Dual objective multiconstraint swarm optimization based advanced economic load dispatch

In electric power system, the vital topic to be mooted is economic load dispatch (ELD). It is a non-linear problem with some unavoidable constraints such as valve point loading and ramp rate constraint. For solving ELD problem distint methods were devised and tried for different electric supply systems yielding slow convergence rates. To achieve fast convergence, dual objective multi constraint swarm optimization based advanced economic load dispatch (DOMSOBAELD) algorithm is proposed making use of simulated values of real power outages of a thermal power plant as initial estimates for PSO technique embedded in it and used for optimizing economic dispatch problem in this article. DOMSOBAELD method was developed in the form of amalgamating fluids. Presence of power line losses, multiple valves in steam turbines, droop constraints and inhibited zones were utilized to optimize the ELD problem as genuinely approximate as possible. The results obtained from DOSOBAELD are compared with particle swarm optimization (PSO), PSOIW and differential particle swarm optimization (DPSO) techniques. It is quite conspicuous that DOMSOBAELD yielded minimum cost values with most favourable values of real unit outputs. Thus the proposed method proves to be advantageous over other heuristic methods and yields best solution for ELD by selecting incremental fuel cost as the decision variable and cost function as fitness function

Solution To Constrained Economic Load Dispatch

The power system in modern world has grown in complexity of interconnection and power demands. The focus has now shifted to enhancing performance, increasing customer focus, lowering cost, reliability and clean power. In this changed modern word where we face scarcity of energy, with an ever increasing cost of power generation, environmental concerns necessitate some sort of optimum economic dispatch. Particle Swarm Optimization (PSO) is used to allot active power among the generating stations which satisfy the system constraints and thereby minimizes the cost of power generated. The feasibility of this method is analysed for its accuracy and its rate of convergence. The economic load dispatch problem is carried for three and six unit systems using PSO and conventional lagrange method for both cases i.e. neglecting and including transmission line losses. The results of PSO method was compared with that of conventional method and was found to be superior. The convergence characteristics in PSO method were also found both for loss included and loss neglected case. The conventional optimization methods are unable to solve many complex problems due to convergence of local optimum solution. Particle Swarm Optimization (PSO) since its initiation during the last 15 years, has been a great solution to the practical constrained economic load dispatch (ELD) problems. The optimization technique is evolving constantly to provide better and fast results

Solving optimal generation dispatch problem in power networks through pso and lambda iteration techniques

Efficient solution to the problem of economic dispatch of network generators has been a growing concern to power system utilities in recent times. This is aimed at determining the optimal allocation of the total network demand among the available generating units such that the total cost of generation is reduced while maintaining an acceptable generation output subject to specified system constraints. This paper, therefore, attempts to resolve this issue from two main perspectives; Lambda Iterative-based approach and Particle Swarm Optimization (PSO) technique. The theoretical backgrounds as well as the mathematical formulations for the two approaches are presented. The standard IEEE 14-Bus, IEEE 30-Bus and the Indian 62-Bus networks are used as case studies to present illustrative examples for the approach. The simulation results obtained using the two approaches are presented and compared. The comparisons of the results obtained show that the two approaches are suitable for providing efficient solutions to economic dispatch problems in large power networks.Keywords: economic dispatch; network demands; lamda iterative; particle swarm optimization;generation cos

Comparisional Investigation of Load Dispatch Solutions with TLBO

This paper discusses economic load dispatch Problem is modeled with non-convex functions. These are problem are not solvable using a convex optimization techniques. So there is a need for using a heuristic method. Among such methods Teaching and Learning Based Optimization (TLBO) is a recently known algorithm and showed promising results. This paper utilized this algorithm to provide load dispatch solutions. Comparisons of this solution with other standard algorithms like Particle Swarm Optimization (PSO), Differential Evolution (DE) and Harmony Search Algorithm (HSA). This proposed algorithm is applied to solve the load dispatch problem for 6 unit and 10 unit test systems along with the other algorithms. This comparisional investigation explored various merits of TLBO with respect to PSO, DE, and HAS in the field economic load dispatch

Solution of Dynamic Economic Load Dispatch (DELD) Problem With Valve Point Loading effects and Ramp rate limits Using PSO

Dynamic economic load dispatch (DELD) is one of the major operational decisions in power system operation and control. It is a Dynamic problem due to dynamic nature of Power system and the large variation of load demand. This absolute problem is normally solved by discretisation of the entire dispatch period into a number of small time intervals over which the load is assumed to be constant and the system is considered to be in temporal steady state. This paper presents particle swarm optimization technique to solve the DELD problem for the determination of the global or near global optimum dispatch solution. To illustrate the effectiveness of the proposed approach, three test systems consisting of 5,10 and 15 generating units, with incorporation of load balance constraints, operating limits, valve point loading, ramp constraints and network lossesare considered and tested. The comparison of numerical results demonstrate the performance and applicability of the proposed method. Keywords: Dynamic economic load dispatch (DELD), Particle Swarm Optimization, Valve - point loading effect, Ramp Rate Limits.DOI:http://dx.doi.org/10.11591/ijece.v1i1.6

Improved Fitness Dependent Optimizer for Solving Economic Load Dispatch Problem

Economic Load Dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to Economic Load Dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness Dependent Optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness Dependent Optimizer (FDO) examines the search spaces based on the searching approach of Particle Swarm Optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have carried out Fitness Dependent Optimizer to solve the Economic Load Dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of Fitness Dependent Optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for Fitness Dependent Optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multi-dimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. Empirical results obtained using the enhanced variant of the Fitness Dependent Optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional Fitness Dependent Optimizer. The experimental study obtained 7.94E-12.Comment: 42 page

Solution to economic load dispatch using PSO

The modern power system around the world has grown in complexity of interconnection and power demand. The focus has shifted towards enhanced performance, increased customer focus, low cost, reliable and clean power. In this changed perspective, scarcity of energy resources, increasing power generation cost, environmental concern necessitates optimal economic dispatch. In reality power stations neither are at equal distances from load nor have similar fuel cost functions. Hence for providing cheaper power, load has to be distributed among various power stations in a way which results in lowest cost for generation. Practical economic dispatch (ED) problems have highly non-linear objective function with rigid equality and inequality constraints. Particle swarm optimization (PSO) is applied to allot the active power among the generating stations satisfying the system constraints and minimizing the cost of power generated. The viability of the method is analyzed for its accuracy and rate of convergence. The economic load dispatch problem is solved for three and six unit system using PSO and conventional method for both cases of neglecting and including transmission losses. The results of PSO method were compared with conventional method and were found to be superior. The conventional optimization methods are unable to solve such problems due to local optimum solution convergence. Particle Swarm Optimization (PSO) since its initiation in the last 15 years has been a potential solution to the practical constrained economic load dispatch (ELD) problem. The optimization technique is constantly evolving to provide better and faster results