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

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  • Kyungcheol Shin

    (School of Electrical Engineering, Anam Campus, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea)

  • Jinyeong Lee

    (Electricity Policy Research Center, Korea Electrotechnology Research Institute (KERI), Uiwang 16029, Republic of Korea)

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.

Suggested Citation

  • Kyungcheol Shin & Jinyeong Lee, 2024. "Investment Decision for Long-Term Battery Energy Storage System Using Least Squares Monte Carlo," Energies, MDPI, vol. 17(9), pages 1-15, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2019-:d:1382323
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    References listed on IDEAS

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    5. Omar J. Guerra, 2021. "Beyond short-duration energy storage," Nature Energy, Nature, vol. 6(5), pages 460-461, May.
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    Cited by:

    1. Minseok Jang & Sung-Kwan Joo, 2025. "Pattern-Aware BiLSTM Framework for Imputation of Missing Data in Solar Photovoltaic Generation," Energies, MDPI, vol. 18(17), pages 1-20, September.

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