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Smart Grids

Smart Grids. Institute for Energy and Environment Department of Electronic & Electrical Engineering University of Strathclyde. Active Network Management (ANM) and Smart Grids. Paradigm shift from “fit & forget” Accepted characteristics: Distributed generation Renewable

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Smart Grids

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  1. Smart Grids Institute for Energy and Environment Department of Electronic & Electrical Engineering University of Strathclyde

  2. Active Network Management (ANM) and Smart Grids Paradigm shift from “fit & forget” Accepted characteristics: Distributed generation Renewable Monitoring, comms and control Preventive and corrective actions Flexible & adaptable Embedded intelligence Autonomy has expertise and capability/capacity across the range of research and development areas essential to the delivery of the Smart Grid Acknowledgement: Alinata, Victoria, Australia

  3. Active Networks • Taking the lead from market opportunities and energy policy distribution networks are becoming more ‘active’ (transmission networks already being active): • Responsive customers (DSM, Home Automation, etc.) • Smart Metering • Distributed, micro scale and renewable generation • Electric Vehicles • Distribution Automation led by security and reliability drivers • Energy storage • Interaction of energy networks (electricity, gas, heat, etc.) • Cooperation with transmission

  4. EU SmartGrids agenda • EU wide vision and research and deployment ageda for moves towards smarter grids

  5. SmartGrid: Proposed Definition • A SmartGrid is an electricity network that can intelligently integrate the actions of all users connected to it (generators, consumers and those that do both) in order to efficiently deliver sustainable, economic and secure electricity supplies.

  6. SmartGrid Definition • A SmartGrid employs innovative products and services together with intelligent monitoring, control, communication, and self-healing technologies to: • better facilitate the connection and operation of generators of all sizes and technologies • allow electricity consumers to play a part in optimising the operation of the system • provide consumers with greater information and choice in the way they secure their electricity supplies • significantly reduce the environmental impact of the total electricity supply system • deliver enhanced levels of reliability and security of supply

  7. Aura-NMS project • Autonomous Regional Active Network Management System (Aura-NMS) project with ABB, Scottish Power, EdF-Energy – moving towards demonstration • Novel network management approaches being deployed on ABB COM600 computer – lab concept demonstration in 2008 • Different algorithms tested: • Constraint satisfaction for thermal and voltage constraint management • OPF for thermal constraint management • Current tracing for thermal constraint management

  8. AuRA-NMS: Autonomous Regional Active Network Management System Using: • Distributed hardware (ABB COM600 Industrial PC) • Distributed, agent based, control software Aim to provide: • Plug and play functionality • Enhanced network control • Initial functions: • Thermal Management • Voltage Control • Reconfiguration • Distributed Intelligence research aspects: • Autonomous behaviour: • Automatically planning and executing network management functions • Automatically reacting to control decisions from other AuRA-NMS functions • Negotiation and arbitration to determine correct actions to take • System integration: • New agents automatically integrate with existing control functions • Harmonisation with Common Information Model and IEC 61850

  9. Orkney ANM project • Orkney Min/Max demand: 8/31MW (11,500 customers) • 33kV submarine cables: 2 x 20MW import/export • Generation mix of wind and gas (wave and tidal). • Requirement for generation constraint management system: • DTI funded study – 2003/2004 • RPZ application – 2005 • Technical and commercial development – 2006-2008 • Prototyping and Trial – November 2006 • Full deployment – 2008/2009 • Commercialisation

  10. Orkney ANM project • Each zone has a thermal limitation on generation output at any given time • Whole system has a further thermal limit on generation output • Real time control of wind and marine generating units based on measurements and control logic • Technology being taken forward by university spin-out company:

  11. Renewables Integration: Multiple Objective Optimisation Distributed Energy Resource Planning • Multiple objective optimisation framework for DER integration • Outer loop DER investment, location and capacity optimisation • Inner loop operational optimisation (OPF) including dispatch/curtailment decisions • Work developing from DG/ANM to energy storage, electric vehicles and other network constraints

  12. Shetland Energy Storage project • Different energy storage roles considered: • Wind energy capture • Voltage support • Reserve/response • Mix of generation technologies: • Wind power • Gas turbines • Diesel engines • VRB and NaS battery technologies considered to support system and enhance wind connection potential

  13. EVs in our dreams …

  14. And back in the real world … • City car requires 60 mile range • in the US 75% of daily commutes are 40 miles or less • In the UK … • This requires a 22kWh battery approximately • This requires 7 hour charge at 3kW (maximum from 13A socket) • 2030 energy requirement in range 10 - 40 TWh p.a. (cf. 360 TWh GB electrical energy demand) • So a long charge but at a slow rate • High powered, fast charging is possible but new infrastructure is required

  15. Electric Vehicles Grid Integration • Statistical model of domestic car use has been constructed • Privately owned cars are utilised only 5.2% of time of transportation, thus they are available up to 94.8% of time as responsive load or provide secondary function, V2G or G2V • Statistical model of electric vehicle use now possible • Integration with network management schemes

  16. Markets and Economics • Market structures and mechanisms for highly distributed energy futures with consumer participation • Transmission and Distribution system pricing alternatives for future system scenarios • Generation scheduling and unit commitment using OPF • Time of Use (ToU) and alterative pricing studies

  17. Asset Management Sensor design Data acquisition Partial discharge and RF measurement Intelligent data analysis Automated diagnostics Distributed intelligence architectures Asset management methods

  18. Demand Side Participation • Smart meter trial and consultation inputs • Home Area Networks (HAN) for demand side participations • Laboratory demonstration and calibration of automated home energy management • Energy Systems Research Unit: • BRE Scotland Chair in Energy Utilisation • ESP-r and MERIT software for home energy assessment

  19. SP Energy Demand Research • Data Analysis expertise and support for SP Energy Retail: • Independent investigation of effects of Smart Meters on consumers energy usage behaviour • Part of OFGEMs EDR Project involving 3 other major utilities • Undertaken during summer 2009 with reporting complete prior to SP reporting deadlines • Ongoing support/advice for the remainder of EDR project (until March 2010)

  20. National/Zonal Demand Smart Plug Task Criticality INDO Requests Schedule Time Usage Statistics Advances Best Tariff/ time to use Web Service Request Advances Meter Visualisation and intelligent interpretation of energy usage

  21. Questions?

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