Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method

Ryohei Yokoyama, Kotaro Takeuchi, Yuji Shinano and Tetsuya Wakui

Energy, 2021, vol. 228, issue C

Abstract: The mixed-integer linear programming (MILP) method has been applied widely to optimal design of energy supply systems. A hierarchical MILP method has been proposed to solve such optimal design problems efficiently. In addition, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. The methods of clustering periods by the order of time series, by the k-medoids method, and based on an operational strategy are applied for the model reduction. As a case study, the multiobjective optimal design of a gas turbine cogeneration system is investigated by adopting the annual total cost and primary energy consumption as the objective functions, and the clustering methods are compared with one another in terms of the computation efficiency. It turns out that the model reduction by any clustering method is effective to enhance the computation efficiency when importance is given to minimizing the first objective function, but that the model reduction only by the k-medoids method is effective very limitedly when importance is given to minimizing the second objective function.

Keywords: Energy supply; Multiobjective optimal design; Mixed-integer linear programming; Hierarchical optimization; Time aggregation; Clustering (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:228:y:2021:i:c:s0360544221007544

DOI: 10.1016/j.energy.2021.120505

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