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A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem

机译：基于禁忌搜索的模糊优化单元承诺问题混合优化方法

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摘要

This article presents a solution model for the unit commitment problem (UCP) using fuzzy logic to address uncertainties in the problem. Hybrid tabu search (TS), particle swarm optimization (PSO) and sequential quadratic programming (SQP) technique (hybrid TS—PSO-SQP) is used to schedule the generating units based on the fuzzy logic decisions. The fitness function for the hybrid TS-PSO-SQP is formulated by combining the objective function of UCP and a penalty calculated from the fuzzy logic decisions. Fuzzy decisions are made based on the statistics of the load demand error and spinning reserve maintained at each hour. TS are used to solve the combinatorial sub-problem of the UCP. An improved random perturbation scheme and a simple method for generating initial feasible commitment schedule are proposed for the TS method. The non-linear programming sub-problem of the UCP is solved using the hybrid PSO-SQP technique. Simulation results on a practical Neyveli Thermal Power Station system (NTPS) in India and several example systems validate, the presented UCP model is reasonable by ensuring quality solution with sufficient level of spinning reserve throughout the scheduling horizon for secure operation of the system.
机译：本文提出了一种使用模糊逻辑解决单元不确定性问题的单元承诺问题（UCP）的解决方案模型。混合禁忌搜索（TS），粒子群优化（PSO）和顺序二次规划（SQP）技术（混合TS-PSO-SQP）用于基于模糊逻辑决策调度发电单元。混合TS-PSO-SQP的适应度函数是通过组合UCP的目标函数和根据模糊逻辑决策计算出的罚分来制定的。基于负荷需求误差和每小时保持的旋转储备的统计信息，做出模糊决策。 TS用于解决UCP的组合子问题。针对TS方法，提出了一种改进的随机扰动方案和一种生成初始可行承诺进度表的简单方法。使用混合PSO-SQP技术解决了UCP的非线性编程子问题。通过在印度的一个实用Neyveli热电站系统（NTPS）上进行的仿真结果以及几个示例系统的验证，所提出的UCP模型是合理的，它可以通过确保在整个调度范围内确保足够的旋转备用量的质量解决方案来确保系统的安全运行。

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