A hierarchical optimization approach to robust design of energy supply systems based on a mixed-integer linear model

“…A hierarchical MILP method has been proposed to solve the ordinary optimal design problems efficiently . In addition, this method has been applied to the robust optimal design (Yokoyama et al, 2021). In this paper, the method is applied to solve the three MILP problems with both the DVs and OVs.…”

Section: Application Of the Hierarchical Optimization Methodsmentioning

confidence: 99%

“…Since the three MILP problems are of different types, the original hierarchical MILP method is applied to each problem without and with its modification. For the details of the hierarchical MILP method and its application to the robust optimal design, refer to the work of and Yokoyama et al (2021).…”

Section: Application Of the Hierarchical Optimization Methodsmentioning

confidence: 99%

“…For systems with complex configurations, it may be difficult to apply the robust optimization method directly because it may needs long computation times. Thus, a hierarchical optimization method is applied to some optimization calculations necessary in the robust optimization method (Yokoyama et al, 2021). To show how the proposed approach is effective, a case study is carried out on the optimal design of a cogeneration system with a practical configuration using the robust optimization method combined with the hierarchical optimization method.…”

mentioning

confidence: 99%

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An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation

Yokoyama¹,

Shinano

Wakui³

2023

Front. Energy Res.

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It is important to design multi-energy supply systems optimally in consideration of their operations for variations in energy demands. An approach for efficiently solving such an optimal design problem with a large number of periods for variations in energy demands is to derive an approximate optimal design solution by time series aggregation. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. In this paper, an effective approach for time series aggregation is proposed to derive an approximate optimal design solution and evaluate a proper gap between the upper and lower bounds for the optimal value of the objective function based on a mixed-integer linear model. In accordance with aggregation, energy demands are relaxed to uncertain parameters and the problem for deriving an approximate optimal design solution and evaluating it is transformed to a three-level optimization problem, and it is solved by applying both the robust and hierarchical optimization methods. A case study is conducted on a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to derive much smaller gaps as compared with those obtained by a conventional approach.

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Multicriteria adjustable regret robust optimization for building energy supply design

Halser,

Finhold,

Leithäuser

et al. 2024

Energy Reports

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A hierarchical optimization approach to robust design of energy supply systems based on a mixed-integer linear model

Cited by 6 publications

References 27 publications

Optimal stochastic scheduling of plug-in electric vehicles as mobile energy storage systems for resilience enhancement of multi-agent multi-energy networked microgrids

Optimal stochastic scheduling of plug-in electric vehicles as mobile energy storage systems for resilience enhancement of multi-agent multi-energy networked microgrids

An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation

Multicriteria adjustable regret robust optimization for building energy supply design

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