Data Analytics for Future Energy Systems

Transcription

1 Data Analytics for Future Energy Systems Dr Stuart Galloway & Dr Bruce Stephen Advanced Electrical Systems Group Institute for Energy and Environment Department of Electronic and Electrical Engineering University of Strathclyde Glasgow G1 1XW United Kingdom {stuart.galloway

2 Introduction Smart Grid is essentially about data More informed power system: sensing at an extent and rate previously unknown Q: How to get business value from this investment in monitoring? New services? Augment existing services? A: Understand current operation & constraints

3 Characteristics of Demand WHAT DRIVES THE NETWORK?

4 Demand Smart Meters/BEMS 30 minute reads Understand demand to manage it DSM When used/when not Realign peak demand with renewable generation All data driven!

5 Loads on the LV network were always assumed to have a high degree of variability to them due to the nature of domestic routine AMI & BEMS deployment allows much greater insight into the behaviour of these loads Patterns are difficult to capture given the complex nature of the data High dimensional how do they all relate? Non-stationary (many sub-behaviours observed e.g. weekends, holidays) Tools have been developed for our past projects that: Formulate classes of energy use and automatically categorise residents Quantify behavioural consistency in terms of movers and stayers Forecast aggregated residential load Model appliance usage and its variability Wet appliances Heating (space and water) EV Charging? Patterns repeat in load profiles both across days and customers Load characteristics can be attributed to appliance usage patterns, which can also be quantified

6 Power System Operation and Control HOW DOES THE NETWORK BEHAVE?

7 Network Operation Load flows Power flow directions in the network Voltage excursions along feeders State estimation Infer unmeasured quantities from measured ones Data model drives physical models Real loads are not homogenous Homogenous assumption may result in different outcomes

8 Topology is OK but the 16 real interest comes from what and where (how far) loads are attached from the feeder end. Solution is to superimpose the network model on an existing geographic housing layout kv substation Feeder/Microgrid Model kV kv 11kV Use to evaluate power system health metrics (load flows, voltages etc.) and inform. Load models plug into point loads and resulting aggregate can be evaluated (minus losses) at a desired point e.g. grid infeed Single laid Double laid Triple laid but what is realistic? Realism is important from the DNO perspective as the following examples illustrate

9 Rural/Suburban Heterogeneous properties of a similar vintage (1973), generous but uneven spacing, increasing PV penetration one infeed transformer possibly specified for fewer houses than were eventually built. Stephen, B., Mutanen, A., Galloway, S., Burt, G. & Jarventausta, P. (2013) Enhanced load profiling for residential network customers. IEEE Transactions on Power Delivery. ISSN (In Press) Stephen, B., Isleifsson, F., Galloway, S., Burt, G. & Bindner, H. (2013) Online AMR domestic load profile characteristic change monitor to support ancillary demand services. IEEE Transactions on Smart Grid. ISSN (In Press)

10 Condition Monitoring HOW DOES THE NETWORK AGE?

11 Network Monitoring Condition monitoring Could be SCADA, could be more Whats right, whats wrong? Huge volumes of data What does fault(s) look like? How to predict fault onset?

12 Fault Detection/Diagnosis Offshore wind Generation Look at the way the power curve behaves Plant wear and degradation How do changes manifest? Transmission Power Transformers Partial Discharge detection Different frequency compositions What types of faults do they represent? Distribution Switchgear Trip coil testing Mechanism and control system faults What does the shape of the test record indicate? Stephen, B., Galloway, S.J., McMillan, D., Hill, D.C. & Infield, D.G. (2011) A copula model of wind turbine performance. IEEE Transactions on Power Systems, 26 (2). pp ISSN Baker, P., Stephen, B. & Judd, M. (2013) Compositional modelling of partial discharge pulse spectral characteristics. IEEE Transactions on Instrumentation and Measurement, 62 (7) ISSN Stephen, B., Strachan, S.M., McArthur, S.D.J., McDonald, J.R. & Hamilton, K. (2007) Design of trip current monitoring system for circuit breaker condition assessment. IET Generation Transmission and Distribution, 1 (1). pp ISSN

13 Asset Management and Replacement HOW CAN THE BUSINESS MAKE INFORMED DECISIONS?

14 Asset Management Track fault occurrences/onsets on individual plant Look across whole fleet Inter and intra plant faults Manage/model lifecycle of fleet Spares inventory management Identify subsystems failures Repair prioritisation

15 Intra-Plant Condition Who is most similar who will fail next?

16 Markov chain type models How do assets move through their lifecycles over time? Fault tree type models How do systems of components fail? Wilson, Graeme and McMillan, David (2013) Modeling the effects of seasonal weather and site conditions on wind turbine failure modes.in: ESREL 2013, , Amsterdam.

17 How to go forward? Power Systems can benefit from Big Data Hence Smart Grid - Operational robustness & business value through informed operation Good physical models and domain knowledge exist Black box type models may fail to capitalise on this Other Smart Grid stakeholders could see benefit from these outcomes. How can we all work together? IT Service Providers Community Energy Groups Distribution Network Operators Work with Power Systems engineers to develop models that leverage big data insight with well understood domain knowledge

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