Institute of Energy and Sustainable Development
Smart grids: technologies, markets and communities
The Open University 4th April 2014
Dr Vijay Pakka Senior Research Fellow vpakka@dmu.ac.uk
The Open University 4 th April 2014 Dr Vijay Pakka Senior Research - - PowerPoint PPT Presentation
Smart grids: technologies, markets and communities The Open University 4 th April 2014 Dr Vijay Pakka Senior Research Fellow vpakka@dmu.ac.uk Institute of Energy and Sustainable Development Transition to a Smart Grid A smart grid is an
Institute of Energy and Sustainable Development
Dr Vijay Pakka Senior Research Fellow vpakka@dmu.ac.uk
Institute of Energy and Sustainable Development
connected to it — generators, consumers and generator/consumers — in order to ensure an economically efficient, sustainable power system with low losses and a high quality and security of supply and safety.” - EC Smart Grid Task Force, 2010
Courtesy: Electric Power Research Institute
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Smart Grid will make possible high levels of penetration of renewables by:
intermittent generation
protection, ancillary services, etc.
Institute of Energy and Sustainable Development
□ System that collects data from sensors within a plant or remote locations and processes centrally to manage and control devices on the field
□ Topology processor □ State Estimation □ Three phase balanced power flow □ OPF (Optimal Power Flow) □ Contingency analysis □ Short circuit analysis □ Relay protection coordination
Institute of Energy and Sustainable Development
□ Optimal Network Reconfiguration □ Integrated volt/VAR control □ Optimal Capacitor and VR sizing and placement □ Optimal DG sizing and placement □ Load Modelling and Estimation □ State Estimation □ Outage Management Systems □ Fault Detection, Isolation and Restoration □ Interface to GIS
Institute of Energy and Sustainable Development
hours on the few days when wholesale prices are the highest. CPP effectively transfers the cost of generations especially for those few hundred hours when supply costs are very high.
reducing critical-peak usage. A baseline load needs to be established beforehand.
day-ahead of the critical event, thus allowing the operational flexibility for the utilities and DSOs.
system conditions and marginal costs if any.
Price signals transmitted to consumers either a day-ahead or hour-ahead the actual time of delivery.
Institute of Energy and Sustainable Development
The Community Energy Cooperative’s Energy-Smart Pricing Plan (Illinois) 2003 – 2005
p.m. to midnight) and on “high price days” when provided with additional notifications.
in the electricity price.
responding to a spike in a single hourly price.
influencing customers.
Institute of Energy and Sustainable Development
Average Load Profiles
High Consumption Households on Super Peak Event Days, 2004 - 2005 The energy management technology included the following components:
system components
communicating whole-house interval electricity meter capable of recording consumption data in 15-minute intervals
conditioning loads
selected loads (eg pool pumps and spas)
software.
a Super Peak event.
respond to prices.
2005.
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
□ To develop a common toolbox, allowing pan-European TSOs to increase coordination and harmonise operating procedures. □ To carry-out the operational dynamic simulations in the context of a full probabilistic approach, thus going beyond the current ‘N-1’ approach and optimising the transit capacities of the grid over different areas (national, regional, pan-European) and timescales (two-days ahead, day-ahead, intra-day, real-time).
□ Objective to increase network capacity by 20% □ To target energy efficiency and demand reduction measures for industrial and commercial customers in cooperation with the buildings research establishment, energy supply companies and an independent party. □ Focus on voltage optimisation, power factor correction and low energy appliances that do not directly require customer behaviour change. □ Dynamic rating of network assets to create additional headroom where possible □ Flexible network control to help re-balance network loading using neighbouring network groups to support demand □ Integration of voltage regulation and power compensation equipment to release voltage constrained capacity, and to assist with re-balancing the network.
Institute of Energy and Sustainable Development
□ Installation of 500 Li-ion energy storage systems (total 3 MWh of energy) at customers’ premises together with PV and energy load controllers. □ Energy load controllers, connected through a communication link with the system
demand and grid conditions. □ Control of local storage is to ensure the real-time balance between electricity demand and production at consumers’ premises.
Institute of Energy and Sustainable Development
▫ Complex Adaptive Systems Cognitive Agents and Distributed Energy ▫ 3-year EPSRC sponsored project ▫ Partners: E.On, Ecotricity, Cranfield Univ, CSIRO (Australia), NEF ▫ Complexity Science based investigation into the Smart Grid concept ▫ Predominantly at the Distribution level
▫ Agent-based Modelling of Electricity Networks ▫ 3-year EPSRC sponsored project ▫ Partners: E.On, NEF, EIFER (Karlsruhe), E.On, Western Power ▫ Builds on the CASCADE model, but predominantly at the Transmission level.
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
□ Central & Distributed Generators □ Traders □ Suppliers □ System Operators □ Consumers
□ Networks: Electrical networks, Communication networks □ Transformers and Voltage Regulators □ Switches, Capacitors □ Loads □ Smart meters Intelligent programmable devices
□ Information transfer and signalling processes □ Power flow and optimal dispatch calculations □ Trading rules and market mechanisms
Institute of Energy and Sustainable Development
Commercial aggregation of demand profiles of Prosumers Market participation: Bids and Offers based on aggregate profiles Derives and transmits smart signal to Prosumers Aggregates change in demand Learns new price signal based on earnings Satisfies physical constraints of distribution network
Institute of Energy and Sustainable Development
power supply and tap changers
power quality
management
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Coal Plants = 4 CCGT Plants = 7 Wind Farms = 3
System Imbalance System Prices
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
MW
200 400 600 800 1000 1200 1400 1600 Day-Ahead At Gate-Closure
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
£/MWh
20 25 30 35 40 45 50 SSP SBP
Institute of Energy and Sustainable Development
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
MW
500 1000 Day-Ahead At Gate Closure
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
£/MWh
10 20 30 40 50 60 70 SSP SBP
Coal Plants = 2 CCGT Plants = 3 Wind Farms = 44
System Imbalance System Prices
Institute of Energy and Sustainable Development
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
MW
500 1000 1500 Day-Ahead At Gate Closure
Settlement Periods
20 40 60 80 100 120 140 160 180 200 220 240 260
£/MWh
10 20 30 40 50 60 70 SSP SBP
Coal Plants = 1 CCGT Plants = 2 Wind Farms = 64
System Imbalance System Prices
Institute of Energy and Sustainable Development
0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2000 2500 3000 3500 4000 4500 5000 5500 6000 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
Normalized Price Signal Demand in MW Settlement Periods
After DR (e=10%) After DR (e=30%)
Price Signal
Price Elasticity Residential
Commercial
Industrial
Coal plants – 4, CCGT – 7, Wind farms – 3
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Substation Transformer
Distribution Transformer
UG cables
Primary Main
abc cba bc ca abc a c a
Lateral
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Power Flows, Voltages, Currents Component Assessor Classification:
Objects:
Component Models Network data Component Data
Topology Assessor
Sweep Methods Matrix Methods
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Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development
Institute of Energy and Sustainable Development