Social network search algorithm for combined heat and power economic dispatch

Combined heat and power economic dispatch (CHPED) is a critical, non-linear, non-convex, non-differential, and constrained optimization problem in power systems that aims to minimize the total fuel cost and provide precise power and heat demands. In this paper, a novel optimization algorithm, namely social network search algorithm (SNS), is applied to solve the challenging CHPED problem. SNS mimics social network users' behavior to increase popularity by modeling the different decision moods in expressing their views. The performance of SNS is investigated on five test systems considering valve-point loading effects, prohibited operating zones, and transmission power losses. The simulation results of SNS are compared with those of driving training-based optimization (DTBO), egret swarm optimization algorithm (ESOA), manta ray foraging optimization algorithm (MRFO), particle swarm algorithm (PSO), crow search algorithm (CSA), and various reported algorithms. The comparison results show that the SNS algorithm outperforms the other algorithms in terms of solution quality and provides better than or as well as the best-reported solution, resulting in enormous annual cost savings and significant economic benefits for the power systems while satisfying consumers demands of heat and power, which confirms the effectiveness of the proposed SNS algorithm in solving different CHPED problems.

Automated summary

In this paper, the social network search algorithm (SNS) is applied to solve the combined heat and power economic dispatch (CHPED) problem. The performance of SNS is compared to other optimization algorithms, and it is proven to be effective in solving CHPED problems.