A Hybrid Heuristic Solution for Packetized Energy Transactions in Smart Homes

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.