An efficient mixed-variable generation operator for integrated energy system configuration optimization

Abstract

Energy consumption has skyrocketed as society has progressed, resulting in energy crises and environmental issues. New energy technologies are being developed to address these challenges, and technologies such as the cogeneration of electricity, heating, and cooling are being used to improve energy utilization. The configuration of an energy system has a critical impact on the economics of the system, its ability to supply energy, etc. This study establishes a multiobjective mixed-variable configuration optimization model for a comprehensive combined cooling, heating, and power energy system. The model considers equipment types (categorical variables), equipment quantities (integer variables), and critical parameters (real variables) as optimization variables, where economic performance, the proportion of renewable energy, supply shortage, and dependence on the external power grid are considered unoptimized objectives. This is a multiobjective and mixed-variable problem, and the combination of the two properties poses an excellent challenge for evolutionary algorithms in optimizing the model. Therefore, we propose an efficient generating operator based on a fully connected weighted neural network, called the FCWN operator. This operator overcomes the challenge of the discrete variable space and broken neighborhood relationships in mixed-variable problems. During the algorithm search process, search history information is used to update the fully connected weight network for distribution estimation of the variable space. Offspring are generated based on the fully connected network to improve the algorithm’s search efficiency. In the experimental section, we construct a model with 20 variables and conduct simulation-based configuration optimization for scenarios with 3, 5, and 8 available equipment types. The final obtained Pareto frontier solution set is evaluated using the hypervolume metric, a widely used multiobjective evaluation metric. The Wilcoxon rank sum test on the experimental results shows that the proposed algorithm has better results than other state-of-the-art algorithms at a 95% confidence level.