Risk-revenue analysis of behind-the-meter energy storage system considering load uncertainties

Due to the increase in global energy consumption and carbon dioxide emissions, new energy are eagerly expected to be widely put into application. The installation of photovoltaic systems will increase the fluctuation and uncertainty of load, which will have a certain impact on the planning of customer-side energy storage systems (ESSs). The current tariff policy and the scenarios of energy storage devices have made strict requirements on the ability to control load. In this paper, an energy storage revenue assessment method based on portfolio theory is proposed. The uncertainty of load is analysed by non-parametric kernel density estimation (KDE). Then a factor is used to measure the risk of reducing the load, which is coped with the ESS. The capacity and operation strategies of ESS is optimally allocated by linear programming (LP). Then a method based on portfolio theory is used to quantify the combined impact of risk and revenue, which provides a certain reference for the determination of the capacity of the behind-the-meter (BTM) ESS and the operation strategy.