Ryohei YOKOYAMA’s research while affiliated with Osaka University and other places

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.

To reduce both the platform motion and dynamic loads of floating offshore wind turbine-generator systems, feedforward control for high wind speed regions is developed by combining with the wind speed previewed by a nacelle-mounted lidar. First, the wind speed preview using a nacelle-mounted lidar was simulated by considering the floating platform velocity and the temporal difference of laser casting. Feedforward control, in which the blade pitch is manipulated according to the preview wind speed so as to maintain the rated generator speed, is combined with gain-scheduling feedback control of the generator speed. This feedforward control is characterized by employing the first-order lag filter with the delay compensator for the preview wind speed. The effectiveness of the developed feedforward-feedback controller is analyzed through an aero-elastic-hydro-control coupled nonlinear dynamic simulation of a 5-MW floating offshore wind turbine-generator system under turbulent wind fields and irregular wave height variations. The feedforward-feedback controller provides the stabilization of the platform pitching motion and the reduction in the dynamic load variations at the blade root and drivetrain as well as the tower base in comparison to the conventional gain-scheduling feedback control of the generator speed. Moreover, the sensitivity of the settings of the first-order lag filter and the delay compensator in the wind speed preview is clarified for their optimal design.

To reduce both the platform motion and dynamic loads of floating offshore wind turbine-generator systems, feedforward control for high wind speed regions is developed by combining with the wind speed previewed by a nacelle-mounted lidar. First, the wind speed preview using a nacelle-mounted lidar was simulated by considering the floating platform velocity and the temporal difference of laser casting. Feedforward control, in which the blade pitch is manipulated according to the previewed wind speed so as to maintain the rated generator speed, is combined with gain-scheduling feedback control of the generator speed. This feedforward control is characterized by employing the first-order lag filter with the delay compensator for the previewed wind speed. The effectiveness of the developed feedforward-feedback controller is analyzed through an aero-elastic-hydro-control coupled nonlinear dynamic simulation of a 5-MW floating offshore wind turbine-generator system under turbulent wind fields and irregular wave height variations.

To efficiently solve long-term operational planning problems of energy storage and supply systems, a near-optimal solution method based on the shrinking and receding horizon approaches was developed. A lower bound of the original problem is first calculated by solving a simplified problem, in which the number of binary variables is reduced and the related constraints are simplified. Then, a short-term operational planning problem, with a shrunken planning horizon length, is solved by providing the simplified problem results as the terminal condition for stored energy. Within this problem, the original binary variables and constraints are considered. The short-term operational planning is updated using a receding horizon approach, in which the initial and terminal conditions are provided by the previous short-term operational planning results and simplified problem results, respectively. The solution, obtained by connecting the solutions in all the updating horizons, is regarded as a near-optimal solution of the original problem. The developed method was applied to solve a long-term operational planning problem for an energy storage and supply system, which includes a photovoltaic unit and a metal hydride tank with a water electrolyzer. The results showed that the developed method could find a better solution in a shorter computation time than the conventional methods.

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.