Utilization of the Energy Storage System for the Energy Arbitrage and Peak Shaving

Vitlinsky, Igor D.; Cho, Gvan Chun; Smotrov, Nikolay N.; Bitkulov, Kirill R.; Umurzakov, Denis D.

doi:10.1109/reepe53907.2022.9731459

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EOL is the battery capacity up to the facility warranty period, considering ESS degradation. The initial design capacity of the ESS must satisfy the capacity demanded by the owner until the end of the warranty period, and additional design capacity to extend the warranty lifespan should not be added during operation [40,41]. In accordance with Korea's EOL system and the characteristics of lithium-ion batteries, the DoD is set to 80% [42][43][44].…”

Section: Case Study 41 Ess Parameter and Crf Settingmentioning

confidence: 99%

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

Shin,

Lee

2024

Energies

View full text Add to dashboard Cite

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.

show abstract

Section: Case Study 41 Ess Parameter and Crf Settingmentioning

confidence: 99%