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An improved quantum-behaved particle swarm optimization method for short-term combined economic emission hydrothermal scheduling

机译：短期组合经济排放水热调度的改进量子行为粒子群算法

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摘要

This paper presents a modified quantum-behaved particle swarm optimization (QPSO) for short-term combined economic emission scheduling (CEES) of hydrothermal power systems with several equality and inequality constraints. The hydrothermal scheduling is formulated as a bi-objective problem: (ⅰ) minimizing fuel cost and (ⅱ) minimizing pollutant emission. The bi-objective problem is converted into a single objective one by price penalty factor. The proposed method, denoted as QPSO-DM, combines the QPSO algorithm with differential mutation operation to enhance the global search ability. In this study, heuristic strategies are proposed to handle the equality constraints especially water dynamic balance constraints and active power balance constraints. A feasibility-based selection technique is also employed to meet the reservoir storage volumes constraints. To show the efficiency of the proposed method, different case studies are carried out and QPSO-DM is compared with the differential evolution (DE), the particle swarm optimization (PSO) with same heuristic strategies in terms of the solution quality, robustness and convergence property. The simulation results show that the proposed method is capable of yielding higher-quality solutions stably and efficiently in the short-term hydrothermal scheduling than any other tested optimization algorithms.
机译：本文提出了一种改进的量子行为粒子群算法（QPSO），用于具有多个等式和不等式约束的水火发电系统的短期组合经济排放调度（CEES）。将水热调度表述为一个双目标问题：（ⅰ）最小化燃料成本和（ⅱ）最小化污染物排放。通过价格惩罚因子将双目标问题转换为单个目标。所提出的方法称为QPSO-DM，将QPSO算法与差分变异操作相结合以增强全局搜索能力。在这项研究中，提出了启发式策略来处理等式约束，尤其是水动态平衡约束和有功功率平衡约束。还采用了基于可行性的选择技术来满足储层存储量的限制。为了显示该方法的有效性，进行了不同的案例研究，并将QPSO-DM与差分进化（DE），具有相同启发式策略的粒子群优化（PSO）在解决方案质量，鲁棒性和收敛性方面进行了比较属性。仿真结果表明，所提出的方法能够在短期热液调度中稳定，高效地产生更高质量的解决方案，其效果优于其他任何经过测试的优化算法。

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