A modified genetic algorithm for combined heat and power economic dispatch

Abstract Combined heat and power economic dispatch (CHPED) is an important issue in the energy field, since it has great potential in improving the utilization efficiency of power and heat energies. This paper proposes a modified genetic algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. MGA attains the smallest mean and standard deviation related to 50 cost values for every instance, thus it has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.