因應能源短缺以及發電成本高漲等問題,機組調派最佳化以及併入再生能源皆是重要的研究方向。本論文提出應用基因演算法求解考量太陽光電不確定性因素之實功發電與彈性需量調派最佳化問題。本文在發電機組調派與彈性負載調派的編碼問題中,設計兩種長度可變之編碼方法與其交配突變法,並使用高斯分布為太陽光電建置隨機分布模型,以點估測法進行取樣,達到在調派最佳化中考慮太陽光電不確定性之目的。本文所提出之基因演算法應用在工廠系統中,在符合各種發電與需量限制條件下,成功達成總發電成本最低之目的。最後針對四種參數值設計不同情境進行模擬,並討論限制參數以及太陽光電如何影響本演算法之收斂表現。
Studies on unit commitment (UC) and renewable energy are now important topics as a result of shortage of energy. This thesis presents a novel method based on Genetic Algorithms for solving unit and elastic load commitments in a factory power system considering uncertain renewables. Two encodings are proposed for the unit and elastic load, incorporating with new crossover and mutation operations. Point Estimate Method (PEM) is used to sample the uncertain photovoltaic generation, modeled by a Gaussian distribution. The proposed method can attain the optimal cost of power generation subject to various operational constraints in the factory power system. This work obtains simulation results through four scenarios specified by different constraints. Simulation results show the applicability of the proposed method and validate the algorithm.