Packetized Energy Management Controller for Residential Consumers

TL;DR

This paper presents a packetized energy management controller for residential PV systems that optimizes energy storage and load scheduling to reduce system costs, using heuristic algorithms like GA, BPSO, and DE.

Contribution

It introduces a novel packetized energy management controller that jointly optimizes load scheduling, energy transactions, and battery degradation using established heuristics.

Findings

GA reduces system cost by up to 4.7%

BPSO reduces system cost by up to 5.14%

DE reduces system cost by up to 1.35%

Abstract

In this paper, we investigate the management of energy storage control and load scheduling in scenarios considering a grid-connected photovoltaic (PV) system using packetized energy management. The aim is to reduce an average aggregated system cost through the proposed \textit{packetized energy management controller} considering household energy consumption, procurement price, load scheduling delays, PV self-sufficiency via generated renewable energy and battery degradation. The proposed approach solves the joint optimization problem using established heuristics, namely genetic algorithm (GA), binary particle swarm optimization (BPSO), and differential evolution (DE). Additionally, the performances of heuristic algorithms are also compared in terms of the effectiveness of load scheduling with delay constraints, packetized energy transactions, and battery degradation cost. Case studies have been provided to demonstrate and extensively evaluate the algorithms. The numerical results show that the proposed packetized energy management controller can considerably reduce the aggregated average system cost up to 4.7\%, 5.14\%, and 1.35\% by GA, BPSO, and DE, respectively while meeting the packetized energy demand and scheduling delays requirements.