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

Ayesha Abbasi, Kiran Sultan (), Sufyan Afsar, Muhammad Adnan Aziz and Hassan Abdullah Khalid
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Ayesha Abbasi: Department of Electrical and Computer Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan
Kiran Sultan: Department of CIT, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Sufyan Afsar: Department of Electrical Engineering, Bahria University, Islamabad 44000, Pakistan
Muhammad Adnan Aziz: Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan
Hassan Abdullah Khalid: Center for Advanced Studies in Energy, National University of Science and Technology, Islamabad 44000, Pakistan

Energies, 2023, vol. 16, issue 13, 1-22

Abstract: 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.

Keywords: microgrid; demand response; load scheduling; peak shaving; PV; battery energy storage; electric vehicle (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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