Optimal Dispatch of Generation with Valve Point Loading using Genetic Optimization Technique (original) (raw)
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Improved genetic algorithm for economic load dispatch with valve-point loadings
… Electronics Society, 2003 …, 2003
Economic load dispatch is one of the optimization problems in power systems. This paper presents an improved genetic algorithm for economic load dispatch with valve-point loadings. New crossover and mutation operations are introduced. The solutions of the economic load dispatch with valve-point loadings under three cases are solved by the improved genetic algorithm. Test results are,given and compared with those from different published genetic algorithms. It will be shown that the proposed improved genetic algorithm performs better than the published genetic algorithms.
Solution of Economic Dispatch Problem through Genetic Algorithm
To provide high-quality and reliable power supply at the lowest possible cost while operating to meet the limits and constraints imposed on the generating units is the main objective of electric power utilities. The economic load dispatch (ELD) is essential in the operation of power system. A global optimization technique 'Genetic Algorithm' (GA) is presented. The algorithm utilizes information of candidate solutions to evaluate their optimality. As a case study, this technique is applied to the solution of the economic dispatch (ED) problem of thermal generating units. A smooth quadratic function is used to represent the fuel cost of each generating unit. The B-coefficient method is used to model the transmission losses. Using two different methods " Lambda-Iteration method " and " Genetic Algorithm method " the cost function is derived at different loads. These two different methods proposed to reduce the generation cost using data of different units. GA has been identified as more suitable than LI. It is free from nature of graph of the cost function. It exhibits very good performance on the majority of the problems applied.
Economic Load Dispatch in Power System using Genetic Algorithm
International Journal of Computer Applications, 2013
Evolutionary algorithms are becoming an important aspect of artificial intelligence and are successfully applied to a variety of optimization problems. This paper presents genetic algorithm and quadratic programming concepts in solving economic load dispatch in which the total cost of generating power is minimized with a valve point loading effect while satisfying the load demand irrespective of transmission line losses. This work aims in modeling the economic load dispatch problem with transmission loss and is being applied to the test systems i.e. IEEE 14 BUS and IEEE 30 BUS using MATLAB.
Performance Analysis of Evolutionary Optimization Techniques for Economic Dispatch Problem.pdf
International Journal of Advances in Computer and Electronics Engineering, 2019
This paper deals with comparisons of different evolutionary optimization techniques for the solution Economic Dispatch Problem (EDP) in power system. The main principle behind the evolutionary techniques is inspired by the biological evolution of the living organisms. Economic Dispatch problem deals with optimizing the operational cost of the online generators with fulfilling the consumer end power demand. Scheduling the practical generation units involves the complex limitations. Among these limitations valve point loading effect is included in this work for increased convolution. In this paper, the evolutionary optimization techniques such as the Real Coded Genetic Algorithm (RCGA), Binary Coded Genetic Algorithm (BCGA), Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Artificial Immune System (AIS) are simulated for EDP for its performance analysis. These optimization algorithms are validated for three, thirteen and forty generating systems and their results are compared. From these results, the elite and the most suitable algorithm are found for solving the EDP.
Hybrid Genetic Algorithm for Optimizing Environmental/Economic Power Dispatch
2010
The conventional economic power dispatch is a non-linear optimization problem with several constraints. The environmental issues concerning the pollutant emissions produced by fossil based thermal generating units became a matter of concern in recent years. Accordingly, minimization of emissions by dispatch of power generation is very desirable. The problem is how to supply all electrical loads at minimum cost taking the environmental issues into account (minimum pollution). Environmental/Economic dispatch is a multi-objective problem treats economic and pollutant emissions. This multi-objective problem is converted into single objective function using a modified price penalty factor approach to calculate environmental /economic power dispatch problem. A commonly used technique to solve this problem is to apply genetic algorithm to a small number of generations to get near optimum economic solution for the power system dispatch. This paper presents an application of hybrid genetic algorithm (HGA) to achieve an optimal solution for the Combined Economic Emission Dispatch problem (CEED). The optimum solution obtained by the proposed technique is faster and more efficient than that obtained by using both the Conventional Optimization methods (CM) and simple Genetic Algorithm (GA). The proposed algorithm is tested on standard IEEE 30-bus model system.
2021
The Economic Load Dispatching Problem (ELDP) in power system's operational planning is of an immense importance. The power generation must fulfillment parity and disparity constraints. In order to satisfy the economical operation of the power system, the generated power must meet the load demand with minimum losses because the cost of storing the generated electrical energy is very high. The input-output characteristic curves of the modern thermal generating units are equipped with turbines having multi-valves steam input have non-convexity nature, this results in ELDP. Though this problem is discussed significantly but mostly analyzed using different mathematical approaches by utilizing the Quadratic Cost Curves (QCC) but the fact remains that the actual ELDP is non-convex whereas the QCC is convex. The multi-valve steam input based thermal power plants produce ripple like heat rates, which cannot be properly signified using convex approach thus can result in high cost. In this work, ELDP is modeled using Real Coded Genetic Algorithm (RCGA)and convex and the non-convex economic dispatch problem has been investigated for Thermal Power Station (TPS) Jamshoro as a case study. The SYRAP (System Reservoir and Plants) software is used for economic operational loading of WAPDA power plants based on the incremental cost curves. The obtained results of TPS are validated with 6-machine IEEE standard test system with the simulation model for both nonconvex and convex economic dispatch. The obtained results confirm the minimum power generation cost by non-convex method as compared to convex approach.
International Journal of Smart Electrical Engineering, 2013
Nowadays, economic load dispatch between generation units with least cost involved is one of the most important issues in utilizing power systems. In this paper, a new method i.e. Water Cycle Algorithm (WCA) which is similar to other intelligent algorithm and is based on swarm, is employed in order to solve the economic load dispatch problem between power plants. In order to investigate the effectiveness of the proposed method in solving non-linear cost functions which is composed of the constraint for input steam valve and units with different fuels, a system with 10 units is studied for more accordance with literatures in two modes: one without considering the effect of steam valve and load of 2400, 2500, 2600 and 2700 MW and the other one with considering the effect of steam valve and load of 2700 MW. The results of the paper comparing to the results of the other valid papers show that the proposed algorithm can be used to solve in any kind of economic dispatch problems with prop...
Economic emission dispatch of thermal generating units using genetic algorithm technique
International Journal of Enterprise Network Management, 2011
Well-established conventional algorithms are available for solving the economic emission dispatch (EED) problem. But the recent trend is to use the tools such as genetic algorithms (GAs) evolutionary programming technique, etc., because of some of their superior qualities. In this paper, GA technique has been applied for solving EED problem. This GA technique represents a class of general purpose stochastic search techniques which simulate natural inheritance by genetics illustrated by considering a five-bus system.
Genetic Algorithm based Cost Optimization Model for Power Economic Dispatch Problem
The Economic Dispatch Problem (EDP) is the optimal allocation of the load demand among the running generators while satisfying the power balance equations and the unit's operating limits. In an electrical power system, a continuous balance must be maintained between electrical generation and varying load demand, while the system frequency, voltage levels, and security also must be kept constant. Furthermore, it is desirable that the cost of such generation be minimal. Numerous classical techniques such as LaGrange based methods, linear programming, non-linear programming and quadratic programming methods has been reported in the literature. The solution of the economic dispatch problem using the classical approach presents some limitations in its implementation. One of such limitations is that there exists the possibility for this approach to be caught at the local minima when the cost functions are non-convex or piecewise discontinuous in the functional space. Furthermore, treatments of operational constraints are very difficult using the classical approach. This thesis explored the application of genetic algorithm to solve the problem of Economic power dispatch in order to circumvent the above stated limitations. Genetic Algorithms (GAs) are numerical optimization algorithms based on the principle inspired from the genetic and evolution mechanisms observed in natural systems and population of living being. Both the GA and the classical approach using the LaGrange based methods are implemented using C++ in order to be able to verify the performance of the GA approach in practical applications. Case studies of a three generators and Nigerian Grid systems are considered for two different power demands. Economic Dispatch without transmission losses were considered in both case studies. Results showed a significant improvement with the method of genetic algorithm over the classical method. This is expected to help power service companies to maximize profit while maintaining reliability and security of supply.