Reserve Constrained Dynamic Dispatch of Units With Valve-Point Effects

Victoire, T. Aruldoss Albert; Jeyakumar, A. Ebenezer

doi:10.1109/tpwrs.2005.851958

Cited by 233 publications

(88 citation statements)

References 24 publications

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“…It clear from the table that the proposed method produces much better results compared to recently reported different method for solving DED problem. Table 6 Comparison of results for 10-unit systems Method Production cost ($) Computing time (s) Hybrid EP-SQP [11] 1031746 NA DGPSO [12] 1028835 NA Hybrid PSO-SQP [13] 1027334 NA ICA [14] 1022205.6846 NA Proposed 1001746.4962 4.2798 NA: Not Available…”

Section: ) Case Studymentioning

confidence: 99%

“…Most of these methods are not applicable for non-smooth or non-convex cost functions. To overcome this problem, many stochastic optimization methods have been employed to solve the DED problem, such as genetic algorithm (GA) [6], simulated annealing (SA) [7], differential evolution (DE) [8,9], particle swarm optimization (PSO) [10], hybrid EP and SQP [11], deterministically guided PSO [12], hybrid PSO and SQP [13], and imperialist competitive algorithm (ICA) [14]. Many of these techniques have proven their effectiveness in solving the DED problem without any or fewer restrictions on the shape of the cost function curves.…”

Section: Introductionmentioning

confidence: 99%

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A Modified Differential Evolution Algorithm for Solving NonConvex Dynamic Economic Dispatch Problems

Hardiansyah¹,

Fuazen²

2017

IJARCSSE

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Abstract-In this paper, a modified differential evolution (MDE) algorithm is presented to solve non-convex dynamic economic dispatch (DED) problems considering valve-point effects, the ramp rate limits and transmission losses. The practical DED problems have non-smooth cost function with equality and inequality constraints, which make the problem of finding the global optimum difficult when using any mathematical approaches. The proposed MDE algorithm is inspired by three ideas: (1) use of opposition based learning to generate the initial population, (2) use of tournament best process to generate mutant vector to explore the region around the tournament best individual, (3) use of a single set population in contrast to the two set population as in basic DE. The feasibility of the proposed method is validated on 5 and 10 units test system for a 24 h time interval. The results are compared with the results reported in the literature.Keywords-Modified differential evolution, dynamic economic dispatch, non-smooth cost functions, ramp rate limits, valve-point effects I. INTRODUCTIONThe electrical power systems are interconnected in order to obtain the benefits of minimum generation costs, maximum reliability and best operational conditions, such as sharing of power reserve, improving the stability and operating on emergency situations. Thus, the optimization problem of the economic dispatch of electrical power system is relevant to accomplish requirements of quality and efficiency in power generation. Dynamic economic dispatch (DED) is one of important problems in power system operation and control, which is used to determine the optimal schedule of generating outputs on-line so as to meet the load demand at the minimum operating cost under various system and operating constraints over the entire dispatch periods. DED is an extension of the conventional economic dispatch (ED) problem that takes into consideration the limits on the ramp rate of generating units to maintain the life of generation equipment [1,2].Since the DED problem was introduced, several optimization techniques and procedures have been used for solving the DED problem with complex objective functions or constraints. There were a number of classical methods that have been applied to solve this problem such as gradient projection method, Lagrange relaxation, and linear programming [3][4][5]. Most of these methods are not applicable for non-smooth or non-convex cost functions. To overcome this problem, many stochastic optimization methods have been employed to solve the DED problem, such as genetic algorithm (GA) [6], simulated annealing (SA) [7], differential evolution (DE) [8,9], particle swarm optimization (PSO) [10], hybrid EP and SQP [11], deterministically guided PSO [12], hybrid PSO and SQP [13], and imperialist competitive algorithm (ICA) [14]. Many of these techniques have proven their effectiveness in solving the DED problem without any or fewer restrictions on the shape of the cost function curves.Differential evolution (DE) algorithm introd...

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Section: ) Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Modified Differential Evolution Algorithm for Solving NonConvex Dynamic Economic Dispatch Problems

Hardiansyah¹,

Fuazen²

2017

IJARCSSE

View full text Add to dashboard Cite

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“…There are two methods that can be used to calculate the transmission loss: the power flow method [27] and the B-coefficients method (Kron's formula) [23]. In this paper, the B-coefficients method is adopted in line with the previous research done in [12,16,26] based on following formula:…”

Section: System and Operational Constraintsmentioning

confidence: 99%

Modified particle swarm optimization for economic-emission load dispatch of power system operation

Abdullah

Bakar²,

Rahim³

et al. 2015

Turk J Elec Eng & Comp Sci

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This paper proposes a modified particle swarm optimization considering time-varying acceleration coefficients for the economic-emission load dispatch (EELD) problem. The new adaptive parameter is introduced to update the particle movements through the modification of the velocity equation of the classical particle swarm optimization (PSO) algorithm. The idea is to enhance the performance and robustness of classical PSO. The price penalty factor method is used to transform the multiobjective EELD problem into a single-objective problem. Then the weighted sum method is applied for finding the Pareto front solution. The best compromise solution for this problem is determined based on the fuzzy ranking approach. The IEEE 30-bus system has been used to validate the effectiveness of the proposed algorithm.It was found that the proposed algorithm can provide better results in terms of best fuel cost, best emissions, convergence characteristics, and robustness compared to the reported results using other optimization algorithms.

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“…Dynamic economic and environmental dispatches (DEED) are both crucial objectives in this scheduling task because a small percentage improvement may potentially bring significant cost savings and operational improvements [1,2]. The goal of economic dispatch is to minimize the cost by determining the power production of thermal power plant units, while managing system constraints, balancing power production and load demand, and meeting plant operational requirements, e.g.…”

Section: Introductionmentioning

confidence: 99%

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Yang

Niu

et al. 2014

J. Mod. Power Syst. Clean Energy

114

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Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

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Reserve Constrained Dynamic Dispatch of Units With Valve-Point Effects

Cited by 233 publications

References 24 publications

A Modified Differential Evolution Algorithm for Solving NonConvex Dynamic Economic Dispatch Problems

A Modified Differential Evolution Algorithm for Solving NonConvex Dynamic Economic Dispatch Problems

Modified particle swarm optimization for economic-emission load dispatch of power system operation

A self-learning TLBO based dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

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