Cogeneration plays an increasingly important role in energy utilization, and combined heat and power economic dispatch (CHPED) becomes an important optimization task in the economic operation of the power system. In this paper, biogeography-based learning particle swarm optimization (BLPSO) is proposed to solve the CHPED problem considering various constraints. In contrast to PSO, BLPSO adopts a biogeography-based learning model with improved updating equations to overcome premature convergence. Moreover, fitness information based migration is used to construct the learning exemplar and enhance search accuracy. A repair technique is also employed to handle the system constraints and guide the solutions toward feasible zones. BLPSO is applied to solve four CHPED problems with constraints including power output balance, heat production balance, feasible operating area of cogeneration units, system transmission loss and prohibited operating zones. The experimental results show that BLPSO outperforms the state-of-the-art methods in terms of solution accuracy and stability. Therefore, BLPSO can be regarded as a promising alternative for the CHPED problem.

热电联产在能源利用中发挥着越来越重要的作用，热电联产经济调度（CHPED）成为电力系统经济运行中的重要优化任务。本文提出了基于生物地理学的学习粒子群算法（BLPSO），以解决各种约束条件下的CHPED问题。与PSO相比，BLPSO采用了基于生物地理学的学习模型，该模型具有改进的更新方程来克服过早的收敛性。此外，基于适应度信息的迁移用于构造学习样本并提高搜索准确性。还采用维修技术来处理系统约束并将解决方案导向可行区域。BLPSO被用于解决四个CHPED问题，其中包括功率输出平衡，热量产生平衡，热电联产机组的可行运行区域，系统传输损耗和禁止运行区域。实验结果表明，BLPSO在解决方案的准确性和稳定性方面均优于最新方法。因此，可以将BLPSO视为解决CHPED问题的有希望的替代方法。