Deakin University

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Informed decision making of battery storage for solar-PV homes using smart meter data

journal contribution
posted on 2019-09-01, 00:00 authored by Hong Xian LiHong Xian Li, Peter HoranPeter Horan, Mark LutherMark Luther, Tarek Ahmed
As the cost of solar photovoltaic (PV) systems decreases and incentives such as feed-in tariffs (FiTs) are offered, solar-PV homes are becoming popular. Furthermore, solar-PV homes integrated with hybrid or electric vehicles (EVs) are emerging as a paradigm for future homes. Given the fact that there exists a considerable price difference between grid electricity supply and FiTs, decision making of energy storage using batteries becomes an imperative topic. This research proposes an innovative and generic framework for the decision making of energy storage using batteries based on Smart Meter data, which incorporates the actual energy generation and consumption patterns of solar-PV homes. The proposed energy storage decision is based on a developed economic model, with the consideration of the electricity price from the grid, the FiTs, and the storage cost using batteries (i.e. the average price per kWh of battery capital cost and maintenance cost). Moreover, an intelligent algorithm is developed to calculate the electricity quantities, i.e. electricity supplied from the grid, fed into the grid and stored in a battery of a given capacity, based on the monitored data obtained from a Smart Meter over a year.​ The results reveal that at the present utility prices of $0.3/kWh and a feed-in-tariff of $0.10/kWh in the studied region, energy storage with a battery cost of $0.2 /kWh or more is excessive and not economically feasible. However, with the increasing cost of electricity in recent years and constant changes in battery price, the outcomes could quickly reverse. This research contributes an innovative framework for battery storage decision-making of solar-PV homes, based on economic analysis and Smart Meter data.



Energy and buildings




491 - 502




Amsterdam, The Netherlands





Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2019, Elsevier B.V.