A novel hybrid harmony search and particle swarm optimization method for solving combined heat and power economic dispatch

M. Nazari-Heris, Amir Fakhim-Babaei, B. Mohammadi-ivatloo
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引用次数: 8

Abstract

Combined heat and power (CHP) units are able to generate power and heat, simultaneously. The main objective of CHP economic dispatch (CHPED) problem is to provide optimal heat and power production of cogeneration units with minimum operation cost of supplying heat and power demand. The CHPED problem should be studied considering several operational and electrical equality and inequality constraints consisting of valve-point loading effects of conventional thermal plants, power transmission loss of the system, power and heat capacity production limits of the plants, and heat and power load demand balance. Moreover, heat and power produced by cogeneration plants have bidirectional dependency, which results to complexity of the CHPED problem. In this study, a novel combination of harmony search (HS) algorithm and particle swarm optimization (PSO) method is proposed for the solution of non-convex non-linear CHPED problem. The proposed optimization technique is employed on two large-scale CHP systems for evaluating the performance of the proposed method. The large-scale CHPED problem is solved applying the proposed hybrid method, which demonstrates the effectiveness of the method in terms of operational cost and convergence characteristics.
求解热电联产经济调度的一种新的混合和谐搜索和粒子群优化方法
热电联产(CHP)装置能够同时发电和发热。热电联产经济调度问题的主要目标是使热电联产机组在满足热电需求的最小运行成本下实现最优的热电生产。研究热电联产问题时,应考虑常规热电厂的阀点负荷效应、系统的输电损耗、热电厂的功率和热容量生产极限以及热电负荷需求平衡等运行和电气的等式和不等式约束。此外,热电联产电厂产生的热量和电力具有双向依赖性,这导致了热电联产问题的复杂性。针对非凸非线性CHPED问题,提出了一种将和谐搜索(HS)算法与粒子群优化(PSO)算法相结合的求解方法。将所提出的优化技术应用于两个大型热电联产系统,以评估所提出方法的性能。应用该方法解决了大规模热电联产问题,从运行成本和收敛特性两方面验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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