Optimal Demand Response Using Battery Storage Systems and Electric Vehicles in Community Home Energy Management System-Based Microgrids

• DOI: 10.3390/en16135024
• Publication Date: 2023-06-29T05:43:13Z
• Authors: Ayesha Abbasi; Kiran Sultan; Sufyan Afsar; Muhammad Adnan Aziz; Hassan Abdullah K...
• Keywords: Technology; T; PV; 02 engineering and technology; peak shaving; 7. Clean energy; microgrid; demand response; load scheduling; peak shaving; PV; battery energy storage; electric vehicle; microgrid; battery energy storage; demand response; 0202 electrical engineering, electronic engineering, information engineering; load scheduling

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.