Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme. \ua9 2013 Alma Y. Alanis et al

Review of Optimization Techniques for Sizing Renewable Energy Systems

The growing evidence of the global warning phenomena and the rapid depletion of fossil fuels have drawn the world attention to the exploitation of renewable energy sources (RES). However standalone RES have been proven to be very expensive and unreliable in nature owing to the stochastic nature of the energy sources. Hybrid energy system is an excellent solution for electrification of areas where the grid extension is difficult and not economical. One of the main attribute of hybridising is to be able to optimally size each RES including storages with the aim of minimizing operation costs while efficiently and reliably responding to load demand. Hybrid RES emerges as a trend born out of the need to fully utilize and solve problems associated with the reliability of RES. This paper present a review of techniques used in recent optimal sizing of hybrid RES. It discusses several methodologies and criteria for optimization of hybrid RES. The recent trend in optimization in the field of hybrid RES shows that bio-inspired techniques may provide good optimization of system without extensive long weather data

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems