Scheduling of Community Integrated Energy Systems with an Electric Vehicle Charging Station

What is it about?

A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users’ energy consumption and electric vehicles’ behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

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Photo by Alex Eckermann on Unsplash

Photo by Alex Eckermann on Unsplash

Why is it important?

1) To coordinate flexible demand responses and multiple renewable generations uncertainties, a novel bi-level optimal dispatching model for the CIES with an EVCS is established in this paper, where three different operating modes of EVs (charging, discharging and providing spinning reserves) are fully explored. 2) To promote a balance between energy supply and demand, a new integrated demand response program considering flexible thermal comfort requirements of users is designed by introducing a predictive mean voting (PMV) index. By this means, the electricity and heating demands of the CIES are met while maintaining a user comprehensive satisfaction within an acceptable range. 3) To further explore the potential of demand response, a dynamic pricing mechanism that combines time-of-use (TOU) and real-time (RT) pricing is proposed. This mechanism can flexibly guide users’ energy consumption and EVs charging/discharging behaviors to consume renewable energy. 4) A simulation test was performed on a CIES located in North China to verify the effectiveness and superiority of the proposed method. The influences of main control parameters on the performance of the proposed method have also been analyzed in detail.

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