Energy Technology Challenges

Transcription

1 Energy Technology Challenges Rapid Innovation and Safety Can Co-exist Professor Phil Taylor BEng EngD CEng SMIEEE FIET FHEA Director, EPSRC National Centre for Energy Systems Integration Deputy Pro Vice Chancellor of SAgE Faculty, Newcastle University Head of the School of Engineering Siemens Professor of Energy Systems

2 Overview Whole systems approaches to energy Energy Storage Autonomic Energy Systems Cyber Security Safety Challenges and Opportunities

3 UK Energy Sector Fragmented across many dimensions Within Industry, within government, within academia, between each of these sectors Carbon still too high although progress being made in some sectors Lots of work left to do in heat and transport Energy costs too high for some sectors of society and industry Given huge transition energy still affordable for many we need to be realistic about costs Security of supply adequate for most customers at the moment Some customers in remote areas poorly served Cyber security vulnerabilities could grow with ICT roll out Climate Change n-1 no longer adequate Geo political events still a concern Safety Trilemma or Quadrilemma?

4 Distribution networks: Base case Electricity distribution network Gas distribution network ELY+P2G Gas transmission network CHP 4

5 Distribution networks: Scenario1: Fault in the electricity network Electricity distribution network Gas distribution network ELY+P2G Gas transmission network CHP 5

6 EPSRC National Centre for Energy Systems Integration 20M multi-disciplinary research centre investigating the Whole Energy System Multi-vector energy systems analysis utilising Big Data and Supercomputer techniques and technology Funded by EPSRC, a consortium of 5 research intensive universities, Siemens and other industry partners Address current limitations: Uncertainty, Calibration, Behavioural dynamics, Spatial and Temporal variations, Representing interdependencies Addressed by: Co-evolutionary approach to supply and demand, Stochastic Programming, Agents, System of Systems, Fine Grain Data, Quantitative and Qualitative, Expert Judgement, Multi-Disciplinarity, soft linking between models, High Performance Computing Framework, Some of our strategic industrial and governmental partners Some of our living labs and demonstrator facilities for research and validation Findhorn Community Newcastle helix Siemens Smartgrid Lab Thames Valley Vision Integrel Utility Scale Gas and Electricity Distribution Laboratory Cockle Park Farm

7 Flexibility from Energy Storage Selection and Characterisation of multiple network services Sizing and technology selection End-to-End Modelling of Energy Storage Systems Grid-scale Facilities and Storage Emulation Cloud-based platforms and portfolio energy storage systems

8 Energy Storage Material Degradation through to Electrical Network Services Battery Model Network Model Power Converter Model

9 Transfor Power (k No ESS With ESS - float SoC 100% End-to-end modelling: Simulation Results time (h) ESS power (kw) Floating SoC 100%, Max. SoC 100% Floating SoC 30%, Max. SoC 80% Model 9Results time (h) Fitted Mathematical Models SoC (%) Floating SoC 100%, Max. SoC 100% Floating SoC 30%, Max. SoC 80% time (h) 0.01 Capacity Loss (Ah) time (h) Energy losses Floating SoC 100%, Max. SoC 100% Floating SoC 30%, Max. SoC 80% time (h)

10 Cloud Enabled Energy Systems Lab Mindsphere at Newcastle helix Sodium Nickel Chloride Batteries Redox flow Battery Lithium Ion Batteries Supercapacitors Battery Emulator

11 Autonomic Power System

12 Self* Network Operation and Control Schematic

13 II. How to zone? (a) Proximity metric: definition of distance among buses Definition of merging criteria Zoning methodology Clustering validation criteria Zonal centroid identification (pilot node) Zoning methodologies (examples of existing and own ones): Hierarchical clustering single distance (HCSD) Hierarchical clustering MVAr control space (HCVS) Spectral Clustering (SKC) Fuzzy Clustering (FCM)

14 II. How to zone? (b) Performance of a zoning decision -a greater performance signifies reduced losses & enhanced security OPI (%) HCSD HCVS SKC FCM Robustness of a zoning decision -testing the effect of uncertainty on the measurements (e.g. imperfect prediction, noisy or corrupted data) OPI(%) HCVS SKC FCM HCSD accurate data error 2% error 4% error 6% error 8% error 10%

15 III. Static vs. adaptive zoning.. Questioning the feasibility. Fast enough (<1 min) for large scale network ( e.g buses test network)? Availability of measurement and telecommunication infrastructure? Zoning methodologies HCSD HCVS SKC FCM Execution time (sec) Questioning the value. 5 0 static PI thres =20 PI thres =10 PI thres =1 Performance enhancement vs. reconfiguration threshold (OPI) HCSD HCVS SKC FMC September 2014 Barbara Alimisis

16 Performance Algorithm Selection: Why Do It? Algorithm A best Algorithm A best Algorithm B best A B Network states Potential to provide better performance by selecting algorithms for each state, instead of using one algorithm for all states

17 Application: Power Flow Management Power flow management algorithms Case study power systems Varying system states Algorithm selector designs k+ 20k+ implemented & tested used for testing simulated per system developed & evaluated

18 Application: Power Flow Management Example: IEEE 57-bus system, 10,000 states Algorithm / Selector No. of overloads (count) Curtailed energy (MWh) Best Individual Algorithm (OPF) ,900 Best Direct Selector ,734 Best EPM-based Selector ,646 Optimal Selections ,087 Algorithm selection reduces the number of overloads

19 Cyber Security - Network Configuration Observer Architect Gateway Error Generator 340 Customers with profiles 4 PV Generators with profiles 4 Active Aggregates ( 4 Dormant) 4 Central Core Agents

20 Self Organising Architecture Results Attacks involving up 24% of the customer population examined Comparison between static and self-organising architectures Self-organisation reduced computational burden by at least 65% Self-Organisation prevented control degradation experienced in static testing Feeder end voltage profiles during a continuous attack involving 24% of the customer population Message response times between customer and controller during a continuous attack involving 24% of the customer population Message congestion during a sequential attack involving 24% of the customer population

21 Summary Many Challenges, Many Possible Futures Complexity, Climate Change, Cyber, Automation, Interconnectivity Electrical Networks are at the Nexus between Transport, Gas, Water, Heat Systems Lots of Safety Opportunities and Challenges Increased Automation, AI and Data Science New Materials, Many Devices Connecting and Disconnecting from Network Frequently New ways of working required, new skills required, multi-skills Need Rapid Innovation but Safely - Demonstration, Living Labs