Social cognitive optimization with tent map for combined heat and power economic dispatch

TL;DR

This paper introduces a novel social cognitive optimization algorithm with tent map (TSCO) to efficiently solve the complex combined heat and power economic dispatch (CHPED) problem, demonstrating superior performance over existing methods.

Contribution

The work presents a new TSCO algorithm with adaptive constraint handling specifically designed for CHPED, improving convergence speed and solution quality.

Findings

TSCO outperforms existing algorithms in convergence speed.

TSCO achieves higher solution quality in CHPED cases.

Numerical results validate the effectiveness of TSCO.

Abstract

Combined heat and power economic dispatch (CHPED) problem is a sophisticated constrained nonlinear optimization problem in a heat and power production system for assigning heat and power production to minimize the production costs. To address this challenging problem, a novel social cognitive optimization algorithm with tent map (TSCO) is presented for solving the CHPED problem. To handle the equality constraints in heat and power balance constraints, adaptive constraints relaxing rule is adopted in constraint processing. The novelty of our work lies in the introduction of a new powerful TSCO algorithm to solve the CHPED issue. The effectiveness and superiority of the presented algorithm is validated by conducting 2 typical CHPED cases. The numerical results show that the proposed approach has better convergence speed and solution quality than all other existing state-of-the-art algorithms.