Research on optimal scheduling of combined cooling, heating and power system based on MOABC algorithm

王文静; 于立涛; 撖奥洋; 张智晟

doi:10.3969/j.issn.1009-1831.2019.04.011

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Research on optimal scheduling of combined cooling, heating and power system based on MOABC algorithm

更新时间：2026-03-05

- Research on optimal scheduling of combined cooling, heating and power system based on MOABC algorithm
- Vol. 21, Issue 4, (2019)
- 作者机构：
  
  1. 青岛大学电气工程学院,山东,青岛,266071
  2. 国网青岛供电公司,山东,青岛,266002
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2019.04.011
  CLC：
- Published：2019
- 稿件说明：
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王文静, 于立涛, 撖奥洋, et al. Research on optimal scheduling of combined cooling, heating and power system based on MOABC algorithm[J]. 2019, 21(4).
DOI：

王文静, 于立涛, 撖奥洋, et al. Research on optimal scheduling of combined cooling, heating and power system based on MOABC algorithm[J]. 2019, 21(4). DOI： 10.3969/j.issn.1009-1831.2019.04.011.

摘要

综合能源系统包含多种分布式能源，各能源相互补充，能有效提高能源的利用率，在经济、环保等方面具有显著优势。冷热电联供系统作为综合能源系统的重要补充，具有灵活可靠、高效清洁等优点，现已得到广泛的发展和重视。综合考虑各微源的发电特性和冷热电负荷需求，建立了包含燃料电池、微燃机、余热锅炉、吸收式制冷机和蓄能装置的多目标冷热电联供系统模型。该模型考虑分时电价对冷热电联供系统的影响，以经济成本和环境成本作为目标函数，提出基于Pareto理论的多目标蜂群算法作为模型的求解算法。以实际冷热电联供系统为算例进行仿真，验证了所提模型的有效性，并与多目标粒子群算法进行对比，结果表明，采用基于Pareto理论的多目标蜂群算法能更有效地降低经济成本和环境成本。

Abstract

The integrated energy system contains a variety of distributed energy sources

each of which complement seach other

effectively improves energy utilization

and has significant advantages in terms of economy and environmental protection. As an important supplement to the integrated energy system

the combined cooling

heating and power system has the advantages of flexibility

reliability and high efficiency

and is now widely developed and valued.Considering the power generation characteristics of each micro-source and the demand of cold and heat load

a multi-objective cogeneration system model including fuel cell

micro-combustion engine

waste heat boiler

absorption chiller and energy storage device is established.The model considers the impact of time-of-use electricity price on micro-grid system. Taking economic cost and environmental cost as the objective function

a multi-target bee colony algorithm based on Pare-to theory is proposed as the model solving algorithm. The actual cooling and power supply system is used as an example to verify the effectiveness of the proposed model

and compared with the multi?objective particle swarm optimization algorithm. The results show that the multi?target bee colony algorithm based on Pareto theory can be more effective

and reduce economic and environmental costs.

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