Energy hub optimization with heuristic optimization algorithms

In this thesis study, an energy hub (EH) model integrated with wind and solar energy was proposed. Moreover, a hibrit optimization algorithm was developed for the optimization of proposed energy hub model. In this context, the thesis study studied on two issues. Firstly, a model was proposed where the energy sources at the input of the energy hub were electricity, wind energy, solar energy, natural gas and heat, while electricity, heat, cooling and compressed air were produced at the output. Based on this model, 69 EH structures were created. By using these EH structures, four different scale test systems were created. In the study in which EH optimization was handled, three objective functions were used as minimizing the total energy hub cost, minimizing the total energy hub losses, and minimizing both the energy hub cost and losses. Contrary to the problems in the literature in which the total energy hub cost was minimized, the cost model of wind and solar energy was calculated as direct cost, penalty cost and reserve cost. Thus, a new problem has been proposed to the literature. The second topic addressed in the thesis study was the design of the meta-heuristic search (MHS) algorithm. Accordingly, it was aimed to improve the search performance of the LSHADE with Semi-Parameter Adaptation Hybrid with CMA-ES (LSHADE-SPACMA) algorithm in order to eliminate the premature convergence problem. For this reason, the mutation strategy used in the algorithm was redesigned using the Fitness-Distance Balance (FDB) method and the proposed algorithm was named FDB-LSHADESPACMA. A comprehensive experimental study was carried out using the CEC2017 and CEC2020 benchmark problems to test and validate the performance of the FDB-LSHADESPACMA algorithm. In this direction, 14 up-to-date meta-heuristic search (MHS) algorithm were selected. Energy hub optimization problems given in this study were solved with the proposed algorithm. Accordingly, twelve case studies were created using four EH test systems and three objective functions. All results obtained from FDB-LSHADESPACMA algorithm and competitor MSA algorithms were evaluated using Friedman Test and Wilcoxon signed-rank test. According to results, it was shown that the FDB-LSHADESPACMA algorithm achieved better results and showed superior performance than the competing algorithms.