隨著電力需求在高峰時段的不斷增長，需量反應（Demand Response）成為了一種常見的負載管理方式。然而，基於激勵的需量反應計劃存在不穩定性以及不確定性。因此，過去經常利用直接控制負載或用戶設備的方式來確保能夠達成需量反應目標。然而，該種方法在普通住宅間無法普遍實施，因此本文討論了藉由僅給予住宅降載激勵及降載訊號的方式下，如何評估當中的不穩定性和不確定性，並提出尋找可行計畫的演算法。
本研究中，提出了一種兩階段基因演算法，旨在最大化給定預算內負荷減少量的實現概率。通過引入約束放寬和基因修復技術，找出可行性初始族群以及提升算法的效率，從而確保需求響應計劃的有效性。

With the continuous growth of electricity demand during peak periods, demand response(DR) has become an effective tool for load management. However, incentive-based DR programs have instability and uncertainty. Therefore, in the past, direct control of loads or user equipment was often used to ensure DR targets were met. However, this method cannot be widely implemented in ordinary households. This paper discusses how to evaluate the existing instability and uncertainty by only providing load reduction incentives and signals to households and proposes an algorithm to find feasible plans.
This paper proposes an innovative 2-phase Monte Carlo Simulation Genetic Algorithm (2P-MCSGA) to maximize the probability of achieving load reduction within a given budget. Our research focuses on combining Monte Carlo simulation with genetic algorithms and introducing constraint relaxation and gene repair techniques to improve solution feasibility and algorithm efficiency, ensuring the feasibility and effectiveness of the DR plan.

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