The value of local electricity storage in a smart grid: How important is intermittency?
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1 The value of local electricity storage in a smart grid: How important is intermittency? Pedro Crespo Del Granado (Jointly with Prof. Stein W. Wallace and Dr. Zhan Pang) International Conference on Stochastic Programming, Bergamo, July 8 th 2013
2 Presentation outline 1. Motivation + Research Goals Smart Grids, renewable, energy storage 2. Community Energy Storage A stochastic optimization model Wind Scenario Generation 3. A UK Case Study Implementation Value of energy storage Intermittency 4. Summary and final remarks
3 2 Main motivations
4 Residential Areas Biomass/waste Office Buildings Nuclear Utilities Gas/Coal Plant Hydro Power Farms Sub-Station Building Complexes (community) Apartment Buildings
5 Future Smart Grid Energy System Electrical Cars Residential Areas Biomass/waste Biofuels Office Buildings Nuclear Gas/Coal Plant Utilities Smart Houses Offshore Windmills Hydro Power Farms Onshore Wind Farms Sub-Station Solar energy Building Complexes or Communities Industrial Parks & factories CHP Apartment Buildings Storage
6
7 Pathway to energy storage in a Smart Grid UK and EU policies: Carbon reductions Renewables 20-30% of supply by 2020 Mostly wind More inflexibility in the system New Energy Supply Portfolio, Decentralized Generation System Smart Grids Demand side measures Energy efficiency Electricity Storage is becoming a Reality (it will be a mature technology within the next decade)
8 Research Approach The research objective is to assess, through OR modelling, the value and integration role of energy storage technologies in the deployment of renewable energy in a future smart grid. The research methodology follows a bottom-up approach that models energy systems integration with storage technologies and renewable supply, starting from the end user perspective (e.g. houses, buildings and communities) and then scaling up to the market or grid level.
9 Future Smart Grid Energy System Electrical Cars focus on end-user hybrid generation: Households Communities Residential Areas Biomass/waste Office Buildings Nuclear Utilities Smart Houses Gas/Coal Plant Offshore Windmills Hydro Power Farms Onshore Wind Farms Sub-Station Solar energy Building Complexes Community CHP Apartment Buildings Storage
10 Key Questions What is the value of energy storage for the enduser and the grid? Value of Flexibility! What are the economic implications of end-user storage and demand side measures for energy markets and the grid?
11 Local energy storage problem Point of view: A large end user (industrial site, office building complex, university campus) Assumption: Two-way communication, smart metering Minimize cost of purchasing electricity Decision on the battery charge/discharge given wind realizations The End user only buys from the grid
12 Community energy storage Medium Size Battery considered Wind must be modeled stochastically as some of the value of a storage unit comes from meeting the random wind. This model provides an assessment on the value of energy storage; not an operational model Grid connection Medium Size Battery Community with micro grid
13 The model Input Time varying : Wind Demand Wholesale Spot Prices Storage Unit Parameters: Charge rate Discharge rate Efficiencies A day divided in 48 periods: Model Objective: Minimize cost of grid consumption Model Constraints: Storage Capacity & Efficiency Demand supply balance Wind generation uncertainty Storage balance (Intertemporality) Output Battery Charging/Discharging decisions New grid consumption pattern
14
15 Modeling wind uncertainty Short term wind forecasting models (Pinson & Madsen, 2008) Wind prediction errors distribution fitted to sampling method (Hodge & Milligan 2011) Data divided into subsets : low, medium, high wind realizations
16 A real case study
17 A case study implementation Lancaster University campus distributed generation mix: A 2MWh Combined heat & power engine (CHP) Biomass boiler 3 High efficient thermal gas boilers (4.9MWh) Micro-grid transformers 2.3MWh Wind turbine
18 Case Study basic features Data Used the campus half hourly data electricity consumption in 2010 National Grid sport prices in 2010 Wind speeds recorded in 2010 Storage Considered Different sizes of Vanadium redox battery (VRB), roundtrip efficienfy:0.81 Pedro Crespo Del Granado, Lancaster University The value of local energy storage: How important is intermittency? Bergamo, July 8th, 2013
19 Scenario Generation example
20 Storage role
21 cost saving Value of energy storage Energy Storage cost savings 0.0% -5.0% 4MW 8MW 15MW 24MW -10.0% -15.0% -20.0% -25.0% 4MW 8MW 15MW 24MW Storage Size -5.8% -9.5% -15.2% -21.5% 0 15% 30% 45% Wind Generation penetration
22 Analysis on Intermittency Key Results: Ideal size of energy storage 8-12MW Or a ratio: storage size over daily demand= 20% 10-12% energy savings
23 Summary Value of energy storage, for a large end user with medium size electricity storage, is around 10-19% savings of electricity cost in a smart grid setting. Implications for the grid: Peak shaving leads to more efficient units being used for production. So if we can reduce maximal demand we will increase the flexibility in the system Smart Grid:Understanding the new flexibility of the system More wind will increase price variation. Future/current research Economic Impact to electricity prices and supply equilibrium The end user and the unit commitment problem
24 Thank you for listening and learning!
25 Why this is important?? An ongoing real global problem To help policy makers and other players to make investment decisions Mismatch between supply and demand Value of flexibility and information in real time
26 Residential Areas Biomass/waste Office Buildings Nuclear Utilities Gas/Coal Plant Hydro Power Farms Sub-Station Building Complexes (community) Apartment Buildings
27 Future research plans 1. Unit commitment problem -Test the system flexibility 2. Contribution to supply-demand equilibrium modelling -Market mechanisms to the integration of intermittent energy sources 3. Evaluate investments in the home energy system and in new technologies -Develop business models: Policy implications and incentives
28 Pedro Crespo Del Granado, Lancaster University Modelling the value of end-user energy storage in smart grids Rome, July 3 rd, 2013
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