Goal-oriented heuristic dynamic programming for scheduling of virtual energy hubs with management of intelligent parking lot

The increasing complexity of multi-carrier energy systems (MCESs) has introduced significant challenges in the current energy systems necessitates seamless integration and optimization. To address this issue, this work proposes an effective configuration of a virtual energy hub (VEH) to schedule and make multi-energy systems active inside energy markets. In particular, the framework of VEH dynamically schedules the system in such a way that addresses the management of intelligent parking lot (IPL) alongside the MCESs context. The proposed structure of VEH not only facilitates the optimal operation of MCES but also formulates an advanced model to integrate the EV unit into the studied energy plant. This model of VEH is effectively developed to operate within both the thermal and electrical market where it brings more flexibility and efficient energy interactions. To demonstrate the real-world applicability of the proposed structure, this model considers the inherent uncertainties corresponding to the EV behavior, including stochastic changes in their arrival and departure times, and their state of charge (SOC). Furthermore, the electrical energy generated by renewable resources and energy prices have an uncertainty factor which imposes more complexity to the model of VEH. The Goal-Oriented Heuristic Dynamic Programming (Go-HDP) is adopted to solve the scheduling problem to reach the maximum profit from the integrated system. In this framework, the Go-HDP with multi-neural nets (goal, critic, and action nets) is utilized to dynamically respond to the system requirements by interacting as an agent with the environment. By maximizing a reward function that is defined based on the system characteristics, the neural nets of Go-HDP are trained in the iterative process. The comprehensive simulation examinations under typical scenarios of the virtual hub are made to ascertain the feasibility of the proposed framework.