Economic Load Dispatch in Power System by Hybrid Swarm Intelligence

Abstract

Conserving energy and efficient power systems are very important for us to reduce pollution levels and also reduces wasted fuel resources which are already depleting on the planet. In power operation system, the potential of energy conservation and also much less emission of greenhouse gas because of the wise usage of cleaner non-renewable fuels burned in combined heat and power (CHP) models like the natural gas which provide them benefit from the usual electric power systems. Mixed generator systems have been widely employed by the industry. The industry which requires both power and heat can supply the demands with cogeneration of heat-power systems. Cogeneration (CHP) systems could be constructed in cities and used in the form of distributed electricity sources. To get the optimal usage of CHP devices, economic load dispatch (ED) should be requested more for the process of energy conservation. Economic load dispatch plays a vital role and a large number of different approaches and methods have been used in solving such kind of problems. The methods like lambda-iteration and Gradient are used for finding out the optimized solution of nonlinear problem. The purpose of this thesis is to utilize the algorithmic optimization approaches like particle swarm optimization (PSO), genetic algorithm (GA) and PSO-GA. In this work, the method of PSO-GA Optimization is used to find out the minimized cost at four of the generating units of heat and power. The base of the work is already published where in the loss coefficients are also presented with max-min cost function and power limit. This work is implemented in the MATLAB simulation environment. The work starts by initializing the load/power and then generators load power flow. The generators are allocated to initialize the cost and this cost is optimized by PSO with GA. At the end the experimental results of GA ,PSO and PSO-GA algorithm is equated with each other and it seems better convergence is achieved by PSO-GA Algorithm.

Quantum behaved artificial bee colony based conventional controller for optimum dispatch

“…where, 𝑃 𝑖 𝑡 is local attractor of particle 𝑖 = iteration number, mbest t denotes magnificent mean positions in the i th iteration and 𝛼 is the contraction expansion coefficient for regulating the concurrence rate of the QPSO adopted at par with Tahyudin and Nambo [24], Giri et al [25], Sui et al [26]. Identities 𝑢 𝑖 𝑡 and rand v are functions of uniform probability distribution in the range [0, 1].…”

Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview

“…In a study carried out in 2019, the performance of the GA, PSO, and PSO-GA algorithms was compared for optimizing the economic dispatch of a CHP plant. It was shown that the PSO-GA algorithm obtained the best results [117]. Additionally, in an optimal scheduling problem, an evolutionary algorithm, the DE, and a swarm-based algorithm, the bird mating optimization (BMO), were combined by Bornapour et al [118].…”