A Hybrid Heuristic Solution for Packetized Energy Transactions in Smart Homes

Abstract

This article presents a packetized energy management (PEM) strategy for optimizing the joint problem of energy storage control and load scheduling in grid-connected photovoltaic (PV) systems, aiming to minimize aggregated system costs for smart homes. The framework considers factors such as energy consumption, procurement costs, load scheduling delays, PV system self-sufficiency, and battery degradation. A new hybrid heuristic algorithm, called enhanced exploration and exploitation until convergence (EX-con), is proposed to solve the joint optimization problem. EX-con combines differential evolution (DE) and binary particle swarm optimization (BPSO) algorithms to enhance efficiency of the search process and diversity of the search space. A case study demonstrates the algorithm’s ability to minimize costs while meeting energy demands, maintaining quality-of-service (QoS) standards, and complying with operational constraints.