Real-Time Hierarchical Energy Flexibility Management of Integrated Hybrid Resources

This paper proposes a novel real-time hierarchical energy flexibility management model for integrated hybrid resources (IHRs) in power distribution systems. In the proposed model, each IHR is a combination of different distributed energy resources (DERs) and electric vehicle (EV) chargers that are controlled and dispatched as a single resource in power distribution system. The proposed hierarchical model utilizes a reinforcement learning model, deep deterministic policy gradient, in the IHR controller to dispatch DERs and EVs locally, while a central controller ensures the feasibility and deliverability of the dispatched solution. More specifically, the IHR controller determines the energy dispatches and sends the required net active power as well as the upper and lower boundaries of net reactive power of the IHR to the central controller. The central controller performs power flow analysis and adjusts the received net active power and determines the net reactive power dispatch of each IHR. The proposed model is implemented on IEEE 123-bus test power distribution system with different penetration of DERs and EVs. The simulation results showcase the efficacy of the proposed model in capturing the energy flexibility of DERs and EVs to reduce the operation cost of power distribution system. Further, the robustness and computational efficiency of the proposed model compared to optimization model in dispatching DERs and EVs while meeting the power distribution system constraints is highlighted.