The dynamic economic emission dispatch of the combined heat and power system integrated with a wind farm and a photovoltaic plant

Abstract

Two renewable energies are included in the combined heat and power (CHP) system to optimize its energy configuration, and they are wind power generation and photovoltaic power generation, respectively. Furthermore, a global nondominated sorting genetic algorithm II (GNSGA-II) is proposed to confront the combined heat and power dynamic economic emission dispatch (CHPDEED) with renewable energies. GNSGA-II produces offspring individuals by a crossover with negative exponential distribution, and it is able to carry out global search in the decision space. GNSGA-II also assigns an adaptive weight to original crowding distance to give consideration to both crowding degree and evenness of each individual in the objective space, which is beneficial for improving the evenness of the Pareto set. In addition, a constraint handling approach is proposed to satisfy all constraints, such as power generation limits, heat generation limits, capacity limits of the CHP units, power balances, heat balances, ramp rate limits and spinning reserve requirements. Seven multi-objective evolutionary algorithms (MOEAs) are used to solve the four CHPDEED scenarios with or without renewable energies, and GNSGA-II outperforms the other six MOEAs. It does not only obtain larger hypervolumes and coverage rates, but also obtain relatively small spacings. For the four compromise solutions of GNSGA-II, the generation costs of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 0.51%, 0.34% and 4.1% higher than that of Scenario 1. In the meantime, the pollutant emissions of Scenario 2, Scenario 3 and Scenario 4 are, respectively, 54.78%, 19.05% and 71.45% lower than that of Scenario 1.