Bio-inspired Computational Heuristics Integrated With Active-set Method Using Particle Swarm Optimization To Study Economics Load Dispatch Problems Involving Stochastic Wind Power

The economic load dispatch and the optimal reactive power dispatch problems are associated with the optimal power flow problem.The economic load dispatch is a process of distributing generation among the obtainable units to minimize the fuel cost as well as satisfying the inequality and equality constraints.Whereas,the optimal reactive power dispatch solution has taken as an important consideration in the electric power networks for reliable and secure operation.It is a non-linear and non-convex multi-model problem which have continuous and discrete variables.The associated solution comprises to solve these problems can help in improving the voltage profile,voltage stability as well as reduce the cost and line losses.However,the research is based on covering both fields related to the optimal power flow,where the different optimization techniques have been studies to solved these optimization issues.The details are summarized as follows:To study economic load dispatch problem considering valve point loading effect with stochastic wind power,the novel optimization technique of bio-inspired computational heuristic algorithm(BCHAs)hybrid with the active-set algorithm(ASA)is used.These BCHAs are developed through variants of genetic algorithms based on different set of routines for reproduction operators in order to make exploration and exploitation in the entire search space for finding the global optima,while the ASA is used for rapid local refinements of the results.The designed schemes are estimated on different load dispatch systems consist of combination of thermal generating units and wind power plants with and without valve point loading effects.The accuracy,convergence,robustness and complexity of the proposed schemes has been examined through comparative studies based on sufficient large number of independent trails and their statistical observations in terms of different performance indices.In addition,to solve the optimal reactive dispatch problem the two novel optimization algorithms have been proposed and develop in the research such as grey wolf algorithm(GWO)and Shannon entropy based fractional particle swarm optimization gravitational search algorithm(FPSOGSA-Entropy).To alleviate the drawback of PSOGSA the fractional and Shannon entropy-based techniques are implemented into the algorithm which enhanced memory effect,stability and robustness of the algorithm.The GWO optimizer is tested on two test cases of IEEE30 standards specially,for 13 and 19 variables in order to get three fitness objectives for instance;transmission line losses(Plosses,MW),voltage deviation(VD)and voltage stability index(VSI)as well as cost of energy in($).During computing all fitness objectives,the minimum fitness values are possibly achieved by the finest settings of control variables.The simulation results are compared with other artificial intelligence methods in previous literature to ensure the superior performance of the GWO for ORPD problem.The consistency of GWO will further be validated through detailed statistical analysis including histogram illustrations,boxplots,empirical CDF plot,probability plot and plot of minimum fitness during each independent trial.The novel design of FPSOGSA-Entropy is tested on two bus standards of IEEE 30 and 57 to solve the optimal reactive power dispatch problems in order to find the two objective functions;minimization of power line losses and voltage deviation.The superior performance of the proposed FPSOGSA-Entropy is further verified with the results of simple FPSOGSA for both single and multiple runs through comparative analysis study with state of art counterparts for each scenario of optimal reactive power dispatch problems.