@inproceedings{YokoyamaTakeuchiShinanoetal., author = {Yokoyama, Ryohei and Takeuchi, Kotaro and Shinano, Yuji and Wakui, Tetsuya}, title = {Effect of Model Reduction by Time Aggregation in Multiobjective Optimal Design of Energy Supply Systems by a Hierarchical MILP Method}, series = {Conference}, booktitle = {Conference}, pages = {627 -- 639}, 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 effi- ciently. As one of the strategies to enhance the computation efficiency furthermore, a method of reducing model by time aggregation has been proposed to search design candidates accurately and efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method and model reduction by time aggregation are applied to the multiobjective optimal design. In applying the model reduc- tion, the methods of clustering periods by the order of time series, based on an operational strategy, and by the k-medoids method are applied. As a case study, the multiobjective optimal design of a gas turbine cogeneration system with a practical configuration is investigated by adopting the annual total cost and pri- mary energy consumption as the objective functions to be minimized simultaneously, 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. It also turns out that the model reduction only by the k- medoids method is effective very limitedly when importance is given to minimizing the second objective function.}, language = {en} } @article{YokoyamaTakeuchiShinanoetal., author = {Yokoyama, Ryohei and Takeuchi, Kotaro and Shinano, Yuji and Wakui, Tetsuya}, title = {Effect of model reduction by time aggregation in multiobjective optimal design of energy supply systems by a hierarchical MILP method}, series = {Energy}, volume = {228}, journal = {Energy}, doi = {https://doi.org/10.1016/j.energy.2021.120505}, 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.}, language = {en} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {Evaluation of Design Alternatives for a Gas Turbine Cogeneration Plant Based on Multiobjective K-Best Solutions}, series = {International Conference on Power Engineering-2019}, journal = {International Conference on Power Engineering-2019}, pages = {1112 -- 1117}, abstract = {To realize the best performance in energy supply, it is an important task to determine types, capacities, and numbers of energy conversion equipment appropriately. It is also necessary to take account of seasonal and hourly variations in energy demands and corresponding operational strategies of equipment. The mixed-integer linear programming (MILP) method has been utilized widely to derive the optimal solution for such a design problem. From the design viewpoint, it is important to generate not only the optimal solution but also suboptimal ones which follow the optimal one without omission, what are called K-best solutions. In this paper, an MILP method utilizing the hierarchical relationship between design and operation variables is applied along with some strategies to efficiently drive K-best solutions of a multiobjective optimal design problem of a gas turbine cogeneration system for district energy supply. The annual total cost and primary energy consumption are adopted as the objective functions to be minimized. A case study is conducted, and K-best solutions with an allowable increase in the value of the combined objective function are derived for each value of the weight for the annual total cost. Through the study, it is clarified how the weight affects the computation time, the number of the K-best solutions, and the design specifications and the values of the objective functions for the K-best solutions.}, language = {en} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {エネルギー供給システムの設計および運用の 階層的関係を考慮した最適化 (蓄エネルギー機器を有するシステムへの適用)}, series = {第40回エネルギー・資源学会研究発表会講演論文集}, journal = {第40回エネルギー・資源学会研究発表会講演論文集}, pages = {398 -- 403}, abstract = {Mixed-integer linear programming (MILP) methods have 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, some strategies have been proposed to enhance the computation efficiency furthermore. As one of the strategies, a method of reducing model by time aggregation has been proposed to search design candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method with the strategies has been extendedly applied to the optimal design of energy supply systems with storage units. Especially, the method of re- ducing model is extended by aggregating representative days and sampling times differently in consideration of the characteristics of storage units. A case study is conducted on the optimal design of a gas turbine cogeneration system with a thermal storage unit for district energy supply. Through the study, it turns out the hierarchical MILP method is effective to derive the optimal solution as compared with a conventional method. It also turns out that the model reduction with the special time aggregation is effective to shorten the computation time as compared with that without time aggregation in case that the number of candidates for equipment capacities is relatively small.}, language = {ja} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {An effective approach for deriving and evaluating approximate optimal design solutions of energy supply systems by time series aggregation}, series = {Frontiers in Energy Research}, volume = {11}, journal = {Frontiers in Energy Research}, doi = {10.3389/fenrg.2023.1128681}, pages = {1 -- 14}, abstract = {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.}, language = {en} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {時間集約によるエネルギー供給システムの 近似最適設計解の導出および評価 (階層的最適化を援用した設計解の評価)}, series = {エネルギー・資源学会第38回エネルギーシステム・経済・環境コンファレンス講演論文集}, journal = {エネルギー・資源学会第38回エネルギーシステム・経済・環境コンファレンス講演論文集}, pages = {468 -- 473}, abstract = {For the purpose of attaining the highest performance of energy supply systems, it is important to design the systems optimally in consideration of their operational strategies for seasonal and hourly variations in energy demands. An ap- proach to solve such an optimal design problem with a large number of periods efficiently is to derive an approximate optimal design solution by aggregating periods with a clustering method. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. The purpose of this paper is to provide a time aggregation method for deriving aprroximate optimal design solutions and evaluting their values of the objective function. Especially, a method of evaluating design solutions is presented here using both methods of evaluating the robustness under uncertain energy demands and solving optimal design problems by a hierarchical approach. A case study is conducted for a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to evaluate effective lower bounds for the optimal value of the objective function as compared with those obtained by a conventional approach.}, language = {ja} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {時間集約によるエネルギー供給システムの 近似最適設計解の導出および評価 (階層的最適化を援用した近似最適設計)}, series = {第41回エネルギー・資源学会研究発表会講演論文集}, journal = {第41回エネルギー・資源学会研究発表会講演論文集}, pages = {144 -- 148}, abstract = {For the purpose of attaining the highest performance of energy supply systems, it is important to design the systems optimally in consideration of their operational strategies for seasonal and hourly variations in energy demands. An ap- proach to efficiently solve such an optimal design problem with a large number of periods for variations in energy de- mands is to derive an approximate optimal design solution by aggregating periods with a clustering method. However, such an approach does not provide any information on the accuracy for the optimal value of the objective function. The purpose of this paper is to propose a time aggregation approach for deriving suitable aprroximate optimal design solutions and evaluting their values of the objective function accurately. This time aggregation approach is realized by combining a robust optimal design method under uncertain energy demands and a hierarchical approach for solving large scale optimal design problems. A case study is conducted for a cogeneration system with a practical configuration, and it turns out that the proposed approach enables one to evaluate effective upper and lower bounds for the optimal value of the objective function as compared with those obtained by a conventional approach.}, language = {ja} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {Day and time aggregations for optimal design of energy supply systems with storage units by a hierarchical MILP method}, series = {PROCEEDINGS OF ECOS 2022 - THE 35 TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS}, journal = {PROCEEDINGS OF ECOS 2022 - THE 35 TH INTERNATIONAL CONFERENCE ON EFFICIENCY, COST, OPTIMIZATION, SIMULATION AND ENVIRONMENTAL IMPACT OF ENERGY SYSTEMS}, pages = {1891 -- 1902}, abstract = {Mixed-integer linear programming (MILP) methods have 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, some strategies have been proposed to enhance the computation efficiency furthermore. As one of the strategies, a method of reducing model by time aggregation has been proposed to search design candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method with the strategies is extendedly applied to the optimal design of energy supply systems with storage units. Especially, the method of reducing model is extended by aggregating both representative days and sampling times separately in consideration of the characteristics of energy storage units. A case study is conducted on the optimal design of a cogeneration system with a thermal storage tank. Through the study, it turns out the hierarchical MILP method is effective to derive the optimal solutions in short computation times. It also turns out that the model reduction with day and time aggregations is effective to shorten the computation times furthermore when the number of candidates for equipment capacities is relatively small.}, language = {en} } @article{YokoyamaShinanoWakui, author = {Yokoyama, Ryohei and Shinano, Yuji and Wakui, Tetsuya}, title = {Day and time aggregations for optimal design of energy supply systems with storage units by a hierarchical MILP method}, series = {The 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2022)}, journal = {The 35th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems (ECOS 2022)}, pages = {1891 -- 1902}, abstract = {Mixed-integer linear programming (MILP) methods have 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, some strategies have been proposed to enhance the computation efficiency furthermore. As one of the strategies, a method of reducing model by time aggregation has been proposed to search design candidates efficiently in the relaxed optimal design problem at the upper level. In this paper, the hierarchical MILP method with the strategies is extendedly applied to the optimal design of energy supply systems with storage units. Especially, the method of reducing model is extended by aggregating both representative days and sampling times separately in consideration of the characteristics of energy storage units. A case study is conducted on the optimal design of a cogeneration system with a thermal storage tank. Through the study, it turns out the hierarchical MILP method is effective to derive the optimal solutions in short computation times. It also turns out that the model reduction with day and time aggregations is effec- tive to shorten the computation times furthermore when the number of candidates for equipment capacities is relatively small.}, language = {en} } @inproceedings{YokoyamaShinanoWakayamaaetal., author = {Yokoyama, Ryohei and Shinano, Yuji and Wakayamaa, Yuji and Wakui, Tetsuya}, title = {Optimal Design of a Gas Turbine Cogeneration Plant by a Hierarchical Optimization Method With Parallel Computing}, series = {ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition}, volume = {3}, booktitle = {ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition}, abstract = {To attain the highest performance of energy supply systems, it is necessary to rationally determine types, capacities, and numbers of equipment in consideration of their operational strategies corresponding to seasonal and hourly variations in energy demands. Mixed-integer linear programming (MILP) approaches have been applied widely to such optimal design problems. The authors have proposed a MILP method utilizing the hierarchical relationship between design and operation variables to solve the optimal design problems of energy supply systems efficiently. In addition, some strategies to enhance the computation efficiency have been adopted: bounding procedures at both the levels and ordering of the optimal operation problems at the lower level. In this paper, as an additional strategy to enhance the computation efficiency, parallel computing is adopted to solve multiple optimal operation problems in parallel at the lower level. In addition, the effectiveness of each and combinations of the strategies adopted previously and newly is investigated. This hierarchical optimization method is applied to an optimal design of a gas turbine cogeneration plant, and its validity and effectiveness are clarified through some case studies.}, language = {en} }