Operation planning of complex combined heat and power systems based on multi-objective optimization

doi:10.3785/j.issn.1008-973X.2026.01.014

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (1): 148-157 DOI: 10.3785/j.issn.1008-973X.2026.01.014

Operation planning of complex combined heat and power systems based on multi-objective optimization

Jianhong CHEN(

),Guoyun WANG,Keqing ZUO,Hongkun ZHANG,Yanke BAO

College of Energy Engineering, Zhejiang University, Hangzhou 310027, China

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Abstract

The expansion, transformation, and upgrading of thermal power plants have significantly increased the complexity of combined heat and power (CHP) systems in terms of the number, type, and performance degradation level of units. A multi-objective optimization model based on the energy efficiency, exergy, economy and environmental factors was constructed for the unit operation planning of complex CHP systems, with comprehensive consideration of the influencing factors such as the unit commitment status and ramping rates of the units during the actual operation process. The entropy weight method was introduced to objectively allocate weights to each objective, transforming the multi-objective optimization problem into single-objective optimization problems. A dynamic programming algorithm was employed to solve the problems, ensuring the determinacy and objectivity of the optimal solution. Taking the complex CHP system of a thermal power plant as a case study, the heat and power load demand over a 24-hour period was randomly selected for operation planning and analysis. The comparison between the optimization results and the results of the original design scheme indicated that the operating cost was reduced by 79 600 yuan per day after optimization. The proposed model can effectively improve the operation economy and reliability of complex CHP systems.

Key words： complex combined heat and power system multi-objective optimization operation planning entropy weight method dynamic programming method

Received: 04 December 2024 Published: 15 December 2025

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Cite this article:

Jianhong CHEN,Guoyun WANG,Keqing ZUO,Hongkun ZHANG,Yanke BAO. Operation planning of complex combined heat and power systems based on multi-objective optimization. Journal of ZheJiang University (Engineering Science), 2026, 60(1): 148-157.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.01.014 OR https://www.zjujournals.com/eng/Y2026/V60/I1/148

基于多目标优化的复杂热电联产系统运行规划

热电厂的扩建、改造和升级显著增加了热电联产系统在机组数量、种类及性能劣化程度等方面的复杂性. 针对复杂热电联产系统的运行规划，综合考虑机组在实际运行过程中的启停状态、爬坡速率等影响因素，构建基于能效、?、经济、环境等的多目标优化模型. 引入熵权法客观地分配各目标的权重，将多目标优化问题转化为单目标优化问题；采用动态规划算法进行求解，确保最优解的确定性和客观性. 以某热电厂的复杂热电联产系统为例，随机选取24 h的热电负荷需求进行运行规划和计算分析，并将优化结果与原设计方案进行对比，结果表明优化计算后运行成本降低了7.96万元/d. 所提模型能够有效地提升复杂热电联产系统运行的经济性与可靠性.

关键词： 复杂热电联产系统, 多目标优化, 运行规划, 熵权法, 动态规划法

Fig.1 Diagram of subproblem decomposition in operation planning of complicated combined heat and power systems

Fig.2 Schematic diagram of economic cost calculation under unstable load conditions

Fig.3 Calculation flowchart of weighted single-objective optimization model based on entropy weight method

Fig.4 Diagram of relationship between energy utilization efficiency and energy flow for different units

Tab.1 Steam turbine initiation methods

Fig.5 Flowchart of solution process for operation planning of complex CHP systems

Fig.6 System topology diagram of case thermal power plant

Fig.7 Thermoelectric load curves of thermal power plant in 3×24 h

Fig.8 Thermoelectric load curves of thermal power plant in 3×24 h after simplification treatment

Tab.2 Results of quantitative calculations for each plan

Fig.9 Cost curves of operating schemes for each unit after optimization

Fig.10 Power operation planning curves for each unit of thermal power plant over a 24-hour period

Fig.11 Operation planning curves of medium-pressure steam extraction volume for each unit of thermal power plant over a 24-hour period

Fig.12 Operation planning curves of low-pressure steam extraction volume for each unit of thermal power plant over a 24-hour period


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