Resumen
Energy demand is rising sharply due to the technological development and progress of modern times. Neverthless, traditional thermal power generation has several diadvantages including its low energy usage and emitting a lot of polluting gases, resulting in the energy depletion crisis and the increasingly serious greenhouse effect. In response to environmental issues and energy depletion, the Combined Cooling, Heating and Power system (CCHP) combined with the power-generation system of renewable energy, which this work studied, has the advantages of high energy usage and low environmental pollution compared with traditional thermal power generation, and has been gradually promoted in recent years. This system needs to cooperate with the instability of renewable energy and the dispatch of the energy-saving system; the optimization of the system has been researched recently for this purpose. This study took Xikou village, Lieyu township, Kinmen county, Taiwan as the experimental region to solve the optimization problem of CCHP combined with renewable energy and aimed to optimize the multi-objective system including minimizing the operation cost, minimizing the carbon emissions, and maximizing the energy utilization rate. This study converted the original multi-objective optimization problem into a single-objective optimization problem by using the Technique for Order Preference by Similarity to and Ideal Solution (TOPSIS) approach. In addition, a hybrid of the simplified swarm optimization (SSO) and differential evolution (DE) algorithm, called SSO-DE, was proposed in this research to solve the studied problem. SSO-DE is based on SSO as the core of the algorithm and is combined with DE as the local search strategy. The contributions and innovations of the manuscript are clarified as follows: 1. a larger scale of CCHP was studied; 2. the parallel connection of the mains, allowing the exchange of power with the main grid, was considered; 3. the TOPSIS was adopted in this study to convert the original multi-objective optimization problem into a single-objective optimization problem; and 4. the hybrid of the DE algorithm with the improved SSO algorithm was adopted to improve the efficiency of the solution. The proposed SSO-DE in this study has an excellent ability to solve the optimization problem of CCHP combined with renewable energy according to the Friedman test of experimental results obtained by the proposed SSO-DE compared with POS-DE, iSSO-DE, and ABC-DE. In addition, SSO-DE had the lowest running time compared with POS-DE, iSSO-DE, and ABC-DE in all experiments.