Smart grids as Common-pool Resources

Managing Electrical Vehicle Charging through Evolving institutions

Amineh Ghorbani

Energy and Industry GroupFaculty of Technology, Policy and Management

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Smart grids

• Mitigation strategies in cities• Adaption of renewable energy systems

• Solar panels• Electric Vehicles

• Electricity grids need to be smart to • Manage the inflow and outflow of electricity: consumers vs.

prosumers• Huge overloads: electric cars

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Managing smart grids

• Centralized vs decentralized • What happens to my privacy?• It’s too difficult, can somebody do it for me?

• Multi-agent systems• Autonomous entities called agents to manage energy usage• Agents represent households

Maximize profit – self-interested• Minimizes user interaction but still requires central control

Household information needs to be shared•Attractive on the consumer side, but what about grid

operators?

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Smart grids as Common-pool resources

•Grids are CPR because:• Users cannot be excluded from them• They are limited in the amount of electricity they can provide • (very similar to irrigation systems)

Question:Can viewing smart grids as common-pool resources that are self-organized

by artificial agents allow more efficient management of the grid?While minimizing information sharing and user interaction.

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The grid and the tragedy

Tragedy of the commons

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A CPR-oriented multi-agent system design

• Assumption: only considering EV owners as a starting point. • Electricity as resource unit• Artificial agents come up with behavioral strategies about their

electricity usage patterns• Shared strategies will eventually turn into institutional rules. • No cheating no monitoring, no sanctioning

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Design Elements

1. Agent properties and attributes2. Structure of behavioral strategies

• Voltage rules vs. Time rules3. Selection of behavioral strategies

• Learning• Copying• Innovating

4. Selection of institutions• Voting • One institutions selected at a time• Structure: ADICO• Change over time

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Results – Performance indicators

• Average state of charge (SOC)• Dependent on the node of the agent (heterogeneous) ,

distance from the main transformer• SOC of all agents• SOC of the weaker agents

• Number of weak failures: unable to charge fully• Number of strong failures: unable to charge sufficiently

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Results – First look

• Comparison with and without software agents• The average SOC over all agents is increased.• There is a redistribution of access from advantaged agents to

the disadvantaged.• The average number of weak and strong failures decreases

• Comparison with and without institutions• The average SOC is significantly bigger with institutions• Weaker agents perform better with institutions• The average number of weak failures is reduced but not

always• The average number of hard failures is reduced.

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Results – Under what conditions does the model perform best?

1- What type of rules?Voltage Rules work much better than time based rules

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Results – change of institutions over time

The adaption and change of institutions increases the performance of the model over time.

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Conclusions • Viewing Smart grids as CPR systems and allowing self-

organization and endogenous institutions can result in a significant increase in the performance of grid.

• A mutli-agent system design can minimize user interaction, and allow decentralized control.

• More adjustments and reconfigurations of the design required to maximize the optimal conditions.

Thank you for your attention!