Optimal capacity configuration of CCHP system with improved operation strategies using improved multi-objective multi-universe algorithm

In this study, a multi-objective capacity optimization model for a combined cooling, heating, and power (CCHP) system is established, to determine the optimal configuration scheme for various strategies. We develop improved-following-the-thermal-load (IFTL) and improved-following-the-electric-load (IFEL) strategies to properly manage the energy flow. Under the IFTL and IFEL strategies, the redundant energy generated in the operation of CCHP system is fully utilized, which effectively reduces the fuel consumption and improves the energy efficiency of the system. Furthermore, an improved multi-objective multi-verse optimization (IMOMVO) algorithm—which can effectively optimize the configuration of the CCHP system under different strategies—is proposed; it incorporates an opposition-based learning mechanism, dominance rank, population-guidance mechanism, and seagull attacking operator into the conventional MOMVO algorithm. The optimal solution of each strategy under energy, economy, and environment objective functions can be obtained using the Technique for Order of Preference by Similarity to Ideal Solution. A large hotel equipped with CCHP systems operating under IFTL, IFEL, following-the-thermal-load, following-the-electric-load, and following-the-hybrid-electric–thermal-load strategies are examined. The results demonstrate that the Pareto solutions obtained using proposed IMOMVO algorithm are evenly distributed and can provide a set of representative solutions; furthermore, the system configuration under the proposed IFEL strategy can achieve energy efficiency, carbon-dioxide-emission-reductions, primary energy saving, and annual-cost-saving ratios of 67.09%, 47.91%, 31.65%, and 14.94%, respectively; therefore, it outperforms other strategies.