Genetic Algorithm Solution to Unit Commitment Problem (original) (raw)
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Implementation of Genetic Algorithm for Optimal Unit Commitment in a Power System
2018
The Genetic Algorithms (GA) are general optimization techniques based on principle inspired from the biological evolution. A simple GA algorithm implementation using the standard crossover and mutation operator could locate near optimal solutions but it most cases failed to converge to optimal solution. However, using the varying quality function techniques and adding problem specific operators, satisfactory solution was obtained. Test result, for systems of up to 100 units and comparisons with result obtained using Lagrangian relaxation and Dynamic programming are also reported. In this research, a genetic algorithm was applied to the unit commitment scheduling problem. A genetic It is hoped that the concurrent processing will enable the algorithm to operate within the needed response time of an electric utility power broker. The goal of this research is to determine if a genetic algorithm can be implemented to find good unit commitment schedules.
Optimization of Unit Commitment Problem Using Genetic Algorithm
Int. J. Syst. Dyn. Appl., 2021
The main objective of the paper is to minimize the use of conventional generators and optimize the fuel cost. To minimize the use of conventional generators, solar thermal power plant (STPP) is proposed in this paper. An approach for optimal location of STPP is also proposed in this paper. To minimize the fuel cost, firstly unit commitment (UC) is applied in conventional generators. Then genetic algorithm (GA) is used to optimize the fuel cost of committed generators. The suggested method is tested on an IEEE 14 bus test system for 24 hr. schedule with variable load. The effectiveness of the proposed methodology is illustrated in three cases. Case 1 is used to identify the STPP location to reduce the fuel cost of conventional generator. In Case 2, unit-commitment is applied to save considerable fuel input and cost. In order to optimize the committed fuel cost, a genetic algorithm is applied in Case 3.
Solution of unit commitment problem using enhanced genetic algorithm
2014 Eighteenth National Power Systems Conference (NPSC), 2014
In this paper, enhanced genetic algorithm (EGA) is used to solve the short-term unit commitment problem (UCP) and the enhanced lambda iteration (ELI) method is used to solve the economic dispatch (ED) sub-problem. Based on EGA, the problem specific operators have been integrated in the simple GA algorithm and thus, enhanced the quality of the solution. Performance of EGA is tested on 2 test systems comprising of 4unit and 10-unit over the scheduling time horizon of 8 hours and 24 hours respectively. Results demonstrate that the proposed method is superior to the other reported methods in the literature.
A Genetic Algorithm Approach to Solve Unit Commitment Problem
In this a genetic algorithm based approach to resolve the thermal unit commitment (UC). During this approach load demand, the most objective of getting a possible unit combination for every loading condition whereas subjected to a range of constraints. The model during this study contains four-generation units and also the 8-hour daily load demand. The results are compared between the dynamic programming (DP) and genetic algorithm the achieved results prove the effectiveness, and validity of the planned approach to unravel the large-scale UC. In the results indicating comparison of the cost solutions is using the genetic algorithm and the Dynamic Programming. The MATLAB Tool box is used to find the result.
European Transactions on Electrical Power, 2007
This paper presents an approach for solving the unit commitment problem based on genetic algorithm with integer representation of the unit start-up and shutdown times. The new definition of the decision variables in the unit commitment problem reduces the solution space and computational time of the genetic algorithm. The method incorporates time-dependent start-up costs, demand and reserve constraints, minimum up and down time constraints, ramp rate limit constraints, and units power generation limits. Penalty functions are applied to the infeasible solutions. Test results showed an improvement in effectiveness and computational time compared to results obtained from genetic algorithm with standard binary representation of the unit states and other techniques.
International Journal of Electrical Power & Energy Systems, 1996
A genetic algorithm is a random search procedure which is based on the survival of the fittest theory. This paper presents the genetic algorithm applied to the unit commitment scheduling problem and to the economic dispatch of generating units. The first half of the paper applies the genetic algorithm to the unit commitment scheduling problem, which is the problem of determining the optimal set of generating units within a power system, to be used during the next one to seven days. Thejrst halfof the paper
A Comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem
IEEE Access
Unit Commitment (UC) is a key task in electric power system operation, aiming at minimizing the total cost of power generation. It is essential to monitor wide range of activities and practices of UC necessary to determine the operating plan of generating units. The UC problem is particularly crucial, when the behavior of loads at every hour interval, is oscillatory and with different operational constraints and environments. Many works have been proposed, with different optimization methods to solve the UC problem. This paper gives a detailed review of the evolutionary optimization techniques, employed for solving UC problem, by collecting them from lots of peer reviewed published papers. This review was carried out under many sections, based on various evolutionary optimization techniques, to help new researchers, dealing with modern UC problem solutions, under different situations of power system.
Optimal Unit Commitment of Power System Using Fast Messy Genetic Algorithm
This paper presents a new approach via improved version of genetic algorithm to solve optimal unit commitment (UC) problem. Fast Messy Genetic Algorithm (FMGA) is applied to the calculation of optimal unit commitment problem. The test results demonstrate that not only the FMGA procedure consider is the constraints very well, but also has some advantages, such as good convergence, fast calculating speed and high precision. A ten unit power system was used as a numerical example to test the new algorithm. The optimal scheduling of on line generation units could be reached in the testing results while satisfying the constraints of the objective function. Numerical results indicate that the performance of the FMGA algorithm outperforms the other algorithms and achieves minimum generation cost.
Optimum Unit Commitment for Thermal Power Plants - A Genetic Algorithm Approach
2009 Annual IEEE India Conference, 2009
This paper presents genetic algorithm method for unit commitment of thermal power plant. The optimum allocation of generations (for a given plant load) to different units of a plant is called unit commitment (UC). It can be easily shown that the optimal operation of the units at a thermal power station can be achieved when the incremental fuel cost (incremental cost) of all the units are equal. A new fitness function is defined in this paper, which combines (i) equal incremental cost (IC) criteria, and (ii) generation and load balance constraint. Generation of each unit is taken as variable. The minimum and maximum generation limits of the units are incorporated with the help of lower and upper bounds of variable. The genetic algorithm (GA) optimization method is employed to estimate the optimum allocation of generations to different units of plant, making use of the fitness function. Computer programs (using MATLAB) have been developed for optimum UC, using GA technique.
Mathematical Problems in Engineering, 2014
Due to the continuous increase of the population and the perpetual progress of industry, the energy management presents nowadays a relevant topic that concerns researchers in electrical engineering. Indeed, in order to establish a good exploitation of the electrical grid, it is necessary to solve technical and economic problems. This can only be done through the resolution of the Unit Commitment Problem. Unit Commitment Problem allows optimizing the combination of the production units’ states and determining their production planning, in order to satisfy the expected consumption with minimal cost during a specified period which varies usually from 24 hours to one week. However, each production unit has some constraints that make this problem complex, combinatorial, and nonlinear. This paper presents a comparative study between a strategy based on hybrid gradient-genetic algorithm method and two strategies based on metaheuristic methods, fuzzy logic, and genetic algorithm, in order t...