Shuffled differential evolution for large scale economic dispatch

Reddy, A. Srinivasa; Vaisakh, K.

doi:10.1016/j.epsr.2012.11.010

Cited by 123 publications

(29 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The optimal real power generation output, the FC, emission and power loss obtained for the 40-unit test system using various intelligent techniques are given in Table XIV. It can be inferred from Table XIV that the minimum production cost obtained using the CRO technique is comparatively smaller with respect to those obtained by TLBO [21], quasi-oppositional TLBO (QOTLBO) [21], GA-API [42] and stochastic DE (SDE) [44] discussed in this paper. The convergence of optimal solution using CRO is shown in Figure 5, where only about 55 iterations are needed to find the near-optimal solution.…”

Section: Case 2 (With Wind Firms)mentioning

confidence: 74%

Economic emission dispatch for wind-fossil-fuel-based power system using chemical reaction optimisation

Roy¹,

Hazra²

2014

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

Summary This paper presents a chemical reaction optimisation algorithm based on the chemical molecular reaction to optimise total cost in wind–fossil‐fuel‐based power systems, while all operating constraints are satisfied. With the continuing search for alternatives to conventional energy sources and global warming, it is necessary to include wind energy conversion system generators in the economic load dispatch problem. The random nature of wind power is modelled using Weibull probability distribution function. Because of uncertainty in wind power, wind cost is generalised by including overestimation and underestimation cost of available wind power. The proposed approach is examined and tested on the standard Institute of Electrical and Electronics Engineers 30‐bus test system having three conventional thermal generators, two gas generators, one oil generator and two wind turbines and a 40‐unit test system having three wind turbines and 37 thermal generating units. The simulation results show that the proposed algorithm gives comparatively better operational cost compared with other optimisation techniques. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Section: Case 2 (With Wind Firms)mentioning

confidence: 74%

Economic emission dispatch for wind-fossil-fuel-based power system using chemical reaction optimisation

Roy¹,

Hazra²

2014

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

show abstract

“…It can be seen from the graph and the table that the minimum cost is rst reached by using SCA algorithm and the rest of the algorithms take more time as compared to SCA. According to Table 1, the minimum fuel cost for 13 generator units in the proposed algorithm is 24512.6085 $/hr, which is better than the those in SDE [37] and ORCCRO [39]. The minimum, maximum, and average fuel costs obtained after 50 trials are presented in Table 2.…”

Section: Simulations and Resultsmentioning

confidence: 94%

“…Test case 1: 13 generator units have been considered in test system 1 with transmission losses. The input data for the transmission loss is taken from the study by Srinivasa and Vaisakh [37]. The total power demand is 2520 MW.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…The number of search agents used is 50 in this case. In test case 1, the results of the SCA algorithm are compared with those of the Oppositional Real Coded Chemical Reaction Optimization (ORCCRO) [39] and Stochastic Di erential Equation (SDE) [37] optimization techniques. It can be seen from the graph and the table that the minimum cost is rst reached by using SCA algorithm and the rest of the algorithms take more time as compared to SCA.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…Table 6 illustrates the minimum, maximum, and average fuel costs of various optimization techniques after 50 trials. The comparison of the convergence characteristics of the SCA with those of GAAPI [39] and SDE [37] is illustrated in Figure 4. Looking at the tabular and graphical data, it is clear that the minimum fuel cost obtained by the SCA is better that those by other techniques like GAAPI [39], DE/BBO [43], SDE [37], and BBO [43].…”

Section: Simulations and Resultsmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Study of Economic Load Dispatch using Sine Cosine Algorithm

Bhattacharjee

Patel

2018

Scientia Iranica

View full text Add to dashboard Cite

Economic Load Dispatch (ELD) is an important part of cost minimization procedure in power system operation. Di erent derivative and probabilistic methods are used to solve ELD problems. This paper proposes a powerful Sine Cosine Algorithm (SCA) to explain the ELD issue including equality and inequality restrictions. The main aim of ELD is to satisfy the entire electric load at minimum cost. The SCA is a population-based probabilistic method, which guides its search agents that are randomly placed in the search space towards an optimal point using their tness functions and keeps a track of the best solution achieved by each search agent. SCA was used to solve the ELD problem due to its favorable exploration and local optima escaping technique. This algorithm con rmed that promising areas of the search space were exploited to have a smooth transition from exploration to exploitation using sine and cosine functions. Simulation results proved that the proposed algorithm surpassed other existing optimization techniques in terms of quality of the solution obtained and computational e ciency. The nal results also proved the robustness of the SCA.

show abstract

Dynamically controlled particle swarm optimization for large-scale nonconvex economic dispatch problems

Jadoun

Gupta

Niazi

et al. 2014

Int. Trans. Electr. Energ. Syst.

View full text Add to dashboard Cite

This paper proposes a dynamically controlled particle swarm optimization method to solve nonconvex economic dispatch problem of large dimensions. It essentially aims to improve the performance of the conventional particle swarm optimization by suggesting improved cognitive and social components of the particle's velocity through preceding and aggregate experience of the swarm, respectively. The control parameters of the governing equation are controlled dynamically by introducing new exponential functions. The overall methodology effectively regulates the velocity of the particles during their whole course of flight and results in substantial improvement. The effectiveness of the proposed method has been investigated on 40 generators and 140 generators test generating systems by considering general operational constraints. The application results show that the proposed method is very promising to solve large-dimensional economic dispatch problems.greatly depends on its parameters tuning [7], and it often suffers from the problems such as being trapped in local optima because of premature convergence [8], lack of efficient mechanism to treat the constraints [9], loss of diversity and performance in optimization process [1], and so on. PSO is a population-based optimization technique in which the movement of the particles is governed by the two stochastic acceleration coefficients, that is, cognitive and social components and the inertia component [1,6,7].Several modified versions of PSO have been reported in the recent past to enhance the performance of the conventional PSO by modulating inertia weight, improvising cognitive and social behavior, using constriction factor approach, or modifying the control equation of the PSO, and some of them can be discussed here.Some experiments with the inertia weight have been reported in Refs [6,7,[10][11][12][13]. Chaotic PSO in Ref. [11] proposed adapted inertia weight that varies dynamically with fitness value for exploration, and a chaotic local search was used to determine the particle position for better exploitation. The improved PSO in Ref.[12] suggested chaotic inertia weight that decreases and oscillates simultaneously under the decreasing line in a chaotic manner. In this way, additional diversity is introduced, but it requires tuning of chaotic control parameters. In Ref.[13], the inertia coefficient is controlled with respect to the objective function to provide unique velocity in the convergent direction of the best particle. In this approach, the velocity of the best particle stagnates, whereas the rest of the particles increase. This enhances the chances of local trapping, and to avoid it, an empirical formula is used to limit particles' velocities. Many researchers [10,[14][15][16] have attempted to vary the cognitive and social behavior of the swarm during the search process by dynamically controlling the acceleration coefficients within maximum and minimum bounds. Again, the determination of limiting values of the acceleration coefficients is a difficult ta...

show abstract

Shuffled differential evolution for large scale economic dispatch

Cited by 123 publications

References 30 publications

Economic emission dispatch for wind-fossil-fuel-based power system using chemical reaction optimisation

Economic emission dispatch for wind-fossil-fuel-based power system using chemical reaction optimisation

A Comparative Study of Economic Load Dispatch using Sine Cosine Algorithm

Dynamically controlled particle swarm optimization for large-scale nonconvex economic dispatch problems

Contact Info

Product

Resources

About