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首页> 外文会议>Australasian conference on artificial life and computational intelligence >A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment
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A MOEA/D with Non-uniform Weight Vector Distribution Strategy for Solving the Unit Commitment Problem in Uncertain Environment

机译：具有非均匀重量载体分布策略的MOEA / D，用于解决不确定环境中的单位承诺问题

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摘要

In this paper, a multiobjective evolutionary algorithm based on decomposition (MOEA/D) based is proposed to solve the unit commitment (UC) problem in uncertain environment as a multi-objective optimization problem considering cost, emission, and reliability as the multiple objectives. The uncertainties occurring due to thermal generator outage and load forecast error are incorporated using expected energy not served (EENS) reliability index and EENS cost is used to reflect the reliability objective. Since, UC is a mixed-integer optimizar tion problem, a hybrid strategy is integrated within the framework of decomposition-based MOEA such that genetic algorithm (GA) evolves the binary variables while differential evolution (DE) evolves the continuous variables. To enhance the performance of the presented algorithm, novel non-uniform weight vector distribution strategies are proposed. The effectiveness of the non-uniform weight vector distribution strategy is verified through stringent simulated results on different test systems.
机译：在本文中，提出了一种基于分解（MOEA / D）的多目标进化算法，以解决不确定环境中的单位承诺（UC）问题作为考虑成本，发射和可靠性作为多目标的多目标优化问题。使用预期的能量（Eens）可靠性指数并使用Eens成本来反映由于热发电机中断和负载预测误差引起的不确定性并入。由于UC是混合整数优化的问题，但混合策略集成在基于分解的MOEA的框架内，使得遗传算法（GA）在差分演进（de）演变的连续变量时演变为二进制变量。为了增强所提出的算法的性能，提出了新的非均匀重量载体分布策略。通过对不同测试系统的严格模拟结果验证了非均匀重量载体分布策略的有效性。

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