Dynamic Economic Dispatch Solution using an Enhanced Real-Quantum Evolutionary Algorithm

Babu, G. Suresh; Das, D. Bhagwan; Patvardhan, C.

doi:10.1109/icpst.2008.4745185

Cited by 7 publications

(8 citation statements)

References 10 publications

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“…The performance of CSDE/best/1 was also compared with other recently published methods from the literature including SQP (Attaviriyanupap et al, 2002), EP (Attaviriyanupap et al, 2002), Hybrid EP -SQP (Attaviriyanupap et al, 2002), PSO -SQP (Victoire & Jeyakumar, 2005b), modified hybrid EP (MHEP) -SQP (Victoire & Jeyakumar, 2005a), Modified DE (Yuan et al, 2008), QEA (Babu et al, 2008), hybrid DE ), Shor's r-algorithm , IPSO , AHDE (Lu et al, 2010), CE and ECE (Immanuel Selvakumar, 2011), chaotic sequence-based DE (He, Dong, Wang, & Mao, 2011), ABC, PSO and GA (Hemamalini & Simon, 2011a), AIS (Hemamalini & Simon, 2011b), HQPSO (Chakraborty, Senjyu, Yona, & Saber, 2011), hybrid DE -SQP (Elaiw, Xia, & Shehata, 2012) and BCO -SQP (Basu, 2013). The results can be found in Table 10.…”

Section: Comparison With Previously Published Resultsmentioning

confidence: 99%

“…In addition, the four main parameters in CSDE, e.g. NP, F, CR and r m , were also investigated to demonstrate their effects on the performance of the overall (Dieu & Ongsakul, 2010) 2,185,413 Hybrid HNN -QP (Attaviriyanupap et al, 2002) 2,196,210 LP (Babu et al, 2008) 2,196,939 EP (Direct) (Babu et al, 2008) 2,196,608 EP (Adapted) (Babu et al, 2008) 2,196,462 EP þ SQP (Babu et al, 2008) 2,196,439 HSSDED (Babu et al, 2008) 2,196,440 ERQEA (Babu et al, 2008) 2,196,440 CSDE/best/1 2,185,400…”

Section: Discussionmentioning

confidence: 99%

“…This is the New England System which has 10 generating units, 39 buses and 46 transmission lines with quadratic cost curves, the requirement being to schedule over a period of 12 h (Attaviriyanupap et al, 2002) 1,051,163 --EP (Attaviriyanupap et al, 2002) 1,048,638 -80 £ 50,000 Hybrid EP -SQP (Attaviriyanupap et al, 2002) 1,031,746 -60 £ 30,000 MHEP -SQP (Victoire & Jeyakumar, 2005a) 1,028,924 1,031,179 60 £ 30,000 PSO -SQP (Victoire & Jeyakumar, 2005b) 1,027,334 1,028,546 100 £ 30,000 Modified DE (Yuan et al, 2008) 1,031,612 1,033,630 100 £ 20,000 QEA (Babu et al, 2008) 1,022,422 1,024,340 -hybrid DE 1,031,077 -100 £ 10,000 Shor's r-algorithm 1,038,976 --IPSO 1,023,807 1,026,863 20 £ 2000 AHDE (Lu et al, 2010) 1,020,082 1,022,474 60 £ 2000 CE (Immanuel Selvakumar, 2011) 1,022,701 1,024,024 50 £ 1000 ECE (Immanuel Selvakumar, 2011) 1,022,271 1,023,334 50 £ 1000 Chaotic sequence-based DE (He et al, 2011) 1,023,432 1,026,475 600 £ 410 ABC (Hemamalini & Simon, 2011a) 1,021,576 1,022,686 200 £ 300 PSO (Hemamalini & Simon, 2011a) 1,027,679 1,031,716 200 £ 300 GA (Hemamalini & Simon, 2011a) 1,033,481 1,038,014 200 £ 300 AIS (Hemamalini & Simon, 2011b) 1,021,980 1,023,156 100 £ 300 HQPSO (Chakraborty et al, 2011) 1,031,559 1,033,837 50 £ 1500 HHS (Pandi & Panigrahi, 2011) 1,019,091 -100 £ 2000 IGA -NSPF (Ganjefar & Tofighi, 2011) 1,019,145 1,021,453 100 £ 6000 Hybrid DE -SQP (Elaiw et al, 2012) 1,030,500 -60 £ 20,000 BCO-SQP (Basu, 2013) 1,032,200 -200 £ 300 CSDE/best/1 1,019,536 1,022,883 10 £ 6000 Q. Niu et al 18 Table 11. Best solution obtained using the CSDE/best/1 method for power system 1.…”

Section: Power System 2: 10-unit 12-h Systemmentioning

confidence: 99%

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Differential evolution combined with clonal selection for dynamic economic dispatch

Niu

Irwin

2014

Journal of Experimental & Theoretical Artificial Intelligen

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Dynamic economic load dispatch (DELD) is one of the most important steps in power system operation. Various optimisation algorithms for solving the problem have been developed; however, due to the non-convex characteristics and large dimensionality of the problem, it is necessary to explore new methods to further improve the dispatch results and minimise the costs. This article proposes a hybrid differential evolution (DE) algorithm, namely clonal selection-based differential evolution (CSDE), to solve the problem. CSDE is an artificial intelligence technique that can be applied to complex optimisation problems which are for example nonlinear, large scale, non-convex and discontinuous. This hybrid algorithm combines the clonal selection algorithm (CSA) as the local search technique to update the best individual in the population, which enhances the diversity of the solutions and prevents premature convergence in DE. Furthermore, we investigate four mutation operations which are used in CSA as the hyper-mutation operations. Finally, an efficient solution repair method is designed for DELD to satisfy the complicated equality and inequality constraints of the power system to guarantee the feasibility of the solutions. Two benchmark power systems are used to evaluate the performance of the proposed method. The experimental results show that the proposed CSDE/best/1 approach significantly outperforms nine other variants of CSDE and DE, as well as most other published methods, in terms of the quality of the solution and the convergence characteristics.

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Section: Comparison With Previously Published Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Power System 2: 10-unit 12-h Systemmentioning

confidence: 99%

See 1 more Smart Citation

Differential evolution combined with clonal selection for dynamic economic dispatch

Niu

Irwin

2014

Journal of Experimental & Theoretical Artificial Intelligen

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“…The detailed data of this system can be found in [16]. Due to the unavailability of data and comparison purposes, the test problem without P loss is solved using ADE and compared with other algorithms in the literature, such as quadratic programming (QP) [20], augmented Lagrange hopfield network (ALHN) [20], a hybrid evolutionary programing and sequential quadratic programing based algorithm (EP-SQP) [21], PSO and different variants of DE [16]. The results are shown in Table 2, which reveal the superiority of ADE to the state-of-the-art-algorithms.…”

Section: Case-2mentioning

confidence: 99%

Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems

Elsayed

Zaman

Sarker

2015

Proceedings in Adaptation, Learning and Optimization

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The objective of a dynamic economic dispatch problem is to determine the optimal power generation from a number of generating units by minimizing the fuel cost. The problem is considered a high-dimensional complex constrained optimization problem. Over the last few decades, many differential evolution variants have been proposed to solve this problem. However, such variants were highly dependent on the search operators, control parameters and constraint handling techniques used. Therefore, to tackle with this shortcoming, in this paper, a new differential evolution framework is introduced. In it, the appropriate selection of differential evolution operators is linked to the proper combination of control parameters (scaling factor and crossover rate), while the population size is adaptively updated. To add to this, a heuristic repair approach is introduced to help obtaining feasible solutions from infeasible ones, and hence enhancing the convergence rate of the proposed algorithm. The algorithm is tested on three different dynamic dispatch problems with 12 and 24 hours planning horizons. The results demonstrate the superiority of the proposed algorithm to the state-of-the-art algorithms.

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“…All scenarios with voltage fluctuation constraints defined by Computer Business Equipment Manufacturers Association (CBEMA) are considered. The quantum evolutionary algorithm (QEA) [18]- [20] is used to minimize both MW loss and new SC cost while satisfying operational constraints and the voltage fluctuation constraints. Compared with traditional methods, computation complexity can be simplified and the computation time can be markedly reduced to avoid convolution calculation.…”

Section: Introductionmentioning

confidence: 99%

Optimal VAR Planning Considering Intermittent Wind Power Using Markov Model and Quantum Evolutionary Algorithm

Hong

Pen

2010

IEEE Trans. Power Delivery

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Techniques for distributed generations (DGs) have attracted increasing attention due to their effects on environmental sustainability and the reduction in traditional megawatt (MW) generation expansion. Wind farms are one of the DGs and have intermittent characteristics. This paper presents a method for static VAR planning considering existing wind generator voltages and transformer taps as controllers to regulate the voltage profile in a distribution system with wind farms. Wind power generations and bus loads are modeled with the Markov model. The probabilities and durations of the operation states are obtained. Through a quantum evolutionary algorithm, the cost of static VAR compensators and MW loss in the system are minimized and the operational constraints are fulfilled. The applicability of the proposed method is verified through simulations using a standalone 25-bus (Penghu) system and a 17-bus system. Index Terms-Intermittence, Markov model, quantum evolutionary algorithm, VAR planning, voltage fluctuation.

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Dynamic Economic Dispatch Solution using an Enhanced Real-Quantum Evolutionary Algorithm

Cited by 7 publications

References 10 publications

Differential evolution combined with clonal selection for dynamic economic dispatch

Differential evolution combined with clonal selection for dynamic economic dispatch

Automated Differential Evolution for Solving Dynamic Economic Dispatch Problems

Optimal VAR Planning Considering Intermittent Wind Power Using Markov Model and Quantum Evolutionary Algorithm

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