Agent-Based Micro-Storage Management for the Smart Grid

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Agent-Based Micro-Storage

Management for the Smart Grid

Perukrishnen Vytelingum, Thomas D. Voice, Sarvapali D. Ramchurn, Alex Rogers and Nicholas R. Jennings

University of Southampton

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iDEaS : Intelligent Decentralised Energy-Aware Systems

Outline

• Energy Domain

• Smart Grid

• Agent-Based Micro-Storage Management

– Game theoretic analysis

– Adaptive storage mechanism – Empirical evaluation

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Development and growth since the start of the industrial revolution due to fossil fuels

• Fossil Fuels

– Coal (1800+) – Oil (1900+) – Gas (1960+)

• Concentrated solar energy collected over millions of years

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Continued use of fossil fuels is challenged by three impending factor

• Finite resources

– Demand outstrips production capacity

• Energy security

– Resources are not evenly distributed

• Climate Change

– Increasing atmospheric CO2 concentration

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Addressing these issues require challenging changes in the way we use energy

• 80% reduction in CO2 by 2050

– Increased energy efficiency through electrification

• Transport

• Heating

– Low carbon

• Wind

• Solar

• Hydro

• Nuclear

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2008 Climate Change Act

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Current electricity networks are challenged by these proposed changes

• Ageing infrastructure designed for a small number of large generators

– Supply follows demand

• Fuel inefficiency

• Peak demand

• Fixed prices

• Fred Schweppe proposed the need for a more dynamic grid (1978)

– Spot pricing and homeostatic control

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The Smart Grid represents a modern vision of a dynamic electricity grid

Imagine the possibilities: electricity and information flowing together in real time, near-zero economic

losses from outages and power quality disturbances, a wider array of customized energy choices, suppliers

competing in open markets to provide the world’s best electric services, and all of this supported by a new

energy infrastructure built on superconductivity,

distributed intelligence and resources, clean power, and the hydrogen economy.

US Department of Energy (2009)

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The Smart Grid represents a modern vision of a dynamic electricity grid

Imagine the possibilities: electricity and information flowing together in real time, near-zero economic

losses from outages and power quality disturbances, a wider array of customized energy choices, suppliers

competing in open markets to provide the world’s best electric services, and all of this supported by a new

energy infrastructure built on superconductivity,

distributed intelligence and resources, clean power, and the hydrogen economy.

US Department of Energy (2009)

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We investigate how micro-storage can

address the three challenges posed above

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• How can consumer owned, small scale, storage devices be deployed within a Smart Grid?

– How much storage is required?

– How should storage be managed?

– Does the electricity network benefit?

• Load and Diversity Factor

• Carbon Emission Reduction

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iDEaS : Intelligent Decentralised Energy-Aware Systems ¹⁰

½ hour periods

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Demand (kW) 0

Smart Home

Macroscopic Market Model

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We perform two types of analysis on a

system composed of multiple such homes

• Game theoretic analysis

– What does equilibrium look like?

• Adaptive storage strategy

– A best-response day-ahead storage computation – A learning mechanism to adapt to changing

market prices (as total demand is changing now that agents are changing their individual demand)

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• Individual payoff of each agent:

• Seek to find the storage profile subject to two conditions:

We exploit the price signal to perform an aggregate game theoretic analysis

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No net charge

Battery capacity

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We exploit the price signal to perform an aggregate game theoretic analysis

• Calculating the Nash equilibrium simplifies to minimising:

• Characterised by two price points:

– Charging price point – Discharging price point

• We can solve for aggregate and individual storage profiles.

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We use linear programming to optimise the storage profile within individual homes

• Best-response storage computation

• Solve using CPLEX subject to same constraints as before (with one change):

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We need to apply a two rate learning approach to optimise storage over time

• Use moving average prediction of prices

• Find storage capacity that will minimise cost of electricity

– Adapt capacity toward this value:

• Again, find the best storage profile

– Adapt existing storage toward this profile:

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We perform an empirical evaluation using price and carbon data from the UK grid

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• Simulate 1000 homes with randomly allocated storage capacity and load profiles (generated from UK averages).

• Use UK grid data for the macroscopic market model

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Nash Equilibrium Storage Profile Storage Profile over 100 Trading Days

Electricity Prices over 100 Trading Days Mean-Squared Deviation from Nash Equilibrium

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Empirical Evaluation

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We see improvements in system-wide metrics with varying storage uptake

18 Diversity Factor (DF)

Ratio of the sum of individual maximum demand to the maximum total demand.

Load Factor (LF)

Average power divided by peak power.

Carbon Emissions Reduction

Gird carbon intensity

correlated to total demand.

System-wide Grid Performance

Proportion of Population with Storage

Factor

4 kWh

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Conclusions and Future Work

• Conclusions

– Shown how an adaptive agent-based storage strategy is able to reach equilibrium solution

– Demonstrated desirable system-wide properties at this equilibrium

• Future Work

– Better understand convergence critieria – Improved market modelling

• Interaction between domestic, industrial and commercial use

– Drive demand with real smart meter data

• Predict future demand and price (Gaussian processes)

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