WISE software
Use of water reservoirs for energy storage
Work package 4
2016
Title: Authors :
WISE software, Kristian Duch Søndergaard, Sweco Denmark A/S Use of water reservoirs for energy storage Elham Ramin, Sweco Denmark A/S
Publisher: Photo:
Miljøstyrelsen - Strandgade 29 1401 København K Illustration: www.mst.dk -
Year: Map:
2016 -
ISBN nr.
2 WISE software
Ansvarsfraskrivelse:
Miljøministeriet offentliggør rapporter og indlæg vedrørende forsknings- og udviklingsprojekter inden for miljøsektoren, som er finansieret af Miljøministeriet. Det skal bemærkes, at en sådan offentliggørelse ikke nødvendigvis betyder, at det pågældende indlæg giver udtryk for Miljøministeriets synspunkter. Offentliggørelsen betyder imidlertid, at Miljøministeriet finder, at indholdet udgør et væsentligt indlæg i debatten omkring den danske miljøpolitik. Må citeres med kildeangivelse.
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Contents
Contents ...... 4
1. Introduction ...... 5 1.1 Driving force - Why this project? ...... 5 1.2 The concept: What can WISE do ...... 5 1.3 Workflow for developing the WISE program ...... 6 1.4 Vulnerability of abstraction wells and water quality ...... 6
2. Description of WISE ...... 8 2.1 WISE, software architecture ...... 8 2.2 Optimization method ...... 9 2.2.1 Optimization algorithm ...... 9 2.2.2 Constraints and configurations ...... 10 2.2.3 Convergence and robustness ...... 11 2.3 Optimization process ...... 11 2.4 WISE interfaces ...... 12 2.4.1 Control systems ...... 12 2.4.2 NordPoolSpot ...... 12 2.4.3 Energinet.dk ...... 12
3. Proof of concept ...... 13 3.1 Proof of concept, level 1 - Conceptual model ...... 13 3.2 Proof of concept, level 2: HOFOR case ...... 15 3.2.1 Problem formulation ...... 16 3.2.2 Optimization results ...... 17 3.2.3 Savings ...... 18 3.3 Proof of concept, level 3 - VCS case...... 19 3.3.1 Problem formulation ...... 21 3.3.2 Optimization Results ...... 22 3.3.3 Savings ...... 22 3.3.4 Usage of potential storage volume ...... 24
4. Conclusions ...... 25
5. Perspective ...... 26 5.1 Business plan ...... 26 5.2 Business opportunities ...... 26 5.3 Communication and presentation activities ...... 26
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1. Introduction
The current report describes the development of WISE software, a Water Intelligent Saving Engine developed by SWECO in collaboration with VandCenter Syd, HOFOR and Aarhus Vand, which are among the largest water utilities in Denmark. Development of WISE is part of the “Future Water” project funded by VTUF and the Ministry of Environment and initiated by VandCenter Syd (VCS) with the purpose of developing tools to support efficient and sustainable operation in drinking water utilities (http://futurewatercity.com/).
1.1 Driving force - Why this project?
In the framework of Climate Action by European commission, an objective is set to limit global warming by reducing the emission of greenhouse gases to at least 50% of 1990 levels by 2050. Drinking water utilities can contribute to this vision by reducing the CO2 emissions of their pumping systems. To do so it is necessary to develop optimization tools that help water utilities to use storage volumes at waterworks and in the distribution network in a way that enables them to postpone energy consumption to “green” hours with lower CO2 emission factors. In fact, water supplies have the possibility of using more green energy and at the same time achieving economical savings. Furthermore, many utilities in Denmark have the vison of becoming CO2-neutral in the near future. For the above reasons the initiative was taken to develop a software that is able to help water supply utilities to:
1) Take advantage of smart grid (water reservoirs = batteries)
2) Exploit more fossil free energy
3) Move from constant energy prices to market based prices (= economical savings)
4) Reduce total energy costs as much as possible
5) Document and reduce CO2 emissions in relation to energy consumption
6) Automatic and on-line control water production and pumping in relation to chosen priorities.
1.2 The concept: What can WISE do
Optimization of pumping operation in water distribution systems is not a trivial task. It requires managing storage facilities at water works and in the network at elevated locations. Storage facilities are mainly used to pressurize the system and provide storage for reducing max. pumping flow and emergency situations (Figure 1). However, reservoirs can also be used to optimize the operation of pumps. Most utilities have enough pumping capacity to pump more water when the price is low (and green) to fill their reservoirs. Then during peak hours with high electricity prices, they can supply the consumers mostly with the stored water in the elevated reservoirs and thereby minimize pumping from water works. These reservoirs play the role of "batteries", which are able to store the hydraulic energy with an efficiency close to 100%. This concept also applies to the reservoirs at each water work to optimize the operation of pumps at the abstraction wells.
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FIGURE 1 ELEVATED RESERVOIRS AS BATTERIES FOR STORAGE OF HYDRAULIC ENERGY
Using the most out of storage volumes requires the use of advanced tools that can take into account the time-varying consumption rates, electricity tariffs, CO2 emission factors, as well as various constraints on water abstraction, pumping capacity and storage volumes.
The WISE software is capable of connecting to external sources such as SCADA/SRO systems as well as online servers to retrieve dynamic data on water consumption, electricity price and CO2 emission factors etc. Based on these data, WISE performs an optimization analysis every hour to find the most economical operational strategies for the next 24 hours.
1.3 Workflow for developing the WISE program
The following steps have been performed for development of the WISE program:
Requirement analysis for the optimization of water supply systems Architectural design of the software Implementation and coding Testing (Proof of concept)
The WISE program has been evaluated through three levels of proof of concept to confirm that the developed software can deliver the required capabilities:
Level 1: Using a conceptual model to test the performance of the software in terms of programmability, expandability, simplicity, stability/robustness and speed.
Level 2: Optimizing a simple existing water supply system using online data to test the trends and savings.
Level 3: Optimizing a complex existing water supply system using online data to test the automation of operation control through SCADA systems.
1.4 Vulnerability of abstraction wells and water quality
An even abstraction, to ensure a high and a stable quality of drinking water, has been the rule of thumb in Denmark for years. This practice is being challenged when using WISE, because it wants to increase abstraction when electricity is cheap and green.
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Therefore, the influence of fluctuating abstraction on the long term water quality has been investigated as a sub project by Aktor Innovation. The results have been published in the Danish technical magazine “DanskVand” in October 2014 with the title “Jævn indvinding er godt for vandkvaliteten – en sandhed med modificationer”.
The overall conclusion is that most of the aquifers exploited for drinking water purposes in Denmark will not experience deteriorated water quality due to fluctuating abstraction. WISE does not so far carry out any automated water quality calculations. Therefore, before applying WISE the vulnerability of all well fields involved should be investigated through an external study using the categorization of aquifers and the water quality model developed by Aktor Innovation in the sub- project.
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2. Description of WISE
2.1 WISE, software architecture
The architecture of WISE is component based, meaning that every component of the software functions independently of the others. An overview of the whole component design of the software is shown in FIGURE 2, which also illustrates the integration of WISE to the external systems in order to obtain the information for the optimization analysis. The external systems are mainly the SCADA/OPC system of the water distribution network, as well as online sources publishing the electricity price and CO2 emission factors, namely Nordpoolspot and Energinet.dk. Data from the SCADA/OPC system are retrieved instantly, whereas the 24-hour data for the electricity price and CO2 emission factor are published the day before.
User interface OPC 1) Key values 2) Control strategies 3) Alarms / Error messages
Input-user interface Virtuel OPC
WISE – boundry interface
Output database Output-funcions
WISE Controlers
Input database Input-functions
OptimerizationOptimizationalgorithm algorithm(DLL (DLL))
Data validation Firewall Nordpoolspot (kr/kwh) Energinet (CO2/kWh)
FIGURE 2 WISE SYSTEM COMPONENT DESIGN AND CONNECTIONS TO EXTERNAL SERVERS
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2.2 Optimization method
Optimization of water distribution systems in WISE is formulated as a multi-objective optimization problem, in which the best combination of the values of the decision variables needs to be identified in terms of certain objectives, such that a number of constraints are satisfied.
The two main objectives are to minimize cost and CO2 emissions of the water works operation. The decision variables are the water levels in reservoirs and the distribution fraction between the waterworks. The constraints are pumping and storage capacities as well as abstraction limitations from the wells.
2.2.1 Optimization algorithm
Among many multi-objective optimization algorithms, genetic algorithm is shown to be an effective method in optimization of water distribution systems. The “state-of-the-art” multi- objective genetic algorithm, non-dominated Sorting Genetic Algorithm (NSGA-II, Deb et al., 20021), is used in WISE. The optimization process in the NSGA-II method is summarized in Figure 3. The additional features of NSGA-II besides selection, crossover, and mutation as compared to the traditional genetic algorithms are assuring elitism by combining parent and child and generating a global population. Additionally, crowding distance comparison on the solutions with the same ranking is performed. Finally, an efficient constraint handling method referred to as constrained tournament method by Deb et al. (2002)1 is used to give feasible solutions a priority over infeasible solutions.
FIGURE 3 OPTIMIZATION PROCESS USING MULTI-OBJECTIVE GENETIC ALGORITHM NSGA-II (WU ET AL., 2010)
1 Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." Evolutionary Computation, IEEE Transactions on 6.2 (2002): 182-197.
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2.2.2 Constraints and configurations
In WISE it is possible to apply several constraints that the optimization algorithm has to take into account. The following constraints and configurations are applied:
Water towers / elevated reservoirs: Level/volume graph Max. and min. levels (limits) In service: Yes/No
Well fields: EQ- graph, well pumps Max. possible production (capacity) Max. and min. abstraction (limits) Max. and min. abstractions (capacity) Yearly abstraction permit Yearly min. production (limit)
Water works: EQ- graphs, pumps Max. and min. pumping (capacity) Max. and min. pumping (limits) Control pressure in pipe network Min. pressure in pipe network Reservoirs: o Level/volume graph o Max. and min. levels (limits) o In service: Yes/No
Booster pumps: Yearly intake permit EQ- graph, pumps Max. and min. pumping (capacity) Max. and min. pumping (limits) Control pressure in pressure zone Min. pressure in pressure zone
Most of the constraints and configurations are applied via the WEB user interface made for WISE. The WEB interface is showed in Figure 4.
FIGURE 4 WEB USER INTERFACE FOR APPLYING CONSTRAINTS AND CONFIGURATIONS IN THE WISE OPTIMIZATION.
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2.2.3 Convergence and robustness
The efficiency of genetic algorithm is very much dependent on its parameter values. The main parameters of NSGA-II are: Number of population Number of generation Cross-over probability Mutation probability
To identify the optimum combination of parameters for WISE, several sensitivity tests have been performed. It is found that the number of population and the mutation probability are the most crucial parameters for convergence and robustness of the algorithm. High numbers of populations and low mutations result in high explorations of the possible values for the decision variables and increase the chance of finding an optimum solution.
2.3 Optimization process
Figure 5 illustrates the components and data flow in the optimization process that is programmed in WISE.
SCADA/OPC Decision variables
Water tower Waterworks Actual Distribution water level Fraction water level water Levels
Import/export Tower volume Waterworks change volume change
Static data Total pumping Discharge flow Abstraction EQ curve flow flow
HV curve Constraints handling
Online server
EL price Energy
CO2 factor CO2 COST
FIGURE 5 OPTIMIZATION PROCESS AND DATA FLOW IN WISE PROGRAM
The optimization of water supply network is performed for the following hour, which has both the electricity price and CO2 emission factor available on the online servers. The maximum optimization period is set to be no more than 24 hours due to computation time constraints.
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We must note that the water consumption is obtained instantly from the SCADA system for the current hour. In order to forecast the hourly values of import/export of water in the system for the following hours, the values from the last three weeks of historical data stored in the database are used. This way we also take into account the correlation of daily consumption profile to the type of the day (weekday vs. weekend).
2.4 WISE interfaces
The input data to WISE software are imported through an interface from the following servers:
Control systems (SCADA, OPC) NordPoolSpot Energinet.dk
2.4.1 Control systems Most of the water utilities supervise and control the operation of their machineries using a series of control systems and tools such as SCADA (Supervisory Control and Data Acquisition), which is the equivalent of SRO (Styring Regulering Overvågning) in Denmark. The communication of WISE with SCADA is through an OPC (OPL for Process Control) interface.
2.4.2 NordPoolSpot Nord Pool is one of the oldest and leading electricity exchange markets, which has a market share of about 70% in the Nordic region. The pricing is based on the intersection between the aggregated supply and demand curves, when every day at noon, the participants submit their bids for every hour of the succeeding day.
2.4.3 Energinet.dk Energinet is a non-profit company in Denmark owned by the Ministry of Climate, Energy and Building that is responsible for maintaining the security of electricity and gas supply in Denmark. Energinet calculates the emission of greenhouse gases of the energy system as a whole in Denmark. Therefore, Energinet supports the use of fossil free energy sources such as wind turbines, biomass and solar panels. Energinet has an online FTP server to get the values on the calculated hourly emission factors.
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3. Proof of concept
This section presents the results of three level testing of the WISE software to confirm its performance:
Level 1: Using a conceptual model to test the performance of the software in terms of accuracy, stability/robustness, speed, simplicity, programmability and expandability.
Level 2: Optimizing a simple existing water supply system using online data to test the trends and savings. Herlev supply area, which is a suburb to Copenhagen and a part of HOFOR, was used as test facility.
Level 3: Optimizing a complex existing water supply system using online data to test the automation of operation control through SCADA systems. VandCenterSyd supply area, which is supplying most of Odense, the third largest city in Denmark, was used as test facility.
3.1 Proof of concept, level 1 - Conceptual model
The optimization algorithm was verified based on the following criteria:
Accuracy: Delivering reliable results, trends and correlations
Stability/robustness: Produces constantly acceptable “accuracy”
Speed: Fast performance to apply to real-time control
Simplicity: Easy to understand and maintain
Programmability: Easy to manipulate the code and adapt to new functionalities
Expandability: Able to handle Multi-zone and other complex cases. Further development is possible.
For this purpose, a simple conceptual model was set up consisting of a water tower and a pump. The conceptual model was then tested using arbitrary dynamic data for consumption rate and electricity prices (see Figure 6) in three different scenarios. The results are listed in Figure 7. Most importantly, the results show the right trends, meaning that when the electricity prices are low, reservoirs are filling and when the price is high, reservoirs are emptying.
From this assessment (Table 1) it was concluded that NSGA II had the right performance and that the WISE software therefore should be based on this optimization algorithm.
Optimization algorithm, NSGA-II Accuracy High Stability/robustness High Speed Conceptual model: 10 sec (calculated) Simplicity Low / Medium
Programmability Medium HOFOR case: ~ 5 min (estimated) Expandability Very high TABLE 1 ASSESMENT ON THE PREVCSFORMANCE case: ~ 30 OF min THE (estimated) OPTIMIZATION ALGORITHM, NSGA II
Multi-zone cases: ~ 40 min (estimated)
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FIGURE 6 THE CONCEPTUAL MODEL WITH DYNAMIC WATER CONSUMPTION AND ELECTRICITY PRICE. THE CO2 EMISSION FACTOR IS SET TO A CONSTANT VALUE OF 366 [G/kWh].
Accuracy Convergence and stability
Scenario 1: Constant flow rate and variable El price
Scenario 2: Variable flow rate and constant El price
Scenario 3: Variable flow rate and El price
FIGURE 7 OPTIMIZED VOLUME CHANGE IN WATER TOWER TO MINIMIZE THE COST DURING ELECTRICITY PRICE AND CONSUMPTION RATE VARIATION IN 24 HOURS.
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3.2 Proof of concept, level 2: HOFOR case
N
PZ 1 Herlev PZ 2
FIGURE 8 LOCATION OF HERLEV MUNICIPALITY AND THE DIVISION IN TWO HYDRAULIC DISCONNECTED PRESSURE ZONES (PZ) NAMED “PZ1” (NORTH) AND “PZ2” (SOUTH). HERLEV WATER TOWER IS LOCATED IN PZ1.
Herlev municipality imports all of its drinking water from HOFOR’s water supply network through two pressure zones, which in normal operation mode are hydraulically disconnected. Key figures covering Herlev pressure zone 1 (North) are listed in Table 2. Hjortespring booster pump station delivers all drinking water to pressure zone 1 (North) in normal operation. However, in emergencies water can be supplied from “PST Klausdalsbrovej” (pump station), “MB Borgerdiget” (District metering well) and “MB Violinvej” (District metering well). Herlev water tower (also called Hjortespring Water tower) connects to the booster station to control the water supply in pressure zone 1. Figure 9 shows the conceptual model of Herlev water supply system in pressure zone 1 that was applied in the WISE-model and used in the proof of concept level 2.
Søndersø-Tinghøj transport pipe
M MB 1 (Kildegården)
Hjortespring booster station only import
Herlev water tower M MB PST Pressure zone 1 2 Klausdalsbrovej (Nord)
M
MB Borgerdiget M M MB Violinvej
Import and Import and export export
FIGURE 9 CONCEPTUAL MODEL OF HERLEV WATER SUPPLY NETWORK IN PRESSURE ZONE 1, NORTH.
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Key Figures 2015, Herlev pressure zone 1 (north) Units Value Water works No 0 Pressure zones No 1 Water towers and reservoirs in the water supply network No 1 Emergency connections No 3 Water consumption (Pumped into the network) m3/yr 1,073,136 Electricity price (HOFOR) excl. taxes and VAT1) Kr./kWh 0.2113 Energy consumption, PST Hjortespring (pump station) kWh/yr 99,799 Pressure level (= level of water table in water tower) - Max. Masl 66.10 - Min., in service Masl 62.30 - Bottom plate Masl 57.50 - Normal Masl 64.20 Pressure, suction side of PST Hjortespring Masl 47.59 Avg. lift, PST Hjortespring mWC 16.61 Level of pressure transducer (pressure side of PST Hjortespring) Masl 37.26 1) FlexPricing, Midtjysk Elhandel A/S taken from invoice covering November 2015
TABLE 2 KEY FIGURES FOR HERLEV PRESSURE ZONE 1 (NORTH).
Minimum Maximum Herlev water tower Volume 800 m3 2900 m3 Pumping capacity 140 m3/h or off 1000 m3/h Hjortespring pumping station Target pumping 140 m3/h or off 375 m3/h flow TABLE 3 CONSTRAINTS ON HERLEV WATER SUPPLY SYSTEM.
3.2.1 Problem formulation The simple formulation of Herlev water supply in pressure zone 1 is illustrated in Figure 10 consisting of a booster pump and a water tower. The capacity constraints on the pump and water tower are listed in Table 3. The hourly variations in the electricity price, Co2 emission factor and the consumption rate are shown in Figure 11.
Water tower
Booster pump Consumption
FIGURE 10 FORMULATION OF HERLEV WATER SUPPLY SYSTEM ZONE 1.
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Consumption rate 200
/h
3 100
Flow m
0 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03 Electricity price 0.5
0
DKK/kWh
-0.5 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03 CO2 emission factor 600
400
/kWh
2 200
kg COkg 0 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03
FIGURE 11 DYNAMIC INPUT USED FOR THE OPTIMIZATION OF HERLEV WATER SUPPLY SYSTEM
3.2.2 Optimization results
The Herlev water supply system was optimized by WISE software during February 2016. The results are presented here in terms of trends and consistency, as well as the achieved savings as compared to constant operation strategy.
The optimized operation of the booster pump in pressure zone 1 for the last two weeks of February 2016 is illustrated in Figure 12, which shows the suggested pumping flow that follows the opposite of variations in the electricity price. In terms of trends and consistency, the optimization algorithm in WISE performs nicely.
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El price 400 0.5 Pumping flow Average consumption rate
/h
3 200 0
Flow m
Elprice (DKK/kWh)
0 -0.5 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03
El price 400 0.4 3 Filling the tower 3 Emptying the tower
200 0.2
0 0
Volume change intower, Volume change m -200 -0.2 intower, Volume change m 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03
FIGURE 12 THE OPTIMIZED OPERATION OF THE BOOSTER PUMPSTATION IN HERLEV BY WISE SOFTWARE
3.2.3 Savings
Optimization of booster pump operation at Herlev resulted in 3 to 11 % savings in terms of COST and CO2 respectively. Figure 13 shows the variations in the hourly savings on variation in the electricity price in the period where the Herlev boosting pump was optimized.
Total CO saving:3 % 2 150 100 50 0 -50
CO2 saving % CO2 saving -100 -150 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03
Total Cost saving:11 % 150 100 50 0 -50
Cost saving % Cost saving -100 -150 19/02 20/02 21/02 22/02 23/02 24/02 25/02 26/02 27/02 28/02 29/02 01/03 02/03
FIGURE 13 SAVINGS IN TERMS OF CO2 AND COST BY OPTIMIZING THE BOOSTER PUMPING STATION AND WATER TOWER IN HERLEV USING WISE SOFTWARE
The achieved savings is in a very acceptable range considering the limitation in pumping the high flow rates with much higher energy consumption than the low flow rates as compared to the moderate changed in the electricity price.
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3.3 Proof of concept, level 3 - VCS case
N
Water supply areas in Odense municipality
VCS Other water utilities Storage tanks
FIGURE 14 LOCATION OF ODENSE MUNICIPALITY AND THE DIVISION IN WATER SUPPLY AREAS.
FIGURE 15 LOCATION OF WATER WORKS AND ELEVATED RESSERVOIRS OWEND AND RUN BY VCS.
VCS water utility company operates one of the largest and oldest water supply system in Denmark (Figure 14 and Figure 15). VCS supplies industries and 165,000 residents with about 9.2 million m3 water a year through 1,000 km pipe network and 5 water works. Two reservoirs on elevated ground, namely Bolbro I + II and Sanderum, are used to handle the daily consumption variations. The water supply area under VCS operation is divided into 11 different pressure zones due to the elevation differences; however, it is only pressure zone 1 that has elevated reservoirs (Table 4 and Table 5). Figure 16 shows the conceptual model of the VCS water supply system that was applied in the WISE-model and used in the proof of concept level 3.
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FIGURE 16 CONCEPTUAL MODEL OF THE VCS WATER SUPPLY NETWORK APPLIED IN WISE.
Key figures for 2014 and 2015, VCS Units Water supply area (all pressure zones) Pressure zones No. 11 Pressure zones with water towers / elevated reservoirs No. 1 Water consumption (pumped into the network) -2014 m3/yr. 9,276,002 -2015 m3/yr. 9,125,824 Residents No. 165,000 Electricity price excl. taxes and VAT (2015), set price Kr./kWh 0.3356 Pressure zone 1 Pressure level Masl 41.5 Energy consumption, water works (2014) kWh/yr. 1,226,618 Energy consumption, well fields (2014) kWh/yr. 515,015 Water consumption, 2014 (pumped into the network) m3/yr. 7,761,959 TABLE 4 KEY FIGURES FOR THE WATER SUPPLY RUN BY VANDCENTERSYD (VCS).
Pressure zone 1 Units Holmehave Hoved Lunde Lindved Værket Værket Værket Værket, Z1 Avg. geometric lift Booster pumps at water work mWC 10.7 29.1 45.6 18.8 Well pumps mWC 9.3 0 (-6.5) 7.8 11.0 11.0 Sum mWC 20.0 29.1 36.9 56.6 29.8 TABLE 5: AVERAGE GEMETRIC LIFT FOR THE WATER WORKS LOCATED IN PRESSURE ZONE 1.
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3.3.1 Problem formulation The optimization was applied to the VCS water supply system including three water towers and the four water works in pressure zone 1. Only one water tower with the volume equal to the three water towers at VCS is considered in the calculations. The schematic formulation of the problem is shown in Figure 1. The VCS waterworks have certain constraints on the pumps, treatment capacity and abstraction permit, which are summarized in Table 6. The hourly variations in the electricity price, Co2 emission factor and the consumption rate are shown in Figure 17.
FIGURE 1 FORMULATION OF VCS WATER SUPPLY SYSTEM WITH 4 WATERWORKS AND A WATER TOWERS
Unit Holmehave Hoved- Lindved- Lund- værket værket værket
Reservoir capacity Treatment capacity m3/day 1,000 1,000 1,000 1,000 Volume m3 2,000 2,500 1,450 1,000 Min. Level m 0.90 1.00 1.22 1,00 Max. Level m 2.45 3.50 2,99 3.00 Abstraction capacity Min. pump capacity m3 150 42.1 47 21.7 Max. pump capacity m 1,000 670 482 165 Target min. pump m 200 283 103 45 Target max. pump m 700 482 348 120 Abstraction permit mio.m3/yr. 5.5 1.8 2.1 1.0 Minimum production mio.m3/yr. 0 0 0 0 Discharge capacity Min. pump capacity m3 136 195 49.14 37.7 Max. pump capacity m 760 575 450 148 Target min. pump m 136 342 104 63 Target max. pump m 613 360 300 110 TABLE 6 VCS WATER WORKS CONSTRAINTS
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Consumption rate 2000
/h 3 1000
m
0
Electricity price 0.4
0.3
0.2
0.1
Dkk/kWh
0
CO2 emission factor 600
/kWh
2
kg CO 0 01/02 03/02 05/02 10/02 13/02 15/02 17/02 19/02 21/02 23/02 25/02 27/02 29/02
FIGURE 17 DYNAMIC INPUT USED FOR THE OPTIMIZATION OF VCS WATER SUPPLY SYSTEM. CONSUMPTION RATE IS CALCULATED AS: QCONSUMP = QTOTAL CONSUMP + QMUNKDRUP – QDALUM WW, PRODUCTION.
3.3.2 Optimization Results
The optimized operation of the four water works in pressure zone 1 for February 2016 is illustrated in Figure 18, which shows that filling and emptying of reservoirs follow the opposite of variations in the electricity price. For the complex water supply system at VCS, the optimization algorithm in WISE performs just as nicely as in the simple case (Herlev pressure zone 1).
Bolbro 1+2 + Sanderum reservoirs reservoirs
FIGURE 18 VCS UTILITY: OPTIMIZED OPERATION DONE BY THE WISE SOFTWARE.
3.3.3 Savings
Optimization of the operation at VCS resulted in 9 to 8 % savings in terms of COST and CO2 respectively. Transformed into unit cost and emissions and compared to the yearly production in
2014, this adds up to a yearly saving of 18,629 DKK/yr. and 31,824 kg CO2/yr. (Table 7, Table 8 Table 9).
The achieved savings are less than the expected potential of 20 % savings. The reason for this is mainly the very low variation in electricity price during the test period combined with the applied constraints on especially the max. and min. pumping limits. Never the less, the project team still
22 WISE software believes that 15-20 % savings on the electricity cost (market price) or CO2 emission are achievable. WISE will continue to operate at VCS to verify the full potential of WISE.
VCS - 2014 Yearly water production (2014)
Waterworks m3/year % Holmehaveværket 2,948,772 38.0% Lindvedværket 1,399,312 18.0% Hovedværket 2,732,954 35.2% Lundeværket 680,921 8.8% Sum 7,761,959 100% TABLE 7: YEARLY WATER PRODUCTION ETC. AT VCS IN 2014.
VCS – Average Average hourly Fraction Cost1) CO21) production (2014) (WISE, Feb 2016) Waterworks m3/h % Dkk/h Kg CO2/h Holmehaveværket 408 38.0% 3,8 7,6 Lindvedværket 67 18.0% 2,5 5,0 Hovedværket 273 35.2% 11,3 6,3 Lundeværket 207 8.8% 3,2 22,3 Sum 955 100% 20,8 41,1 TABLE 8 OPTIMIZED OPERATION AT VCS USING WISE. 1) CALCULATED USING EQ-GRAPHS, VALUES OF ELECTRICITY PRICE AND CO2 EMISSIONS COVERING FEBURARY 2016 AS LOGGED IN WISE.
Unit COST Unit CO2 COST1) CO21) Dkk/m3 Kg CO2/m3 Dkk/yr Kg CO2/yr VCS – Average 0.0258 0.0511 200,259 396,636 VCS – WISE, Feb 2016 0.0234 0.0470 181,630 364,812 Saving 9% 8% 18,629 31.824 TABLE 9: SAVINGS. 1) IN THE CALCULATION, THE YEARLY WATER PRODUCTION IN 2014 IS USED (7.761.959 M3/YR).
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3.3.4 Usage of potential storage volume
Lower bound Upper bound Optimized ]
3 29800
] 3 24800
19800 Maximum possible usage of 14800 storage capacity
9800
Storage volume [m
Water tower volume usage [m usage volume tower Water 4800
00:00 01:00 02:00 03:00 04:00 05:00 06:00 07:00 08:00 09:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 00:00
FIGURE 19 AN EXAMPLE OF THE VOLUME USAGE IN THE ELEVATED RESERVOIRS AFTER WISE OPTIMIZATION. LOWER AND UPPER BOUNDS ARE DEFINED BASED ON THE TOTAL PUMPING CAPACITY.
Based on an hourly analysis of the pumping capacity and the available storage volume in the tower, the following usage of the storage volume in the water tower could be estimated:
Max. available storage volume (between max. and min. level limitations) 26,350 m3 Max. possible usage due to max. pumping capacity (between upper and lower bound in a day) 4,251 m3 WISE usage of storage capacity (between max. and min. optimized level in a day) 3,763 m3 WISE usage of available capacity 14.3% WISE usage of possible capacity 88.5%
These values indicate that the WISE optimization of water supply in VCS is mainly limited by the low pumping capacity at the water works in order to use the most out of the storage volume in the water towers. In order to increase the usage of storage volume in a longer term, the end water level in each optimization run is determined based on the average electricity price of that day as compared to the average price three weeks before. Figure 20 shows the volume change in the tower during the first two weeks of July, where more than 36% of the tower volume could be used for optimization, twice more than the 14% usage of storage volume in a day of optimization as shown above.
4 x 10 3.8 WatertowerStorage volume storage [m planning3] [m3] 0.4
El price
3.6 0.35
] ]
3 3 3.4 0.3
3.2 0.25
3 0.2
2.8 0.15
Elprice [DKK/kWh]
Watertower [mvolume Storage volume [m volume Storage
2.6 0.1
2.4 0.05 01/07 03/07 05/07 08/07 10/07 12/07 14/07
FIGURE 20 STORAGE PLANNING AT ELEVATED RESERVOIRS IN JULY 2016 BASED ON AVERAGE DAILY ELECTRICITY PRICE AS COMPARED TO THREE WEEKS BEFORE.
24 WISE software
4. Conclusions
The project has succeeded in developing an optimization tool that enables water utilities to use reservoirs as energy storage and thereby make it possible to postpone energy consumption to cheap and “green” hours.
The achievement includes:
1) Finding and developing an appropriate optimization algorithm 2) Developing and programming a flexible software architecture that can be applied to both simple and complex water supply networks in Denmark and abroad. 3) Developing a WEB interface for changing constraints and configurations as well as showing up to 20 different graphs for analyzing data.
Futhermore, the 3 levels of proof of concept have showed that WISE are able to:
1) follow the wanted trends. High electricity prices produce reduced pumping and vice versa. 2) make economical and CO2 savings up to 9-11%. At VCS, this adds up to a yearly saving of
about 19,000 DKK/yr. and 32,000 kg CO2/yr. However, this is less than the expected potential of 20 % savings. The reason for this is mainly the very low variation in electricity price during the test period combined with the applied constraints on especially the max. and min. pumping limits. Never the less, the project team still believes that 15-20 % savings on the electricity cost (market price) or CO2 emission are achievable. WISE will continue to operate at VCS to verify the full potential of WISE. 3) automatic and on-line control the water production and pumping in relation to chosen priorities. 4) connect to external sources such as SCADA/SRO systems as well as online servers to
retrieve dynamic data on water consumption, electricity price and CO2 emission factors etc. 5) perform an optimization analysis every hour to find the most economical operational strategies for the next 24 hours.
WISE software 25
5. Perspective
5.1 Business plan
The WISE software is easily converted into different language versions. This means that the software is prepared to be exported to drinking water utilities abroad. However, Danish utilities will be targeted in the beginning to get even more documentation that proves the potential of WISE.
WISE is running in test mode at VCS until December 2016. Results from the final test runs is then going to be used to initiate a closer dialogue with other Danish utilities.
5.2 Business opportunities
Sweco has a strong network and market position in all of Europe. However, the Scandinavian countries and especially Sweden will be our main focus in the beginning when taking WISE abroad. These countries have in many places great elevation differences within water supply areas and they have varying electricity prices due to implementation of green energy produced from especially windmills (in Denmark), hydropower etc.
5.3 Communication and presentation activities Throughout the project period, WISE has been presented on numerous occasions. These include: 1. Presentation at “Dansk Vand Conference” 2014 2. IFAT Messe Munchen. Stand at the Danish pavillon, May 2014 3. Presentation at “Dansk Vand Conference” 2015 4. Presentation at “Young Water Professionals Denmark Conference and Workshop” 2015
In addition, WISE has been presented on several occasions in relation to the mother project “Future Water” and at the website http://futurewatercity.com/.
26 WISE software
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