A Surrogate-Based Optimization Method for Solving Economic Emission Dispatch Problems with Green Certificate Trading and Wind Power

To reduce the impact of greenhouse effect, the deployment and utilization of renewable energy sources such as wind power has become an inevitable trend. Therefore, the decision of economic emission dispatch (EED) problems is particularly important. In this paper, to incentivize renewable energy generation, the green certificate trading mechanism is introduced to solve from the economic level. However, as the dimensions of the EED problems are increased, the current methods cannot make proper scheduling decisions in a short time. Therefore, the EED problems are categorized as computationally expensive EED problems. To solve the above problems, a surrogate-based multi-objective optimization method is proposed. On one hand, the artificial neural network (ANN) surrogate models are proposed to replace the traditional objective function, which greatly reduces the time to obtain feasible decisions. On the other hand, a modified multi-objective gray wolf optimizer (MOGWO) is proposed to execute EED optimization accurately and quickly. This algorithm improves the search ability and convergence of the original MOGWO algorithm through improving the position update strategy and introducing the difference algorithm. The effectiveness of the surrogate-based MOGWO is testified through simulations of benchmark functions and computationally expensive EED optimization problems within the actual Taipower 40-unit test system.