Abstract
This paper provides models for managing and investigating the power flow of a grid-connected solar photovoltaic (PV) system with an energy storage system (ESS) supplying the residential load. This paper presents a combination of models in forecasting solar PV power, forecasting load power, and determining battery capacity of the ESS, to improve the overall quality of the power flow management of a grid-connected solar PV system. Big data tools were used to formulate the solar PV power forecasting model and load power forecasting model, in which real historical solar electricity data of actual solar homes in Australia were used to improve the quality of the forecasting models. In addition, the time-of-use electricity pricing was also considered in managing the power flow, to provide the minimum cost of electricity from the grid to the residential load. The output of this model presents the power flow profiles, including the solar PV power, battery power, grid power, and load power of weekend and weekday in a summer season. The battery state-of-charge of the ESS was also presented. Therefore, this model may help power system engineers to investigate the power flow of each system of a grid-connected solar PV system and help in the management decision for the improvement of the overall quality of the power management of the system.
Highlights
Renewable energy sources have been rapidly growing globally because of their advantages that minimized dependency on fossil fuels and reduced pollution and provide a cost-effective, reliable, and secured system [1]
Compared with other previous approaches which presented the power flow management of a grid-connected solar PV system, this paper presents three design models which combine forecasting solar PV power, forecasting load power, and determining battery capacity of the energy storage system (ESS), to develop the power flow management of the grid-connected solar PV system using time-of-use electricity pricing to provide the minimum cost of electricity from the grid to the residential load
This paper presented models in managing the power flow of each system of the grid-connected solar PV system with ESS
Summary
Renewable energy sources have been rapidly growing globally because of their advantages that minimized dependency on fossil fuels and reduced pollution and provide a cost-effective, reliable, and secured system [1]. Compared with other previous approaches which presented the power flow management of a grid-connected solar PV system, this paper presents three design models which combine forecasting solar PV power, forecasting load power, and determining battery capacity of the ESS, to develop the power flow management of the grid-connected solar PV system using time-of-use electricity pricing to provide the minimum cost of electricity from the grid to the residential load. Power forecasting model were from actual solar homes in Australia for three years, and collected every minutes to efficiently developed based on big data tools using real historical solar electricity data. Australia forof three years, data and collected every 30 minutes to efficiently handle battery capacity of the ESS was determined based on these historical solar power data and and manage the large amount of historical data in formulating these forecasting models. 3, power, forecasting load power, and determining battery capacity and managing the power flow of to illustrate the effectiveness of theare design models to present power flow profiles of each the grid-connected solar PV system described in and
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