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Optimal energy storage sizing and demand response control for electric vehicle charging stations in port facilities

Gkiata Ioanna

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Summary

The rapid expansion of electric vehicle charging stations has introduced significantburden to the electrical grid. Even though the installation of energy storage systems can help mitigate this, the conventional approach of requiring an individual energy storage system attached to each respective charging station is inefficient and expensive. A more promising alternative is the introduction of a single shared energy storage to the charging station infrastructure. However, its effective management is considered highly challenging due to complex coupling constraints, conflicting interests between interconnected charging stations, as well as the inherent uncertainty arisen from renewable energy generation. Furthermore, in recent years, the technological advances in electrification of transportation and optimal charging have been at the spotlight as sustainable approaches for the development of green port facilities.In this thesis, a novel, data-driven energy management framework for interconnected electric vehicle charging stations is presented. The proposed system tackles the problems of aggregated demand response control, economic dispatch and optimal shared storage sizing. Firstly, the problem of demand response control of a large fleet of electric vehicles is examined. The aggregated charging profile of electric vehicles is computed by utilizing a virtual battery model and Time of Use pricing strategies. Then, a theoretically rigorous, distributed probabilistic robust model predictive control algorithm based on scenario optimization and alternating direction method of multipliers is designed to deal with the economic dispatch of charging stations. This is accomplished by employing the approach of dynamic virtual storage capacities purchased by charging stations and the consideration of aggregated electric vehicle load profiles and renewable power generation uncertainty. Finally, to achieve the efficient and cost-effective utilization of the actual shared physical energy storage system, the dynamic virtual capacities previously computed are used to maximize the expected profit of the energy storage system operator, leading to the optimal sizing of the shared physical storage. Simulation case studies in the port area of Hamburg, Germany underline the high effectiveness and reliability of the proposed overall energy management system.

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