SRDC 9.2 (D) Evidence Reportsset203 NTVV

New Thames Valley Vision

SSET203

LCNF Tier 2 SDRC 9.2(d) Evidence Report Develop and Trial Method of Optimising Network Monitoring Based on Installation of First 100 Substation Monitors

Prepared By Document Owner(s) Project/Organization Role Gideon Evans Project Manager Nigel Bessant Project Delivery Manager

Employment Manual Version Control Version Date Author Change Description 1.0 30 04 2014 Gideon Evans Final SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision

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SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision Contents

Summary

1.1 Criteria 9.2 (b) 6

1.2 Background 7

1.3 Link to Methods and Learning Outcomes 9

2 Substation Monitor Installations

2.1 Substation Selection Process 11

2.2 Data from substation Monitoring 13

2.3 Substation Monitoring Data in PowerOn Fusion 14

2.4 Substation Monitoring Data in Pi Process Book 15

3 Optimised Substation Monitoring – Substation Categories

3.1 Substation categories 18

3.2 Analysis of Substation Data by Category 18

3.3 Recommendations for Future Substation Monitoring 19

4 Optimised Substation Monitoring – DNO Operational Requirements

4.1 Availability of Data 21

4.2 Summary of Data 21

43 Uses for Streamed Data 25

4.4 Uses for Periodic Data 26

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision

4.5 Uses for Alarms 28

4.6 Local Depot Review of Data 31

5 Optimised Substation Monitoring – Virtual Monitoring

5.1 Virtual Monitoring (Buddying and Aggregation) 35

5.2 Comparison with Real Substation Monitoring 35

5.3 Reducing Monitoring at Substations 36

6 Conclusions

6.1 Convergence of Recommendations - Location 37

6.2 Convergence of Recommendations – What to Monitor 38

7 Next Steps

7.1 Project Substation Monitoring Installations 39

7.2 Refinement of Recommendations 40

8 Appendices 42

Appendix 1 University of Reading Categorisation of Substations Attached

Appendix 2 University of Reading Selection Procedure for Substation Attached Monitor Locations

Appendix 3 Substations Selected Attached

Appendix 4 Data from Substation Monitoring – Pi Process Book Attached

Appendix 5 University of Reading Initial Analysis of First 100 Attached Substations Where Monitoring was Installed

Appendix 6 University of Oxford 12 Month Analysis of First 100 Attached Substations Where Monitoring was Installed

Appendix 7 Schedule of Available Substation Monitoring Analogues. Attached

Appendix 8 First 100 Substations – Operational Data Overview Attached

Appendix 9 Operational Use of Substation Monitoring Data - Case Attached Studies

Appendix 10 Strategy for the Use of Alarms on the LV Network Attached

Appendix 11 University of Reading Feeder Demand Predictions Attached

1 Summary

1.1 Criteria 9.2(d) Successful Delivery Reward Criteria 9.2 (d) Criterion: Develop and Trial Method of Optimising Network Monitoring Based on Installation of First 100 Substation Monitors

Evidence: Prepare Report reviewing optimal deployment of monitors based on installation of first 100 substation monitors.

SSEPD confirms that this criterion has been met.

This document provides details of the review of the deployment of the first 100 substation monitors, and presents the findings identified in line with the evidence criteria specified for the Successful Delivery Reward Criteria (SDRC).

It is confirmed that:

 The 100 substation monitors installed by April 2013 have been operating and providing data for analysis for a year.  The data provided has been analysed against the original selection criteria to recognise if these criteria usefully inform the selection of future substations to be monitored.  The data provided has been compared with aggregated end point data to understand if the availability of smart meter data in the future can influence the substations that need monitoring.  The data has been reviewed in terms of operational relevance and direct input to network planning decision making.  The outcome of learning points from each analysis stage has been described in terms of its influence on the optimisation of future substation monitoring deployments.

1.2 Background

The NTVV project requires half hourly energy data to be captured from substations (and end points) on the low voltage network so that energy usage patterns can be identified, categorised and forecast, and compared with aggregated data from end points. With this information it is expected that meaningful forecasts can be made regarding the future loading of the low voltage network.

Some LV feeders have little or no headroom whilst others have a reasonable amount available for historic network reasons. The substation monitoring will provide real data for comparison with the estimates and forecasts. The substation monitoring will also provide data to support the development of the most appropriate deployment strategy and operating regimes for new network operating methods identified.

The data provided from substation monitoring is clearly important for the NTVV project to be able to better understand the LV network, but it is accepted that one of the learning outcomes required from each area of analysis is how the LV network can be most effectively managed using the least amount of substation monitoring data, and how to recognise which substation locations bring the greatest benefit to the DNO from the data.

SDRC 9.2 (d) was established to acknowledge the clear focus given to the optimisation of substation monitoring, both in terms of quantity and location. In particular, the project has sought to:

 minimise the hardware deployed so that the cost is minimised while providing the maximum ultimate benefit to customers  take a phased approach to monitoring deployments, and has targeted substations with larger numbers of feeders and connected customers (to increase the quantity of data received per installation) while facilitating the targeting of substation categories proposed by the University of Reading team for the best statistical analysis  Review the data from the first 100 substation monitoring installations to allow the next batch of 100 substations to be selected for the greatest data benefit to the project

 Identify any correlations between substation energy usage data and other publicly available data such as council tax bands to better perform above a standard statistical approach

Only very weak data correlation between substation energy data and property size, homogeneity and density was found and it was calculated that from a relevant substation population of 500, to achieve a 99% level of confidence that the correlation is correct, a minimum of 294 substations would need to be monitored. Hence it was decided to deploy all 325 substation monitors, allowing for anticipated difficulties at some substation sites, typically with communications signal strength.

Consideration was also given to the optimal use of this substation data by business-as- usual teams in both operational and planning environments, and new approaches to minimising monitoring which will be revealed throughout the course of this project

1.3 Link to Methods and Learning Outcomes Method 3 as defined for NTVV (see SET203 New Thames Valley Vision bid submission) proposes the development of mathematical techniques to reduce the need for new and extensive low voltage network monitoring that might be required to manage and design low voltage networks to meet the needs of the new low carbon technologies. Mathematical models are to be developed by the University of Reading using data from end point monitors now installed in customers’ premises, and subsequently from devices measuring the energy profiles of low voltage feeder cables from distribution substations. The substation monitors were installed in line with SDRC 9.2 (b).

To meet SDRC 9.2 (b) 100 substation monitors were installed at distribution substations in the Bracknell area, commissioned and data collection established. The substation monitors are GE Digital Energy Multilin DGCM devices. These were configured for use at distribution substations with up to 6 low voltage feeders, with communications achieved using a Westermo MR-310 modem, and GPRS/3G SIM card connected to the Vodafone network.

The substation data is transmitted to an SSEPD server via a Vodafone access point, and fed into a front end processor (FEP) of the PowerOn Fusion system, established in the shadow environment in the SSEPD control room. From PowerOn Fusion the data is available for storage in SSEPD’s PI Process Book, and is subsequently available for sharing with the University of Reading.

Successful completion of Learning Outcome 1 requires an understanding of the interaction between the network and individual customers in order to optimise network investment. The selection of the initial 100 substations locations to be monitored was made with guidance from the University of Reading to ensure that the mix of customer types fed from the substations was a representative sample. Combined with end point monitoring data this substation data will be used to predict future demand on the network and better inform investment decisions relating to the low voltage network.

Successful completion of Learning Outcome 2 requires improved modelling to enhance network operation procedures and to provide a management tool for planning and

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision investment on the low voltage distribution network. The University of Reading are using the data from the substation monitors to develop the model.

Successful completion of Learning Outcome 3 looks to optimise the deployment of monitoring through the use of modelling to reduce the need for and enhance the information provided by monitoring.

2 Substation Monitor Installations

2.1 Substation Selection Process To be able to install 100 substation monitors, SSEPD consulted with the University of Reading to identify appropriate locations. Their objective was to achieve substation monitoring for a good mix of properties in Bracknell that were statistically relevant, and to achieve a good coverage of one or more high voltage feeders. Substations were allocated to a matrix of property categories based on density (number of customers per feeder) and homogeneity (mix of types of property connected to the feeder). The matrix of substations is shown in Appendix 1 University of Reading Categorisation of Substations.

Actual substations to be monitored were selected from the matrix, taking into account the coincident requirements for end point monitoring (as previously indicated for End Point Monitors, SDRC 9.2(a)) and practical considerations identified during a survey of the substations. A more detailed description of the criteria is found in Appendix 2 University of Reading Selection Procedure for End Point Monitor Locations.

The actual substations selected are listed in Appendix 3 Substations Selected

Figure 1 Substation Monitoring Installed at Merryhill Substation

Figure 2 Installed Substation Monitor

2.2 Data from Substation Monitoring Three types of data have been obtained from substation monitors. These are:

Real time Immediate transmission (streaming) of measured values These include current, voltage, power and harmonic content

Periodic Half hourly calculated values These include Current (maximum, minimum and mean), Voltage (maximum, minimum and mean) energy (real positive and negative, reactive positive and negative) and harmonic content)

Alarms These include Voltage (high and low) and Current (high high and high)

The real time values are transmitted from the substation monitor direct to the PowerOn Fusion system for storage in Pi Historian. Any gap in communications in the GSM / UMTS systems due to signal strength fluctuations or communication network congestion will result in loss of data; there is no storage of this type of data. As might be expected, the volume of data is large and some gaps in the data are inevitable for the reasons described.

Periodic data is calculated in the substation monitor and stored for subsequent transmission to PowerOn Fusion when polled. Storage in the device is limited to 14 days and it would be expected that the data is polled at least once a day. When the substation monitor is streaming real time values the half hourly data will naturally be polled as soon as it is available, logically just after the completion of each half hour. The University of Reading team requested that this data be calculated on a half hourly basis for consistency with end point monitor data (smart meter data), although the period can be varied (1, 5, 10, 30 and 60 minute options are possible).

2.3 Substation Monitoring Data in PowerOn Fusion PowerOn Fusion is the primary tool in which data monitored at substations is visible to operational users.

Figure 3 PowerOn Fusion – Substation Monitor Data – Trevelyan Substation

It can be seen that all analogues for every phase of every feeder and the busbar are visible. Real time streamed analogues are shown in yellow boxes and calculated periodic (half hour) data is shown in white boxes. Alarms are shown in squares.

This presentation of data is intended to allow any user with access to the PowerOn Fusion system to quickly view actual values in real time, and to make immediate operational decisions based on current circumstances. An example could be the connection of a back feed that is only permitted when the load current has dropped below a threshold; the load can be monitored at the substation that is to be off-loaded, and similarly, the substation that will pick-up the load can be checked to confirm that the load current does not exceed the threshold.

PowerOn Fusion is a useful operational data viewing medium, but it does not store the data; it sends the data to Pi Process Book for storage.2.4 Substation Monitoring Data in Pi Process Book

Many operational decisions are made following some analysis of trends in data, and for this purpose Pi Process Book is used. Real time information alone in PowerOn Fusion is insufficient for this purpose and Pi Process Book allows many different analogues to be displayed individually or in multiple on a graph, and for any time period from seconds to months or years.

Figure 4 Pi Process Book – Trevelyan Substation Busbar Analogues

For periodic data that has been obtained on a half hourly basis there are 48 values in a day, 336 in a week and over 17,000 in a year. It is practical to display these data volumes for these periods, and most users are able to identify relevant trends in the data in these periods. Figure 5 below shows typical half hour values, in this case the mean busbar voltages at Trevelyan substation.

Figure 5 Trevelyan Substation Busbar Mean (Half Hour) Voltage Analogues For streamed data there are over 17,000 values for each analogue in a day, 121,000 in a week and 6,300,000 in a year. It is possible to view such volumes of data, but system performance is inevitably a limitation, and most benefit is gained from such high resolution data by viewing shorter periods. Figure 6 blow shows the corresponding streamed data (Busbar Voltages) for comparison with Figure 5.

Figure 6 Trevelyan Substation Busbar (Instantaneous) Voltage Analogues

Further images of the different types of data, viewed graphically in Pi Process Book can be seen in Appendix 4 Data from Substation Monitoring – Pi Process Book.

A further key feature of the Pi Process Book is that it allows data to be exported for use in other applications such as Microsoft Excel or Matlab. For the NTVV project, the majority of DNO operational users use the data directly in Pi Process Book and the majority of University users access the data in Matlab.

3 Optimised Substation Monitoring – Substation Categories

3.1 Substation categories At the start of the NTVV project it was recognised that it would not be possible to fit substation monitoring at all substations, so a choice would have to be made as to which substations to target. The University of Reading team chose to group substations into a matrix that considered the types of properties connected in terms of homogeneity and density. This could be achieved from information that was widely available in the public domain (e.g. council tax bands, Google Earth, etc) and did not require any DNO data other than the connectivity of properties to their feeding substation.

The names of the substations selected and their groupings are shown in Appendix 1 University of Reading Categorisation of Substations. Further details about the substations including the number of feeders and numbers of customers connected is included in Appendix 3 Substations Selected.

It was expected that analysis of the energy consumption data collected would reveal some trends that may inform the selection of substations for subsequent monitoring installations for the NTVV project, and also potential learning that will inform DNOs about the benefits of monitoring particular categories of substations.

3.2 Analysis of Substation Data by Category The University of Reading have received data from the first 100 substation monitors since April 2013 and made initial observations in July 2013. At that stage the joint priority was to establish whether further substation monitoring was required for the NTVV project, and if so, which substations should be targeted so far as this could be informed from the data received at that point in time. The observations made are included in Appendix 5 University of Reading Initial Analysis of First 100 Substations where Monitoring was Installed.

It was apparent that more data would be required, and the monitors would need to operate for longer than the three months that they had operated at that time, before definite conclusions could be drawn. The primary recommendation for the NTVV project was that “more data was required in each of the groups and the benefit from more

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision information (substations with more feeders, say) regardless of group far outweighs a focus on any one given group”. This has led to a second and third tranche of monitoring installations in the project being targeted at substations with four, five and six feeders (in preference to two and three feeder substations).

Now that the first 100 substation monitoring installations have been operating for nearly a year, it is expected that relevant trends and observations may become more apparent and lead to conclusions that may allow a DNO to target future substation monitoring. The detailed analysis in Appendix 6 University of Oxford 12 Month Analysis of First 100 Substations Where Monitoring was Installed provides further details of observations made. The main observation is that the 282 feeders examined show no strong correlation between substation energy data and property size, homogeneity and density. Random sampling was therefore recommended.

3.3 Recommendations for Future Substation Monitoring

In the absence of any tangible correlation the University of Oxford team1 recommended that a random sampling approach be adopted, aiming for a 99% confidence level for a normally distributed population. The quantity of substations required for analysis is understood as:

99% confidence, population size of 500, install monitoring at up to 294 substations 95% confidence, population size of 500, install monitoring at up to 223 substations

There are over 600 substations in Bracknell, but after excluding the HV switching stations and single customer (LV or HV) substations the actual number of relevant substations is approximately 500 (the actual number changes on a weekly basis reflecting developments in the area). To achieve the optimum statistical target number of installations (to achieve 99% confidence) therefore required monitors to be installed at 294 substations.

University of Reading is a project partner with the New Thames Valley Vision. To perform some of the analysis required in support of the New Thames Valley Vision, the University of Reading has placed a contract with the University of Oxford. Scottish and Southern Energy Power Distribution is pleased to have the benefit of the combined expertise from both the Universities of Reading and Oxford in this project.

In practise, experience to date has confirmed that data losses due to communications problems and other technical difficulties may reduce the effective count of installed substation monitors by up to 10%, so if data is required from 294 sites, installations would have to reach approximately 110% of 294, ie 323 sites. For the NTVV project this meant that all 325 substation monitors allowed for would be deployed.

As no feeder or substation level correlation has been identified at this stage it is not possible to make a recommendation, based on substation categorisation, for substation monitoring deployments beyond the project. At the end of the NTVV project when significantly more data has been assessed, both by count of substations and by duration of operation, a recommendation may be possible.

4 Optimised Substation Monitoring – DNO Operational Requirements

4.1 Availability of Data The existing low voltage network is operated predominantly on a reactive basis, with depot teams responding to requests for new supplies, or responding to incidents, typically where customers have experienced a loss of supply. Any data relating to the network that supports these existing priorities is valued. Statistically, only a small number of substations or low voltage feeders are involved in such issues in a town the size of Bracknell in any given period, and it follows that the number of locations where direct operational benefit is gained from substation monitoring is also small. The locations of interest on a given day are typically unknown in advance, and it is therefore not possible to target the installation of monitoring on this basis.

Streamed data in particular is very vulnerable to communications difficulties (e.g. poor GSM signal strength), as this results in gaps in the data. This is not ideal, but experience to date, where users are unaccustomed to receiving any centralised data, this has not proved to be a problem. It might be anticipated that expectations will rise as users become more familiar with what data is routinely available and how they can use this data to operate the network in a manner that brings the greatest benefits to customers.

4.2 Summary of Data A summary of the types of analogue available at each substation is included in Appendix 7 Schedule of Available Substation Analogues. A summary of the data collected from each substation since the date of installation (January 2013 to March 2013) can be seen in Appendix 8 First 100 Substations – Operational Data Overview. This schedule records, for each substation, and for each analogue, the highest of the high values, the lowest of the low values, and the mean of the average (RMS) values. Energy values are cumulative and for these the most recent (highest value) is logged in the schedule. For each analogue type, rules were set that resulted in individual values being highlighted. In this schedule highlight colours based on the following rules were applied:

Voltage 254 < V < 216

Harmonic Content > 5% Harmonic Content >8%

Reactive Energy >10% of Energy +ve

Energy -ve > 0

Maximum Demand >100% Maximum Demand >130% Alarm Count > 0 Table 1 Colour Key for Analogue Thresholds

The objective of the highlighting was to give immediate visibility to those substations or feeders that have operated outside of thresholds of interest, and to allow those substations or feeders to be investigated further. In aspects where large numbers of substations or feeders are highlighted, further consideration was given to how many phases (one, two or all three) were showing the same characteristic. A single minor breach of a threshold was seen as less important than a large breach involving two or possibly all three phases. This was about prioritising the effort of investigation of a potentially large count of substations or feeders.

A statistical analysis reveals that of 100 substations (with 382 feeders in total) monitored:

Busbar – Thresholds Exceeded Count L1 L2 L3 Average Voltage greater than 254 volts 6 16 9 10 Harmonic Content greater than 5% or 8% 56 53 45 51 Reactive Energy greater than 10% of real energy 40 26 29 33 Energy (-ve) greater than zero 20 17 20 19 Maximum Demand greater than 100% or 130% 15 18 18 17 Feeders - Thresholds Exceeded Count L1 L2 L3 Average Reactive Energy greater than 10% of real energy 91 96 126 104 Energy (-ve) greater than zero 85 89 85 86 Maximum Demand greater than 100% or 130% 29 23 30 27 Table 2 Count of Analogue Limits Exceeded During First 12 Months of Operation

The investigation into each substation or feeder with highlighted analogues started with an assessment of the data recorded in Pi Process Book which records all the actual values in the period of analysis (January 2013 to January 2014). If this revealed that the excursion was “seldom” and in the first few months of the period under review, then little further investigation is justified. On the other hand, if the excursion was seen to be “frequent” and “recent” (within the last month or two of the period under review), then the justification to pursue the investigation further was made. It was then appropriate to assess if there was a pattern (e.g. did the excursion happen at the same time each day or week? To what extent? On one or all phases simultaneously?)

It was possible for any substation or feeder that the data was corrupted due to communications problems, equipment or sensor malfunctions, or manual errors introduced in the early stages of the installation and commissioning works. All such issues were resolved where possible, but the data stored prior to the point of resolution is vulnerable to misinterpretation. Clearly it was important to distinguish substations and feeders from further investigation where the problem could be quickly and directly linked to the monitoring process or equipment; in this case it followed that the monitoring process and equipment became the subject of the investigation and the issues had not already been identified and resolved.

High Voltage was seen to occur at approximately 10% of substations at least once during the 12 month period. Note that this refers to high voltage at the substation and not to any customer’s supply point (to which the Electricity Safety Quality and Continuity Regulations apply). In one example (Cannon Hill substation), the circumstances were

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision analysed in detail. It was found that the transformer was operating on the wrong tap position; this was promptly corrected by the local depot team. Further details can be found in Study 1 in Appendix 9 Operational Uses of Substation Monitor Data – Case Studies.

High Harmonic Content occurred at 51% of substations. Historic data about harmonic content is limited to specific sites investigated for very specific reasons, so it is not possible to make any comparison. Closer inspection has revealed that the occurrences of high harmonic content are mostly at times when the load on the network is less (i.e. over night). It is suspected that this may be caused by street lighting, and the effect on the voltage is seen as the non lighting load on the network is much less.

Large quantities of reactive energy may be expected at substations feeding non domestic customers. This would be expected where there are large motors or significant quantities of traditional ‘low-frequency’ fluorescent lighting; in an area like Bracknell it is likely that this is linked to air conditioning and catering type equipment. The proliferation of switched mode power supplies may also result in reducing power factors as the current draw from these devices is often smoothed through the use of reactive components.

Real Energy (-ve) is a measure of embedded generation on the low voltage network feeding back from the feeder into the substation busbars, or from the substation busbars through the transformer into the high voltage system. In small quantities, and with no adverse impact on voltage anywhere on the network, this is not a problem. However, as the take-up of solar panels and other embedded generation technologies grows, this parameter is likely to grow and may be an important indicator of the feeders or substations where high voltage related problems are likely to be encountered first, particularly at remote ends of a feeder. An example is analysed in Study 14 in Appendix 9 Operational Uses of Substation Monitor Data – Case Studies.

Maximum Demand is calculated by dividing the maximum current measured by the feeder or transformer rating in amps. Correct calculations require correct fuse size and transformer rating information to be loaded into the database. Large overload concerns may reflect lag in updating the database following a recent reinforcement project, rather

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision than a true overload. Most distribution transformers in the SEPD area are designed to withstand a 30% overload on a cyclic basis, and transformers loaded in the range 100% to 130% may be completely satisfactory if the nature of the loading is within the cyclic capability. The concern at these sites is the extent to which the loading will further increase, and clearly there is no headroom left to pick up the load from electric vehicle charging if this coincides with the periods when the load already exceeds 100%.

Having full access to the maximum demand information on a continuous basis allows considerably greater analysis of the actual loading compared with the quarterly reads that may be expected from traditional maximum demand indicators (MDIs). An example analysis is given in Study 8 in Appendix 9 Operational Uses of Substation Monitor Data – Case Studies.

4.3 Uses for Streamed Data Streamed data is very onerous to retrieve from site in terms of site monitoring equipment functionality, communications systems, head end processing and storage. Ultimately these aspects manifest as costs, and it follows that a DNO would expect to avoid drawing on streamed data unless it is actually very valuable.

Operational staff find access to actual real time current and voltage values invaluable in understanding the operational status of the network. Benefits include:

Supply Restoration  Provision of immediate information for decision making  Provision of immediate information to keep customers informed  Records of actual times when faults occurred, and supplies restored

Network Abnormalities (Voltage or Power Quality)  Quick initial assessment of the state of the network  Provision of immediate information to keep customers informed  Records of actual times when issues occurred

Operations (Construction or Maintenance)

 Quick initial assessment of the state of the network at the time (eg linking for a shutdown)  Records of actual times when trends show that operations can or cannot be carried out.

Control of Network Management Tools  Accurate real time information about current and voltage may become crucial to the control of energy storage (See Section 7 Next Steps).  Accurate real time information about current and voltage may become a trigger to activate some form of Automated Demand Response (See Section 7 Next Steps).

4.4 Uses for Periodic Data Periodic data is derived from original measured values in the monitoring device, and for a half hour period, only 48 values are created, transmitted, managed and stored in a 24 hour period. From a data management point of view, there are 360 streamed values for every periodic value; clearly periodic values are less onerous to handle and the marginal cost of each analogue is considerably less than for a streamed value. Benefits of periodic values include:

Operational Use  Records of “maximum” or “minimum” values can be easily assessed for significant periods (days, weeks, months, and possibly even years). This may be helpful in building a “big picture” view of the network performance to support decisions such as the suitability of a backfeed, or generator sizing for planned works.  Speed of access to data for large periods. To make operational decisions, operational users have to strike a balance between seeking more accurate information (e.g. going to site to take readings of current or voltage) compared to relying on experience, intuition and judgement. The quicker they can access real trustworthy information (from monitoring equipment) the more likely they are to follow the more objective approach to decision making.

Network Planning  Accurate and detailed historic loading information from monitoring, particularly maximum and mean currents can be used to inform planners as they respond to requests from customers requesting new connections and connections of increased capacity.  As for operational users, speed of access to data for large periods will bring a benefit. To make planning decisions, planners have to strike a balance between seeking site based readings or employing more expedient heuristic techniques. The quicker they can access real trustworthy information (from monitoring equipment) the more likely they are to follow the more objective approach to decision making.  Power quality information such as voltage harmonic content can be drawn upon, both to assist in initial decision making (e.g. permitting a particular load to be connected) and also in assessing the consequence (e.g. observing that harmonics have not exceeded an agreed threshold). The monitoring system provides good quality information of “before” and “after” that can be used to objectively inform commercial and technical discussions with customers.

Customer Service  Enquiries from customers are diverse, including requests for new connections, concerns about their own service (load or voltage), or concerns about the performance of the network. The availability of comprehensive periodic data can allow the DNO to respond objectively, and if a network performance issue is identified, this can be addressed in an informed manner, in line with other agreed procedures.  Speed of response is crucial in meeting customer expectations and in complying with agreed procedures. Having access to a database that already contains the relevant periodic analogues is much quicker than visiting site for each request, deploying localised monitoring equipment for a week, then returning to site to recover the equipment, and downloading the data. For the NTVV project, values such as energy (all four quadrants of real and reactive energy) are being recorded. They will be used within the project to support project

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision aspects of “Forecasting” and “Aggregation”. It is possible that an outcome of these investigations is that a DNO requires these analogues for future use, but at this stage operational and planning users have not shown any interest in these parameters.

4.5 Uses for Alarms The traditional mechanism of alarms on the low voltage network is a phone call from a customer informing the DNO that their “lights had gone out” or that they were suffering from some other problem such as “an intermittent supply”. Protection is predominantly provided with fuses, and there is no electronic relay or communications functionality provided at distribution substations.

The installation of distribution substation monitoring equipment inherently provides an electronic relay (with ability to compare real measured electrical values with predetermined thresholds), transmission of data via GSM or UMTS (a means to transmit alarms back), and a head end system (Distribution Management System) to receive, display and process for storage large quantities of data. It follows that if the infrastructure and costs are justified by the value of the substation monitoring data, then the additional cost of providing alarms can be thought of as negligible. In practice, allowance must also be made for managing alarms (where are they displayed, who is responsible for responding to them, how are they cleared etc).

Alarms Available The following alarms were established at the time of commissioning the first 100 substation monitors: FeederHigh Current (80% of fuse rating) FeederLow Current (5% of 100A, ie 5A) BusbarHigh High Current (120% of rating) BusbarHigh Current (95% of rating) BusbarHigh Voltage (110% of 230, ie 254 Volts) BusbarLow Voltage (94% of 230, ie 216 Volts) BusbarNo Volts (5% of 230, ie 11.5 Volts)

Observations

For the project to date, only one user has been reviewing alarms in detail, and this role has been focussed on ensuring that the alarms were functioning correctly in line with expectations, and this has taken place during working hours only. As other users gain access to alarms expectations will evolve, and this will influence policy, procedure and training recommendations accordingly. For now, observations are linked to functionality.

Busbar – Alarm Count L1 L2 L3 Average High Voltage Alarms 26 38 38 34 Low Voltage Alarms 3 5 6 5 High High Current Alarms 2 3 2 2 High Current Alarms 8 3 4 5 Table 3 Count of Alarms Triggered During First 12 Months of Operation

 Operation – alarms are communicated on an “unsolicited” basis (i.e. alarms are transmitted by the site equipment without waiting to be polled by the front end processor (FEP)). Alarms are received correctly and in a timely manner during normal system operation. The absence of sufficient GSM or UMTS signal strength will clearly hold up the transmission of the alarms; this is not critical for the project, but if they become relied upon to support a business process, then this could completely change expectations of the communications system deployed.

 Response – as above actual receipt of alarms in real time (in practice, within a minute or two) of an event occurring, is possible most of the time, but may not be all of the time. The real question is whether this is actually necessary? Apart from alarms that indicate a loss of supply, little benefit is gained from trying to respond immediately. For example, if two or three high voltage alarms came in overnight from two or three different substations, and the recorded voltage is seen to be one volt over the threshold, value is gained by recognising the pattern of what has occurred (e.g. are all substations fed from the same high voltage feeder, timing of alarms consistent, extent of excursion etc), and then despatching a team to investigate on site in an informed manner a day or two later (this scenario could be the result of a defect in high voltage control scheme). Alarms draw attention to abnormal network values, but do not necessarily require an

emergency response (analogous to HV switching carried out in response to HV alarms).

 Volume of Alarms – experience of operation for the first 100 substations quickly indicated that many feeders operate frequently in the current range zero to 5 amps. 5 Amps is the minimum value that the GE DGCM monitor can respond to, and the consequence was that there were large numbers of low current alarms that provided absolutely no value to the project team or other prospective users. The original objective was to receive an alarm when a fuse had operated by establishing that the current was (or very close to) zero. 5 Amps was perceived as an acceptable compromise, but the learning outcome is that typical low voltage network feeders operate to very low loading levels and any measuring or alarming capability should take this into account. Low current alarms have now been disabled to avoid burdening the project systems and users with unwanted information.

 Real Network Issues – actual alarms (e.g. high voltage) do raise concerns that the network is not operating correctly and give a reason to investigate further and resolve actual network problems BEFORE the problem manifests itself as a larger problem that actually causes a problem to the customers connected to the network. Such issues can typically be resolved in a planned manner during normal working hours, rather than in a reactive manner following a complaint from a customer, typically involving higher costs due to out of hours working and extensive effort required to keep the customer informed.

 Alarm Development – recognising that a fuse has blown by means of an alarm would add immediate benefit to existing depot users and to the network management centre. Provision of this type of alarm is not a primary requirement of the project, and is constrained by the equipment used (5 Amp small current limitation) and the inability to make a voltage connection to the feeder cables on the outgoing side of the fuses. It is hoped to develop and refine a virtual alarm that can be calculated in the DGCM monitor that assesses the neutral current and provides a reliable indication. Other similar ideas may evolve as depot users start to become familiar with what is possible.

Further comments on the experiences gained with the provision of alarms on the low voltage network are given in Appendix 10 Strategy for the Use of Alarms on the LV Network.

4.6 Local Depot Review of Data The Slough Depot team that are responsible for the low voltage network in the Bracknell area have been given access to the substation monitoring data, as well as the summary of data seen in Appendix 8 First 100 Substations – Operational Data Overview. As an operational team, they are familiar with PowerOn Fusion, so access to the system is not an area requiring significant learning, but access to the data and recognising when they can benefit from exploring the data around their business as usual responsibilities is a new area of focus. To support the process a project team representative has been working in the Depot Environment on a weekly basis, both to help the Depot team when they take interest, and also to understand their day-to-day issues with a view to leading them to the data that can answer their queries when they have not already thought to do so.

Specific analysis and observations are recorded in Appendix 9 Operational Use of Substation Monitoring Data – Case Studies.

4.7 Recommendations for Future Substation Monitoring Taking into account the practical possibilities and limitations of equipment, communications and systems, and consideration of the benefits obtained by existing users and prospective users, the recommended substation monitoring for future deployments would be as follows.

Choice of Locations to Monitor Operational users of monitoring data (including alarms) have an inevitable desire (if not expectation) to have data from all distribution substation sites. Justification for deployment at all sites cannot be made from the observations, experience and trials to date (based on 12 months’ worth of data), but logical suggestions are:

Deploy monitoring at substations where:

 New substations are built to provide new supplies to customers as these have uncertain loading (and the marginal cost of monitoring equipment pre-fitted on new substation plant would be assumed to be less than for retrofitting);  Existing substations with known problems, particularly relating to load, but not necessarily significantly overloaded on three phases;  Existing substations feeding a very dynamic customer base (those where customers frequently change), more likely in non-domestic, or mixed use areas  Existing substations where the take up of low carbon technologies is already advanced or forecast to be problematic

This combination of substation sites will give a DNO the operational data that it needs most of the time, without the volume of data and associated costs for monitoring all substations.

Requirement for Streamed Data The benefits of streamed data are clear at the time that they are called upon, but for most substations (statistically) no problems occur for many years and the disproportionate communications and data storage costs outweigh the benefits for most sites. A suggested deployment strategy with regard to streamed data would be as follows.

 Modify substation monitoring device to store a rolling one month period of streamed data at the device, and configure the FEP to retrieve this data only when requested.  For those substations where there are known ongoing operational issues, turn on the streaming;  For those substations where energy storage or ADR calls upon streamed data directly turn on the streaming.

The default arrangement should be that streaming is turned off, but that any substation can have it turned on, and the rolling month’s worth of data held in the device can be retrieved if required following a fault, planning requirement or query from a customer.

Requirement for Periodic Data Periodic data is very efficient at conveying useful information to operational and planning users. By definition, if monitoring is deployed then this is the data that would be expected to be retrieved from site by default. The choice to be made is more about which analogues provide benefit, and which ones do not. The suggestion is as follows.

Analogues that should be monitored:  Feeder current analogues (maximum, mean and minimum)  Busbar current analogues (Maximum and mean)  Busbar voltage analogues (maximum, mean and minimum)  Busbar voltage harmonic content

The justification and benefit from energy values has yet to be established (for use beyond the project), and if required, are more likely to benefit the modelling of the network to support medium term and longer term planning and investment decisions.

Requirement for Alarms Of the alarms available to date the following alarms would be expected to be available in a future deployment. Busbar high voltage Busbar low voltage Busbar high high current Busbar high current Feeder high current

The identification of a blown fuse is a fundamentally desirable notification that would be valued as an alarm, but is not directly available since this would require additional voltage measurements on the outgoing side of each fuse. Further analysis of neutral currents and the establishment of an alarm based on a neutral current threshold being reached is to be investigated for radial LV circuits.

Further “virtual” alarms may be derived in PowerOn Fusion for the benefit of users by recognising the features of other analogues. Examples include:

 Phase to Phase Fault (one or two phase currents drop to zero, while another phase current increases)  Current Sensor Failure (half hour mean, maximum and minimum currents converge on same value for two or more consecutive half hours)

These will be further investigated during the remainder of the project.

5 Optimised Substation Monitoring – Virtual Monitoring

5.1 Virtual Monitoring (Buddying and Aggregation) By successfully categorising customers, buddying and aggregating demand profiles, it is anticipated that a DNO will be able to model the load on the low voltage network using only very small amounts of real monitored data. The applicability of buddied profiles to individual customers in the absence of real (smart meter type) data has been established (see SDRC9.5(c) Evidence Report). It was then necessary to aggregate the data at the feeder level, and in particular, to compare the predicted power flow characteristics against substation monitoring data. A close correlation between aggregated data (virtual monitoring) and real monitored data suggested that less real monitoring may be required.

Further consideration is being given to how far virtual monitoring could reduce the necessary number of substation monitoring points and estimate the optimal location of the remaining monitoring points.

5.2 Comparison with Real Substation Monitoring To be able to emulate aggregated energy profiles on the low voltage network a number of steps have been followed:  Feeder identified for analysis  End point data obtained from the 250 monitored customers (where the data was successfully transmitted)  For each customer on the selected feeder the Least-Error Matching algorithm was run. This was developed at the University of Reading, producing an allocation of a buddy to each customer. This is described in detail in Section 3 of Appendix 11 University of Reading Feeder Demand Predictions.  The energy profiles allocated to each customer were run through the network modelling environment (NME) to calculate the total energy profile of the feeder;  The last stage was to use the figures to calculate the error in the model as well as comparing to real substation monitoring.

5.3 Reducing Monitoring at Substations It was expected that if the buddying and aggregation is successful (accurate enough to allow a DNO to make relevant decisions), then buddying may be thought of as “virtual monitoring”, and for those feeders where buddying has been applied to end points, less substation monitoring may be required. To study this it was necessary to:

 Assess the error measures of the buddying technique against actual substation monitor energy readings to assess the accuracy of the buddying.  Assess the stresses on the network under the buddying scenario and suggest roughly how accurate the buddying needs to be in order to be sure of whether or not there are network issues.  Compare the buddying accuracy with the accuracy required.  Identify any patterns in which properties and/or feeders are potential issues.

The analysis carried out to date can be seen in Section 4 Reduction in the necessary number of network monitoring points of Appendix 11 University of Reading Feeder Demand Predictions.

5.4 Recommendations for Future Substation Monitoring

Only three substations have been considered to date. The buddied profiles created have been successfully loaded and run in the network modelling environment, and demonstrated that when simulated increased loading scenarios are applied, network excursions can be successfully revealed. Assessment of buddying accuracy has not revealed conclusively the scale of reduction in monitoring that could be achieved.

It remains expected that for some networks the buddying will be sufficiently good and the network headroom known to be significant, in which case substation monitoring equipment will not be required. On the other hand, some substations are very unpredictable, in which case buddying is less likely to be successful; such substations may always need to be monitored.

6 Conclusions

6.1 Convergence of Recommendations - Location

For Substation Categorisation no recommendation can be made at this stage regarding location as no correlation between feeder demand and customer criteria have been identified to date. Some correlation may become apparent following further analysis with much more data throughout the project.

The main recommendations from DNO Operational Requirements with regard to choice of location for monitoring were:  New substations built to provide new supplies to customers;  Existing substations with known problems, particularly relating to load, but not necessarily significantly overloaded on three phases;  Existing substations feeding a very dynamic customer base (those where customers frequently change), more likely in non-domestic, or mixed use areas  Existing substations where the take up of low carbon technologies is already advanced or forecast to be problematic

Assessment of Virtual Monitoring has started and will continue for large quantities of substations throughout the project. Buddying and aggregation of customers’ energy profiles has been seen to work, but the judgement of precise criteria for when real monitoring is required cannot be made at this stage. The generalised expectation that substations regarded as predictable and with reasonable headroom available may not require monitoring, and those where the demand is volatile and close to limits will require monitoring does remain reasonable.

The anticipated monitoring requirements of based on Virtual Monitoring are convergent with the proposed monitoring requirements for DNO Operational Requirements. For the NTVV project all 325 available substation monitors have been committed to substations predominantly with the greatest count of feeders; this approach has been adopted to obtain the greatest amount of data using the available resources, and this will help to validate the recommendations made at the end of the project.

6.2 Convergence of Recommendations – What to Monitor

Streamed Data In terms of DNO Operational Requirements, Operational users will always have a preference for streamed data (eg current, voltage and power) being fully available but the costs and resources required to do so are substantial. The suggested strategy was that monitoring equipment installed should have the capability for streaming built in and available to turn on, but this would be turned off by default except in certain circumstances such as specific operational needs (eg faults), energy storage control, and possibly ADR.

The analysis of Substation Categories and Virtual Monitoring do not explicitly draw on streamed data and, when completed later in the project, are not expected to inform this recommendation.

Periodic Data The analysis of Substation Categories and Virtual Monitoring both draw on half hourly real and reactive energy values, and for every site where monitoring is installed, it would be expected that these values are available.

The provision of current and voltage analogues (maximum, mean and minimum) for feeders and busbars is fundamental to DNO Operational Requirements, providing benefit to operational and planning users. Other calculated values such as energy and harmonic content provide a more selective value, but can be provided for negligible extra resources, given that the primary currents and voltages are already being monitored.

Alarms Alarm functionality is generally inherently available in monitoring systems, and appropriately managed current and voltage alarms for feeders and busbars provide a helpful management tool for DNO Operational Requirements. Added value can be gained by further establishment of virtual alarms. The real challenge is to identify the most appropriate strategy for which alarms to respond to in real time, and which ones to assess purely on a retrospective statistical basis. The analysis of Substation Categories and Virtual Monitoring do not require the provision of alarms.

7 Next Steps

7.1 Project Substation Monitoring Installations

Following the initial assessment of substation monitoring data by the University of Reading team in July 2013, as recorded in Appendix 5 University of Reading Initial Analysis of First 100 Substations Where Monitoring is Installed, it was agreed that targeting substations with larger numbers of customers and feeders would provide the greatest benefit to the project. With this in mind a second batch of 110 sites were identified, substation monitors ordered and preparations made for installation. These installations are being carried out in parallel with this optimisation review.

One of the learning outcomes of the first batch of installations was that practical difficulties including low signal strength could be expected to affect 10% of sites; to provide effective complete data from 100 sites required an overpopulation of 10 further sites, bringing the total to 110 sites. The total number of substation monitors supplied is now 220 with just over 200 installed to date. In the light of the Substation Categorisation analysis (random sampling requiring a monitoring installation count of 294 sites to achieve a confidence level of 99%), the total project allowance of 325 monitors will be deployed. This should provide confidence that quality continuous data can be obtained from approximately 300 installations; for those sites where communication difficulties prove impractical or uneconomic to resolve, those devices can be relocated to other unmonitored sites to optimise the availability of data for the project.

The learning gained during the first batch of 100 installations in various aspects has informed the installation process for the second batch. Examples include the mounting arrangements of the antennas (detached from enclosures and mounted as high as possible), setting the resolution of the voltage analogues to 0.1 volt, and ensuring that the periodic values could be polled such that only the required 48 values are transmitted per day. These aspects will now be applied retrospectively to the first batch of substation monitors to improve the quality of the data.

For the project the primary objective is to ensure that the data from all selected substations is measured correctly and transmitted completely, and so far as possible, the

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision operation of devices on site and the PowerOn Fusion and Pi Process Book systems are operated as efficiently as possible to the end of the project. It is possible that as learning is derived in other aspects of the project (eg the deployment of energy storage devices) new requirements for monitoring may be identified (eg location, type of data, resolution of data etc) and it is equally important that the systems are managed with a degree of flexibility to allow for these new needs to be assessed and fulfilled with the available resources where possible.

7.2 Refinement of Recommendations

There are many operational uses for the substation monitoring data and the DNO Operational Requirements section details the possibilities and recommendations.

The analysis of Substation Categories has been unable to show a strong correlation between energy demand at the substation level and customer attributes; there is a clear requirement to adopt a random sampling technique, and this requires approximately 200 monitors to be installed in addition to the 100 monitors installed for the initial assessment. The data from a much larger quantity (approximately 300 sites) operating over a longer period (up to 3 years) will allow any correlation to be fully assessed, and recommendations made.

To date, operational users have had access to the substation data under supervision of the project team. Operational users need time to make their own discoveries with the data and to grow their confidence in using it. An ongoing programme of hands-on use, combined with targeted discussions with the project team is planned for the next few months to test the actual benefits of the data, and to test the recommendations regarding the locations where the greatest benefit from monitoring is derived.

Virtual Monitoring has been demonstrated to work, but considerably more studies are required to be run and assessed before conclusions can be drawn about the confidence with which virtual monitoring can be used in lieu of real monitoring. Further studies will be run with the remainder of the existing 100 substation monitor sites (only three have

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision been used to date), and also the 200 additional substation monitors currently being installed.

These next steps will allow the recommendations to be refined and to establish a methodology for determining the number and distribution of substation monitoring for maximum benefit to a DNO.

SRDC 9.2 (d) Evidence Report SSET203 NTVV New Thames Valley Vision 8 Appendices

Appendix 1 University of Reading Categorisation of Substations See separate document attached.

Appendix 2 University of Reading Selection Procedure for Substation Monitor Locations See separate document attached.

Appendix 3 Substations Selected See separate document attached.

Appendix 4 Data from Substation Monitoring – Pi Process Book See separate document attached.

Appendix 5 University of Reading Initial Analysis of First 100 Substations Where Monitoring is Installed See separate document attached.

Appendix 6 University of Oxford 12 Month Analysis of First 100 Substations Where Monitoring was Installed See separate document attached.

Appendix 7 Schedule of Available Substation Monitoring Analogues. See separate document attached.

Appendix 8 First 100 Substations – Operational Data Overview See separate document attached.

Appendix 9 Operational Use of Substation Monitoring Data – Case Studies See separate document attached.

Appendix 10 Strategy for the Use of Alarms on the LV Network See separate document attached.

Appendix 11 University of Reading Feeder Demand Predictions See separate document attached.