考慮再生能源不確定性下的機組組合最佳化問題

Abstract

隨著全球減碳的趨勢及需求，再生能源發電量佔比不斷提高，電力系統運行的不確定性增加，輔助服務的加入減緩不確定性的影響，但也增加了電網營運時決策的複雜性。本研究針對機組組合最佳化問題，考量電網營運的日前與即時的兩階段決策過程和再生能源的不確定性，建立雙層最佳化模型。利用分段線性化法將模型轉換為混整數規劃模型，分別利用隨機最佳化求解多種情境下的最小期望值，及穩健最佳化法結合變數與限制式生成演算法求解最糟情境的最佳解。個案分析採用模擬的六匯流排系統，隨機最佳化結果顯示在再生能源較高的時段會採用較多的輔助服務，穩健最佳化結果顯示在再生能源變化較大的時段會需要啟動更多的可控式發電機台以達到電網平衡。

With the global trend for carbon reduction, the proportion of renewable energy generation is continuously increasing, which in turn elevates the uncertainty in power system operations. The integration of ancillary services helps mitigate this uncertainty but also adds complexity to decision-making in grid operations. This study addresses the unit commitment optimization problem by considering the two-stage decision-making process—day-ahead and real-time operations—under renewable energy uncertainty, establishing a bi-level optimization model. The model is transformed into a mixed-integer programming model using the piecewise linearization. The stochastic optimization is employed to solve for the minimum expected cost across various scenarios, while the robust optimization, combined with a column-and-constraint generation algorithm, is used to find the optimal solution under the worst-case scenario. The case study of a simulated six-bus system demonstrates that stochastic optimization results in greater use of ancillary services during periods of high renewable energy output, while robust optimization requires the activation of more controllable generators to maintain grid balance during periods of significant renewable energy variability.