Yoshikazu Fukuyama

Parallel particle swarm optimization for reactive power and voltage control investigating dependability

This paper presents a parallel particle swarm optimization (PSO) technique for reactive power and voltage control (Volt/Var Control: VVC) investigating dependability. High penetration of renewable energies and deregulation of power systems force power flow to change suddenly and operators in control centers have to control voltage in wider power systems. Therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. This paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC with IEEE 14, 30, 57, and 118 bus systems.

Total optimization of smart community by Differential Evolution considering reduction of search space

This paper proposes a total optimization method of a smart community (SC) by Differential Evolution (DE) considering reduction of search space. Japanese experts has developed various sectors of SC models such as an electric utility model, an industry model, and a building model. This paper utilizes the models and tries to optimize whole of a SC in order to minimize total energy costs and shift electric power load peak of the SC. The simulation results by the proposed method are compared with the results by a Particle Swarm Optimization (PSO) based method.

Reactive Tabu Search for job-shop scheduling problems

A Job-shop scheduling problem (JSP) can be formulated as a combinatorial optimization problem. Up to now, the problem has been solved by various metaheuristic methods such as Genetic Algorithm (GA) and Tabu Search (TS). Recently, practical methods for JSP considering various practical constraints have been developed. However, although one of the challenges for practical production environment is considering various kinds of small quantity production, methods for JSPs considering the situation have not been developed yet. This paper presents a reactive tabu search for JSP toward handling various kinds of small quantity production.

Reactive Tabu Search for Job-shop scheduling problems considering peak shift of electric power energy consumption

The Job-shop scheduling problem (JSP) can be formulated as a combinatorial optimization problem. Up to now, the problem has been solved by various evolutionary computation techniques such as Genetic Algorithm (GA) and Tabu Search (TS). Recently, practical methods for the JSP considering various practical constraints have been developed. However, methods for the JSP considering peak shift of electric power energy consumption have not been developed yet. This paper presents an application of Reactive Tabu Search for the JSP considering the peak shift of electric power energy consumption.

Optimal operational planning of energy plants by differential evolutionary particle swarm optimization

This paper presents optimal operation planning of energy plants by differential evolutionary particle swarm optimization (DEEPSO). The problem can be formulated as a mixed integer nonlinear optimization problem and various metaheuristics such as particle swarm optimization (PSO) and differential evolution (DE) have been applied. However, solution quality can be improved and this paper applies recently developed DEEPSO for optimal operational planning of energy plants in order to improve solution quality. The average solutions by the proposed method is about 12% lower than those by PSO.

Parallel multi-population differential evolutionary particle swarm optimization for voltage and reactive power control in electric power systems

This paper proposes parallel multi-population differential evolutionary particle swarm optimization (DEEPSO) for voltage and reactive power control in electric power systems. The problem can be formulated as a mixed integer nonlinear optimization problem and various evolutionary computation techniques such as Differential Evolutionary particle swarm optimization (DEEPSO) and differential evolution (DE) have been applied so far. However, since there is still room for improvement on solution quality, this paper applies parallel multi-population DEEPSO in order to improve solution quality. The proposed method is applied to IEEE 118 bus system. The simulation results show the appropriate number of sub-populations and migration intervals, and verify improvement of solution quality compared with the conventional parallel DEEPSO based method.

Estimation of missing data of showcase using autoencoder

This paper proposes an estimation method of missing data of showcase using autoencoder (AE). When various accidents such as frost formation in the refrigerators and refrigerant leakage happen, vendors of showcases have to treat the accidents as quickly as possible. Therefore, various measured data of showcases such as temperatures or pressures of some portions of showcases have to be gathered correctly and symptoms of the accidents should be estimated in advance. However, in rare cases, there is a possibility to miss to gather the data due to various on-site conditions. The proposed method is applied to missing data of actual showcase and the effectiveness of the proposed method is verified.

Dependability verification of parallel differential evolutionary particle swarm optimization based voltage and reactive power control

This paper evaluates dependability of parallel differential evolutionary particle swarm optimization (DEEPSO) based voltage and reactive power control (Volt/Var Control: VVC). Considering large penetration of renewable energies and deregulated environment of power systems, VVC is required to realize faster computation to larger-scale problems and one of the solutions for the problem is applications of parallel and distributed computing. Since power system is one of the infrastructures of social community, not only fast computation, but also sustainable control (dependability) is strongly required for VVC. The simulation results with IEEE 118 bus systems indicate that parallel DEEPSO is superior to parallel PSO and parallel differential evolution (DE) from the dependability point of view.

Parallel particle swarm optimization for reactive power and voltage control verifying dependability

This paper presents a parallel particle swarm optimization (PSO) for reactive power and voltage control (Volt/Var Control: VVC) in electric power systems verifying dependability of the control. Considering high penetration of renewable energies and deregulation of power systems, electric power flows can change suddenly and operators in control centers have to control voltage in wider power systems. Therefore, VVC is required to shorten the control interval and handle larger-scale power systems. One of the solutions for this problem is applications of parallel and distributed computing. Since electric power systems is one of the infrastructures of social community, sustainable voltage control is crucial and dependability is strongly required for VVC. This paper investigates not only fast computation by parallel computation but also dependability of parallel PSO for VVC. The results are meaningful for practical parallel computation by PSO in actual VVC operations.

Total Optimization of Smart City by Global-Best Modified Brain Storm Optimization

This paper proposes a total optimization method of a smart city (SC) by Global-best Modified Brain Storm Optimization (GMBSO). Almost all countries have a goal to reduce CO2 emission as the countermeasures of global warming. In addition, these countries have conducted SC demonstration projects. The problem of the paper considers CO2 emission, energy cost, and electric power load at peak load hours. In order to solve the problem, Differential Evolutionary Particle Swarm Optimization (DEEPSO), Modified Brain Storm Optimization (MBSO), and Global-best Brain Storm Optimization (GBSO) have been applied to the problem. This paper proposes a novel evolutionary computation method, called Global-best Modified Brain Storm Optimization (GMBSO), which is a combined method of GBSO and MBSO in order to obtain better results. The total optimization of SC is solved by the proposed GMBSO based method. The results by the proposed GMBSO based method is compared with those by conventional DEEPSO, BSO, only GBSO, and only MBSO