Multi-objective Bi-level Programming for Environmental Constrained Electric Power Generation and Dispatch via Genetic Algorithm (original) (raw)
Related papers
Advances in Intelligent Systems and Computing, 2014
This article demonstrates how trilevel programming (TLP) in a hierarchical decision structure can be efficiently used for modeling and solving reactive power dispatch (RPD) problems of electrical power system by using genetic algorithms (GAs) in the framework of fuzzy goal programming (FGP) in uncertain environment. In the proposed approach, various objectives associated with a RPD problem are considered at three hierarchical levels in a planning horizon. In the solution process, a GA scheme is employed to obtain the individual values of objectives and thereby to evaluate the developed FGP model to reach a solution for optimal RPD decision. The proposed approach is tested on the standard IEEE 6-Generator 30-Bus System.
Trans Stellar Journals, 2021
In this paper, the problem of load dispatch of Thermal power generation considering all the various system constraints is discussed using generalized reduced gradient (GRG) algorithm and Evolutionary algorithm nonlinear optimization and the results are compared. The most economical operating costs can be obtained by distributing the load demand to the interconnected generators i.e., Load dispatch, while considering their various constraints. The operation of Thermal Power plant is very expensive and also each generating unit usually has a unique cost-per-hour characteristics, minimum and maximum operating powers, ramp rates, prohibited operating zones, emission coefficients. In this paper, ELD is calculated for IEEE-30 bus system connected to six thermal power plants. Here transmission line losses are also taken into consideration. ELD is calculated using GRG algorithm and Evolutionary algorithm and the results are compared. These algorithms present in Solver tool of Excel add-in program are applicable to arrive at an optimum value (maximum or minimum value) in an objective cell within predetermined constraints or limits. The results on optimizations have shown that the GRG algorithm is better compared to Evolutionary algorithm in solver in a given time, as the evolutionary algorithm takes more time to converge and more number of iterations to arrive at the conclusions to find a feasible solution.
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.
Comparison of Analytical and Heuristic Techniques for Multiobjective Optimization in Power System
Advances in Computational Intelligence and Robotics
Due to liberalization in the power market stake of the Distributed Generation (DG) in the power industries has increased radically. Integration of DG will result is the change in the operating conditions of the existing power system network. Due to this DG has drawn attention of utility providers, policy makers and, to effectively use the DG, several researchers also. Inclusion of DG in the existing power system may enhance its power transfer capacity, voltage profile, reliability and it can also reduce the overall system losses if installed in proper capacity and at proper place. Benefits of DG can be efficiently extracted only if an appropriate capacity of DG is introduced in the existing power system at appropriate place. This chapter proposes an analytical and heuristic approach suggesting the optimum size and location of type-1 and type-2 DG. The proposed method is implemented on the IEEE-13 bus radial distribution network (RDN) and result shows the validity of the proposed met...
Economic Load Dispatch of Three Bus Thermal Generator Using Fuzzy Logic Technique
Asian Journal of Electrical Sciences
This paper gives details/information about economic load dispatch using manual method and optimized fuzzy method. Using manual method, we get lambda ‘λ’ one of the inputs to the fuzzy by using different demand values. The calculations of manual method and fuzzy results are both mentioned in this paper. The whole analysis is done considering transmission losses.
Procedia Computer Science, 2015
A power system with many generating units should run under economic condition. The operating cost must be minimized for any feasible load demand. However, along with the cost, the environmental emissions should also be considered, that make it multiobjective optimization problem. In this paper, we have used the standard IEEE test bus systems as a model power system for solving the economic load dispatch (ELD) problem considering different size of system. A comparison of simulation output is made between the results obtained by applying conventional Newton Raphson and Lagrangian multiplier (LM)algorithms and proposed genetic algorithm (GA). From the simulation results, it is observed that, the proposed GA based approach provides better compromised solutions between the two objectives i.e. cost and emission effectively.
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.
Soft Computing Approach for Optimal Electric Power Generation and Dispatch
2018
This article describes how genetic algorithm (GA) can be efficiently used to fuzzy goal programming (FGP) formulation for optimal power flow in operational and planning phases of power system. In the proposed approach, the objectives of the problem for optimal power flow computation are fuzzily described. In the model formulation of the problem, the membership functions of the defined fuzzy goals are characterized first to measuring the degree of achievement of the specified aspiration levels of the objective goals in the decision making context. Then, the goal achievement function under the minsum FGP to minimize the regret arises due to under-deviations from the highest membership value (unity) of the defined membership goals to the extent possible is constructed for making optimal power flow decision in the decision making environment. In the solution process, the proposed GA method is used in an iterative manner to reach a satisfactory decision on the basis of needs and desires of the decision maker (DM). To illustrate the potential use of the approach, the problem tested on IEEE 6-Generator 30-Bus System is considered and the model solution is compared with the solutions obtained in the previous study. IndexTerms-Fuzzy goal programming, Goal programming, Genetic algorithm, Membership function, Optimal power flow.