Energy Storage System Investment Decision Based on Internal Rate of Return

The continuous integration of new energy sources has aggravated the fluctuation of power load in power systems. In recent years, the rapid development of energy storage technology has matched the demand for the balance of supply and demand of power load. At the same time, the peak and valley electricity price policy of power system makes it possible for the investor to make a profit with the investment of building energy storage systems. So it is necessary to plan the construction of the energy storage system from the perspective of investor. Based on the internal rate of return of investment, considering the various financial details such as annual income, backup electricity income, loan cost, income tax, etc., this paper establishes a net cash flow model for energy storage system investment, and uses particle swarm optimization algorithm based on hybridization and Gaussian mutation to get the energy storage capacity that maximizes the internal rate of return of the investment. And this internal rate of return is compared with the set internal rate of return of the investment to determine whether the energy storage system is worth building. The paper illustrates the effectiveness of the investment planning model through the planning process of two users.