Multi-objective optimization of a hybrid distributed energy system using NSGA-II algorithm
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In this paper, a multi-objective optimization model is established for the investment plan and operation management of a hybrid distributed energy system. Considering both economic and environmental benefits, the overall annual cost and emissions of CO2 equivalents are selected as the objective functions to be minimized. In addition, relevant constraints are included to guarantee that the optimized system is reliable to satisfy the energy demands. To solve the optimization model, the non-dominated sorting generic algorithm II (NSGA-II) is employed to derive a set of non-dominated Pareto solutions. The diversity of Pareto solutions is conserved by a crowding distance operator, and the best compromised Pareto solution is determined based on the fuzzy set theory. As an illustrative example, a hotel building is selected for study to verify the effectiveness of the optimization model and the solving algorithm. The results obtained from the numerical study indicate that the NSGA-II results in more diversified Pareto solutions and the fuzzy set theory picks out a better combination of device capacities with reasonable operating strategies.
Keywordsmulti-objective optimization hybrid distributed energy system non-dominated sorting generic algorithm II fuzzy set theory Pareto optimal solution
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This work was supported in part by the National Natural Science Foundation of China (Grant No. 71804106), Shanghai Sailing Program (No. 17YF1406800), Shanghai Chenguang Program (No. 17CG57) and The Key Fund of Shanghai Science Technology Committee (No. 16020500900).
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