Maximizing Investment Value of Small-Scale PV in a Smart Grid Environment

TL;DR

This paper presents an optimization method using particle swarm algorithms to maximize the investment value of small-scale PV systems in smart grid environments by leveraging smart meter data.

Contribution

It introduces a PV sizing and orientation optimization approach that considers dynamic tariffs and smart meter data for improved investment decisions.

Findings

Optimized PV configurations can significantly increase investment value.

The method effectively compares different retailer tariffs and identifies the most cost-effective options.

Application to real data demonstrates practical utility and improved decision-making.

Abstract

Determining the optimal size and orientation of small-scale residential based PV arrays will become increasingly complex in the future smart grid environment with the introduction of smart meters and dynamic tariffs. However consumers can leverage the availability of smart meter data to conduct a more detailed exploration of PV investment options for their particular circumstances. In this paper, an optimization method for PV orientation and sizing is proposed whereby maximizing the PV investment value is set as the defining objective. Solar insolation and PV array models are described to form the basis of the PV array optimization strategy. A constrained particle swarm optimization algorithm is selected due to its strong performance in non-linear applications. The optimization algorithm is applied to real-world metered data to quantify the possible investment value of a PV installation under different energy retailers and tariff structures. The arrangement with the highest value is determined to enable prospective small-scale PV investors to select the most cost-effective system.