Heuristic optimization technique for hydrothermal scheduling considering pumped storage unit

R. S. Patwal, Nitin Narang
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引用次数: 2

Abstract

In this paper presence of pumped-storage unit is considered for understanding its effect over the optimal scheduling of multi-reservoir cascaded hydrothermal Plants. In Present scenario pumped storage units are becoming a valuable part of power system plants for energy and water conservation and accomplishing the need of high power demands. To maximize the water as fuel input in hydroelectric system pumped storage units are added with an advantage of operating in generating as well as pumping mode. Hydrothermal scheduling is an important aspect and is performed to minimize the operating cost of thermal power generation but considering a pumped storage unit other practical problems are also resolved. The purpose of this research is to implement a heuristic optimization technique for optimal economic scheduling of hydrothermal units considering a pumped storage unit. In proposed heuristic optimization technique, global best solution obtained from Particle Swarm Optimization (PSO) technique is further improved by applying different mutation strategies. The modified global best solutions are compared and most improved global best solution is chosen as a final solution and used for further iterations. The feasibility and efficiency of heuristic optimization technique be validated through a test system containing four hydro plants, three thermal plants and single pumped storage unit. The results demonstrate that the heuristic optimization technique can get a better solution in comparison with PSO technique.
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考虑抽水蓄能机组的热液调度启发式优化技术
本文考虑了抽水蓄能机组的存在,以了解其对多水库级联热液电站优化调度的影响。目前,抽水蓄能机组已成为电力系统电厂节能、节水和满足高功率需求的重要组成部分。为了最大限度地提高水力发电系统中作为燃料输入的水,增加了抽水蓄能机组,其优点是在发电和抽水模式下运行。水热调度是一个重要方面,是为了使火电发电的运行成本最小化而进行的,但考虑到抽水蓄能机组,也解决了其他实际问题。本研究的目的是实现一种考虑抽水蓄能机组的热液机组经济最优调度的启发式优化技术。在启发式优化技术中,通过引入不同的突变策略,进一步改进粒子群优化(PSO)技术得到的全局最优解。对改进后的全局最优解进行比较,选择改进最大的全局最优解作为最终解,用于进一步的迭代。通过包含4座水电厂、3座火电厂和1座抽水蓄能机组的试验系统,验证了启发式优化技术的可行性和有效性。结果表明,启发式优化技术比粒子群优化技术能得到更好的解。
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