可再生能源與儲能系統整合之機組組合最佳化問題

Abstract

隨著可再生能源滲透率快速攀升，風力與太陽能等間歇性電源對傳統電力系統造成供需失衡與調度成本上升等挑戰。為解決此問題，本研究建立一個整合可再生能源、電池儲能系統（Battery Energy Storage System, BESS）、補充備轉容量（Supplemental Reserves）與需量反應（Demand Response, DR）的兩階段隨機機組組合最佳化模型（Two-Stage Stochastic Unit Commitment）。在日前階段，模型於可再生能源不確定性下決定機組啟停與輔助服務配置決策，在即時階段則針對多情境進行最佳化調度。本研究以 IEEE 24 匯流排可靠性測試系統進行模擬分析，並透過隨機抽樣生成風速與日照情境。模擬結果顯示，納入 BESS 與輔助服務可有效降低營運成本與可控式機台升降功率頻率、減少可再生能源棄電並提升系統穩定性。此外，模型亦能提供不同可再生能源滲透率下最佳 BESS 容量的決策。綜上所述，本研究所提出之模型可作為未來電力市場中可再生能源與需求端資源協同調度的實用工具。

With the rapid increase in renewable energy penetration, intermittent sources such as wind and solar power pose significant challenges to traditional power systems, including supply–demand imbalances and rising dispatch costs. To address these issues, this study develops a two-stage stochastic unit commitment optimization model that integrates renewable energy, battery energy storage systems (BESS), supplemental reserves, and demand response (DR). In the day-ahead stage, the model determines unit commitment and ancillary service allocation under renewable generation uncertainty; in the real-time stage, it performs optimal dispatch across multiple scenarios. Case studies based on the IEEE 24-bus Reliability Test System are conducted, with wind speed and solar irradiance scenarios generated through stochastic sampling. The simulation results show that incorporating BESS and ancillary services can effectively reduce operating costs and the ramping frequency of controllable units, mitigate renewable curtailment, and improve system stability. Furthermore, the model provides decision support for determining the optimal BESS capacity under different levels of renewable penetration. Overall, the proposed model offers a practical tool for the coordinated dispatch of renewable and demand-side resources in future electricity markets.