Volt/Var control in a distribution system by a fuzzy optimization approach

Short-Term Load Forecasting Based on Wavelet Transform and Least Squares Support Vector Machine Optimized by Improved Cuckoo Search

“…Most load forecasting theories are based on time series analysis and auto-regression models, including the vector auto-regression model (VAR) [2,3], the autoregressive moving average model (ARMA) [4][5][6], and so on. Time series smoothness prediction methods are criticized by researchers for their weakness of non-linear fitting capability.…”

NSGA-II with local search for a multi-objective reactive power compensation problem

“…Two objective functions have been considered associated both with the minimization of the system resistive losses (18) and the capacitor deployment cost (19):…”

“…9 corresponds to a physical solution, with power losses and cost computed through expressions (18) and (19), and a compensation profile associated with capacitors installed along the network. The objective function values of some selected representative solutions are presented in Table 3, and the physical characterization of three solutions are displayed in Tables 4-6.…”

“…More recently, heuristics [11] and mainly meta-heuristics, such as Simulated Annealing [12][13][14], Ant Colony [15], Particle Swarm Optimization [16], Plant Growth Simulation Algorithm [17] and Tabu Search [18], Fuzzy based techniques [19] and Genetic Algorithms [20], have been used to deal with the problem. Because of the characteristics of the VAR planning problem, which is a combinatorial non-linear multi-objective mixed integer problem, metaheuristics can cope with model complexity and tractability, reduce the exhaustive search in large spaces and lead to (near) Pareto optimal solutions.…”

A Stochastic Programming to Volt/VAR/Total Harmonic Distortion Control in Distribution Networks Including Wind Turbines

“…In these works, the OLTC tap position planning and shunt capacitor on/off switching states have been done based on an optimal time-interval division for the forecasted daily load to decrease energy losses and improve power quality; however, there are some distribution networks uncertainties, such as load demand (LD) and renewable energy electricity generation. In this regard, for the second category, stochastic volt/VAR control with uncertain values for some random variables regardless of harmonic consideration were discussed in some works [20][21][22]. A probabilistic analysis based on a 2m-point estimated method has been employed to solve the daily volt/VAR control problem in distribution systems with uncertainty in LD and electrical power generation [20,21].…”