Heuristic optimization based approach for identification of power system dynamic equivalents

Rueda, José L.; Cepeda, Jaime C.; Erlich, I.; Echeverría, D.; Argüello, Gabriel

doi:10.1016/j.ijepes.2014.07.012

Cited by 21 publications

(25 citation statements)

References 10 publications

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“…start and P i . shut , we use (18) to calculate the spinning reserve capacity in (7) when sampling the feasible unit on-off schemes:…”

Section: Representative Set Of Feasible Unit On-off Schemesmentioning

confidence: 99%

“…Since the inherent nature of mixed-integer nonlinearity of the UC problem, many algorithms have been used to search the optimal UC scheme, such as the dynamic programming method [2,3], heuristic methods [4][5][6][7], the mixed integer programming method [8,9], the Lagrangian relaxation method [10,11], and the genetic algorithm method [12,13]. Generally, the common way is to turn the UC problem into a two-stage optimization problem, with determination of unit on-off scheme as the first stage and scheduling the generation output as the second stage.…”

Section: Introductionmentioning

confidence: 99%

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Ordinal optimization theory to solve large‐scale power system unit commitment

Xie

Zhu

et al. 2017

IEEJ Transactions Elec Engng

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The complexity of the multiperiod dynamic unit commitment problem makes it difficult or even unviable to find the global optimal solution. Ordinal optimization provides a simulation-based approach suitable for solving this kind of problem. It uses crude models and rough estimates to derive a small set of unit commitment schemes for which simulations are necessary and worthwhile to find good enough solutions with drastically reduced computational burden. The 10-100 thermal units standard test example and the case of an actual provincial power system with 128 units verify the feasibility of ordinal optimization to solve the large-scale dynamic unit commitment problem.Yuxin Du (Non-member) received the B.S. degree from Nanchang University, Nanchang, China, in 2015. Now she is pursuing the M.S. degree from

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“…start and P i . shut , we use (18) to calculate the spinning reserve capacity in (7) when sampling the feasible unit on-off schemes:…”

Section: Representative Set Of Feasible Unit On-off Schemesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ordinal optimization theory to solve large‐scale power system unit commitment

Xie

Zhu

et al. 2017

IEEJ Transactions Elec Engng

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“…So far, many efforts are devoted to reduce the power system model using classical approaches based on modal analysis [4][5][6][7][8][9], coherency concept [10,11], or a combination of both [12]. However, using classical approaches may not be a good choice due to the continuous growth of the network and the non-availability of neighbors' system parameters and configurations that are the key information of these analytical approaches [13,14]. To overcome these shortcomings of classical approaches, measurement based approaches have been proposed [15,16].…”

Section: Introductionmentioning

confidence: 99%

“…The performances of recently developed metaheuristic algorithms to solve the dynamic equivalence problem are addressed in [14,[33][34][35][36]. Although optimization algorithms such as PSO [34], Grey Wolf Optimizer (GWO) [35] and Salp Swarm Algorithm (SSA) [36], are able to solve the dynamic equivalence problem, accuracy of the solution is not guaranteed.…”

Section: Introductionmentioning

confidence: 99%

A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

Benmiloud

Arif

2019

Journal of Electrical Engineering

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Dynamic equivalent (DE) is an important process of multi-area interconnected power systems. It allows to perform stability assessment of a specific area (area of interest) at minimum cost. This study is intended to investigate the dynamic equivalent of two relatively large power systems. The fourth-order model of synchronous generators with a simplified excitation system is used as equivalent to the group of generators in the external system. To improve the accuracy of the estimated model, the identification is carried in two stages. First, using the global search Sine Cosine Algorithm (SCA) to find a starting set values, then this set is used as starting point for the fine-tuning made through the Pattern Search (PS) algorithm. To increase the reliability of the model’s parameters, two disturbances are used to avoid the identification based on a specific event. The developed program is applied on two standard power systems, namely, the New England (NE) system and the Northeast Power Coordinating Council (NPCC) system. Simulation results confirm the ability of the optimized model to preserve the main dynamic properties of the original system with accuracy.

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“…Despite its similarities to some of the popular heuristic methods, the uniqueness of the MVMO is the use of a special mapping function to mutate candidate solutions according to the statistics of the best candidate solutions. MVMO has shown promising performance in some other optimization problems of power systems [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Dynamic security enhancement of power systems using mean-variance mapping optimization

Kucuktezcan¹,

Genç²

2017

Turk J Elec Eng & Comp Sci

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Abstract:In this paper, a preventive control action that involves both generation rescheduling and load curtailment is proposed for enhancing the dynamic security of large interconnected power systems. The control action is formulated as a security-constrained optimization problem that is solved by mean-variance mapping optimization (MVMO) integrated with a self-adaptive penalization technique and artificial neural networks to develop a fast and effective methodology.The proposed methodology is applied to a 16-generator 68-bus test system to solve the security-constrained optimization problem with both continuous and discrete decision variables. To find a proper and cost-effective solution for the control actions within an acceptable time, dynamic security assessment methodology based on artificial neural networks is integrated into the optimization process for predicting the violations of security constraints brought about by the candidate solutions. The proposed method effectively integrates a variety of popular heuristic optimization algorithms, including MVMO, differential evolution, particle swarm optimization, genetic algorithms, big bang-big crunch, and artificial bee colony. MVMO outperforms all the others in various aspects such as reliability and robustness.

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Heuristic optimization based approach for identification of power system dynamic equivalents

Cited by 21 publications

References 10 publications

Ordinal optimization theory to solve large‐scale power system unit commitment

Ordinal optimization theory to solve large‐scale power system unit commitment

A novel measurement-based procedure for dynamic equivalents of electric power systems for stability studies using improved sine cosine algorithm

Dynamic security enhancement of power systems using mean-variance mapping optimization

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