Dual‐layer power scheduling strategy for EV‐ESS‐controllable load in bi‐directional dynamic markets for low‐cost implementation

A novel energy management algorithm (EMA) is proposed for a smart home with electric vehicle (EV), energy storage system (ESS), and bidirectional energy transfer with the grid that can be implemented on a low‐cost home energy management system (HEMS). The proposed algorithm is composed of online and offline layers and it takes into consideration the controllable load and battery degradation as well as vehicle to home (V2H) and home to grid (H2G) services. As the main objective of this study is to present a low‐cost alternative to the existing optimization‐based energy management algorithms, a rule‐based algorithm is proposed to schedule the operation of EV, ESS, and controllable load, which requires low computational power and memory. In this regard, the major deficiency of rule‐based algorithms in bi‐directional markets, which is their inability to address the relative nature of feed‐in tariffs has been tackled in this work. Omitting this issue could cause the rule‐based algorithms to be incapable of dispatching EV and ESS within bi‐directional markets efficiently. The proposed algorithm incorporates a fuzzy‐rule‐based offline layer along with a modifying on‐line layer. The functionality of the presented EMA has been validated experimentally using a hardware‐in‐the‐loop (HIL) setup, which confirmed major improvements in revenue, cost of energy, and peaks of power for a home.