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The IUP Journal of Electrical and Electronics Engineering:
An SFLA Approach to Solving Profit-Based
Unit Commitment Problem Under Deregulation†
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In this paper, a Shuffled Frog Leaping Algorithm (SFLA) is proposed to address the Profit-Based Unit Commitment (PBUC) problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi-constrained, nonlinear optimization problem. Some of the recently developed algorithms like GA, PSO, BBO, ACO, etc. are available to solve this complex problem, but so far there has been no such ideal technique which can completely handle the computational time and the large dimensionality of the problem. In this scenario, an attempt is made to solve this problem using SFLA. The problem is formulated as a bi-objective optimization problem with profit maximization of generation companies as one objective and emission level limitation of generating units as second objective. The proposed algorithm is tested on IEEE 10 unit 10 hours load demand as input data for simulation using MATLAB. The results are compared with GA and PSO. From the results, it is observed that SFLA effectively handles the dimension of the problem and achieves maximum profit and minimum production cost with less computational time.

 
 

Unit commitment (Wood and Wollenberg, 1984) is a nonlinear mixed integer optimization problem that involves determining the scheduling of the generating units based on the load demand while satisfying a set of constraints. The Unit Commitment Problem (UCP) can be analyzed in both regulated framework (Snyder et al., 1987) and under deregulation (Allen and Illic, 1997; and Bavafa et al., 2008). The traditional regulated framework is more like vertically integrated structure and involves determining the on and off states of the units with production cost minimization as main objective (Conejo, 2010; and Delarue, 2010). Under deregulation, the monopoly of the vertically integrated structure no longer remains the same, but it changes to horizontal structure with no holding of generation, transmission and distribution together (Ouyang and Shahidepour, 1992; and Dimitroulas and Georgilakis, 2011). In regulated framework, utilities run UC with the condition that demand and reserve must be met. But in the restructured power system, the demand constraint changes to less than or equal to the predicted demand. This problem is called Profit-Based Unit Commitment (PBUC) problem (Shahidehpour et al., 2002; and Padhy, 2004). On the other hand, UC under deregulated environment changes its objective from minimization of the cost to maximization of profit. The PBUC problem (Charles and Gerald, 2000) is a highly complex, nonlinear, non-convex and mixed integer optimization problem. Some of the recently developed algorithms like Genetic Algorithm (GA) (Charles and Gerald, 2000) and PSO (Reglend et al., 2010) are used to solve the problem. Most of the heuristic algorithms seem to be fast and reliable, but they have the problem of convergence with large-scale power systems. The GA solution to the PBUC problem involves generation of initial binary population in terms of strings. Each string is a potential solution to the problem and its fitness value can be calculated by satisfying minimum up and down time constraints. The population is evaluated in terms of best fitness value, and for each iteration, the best fitness value is stored. The next generation of population is selected based on the selection rate, crossover and mutation. This process is repeated till the maximum number of iterations and finally the best solution is stored. So basically, the GA uses genetic operators for obtaining the final solution. During this process, the time for convergence may take longer.

 
 
 

Electrical and Electronics Engineering Journal, Profit-Based Unit Commitment (PBUC) problem, Shuffled Frog Leaping Algorithm (SFLA) , Deregulation, Profit