基于混沌自适应粒子群算法的冷热电联供系统优化

贠保记; 白森珂; 张国

引用本文:	贠保记,白森珂,张国.基于混沌自适应粒子群算法的冷热电联供系统优化[J].电力系统保护与控制,2020,48(10):123-130.
	YUN Baoji,BAI Senke,ZHANG Guo.Optimization of CCHP system based on a chaos adaptive particle swarm optimization algorithm[J].Power System Protection and Control,2020,48(10):123-130

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基于混沌自适应粒子群算法的冷热电联供系统优化
贠保记^1,2,白森珂¹,张国³
(1.西安科技大学，陕西西安 710054;2.西安西瑞控制技术股份有限公司，陕西西安 710077; 3.长庆油田分公司西咸公用事业处，陕西西安 710018)

摘要:

在研究冷热电联供系统优化运行的过程中，为了更好地优化调度冷热电联供系统中各设备的出力，提出了基于Tent映射的混沌搜索和非线性自适应粒子群算法相结合的优化算法。建立了一个包含风机、光伏、微燃机和燃气锅炉等主要设备的冷热电联供系统模型。以联供系统的运行成本、污染物排放量和能源利用率为目标，建立了多目标优化模型。在满足设备出力、功率平衡等约束条件下，利用Matlab进行了优化求解。仿真结果表明，所提出的冷热电联供系统优化方法，可以有效地提高经济效益，减少污染排放，提高能源利用率。

关键词: 冷热电联供多目标优化混沌搜索自适应粒子群算法

DOI：10.19783/j.cnki.pspc.190875

分类号:

基金项目:陕西省重点产业创新(群)项目(2019ZDGU18-01)

Optimization of CCHP system based on a chaos adaptive particle swarm optimization algorithm

YUN Baoji^1,2,BAI Senke¹,ZHANG Guo³

(1. Xi'an University of Science and Technology, Xi'an 710054, China;2. Xi'an Xirui Control Technology Co., Ltd., Xi'an 710077, China;3. Xixian Public Utility Department, Changqing Oilfield Branch, Xi'an 710018, China)

Abstract:

In the process of studying the optimal operation of a CCHP system, in order to better optimize the output of each piece of equipment in the dispatching CCHP system, an optimization algorithm combining chaotic search based on a Tent map and a non-linear adaptive particle swarm optimization algorithm is proposed. A model of the CCHP system including wind turbine, photovoltaic, micro-gas turbine and gas boiler is established. A multi-objective optimization model is established for the operation cost, pollutant emission and energy utilization of the CCHP system. Under the constraint of equipment output and power balance, the optimization is carried out by Matlab. The simulation results show that the proposed optimization method for the CCHP system can effectively improve economic benefits, reduce pollution emission and improve energy utilization. This work is supported by Key Industry Innovation (Mass) Project of Shaanxi Province (No. 2019ZDGU18-01).

Key words: combined cooling heating and power (CCHP) multi-objective optimization chaotic search adaptive particle swarm optimization

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