Specialized differential evolution technique to solve the alternating current model based transmission expansion planning problem

Torres, Santiago P.; Castro, Carlos A.

doi:10.1016/j.ijepes.2014.12.016

Cited by 25 publications

(13 citation statements)

References 15 publications

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“…2 that the final simulation results by any independent run are the same with those of actual digital system as shown in Eq. (10). They reveal that the proposed algorithm can accurately estimate the actual system parameters and has a good robustness as well.…”

Section: An Illustrative Examplementioning

confidence: 87%

“…It is also a population-based algorithm with multiple direction searching. Recently, a variety of engineering optimization problems have been solved and explored by using the DE algorithm (1,(8)(9)(10)(11) .…”

Section: Introductionmentioning

confidence: 99%

“…In [10], a specialized DE technique to solve the transmission expansion planning (TEP) problem was developed and some comparisons were performed with other swarm methods. Moreover, an optimization problem of heliostat field layout in solar central receiver systems on annual basis has been solved using the DE algorithm (11) This paper will focus on the parameter estimation of the bilinear digital system by the DE optimal algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parameter Estimation of Bilinear Digital Systems Using Differential Evolution Algorithm

Chang

Shih

Hsiao

et al. 2015

Proceedings of the 3rd International Conference on Intelligent Systems and Image Processing 2015

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In this paper, we propose a novel parameter estimation scheme for bilinear digital systems based on using the differential evolution (DE) algorithm. The DE algorithm is one of evolutionary computations and is fully with real-valued operations during the optimization. It has been proven to be an excellent searching algorithm in solving a variety of practical engineering optimization problems.With the use of the DE algorithm, the parameter estimation problem especially for the bilinear digital system is considered. By minimizing the error signal between the actual output and model output, the system coefficients can accurately be estimated. Finally, a large number of independent runs with different sets of initial conditions are examined to confirm the algorithm's robustness and feasibility.

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Section: An Illustrative Examplementioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parameter Estimation of Bilinear Digital Systems Using Differential Evolution Algorithm

Chang

Shih

Hsiao

et al. 2015

Proceedings of the 3rd International Conference on Intelligent Systems and Image Processing 2015

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“…It is accepted when the design is complicated, non-linear, or requires several uncertain parameters. The probability descriptions of case voltages and branch power flows can be achieved accurately by MCS [30,31], though it usually consumes significant computation effort. Hence, it is not appropriate for online applications or where other programs work accurately [32,33].…”

Section: Popf Methodsmentioning

confidence: 99%

“…The lower and upper initial population (

m_{j truemin}

and

m_{j truemax}

) are limited since the generation resizing case is regarded. The DEPs are very sensitive concerning the control parameters, and the right choices for them, which guarantees a faster confluence and good results [21, 31]. The algorithm advances by initialising the population of individuals and evaluating the fitness function, where the fitness function is the objective function (explained in Section 3), followed by checking the constraints (see Section 4).…”

Section: Plan Solution Algorithmmentioning

confidence: 99%

Probabilistic transmission expansion planning considering distributed generation and demand response programs

Hejeejo

Qiu

2017

IET Renewable Power Generation

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Transmission expansion planning (TEP) is generally determined by peak demands. To improve the efficiency and sustainability of energy systems, attention has been paid to demand response programs (DRPs) and distributed generation (DG). DRPs and DG will also have significant impacts on the controllability and economics of power systems, from short-term scheduling to long-term planning. In this study, a non-linear economic design for responsive loads is introduced, based on the price flexibility of demand and the customers' benefit function. Moreover, a probabilistic multi-objective TEP model which considers DRPs is also proposed. A probabilistic analysis method, the so-called Monte-Carlo simulation method, is implemented to handle the uncertainty of the loads, DRPs and DG in the TEP problems. Due to the problems' non-convex formulations, a non-dominated sorting differential evolution program is used to solve the TEP problems. The proposed TEP model can find the optimal trade-off between transmission investment and demand response expenses. The planning methodology is then demonstrated on an IEEE 118-bus system in order to show the feasibility of the proposed algorithm.

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Metaheuristics for Transmission Network Expansion Planning

Chicco

Mazza

2020

Transmission Expansion Planning: The Network Challenges of the Energy Transition

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Specialized differential evolution technique to solve the alternating current model based transmission expansion planning problem

Cited by 25 publications

References 15 publications

Parameter Estimation of Bilinear Digital Systems Using Differential Evolution Algorithm

Parameter Estimation of Bilinear Digital Systems Using Differential Evolution Algorithm

Probabilistic transmission expansion planning considering distributed generation and demand response programs

Metaheuristics for Transmission Network Expansion Planning

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