研究以人工蜂群（Artificial Bee Colony, ABC）及粒子群演算法（Particle Swarm Optimization, PSO）探討配電系統中電池儲能系統之位置與容量配置問題，比較兩種策略：系統尖峰負載資料進行配置，完整二十四小時負載資料進行配置。兩種策略所得配置在相同潮流條件下進行整日運轉模擬，並以傅立葉級數參數化儲能能量變化曲線，以建立具週期性之整日能量軌跡。以 D. Das 等人提出之 15 匯流排系統簡化為 5 匯流排架構，作為方法比較之測試平台，探討模型在相同系統架構、成本假設與評估程序下，相對於無 BESS 基準之改善程度。結果顯示基於尖峰負載資料所得到之配置改善幅度接近完整二十四小時負載曲線所得到之配置，且前者相對無 BESS 基準之成本改善比例較後者高出約 0.51 個百分點。另以完整 15 匯流排系統作驗證並得到相似結果，顯示縮減模型所得結果趨近完整模型。因此在配電系統資料蒐集受限或需進行快速初步規劃時，尖峰負載導向模型可作為低資料需求之規劃工具，提供未來在資料蒐集與分析層面上的參考。

This study employs the Artificial Bee Colony (ABC) algorithm and Particle Swarm Optimization (PSO) to investigate the optimal siting and sizing of battery energy storage systems (BESSs) in distribution networks. Two planning strategies are compared: a peak-load-based strategy and a strategy based on the complete 24-hour load profile. The BESS configurations obtained from the two strategies are evaluated through full-day operational simulations under identical power-flow conditions. The stored-energy trajectory of each BESS is parameterized using a Fourier series to ensure that the resulting charging and discharging behaviors satisfy the capacity and operational constraints. A reduced 5-bus system derived from the 15-bus distribution system proposed by Das et al. is adopted as the test platform for model comparison. Under the same system structure, cost assumptions, and evaluation procedure, the cost reduction achieved by each model is evaluated relative to the no-BESS baseline. The results show that the configuration obtained using peak-load data achieves a cost reduction close to that obtained using the complete 24-hour load profile. Moreover, in the studied case, the cost-reduction rate achieved by the peak-load-oriented model relative to the no-BESS baseline is approximately 0.51 percentage points higher than that achieved by the 24-hour-load-oriented model. Validation using the full 15-bus system yields a similar trend, indicating that the results obtained from the reduced model are close to those obtained from the full model. Therefore, when distribution-system data collection is limited or rapid preliminary planning is required, the peak-load-oriented model can serve as a planning tool with relatively low data requirements. The findings provide a practical reference for future data collection and planning analysis of BESS deployment in distribution systems.

[1] BloombergNEF, “Lithium-Ion Battery Pack Prices Fall to $108 Per Kilowatt-Hour, Despite Rising Metal Prices: BloombergNEF,” Press Release, Dec. 9, 2025.
[2] National Renewable Energy Laboratory, “2024 Annual Technology Baseline (ATB): Utility-Scale Battery Storage,” 2024 [Accessed: Oct. 15, 2025] (https://atb.nrel.gov/electricity/2024/utility-scale_battery_storage).
[3] 經濟部能源署，「備用供電容量管理辦法」及淨尖峰能力因子相關說明（含 4 小時基準）[Accessed: Jun. 3, 2025] (https://www.moeaea.gov.tw/ECW/populace/content/ContentDesc.aspx?menu_id=26818)。
[4] C. K. Das, O. Bass, G. Kothapalli, T. S. Mahmoud, and D. Habibi, “Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm,” Applied Energy, vol. 232, pp. 212–228, 2018, doi: 10.1016/j.apenergy.2018.07.100.
[5] D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, pp. 459–471, 2007, doi: 10.1007/s10898-007-9149-x.
[6] J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks (ICNN’95), 1995, pp. 1942–1948, doi: 10.1109/ICNN.1995.488968.
[7] J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 2nd ed. Cambridge, MA, USA: MIT Press, 1992.
[8] R. Storn and K. Price, “Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, pp. 341–359, 1997, doi: 10.1023/A:1008202821328.
[9] N. K. Singal, “Calendar Life vs Cycle Life: What’s the Difference and Why It Matters?,” LinkedIn Pulse, May 24, 2025.
[10] B. Xu, A. Oudalov, A. Ulbig, G. Andersson, and D. S. Kirschen, “Modeling of lithium-ion battery degradation for cell life assessment,” IEEE Transactions on Smart Grid, vol. 9, no. 2, pp. 1131–1140, Mar. 2018.
[11] Contemporary Amperex Technology Co., Limited (CATL), Energy Storage System Product Brochure/Datasheet, Nov. 2024.
[12] T. Frambach, R. Liedtke, and E. Figgemeier, “Battery sizing of 48 V plug-in hybrids considering calendar and cycle degradation,” Journal of Energy Storage, vol. 60, art. no. 106681, Apr. 2023, doi: 10.1016/j.est.2023.106681.
[13] N. Jayasekara, M. A. S. Masoum, and P. J. Wolfs, “Optimal operation of distributed energy storage systems to improve distribution network load and generation hosting capability,” IEEE Transactions on Sustainable Energy, vol. 7, no. 1, pp. 250–261, Jan. 2016, doi: 10.1109/TSTE.2015.2487360.
[14] 台灣電力股份有限公司，「各種發電方式之發電成本（114年，截至10月底止）」[Accessed: Oct. 28, 2025] (https://www.taipower.com.tw/2289/2363/2373/2375/10359/normalPost)。
[15] 張文奇、林素真、蔡森洲、蘇嬛嬛、施恩、古文潔，〈供電瓶頸地區需量反應措施之研究〉，《台電工程月刊》，第819期，105年11月。
[16] National Renewable Energy Laboratory (NREL), “Simple Levelized Cost of Energy (LCOE) Calculator Documentation,” Energy Systems Analysis, last updated Dec. 6, 2025 [Accessed: Oct. 28, 2025] (https://www.nrel.gov/analysis/tech-lcoe-documentation.html).
[17] S. N. Ghoreishi, A. Clausen, and B. Nørregaard Jørgensen, “Termination Criteria in Evolutionary Algorithms: A Survey,” in Proc. 9th Int. Joint Conf. on Computational Intelligence (IJCCI), 2017, pp. 373–384, doi: 10.5220/0006577903730384.
[18] IEEE RTS-96 / University of Washington Power Systems Test Case Archive (UW PSTCA), R. Christie, “Reliability Test System (RTS) – data (Hourly Loads).”
[19] D. Das, D. P. Kothari, and A. Kalam, “Simple and efficient method for load flow solution of radial distribution networks,” International Journal of Electrical Power & Energy Systems, vol. 17, no. 5, pp. 335–346, 1995, doi: 10.1016/0142-0615(95)00050-0.
[20] R. D. Zimmerman, C. E. Murillo-Sánchez, and R. J. Thomas, “MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education,” IEEE Transactions on Power Systems, vol. 26, no. 1, pp. 12–19, Feb. 2011, doi: 10.1109/TPWRS.2010.2051168.
[21] R. D. Zimmerman and C. E. Murillo-Sánchez, MATPOWER User’s Manual, Version 8.1, 2025, doi: 10.5281/zenodo.15871661.