SRDC 9.2 (D) Evidence Reportsset203 NTVV

SRDC 9.2 (D) Evidence Reportsset203 NTVV

SRDC 9.2 (d) Evidence ReportSSET203 NTVV

New ThamesValley Vision

New ThamesValley 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

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Contents

Page
1 / 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
4.5
4.6 / Uses for Alarms
Local Depot Review of Data / 28
31
4.7 / Recommendations for Future Substation Monitoring / 31
5 / Optimised Substation Monitoring – Virtual Monitoring
5.1 / Virtual Monitoring (Buddying and Aggregation) / 35
5.2
5.3 / Comparison with Real Substation Monitoring
Reducing Monitoring at Substations / 35
36
5.4 / Recommendations for Future Substation Monitoring / 36
6 / Conclusions
6.1 / Convergence of Recommendations - Location / 37
6.2 / Convergence of Recommendations – What to Monitor / 38
7 / Next Steps
7.1
7.2 / Project Substation Monitoring Installations
Refinement of Recommendations / 39
40
8 / Appendices / 42
Appendix 1 / University of Reading Categorisationof Substations / Attached
Appendix 2 / University of Reading Selection Procedure for Substation Monitor Locations / Attached
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 Substations Where Monitoring was Installed / Attached
Appendix 6 / University of Oxford 12 Month Analysis of First 100 Substations Where Monitoring was Installed / Attached
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 Studies / Attached
Appendix 10 / Strategy for the Use of Alarms on the LV Network / Attached
Appendix 11 / University of Reading Feeder Demand Predictions / Attached

1 Summary

1.1Criteria 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 100substation 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.

1.2Background

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 methodsidentified.

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 howto 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
  • Reviewthe 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

1.3Link 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 ProcessBook, 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 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 andenhance the information provided by monitoring.

2 Substation Monitor Installations

2.1Substation 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 Bracknellthat 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 2University of Reading Selection Procedure for End Point Monitor Locations.

The actual substations selected are listed in Appendix 3 Substations Selected

SAM 2299

Figure1 Substation Monitoring Installed at Merryhill Substation

SAM 2310

Figure2 Installed Substation Monitor

2.2Data 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

PeriodicHalf 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).

2.3Substation Monitoring Data in PowerOn Fusion

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

Figure3 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.
2.4Substation Monitoring Data in Pi Process Book

Many operational decisions are made following some analysis of trends in data, and for this purposePi 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.

3Optimised Substation Monitoring – Substation Categories

3.1Substation 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 monitorssince 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 ReadingInitial 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 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 6University of Oxford12 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.

3.3Recommendations 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: