# Optimal Selection of Small-Scale Hybrid PV-battery Systems to Maximize Economic Benefit Based on Temporal Load Data

## TL;DR

This paper presents an optimization method using quantum-behaved particle swarm optimization to select the most cost-effective hybrid PV-battery system for residential consumers based on their load profiles, considering current market conditions.

## Contribution

It introduces a novel optimization strategy tailored to individual load data for selecting PV-battery systems, accounting for economic factors and retail electricity plans.

## Key findings

- Optimized system configurations significantly reduce electricity costs.
- The method effectively adapts to different load profiles and market conditions.
- Results demonstrate improved economic benefits over standard sizing approaches.

## Abstract

Continued advances in PV and battery energy storage technologies have made hybrid PV-battery systems an attractive prospect for residential energy consumers. However the process to select an appropriate system is complicated by the relatively high cost of batteries, a multitude of available retail electricity plans and the removal of PV installation incentive schemes. In this paper, an optimization strategy based on an individual customer's temporal load profile is established to maximize electricity cost savings through optimal selection of PV-battery system size, orientation and retail electricity plan. Quantum-behaved particle swarm optimization is applied as the underlying algorithm given its well-suited application to problems involving hybrid energy system specification. The optimization strategy is tested using real-world residential consumption data, current system pricing and available retail electricity plans to establish the efficacy of a hybrid PV-battery solution.