Prototype Field Tests. Test Results

D6.2

Programme FP7 – Cooperation / Energy

Grant agreement number 207643

Project acronym ADDRESS

Type (distribution level) Public

Date of delivery May 31st, 2013

Report number D6.2

Status and Version V 1.0

Number of pages 123

WP/Task related WP6 – T6.3

WP/Task responsible KEMA / EDF-SA, Enel Distr., Iberdrola

Mathieu Caujolle, Luc Glorieux, Philippe Eyrolles, Julien Le Baut, Radouane Irhly, François-Xavier Toledo, Regine Belhomme, Francesco Naso, Olena Morozova, Giovanni Author(s) Valtorta, Dominic Ectors, Pieter Kropman, Jitske Burger, Jan Maarten van der Valk, Ignacio Delgado, Roberto González

EDF-SA, Enel Distribuzione, VITO, KEMA, Iberdrola Partner(s) Contributing Distribución.

ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Document ID Test Results

 ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. proof Test Results_v1.0.pdf

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

Executive Summary

The figure one gives an overview of the ADDRESS project where the new functionalities that should be implemented between the players involved in the Network System are depicted. During the project, three field tests have been carried out in order to test and assess all these new functionalities: Spain, Italy and France. The Spanish field test is dedicated to the validation of the downstream part of the ADDRESS chain, from aggregation platform to controllable appliances at consumers’ premises. The Italian test is dedicated to the validation of the upstream part of the ADDRESS chain, from AD buyers to aggregation platform, with a focus on DSO and grid operation on a large MV network, and to the effect of Active Demand (AD) visible at HV level and the French site focuses on the validation of the whole ADDRESS “chain”: from AD buyers to controllable appliances at consumers’ premises but on a smaller scale, with several tens of consumers and with one MV feeder and several LV networks.

DSO

TSO Consumers

AC Retailer Other non

controlled usages Washing Balancing Aggregation machines Responsible platform Meter or

system Party equivalent Smart plugs

Producer EB Water bilateralrelationships heaters

Market,contracts or direct

Maybe other Electric

controlled usages heating Playersfunctions or ofthe electricity

Italian Field Test Spanish Field Test

French Field Test Figure 1. Field tests at a glance.

This document is intended to provide a description of the results reached in the field test carried out in three sites with the aim of validating the ADDRESS concepts and based on the different test cases defined in previous Deliverables (Deliverable 6.1) [1]. In the following Deliverables (Deliverable 6.3 and Deliverable 6.4) [12], [13] the assessment and effectiveness of these results are presented regarding the different objectives defined at the beginning of the ADDRESS project. The following figure depicts this relationship.

D6.1 (Public) D6.2 (Public) D6.3 (Confidential) D6.4 (Confidential)

Field Tests scripts and Technical results of Assessment of field Evaluation ofthe description field tests tests results+ Kema effectiveness of the Validation ADDRESS concepts

Figure 2. Relationship between Field Tests Deliverables

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

Spanish Field Test The location of the Spanish field test site is the city of Castellón de la Plana, a Mediterranean city where Iberdrola is the main retail and distribution company, with more than 100.000 points of supply, 600 secondary substations and 7 primary substations. For this test site, Iberdrola recruited 263 consumers. Six test cases have been carried out, one test case for each specific aspect of the ADDRESS chain to be tested:

- Test case 1: Functionality of the Aggregator ToolBox. The focus is on the validation of the functionality of the Aggregator Toolbox (ATB). The input to the ATB is the power to be increased or reduced, the time and the duration of the signal; the output is the signals to send to each Energy Box (EBox). - Test case 2: Functionality of the EBox. The focus is on the analysis of the behaviour of the EBox when Aggregator’s signal is received, how it is interpreted and which are the signals sent to the smart plugs and smart loads. - Test case 3: Functionality of the smart devices. The objective of this test is the analysis of the communications between the EBox and the smart devices based on the output from the EBox algorithm. - Test Case 4: Global analysis. In these tests, the consumers’ response is analyzed from Aggregator point of view. The objective of these tests is the assessment of the demand side management and behaviour from all the consumers. - Test Case 5: Consumers’ behaviour according to incentives. In these tests, the objective is to analyze how the consumers’ behaviour varies for varying incentives. - Test Case 6: Consumers’ behaviour according to duration. These tests have an objective similar to the above set 5: to analyze the response of the consumers with respect to the duration of price&volume signal.

Italian field test The Italian test site is Carpinone (Molise Region, Centre of Italy), where there is a MV network with several MV generation sources. In addition, the grid has over capacity which enables tests to be performed without endangering the quality and availability of supply. A storage system is installed to emulate demand increase by charging the battery and demand decrease by discharging the battery. Tests developed to validate Italian field test have been grouped into four categories: - Test case 1: The DSO as the AD product validator. In the test, the DSO receives AD bid proposals large enough to push the network to some limit; so proposals have to be curtailed by the DSO validation algorithms, and the curtailed bids should to be published in the MVCC interface. Then, the curtailed AD products are be activated through emulation (by means of the storage system or by modifying load/generation on the network and the real network operation will be observed) - Test case 2: The DSO as the buyer of AD products. In this case, a network constraint is simulated by changing the thermal limit of a MV cable in the network under the test. The real network operation that will be observed will have to comply with the DSO forecast.

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- Test case 3: limitation of the power flow rising to the transmission network. Localized AD products to limit the power flow towards the transmission system. In this test, the AD contribution of specific load areas are programmed within the day-ahead market. - Test case 4: MVCC algorithms reaction to network changes.

French field test This field tests took place on two islands (Houat & Hoëdic) where about 30 households were involved with some of them acting as a reference (not participating in active demand process). These islands are connected to the main land by one MV underwater cable. Distribution is done via 8 MV/LV transformers. In the French field tests different scenarios have been carried out to assess all the implemented technology. Nearly all of them concern the complete ADDRESS architecture. The objectives of these scenarios are mainly the following:  Assess the technical feasibility of the complete implemented ADDRESS system and evaluate its performance on real field data.

 Assess how Active Demand can meet the needs of the electricity system players using bilateral contracts (SRP and/or CRP) or market offers. The provision of AD services was based on the simulation of possible problems or needs identified by electricity system players (DSO, TSO, BRP…) or by other players (retailer, RES producers – PV in our case…). The AD services such as power reserve for imbalance management, load shaping (load increase or decrease) for technical or economical optimizations, voltage control and power control to relief overloads or network congestions were considered.  Simulate the market interactions of aggregation entities and other electricity system players and assess the potential impact of AD on the prices of an electricity market.

 Analyze the impact on the network of the delivered AD volumes. The effects of the AD product volumes forecasted and submitted by the aggregation entity are compared with the actual ones resulting from the load variations observed on the field.

 Study the response of the consumer portfolio to the incentive signals defined by the aggregation entities and sent to the Energy Boxes. This study performed on both local (consumer) and global (cluster of consumers) levels allowed us to verify if, how and under which conditions the initial AD need was fulfilled. The forecasted behaviours of the consumer appliances and of the global cluster are compared to the ones measured on the field.

 Finally, consumers’ acceptance and commitment were also assessed but this topic is out of the scope of this deliverable. In this respect, the studies carried out and the result obtained are described in ADDRESS Deliverable D5.2 [7].

The results of the three field tests are described in the present deliverable. Their main conclusions are presented at the end of this report. Assessment and evaluation of all the tests are presented in the Deliverable 6.3 [12].

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Table of contents Executive Summary ...... 2 Table of contents ...... 5 List of figures ...... 6 List of tables ...... 9 1. Introduction ...... 10 1.1. Scope of the document ...... 10 1.2. Structure of the document ...... 10 1.3. Notations, abbreviations and acronyms...... 10 1.4. Acknowledgements ...... 11 2. Prototype Field Tests...... 13 2.1. Introduction ...... 13 3. Spain ...... 14 3.1. Description of the test ...... 14 3.1.1. Players involved and architecture of the system ...... 14 3.1.2. Equipment installed ...... 15 3.1.3. Exchanged messages between actors ...... 16 3.2. Results ...... 17 3.2.1. Assessment of the ADDRESS technology ...... 17 3.2.2. Problems encountered ...... 38 4. Italy ...... 43 4.1. Description of the test ...... 43 4.1.1. Location ...... 43 4.1.2. Players involved ...... 44 4.1.3. Architecture of the system ...... 45 4.1.4. Equipment installed ...... 46 4.1.5. Exchanged messages between players ...... 49 4.2. Results ...... 49 4.2.1. Assessment of the ADDRESS technology ...... 49 5. France ...... 63 5.1. Description of the test ...... 63 5.1.1. Location ...... 64 5.1.2. Players involved ...... 65 5.1.3. Architecture of the System ...... 65 5.1.4. Equipment installed ...... 66 5.1.5. Test conditions ...... 74 5.2. Results ...... 87 5.2.1. Test execution ...... 87 5.2.2. Assessment of the technical performance on the consumer side ...... 88 5.2.3. Provision of AD services at a cluster level ...... 94 5.2.4. Market simulator...... 106 5.2.5. Complete scenario execution ...... 116 6. Conclusions ...... 122 7. References ...... 125 7.1. Project documents ...... 125 7.2. External documents ...... 125 8. Revisions ...... 126 8.1. Revision history ...... 126

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List of figures

FIGURE 1. FIELD TESTS AT A GLANCE...... 2 FIGURE 2. RELATIONSHIP BETWEEN FIELD TESTS DELIVERABLES ...... 2 FIGURE 3. CONCEPTUAL ARCHITECTURE OF THE SPANISH FIELD TEST...... 15 FIGURE 4. FULLY EQUIPPED HOME...... 16 FIGURE 5. EXCHANGED MESSAGES BETWEEN PLAYERS...... 17 FIGURE 6. METHODOLOGY FOR DAILY CLUSTERING...... 18 FIGURE 7. CONSUMERS’ CLASSIFICATION. MAIN VARIABLES. SPRING...... 20 FIGURE 8. PROTOTYPES FOR SPRING / WORKING DAYS...... 20 FIGURE 9. LOAD PROFILE FROM SPANISH TESTS – SPRING WORKING DAY...... 22 FIGURE 10. LOAD PROFILE FROM SPANISH TESTS – SPRING HOLIDAY ...... 23 FIGURE 11. SPANISH FIELD TEST - RESULTS OF TEST CASE 1...... 26 FIGURE 12. POWER REDUCTION REQUEST AT 19:00, WORKING DAY – DEMAND FROM SMART PLUGS...... 27 FIGURE 13. POWER REDUCTION REQUEST AT 19:00, HOLIDAY – DEMAND FROM SMART PLUGS. ... 28 FIGURE 14. POWER INCREASE REQUEST AT 16:00, HOLIDAY – DEMAND FROM SMART PLUGS...... 28 FIGURE 15 EBOX COMPARISON - OVERRIDE MODE...... 29 FIGURE 16. SPANISH FIELD TEST - RESULTS FOR PLUGS FOR SHIFTABLE DEVICES...... 30 FIGURE 17. SPANISH FIELD TEST – RESULTS FOR INTERRUPTIBLE DEVICES...... 31 FIGURE 18. SPANISH FIELD TEST. MAXIMUM DURATION...... 31 FIGURE 19. SPANISH FIELD TEST - MINIMUM DISTANCE BETWEEN OFF-PERIODS...... 32 FIGURE 20. SPANISH FILED TEST - NUMBER OF OFF-PERIODS...... 32 FIGURE 21. SPANISH FILED TEST – MANAGEMENT OF SMART WASHING MACHINES ...... 33 FIGURE 22. SPANISH FILED TEST – AD TO REDUCE CONSUMPTION BY 20 KW AT 13:00 TO 14:00 THE 26TH OF JUNE (SUMMER WORKING DAY) – CLUSTER 3 SELECTED ...... 34 FIGURE 23. SPANISH FILED TEST – AD TO REDUCE CONSUMPTION BY 20 KW AT 13:00 TO 14:00 THE 26TH OF JUNE (SUMMER WORKING DAY) – CLUSTER 3 SELECTED – NORMALIZED CURVES ...... 34 FIGURE 24. SPANISH FILED TEST – AD TO INCREASE CONSUMPTION BY 20 KW AT 22:00 THE 25TH OF APRIL (SPRING WORKING DAY) – CLUSTER 1 SELECTED ...... 35 FIGURE 25. SPANISH FILED TEST – AD TO INCREASE CONSUMPTION BY 20 KW AT 22:00 THE 25TH OF APRIL (SPRING WORKING DAY) – CLUSTER 1 SELECTED – NORMALIZED CURVES ...... 35 FIGURE 26. SPANISH FILED TEST – DAILY POWER CONSUMPTION DURING DIFFERENT DAYS WITH THE SAME POWER REDUCTION BUT DIFFERENT INCENTIVES...... 36 FIGURE 27. SPANISH FILED TEST – DAILY POWER CONSUMPTION OF DIFFERENT DAYS WITH THE SAME POWER INCREASE BUT DIFFERENT INCENTIVES ...... 37 FIGURE 28. SPANISH FILED TEST – DAILY POWER CONSUMPTION OF DIFFERENT DAYS WITH THE SAME SIGNAL BUT WITH DIFFERENT DURATION...... 38 FIGURE 29. INDICATORS USED FOR TESTING COMMUNICATIONS FUNCTIONALITY ...... 39 FIGURE 30. NUMBER OF PACKAGES OF DATA RECEIVED FROM E-BOXES EVERY DAY ...... 39

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FIGURE 31. EBOXES THAT HAVE RECEIVED SIGNAL FROM AGGREGATOR, PER MONTH...... 40 FIGURE 32. SMART HOUSEHOLD DEVICES KEPT INTO THE DATABASE VS. THEORETICAL ONES...... 40 FIGURE 33. ACTUALLY RECEIVED SLOTS VS. THEORETICAL ONES...... 41 FIGURE 34. ACTUAL RECEIVED DEVICES VS. THEORETICAL ONES...... 42 FIGURE 35. THE TEST LOCATION AND THE STORAGE SYSTEM IN ITALY...... 43 FIGURE 36. CARPINONE ELECTRIC SYSTEM LAYOUT...... 44 FIGURE 37. FIELD TEST OVERVIEW...... 45 FIGURE 38. ADMS AND MVCC ARCHITECTURE...... 46 FIGURE 39. EQUIPMENT AND MEASUREMENTS IN THE HV/MV SUBSTATION...... 47 FIGURE 40. EQUIPMENT IN THE MV/LV SUBSTATIONS...... 48 FIGURE 41. EQUIPMENT IN THE MV PRODUCERS’ AND CUSTOMERS’ PREMISES...... 48 FIGURE 42. THE WHOLE ADDRESS CHAIN TESTED IN FRANCE ...... 65 FIGURE 43 - OPERATION MODE OF THE ADDRESS ARCHITECTURE CONSIDERED IN THE FRENCH FIELD TESTS...... 66 FIGURE 44. MAIN COMPONENTS OF THE FRENCH DSO PLATFORM AND INTERACTIONS WITH EXTERNAL ACTORS...... 68 FIGURE 45. MAIN COMPONENTS OF THE FRENCH ATB SYSTEM AND INTERACTIONS WITH EXTERNAL ACTORS...... 69 FIGURE 46. INTERACTION OF THE ATB SYSTEM WITH THE MARKET SIMULATOR...... 70 FIGURE 47. AGGREGATION OF SUPPLY BIDS ...... 71 FIGURE 48. EQUIPMENT INSTALLED AT CONSUMER’S PREMISES...... 72 FIGURE 49. HOME SIDE SYSTEM AND EBOX DATABASE...... 74 FIGURE 50 - CLUSTERING METHODOLOGY APPLIED TO THE FRENCH FIELD TESTS ...... 75 FIGURE 51. SPRING CONSUMPTION PROTOTYPES FOR THE 3 DAY TYPES AND THE 2 CONSUMERS TYPES...... 76 FIGURE 52. CONTROL SYSTEM OF THE FRENCH ELECTRICAL WATER HEATERS...... 81 FIGURE 53. CONTROL SYSTEM OF ELECTRICAL WATER HEATERS WITH ADDRESS...... 81 FIGURE 54. ILLUSTRATION OF DIFFERENT CONSUMPTION PROFILES...... 86 FIGURE 55. DAILY EWH CONSUMPTION PROFILE USED FOR THE TESTS...... 86 FIGURE 56. INTERRUPTIBLE LOADS – PEAK-SHAVING...... 90 FIGURE 57. INTERRUPTIBLE LOADS – LIMITED PEAK-SHAVING...... 90 FIGURE 58. SHIFTABLE LOAD – EWH...... 91 FIGURE 59. SHIFTABLE LOAD – SWM ...... 92 FIGURE 60. COMMUNICATION ERRORS OBSERVED DURING THE FRENCH FIELD TESTS...... 93 FIGURE 61. RATE OF SUCCESS OF COMMUNICATION BETWEEN THE EBOXES AND THE ADDRESS CONTROL CENTER...... 94 FIGURE 62. OVERRIDE RATES OBSERVED DURING THE FRENCH FIELD TESTS...... 94 FIGURE 63. MAIN STEPS OF THE METHODOLOGY USED FOR COMPUTING THE CLUSTER BASELINES.97 FIGURE 64. RAW DISTRIBUTION OF THE MEDIAN OF THE CONSUMPTION PROFILE OF THE WEATHER SELECTED GROUPS...... 98 FIGURE 65. EXAMPLE OF OFFSET OBSERVED BETWEEN THE MEAN LOAD CURVE OF THE DAILY

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GROUP AND THE LOAD CURVE OBSERVED OVER A DAY ...... 98 FIGURE 66. EXAMPLE OF CLUSTER RESPONSES TO LOAD DECREASE REQUESTS...... 99 FIGURE 67. EXAMPLE OF CLUSTER RESPONSES TO LOAD INCREASE REQUESTS...... 100 FIGURE 68. EVENING PEAK-SHAVING - OK – SCENARIO NO. 2 OF TABLE 21...... 101 FIGURE 69. EVENING PEAK-SHAVING - FAILED – SCENARIO NO. 4 OF TABLE 21...... 102 FIGURE 70. TO SHIFT NIGHT PEAK - OK – SCENARIO NO. 15 OF TABLE 21...... 102 FIGURE 71. TO SHIFT CONSUMPTION DURING WIND TURBINE PRODUCTION - FAILED – SCENARIO NO. 17 OF TABLE 21...... 103 FIGURE 72. STEPS FOR RUNNING THE MARKET SIMULATOR WITH AGGREGATION ENTITY’S OFFER108 FIGURE 73. ILLUSTRATION OF THE LOAD SHAPING OF EWH CONSUMPTION FOR THE FIRST SCENARIO...... 109 FIGURE 74. ILLUSTRATION OF THE LOAD SHAPING OF EWH CONSUMPTION FOR THE SECOND SCENARIO...... 109 FIGURE 75. SIGNALS CHOSEN FOR FOUR LOAD AREAS...... 114 FIGURE 76. WIND PROFILE FOR THE BRITTANY AREA CONSIDERED IN THE SCENARIO (ACTUAL PROFILE OF MAY 7TH 2012)...... 116 FIGURE 77. AD VOLUME PROFILES REQUESTED ON DIFFERENT LOAD AREAS...... 117 FIGURE 78. LOAD AREAS (IN RED) COMPUTED BY THE ALGORITHM BASED ON THE LV NETWORK OF THE BRITTANY ISLANDS ...... 117 FIGURE 79. COMPARISON OF THE LOAD FORECASTS TO THE ACHIEVED PROFILES FOR 2 LOAD AREAS OF THE BRITTANY ISLANDS...... 118 FIGURE 80. COMPARISON OF THE FORECASTED AND THE ACHIEVED PV PLANT INJECTED POWERS (HOURLY DATA) USING ACHIEVED RADIANCE INFORMATION ...... 118 FIGURE 81. EVOLUTION OF THE UPPER AND LOWER FLEXIBILITY LIMITS (IN KW) COMPUTED FOR SEVERAL LOAD AREAS ...... 118 FIGURE 82. VALIDATION RESULT OF SOME OF THE SRP PRODUCTS SUBMITTED BY THE ATB PLATFORM ...... 119 FIGURE 83. INCENTIVE SIGNALS SELECTED BY THE ATB ALGORITHMS TO MEET THE SRP VOLUMES119 FIGURE 84. CLUSTER RESPONSE TO THE SUBMITTED INCENTIVE SIGNAL OBSERVED ON THE FIELD.120 FIGURE 85. IMPACT OF AD LOAD INCREASE ON THE VOLAGE AND POWER FLOW AT THE ISLAND CONNECTION ...... 120 FIGURE 86. IMPACT OF AD LOAD INCREASE ON THE VOLTAGE LEVELS OF THE ISLAND MV NETWORK121

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List of tables

TABLE 1. ABBREVIATIONS ...... 11 TABLE 2. PEAK PERIODS ...... 19 TABLE 3. CONSUMER CLUSTERING FOR SPRING WORKING DAYS - MAIN INDEXES ...... 21 TABLE 4. SPANISH FIELD TESTS - TEST CASE 1, POWER SIGNAL 20 KW...... 24 TABLE 5. SPANISH FIELD TEST - TEST CASE 1, POWER SIGNAL 10 KW...... 24 TABLE 6. SPANISH FIELD TEST. TEST CASE 2...... 27 TABLE 7. DEVICE COMMUNICATIONS...... 41 TABLE 8. CARPINONE GRID CHARACTERISTICS...... 43 TABLE 9. INVOLVED GENERATORS...... 44 TABLE 10. FIELD TEST DEVICES INSTALLED IN THE MV NETWORK...... 47 TABLE 11. TEST1: AD VALIDATION FOR THE INTRADAY MARKET...... 51 TABLE 12. TEST1BIS: AD VALIDATION FOR THE DAY-AHEAD MARKET...... 52 TABLE 13. AD VALIDATION FOR THE DAY-HEAD MARKET...... 53 TABLE 14. DSO AS AN AD PRODUCT BUYER...... 56 TABLE 15. LOCALIZED AD PRODUCTS TO LIMIT THE POWER FLOW TOWARDS THE TRANSMISSION SYSTEM – TEST 1 ...... 59 TABLE 16. LOCALIZED AD PRODUCTS TO LIMIT THE POWER FLOW TOWARDS THE TRANSMISSION SYSTEM – TEST 2...... 61 TABLE 17. MVCC ALGORITHMS REACTION TO NETWORK CHANGES ...... 62 TABLE 18. CONTROLLABLE LOADS CONSIDERED DURING THE FRENCH FIELD TESTS...... 74 TABLE 19. FRENCH FIELD TESTS – OFF-PEAK PERIODS AND PREFERRED START AND END TIMES OF THE EWH ...... 82 TABLE 20. FRENCH FIELD TESTS SCENARIOS ...... 84 TABLE 21 - TEST EXECUTION OVERVIEW ...... 88 TABLE 21. FRENCH FIELD TESTS – TEST RESULTS...... 101 TABLE 22. MEAN ENERGY CONSUMPTION OF ADDRESS AND REFERENCE GROUPS DURING THE PERIOD OF THE FRENCH FIELD TEST (ALL FIGURES IN KWH) ...... 105 TABLE 23. VARIATION OF THE OFF-PEAK CONSUMPTION RATIO IN PRESENCE OF ADDRESS SYSTEMS ...... 105

TH TABLE 24. TEST RESULTS FOR THE SCENARIO OF THE FEBRUARY 9 , 2012...... 111

TH TABLE 25. TEST RESULTS FOR THE SCENARIO OF THE DECEMBER 25 , 2012...... 112

TH TABLE 26. COMPARISON OF THE RESULTS WITH THE REAL SPOT PRICES OF THE FEBRUARY 9 , 2012 ...... 113

TH TABLE 27. COMPARISON OF THE RESULTS WITH THE REAL SPOT PRICES OF THE DECEMBER 25 , 2012 ...... 113 TABLE 28. TEST RESULTS FOR THE SCENARIO OF MAY 24TH AND 25TH, 2013 ...... 114 TABLE 29. TEST RESULTS FOR THE SCENARIO OF DECEMBER 24TH, 2012 ...... 115

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1. Introduction

1.1. Scope of the document

The scope of this document is to provide a description of the results reached in the three field test carried out in the ADDRESS project: Italian, French and Spanish. The field trials have been developed in order to validate the concepts of ADDRESS through Active Demand (AD) products activated in the trials. The document will present the different features of the three trials and the players involved in all of them. For each site will be depicted: - Players involved; - Main result from the different test cases;

1.2. Structure of the document

The document comprises the following main sections:  Section 1 is the Introduction, highlighting the scope and structure of the document;  Section 2 describes the test cases that have been carried out in the different field tests in order to assess and validate the developments carried out in the project and implemented in the trials. This information refer to the updated definition presented in D6.1  Section 3, 4 and 5 details the main results obtained in the three filed test sites. Main results for the different test scripts carried out during the last months of the project;  Section 6 concludes the document and summarises the main points.

1.3. Notations, abbreviations and acronyms

2PL Two-Phase Locking AD Active Demand ADMS Active Demand Management System CRP Conditional Re-profiling DB Database DBMS Database Management System DER Distributed Energy Resources DG Distributed Generation DMS Distribution Management System DOW Description of Work DSE Distribution System Estimator DSM Distribution System Management DSO Distribution System Operator EBox Energy Box

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EC European Commission EU European Union FT Flexibility Table GF Generation Forecast HW Hardware Java High-level, object-oriented, cross-platform programming language Java COM. Java Communications API LA Load area LF Load Forecast MVCC Multi-version Concurrency Control MVCC Medium Voltage Control Center OLV Off-Line Validation OS Operating System PC Project Coordinator RTU Remote Terminal Unit RTV Real time validation SO System Operator SOM Self-organizing Maps SW Software TB Technical Board TM Technical Manager TSO Transmission System Operator USB Universal Serial Bus WP Workpackage XML Extensible Markup Language Zigbee A low-cost, low-power, wireless mesh networking standard. Table 1. Abbreviations

1.4. Acknowledgements

The following table gives the names and affiliations of the project participants who contributed at different levels to the work leading to the results described in this report. Their contributions are gratefully acknowledged.

PARTNER Contributors ENEL Distribuzione Francesco Naso, Olena Morozova, Giovanni Valtorta KEMA Pieter Kropman, Jitske Burger, Jan Maarten van der Valk EDF SA Mathieu Caujolle, Luc Glorieux, Philippe Eyrolles, Julien Le Baut, Radouane Irhly, François-Xavier Toledo, Regine Belhomme,

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

Jonathan Reynaud, Didier Roland, Yann Pollet. VITO Dominic Ectors Iberdrola Distribución Roberto González, Ignacio Delgado

The careful and thorough reviews made by ADDRESS TB members (Arturo Losi, Regine Belhomme and Giovanni Valtorta) are also gratefully acknowledged. Their comments have significantly contributed to improve the text.

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2. Prototype Field Tests.

2.1. Introduction

ADDRESS project has divided the tests to be carried out into three field test locations where all the functionalities of the equipment developed during the previous years of the project are checked. The test scenarios and detailed test scripts for the three sites were defined and presented in Deliverable D6.1 “Description of test location and detailed test program for (limited) prototype field test, simulations and hybrid tests’’. [1] This section will try to summarize the main objectives for each site that help us to identify the results in the following sections of the Deliverable. - Spain: tests are dedicated to the validation of the downstream part of the ADDRESS chain, from aggregation platform to controllable appliances. Metering and appliances equipment have been installed at 263 customers to put under real conditions the concepts of ADDRESS at a scale in which tangible benefits can be studied. - Italy: field tests are dedicated to the validation of the upstream part of the ADDRESS chain, from AD buyers to aggregation platform, with a focus on DSO and grid operation on a large MV network. The secure and reliable operation of the distribution network has been tested taking into account AD together with distributed generation, energy storage systems and large customers connected at the Medium Voltage (MV) level. The AD has been emulated by means of some DG using RES (hydro) and a storage system. - France: The tests are carried out on two French islands in Brittany with one MV feeder and 8 LV networks. About 30 consumers are involved in the tests. The objective is to validate the whole ADDRESS chain from AD buyers to controllable appliances at consumers’ premises. The capability of AD to provide services to the different electricity players is studied, as well as the impact of AD actions on the distribution grid. In particular the combination of Active Demand in domestic installations and a small renewable (PV) power plant has been tested. Additionally in Spain and France different climate conditions (warm in Spain, cold in France) have ensured different equipment and usage patterns.

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3. Spain

3.1. Description of the test

The location for the Spanish field test for ADDRESS project is the city of Castellón de la Plana, a Mediterranean city where Iberdrola is the main retail and distribution company with more than 100.000 points of supply, 600 secondary substations and 7 primary substations. The aim of this test is to validate the interaction between the Aggregator (the new deregulated function and key mediator between consumers, markets and power system participants which gathers the flexibilities of the consumers to build Active Demand (AD) services and the consumers, through the Energy Box (EBox). The EBox manages the appliances downstream according to signals received from the Aggregator and the user’s preferences. The main functionalities that have been tested refer to: - HAN Communication; - Validation of the EBox (remote user interface, optimization algorithm, interactions) and the rest of equipment (smart plugs, measuring device, smart washing machines, air conditioning management system); - Validation of external communication EBox – Aggregator; - Validation of consumers’ experience. For this test, Iberdrola recruited 263 consumers; they signed a contract and received an incentive for participating in the test. All the information regarding the electricity use and the measurements have been sent everyday to the Aggregator’s server, where data is collected, in order to assess and analyze the consumers’ response to the AD events. With this information and the information gathered through questionnaires and interviews carried out during the test (at the beginning, in the middle and at the end), the consumers have been classified into different clusters and identified with a prototype load curve. This study has allowed to identify the acceptance or rejection of residential consumers about AD and help for future research from different points of view: technical, sociological, and also legal, concerning the definition of tariffs and contracts with the consumers.

3.1.1. Players involved and architecture of the system The actors involved in the Spanish field test are: - Aggregator - EBoxes - Smart plugs - Smart washing machines - Air conditioning management systems Figure 3 depicts the global architecture for the Spanish field test.

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Aggregator DSO

House

Air cond. system Official Meter

Ebox Additional measuring Smart device Washing machine

Smart Plugsx5

Figure 3. Conceptual architecture of the Spanish field test.

In Figure 3, blue lines represent ADDRESS information exchanged, while red lines represent “official” information exchanged through the smart meters installed in all the houses by the Distribution Company.

3.1.2. Equipment installed The equipment installed is: - 263 EBoxes (ZIV) equipped with: • Zigbee communication with smart appliances, smart plugs and measuring device. • GPRS communication with the Aggregator toolbox. - 263 sets of five smart plugs (Philips and ZIV) for the connection to the mains of various appliances: water heater, washing machine, dishwasher, dryer… - 263 additional measuring device that will be used to communicate with the EBox - the Official smart meter, already installed. In some houses (full HAN consumers), the following equipment is installed: - Smart washing machines (25) from Electrolux. - Air conditioning management system (14) from Intesis. A fully equipped home, with all the devices and the communication technology used, is represented in Figure 4.

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Figure 4. Fully equipped home.

3.1.3. Exchanged messages between actors The relationships between the actors have been tested in different test scripts, intended to assess and evaluate all their functionalities. The communication and message exchange between the actors is depicted in Figure 5.

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Figure 5. Exchanged messages between players.

The steps are: 1. EBox receives the AD price&volume signal the day ahead from the Aggregator 2. EBox runs the optimization algorithm for the manageable loads based on the AD signal 3. EBox sends the orders to smart plugs and smart appliances 4. During the day, if the consumer switches on a new manageable appliance, this one sends its request to the EBox 5. In the case of point 4. above, the EBox runs again its internal algorithm 6. In the case of point 4. above, EBox sends again activation signals to appliances 7. EBox records information received from the meter during the day (5’ slots). 8. EBox records information from smart plugs and smart loads during the day (5' slots). 9. EBox sends information from the measurements and status of all the appliances in the house at the end of the day to the Aggregator.

3.2. Results

3.2.1. Assessment of the ADDRESS technology Before depicting the results achieved with the test cases defined in [1], the consumers’ segmentation is shown in order to present the consumer clusters defined to classify consumers in the Aggregator ToolBox (ATB) and to select them for the test cases. The data collected for the study cover a year, from December 2011 until December 2012; during this

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period, the hourly energy consumption for the selected consumers has been stored. The days have been divided based on: - Type of day: Working day / Holiday - Season of the year: Winter / Spring / Summer / Autumn The analysis was carried out through SOM algorithms [2], which allow getting different prototypes for each combination; each consumer will be classified with 8 prototypes: winter working day, winter holiday, spring working day, etc. Figure 6 depicts the methodology used in the analysis.

Daily Curves

g n n i o r s e t a s e u l S C

Winter Spring Summer Autumn

y g a n d i

r f e o t

s e u p l y c T

Working day Holiday

Prototypes

Figure 6. Methodology for daily clustering.

For each study, the following quantities have been taken into account to obtain the daily prototypes: - Hourly energy consumption: 24 energy data - Daily level consumption: Average hourly energy consumption in the day for each load curve - Daily peaks: A daily peak occurs in an hour if the ratio of the energy in the hour to the average hourly energy > 1.3 - Period of the day in which the daily peak is produced: Six periods in a day have been considered, as in Table 2.

Period Hours Early morning 1 – 6 Breakfast 7 – 10 Morning 11 – 13 Lunch 14 – 16 Evening 17 – 20

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Night 21 – 24 Table 2. Peak periods

A set of indexes were considered to identify the groups of consumers: - Average hourly energy consumption in a day

- Number of peaks The number of peaks for each prototype is computed as the average value of the number of peaks per day of all the load profiles assigned to each prototype or pattern. - Peak pattern The objective of this index is to numerically describe when peaks are produced. With reference to the six periods defined in Table 2, the index is defined as a binary six-digit number, such as 000000 - Maximum hourly consumption in a day, and hour These two indexes register the maximum hourly value of energy consumption, and the corresponding hour of the day. - Peak/valley ratio This index is obtained as the quotient of maximum and minimum consumptions in a day, and gives information about the degree of variability of consumption. With the SOM algorithm [2], four clusters have been identified. Figure 7, Figure 8 and Table 3 depict the results of the clustering, for spring season and working days. These images depict the different clusters divided into neurons or the minimum logical area used for analysing the information from consumers. [2]

Load curve classification Peaks pattern per neuron

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Average peak hours per neuron Average Consumption per neuron Figure 7. Consumers’ classification. Main variables. Spring.

Spring - working days 2500

2000

1500 Prototype 1

Prototype 2 Wh Prototype 3 1000 Prototype 4

500

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 8. Prototypes for spring / working days.

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C1 C2 C3 C4 Number of peaks 5.59 6.50 5.17 6.15 Hourly average consumption (Wh) 453.21 720.08 246.62 830.88 Peak pattern 000111 011101 000111 000011 Max consumption 912.31 988.36 441.10 2242.01 Max consumption, hour 23 24 23 23 Min consumption 188.71 393.87 133.30 271.58 Peak/valley ratio 4.83 2.51 3.31 8.26 Number of consumers 105 20 170 5

Table 3. Consumer clustering for spring working days - Main indexes

Based on the consumer cluster identified for the four seasons and the two types of day, tests have been carried out during the last months of the projects.

Test Case 1. Functionality of the Aggregator ToolBox The aim of these test case is the validation of the functionality of the ATB. The input to the ATB are the power to be increased or reduced, the time and the duration of the signal; the output are the signals to send to each cluster. This signal sent by the Aggregator concerns duration, power threshold, start time and incentives. The parameters considered to study the correctness of the operation of ATB are: - Type of day: working day / holiday - Hour of the day: peak hour / off-peak hour - Power demand: increase / reduction The peak hour is the hour of the day where there is regularly a high consumption; the off-peak hour is the moment of day where the consumption is lower than the rest hours of the day. The peak hour and off-peak hour selected to develop this case have been selected according to the hours when peak demand or valley demand is presented in Spain. The consumers of the Spanish field test site have different consumption behaviour depending on the day of the week: working day and holiday. To obtain the power demand from all consumers in a working day, power profiles “working day” of each cluster are multiplied by the total number of consumers belonging to each cluster; the summation of these values depicts the whole power demand available for the ATB. The load profile for holiday is calculated taking into account the power load demand for “holidays” from each cluster and following the same procedure as for working days. Load profiles are shown in Figure 9 and

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Power demand - Spring Holidays 350

300

250

200 Prototype 4

Prototype 3 kWh 150 Prototype 2 Prototype 1 100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Figure 10.

Power demand - Spring Working Days 250

200

150 Prototype 4

Prototype 3 kWh Prototype 2 100 Prototype 1

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 9. Load profile from Spanish tests – Spring Working Day.

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Power demand - Spring Holidays 350

300

250

200 Prototype 4

Prototype 3 kWh 150 Prototype 2 Prototype 1 100

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Figure 10. Load profile from Spanish tests – Spring Holiday

From Figure 9, it is possible to identify the peak and off-peak hour for working day: - Peak-hour: 10, 16 and 22 - Off-peak hour: 6 and 19 For holiday (

Figure 10), the peak and off-peak hour identified are: - Peak-hour: 15 and 22 - Off-peak hour: 6, 13 and 19. The Aggregator has the prototypes of each cluster; to find a solution to accomplish a request, the Aggregator needs to know the type of day when the signal is sent; in addition, in the tests two levels of

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power1 have been considered: 20 kW and 10 kW. The following table present these use cases:

Power 20 kW signal hour working day holiday Peak hour & Decrease power 10 15 22 22 Peak hour & Increase power 10 15 22 22 Off-peak hour & Increase power 6 6 19 13 Off-peak hour & Decrease power 6 6 19 13 Table 4. Spanish field tests - Test Case 1, power signal 20 kW.

Power 10 kW signal hour working day holiday Peak hour & Decrease power 10 10 22 22 Peak hour & Increase power 10 10 22 22 Off-peak hour & Increase power 6 6 19 19 Off-peak hour & Decrease power 6 6 19 19 Table 5. Spanish field test - Test Case 1, power signal 10 kW.

The response from the ATB for a power demand reduction of 20 kW, start time 22h, and duration 1h is reported in Figure 11.

Cluster 1 Duration signal: 1 h

1 According to the total number of consumers, and in order to be able to achieve the AD requirements, the ATB has defined two power thresholds in order to increase or reduce the demand from consumers.

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Start time: 22:00 Incentive: If power consumption in signal time is less than 2 kW the user benefit is 0.01 €/kWh.

Cluster 2 Duration signal: 1 h Start time: 22:00 Incentive: If power consumption in signal time is less than 2 kW the user benefit is 0.01 €/kWh.

Cluster 32 Duration signal: 1 h

2 In order to achieve the AD requirement, the ATB sends two signals to different clusters to reduce the demand and another signal to increase the demand in other moment of the day to try to shift some loads for other period of time.

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Start time: 23:30 Incentive: If power consumption in signal time is less than 2 kW the user benefit is 0.01 €/kWh.

Figure 11. Spanish field test - Results of test Case 1.

Test Case 2: Functionality of the EBox The aim of this test case is the analysis of the behavior of the EBox when Aggregator’s signal is received, how it is interpreted by the EBox and which are the signals that the EBox sends to the smart plugs and smart devices. The parameters considered in the test are: - Power demand: increase / reduction - Number of slots of the price&volume signal: • Two slots (one power threshold and incentive) • Three slots (two power thresholds and incentives) - Hour of the day: peak / off-peak - Duration of the signal: • Two slots: 30m / 1h / 1h30m • Three slots: 45m Slots refer to the power levels. If the signal has two slots, there is a unique level power and two incentives (xx € and 0 €). Depending on the nature of the signal (reducing or increasing the demand), the power consumption should be above or below the limit. For a signal with 3 slots, there are two levels power and three incentives (xx €, yy € and 0 €), receiving a different incentive depending on the level consumption: above, below or between limits. For the significance of the test, a reasonable number of EBoxes should be studied. The number depends on the typology of the house and the equipments installed. Additionally, as explained in the following sections, there is a limitation regarding the communications between EBoxes and Aggregator. This has constrained the selection of EBoxes: for carrying out the test, the best EBoxes from communications’ point of view have been selected. For selecting the Eboxes in order to be able to analyze its behavior according to the information received: 1. different signals were sent in a specific day 2. for this day, information from the Eboxes is received at the end of the day. After several days, we are able to receive information from all the Eboxes. Studied cases are the result of the combination of input parameters and signal; they are summarized in Table 6.

Peak hour Off-peak hour

2 slots 3 slots 2 slots 3 slots 30 min 30 min Increase power Duration 45 min 45 min 1 h 1 h

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1 h 30 1 h 30 30 min 30 min Decrease power Duration 1 h 45 min 1 h 45 min 1 h 30 1 h 30 Table 6. Spanish Field Test. Test case 2.

Figure 12, Figure 13, and Figure 14 depict the response from the smart plugs downstream of EBoxes which received an AD signal from the Aggregator. In the Figures, the measurements from all the smart plugs along the day are shown.

Figure 12. Power reduction request at 19:00, working day – Demand from smart plugs.

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Figure 13. Power reduction request at 19:00, holiday – Demand from smart plugs.

Figure 14. Power increase request at 16:00, holiday – Demand from smart plugs.

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The last test carried out in this case is aimed at checking the internal algorithms of the EBox to verify that the Ebox does not intervene when an Aggregator’s signal is received and the global override is selected by the user. To check for this, a specific Ebox in override mode was selected and its behavior was analyzed in comparison with other Eboxes not in override mode that had received the AD signal. The following image depicts the behaviour from two Eboxes, one of them in override mode and the other one not in override mode. The figure depicts the daily energy consumption from both Eboxes (normalized consumption load curves) which have received an AD signal to reduce the demand at 12:00h during one hour. The consumer’s Ebox not in override mode tries to reduce the demand during the first quarter of hour whereas the consumer’s Ebox in override mode does not work and consumer’s loads are switched on during this hour.

Figure 15 Ebox comparison - OVERRIDE mode.

Test Case 3: Functionality of the smart devices The objective of this test case is the analysis of the communications between EBox and smart devices according to the output s from the EBox algorithm. To verify the functionality of smart plugs and smart appliances, it is necessary to select an adequate number of devices and to analyze their functionally in different days. The total number of smart plugs involved in the tests is 255 divided into 125 to power shiftable loads and 130 to power interruptible loads. For shiftable devices, the specification is: - the next start time is scheduled to occur during the user preferred time and according to the AD signal. For interruptible devices, the specifications are: - max duration of the interruption: 2 slots (30 minutes) - minimum distance between two off-periods: 6 slots (1,5 hours)

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- max number of off-periods: 8 The following paragraphs illustrate the main results of the tests and all the comments for all these results will be presented in Deliverable6.3 [12]

Shiftable devices Out of all the smart plugs involved in the tests, 51% have had their next start time in the preferred time interval (Figure 16). The rest of the smart plugs did not fulfil the user’s preferences, for the following reasons: - Override option is selected in the smart plug  the smart plug works correctly (18%) - No override option selected in the smart plug  the smart plugs does not work correctly (31%)

Figure 16. Spanish field test - Results for plugs for shiftable devices.

Interruptible devices The 86% of the plugs for interruptible devices did fulfil specification during all the days, the 7% between 80% and 100%, the 1% between 50% and 80% and 6% did do well in less than 50% of the days (Figure 17).

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Figure 17. Spanish Field Test – Results for interruptible devices.

For each single specification, the analysis shows that : - max duration of the interruption (2 slots = 30 minutes): fulfilled by 99.81 % of the smart plugs (Figure 18);

Figure 18. Spanish field test. Maximum duration.

- min distance between interruptions (6 slots = 1,5 hours): 94.6% of interruptible smart plugs (Figure 19);

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Figure 19. Spanish field test - Minimum distance between off-periods.

- max number of off-periods (8): all the smart plugs have presented less or 8 off-periods during the day (Figure 20);

Figure 20. Spanish filed test - Number of off-periods.

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Smart washing machine In the Spanish field test there are 25 Smart Washing Machines (SWMs) installed. The behaviour of the SWM is similar to the one of shiftable devices: its start should be scheduled during the preferred operation interval. To evaluate the functionality of the SWM, it was verified that the power consumption occurred within the operation interval set by the user. The result of the analysis is shown in the Figure 21.

Figure 21. Spanish filed test – Management of smart washing machines

Test Case 4: Global analysis For these tests, the consumers’ response is analyzed from the Aggregator’s point of view. The focus is on the behaviour of the consumers regarding the signals received, the day of the week and the hour and the duration of these signals, for reducing consumption in peak hours and increasing it in off-peak hour. To this extent: 1. The Aggregator defines the requirements to increase/reduce the demand during a specific day and hour. 2. The Aggregator runs its internal algorithm and sends AD signals (price&volume signals) to the most suitable clusters. 3. At the end of the day, the measurements from the selected consumers (all the consumers in the selected clusters) are assessed in order to verify if the AD requirement from Aggregator’s has been fulfilled. Due to the limitation in communications, the number of EBoxes that received the AD signal was lower than expected; then, it was necessary to extrapolate the data. Based on the total consumption data received from the EBoxes, the average power consumption was calculated for the selected cluster(s) and compared to the one of the rest of the ADDRESS consumers. It is also interesting to represent the normalized load curves, obtained by dividing the load curve by the maximum value of the load, to check the overall characteristics of the power consumption.

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Figure 22 and Figure 23 depict the results of the tests for reducing consumption. The difference between the consumption curves during the price&volume signal period of validity is apparent.

Reduce -Load curve 180000

160000

140000

120000

100000

Cluster 3 kWh 80000 Without AD Signal

60000

40000

20000

0:30 2:00 3:30 5:00 6:30 8:00 9:00 9:30 0:00 1:00 1:30 2:30 3:00 4:00 4:30 5:30 6:00 7:00 7:30 8:30 0:00

10:30 12:00 13:30 15:00 16:30 18:00 19:00 20:30 22:00 23:30 11:00 11:30 12:30 13:00 14:00 14:30 15:30 16:00 17:00 17:30 18:30 19:30 20:00 21:00 21:30 22:30 23:00 10:00 Figure 22. Spanish filed test – AD to reduce consumption by 20 kW at 13:00 to 14:00 the 26th of June (Summer working day) – Cluster 3 selected

Reduce Demand - Power consumption Normalized 1

0,9

0,8

0,7

0,6

0,5 Cluster 3 Without signal 0,4

0,3

0,2

0,1

2:30 3:00 3:30 6:30 7:00 0:00 0:30 1:00 1:30 2:00 4:00 4:30 5:00 5:30 6:00 7:30 8:00 8:30 9:00 9:30 0:00

10:00 10:30 11:00 13:30 14:00 14:30 17:30 18:00 18:30 21:00 21:30 22:00 12:00 12:30 13:00 15:00 15:30 16:00 16:30 17:00 19:00 19:30 20:00 20:30 22:30 23:00 23:30 11:30 Figure 23. Spanish filed test – AD to reduce consumption by 20 kW at 13:00 to 14:00 the 26th of June (Summer working day) – Cluster 3 selected – Normalized curves

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Figure 24 and Figure 25 depict the results of the tests for increasing consumption. Also in this case, the difference between the consumption curves during the price&volume signal period of validity is apparent; in particular, the load curve for the selected cluster (cluster 1) shows increases during the period of the signal while the load curve of the rest of the consumers decreases.

Figure 24. Spanish filed test – AD to increase consumption by 20 kW at 22:00 the 25th of April (Spring working day) – Cluster 1 selected

Figure 25. Spanish filed test – AD to increase consumption by 20 kW at 22:00 the 25th of April (Spring working day) – Cluster 1 selected – Normalized curves

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Test Case 5: Consumers’ behavior according to incentives In this test case, the analysis is focused on the difference of the consumers’ behavior when they receive a similar price&volume signal but with different incentives. Most of the parameters in these tests have been kept constant in order to give evidence only to the influence of the incentives; they are day of the week, hour of the day to start the price&volume signal, power thresholds, duration of the signal. The analysis considers the aggregated response from the consumers after they receive the Aggregator’s signal. These signals were sent manually from the ATB so as to be able to define the incentives to decrease/increase consumption in a working day/holiday. To develop this test, many signals were sent to the EBoxes, for each working day and holiday. After sending these signals, the following weeks the same signals but with different incentives were sent, with the idea of having two or more signals that only differ in the incentive. Due to communication problems, the number of signals sent had to be bigger than required. In the following figures, two examples are shown to ascertain the influence of the incentives. Two daily load curves for the same day of the week during different weeks and with different incentives are shown. Depending on the conditions of the day, the power demanded varies greatly from one day to another; for this reason, these curves have been normalized for an easy comparison. The first example (Figure 26) refers to a signal sent from the Aggregator to all the consumers (4 clusters) in order to reduce the demand at 19:00 for the same day of the week in different weeks and compare the behaviour from consumers according to the incentives proposed. It is apparent that the curve with the highest incentive has a more pronounced power reduction than the other. In the period when the signal is activated, the energy consumption during the day with more incentive is lower than the day with less incentive.

Figure 26. Spanish filed test – Daily power consumption during different days with the same power reduction but different incentives.

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In the example of Figure 27, which refers to the case of an increase of consumption, it is possible to verify that the total energy demanded during the period of the signal has actually increased, and that the increased consumption has a direct relation with the incentive of the signal.

Figure 27. Spanish filed test – Daily Power Consumption of different days with the same power increase but different incentives

Test Case 6: Consumers’ behavior according to duration This test case has objectives similar to the test case 5; it is intended to analyse the response of the consumers when they receive similar price&volume signals but the duration of the signal is different. As in test case 5, the signals are sent manually from the ATB, this time in order to be able to define different durations. Most of the parameters have been kept constant in order to give evidence only to the influence of the incentives; they are day of the week, hour of the day to start the price&volume signal, power thresholds, incentives. The analysis considers the aggregated response from the consumers after they receive the Aggregator’s signal. These signals were sent manually from the ATB so as to be able to define the duration of the signal intended to decrease/increase consumption in a working day/holiday. To develop this test, many signals were sent to the EBoxes, for each working day and holiday. After sending these signals, the following weeks the same signals but with different durations were sent, with the idea of having two or more signals that only differ in the duration. Due to communication problems, the number of signals sent had to be bigger than required. In Figure 28, an example is shown to ascertain the influence of the duration. Load curves are shown for two different days; for each day, both the power demanded (darker line) and the trend line (lighter line) are shown; the difference between the days is the duration of the signal. The trend line allows to

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see the behavior of the curve at the time of the signal. As it can be observed, the longer the duration of the signal the more difficult to follow the signal. The signal with a duration of 30’ allows Ebox to follow the AD requirement in an easier way than a signal with a duration of one hour and a half. This can be justified by the specifications of controllable devices. The specifications of the interruptible devices don’t allow interruptions with a duration bigger than fifteen minutes; moreover, the minumum distance between two off-periods must be bigger than 1h 30’. Therefore, it is quite difficul to be able to reduce consumption during all the period of the signal by disconnecting all devices at same time.

Figure 28. Spanish filed test – Daily power consumption of different days with the same signal but with different duration.

3.2.2. Problems encountered 3.2.2.1 Communications During the deployment of the test, communications has been one of the main problems. Issues with the reception and recording of information have caused difficulties into the processes of selection and analysis of the results. Next, some indicators will be discussed to show the functionality of communications in the Spanish field test.

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Data Packages Aggregator Signals from from E-boxes to Aggregator to Aggregator E-boxes

E- boxes

ADDRESS_001 ADDRESS_036 … ADDRESS_300

Consumptions from Smart devices to E-boxes Smart Plugs Smart washing AACC machine

Figure 29. Indicators used for testing communications functionality

The first indicator is the number of EBoxes that send data about what had happened in the house the day before. The correct operation in this case would be that every EBox sends every day data packages with information about consumption, interruptions, etc. recorded the day before. However, information is received with some day delay, or not received at all, as it is shown in Figure 30, where the number of EBox data received (y axis) per day (x axis) according to the day the information is checked (see the legend).

Figure 30. Number of packages of data received from E-boxes every day

The second indicator is aimed at checking if communications between Aggregator and EBoxes work correctly. Figure 31 shows the average value and the percentage of EBoxes that have received Aggregator’s signal per month versus the total number of EBoxes in the database. In this figure, the different columns depict the average values of AD signals received by the different Eboxes based on the daily information registered from these ones and collected in the ATB. It should be taken into account that the communication from EBoxes to ATB varied every day and additionally not always the EBoxes were able to communicate correctly with the ATB in order to receive the AD signal the day before.

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Communications from Aggregator to Communications from Aggregator to E-boxes E-boxes 120 100,00% 90,00% 100 80,00% 70,00% 80 60,00% 60 50,00% 40,00% 40 30,00% 20,00% 20 10,00% 0 0,00% January February March April January February March April

Av. Signal Received Av. Signal no received % Signal Received % Signal no received

Figure 31. EBoxes that have received signal from Aggregator, per month.

Next, the communications between smart household devices and the EBoxes is verified. It is checked which is the information about the household devices per each EBox in the database. Results about this indicator are depicted in Figure 32.

Smart Devices Communication

100%

80%

60%

40%

20%

0% January February March April Received devices Non received devices

Figure 32. Smart household devices kept into the database vs. theoretical ones.

The last indicator about communications functionality is the performance of communications between smart devices (plugs, washing machine and AACC) and the EBoxes. It is measured by the number of slots actually received by the EBox from the smart devices compared with the expected value (Figure 33). The theoretical one is the result of the product between 288 slots per day and the number of devices installed.

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Smart Devices Communication

100%

80%

60%

40%

20%

0% January February March April Received slots Non received slots

Figure 33. Actually received slots vs. theoretical ones.

Table 7 proposes a comparison between the number of slots received and the number of theoretical slots, taking into account both the number of devices received (1) (see before) and the number of EBox received (4). In the last case we have obtained the number of theoretical slots (6) taking into account that every house has five smart devices (5) and each device stores information every five minutes (288 slots per day).

Month Devices Theoretical Received E-box Theoretical Theoretical received (1) slots (2) slots (3) received (4) devices (5) slots (6) January 7.663 2.206.944 677.922 2.717 13.585 3.912.480 February 10.822 3.116.736 1.012.258 3.168 15.840 4.561.920 March 9.703 2.794.464 905.988 2.983 13.325 3.837.600 Table 7. Device communications.

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Smart Devices Communication

100%

80%

60%

40%

20%

0% January February March April Accepted Devices Failed Devices

Figure 34. Actual received devices vs. theoretical ones.

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4. Italy

4.1. Description of the test

The main objective of Italian field test is to validate the DSO algorithms and prototypes developed within the ADDRESS project to enable and exploit AD products visible on the MV network. In this test, AD products were emulated by means of a storage system and by modifying load/generation of some MV customers/producers.

4.1.1. Location The Italian test site location is Carpinone (Molise Region, Centre of Italy), where the grid has over capacity which enables tests to be performed without endangering the quality and availability of supply. At the location a storage system is installed to emulate demand increase of a load area by charging the battery and demand decrease by discharging the battery.

Figure 35. The test location and the storage system in Italy.

Some of the major characteristics of the Carpinone test location are outlined in the Table 8 and a graphical outline of the medium voltage network is depicted in the Figure 36.

340 km MV network (300 km overhead lines) 10 MV feeders 157 MV/LV substations 17 MV consumers (total power 13,2 MW) 11 MV DGs (13,25 MW: 5 hydro, 5 PV and 1 biogas) 1 Storage system (1000 kW - 500 kWh) 8110 LV consumers 63 LV DGs (total power 467 kW) Table 8. Carpinone grid characteristics.

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Figure 36. Carpinone electric system layout.

As depicted in Figure 36, the storage system used to emulate AD in the test is installed on the Pesche feeder.

4.1.2. Players involved Some MV consumers and some of the generators connected on the Carpinone HV/MV substation were involved in the field test. In particular, these generators have provided the generation curves utilized by the Generation Forecast algorithm and some of them have become available to disconnect their plants as required by the Test case 3 (section 4.2.1 Assessment of the results). Some of the major characteristics of the involved generators involved are outlined in the Table 9.

Type of generator P (kW) Biogas 771 PV 900 Hydro 2500 Hydro 1600 Hydro 2620 Hydro 1150 Hydro 1150 PV 82 Table 9. Involved generators.

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4.1.3. Architecture of the system The field tests executed in Carpinone by ENEL Distribuzione refer to the left side of Figure 37.

DSO

TSO Consumers

AC Retailer Other non

controlled usages Washing Balancing Aggregation machines Responsible platform Meter or

system Party equivalent Smart plugs

Producer EB Water bilateralrelationships heaters

Market,contracts or direct

Maybe other Electric

controlled usages heating Playersfunctions or ofthe electricity

Italian Field Test Spanish Field Test French Field Test Figure 37. Field Test Overview.

The Italian tests were focused on the so-called upstream of Active Demand: the DSO and TSO part. The scope of the field tests addressed the functional components, which are to be used by the DSO in an active grid environment. The DSO algorithms, Medium Voltage Control Centre (MVCC) prototypes and SCADA functionality (Figure 38) are evaluated, using the requirements and criteria that have already been defined in the design process and have been tested during the laboratory tests. Measurements are set-up to capture the output of the said functions. Upon completion of the test the measurements are compared with the original requirements. The aim of the test effort is to: - warrant the ability to maintain a secure & reliable network management - ensure the usability of (software) functions.

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Figure 38. ADMS and MVCC architecture.

4.1.4. Equipment installed The MVCC developed in the ADDRESS project was installed on the AD Server component that runs on a computer server that resides in the Campobasso Control Centre (see Figure 39). The SCADA system of Campobasso Control Centre provides I/O data to support the DSO functionality.

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Figure 39. Equipment and measurements in the HV/MV substation.

Also the Remote Terminal Units (RTUs) and active/reactive power measurement devices were installed in the HV/MV and in some MV/LV substation, in order to provide real measurements for Distributor State Estimation. The RTU installed in the HV/LV substation transmits measurements to SCADA via IP network. In the Table 10 devices installed in the MV network and their functions are described.

SCADA System implementing Network Control Centre DSO functions to enable and MVCC functions (Campobasso) exploit AD TPT2000 (RTU) HV/MV “Carpinone” substation and Real time network “Carovilli” remote MV busbar measurements for DSO algorithms

RTU, P&Q MV/LV substations & measurement MV producers and consumers devices premises Table 11. Field test devices installed in the MV network.

The installation of the measurement devices in the MV/HV substation (storage included) and in the MV producers and customer’s premises are illustrated in the Figure 40 and Figure 41.

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Figure 40. Equipment in the MV/LV substations.

Figure 41. Equipment in the MV producers’ and customers’ premises.

Due to the installed devices it was possible to collect the following measurements (in addition to measures of current and voltage on the top of the MV lines, transmitted to the SCADA through GSM/GPRS in real time): - P, Q in 10 MV/LV substations (MV/LV transformer load) - P, Q in 4 MV Producers premises - P, Q in 2 MV Consumers premises (MV load) - P, Q in 1 Storage system

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4.1.5. Exchanged messages between players Most of the message exchanged between the MVCC and the external actors involved in the architecture were simulated. In particular, the interactions with the market and the aggregators were simulated by manually creating a total supply bid file (with the AD products characteristics offered to AD market sent to SO for validation) and uploading it in the MVCC interface. The communication with the producers involved in the tests took place via e-mail when it was necessary to require production curves or some plants disconnection for the test execution.

4.2. Results

4.2.1. Assessment of the ADDRESS technology

Test Case 1: the DSO as an AD product validator In this case the DSO receives bids of AD products, which aggregators traded in the market or directly with other energy market players, to be validated. These bids should be curtailed or accepted by the DSO by means of the validation algorithms, and the results of the validation (accepted or curtailed bids) should be published in the MVCC interface. Then, the AD products will be activated by means of emulation and the real network operation will be observed. The target of the test is to ensure that: - The DSO algorithms run in a correct sequence without errors; - The DSO validation algorithms detect and curtail correctly an unfeasible (too large) AD product and publish the response on the MVCC interface. The following scenarios were considered: - Test 1 and Test 1bis: the DSO receives bids for the intraday market that should be curtailed or accepted - Test 2: the DSO receives bids for the day-ahead market that should be curtailed or accepted.

Test Objective Test procedure Expected result Result(OK/FAILED) date/time 29/01/2013 To ensure that 1. Run the GF, LF, All the algorithms OK 14.00 - the DSO FT should run without 15.00 validation errors algorithms 2. Upload the Total OK work properly SupplyBid file with SRP and CRP. The AD product 300 kW TSB2013_01_29 (250 kW SRP + 50 intraday 300kw LA storage crp down da levare.xml kW CRP UP + 90 kW CRP DOWN (100% curtaliled)) from 14.00 till 15.00 was offered on the LA DM602023612L001 (the LA where the storage system is connected)

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3. Run the OLV The OLV correctly OK algorithm accepted the AD products (curtailment factor =0,001)

cim_ieee14_state_rdf _OLV.xml 4. Run the RTV The RTV correctly OK (because we have accepted the CRP also the CRP product product in our TSB)

ValidationResponse_R TV.xml 5. Download the The DSE output before OK DSE outputs before the storage activation the storage are realistic (the activation current and the voltage behaviors were checked and compared with the real measurements in some control points)

outputAdrress 13.41.xml 6. Actuate the AD The storage system is OK products with the correctly activated (the storage system P, Q values in the LV measurement xml file on the LA DM602023612L001 were compared with the previous situation, p. 4) 7. Download the The DSE output during OK DSE outputs after the storage activation the AD product (at 14.20) is realistic activation (redo this (the current and the every 15 min while voltage behaviors were the AD is activated) checked and compared with the real measurements in some control points)

outputAdrress 14.20.xml 8. Verify the network There are no network OK operation by constraints in the comparing the DSE network operation (the outputs before, DSE outputs after the while and after the AD product activation AD products must not exceed the

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activation limits of the test line (+- 10% of nominal voltage)

Table 12. Test1: AD validation for the intraday market.

Notes: 1. The VVC is tested implicitly because it always reacts to achieve the optimal network situation 2. The files related to the tests described above are available on the MVCC interface (for the indicated date/time). The first preliminary observations are: 1. All the algorithms run well and give realistic results. 2. Before the AD product activation, the DSO had no network problems to solve and after the AD product activation by means of the storage system no network constraints appeared (DSE results). 3. The interpretation of DSE results presents some difficulties due to the fact that they are presented in .xml format. A user friendly visualization interface should be developed for the product to be suitable for real applications. 4. The MVCC algorithms behavior was compliant with the expected one.

Test Result Objective Test procedure Algorithms behavior date/time (OK/FAILED) 29/01/2013 To ensure that 1. Run the GF, LF, FT All the algorithms should OK 16.00- the DSO run without errors 17.00 validation 2. Upload the TSB file OK algorithms works with SRP = 300 kW on properly the LA TSB2013_01_29 DM602023612L001 intraday 300kw LA storage - scorta II cat.xml form 16.00 till 17.00 3. Run the OLV OLV has to accept or to OK algorithm and RTV curtail the "big" bids (part (because we have of SRP and/or CRP), also the CRP product curtailed CRP/SRP have in our TSB) to be published on the MVCC interface

cim_ieee14_state_rdf _OLV.xml 4. Download the DSE The DSE output before FAILED outputs before storage storage activation are not activation realistic

outputAdrress 15.20.xml 5. Actuate the AD OK products with the storage system LVMeasurements2013 0129151545.xml

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The P value on the LA DM602023612L001 is different respect to the P before storage activation 6. Download the DSE The DSE gave an error FAILED outputs after the AD product activation (redo this every 15 min while AD is activated) Table 13. Test1Bis: AD validation for the day-ahead market.

The first preliminary observations are: 1. All the algorithms (p.1) runs without errors. 2. Before the AD product activation the DSE gave an unrealistic result, not compliant with the real network situation. This result could be due to the following reasons: significant differences between field measurements and historic data or an unexpected reaction of the DSE to the connection/disconnection of capacitor banks in HV/MV substation. This reliable DSE is needed for real applications.

Results Test Date Objective Test procedure Expected result (OK/FAILED) 12/12/2012 To validate the All algorithms should OK "memory 1. run the GF run without problems function" of the 2. run the LF and in the correct OLV Algorithm. sequence. The memory The algorithms output function should will be automatically read the results updated to the OLV of the last OLV input folder and will and, if the appear in the OLV validated product interface page

is performed in The total supply bid has the same hours been prepared assuming and on the same a SRP load increase of LAof the new 300 kW from 19:00 to TSB2012_12_14 LA storage -300.xml products, the 20:00 on the LA latter will have to DM602023612L001 be curtailed Run the OLV. The OLV should not consequently. Be sure there is no thick receive any error This function is on the Enable button at message in updating requested forthe the bottom of the "input" the input files including evaluation of rectangle the TSB already products of the prepared in the intraday market, previous step. considering the The OLV should not results of the curtail the SRP product OLV of the day assuming a typical load ahead market of (observed from the real the previous day. measurements), on the LA DM602023612L001, from -60 to -100 kW

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The log file should present the normal sequence of operation: server.log Network model GF and LF input upload TSB upload Iterative calculation steps The result of the No curtailment has curtailment matrix should been applied OK be seen both on the interface and on the xml file

cim_ieee14_state_rdf_OLV_20121214182736.xml

The Total Supply Bid has been prepared assuming SRP load increase of 200 kW from 19:00 to 20:00 on the LA DM602023612L001 and a CRP down (load TSB2012_12_14 LA storage -200 + crp 50 con memoria.xml decrease) of 50 kW Run the OLV. The OLV should not Be sure there is the thick receive any error OK (0,845 CF) on the Enable button at message in updating the bottom of the "input" the input files rectangle comprising the TSB already prepared in the previous step. The OLV should curtail cim_ieee14_state_rdf_OLV_20121214184144.xml the SRP product with a curtailment factor value from 0,8 to 1 The OLV should curtail the CRP completely Table 14. AD Validation for the day-head market.

Test Case 2: the DSO as an AD product buyer In this test case, the DSO has to buy AD products in order to solve some expected network constraints. A network constraint is simulated by changing the thermal limit of a MV cable in the network under test. The power flow variation to solve this network problem is calculated preliminarily and the amount has to be equal to/larger than the AD bid that the DSO has to buy on the market. Then, the DSO sends a request to the market and receives a response. In case of positive reply, the AD product is activated by means of the storage system. The observed real network operation has to be compliant with the DSO forecast (the current through the interested cable has to be lower than the modified thermal limit).

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Scope of Date Test procedure Expected result Results the test (OK/FAILED) 19/02/2013 To verify 1. Run the GF, LF, FT All algorithms run without problems and in OK that AD the correct sequence products 2. A network constraint is created by setting a new The changed parameters can be found in OK could be (inferior) thermal limit of the MV cable between DM60- the attached file (row 20847-20853 related used to 4027517 and DM60-2023541 in the MV equipment file. on the segment solve The DSO can solve the problem by means of a load DM604027517_DM602023541_0 network reduction in the adjacent LA. The load reduction is problems simulated by the storage, which injects the energy in the network. MVEquipment_20130 215093725m1.xml 3. Upload the Total SupplyBid file with the AD product on The created TSB contains SRP =50 kW, OK Load Area where the storage is installed. CRP UP = 50 kW and CRP DOWN = 5 kW products that correspondto a Load reduction = 95 kW on the LA 0001DM602023612L101 from 16.00 till 17.00

TSB2013_02_19 pom2 100kw LA storage crp da levare (1).xml 4.The OLV has to curtail/accept and publish the validation The OLV accepted SRP and CRP because OK response they do not exceed the power limit of MV/LV transformer of the LA 0001DM602023612L101 (where the AD product is simulated).

cim_ieee14_state_rdf _OLV.xml

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5. Run the RTV (on demand) because the TSB contains The RTV accepted correctly the SRP and OK the CRP product. CRP

cim_ieee14_state_rdf _OLV.xml

6. Download and analyze the DSE output before the AD The current on the interested cable OK products activation exceeds the 10 A (in this case it is equal to 16,5 A). It means that the DSO could have a network problem if the current on the interested cable is not reduced

outputAdrress 13.45.xml 7. Activate the validated AD product with the storage (100 The values of P, Q on the LA OK kW emission) and analyze the DSE output and LV 0001DM602023612L101in the LV measurements .xml file measurements .xml file has to change with respect to the normal state (before the storage activation)

LVMeasurements2013 0219153826 before storage.xml

LVMeasurements after storage.xml

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8. Download the DSE output while (every 15 min) and There are no network constraints in the real OK after the AD product activation. Verify the network network operation after the AD products operation by comparing the DSE output before and after activation (the current in the indicated the AD products activation cable is less than 10 A (6,5 A) and the voltage does not exceed the limits of the test line (+-10% of nominal voltage)

outputAdrress 16.30.xml

Table 15. DSO as an AD product buyer.

The observations are: 1. All the algorithms run well and give realistic results. 2. Before the AD product activation the DSO had an issue: the current in the interested cable was higher than 10 A (e.g. 17 A) 3. After the AD product activation by means of the storage system the current on the interested cable was less of 10 A (6,5 A) so the DSO problem was solved by means of the AD products. 4. The MVCC algorithms behaviour was compliant with the expected one.

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Test Case 3: Localized AD products to limit the power flow towards the transmission system The target of the test is to limit the power flow rising to the transmission network through the primary substation using the localized AD product. In particular, the AD contribution of specific load areas will be programmed within the day-ahead market. During the test an analysis of historical data of consumption patterns of the primary substation Carpinone was carried out (green busbar); in particular data from December 2011. Years 2012 and 2011 have similar weather and load conditions. A significant reduction of power consumption occurs during the central hours of the morning. At the same hours of Saturdays and Sundays there is also the reversal of power flow to the transmission network. The maximum reverse flow is about 2,3 MW in the day December 26th (vacation day in Italy), the reason for this behaviour is that the network has mainly industrial customers, which reduce their load during the day off. In this case the DSO is configured as: • Distribution network manager: the DSO verifies if any network problems and congestions occur, and verifies the technical possibility to solve the problems with an active demand product • Active Demand product buyer: the buys from the market the most suitable active demand product from an aggregator.

The test steps are: 1. Creation of the total supply bid for the day-ahead market in order to have an AD product from 11.00 to 12.00 of the day after 2. Launch of the online validation and verification 3. Emulation of the AD product the next day 4. Check the distribution state estimator if "network constraints" occur and if limitation of the power flow towards the transmission network through the primary substation at Carpinone does occur

Test 1: 19th December This consumption profile is foreseen: • 11:00 to 11:35 308 kW • 11:35 to 11:45 150 kW • 11:45 to 12:00 50 kW

19th December Test procedure Expected result Notes 1. run the GF All algorithms can run without This test can be executed also for 2. run the LF problems and in the correct the day ahead sequence The algorithms output will be automatically updated on the OLV input folder and will appear Weather_Isernia_2012_12_19.xml in the OLV interface page

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19th December Test procedure Expected result Notes The Total Supply Bid If we simulate the AD products with prepared assuming an the storage system, they have to be SRP load increase of 250 simulated in the LA kW from 11:00 to 12:00 on DM602023612L001 the following LA: DM602023612L001 DM602023541L001 TotalSupplyBid 2012_12_19 limitazione risalita.xml

Run the OLV. The OLV should not receive any Be sure there is no thick on error message in updating the the Enable button at the input files comprising the TSB bottom of the "input" already prepared in the previous rectangle step. The OLV should not curtail the SRP product assuming a typical load (observed from the real measurements), on the LA DM602023612L001, from -60 to -100 kW The log file should present the normal sequence of operation: Network model GF and LF input upload TSB upload Iterative calculation steps The result of the These curtailments were The curtailment factor on the very curtailment matrix can be applied: low value of the CRP up is 1.00 seen both on the interface DM602023612L001: 0% (totally curtailed); contemporarly the and on the xml file DM602023541L001: 77,2% CF on the SRP product is not 0.This error is due to a bug of the iterative calculation process; the error is not fatal and do not compromise the test success

cim_ieee14_state_rdf_OLV.xml In order to provide the planned AD product it is possible to use only the load area DM602023612L001 (Centro Squadre), where the storage is located. These are the following set points for the storage: 11:00-11:35 308 kW 11:35-11:45 150 kW 11:45-11:57 50 kW

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19th December Test procedure Expected result Notes The OLV should not receive any error message in updating the input files comprising the TSB DistributionStateEstimator1200.zip already prepared in the previous step.

The OLV should curtail the CRP completely Table 16. Localized AD products to limit the power flow towards the transmission system – Test 1

The observations are: 1. a strong load reduction occurs from 11 to 12 in the morning, but only in some cases an inversion of the power flow occurs. 2. in case of power flow inversion, the storage cannot always ensure enough power to limit the flow rising to the transmission network through the primary substation Carpinone. 3. in case of storage activation, the connecting cable is not always able to support the requested consumption. It becomes necessary to use also other load areas to provide the requested AD product.

Test 2: 28th January Tests carried on during December showed that the contribution of the storage only not always is enough to limit the flow rising to the TSO network through the primary substation at Carpinone. It is therefore necessary to use other load areas to provide the requested AD product. In particular a production reduction will be requested to t he generators on the network; so, the total supply bid must also include the contribution of AD provided by them. The total supply bid includes: Load Area 0001DM602023612L001 W -250000 Load Area 0001DM602023541L001 W -250000 Load Area 0001DM602023503L001 W -150000 Load Area 0001DM602023541L001 W -150000 Load Area 0001DM602027506L001 W -100000 Load Area 0001DM602031519L001 W -100000 Load Area 0001DM602031518L001 W -200000 Load Area 0001DM602031517L001 W -150000 Load Area 0001DM602031514L001 W -100000

It corresponds to the following requirements for the generators on the network under test:

Generator Line Installed power AD foreseen [kW] [kW]

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Biogas Pesche 700 300 PV Biogas Pesche 950 400 Storage (consumption) Pesche 500 500 TOTAL 1200

In order to provide the AD product, the storage on Load Area “Centro Squadre” (DM602023612L001) is used. The foreseen consumption profile is: • 11:00-11:30 500 kW • 11:30-12:00 0 kW

28th January Test procedure Expected result Notes 1. run the GF All algorithms can run without This test can be executed 2. run the LF problems and in the correct also for the day ahead sequence The algorithms output will be automatically updated to the OLV input folder and will Weather_Isernia_2013_01_28.xml appear in the OLV interface page

The Total Supply Bid was prepared assuming an SRP load increase of 1200 kW from 12:00 to 13:00 on the following LA: TotalSupplyBid 2013_1_28 limitazione risalita new.xml 0001DM602023612L001 kW250 0001DM602023541L001 kW250 0001DM602023503L001 kW150 0001DM602023541L001 kW150 0001DM602027506L001 kW100 0001DM602031519L001 kW100 0001DM602031518L001 kW200 0001DM602031517L001 kW150 0001DM602031514L001 kW100

Run the OLV. Be sure there is no thick on the Enable button at the bottom of the cim_ieee14_state_rdf_OLV_20130128121515.xml "input" rectangle

The log file should present the normal sequence of operation: Network model GF and LF input upload TSB upload

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28th January Test procedure Expected result Notes Iterative calculation steps

The result of the curtailment matrix can be seen both on the interface and on the xml file

In order to provide the planned AD product the following generators are involved: Biogas Pesche (kW700) kW300 requested PV Biogas Pesche (kW950) kW400 requested Storage Pesche (kW500) kW500 requested

TOTAL 1200 kW

The OLV should not receive any error message in updating the input files DistributionStateEstimator1200.zip including the TSB already prepared in the previous step.

The OLV should curtail the CRP completely Table 17. Localized AD products to limit the power flow towards the transmission system – Test 2.

The first preliminary analysis is: 1. a strong load reduction occurs in specific hours (11-12 in the morning), but only in some cases an inversion of the power flow occurs. In case of a power flow inversion the storage cannot always ensure enough power to limit the flow rising to the transmission network through the primary substation at Carpinone. 2. in the case of storage utilization, the connecting cable is not always able to support the requested consumption. It becomes necessary to use also other load areas to provide the requested AD product 3. an economic analysis is required to quantify costs and benefits of the action taken: - costs: penalties for the non-production of generators; - benefits: avoided penalties for the resolution of congestion and / or flow towards the TSO.

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Test Case 4: MVCC algorithms reaction to network changes The aim of this test is to verify the algorithms reaction to the relevant network structure modification. This test was carried out on purpose exploiting a “big” network configuration variation. In fact, this test was performed while the Carovilli operative centre was disconnected due to scheduled maintenance works.

Test Results Objective Test procedure Algorithms behavior date/time (OK/FAILED) 20/02/2013 To verify that the 1. Run the GF, LF, All the algorithms should run OK 16.00- algorithms react FT without errors 17.00 correctly after 2. Download the The Carovilli Operative OK modification of Network model Centre disconnection is the network files and the DSE detectable in the MV structure outputs after the topology .xml file Carovilli Operative Centre disconnection MVTopology_2013022 0003125.xml The DSE output is realistic (the current and voltage values were checked in some control points)

output.xml

Table 18. MVCC algorithms reaction to network changes

The observations are: 1. The MVCC correctly receives the measurements and the network model is correctly updated. 2. The DSE reacts well in case of relevant network structure modification and it is able to give a realistic evaluation of the new network state.

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5. France

5.1. Description of the test

Extensive tests were performed in EDF laboratory to prepare the French field tests. They were conducted in conditions very near to those encountered on the field and allowed to assess the technical feasibility and the performance of the ADDRESS chain components during individual and interaction tests [6]. They also allowed to identify the limitations and the risks linked to the currently implemented solutions. Since these unitary and interactions tests were extensively carried out during the previous testing stage, they are no longer performed in the field tests. The test cases carried out during the French field tests are mostly global scenarios to which all the main components of the ADDRESS architecture contribute. The objectives of the French field tests were mainly the following:  Using the results of the laboratory tests as a basis, one of the aims was to evaluate the technical feasibility and the performance of the complete ADDRESS chain.  During the field tests, it was assessed how AD can meet the needs of the electricity system players using bilateral contracts (SRP and/or CRP). The provision of AD services was based on the simulation of possible problems or actual needs identified by electricity system players (DSO, TSO, BRP…) or by other players (retailer, RES producers – PV in our case…). These needs resulted in appropriate AD requests tested on the field. The AD services such as power reserve for imbalance management, load shaping (load increase or decrease) for technical or economical optimizations, voltage control and power control to relieve overloads or network congestions are considered.  The impact on the network of the obtained AD volumes was examined too. The effects forecasted using the AD product volumes submitted by the aggregation entity and validated by the DSO were compared with the effects caused by the load variations observed on the field.  In the field tests, we also studied the response of the consumer portfolio to the incentive signals defined by the aggregation entities. This study performed on both individual (consumer) and global (cluster of consumers) levels allowed us to verify if, how and under which conditions the initial AD need was fulfilled. The forecasted behaviors of the consumer appliances and of the global cluster were compared to the ones measured on the field.  In addition to the previous test cases, another objective was to test the simulated market interactions of the aggregation entities and other electricity system participants (e.g. retailer, or balancing responsible party). Several market scenarios were performed. We aimed at assessing the impact AD could have on the prices of the simulated electricity market; different market conditions were considered. The integration of the simulated electricity market inside of the complete ADDRESS chain of process was also tested.  Finally, consumers’ acceptance and commitment were also assessed but this topic is out of the scope of this deliverable. In this respect, the studies carried out and the result obtained are described in ADDRESS Deliverable D5.2 [7].

Several cases were tested in order to cover these objectives of the French field tests. Most of them

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are about the complete ADDRESS chain of processes providing different service types. Others concern the electricity market simulator and its interactions with the aggregation entity. All these cases allow us to verify the capability of the implemented ADDRESS architecture to respond to the envisioned situations and to test the performance of its modules on real data. Those involving all the components of the ADDRESS architecture are the following:  Request from the DSO for reducing a local overload constraint,  Request from the TSO for reducing a congestion constraint,  Request from the BRP for managing the imbalance between production and consumption,  Requests in combination with RES for reducing a local voltage constraint anticipated by the DSO or providing AD services to a RES producer,  Requests from the DSO for limiting the global consumption of the islands over a complete day,  Offers providing AD services to an electricity market. It is to be noted that the proposed field test cases are generic enough to be illustrative of most of the services that could be provided by the ADDRESS architecture. The AD requests that have been considered and tested may indeed correspond to the needs of different players, or in other words each of the 31 AD services defined in the Deliverable D1.1 [8] may be associated with at least one of the cases played in the French field tests. The description of the test cases and detailed test scripts performed in the French field tests can be found in Chapter 8 and in the Appendix of Deliverable D6.1 [1].

5.1.1. Location The field tests in France were performed on the two islands of Houat and Hoëdic in the South of the Brittany region, in the West part of France. The network considered is composed of 1 MV feeder and 8 MV/LV substations. The island of Houat is connected to the continent through a 19 km long underwater MV cable, and a second 8.5 km long underwater MV cable connects the island of Houat to the island of Hoëdic. On Hoëdic, one private PV plant with a power of 100 kW peak is connected to the LV grid. At certain periods of the year (e.g. May or June) the PV production may be very high with respect to the consumption of the island. There are a total of 569 consumers for both islands, of which about 373 are permanent consumers. Indeed, there is a high rate of secondary houses. Around 30 domestic consumers have taken part in the tests, leading to a rate of 5 to 8 % of active consumers, which is quite significant for such a demonstration. On these islands a lot of the consumers use electricity for their water heaters (water tanks containing between 150 and 300 litres) taking advantage of the off peak tariff (8 hours per day), and for heating by means of electric heaters. This represents the major part of the electricity consumption for these consumers. The French field tests site is described in more detail in ADDRESS Deliverable D6.1 [1]. As already mentioned, the French field tests are dedicated to the validation of the whole ADDRESS chain, i.e. from the AD buyers to controllable appliances at the consumers’ premises, which is shown on Figure 42. The configuration of the chain and of its different components for the carrying out of the French field tests are described in detail Sections 5.1.3, 5.1.4 and 5.1.5.

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DSO

TSO Consumers Retailer

Other non

controlled usages Balancing Responsible Washing Aggregation machines

system Meter or Party platform equivalent Smart plugs Producer EB

bilateralrelationships Water

Market,contracts direct or heaters

Electric Players or functions the of electricity heating

Figure 42. The whole ADDRESS chain tested in France

5.1.2. Players involved In order to conduct the field test cases described in the previous section, the complete ADDRESS architecture is considered and tested. Depending on the scenario, different players are involved in the tests. In every test script, at least the three following players are to be considered: the DSO, the aggregation entity and the consumers (or more precisely their EBoxes and appliances). The Market Simulator (MS), simplified representation of the Electricity system players and real local players are also considered in some of them.

5.1.3. Architecture of the System As already mentioned, during the laboratory tests, the modules composing the ADDRESS architecture were validated and most of the limitations and risks they present were identified. Based on these results, choices were made regarding the ADDRESS architecture implementation for the French field tests in order to ensure that both the system performance and reliability are guaranteed. Based on the results of the laboratory tests, a two-part system compliant with the ADDRESS architecture and fit to the reliability requirements of a field test was retained for the French field tests. This system is composed of two distinct parts (Errore. L'origine riferimento non è stata trovata.):  the upstream part of the architecture consisting of the DSO Platform, the ATB System, a simplified modeling of the different electricity system players and the Market Simulator. It is managed in off-line mode. Since significant risks of failures of the ATB optimization processes were detected during the laboratory tests, the optimization results could not be directly applied in the EBox database connected to the internet. The ATB and the DSO platforms are run with a local copy of the EBox database (renamed ATB database). Using the database, the whole day-ahead and intra-day sequences are run with the interactions between the DSO and the ATB platforms being considered. If the sequences prove to come to their end and succeed, they are approved for application.  the downstream part consisting of the ecosystem of the Energy Boxes. It is always run connected to the internet in an online mode. The correct operation of the Energy Boxes (EBox) highly depends on its availability and the robustness of the information present in the

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EBox database of the aggregation entity (where the EBoxes get the signals generated by the ATB). In case a problem arises in this part, the consumers could be impacted. Special care is thus taken so that no erroneous data is input in this database.

The link between both parts is ensured by the synchronization of the two EBox and ATB databases. This synchronization occurs only when every precaution has first been taken to ensure that no problem may arise on the field. The strict separation between both parts was also useful to run some cases for which tweaks were required. Indeed, some of the algorithm limitations identified during the laboratory tests required manual interventions in order to by-pass some modules and run the considered test script (e.g. when testing load increasing signals). Detailed information on the system implementation deployed for the French field tests and on the impact of these two separate operation modes on the management of the platforms included in the ADDRESS system will be specified in the following section.

Weather websites DSO Platform Historical Consumption

Upstream server

chain SOAP webservices

Simulated ATB Electricity ATB System Database Market

2-way

Synchronization Downstream

EBoxes Internet chain EBoxes WebServer EBox

EBoxes Database EBoxes

Figure 43 - Operation mode of the ADDRESS architecture considered in the French field tests

5.1.4. Equipment installed

5.1.4.1 Upstream side of the ADDRESS chain: market, DSO and aggregation function

5.1.4.1.1 DSO platform The DSO performs different activities depending on the considered test case

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CIM Weather Network DSO Platform Weather network forecast description websites converters manager

Algorithm Weather data managing database engine LAC Historical Consumption load data GenF LoadF data database server FTC AD flexibility OLV RTV data database Historical ATB AXIS Server databases SOAP

: Interactions of the DSO platform with external actors : Interactions within the DSO platform ( Figure 44.). All of its activities are run using the DSO platform developed by the EDF team and where in particular the algorithms developed for the DSO by the project partners have been integrated. Its specific components and GUIs are used.  The platform is used to anticipate the distribution network constraints and to define the DSO needs in terms of AD products in regards to these constraints. These flexibility needs are transmitted to the aggregation entity in order to adjust the local consumption of its consumers’ portfolio.  The DSO platform operates automatically as the actor responsible for the technical validation of the Active Demand programs. It communicates the flexibility information to every aggregation entity, performs the technical validation of the submitted flexibility programs and returns the curtailed volume to the appropriate aggregation entity.  The DSO platform enables assessing the impact of the real flexibility response of the consumers on the evolution of the distribution network quantities and its constraints.

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CIM Weather Network DSO Platform Weather network forecast description websites converters manager

Algorithm Weather data managing database engine LAC Historical Consumption load data GenF LoadF data database server FTC AD flexibility OLV RTV data database Historical ATB AXIS Server databases SOAP

: Interactions of the DSO platform with external actors : Interactions within the DSO platform

Figure 44. Main components of the French DSO platform and interactions with external actors.

5.1.4.1.2 Aggregation entity and ATB The aggregation entity is the central component of the ADDRESS architecture. It interacts with several actors depending on the considered case.  EBoxes. The main interaction of the aggregation entity with the consumers is through their EBoxes. The aggregation entity defines and “sends” price and volumes signals to the consumers’ EBoxes to obtain modifications of their consumption.  Regulated and deregulated players. The flexibility needs expressed by regulated and deregulated actors are taken into account by the ATB platform algorithms. The signals sent to the EBoxes depend on the requested flexibility volumes and timeslots.  DSO. The aggregation entity takes into account the local flexibility information defined by the DSO in order to assess the offer volume it can respond to and to constrain the selection of the signals that can be sent to the EBoxes. In the same way, the results of the technical validation carried out by the DSO are integrated by the aggregation entity when choosing the signals for the EBoxes.  Market. The aggregation entity can offer flexibility volumes on an external electricity market and take into account the acceptance level of its offer proposal. The management of its algorithms and their interactions with external actors is done by the two platforms developed for the French field tests: (i) the ATB prototype management software, used to generate the content of the ATB database, and (ii) the ATB platform, used to manage the day-ahead and intra-day optimization sequences. Both platforms are part of the system of the aggregation entity, i.e. which will be called here the ATB system (Figure 44)

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EBoxes Internet EBoxes WebServer EB EBoxes Database EBoxes 2-way Synchronization

ATB Consumer prototype Samples management Generation ATB Database Response Signals Forecast Adjustment

Regulated ATB Market Price / Platform Forecasting deregulated actors ATB System Optimization ESB Algorithms Orchestrator Market

SOAP AXIS Server DSO

: Interactions of the ATB platform with external actors : Interactions within the ATB platform

Figure 45. Main components of the French ATB system and interactions with external actors.

5.1.4.1.3 Market simulator The tests performed regarding the Market Simulator (MS) follow several objectives:  Assessing the impact of AD offers generated by the ATB or any aggregation entity on the wholesale market prices,  Analyzing the market acceptance of these AD offers,  Evaluating the potential benefits for the aggregation entity. It should be noticed that the market considered for the tests is the (French) SPOT national energy market where different actors (DSO, TSO, aggregation entities, BRPs…) interact in order to sell or buy volumes depending on their needs and their resource availability. These volumes are aggregated in one global offer and one global demand coming from the forecasting tool (Figure 46.). Other markets could have been considered assuming that they follow similar principles. The SPOT market was favored since the data required for the studies are public.

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Figure 46. Interaction of the ATB system with the Market Simulator.

During the tests, two MSs have been used. The 1st one is the tool developed in Task 5.3 of WP5 and the 2nd one is a tool developed by the EDF team in order to run additional test cases not possible with the 1st simulator.

1. Initial market simulator The market simulator developed in Task5.3 is a JAVA program which operates similarly to a real electricity market. It uses demand and supply bids as inputs and then clears the market. The tool only accepts hourly and blocks bids for a single market area. A bid creation tool was also developed in Excel in order to simplify the creation of the bid files for the test cases. The integration of the hourly bids is realized as follows:  The supply bids and demand bids are aggregated separately: the aggregated curve is the sum, for each price, of the volumes from the different bids (Figure 47.). We thus obtain two curves: the hourly total supply curve and the hourly total demand curve.  The two curves are then compared to find the temporary equilibrium, i.e. the price for which the demand and supply volumes are equal (or intersect). This allowed us to understand several wholesale price issues. But several problems were encountered while integrating the AD offers and motivated the development of an alternative market simulating tool.

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Bid 1 Bid 2

Total bid

Figure 47. Aggregation of supply bids

2. Alternative market simulator The alternative market simulator was developed in Excel by EDF in WP6 in order to run additional test cases. In this new version, the equilibrium point is found by intersecting linearly the price-volume curves of the total offer and total demand. This choice was made to simplify the computation process. However, when the AD offer of the aggregation entity is partially accepted by the market, the market simulator will then use the usual starcase model. So, we are sure that the price of the equilibrium is the price of the AD offer.

5.1.4.2 Downstream part of the ADDRESS chain: Energy Boxes The equipment installed at each consumer’s premises consists of: - one EBox composed of PC, - up to 7 smart wall units to control the electric heaters and the water heater, - up to 3 temperature sensors, - 5 smart plugs to control other types of “classical” electric appliances, - Smart washing machines developed in the project and able to communicate directly with the EBox installed at 7 consumers. The configuration is shown in Figure 48.

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ADDRESS Control Center (EDF Clamart)

At consumers’ premises

Meter EBox PC ADDRESS

Smart washing Electric appliances connected Temperature machine through smart plugs or wall units sensors (7 consumers) • radiators • water heater • white goods…

Figure 48. Equipment installed at consumer’s premises.

The EBox is the computing device installed at the consumer’s premises that contains the energy management software. It manages the controllable devices inside the house (Smart Plugs and Wall Units, Temperature Sensors, Smart Washing Machine...) and communicates with the aggregation platform through the so-called EBox database described in Section 5.1.4.1.2 (and

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EBoxes Internet EBoxes WebServer EB EBoxes Database EBoxes 2-way Synchronization

ATB Consumer prototype Samples management Generation ATB Database Response Signals Forecast Adjustment

Regulated ATB Market Price / Platform Forecasting deregulated actors ATB System Optimization ESB Algorithms Orchestrator Market

SOAP AXIS Server DSO

: Interactions of the ATB platform with external actors : Interactions within the ATB platform Figure 45.) to get the AD requests (Figure 49.). The EBox is always operating in an online mode, i.e. it is always connected to the Internet. The EBox database of the aggregation platform is remotely accessed every 15 minutes with this internet connection using webservices. This remote access allows in particular to detect if a new incentive signal has been added in the EBox database so that the optimization algorithm of the EBox is then launched. During this optimization run the requested load modification operation is taken into account. It is to be noticed that the incentive signals input in the EBox database are either selected by the ATB platform algorithms or manually chosen. During the French field tests, several types of devices have been managed by the EBox. Table 19 summarizes the loads that have been controlled.

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Internet WebServer EBox Database

Figure 49. Home side system and EBox database.

Category Device

Non-Controllable load Fridge, Freezer, Kettle, Micro Wave, Light, PC, TV…

Shiftable load Electrical Water Heater (EWH), Dryer, Washing Machine, Smart Washing Machine, Dishwasher

Interruptible load Electrical Heaters

Table 19. Controllable loads considered during the French field tests.

5.1.5. Test conditions In this Section, we give, for each platform (DSO, ATB, EBoxes...), a global description of the test conditions and of the settings used when performing the field test cases.

5.1.5.1 ATB platform

5.1.5.1.1 ATB database adjustments The laboratory tests showed that a new ATB database was necessary to be able to carry out some of the French field tests. It was then built in accordance with the specificities needed. This database has a better balance between the two types of incentive signals: 55% of the signals are meant for load decrease and 45% of the signals are for load increase. This has to be compared to the 85% / 15% ratio of the initial test database. The parameters of the incentive signals and the cluster prototypes as well as the prototype parameters have also been recalibrated following the lessons learned from the laboratory tests.

5.1.5.1.2 Clustering methodology The prototypes defined in the new database were constituted using the consumption data of the

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consumers taking part in the field experiments. Over a year of consumption data were used for this purpose. After their analysis, several groups have been defined depending on:  the season of the year: Spring / Summer/ Autumn / Winter,  the day type: working days / Saturdays / Sundays or holidays... For each of these groups, two distinct load curve prototypes have been determined. They result from the classification performed on the consumer consumption profiles.

Consumers’ Daily consumption Curves

Season clustering

Spring Summer Winter Autumn

Day type clustering

Working Saturdays Sundays days

Profile clustering

Profile 1 Profile 2

Load Area clustering

Prototype Prototype Prototype (Load Area 1) (Load Area i) (Load Area N) Figure 50 - Clustering methodology applied to the French field tests

An additional clustering level has also to be considered since the complete ADDRESS architecture is tested in the French field tests: prototypes are defined depending on the Load Area attribution of each consumer. It is to be noticed that the prototype load profiles do not change between the Load Areas, only the number of associated consumers and specific characteristics change: some consist of very few consumers while others are a little larger. The methodology used for the prototype clustering is shown in Figure 50.

5.1.5.1.3 Constitution of the prototype consumption profiles Different actions were performed in order to carry out the clustering of the French consumers taking part in the ADDRESS field tests on the Brittany islands.  Consumer classification and consumer type identification The consumption data measured during a period of more than one year were used in order to classify the different consumers and calculate the average consumption of each prototype. A software tool previously developed by EDF for its own studies was applied to this set of consumption data. It allowed us to identify two main types of consumers. Three different day types were also defined even if this 3-part separation was only required for one of the two consumer classes.

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The consumer recruitment was still going-on at the time of the classification and new consumers continued to join. Classification criteria were provided in order to attribute these new consumers to one of the two consumer classes without having to carry out again the whole classification process. It is to be noticed that more classes could have been defined but they were not created given the limited number of consumers taking part in the French field tests.

 Correction of the weather data influence The analysis of the load curves showed a direct impact of the weather, and in particular of the temperature, on the consumers’ consumption profiles. This load data was processed with another tool previously developed by EDF for its own studies in order to correct the variability induced by the temperature on the profiles and normalize them, i.e. estimate the consumption that would have been observed for normalized weather conditions.

 Generation of the prototype consumption profiles Based on the previous results, 24 (4x3x2) prototype consumption profiles were computed for the defined seasons, day types and consumer classes. Some of these prototypes are shown in Figure 51..

2000

1500

1000 Power (w)Power 500

0 0 4 8 12 16 20 24 Time (h) Working Day Saturday Holiday

1200 1000 800 600

Power (W) Power 400 200 0 0 4 8 12 16 20 24 Time (h) Working Day Saturday Holiday Figure 51. Spring consumption prototypes for the 3 day types and the 2 consumers types.

5.1.5.1.4 Appliance modeling Some appliance types defined by default in the ATB database were not applicable to the context of

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the French field tests since all of them they were developed for the Spanish field tests. In particular, the water heaters were first defined in the ATB database as interruptible devices of “low” power (less than 300 W) working continuously. But, in France, the water heaters are of higher power (several kW) and only work during short periods of time (those with off-peak prices) since remote signals sent at the change between peak and off-peak time are used to control such devices. In order to take into account these differences, the water heaters were defined as shiftable devices both in the database and on the field. In the database, the statistical distribution of their starting times was also modified in order to take into account the time intervals corresponding to the price change from peak (high electricity price) to off-peak (low electricity price) periods: these intervals present the highest probability for the water heaters to start their heating cycle. But the adjustments carried out on the modeling of such loads could not fully solve the problem: the statistical distribution of the starting times cannot compensate for the impact of the over-a-day price variations implied by the remote tariff signals used in France. It is to be noted that the EBoxes algorithms take this price information into account when managing their loads, i.e. when shifting or interrupting their consumption following the user’s preferences and the price information.

5.1.5.1.5 ATB platform operation The database generated with the previously defined clusters was used in all the test cases performed for the French field tests described in Deliverable D6.1 [1]. Depending on the case, the ATB platform interacted with the DSO platform or an external electricity market simulator. It is to be noticed that the unitary and standalone tests of the ATB platform were already performed during the laboratory tests. These tests were meant to:  Analyze the usage of the ATB database and evaluate the performance of the algorithms used to build it (consumer prototype generation, cluster flexibility forecasting...),  Assess the capability of the ATB optimization algorithms to supply AD load increase and decrease requests for different seasons, day types and day periods given a fixed database,  Check the interactions of the ATB with the DSO platforms during the validation sequence. The test scripts associated with these lab tests can be found in the Appendix of Deliverable D6.1 [1].

5.1.5.1.6 Scaling the cluster flexibility Some test cases imply that a large amount of flexibility is available in the aggregation function portfolio. Such an amount is not possible given the limited number of consumers taking part in the French field experiment. A scaling factor was applied to the ATB database for these cases in order to simulate a larger presence of AD in the islands. This factor does not modify the flexibility profile that could be offered: during the lab tests, it was assessed that the flexibility forecasted for a cluster of 10 consumers was similar to the one forecasted for a cluster of 100 presenting the same characteristics once scaled appropriately by a factor 10. This scaling property is linked to the statistical method used for the forecasting. The same factor was applied to the flexibility impact observed on the field when comparing the ATB flexibility forecasting with the obtained flexibility.

5.1.5.1.7 Incentive signal selection The ATB platform optimization algorithms are able, in most of the test cases, to select suitable incentive signals to be sent to the associated clusters: in most cases, the chosen signals are directly

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supportive of the volumes of the AD request. This means that load decrease requests are supplied by means of load decrease signals and load increase requests by load increase signals. However, in some cases, the payback effect of the signals is used by the algorithms to supply the request instead of its primary effect. For example, the payback effect of a load increase signal is used to limit the consumption during the time interval associated with the load reduction request. In this document, when used this way, the signals are referred to as “inverse signals”. The rate of occurrence of this phenomenon is smaller than the one observed with the database used for the lab tests, but it still remained. It is directly linked to the flexibility response forecasted by the algorithms applied during the database generation phase. For example, the volumes that load decrease signals can provide are sometimes smaller than those associated with the payback effect of a load increase signal starting before. The problem when using such “inverse signals” is that they rely on the quality of the forecast of the payback effect, which is even harder to forecast than the direct effect of an incentive signal. The measurement and assessment of this payback effect of AD requests at a cluster level is even more complex (see subsection 5.2.3). Thus since “inverse signals” are highly unreliable and may lead to possibly harmful effect at consumers’ premises, when they are selected by the ATB optimization algorithms, they are not applied on the field. The optimization processes are rerun after slightly adjusting the AD request volumes and/or time interval until the signal selection is successful. All these adjustments were run off-line using the ATB database. For some cases, such adjustments could not achieve the expected result and the signal selection had to be performed manually by bypassing the ATB platform algorithms.

5.1.5.1.8 ATB algorithm execution As explained before, the ATB platform has to be bypassed in some cases, mainly because of some limitations of the current ATB implementation. For instance, this happens in situations where the payback effect of signals opposite to the request is used to respond to the expressed AD need, for instance when intra-day load increases are requested. In every other case, the ATB is implied to run the complete day-ahead and the intra-day sequences to ensure that no error appears during one of the optimizations before applying the selected incentive signals to the on-line EBox database.

5.1.5.2 DSO platform The three operation modes of the DSO platform are described in [6]. In the field tests two of them are used depending on the scenarios that are considered:  The accelerated-time mode is the one used in most of the field test cases. In this mode, the platform operates in standalone, manages automatically the interactions with the ATB platform and disregards any scheduling. For example, the AD requests sent by the ATB are considered whenever they are submitted (no time limit or submission interval considered) and the responses to these requests occur immediately at the end of the validation algorithm computation.  The off-line mode is used when the ATB has to be bypassed. In this situation, the DSO algorithms are executed in sequence (sequential tests) with the interactions between the DSO and the ATB platform simulated. The ATB requests are generated using the GUI embedded in the DSO platform for this purpose. NB: A third operation mode of the DSO platform, the scheduled mode, has been tested in laboratory

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but has not been used in the field tests due to communication issues. The field test cases involving the DSO platform are very similar to some of the tests performed in the laboratory (in particular tests scripts 7 and 8 of the DSO algorithms [1]). All these tests aimed at assessing the performance and reliability of the algorithms on an individual level and integrated in the ADDRESS architecture. The main differences between the lab and field tests rest in the context, i.e. in the inputs linked to the tested field scenario. Depending on the field test cases, the tests are performed:  Either on the real network of the Brittany islands operated in nominal or near-nominal conditions;  Or on a modified network constrained by one or more of the following actions: modification of network components (line impedance, transformer rated power…), modification of the PV plant characteristics, adjustments of the forecasted load curves…

5.1.5.3 Energy Boxes

5.1.5.3.1 Configuration of the loads This section details the parameters that have been used to specify the different types of loads and their parameters in the configuration file of the EBoxes. A special focus is given to the Interruptible Loads and to the Electrical Water Heaters as shiftable loads.

5.1.5.3.2 Interruptible loads During the field tests, most of the loads declared as Interruptible Loads were electrical heaters. This is due to the fact that serious issues were met with the Thermal Load type in the laboratory tests [9]. Despite all the effort spent by the developers and EDF team, these issues could not be solved and Thermal load type could not be tested on the field. Electric heaters were thus defined as interruptible loads. Electrical heaters represent an important part in the house consumption. This is why this section focused on the described parameters for electrical heaters. The Interruptible Loads are loads that can be interrupted by the EBox for a given duration. There are four parameters that have to be defined in the configuration file of the EBox:  Duration: it is the maximum duration of each interruption (expressed in timeslots of 15 minutes);  Distance: it is the minimum distance between two consecutive interruptions (in timeslots of 15 minutes);  maxOFF: it is the maximum number of interruptions during one day;  onPower: it is the theoretical power which represents the consumption of the load (in Watt). This parameter is used by the optimizer, but it was not the measured appliance consumption. During the field tests, we initially used the same parameters for every Interruptible Load:  Duration=1, i.e. interruptions of 15 minutes duration to limit the impact on the consumer’s comfort since we cannot control the temperature when electric heaters are defined as interruptible loads;  Distance=6, i.e. a minimum duration of 1h30 between two interruptions;  maxOFF=4, i.e. 1h of interruption at maximum in a same day;  onPower=500 W; Then, it was decided to increase the duration, the distance and the maxOFF. The parameters finally

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used are the following:  Duration=2, i.e. interruptions of 30 minutes duration (which prove to be acceptable from the perspective of consumer’s comfort) ;  Distance=6 , i.e. a minimum duration of 1h30 between two interruptions;  maxOFF=8, i.e. 2h of interruption at the maximum if a same day;  onPower= value tuned using the power measured by the Smart Plug or the Wall-Unit.

5.1.5.3.3 Electrical Water Heater During the French field test, the management of Electric Water Heaters (EWH) was a challenge. Initially, the EWH were supposed to be operated by the EBox as Interruptible Loads. However, this does correspond to the specific nature of the EWH in France (see Section 5.1 or Deliverable D6.1 [1]). So EDF decided to define them as Shiftable Loads which was more appropriate considering their consumption behaviour.

More specifically, this change of the appliance modeling is motivated by several factors:  the EWH is a high power load (~2 or 3 kW depending on the family) consuming only during specific day periods (off-peak periods typically) contrary to the EWH present in Spain that are working continuously;  we could also shift a part of its consumption when the renewable production is high in order to test the combination of AD with RES production.

Shifting these devices was a real challenge since in France a huge number of EWHs are already remotely operated thanks to an electronic relay. This functioning is detailed in Figure 52..

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Figure 52. Control system of the French Electrical Water Heaters.

Both part of Figure 52. present the operation of the electric installation that allows to control the EWHs during peak and off-peak periods. It consists in two circuits: a power circuit which feeds the EWH and a control circuit which manages the power circuit thanks to an electronic relay (2).  During the off-peak period, a signal is sent to the contactor (1) which closes itself: the electronic relay is then switched ON closing the power circuit and feeding the EWH.  During the on-peak period, another signal is sent to the contactor which opens itself: the electronic relay is switched OFF opening the power circuit and switching off the power supply of the EWH. In order to enable the control of the EWH by the ADDRESS system, a Wall-Unit (3) was wired downstream the control circuit and in series with the power part of the normal electrical installation. This additional installation is presented in the Figure 53..

Figure 53. Control system of Electrical Water Heaters with ADDRESS.

Two problematic situations may occur with this implementation:  The starting time of the EWH calculated by the EBox happens during a peak period. The Wall-Unit is switched on whereas the contactor is off.  the EWH will not be fed by the power circuit.  The starting time calculated by the EBox happens just before the end of an off-peak period. The contactor is on and the EWH is fed only for a very short time till the end of the off-peak period.  the water may not be warm enough when the relay is switched off.

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These two situations may cause a discomfort for the consumer (cold water!). Before declaring the EWH as a Shiftable Load, it was therefore necessary to tune the parameters in the EBox in the appropriate way. This was done in three steps.

First phase: EWHs declared as Non-Controllable loads At first, after the EWH had been wired to a Wall-Unit, it was defined as a non-controllable load and therefore it was not operated by the EBox. The objective was to measure the EWH consumption.

At the same time, laboratory tests were performed in the EDF laboratory to determine the best parameters suited to the control of the EWH.

Second phase: EWHs declared as Shiftable loads Declaring the EWHs as Shiftable Loads was not a simple matter since the parameters had to be defined on a case by case basis for each consumer. Indeed, not every customer has the same peak/Off-peak periods: on the two islands, four different types of tariff periods exist. The main difficulty comes from the midday off-peak period because the water must be sufficiently heated during this period. If this condition is not met, the water will not be hot enough by the evening. Concerning the consumers whose off-peak period only happens during the night, the starting time needed to be programmed at the middle of the night and not before midnight because of limitations related with the implementation of the EBox optimization algorithm (one day non-sliding optimization window). To guarantee this point, we used the same configuration for every EWH: Default Consumption Profile:  Period=60 minutes  Power=1500 W  DetectionLevel=50 W  DetectionTime=7 seconds  DetectionOFFLevel=10 W  DetectionOFFTime=600 seconds However, the preferred start and end times of the EWH had to be adapted to each consumer, taking into account its peak/off-peak tariff periods. Table 20 summarizes these choices.

Off-Peak periods Preferred start and end times 01h30-07h30 01h45 – 13h45 12h30-14h30 02h00-07h00 02h15 – 15h30 14h00-17h00 23h30-07h30 00h15 – 03h00 22h30-06h30 00h15 – 03h00

Table 20. French field tests – Off-peak periods and preferred start and end times of the EWH

Despite these modifications, several problems still occurred (e.g. abnormal starting time calculated by the EBox optimizer) causing a discomfort to some consumers. Furthermore, the EWH shifting capability was limited during this phase: it was only possible to shift the EWHs during the Off-Peak

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period which is not necessarily the most interesting situation regarding the field tests cases considered in France. All these reasons led EDF to change the position of the relay.

Third phase: changing the position of the relay For some EWHs, the position of the relay can be changed manually into a “forced operation” position. In this mode, the EWH is no longer operated by the relay (it is always "ON") and starts as soon as the water temperature is below a set point limit. For a limited time during the French field tests the consumers were then asked to change the EWH operation mode themselves to “forced operation”. After this intervention, AD requests have been sent to their EBoxes in order to shift EWH load from off-peak to on-peak periods. This was necessary to test cases where AD is combined with PV production.

5.1.5.3.4 Scenarios to test the provision AD Services During the French Field Tests, the tested scenarios are listed in Table 21 below.

Scenario Request Number Dates (dd/mm/yyyy) Start Duration Type of days time (min) Evening peak-shaving Decrease 1 21/02/2013 18:15 15 Evening peak-shaving Decrease 3 27/02/2013 to 12/03/2013 18:30 15 Morning peak-shaving Decrease 4 14/03/2013 to 03/04/2013 07:45 30 Special evening peak Decrease 1 16/03/2013 21:15 30 shaving (sport on TV) DSO request Decrease 1 20/03/2013 09:45 30 DSO request Decrease 1 22/03/2013 11:00 30 DSO request Decrease 1 25/03/2013 10:00 30 DSO request Decrease 2 27/03/2013 to 28/03/2013 12:00 30 DSO request Decrease 2 28/03/2013 to 01/04/2013 17:00 30 Evening peak-shaving Decrease 7 03/04/2013 to 10/04/2013 20:15 30 in Spring time DSO request Decrease 8 11/04/2013 to 18/04/2013 11:15 30 Morning peak-shaving Decrease 7 19/04/2013 to 25/04/2013 05:30 30 at the end of Off-peak period Night peak-shaving Decrease 7 26/04/2013 to 02/05/2013 02:00 30 (EWH) To shift consumption Increase 2 04/05/2013 to 09/05/2013 02:30 60 during wind turbine production To shift night peak Increase 12 05/05/2013 to 31/05/2013 03:30 60 Night peak-shaving Increase 5 06/05/2013 to 13/05/2013 03:00 60 (EWH)

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Scenario Request Number Dates (dd/mm/yyyy) Start Duration Type of days time (min) To shift consumption Increase 1 12/05/2013 04:00 60 during wind turbine production To shift consumption Increase 6 17/05/2013 to 22/05/2013 13:30 60 during PV production To shift consumption Increase 9 23/05/2013 to 31/05/2013 11:00 60 during PV production Table 21. French field tests scenarios

5.1.5.4 Market simulator or MS

5.1.5.4.1 General assumptions Several assumptions are made in order to test effective impact of AD on an electricity market. We assume that: - Each entity develops a consolidated portfolio strategy, has a good knowledge of his costs and is able to forecast wholesale market prices efficiently. It is thus able to trade-off between his costs and his potential gains using this information; - We only focus on a global electricity market: the national exchange market. In fact, the rules for the national power exchanges are clearly defined. It is also fully representative of the price determination processes used in the power markets and all the data required for the simulation are public. We did not consider local markets; - When assessing AD provider’s costs, Task 5.4 assumes that the AD offers have a marginal impact on the wholesale market, i.e. they do not affect the wholesale prices [9]. This is the assumption used by the ATB platform when defining its AD offers. On the contrary, the main objective of the market simulator is to illustrate the potential impacts of AD on these prices. Both approaches are thus complementary. - Each aggregation entity uses its internal market price forecasts, the flexibility available in its consumers’ portfolio and the consumers’ preferences (focus on comfort, savings…) in order to identify the optimal time slots for the AD volumes to be proposed to the market. Like in Task 5.1 and Task 5.4, we choose to adopt an “epsilon” margin around the forecasted market prices. When testing the price sensitivity, several margin values were considered. They ranged from 10% to 40% below the forecasted market prices when offering AD products reducing demand and from 10% to 40% above the forecasted prices when offering AD products increasing demand3. Hence, this strategy aims at guaranteeing his AD products will be retained with a high probability in the merit order when his forecasting are pertinent; in the other cases, losses are limited thanks to the margins adopted. - In the scenarios tested, we assume that only one aggregation entity is representing all the offers from the different aggregators existing in the market. Using a price-volume signal, it is able to manage the Electric Water Heaters (EWHs). The aggregation entity earns money by taking

3 In order to make the margin significant for market prices around zeros (0€), we have added (subtracted) one euro (1€) to this margin. This is required since the margin is computed relative to the market price itself.

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advantage of appropriate load shifting on the Spot Market.

5.1.5.4.2 EWH scenarios In these scenarios, we focus on a specific appliance, the “electric water heater” (EWH). This choice was made for two reasons: 1. It is a common appliance in France: 9 millions of households are equipped and most of them with thermal storage (see the description on Deliverable D6.1 [1]); 2. The French field tests showed positive results concerning the shifting of these devices. The best time slots for EWHs activation are identified while ensuring comfort criteria. First, we ran the market simulator in order to replay situations that were observed in the actual French market. These tests were performed with the two market simulators, the one developed by VTT (TS_MS_Java) and the one developed by EDF (TS_MS_Excel) as an alternative to bypass some of the other tool limitations. We mainly focused on market situations characterized by unusual market prices. Two different situations were considered in particular:  One with high market prices (9th February 2012 during the cold period where prices reached 1938€ / MWh in France). The corresponding test cases are TS_MS_Java – 1 to – 3 and TS_MS_Excel – 1 to – 3 (described in D6.1 [1]).  One with negative market prices (25th December 2012 where the transitory situation caused by high wind generation made prices drop to -50 €). The corresponding test cases are TS_MS_Java – 4 to – 6 and TS_MS_Excel – 4 to – 6. Then, we created an intermediate scenario using the field test flexibility and where the ATB was bypassed (TS_MS_FRAD – 01). The incentive signals were selected manually on the basis on our own specifications resulting from market forecast and put in the ATB database to be sent and applied by the EBoxes. The resulting flexibility response observed in the field was introduced back into the market simulator (with the appropriate scaling) in order to assess its impact on the market prices. The data we use for the EWH scenarios are public. They concern the electrical consumption of the EWHs in France. The following simplified assumptions have been retained for illustrative purpose. It is to be noticed that they correspond to a nearly ideal situation from the aggregation perspective.  The unit power of one EWH is around 2 kW and we have about 9 million EWHs controlled in France. That gives us a global theoretical power of 18 GW. However, we apply a correction (division) factor of two in order to take into account every constraint that acts on the EWH. With this assumption, we get a global shiftable load of 9 GW in France.  Since the average heating time of an EWH is of about six hours, the maximum storage capacity is 54 GWh. It leads us to a consumption of 20 TWh over a year.  In order to be coherent with the volumes treated in the French market and the ones manipulated by the aggregation entities, the EWH volumes have been scaled (only 15.3%4 of the global volumes were considered for the scenarios)  From public data, we have been able to create a profile that depends on the month, the day of the week and the hour. An overview of these profiles is given below (Figure 54.).

4 For a given day, MAX EWH power of the day / MAX national demand of the day. This ratio is then multiplied by the MAX spot power of the day for estimating the EWH potential of the day.

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Figure 54. Illustration of different consumption profiles.

 The minimum average sourcing cost can be reached using as much as possible the EWHs heater at night, when the prices are low. It supposes that the storage is empty at the beginning of the night. On the contrary, if the storage were at the maximum at night, we should have to wait until the morning when the consumer begins to use hot water and the individual prices are higher.  We assume that the aggregation entity has an exact knowledge of the D+1 market prices, i.e. no error is made on the price forecast. Ideally, the aggregation entity would know the D+2 and D+1 prices considering the daily profile consumption.

 Two options were considered when choosing the reference power, Ptref., i.e. the quantity used by the aggregation entity to prepare its offers. o Using the load corresponding to the water consumption. We consider that the relay controlling the load is always ON. So, the EWH is started as soon as the water temperature becomes lower than the set point temperature limit. o Using the load corresponding to the real energy consumption of the EWHs, i.e. taking into account the control of the starting time thanks to the relay. This reference is already optimized in order that the consumption of energy will be in the off peak periods of the day (periods with individual lower prices). The latter option was chosen because the prices we use for the tests (spot EPEX 2012) are directly related with the EWH load controlled by the relay. The chart below (Figure 55.) represents the average daily consumption profile (from 6:00 pm to 5:00 pm in D+1). It takes into account the national repartition of the off-peak period (not every EWH is managed in the same way). We will use this similar distribution for every day of the week to simplify our cases.

Figure 55. Daily EWH consumption profile used for the tests.

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5.1.5.4.3 ATB and MS interaction In day-ahead, the aggregation platform takes into account the flexibility requests expressed by bilateral contracts (if any) and proposes the remaining flexibility volumes to the electricity market at specific prices. The acceptance of the proposed flexibility is then taken into account by the aggregation platform to define the signals to be sent to each consumer cluster. The optimization algorithms of the ATB platform also take into account the flexibility constraints imposed by the DSO for each Load Area in order to ensure the technical feasibility of the chosen signals. Once the signal selection has ended, the chosen incentive signals are applied on the field. It is to be noted that up-scaling factors are applied to the offered volumes proposed to the market in order to make their impact more significant. The inverse scale is then applied to the accepted offer volumes when the market responds to the aggregation platform. Because of limitations of the current ATB implementation, the acceptance factor of the volumes corresponding to the payback effect of the proposed offers cannot be taken into account by the ATB following optimization; only the offered volume can be modulated by the market acceptance.

5.2. Results

As previously mentioned the field tests in France were mainly focused on assessing the operation and the performance of the complete ADDRESS chain. The test cases and the detailed test scripts are given in Chapter 8 and in the Appendix of Deliverable D6.1 [1]. In this Section, the main results of these tests are presented. A detailed description of a complete test case is also given and the results obtained at each step are illustrated.

5.2.1. Test execution The table below presents an overview of the tests execution performed during the French field tests on the complete platform sequence. The column “Successful” indicates whether the successive steps of the test scripts were carry out successfully or not but does not give any information on regarding the effective delivery of AD flexibility. The results of the tests in terms of AD delivery are given in Section Errore. L'origine riferimento non è stata trovata..

NB: For a detailed description of the test scripts, please refer to Deliverable D6.1 [1].

Objective Success Function Code ID ful AD request from the DSO for local overload 1 OK reduction AD request from the TSO for global congestion Load decrease TS_CAT3_Loa 2 OK services d_Dec reduction 3 AD request from the BRP for imbalance management OK but ATB

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Objective Success Function Code ID ful bypasse d5 AD request to avoid curtailment of a PV plant due to Requests in 1 OK TS_CAT3_Loa voltage constraint combination AD request for compensating the peak production d_Dec 2 OK with RES of a mainland wind farm Over-a-day TS_CAT3_OA AD requests from the DSO for an over-a-day global global load 1 OK D_Lim load limitation limitation Wholesale market prices estimation based on 1,3 forecasted demand and supply OK 4,6 Evaluation of the MS accuracy given the number of TS_MS_Java price/volume couples

Estimation of the AD impact on the wholesale 6 2,5 Failed market prices using the EWH as a shiftable device Market Wholesale market prices estimation based on Simulator forecasted demand and supply Estimation of the AD impact on the wholesale 1 to 6 OK TS_MS_Excel market prices using the EWH as a shiftable device Estimation of the impact of the prices of AD offers on the earnings / savings Test of MS with AD offers related to the load shifting 1 OK TS_MS_FRAD observed in the field tests Integration of TS_CAT3_MA an electricity 1 Provision of AD services to an electricity market OK R_INT market

Table 22 - Test execution overview

5.2.2. Assessment of the technical performance on the consumer side 5.2.2.1 Management of the consumption by the Energy Box As previously mentioned, the management of the electrical heaters defined as Thermal Loads was not possible due to technical issues: as a consequence, it was decided to declare the electrical heaters as shiftable loads. Regarding the EBox specifications, it performs the consumption management of the controlled devices even in the absence of request coming from the aggregation platform, i.e. it optimizes the consumption of the house without any request:  Interruptions are carried out during peak periods so that the savings are maximum for the consumers;

5 The ATB platform had to be bypassed because load increase signals were always selected by the ATB algorithm: instead of using the direct effect of load decrease signals, the payback effect of load increase signals was selected. Load decrease signals were thus selected manually. 6 For several hours, the MS gives unrealistic results significantly different from the ones expected from a theoretical point of view. The calculation of earnings/savings cannot be done without knowing the part of the aggregator's offer that has been accepted by the market.

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 The optimization is done to save a maximum amount of energy, i.e. at the end of the day all the interruptions allowed by the configuration specified in the EBox for the interruptible loads have been made;  The distance between two consecutive interruptions is set so that the impact on the consumer’s comfort limited;  When the priority is given to money savings, Shiftable Loads start during off-peak periods. Moreover, several issues have been encountered during the field operation, such as: - A default in a Wall-Unit. - Stop in the execution of the EB software when a Smart Plug or a Wall-Unit is switched off. In this situation, the load remained switched off until a remote manual action is undertaken to restart the EBox software. - Communication errors between the EBox and the Smart Plugs or the Wall Units. In that case the EBox doesn't receive power measurements and is not able to send ON/OFF orders. - The distance between the EBox and the Smart Washing Machine must be short in order to ensure their communication. NB: according to the privacy rules applicable in France, we are not allowed to present individual results recorded in a consumer house, even if these results are anonymized. Consequently, in the following sections, we will illustrate results obtained at consumers in the French field tests with similar individual data recorded during the EDF lab tests in the multi-energie house and with aggregated results obtained on the field.

5.2.2.2 Peak-Shaving using Interruptible Loads After a signal is received, the EBox runs an optimization to calculate the interruptions of the controlled appliances. If interruptions are still available (i.e. the total number of interruptions for the day is not reached) and the economic incentive sent by the aggregation platform is high enough, the appropriate interruptible loads are switched off at the requested time in order to reduce the consumption below the required power.

AD request Start (date & time) Duration (min) Limit (kW) 15-03-2013 07:45:00 30 0.05

The objective of this price-volume signal is to limit the power of the house below 0.05 kW during 30 minutes. The result is presented on Figure 56..

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0 0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 0:00 P Heater 1 (W ) P Heater 2 (W ) P Heater 3 (W ) P Heater 4 (W ) P Heater 5 (W ) P Heater 6 (W ) Figure 56. Interruptible Loads – Peak-Shaving.

The six electrical heaters (declared as interruptible loads) are switched to an OFF state by the EBox between 7:45 and 8:15.

5.2.2.3 Limited Peak-Shaving using Interruptible Loads In this second example of peak-shaving, the AD request is partially satisfied. This price-volume signal requests a consumption below 0.1 kW between 20:00 and 20:30. As can be seen in Figure 57., the four electrical heaters are interrupted from 20:00 to 20:15 and not until 20:30. So the load decrease request is only partially satisfied. The reason is that one of the electrical heaters is configured in the EBox with an interruption duration of 15 minutes only, for the comfort of the users. Due to the request threshold at 0.1 kW, the EBox cannot find a solution for the second half of the request. AD request Start (date & time) Duration (min) Limit (kW) 30-03-2013 20:00:00 30 0.1

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5.2.2.4 Shiftable Load: Electrical Water Heater In this case, the EWH is declared as a shiftable load. The applied AD requests are designed for shifting the consumption and are sent twice to the EBox during the day first at 15:30 and then at 21:00. There are two power thresholds in order to be sure that the request will be satisfied by the interruptible loads and by the shiftable loads. The results of these requests are presented in Figure 58..

AD requests Start (date & time) Duration (min) Limit (kW) 07-05-2013 15:30:00 1.0 & 60 2.5 07-05-2013 21:00:00 9999999.0

At 14:05, the EBox detects the start of the consumption measured by the Wall Unit of the EWH. The EBox sets this Wall Unit into an OFF-state and calculates its next start time taking into account the AD request, the user's preferences and the tariff. Then, at 15:30 (the request time), the EBox sets the Wall Unit back into an ON-state: the EWH works until water temperature inside the EWH is hot enough. At 18:15, as the water temperature is below the EWH temperature threshold, the EWH starts again. This restart is detected by the EBox which sets the Wall Unit into an OFF-state again and calculates the next start time at 21:00 according to the second AD request (Wall Unit back into an ON-state).

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5.2.2.5 Shiftable Load: Smart Washing Machine In this case, the shiftable load is the ADDRESS Smart Washing Machine (SWM). The applied AD request is designed for shifting its consumption:

AD request Start (date & time) Duration (min) Limit (kW) 2.89 25-04-2013 16:15:00 45 9999999.0

The power threshold (2.89 kW) is adjusted in order to be sure to shift the SWM because this EBox manages also several interruptible loads. The results of this request are presented in Figure 59.. At 14:45, the user starts his/her SWM, selects a washing program and sets the SWM in "remote mode". Then, the EBox receives the consumption profiles from the SWM, calculates the next start time for 16:15 (time of the AD request) and sends this start time to the SWM. At 16:15 on the clock of the SWM, the washing program is started.

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5.2.2.6 Unsuccessful or limited peak-shaving During the French field tests, several scenarios of peak shaving were unsuccessful or limited. These situations were a consequence of the EBox parameters concerning the interruptible loads:  When an AD request is set in intraday and late in the day, it could happen that there is no solution: for instance for this day, all possible interruptions have already been used by several or all the interruptible loads because of the normal behavior of the EBox as an Energy Manager.

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 When the power threshold of the price-volume signal is small and the number of possible "available offs" is limited. 5.2.2.7 Energy Box communication errors

5.2.2.7.1 Between the EBoxes and the appliances (Smart Plugs or Wall Units) The communication errors between the EBox and the Smart Plugs or Wall Units recorded during the French Field Tests are given in Figure 60..

5.2.2.7.2 Between the EBoxes and the ADDRESS control center in Clamart During the French field tests, a supervision system was designed so that the EBoxes installed in the consumers’ house send all their data (data from the so-called IDM file) to the ADDRESS control center (located in the EDF site of Clamart, close to Paris). This sending first occurred on a daily basis and later on a 15 min basis. The rate of success of the communication between the EBoxes and the ADDRESS control center is presented in Figure 61..

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Figure 60. Communication errors observed during the French Field tests.

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Figure 61. Rate of success of communication between the EBoxes and the ADDRESS control center.

5.2.2.8 Overrides With the User Interface of its EBox, each consumer can use the override whenever he/she needs it. Figure 62. shows the override rates observed during the French Field tests.

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Figure 62. Override rates observed during the French Field tests.

5.2.3. Provision of AD services at a cluster level 5.2.3.1 Evaluation of the obtained AD volumes at a cluster level The precise evaluation of the AD flexibility delivered at the cluster level is very difficult. The issue comes from the definition of the “baseline”, i.e. the reference curve representing what would have been the behavior of the cluster without any incentive signals and to which the consumption curve in

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presence of incentive signals is compared to assess the delivery of the AD products. This issue is even more complex in case of small clusters like the ones we have in the French field tests. Indeed in case of large clusters, i.e. clusters composed of hundreds of consumers, the variability of their consumption may be reduced and the baseline computation of the clusters would less sensitive. The difficulties encountered when computing the baseline originate from several factors.  The consumption curve of individual consumers presents a high variability from one year to another and even from one day to the next. This observation already holds for consumers not using local energy managers or taking part into AD experiments. It is all the more accentuated when devices influencing the appliance consumption are involved.  The EBoxes act as energy managing devices when no incentive signal is sent by the aggregation entity. They automatically shift or interrupt some of the appliance consumption depending on the consumers’ preferences. This standalone operating mode introduces new modifications to the “natural” consumption curve.  The incentive signals sent by the aggregation entity for increasing or decreasing the global load of the household modify the initial placement of the appliance consumption. The response of the AD devices will depend on the remaining flexibility available at the time of the request and on the consumer’s preferences. All this variability remains at the cluster level when it only consists of a reduced number of consumers.

It is to be noticed that the baseline can be either defined as the consumption of the cluster without any automated influence (cluster without ADDRESS-like systems) or its consumption when no incentive signal is sent (cluster with ADDRESS-like systems acting only as energy managers). This definition will have to be addressed by the regulation. The TSOs, DSOs, AD providers and retailers should work together in order to agree on this definition, but also on a method for measuring and computing the delivered AD for each load area (LA) and macro load area (MLA) regarding the AD products which have been previously submitted and validated. Many methods are currently studied in order to compute a reliable baseline that can be used for assessing, controlling and remunerating the flexibility provided by AD at consumer or cluster levels: forecasting based methods, control groups based methods... The methodology used for the French field tests is presented in the following paragraphs. In our study, we considered that the baseline is the cluster consumption without any automated influence (1st possibility). This choice was motivated by the data available for the analysis.

5.2.3.1.1 Methodology for the baseline computation The methodology applied for this analysis uses simplified processes because of the limited data available: reduced number of observations with a same incentive, limited number of consumers in the clusters… It should also be noted that the proposed method may no longer be appropriate if AD requests were to be applied over a long time, i.e. if they affect both the reference and the observation intervals. Two data sources were available for assessing the AD flexibility provided by the incentive signals: the consumption data collected by the smart meters installed by the French DSO (ERDF) and the data regularly sent by the EBoxes. We mainly used the consumption measured by the smart meters since the consumption data measured by the EBoxes were not available for an as long period as

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those collected by the meters and additionally they were corrupted over some time periods. The metered data cover the period from December 2011 to May 2013, i.e. to the end of the French experiment. The construction of the baselines is based on the historical consumption and weather data from December 2011 to June 2012. This interval is referred to as the “comparison interval”. An individual reference profile is computed for each consumer and each day where an incentive signal was sent by the aggregation entity to the EBoxes. These references are then aggregated to compute the baseline of the cluster. The methodology used consists of several steps (Figure 63.). They are applied to each day when an AD request was submitted. Let explain the process for a specific day referred to as the “studied day”. (1) First selection of equivalent days regarding weather data A first characterization quantity, i.e. the average wind chill7 temperature, is computed for the studied day. It is compared with those computed for all the days of the comparison interval. The days with similar average wind chill temperatures are kept for further analysis. A two degree difference is allowed.

7 The wind chill is the air temperature perceived by the body on exposed skin due to the flow of cold air.

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C-consumer

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Selection of Historical wind-chill data equivalent days D-day wind-chill profile

Median based Historical consumption profile sorting D-day consumption profile

Median bias correction

Daily reference computation

No D-day is the last of the series? Consumer Yes Level Series reference computation

No C-consumer is the last of the cluster? Yes Cluster Cluster reference Level computation

Figure 63. Main steps of the methodology used for computing the cluster baselines.

(2) Consumption profile sorting based on median value The median value of the consumption profiles observed for the days selected with the temperature information is then computed. This second characterization quantity allows us to discriminate the profiles whose median values are very different from the one calculated during the studied day. These medians are not computed with the consumption curve considered over a whole day; the time interval is chosen in order to improve the probability of finding matching curves. Figure 64. illustrates the distribution of the differences observed between these two medians before any corrective measure is applied. These differences come from different factors such as days where the consumers are absent, etc. Only the consumption profiles whose median difference is small are kept. These profiles are referred to as the “daily group”. They are weighted depending on their similarity with the profile observed over the studied day. Those matching the most are preponderant.

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Figure 64. Raw distribution of the median of the consumption profile of the weather selected groups.

(3) Median bias correction of the “daily group” at consumer level When studying the load curves of daily groups, it was noticed that many of them presented a similar behavior but could not be used as such as a reference because the load profile was shifted by a few hundred watts (Figure 65). In order to compensate this shifting, the median values of the consumption profiles are used again. The offset observed between the studied day and each curve of the daily group is brought back to zero.

32500

27500

22500

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7500 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 Time (h) Realized Baseline Raw Baseline Figure 65. Example of offset observed between the mean load curve of the daily group and the load curve observed over a day

(4) Daily reference computation at consumer level The average consumption curve of the daily group profiles is computed once their median has been corrected. This average profile can be used as a comparison reference with the consumption profile observed during the studied day in order to assess the load decreases or increases induced by the incentive signals sent by the aggregation entity, as well as their payback effect. The profile is referred to as the “daily reference”. At the end of these 4 steps, we have constructed a reference consumption profile for each consumer and for each day where AD requests were submitted by the aggregation entity. (5) Series reference computation at consumer level On the field, series of identical AD requests were applied, i.e. incentive signals with the same characteristics (timeslots, volumes and prices) were sent to all the consumers of a

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cluster several days in a row. These series are meant to assess the variability of the response of a consumer to the same signals as well as the average response of the consumers to these signals. For this purpose, two additional consumption curves are computed using previous data as a basis. (1) The “average series reference” is calculated using the consumption profile of every daily reference determined for the days composing the series. (2) The “average series response” is calculated using the consumption profiles submitted to AD requests of the N days composing the series. It corresponds to the average response profile of the consumer to a specific AD flexibility request. Both curves can be compared to evaluate the mean load increase or decrease that can be expected for each active consumer when submitted to the same signal characteristics. (6) Series reference computation at cluster level The two references previously computed at a consumer level are aggregated to constitute the comparison references of the cluster. They are used to assess the cluster response to specific AD requests.

5.2.3.1.2 Examples of computed baselines and associated consumption profiles The methodology was applied to all the incentive signals applied on the field. In Figure 66. and Figure 67. are represented the baselines computed using this method and the average consumption profile obtained during the considered series. It can be seen that whereas in some cases the increase or decrease clearly appears and can be quantified in other cases it is much more difficult. Only a thorough analysis might say if this is due to the baseline which is not appropriate for this day or to the absence of response (or very limited response) of the cluster.

52500 42500 Load decrease 37500 47500 signal 32500 42500

27500 Power (W) Power 37500 (W) Power Load decrease 22500

32500 signal 17500 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30 18:15 19:15 20:15 21:15 22:15 23:15 0:15 1:15 Time (h) Time (h) Realized Baseline Realized Baseline Figure 66. Example of cluster responses to load decrease requests.

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27500 Load increase Load increase 22500 signal signal 22500 17500

17500 Power (W) Power Power (W) Power 12500 12500

7500 7500 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 Time (h) Time (h) Realized Baseline Realized Baseline Figure 67. Example of cluster responses to load increase requests.

5.2.3.2 Impact of AD on the consumption profile of a cluster

5.2.3.2.1 AD requests carried out The results of the tested scenarios in the French Field tests are listed in Table 23.

Scenario Request Start Duration Result Type time (min) 1 Evening peak-shaving Decrease 18:15 15 OK 2 Evening peak-shaving Decrease 18:30 15 OK 3 Morning peak-shaving Decrease 07:45 30 Limited 4 Special evening peak Decrease 21:15 30 Failed shaving (sport on TV) 5 DSO request Decrease 09:45 30 Limited 6 DSO request Decrease 11:00 30 OK 7 DSO request Decrease 10:00 30 OK 8 DSO request Decrease 12:00 30 Limited 9 DSO request Decrease 17:00 30 OK 10 Evening peak-shaving in Decrease 20:15 30 Failed summer time 11 DSO request Decrease 11:15 30 OK 12 Morning peak-shaving at the Decrease 05:30 30 OK end of Off-peak period 13 Night peak-shaving (Water Decrease 02:00 30 Failed Heaters) 14 To shift consumption during Increase 02:30 60 OK wind turbine production 15 To shift night peak Increase 03:30 60 OK 16 Night peak-shaving (Water Increase 03:00 60 OK,but Heaters) Limited

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Scenario Request Start Duration Result Type time (min) 17 To shift consumption during Increase 04:00 60 Failed wind turbine production 18 To shift consumption during Increase 13:30 60 Limited PV production 19 To shift consumption during Increase 11:00 60 OK PV production Table 23. French field tests – Test results.

The meaning of the column "Results" is the following:  OK: the actions decided by the EBoxes had significantly modified the aggregated consumption of the cluster of consumers.  Failed: there was no measurable effect on the cluster consumption.  Limited: there was a small or limited effect on the cluster consumption.

5.2.3.2.2 Examples of cluster responses to AD requests The next four figures present examples curves of the average consumption per consumer of the French Field test cluster for different scenarios listed in Table 23.

3000 Watts 2900 2800 2700 2600 2500 2400 2300 2200 2100

2000

15:30 16:30 17:00 17:30 18:00 19:00 19:30 20:00 20:30 21:30 22:00 22:30 16:00 18:30 21:00

Realized Baseline

Figure 68. Evening peak-shaving - OK – Scenario No. 2 of Table 23.

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2400 Watts 2200

2000

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18:15 19:15 19:45 20:15 20:45 21:45 22:15 22:45 23:15 18:45 21:15 23:45

Realized Baseline

Figure 69. Evening peak-shaving - Failed – Scenario No. 4 of Table 23.

1500 Watts 1400 1300 1200 1100 1000 900 800 700 600

500

0:30 1:30 2:00 2:30 3:00 4:00 4:30 5:00 5:30 6:30 7:00 7:30 1:00 3:30 6:00

Realized Baseline

Figure 70. To shift night peak - OK – Scenario No. 15 of Table 23.

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1600 Watts 1400

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1:00 2:00 2:30 3:00 3:30 4:30 5:00 5:30 6:00 7:00 7:30 8:00 1:30 4:00 6:30

Realized Baseline

Figure 71. To shift consumption during wind turbine production - Failed – Scenario No. 17 of Table 23.

5.2.3.3 Impact of AD on the energy consumption

5.2.3.3.1 Methodology In this paragraph, we present the methodology used for assessing, at a global level, the possible changes in the energy consumption that may result from the installation of the EBoxes. Context and data available The period considered for this analysis covers the period of the French field tests, i.e. from December 2012 to May 2013. For comparison purpose, the same period is considered for the year 2011/2012 when no EBox was installed in the Brittany islands. Several issues had to be solved when conducting this study. Some of them are explained below:  Most of the consumption data sent by the EBoxes are not reliable enough during the period from the beginning of the experiment to mid-February because of erroneous data measurement (which was solved later) and communication issues. These problems persisted for some boxes after February. Consumption data measured by the smart meters installed by ERDF are therefore used instead. In fact, even if their measurements also present some missing data, they are far more reliable.  The measured consumption is very dependent on the outside temperature: a large part of the consumers’ consumption increases with decreasing temperatures and vice versa.  The variability typical of the studies focusing on single consumers, such as consumer absenteeism, induced additional variability. Such discrepancies are not taken into account in this study.

Constitution of the consumer panel A selection of consumers had to be carried out in order to obtain comparable results. The methodology used to separate the “ADDRESS” group from the “reference” one stands as follows.

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The two resulting groups of consumers are used for assessing the impact of AD on the energy consumption.  Consumers with an EBox installed (“ADDRESS” group) and those without the ADDRESS system are distinguished (“reference” group). When an EBox is installed but cannot control any appliance during the whole period, the consumer is also attributed to the “reference” group.  Only domestic consumers are considered. The consumption data are removed from the study when the EBox is installed in public facilities.  A consumer is removed from the study when too many consumption data are missing during the whole period or any of its sub-periods.

Normalization of the consumption curves against wind chill temperatures The first treatment applied consists in correcting the power measurements with the outside temperature, i.e. in taking into account the impact of weather changes on the consumption. The used data are the wind chill temperature data from the Belle-Ile island weather station. It is the closest weather station to the Houat and Hoëdic islands. The treatment correcting the consumption data makes the following assumption: when daily temperatures are equal, a consumer always displays the same consumption of electricity. Thus, when two corresponding days present different temperatures, a correction is made according to the wind chill temperature. In order to estimate this correction, we consider that a part of the domestic electricity consumption is correlated with the outside temperature (heating appliances…) while the other part is constant and does not depend on this parameter. Based on this assumption, a linear relationship is established between the energy consumed per day and the day temperature. We also reckon that temperatures over 16°C have a lesser impact on the consumption: over this temperature, only the constant part of the consumption remains. The average daily heating degree and the average wind chill are then computed on both considered periods and based on this information, the 2013 consumption is corrected. This correction is computed separately on peak hours and on off-peak hours and the results are then aggregated for the whole cluster.

Assessing the changes in energy consumption on each sub-period of the field tests The field test time is split into several parts related to the stages of the field deployment of the EBoxes and of the testing scenarios. For each test period, the consumption is compared with the consumption over the same period of the year before. Temperature and data corrections are performed so that both periods can be compared and the volumes of the load variations can be estimated. The comparison starts from December 1st 2012 since there is no observation before December 1st 2011 and ends on May 31st.  1st period: After the start of the EBox installation and before the incentive signals are sent on a regular basis (from December 1st until February 14th);  2nd period: From the start of the regular sending of load decrease incentive signals to the EBoxes (February 14th to May 3rd);  3rd period: Load increase incentive signals sent during off-peak hours (May 4th to May 22nd);  4th period: Load increase incentive signals sent during peak hours (May 23rd to May 31st).

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5.2.3.3.2 Results Table 24 presents the consumption of the two groups of consumers considered during the French field tests: the “ADDRESS” group consisting of all the consumers whose appliances are controlled by EBoxes and the “Reference” group where no ADDRESS system was installed. For each group and each year, the mean consumption (raw) observed during the periods of the day with low electricity price (LP), i.e. the off-peak periods, and the one observed during high price (HP – peak period) is presented. The result of the volume correction performed with previous method is illustrated and the resulting energy consumption variation between 2012 and 2013 is expressed.

raw raw raw raw corrected corrected Mean Consumption Group 2012 HP 2012 LP 2013 HP 2013 LP 2013 HP 2013 LP Variation ADDRESS 3306.73 2208.03 3734.75 2393.26 3161.19 2096.43 257.13 Reference 2319.98 1645.20 2449.10 1752.41 2074.94 1632.42 196.55

Table 24. Mean energy consumption of ADDRESS and Reference groups during the period of the French field test (all figures in kWh)

Table 25 presents the variations of the off-peak consumption ratio (LP/Total ratio where Total=LP+HP) in presence of the ADDRESS systems: the off-peak consumption ratio (raw) is presented for both groups and years and the corrected ratio for 2013.

Raw Raw Corrected Group LP/Total ratio 2012 LP/Total ratio 2013 LP/Total ratio 2013 ADDRESS 0.407 0.395 0.403 Reference 0.545 0.552 0.558

Table 25. Variation of the off-peak consumption ratio in presence of ADDRESS systems

Both for the ADDRESS group and the Reference group, the mean variations between 2012 and 2013 in Table 24 correspond to energy reductions. So the EBoxes as well as the actions they trigger when performing standalone optimizations or when responding to flexibility requests do not seem to have a negative effect on the global energy consumption since the energy reductions observed for the ADDRESS group is of the same order of magnitude (around 5%) as the one obtained for the Reference group. They do not seem to have an impact on the share between LP and HP energy consumptions either. The differences between the two groups are not significant enough to be able to conclude to a positive effect of the EBoxes on the energy consumption. NB: it should be recalled that the objective of ADDRESS at the consumer level is not to reduce the energy consumption but rather to shift the consumption to times that more beneficial for the electricity systems as a whole [10], [11].

The above results should be considered with great care because:  the energy consumption between the two groups is not equivalent in terms of total energy consumption even though both categories of consumers have the same types of electric equipment. The size of the two groups is too small to avoid having a few consumers with a large influence on the results for the whole group.  Possible changes in the consumers’ individual behavior (holidays…) can also greatly influence the results considering the size of the groups and the limited historical data available.

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 The duration of the field tests is too short to allow to derive more conclusive results on such topics.

5.2.4. Market simulator 5.2.4.1 EWH scenarios

5.2.4.1.1 Methodology The whole process applied when running the Market Simulator (MS) in the EWH scenarios is presented in Figure 72.. In the first step, we run the MS with only global forecasted data (offer and demand). These volumes originate from exchanges between different actors (DSO, TSO, aggregation entities, BRPs…) that sell or buy volumes depending on their needs and their resource availability. All of these volumes are aggregated into one global offer and one global demand. The purpose of this 1st step is to determine the forecasted market equilibrium without any AD offers. In real life, the global demand and offer volumes are forecasted based on the historical values. However, in our scenarios, the bids are read from the public historical curves. Using this information, we construct a reference (SPOT prices) to compare the results of MS with. In a second step, load shaping is performed. For the scenarios of December 25th and February 9th, it consists in changing the daily EWH reference consumption that has been shown in Figure 55. in order to optimize the purchasing expenses. This shifting isn’t totally free as it highly depends on the consumers’ usage. The challenge is to find the best shifting that satisfies the following points: - The new load positioning has to satisfy comfort constraints; - The shifting appreciation regarding the spot price is maximum. It is to be noticed that the initial placement of the French EWH’s consumption is already statically optimized from a consumer-only point of view. However the aggregation entity, by having accurate price forecasts, can improve this optimization and be rewarded for providing additional services. The output of this load shaping step is summarized in Figure 73. and Figure 74. where:  The green curve represents the normal energy consumption for EWH during this day.  The blue one represents the forecasted EPEX spot prices8.  The grey curve represents the usage of hot water;  The red curve represents the optimized placement of energy consumption for the EWH considering the constraints discussed above. The difference between the optimized consumption and the reference consumption is considered as the volume of the AD offer. Negative AD Volumes mean that the aggregator proposes a load decrease for the concerned period, and positive ones mean that the aggregator/retailer will purchase this extra volume compared to the reference placement (load increase). NB: the sum of the optimized consumptions over the day is equal to the one of the reference consumptions; thus the total of AD volumes is equal to 0. In the third step, we adopt a 20% margin around the real market prices in order to introduce uncertainty in our calculations: 20% below the market prices when offering AD products reducing demand and 20% above the forecasted prices when offering AD products increasing demand. The impact of the value of this margin on the results is also studied here.

8 The real EPEX Spot prices of the considered day are used for the simulations.

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The MS is then run a second time after integrating the AD offers to obtain the new equilibrium points. The fourth step, i.e. the exploitation of the results given by the MS, will be presented and discussed in the Deliverable D6.3 Errore. L'origine riferimento non è stata trovata..

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Figure 72. Steps for running the Market Simulator with aggregation entity’s offer9

9 For the tests, the AD prices will be calculated using the real SPOT market prices instead of the forecasted ones as shown Errore. L'origine riferimento non è stata trovata..

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Figure 73. Illustration of the load shaping of EWH consumption for the first scenario.

Figure 74. Illustration of the load shaping of EWH consumption for the second scenario.

5.2.4.1.2 Results The following tables (Table 26 and Table 27) summarize the results obtained when running the whole MS processes on the considered EWH scenarios. As we can see, the equilibrium prices decrease when the aggregator offers a load decrease and increase when it consumes more than it is supposed to. The average difference between the two

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equilibrium prices (Equilibrium Price 2 – Equilibrium Price 1) over the 24 periods is -1,1 €/MWh for the first scenario and +0.5 €/MWh for the second one. This indicator shows that the AD offers globally implied a decrease in the market prices. However, the real impact on the purchasing expenses should rather be weighted by hourly volumes depending on the part of the AD volumes that has been accepted by the market. This impact will be discussed in the report D6.3 [12]. In order to assess the functioning of the MS, the prices for the equilibrium 1 were compared to the real SPOT prices that were observed during this day. The purpose is to validate the equilibrium calculation algorithm that allows obtaining the equilibrium points. As we can see in Table 28 and Table 29, the results are satisfactory and the relative difference is lower than 1%. However, for some periods (the 8th and 13th periods in Table 28 for example), the difference becomes significant. It is related to the sampling of the demand and offer’s data for some specific periods where these two curves are similar around the SPOT price10. This shows that the MS is very sensitive to the input data: for each hour, the more offer and demand points we have around the forecasted equilibrium the more effective and reliable the results of the equilibrium calculation algorithm will be.

10 This constraint is explained and illustrated in the methodology paragraph (§5.2.1.3.1.15.2.4.1.1).

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Table 26. Test results for the scenario of the February 9th, 2012.

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Table 27. Test results for the scenario of the December 25th , 2012.

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Table 28. Comparison of the results with the real Table 29. Comparison of the results with the real SPOT SPOT prices of the February 9th , 2012 prices of the December 25th , 2012

5.2.4.2 Electric water heaters from the field test

5.2.4.2.1 Methodology In this case, the methodology is quite the same as the one of the two previous EWH scenarios. The only change occurs in the load shaping step: since we use the load shifting that has been obtained in the French field tests rather than the consumption whose placement was optimized, we no longer need to run the placement optimization tool. On the field we succeeded to shift the consumption of 10 water heaters (2 kW each): 5 of them were shifted from May 24th at 11:00pm to May 25th at 4:00 am and 5 were shifted from May 25th at 1:00 am to may 25th at 5:00 am. Since the real clusters are supposed to be much bigger than the one considered in the French field tests, the shifted consumption of the EWH was simply multiplied by 500 in order to have more significant AD offers to propose to the market (10 MWh).

5.2.4.2.2 Results Compared to the 2 first scenarios, the volumes for AD offers are considerably lower. The impact on the market prices is thus not as significant as they are in the 2 first scenarios. However we can notice that, for the 7th period, the 10 MWh load increase has changed the market price by a value of 0.334 €/MWh.

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Table 30. Test results for the scenario of May 24th and 25th, 2013

5.2.4.3 ATB and MS interaction (simulation day: December 24th 2012)

5.2.4.3.1 Methodology The volumes of AD offers are calculated in two different situations for the scenario of December 24th: 1. The first one is the load shifting of the French EWH (same global volume and different consumption hours), 2. The second one is the output of the Aggregation Tool Box (ATB) that has been run with the French field tests parameters. In Figure 75, the AD flexibility volumes proposed by the ATB platform for two of the four Load Areas considered are presented. The reduced volumes available in each cluster (a few kW) explain why the volumes had to be up-scaled before being sent to the MS.

s 2013-06-25 17-09-58 DayAhead-0001F NODE 0258L001 s 2013-06-25 17-09-28 DayAhead-0001F NODE 0013L001

6 16298[19] 15 16022[18] 33005[67] 32660[66] 10 4 offers offers total total 5 2

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-20 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Time (h) Time (h) Figure 75. Signals chosen for four Load Areas.

Even if the volumes of the AD offers proposed by the ATB were up-scaled, the impact on the market prices remains low.

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5.2.4.3.2 Results

Table 31. Test results for the scenario of December 24th, 2012

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5.2.5. Complete scenario execution In this subsection, we present the results obtained at each step of a complete field test case. The case TS_CAT3_RES_Req – 2 presented in Deliverable D6.1 [1] is considered here. The situation considered in the scenario is the following. Based on wind forecasts (Figure 76.), a production peak coming from the wind farms connected to the mainland grid is anticipated by the TSO during the night around 3:00 AM. A load increase request is emitted at a global level in order to compensate this high RES production. We consider that the Brittany islands also contribute to compensate this peak with their available flexibility (since they are connected to the continent through a submarine cable). It is to be noticed that the flexibility considered in the ATB portfolio is much larger than the one actually available on the islands during the field tests. The effects of AD products on the distribution network are thus scaled up in the same way. This is done to make the scenario more realistic.

Wind Speed Max Wind Speed 20

10 Wind Speed (m/s) SpeedWind 5

0 0:00 3:00 6:00 9:00 12:00 15:00 18:00 21:00 0:00 Time (h) Figure 76. Wind profile for the Brittany area considered in the scenario (actual profile of May 7th 2012).

5.2.5.1 AD need definition The AD need expressed by the TSO is formulated in terms of SRP products. These products impact the Load Areas of the Brittany islands during the constrained time interval. Since the flexibility request considered here is global and the ATB algorithms cannot perform an optimization over multiple Load Areas, the requested volumes are split manually between the Load Areas depending on the available flexibility (Figure 77.). Since intra-day SRP are not implemented in the ATB, the product type considered here (SRP) imposes that the AD volumes cannot be adjusted in intra-day in order to fit to more precise forecasts. This could have been achieved using CRP products, but the choice of product type was imposed by another limitation of the ATB: CRP products cannot be used to meet load increase requests in the ATB implemented, only SRP ones can.

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00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 time Figure 77. AD volume profiles requested on different Load Areas.

5.2.5.2 1 DSO forecasting and flexibility computation LoadArea 0258L001 - srp LoadArea 0010L001 - srp The DSO has to perform several actions at the beginning of the scenarios in order to initialize the 0.5 LoadArea 0294L001 - srp different platforms taking part in the scenario. LoadArea 0013L001 - srp

First, the0 Load Areas of the MV network are computed based on the description of the LV network of the Brittany islands (Figure 78).

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Figure 78. Load Areas (in red) computed by the algorithm based on the LV network of the Brittany islands

Then, the forecasting algorithms embedded in the DSO platform forecast the evolution of the consumption profile of each Load Area as well as the production profile of the PV power plant installed on Hoëdic island. In Figure 79., the forecasted consumption profiles of two Load Areas are compared with the achieved ones. In Figure 80., the forecasted and achieved production profiles are compared. It is to be noticed that the curves presented in Figure 80. are generic curves because of confidentiality issues with the actual data from the PV plant. So they are not measurements from the PV plant installed on the Brittany islands, but they illustrate behaviours similar to the ones observed during the field tests with the actual PV plant data. Last, the flexibility limits of each Load Area, i.e. the flexibility table, is computed based on previous information: MV network description, defined Load Areas and forecasts (Figure 81.). This flexibility table is then sent to the ATB platform to be used as constraints by its optimization algorithms when selecting the incentive signals to be sent to the EBoxes.

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180 160 160 140 140 120 120 100 100 80 80

60 60 Power in kW Power 40 kW in Power 40 20 20 - - 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 00:00:00 04:48:00 09:36:00 14:24:00 19:12:00 00:00:00 Time in h Time in h Realized (10min) Forecasted (15min) Forecasted (1h) Realized (10min) Forecasted (15min) Forecasted (1h) Figure 79. Comparison of the load forecasts to the achieved profiles for 2 Load Areas of the Brittany islands.

70 70 60 60 50 50 40 40 30 30 20 20 5 x 10 10

Produced Power (kW) Power Produced 10 Produced Power (kW) Power Produced 5 (kW) Power Produced 0 LA - 0001F NODE 0006L001 - upper 0 LA - 0001F NODE 0258L001 - upper 4.5 0 LA - 0001F 5NODE 0010L001 - upper10 15 20 0 LA - 0001F 5NODE 0294L001 - upper10 15 20 4 Time (h) LA - 0001F NODE 0013L001 - upper Time (h) 3.5 FT Measure - Pprod (10min) Measure - Pprod (1h) Forecast - Pprod (1h) 3 Measure - Pprod (10min) Measure - Pprod (1h) Forecast - Pprod (1h) 2.5 Figure 80. Comparison of the forecasted and the achieved PV plant injected powers (hourly data) using

2 achieved radiance information

00:00 04:00 08:00 12:00 16:00 20:00 00:00 time / 2012-08-22T00:00:00+01:00 5 5 x 10 x 10

LA - 0001F NODE 0006L001 - upper 3 LA - 0001F NODE 0006L001 - lower 5 LA - 0001F NODE 0258L001 - lower LA - 0001F NODE 0258L001 - upper 4.5 2.5 LA - 0001F NODE 0010L001 - lower LA - 0001F NODE 0010L001 - upper LA - 0001F NODE 0294L001 - upper 2 LA - 0001F NODE 0294L001 - lower 4 LA - 0001F NODE 0013L001 - lower LA - 0001F NODE 0013L001 - upper 3.5

1.5 FT FT

1 3

0.5 2.5

0 2

00:00 04:00 08:00 12:00 16:00 20:00 00:00 00:00 04:00 08:00 12:00 16:00 20:00 00:00 time / 2012-08-22T00:00:00+01:00 time / 2012-08-22T00:00:00+01:00

Figure 81. Evolution of the upper and lower flexibility limits5 (in kW) computed for several Load Areas x 10

5.2.5.3 DSO technical validation 3 LA - 0001F NODE 0006L001 - lower LA - 0001F NODE 0258L001 - lower 2.5 The SRP product volumes are submitted by the ATB to the DSO for technical validation.LA - 0001FThe NODE Off 0010L001-Line - lower 2 LA - 0001F NODE 0294L001 - lower Validation algorithm embedded in the DSO platform is launched on message receptionLA and- 0001F theNODE result 0013L001 - lower

1.5 FT

(Figure 82.) is sent back to the ATB for integration. Here,1 the total AD volumes are accepted since no

constraints were detected on the network. 0.5

00:00 04:00 08:00 12:00 16:00 20:00 00:00 time / 2012-08-22T00:00:00+01:00

-0.5

-1

-1.5

Power Power (W) LoadArea 0258L001 - srp

-2 LoadArea 0010L001 - srp PrototypeLoadArea Field 0294L001 Tests, - test srp results -2.5 ADD-WP6-T6.3_Iberdrola_D6.2 – PrototypeLoadArea Field 0013L001 Tests. Test - srp Results Final v1.0 00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 time

1 LoadArea 0258L001 - srp LoadArea 0010L001 - srp 0.5 LoadArea 0294L001 - srp LoadArea 0013L001 - srp

-0.5 Curtailement (PU)

-1

00:00 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 00:00 time Figure 82. Validation result of some of the SRP products submitted by the ATB platform

5.2.5.4 ATB optimization result The optimization algorithms of the ATB platform consider the accepted SRP product volumes and select suitable incentive signals depending on the flexibility limits imposed to each Load Area and the forecasted flexibility response of each cluster (Figure 83.).

s 2013-06-21 17-29-39 DayAhead-0001F NODE 0294L001 s 2013-06-21 17-29-18 DayAhead-0001F NODE 0013L001

20 20 33349[68] 32659[66] reserve reserve 10 10

0 0

-10 -10

Power (kW) Power (kW) Power -20 -20

-30 -30

-40 -40 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 s 2013-06-21 17-29-05 DayAhead-0001FTime (h) NODE 0010L001 s 2013-06-21 17-32-07 DayAhead-0001FTime (h) NODE 0258L001

20 20 32291[65] 32981[67] reserve reserve 10 10

0 0

-10 -10

Power (kW) Power Power (kW) Power -20 -20

-30 -30

-40 -40 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time (h) Time (h) Figure 83. Incentive signals selected by the ATB algorithms to meet the SRP volumes

5.2.5.5 On-field application As explained in the test conditions, the database used by the ATB platform and the one used by the EBoxes are separated (see Errore. L'origine riferimento non è stata trovata.). At the end of the ATB optimization sequence, a signal similar to the one selected by the ATB is entered in the EBox online database. 5.2.5.6 Obtained cluster response The global response of the cluster to the selected incentive signal was then assessed. As seen in Figure 84, a maximum load increase of a little less than 5kW was achieved during the first half hour following the start time of the signal (at 3:00 AM).

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

Load increase 22500 signal

17500 Power (W) Power 12500

7500 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 Time (h) Realized Baseline Figure 84. Cluster response to the submitted incentive signal observed on the field.

5.2.5.7 Effect of AD on the distribution network The effect of the power variations induced by the AD product delivery on the MV network voltage at the island connection as well as on the power flowing towards the islands is illustrated in (a) Without AD (b) With AD Figure 85.. The distribution of the voltage on the MV network of the island is shown in (a) Without AD (b) With AD Figure 86..

In the present case, an impact of the load increase due to AD delivery can indeed be observed but it is very limited. NODE 0008 NODE 0008 NODE 0008 NODE 0008 1 voltage 1 1 referencevoltage 1 0.98 -5%reference 0.98 0.98 -5% 0.98 voltage

0.96 0.96 voltagereference voltage (PU) voltage

voltage (PU) voltage 0.96 0.96 reference-5%

voltage (PU) voltage voltage (PU) voltage 0.94 0.94 -5% 0.940 5 10 15 20 0.940 5 10 15 20 0 5 10time (h) 15 20 0 5 10time (h) 15 20 time (h) time (h) 700 700 700 700 600 600 600 600 500 500 500 500 400 400 400 400

300 300 Active Power (kW)Active Power

Active Power (kW)Active Power 300 300 Active Power (kW)Active Power Active Power (kW)Active Power 0 5 10 15 20 0 5 10 15 20 0 5 10time (h) 15 20 0 5 10time (h) 15 20 time (h) time (h) (a) Without AD (b) With AD Figure 85. Impact of AD load increase on the volage and power flow at the island connection

net net 1.000 Time stamp1.000 : 3h Time stamp : 3h Transformers Transformers 1.000 1.000 1.000 1.000 0.973 1.000 1.000 0.973 Loads Loads Generators Generators 0.999 0.999 0.997 0.999 0.999 0.997 0.999 0.999 0.994 0.999 0.999 0.993 0.999 0.999 0.999 0.994 0.999 0.993 0.999 0.999 0.997 0.999 0.999 0.997 0.999 0.999 0.999 0.999 0.972 0.972 0.999 0.999 0.972 0.972 0.999 0.999 0.999 0.999 0.997 0.999 0.999 0.997 0.999 0.999 0.996 0.996 0.999 0.993 0.993 0.999 0.993 0.993

0.999 0.999 0.996 0.998 0.998 0.996 0.998 0.972 0.972 0.998 0.972 0.972 0.998 0.998 0.971 0.971 0.998 0.998 0.971 0.971 Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results 0.998 0.998 0.971 0.971 0.998 0.998 0.971 0.971 0.998 0.998 0.998 0.998 Final v1.0 0.998 0.998 0.998 0.998 0.971 0.971 0.998 0.998 0.998 0.998 0.971 0.971 0.998 0.998 0.996 0.998 0.998 0.996 1.000 1.000 1.000 1.000 1.000 1.000 0.971 0.971 1.000 0.999 1.000 0.999 0.972 0.971 a) b) 0.999 0.999 0.991 0.963 0.963 0.990 0.961 0.961 0.991 0.991 0.991 0.987 0.990 0.990 0.990 0.985 0.987 0.985 0.999 0.998 0.998 0.998 0.999 0.998 0.999 0.998 0.992 0.991 0.991 0.991 0.990 0.990 0.992 0.991 0.998 0.996 0.996 0.993 0.992 0.9970.989 0.9960.989 0.995 0.991 0.991 0.988 0.988 0.998 0.998 0.992 0.998 0.997 0.991 0.998 0.998 0.993 0.992 0.9980.992 0.998 0.992 0.991 0.991 0.991 0.991 0.991 0.991 0.990 0.990 0.998 0.998 0.988 0.988 0.986 0.986 0.987 0.987 0.985 0.985 0.992 0.992 0.990 0.990

0.992 0.992 (a) Without AD (b) With AD 0.972 0.972 0.998 0.995 Figure0.998 860.995. Impact of AD load increase on the voltage levels of the island MV network 0.998 0.998 0.996 0.996 0.998 0.998 0.996 0.996

0.999 0.993 0.999 0.993

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

6. Conclusions

The ADDRESS project developed three field tests where all the technology was tested and assessed according to the specifications and requirements defined during the project. Based on these specifications, the technology was implemented and divided into the three field tests each focussing on different targets: - Spain: tests on a LV network with demand side management (DSM) from consumers’ point of view. - In Italy: tests on a MV network with several MV generation sources to study the interactions of several DG connection points on the MV network. - French Islands: tests on LV networks connected to a MV feeder network with DSM and with RES connected to the LV level. During the last months of the ADDRESS project, different test cases have been carried out in the three field tests. The main conclusions follow.

Spanish field test The Spanish field test is dedicated to the validation of the downstream part of the ADDRESS chain, to verify the interactions between the Aggregator and the consumers. To develop the tests, at first a consumers’ segmentation was carried out by different seasons and type of day in order to classify them in the Aggregator Toolbox. According to this classification, different clusters were defined in order to categorize the 263 consumers participating to the test. Each cluster has a different load profile according to the type of day (working day or holiday) and the season of the year (winter, spring, summer and autumn). In the end, one consumer is defined with 8 different clusters during all the year. The tests covered a period of 8-10 months, from the middle of September 2012 to the end of May 2013 and beyond. The recruitment and installation process of the equipment was developed during the different months of the pilot. Not all the consumers were participating all the time;. some participated since September, and others from Feb-March 2013 till the end of the test. During the execution of the test, communications have been the main issue. This has hindered developing all tests and obtaining the results. For this, it was necessary to carry out more tests to validate functionality of all the players involved. Due to the communications issue, some test cases defined in previous deliverables [1] could not be developed. The communication with some specific EBoxes was not possible, so most of the results have been analysed in an aggregated way. Despite of it, the analysis of all appliances and players involved in the Spanish field test was achieved. In some cases the results were not as expected but different tests were carried out in order to be able to validate or not the technology implemented. In some tests, in order to achieve a representative value as result of the test, an extrapolated answer from consumers involved in the use case was needed in order to analyse the achieved results. The evaluation of these results is acquainted in the Deliverable 6.3

Italian field test The objective of Italian test was to validate the upstream part of the ADDRESS chain, from AD buyers to aggregation platform, with a focus on DSO and grid operation on a large MV network, and on the effect of AD visible at HV level. The AD was emulated by means of some DG using RES (hydro) and a

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

storage system. The Italian field test cases and scripts were divided into four categories: - Test case 1: The DSO as the AD product validator - Test case 2: The DSO as the buyer of AD products - Test case 3: Localized AD products to limit the power flow towards the transmission system - Test case 4: MVCC algorithms reaction to network changes The observations are: - Most of the tests were carried out successfully. - All the algorithms run well and give realistic results. - The MVCC algorithms behave as expected. Therefore it was possible to test on field the management of the distribution network in presence of Active Demand, allowing the DSO to play both the technical and the commercial roles foreseen in the ADDRESS architecture. The assessment of the results achieved is presented in ADDRESS Deliverable6.3

French field test The main objective of the French field Test was to evaluate the performance and the technical feasibility of the whole ADDRESS chain. A two-part system compliant with the ADDRESS architecture and fit to the reliability requirements of a field test was retained for the French field tests. This system is composed of:  the upstream part of the architecture consisting of the DSO platform, the aggregation platform, a simplified modeling of the different electricity system players and the electricity market simulator;  the downstream part consisting of the ecosystem of the Energy Boxes (and the controlled appliances). It is always run connected to the internet in an online mode. Standardized links were used between some actors (DSO / aggregation functions…) so that they could communicate automatically and perform on their own with a limited manual intervention.

Several cases were tested in order to cover the objectives of the French field tests. Most of them are about the complete ADDRESS chain of processes providing different AD service types to the electricity system players: voltage control, balancing services, load shaping, combination of AD with RES (Renewable Energy Sources)… Other cases concern the electricity market simulator and its interactions with the aggregation entity. All these cases allow to verify the capability of the implemented ADDRESS architecture to respond to the envisioned situations and to test the performance of its modules on real data. Depending on the scenario, different players are involved in the tests. The main cases considered are the following:  Request from the DSO for reducing a local overload constraint,  Request from the TSO for reducing a congestion constraint,  Request from the BRP for managing the imbalance between production and consumption,  Requests in combination with RES for reducing a local voltage constraint anticipated by the DSO or providing AD services to a RES producer,  Requests from the DSO for limiting the global consumption of the islands over a complete day,

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

 Offers providing AD services to an electricity market.

During the French field test, several issues have been encountered both in the downstream and upstream parts of the ADDRESS chain and should be taken into account to analyse the results:  one refers to the development of the Energy Box and their installation in the households. This issue has already been discussed in previous deliverables [9]. The solution adopted allowed to test most of the functionalities regarding the management of the loads by the EBox system.  the electric heating could not be managed as thermal loads due to blocking issues in the EBox software optimisation,  the management of Electric Water Heaters (EWH) also met difficulties. Initially, the algorithms were developed so that the EWH can be operated by the EBox as Interruptible Loads. However, this doesn’t correspond to the specific way the EWH are operated in France. So it was decided to define them as Shiftable Loads which was more appropriate considering their consumption behaviour. So appropriate modifications and delicate parameter tuning were necessary,  The integration of the different components of the ADDRESS chain needed additional developments in particular to compensate for missing functionalities.  Some issues were also encountered in the ATB system (failed ATB optimizations, selection of non-adapted incentive signals...). In a few cases, this led in particular to bypass the aggregation platform in order to run the complete field scenarios concerned by these problems.

Despite of all the above issues, we were able to carry out all the tests planned and to validate the complete ADDRESS chain in the French field tests as expected. Indeed the complete automated sequence (from market simulator, DSO platform and aggregator platform up to the Energy Boxes) could be performed on the field for the designed test cases. The effective impact of AD requests on the consumer consumption could be assessed and compared with the forecasts provided by the aggregation platform. The impact of up-scaled AD requests on the distribution network was also studied. In other words the technical feasibility and performance of the provision of AD services to electricity system players could be assessed and the objective of the French field tests were reached.

As it has already been mentioned above, the assessment of the results obtained in the French field tests will be presented in the following deliverables [12], [13].

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

7. References

7.1. Project documents

List of reference document produced in the project or part of the grant agreement [DOW] – Description of Work [GA] – Grant Agreement [CA] – Consortium Agreement [1] D6.1 - Description of test location and detailed test program for (limited) prototype field test, simulations and hybrid tests. [2] WP1 Subtask 1.2.3ADD-Task1_2-IBD-Subtask123_V3.1.doc [3] Validation of integrated field tests in Spain (Id. ADD-WP6-T6.3) [4] IR6.2. Validation of Italy Field Test (6.2) [5] ADDRESS: Validation of lab and field tests in France (Id. ADD-WP6-T6.3) [6] Internal report I6.1 - Laboratory tests results [7] D5.2 – Key societal factors influencing the adoption of the ADDRESS SmartGrids architecture. Report on the results of WP5 verified by the experience obtained in the field tests (WP6). [8] D1.1 – Conceptual architecture including description of: participants, signals exchanged, markets and market interactions, overall expected system functional behaviour [9] D2.2 - Development of Local Energy Management equipment and integration of algorithms for load, generation and storage control [10] D5.4 - Report outlining business cases for Customers, Aggregators and DSOs in the scenarios detailed in WP1 [11] IR5.1 - Evaluation of Benefits of Active Demand [12] D6.3 – Assessment to what extent the objectives of the field test are met, what prototype (application) performs best. [13] D6.4 - Evaluation of the effectiveness of the ADDRESS concepts in promoting active demand and the large scale integration of DER

7.2. External documents

List of reverence documents not produced in the project (articles, books,….)

Prototype Field Tests, test results ADD-WP6-T6.3_Iberdrola_D6.2 – Prototype Field Tests. Test Results Final v1.0

8. Revisions

8.1. Revision history

Version Date Author Notes 0.1 31/05/13 Ignacio Delgado First release 0.2 14/06/2013 TB members comments 0.3 24/06/13 Ignacio Delgado Updated version with comments from Arturo and Regine 0.4 01/07/13 EDF-SA, Enel comments Distr,Iberdrola 0.5 8/07/13 Ignacio Delgado Updated version 0.6 15/07/2013 PC Comments 0.7 22/07/2013 Iberdrola Updated after PC review 1.0 31/07/2013 PC-QMO Final Approval