Portugal is seen worldwide as a case of success in the large-scale integration of renewables in its power system,especially for wind power.Consistent policies and sound management decisions are fundamental,but a susta...Portugal is seen worldwide as a case of success in the large-scale integration of renewables in its power system,especially for wind power.Consistent policies and sound management decisions are fundamental,but a sustainable process is not possible without the development of endogenous knowledge.This paper summarizes a set of models,both applied by the industry and representing actual technologic advancement,denoting the context of research and innovation in the country that helps to explain such success.Novelties arise in reliability assessment for systems with renewables,active and reactive power control,integration of wind farms,storage,electric vehicle integration,wind and solar power forecasting and distribution operation and state estimation taking advantage of smart grid structures.In all cases,one relevant trait is evident:the pervasive use of computational intelligence tools.展开更多
The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper. An algorithm is designed which makes all particles originally endowed with a certain level energy, what here...The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper. An algorithm is designed which makes all particles originally endowed with a certain level energy, what here we define as EPSO (Energy Strategy PSO). During the iterative process of PSO algorithm, the Inertia Weight is updated according to the calculation of the particle's energy. The portion ratio of the current residual energy to the initial endowed energy is used as the parameter Inertia Weight which aims to update the particles' velocity efficiently. By the simulation in a graph theoritical and a functional optimization problem respectively, it could be easily found that the rate of convergence in EPSO is obviously increased.展开更多
Recently, there is a movement that aims to realize a distributed energy system in Japan. The PV (photovoltaic) generation is especially expected, and it is anticipated that interconnection number of PVs will increas...Recently, there is a movement that aims to realize a distributed energy system in Japan. The PV (photovoltaic) generation is especially expected, and it is anticipated that interconnection number of PVs will increase more and more ha the future. However, an amount of insolation is easily affected by the weather, the output of PV and the supply of electricity becomes unstable. And it causes various problems in a distribution system. Therefore, it is important to forecast the amount of insolation. In previous study, MLPNN (multilayer perceptron neural network) is a general method to forecast the amount of insolation. However, it is in danger of falling into a local solution by only MLPNN. In this study, the authors propose a forecasting method of amount of insolation using MLPNN and EPSO (evolutionary particle swarm optimization). The authors use EPSO in addition to MLPNN to solve the problem. The authors also propose a forecasting method of amount of insolation using other regions weather data for the accuracy improvement.展开更多
基金produced under conditions provided by funding by the ERDF–European Regional Development Fund through the Operational Programme for Competitiveness and Internationalization-COMPETE 2020 within project POCI-01-0145-FEDER-006961by national funds through the FCT-Portuguese Foundation for Science and Technology,as part of project UID/EEA/50014/2013.
文摘Portugal is seen worldwide as a case of success in the large-scale integration of renewables in its power system,especially for wind power.Consistent policies and sound management decisions are fundamental,but a sustainable process is not possible without the development of endogenous knowledge.This paper summarizes a set of models,both applied by the industry and representing actual technologic advancement,denoting the context of research and innovation in the country that helps to explain such success.Novelties arise in reliability assessment for systems with renewables,active and reactive power control,integration of wind farms,storage,electric vehicle integration,wind and solar power forecasting and distribution operation and state estimation taking advantage of smart grid structures.In all cases,one relevant trait is evident:the pervasive use of computational intelligence tools.
基金National Natural Science Foundation of China (No.50408034)
文摘The issue of optimizing the dynamic parameters in Particle Swarm Optimizer (PSO) is addressed in this paper. An algorithm is designed which makes all particles originally endowed with a certain level energy, what here we define as EPSO (Energy Strategy PSO). During the iterative process of PSO algorithm, the Inertia Weight is updated according to the calculation of the particle's energy. The portion ratio of the current residual energy to the initial endowed energy is used as the parameter Inertia Weight which aims to update the particles' velocity efficiently. By the simulation in a graph theoritical and a functional optimization problem respectively, it could be easily found that the rate of convergence in EPSO is obviously increased.
文摘Recently, there is a movement that aims to realize a distributed energy system in Japan. The PV (photovoltaic) generation is especially expected, and it is anticipated that interconnection number of PVs will increase more and more ha the future. However, an amount of insolation is easily affected by the weather, the output of PV and the supply of electricity becomes unstable. And it causes various problems in a distribution system. Therefore, it is important to forecast the amount of insolation. In previous study, MLPNN (multilayer perceptron neural network) is a general method to forecast the amount of insolation. However, it is in danger of falling into a local solution by only MLPNN. In this study, the authors propose a forecasting method of amount of insolation using MLPNN and EPSO (evolutionary particle swarm optimization). The authors use EPSO in addition to MLPNN to solve the problem. The authors also propose a forecasting method of amount of insolation using other regions weather data for the accuracy improvement.