This article provides the solution of the optimal power flow (OPF) problem of medium electrical systems through an artificial intelligence algorithm. The goal is to minimize the total cost of generated fuel and environmental pollution caused by power generation units based on fossils. System performance is also maintained by limiting generator real and reactive power outputs and power flow of transmission lines in acceptable limits. The power flow equations and load balance equation are considered as equality constraints. The performance analysis of this OPF problem using the Particle Swarm Optimization technique is carried out by checking various combinations of values of the associated parameters. The biobjective problem of generation cost and emission dispatch is solved via weighted sum method for different combinations of weights and a multi-objective problem of minimizing power generation cost and flue gases (NOx, CO2, SO2), is solved by a new algorithm named as Multi-Objective PSO (MOPSO) technique, to find out optimal solution and optimal value of weights. Simulation results for the IEEE 30-bus network with 6 generators system show that by proposed method, an optimal solution can be given quickly.

This paper deals with application of evolutionary algorithm (EA) to solve optimal power flow problem in an efficient manner. In this paper a new approach using cuckoo search (CS) method is proposed for solving OPF problem by optimal setting of control variables. Cuckoo search method is a bio-inspired algorithm based on brooding behaviour of cuckoo birds. This algorithm can search for a global solution using multiple paths. Different objective functions as fuel cost minimization and power loss minimization has been considered for optimal active & reactive power dispatch respectively. The proposed method is implemented and evaluated on the IEEE 30-bus system. The simulation results of the proposed approach are compared to others those reported in the literature. The results demonstrate the potential of the proposed approach and show its effectiveness and robustness to solve the OPF problem.

This paper deals with application of meta-heuristic algorithms to resolve the problem of combined economic emission dispatch (CEED) with peak load management for a mediumsized power system in an efficient manner. The objective is to optimize the fuel cost of generation simultaneously minimizing the environmental pollution caused by fossil fuel based power generating units working at their peak limits. In this paper combined problem of minimizing fuel cost and emission of flue gases (NOx, CO2, SO2), is solved using Cuckoo search (CS) and Grasshopper optimization algorithm (GOA) via composite function of all four objectives with help of weight ratios and price penalty factors. Demand Side Management (DSM) measure is also applied at least expensive areas to manage the peak load condition at generating units. The problem is implemented on IEEE 30-bus system with 6 generator units. The simulation results of the CS algorithm for CEED with and without DSM have been compared with the results of GOA algorithm. The compound results obtained by CS algorithm for the problem of CEED with DSM validated its potential.

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This research paper gives solution for Economic Load Dispatch (ELD) problem with considering valve point effect. ELD is the oldest and most important problem of optimal power flow. Objective of the ELD problems is to find out the optimal combination of power outputs of generating units so as to cope up the load demand at minimum cost while satisfying all the equality and inequality constraints. Conventionally, the function of cost for each unit in ELD problems has been approximately represented by a quadratic equation and is solved using various conventional and artificial intelligent techniques of optimization. Unfortunately, high non-linearity is present in the input-output characteristics of generating units' due to presences of prohibited operating zones, valve point loading effects, and multi-fuel effects, etc. Thus, the practical ELD problem is formulated as optimization problem of a non-smooth function with equality and inequality constraints, which cannot be solved by the conventional optimization methods. The performance of Cuckoo Search method and PSO with some modifications is tested on a standard test bed system i.e. IEEE 30-bus 6-generators system.