Investment Decision for Long-Term Battery Energy Storage System Using Least Squares Monte Carlo

Abstract

The use of renewable energy sources to achieve carbon neutrality is increasing. However, the uncertainty and volatility of renewable resources are causing problems in power systems. Flexible and low-carbon resources such as Energy Storage Systems (ESSs) are essential for solving the problems of power systems and achieving greenhouse gas reduction goals. However, ESSs are not being installed because of Korea’s fuel-based electricity market. To address this issue, this paper presents a method for determining the optimal investment timing of Battery Energy Storage Systems (BESSs) using the Least Squares Monte Carlo (LSMC) method. A case study is conducted considering the System Marginal Price (SMP) and Capacity Payment (CP), which are electricity rates in Korea. Revenue is calculated through the arbitrage of a 10 MW/40 MWh lithium-ion BESS, and linear programming optimization is performed for ESS scheduling to maximize revenue. The ESS revenue with uncertainty is modeled as a stochastic process using Geometric Brownian Motion (GBM), and the optimal time to invest in an ESS is determined using an LSMC simulation considering investment costs. The proposed method can be used as a decision-making tool for ESS investors to provide information on facility investments in arbitrage situations.

Cost-Effective Target Capacity Assessment in the Energy Transition: The Italian Methodology

“…In conclusion, flexible energy storage systems are essential for achieving climate and energy objectives, and new methods are being developed to determine their optimal investment timing [62].…”

“…This evidence testifies to the importance of synergic and coordinated grid reinforcements and new storage installation combined solutions. In conclusion, flexible energy storage systems are essential for achieving climate and energy objectives, and new methods are being developed to determine their optimal investment timing [62].…”