With the increasing penetration of wind power and electric vehicle (EV) into the power system, system operators face new challenges for system reliability and generation cost due to the intermittent of wind power. Furthermore, the randomly connected EVs at different time periods and locations add more uncertainty to the power system. In this study, uncertainties in wind power generation and EV charging load are modelled into the day-ahead dynamic economic dispatch (DED) problem, solution feasibility and robustness are discussed, and the bad scenario set is formulated for the day-ahead DED problem. In the obtained model, parameters can be used to adjust the positive bias or conservative bias, charging/discharging power of battery switch stations are also controlled to optimise the total cost of power system. To solve the optimisation problem, the multi-agent bacterial colony chemotaxis algorithm and a mutation strategy based on the cloud theory are developed. The simulation results show that the proposed method is effective, and battery switching station can help to reduce total cost by charging and discharging batteries.

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Robust DED based on bad scenario set considering wind, EV and battery switching station

References

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