A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Pang, Aokang; Liang, Huijun; Lin, Chenhao; Yao, Lei

doi:10.3390/en16021011

Cited by 11 publications

(5 citation statements)

References 50 publications

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“…Assuming a computation time of 8.6 seconds for a 40-unit system, solving a 42-unit problem with the Robust Learning Grey Wolf Optimization would take more than 8.6 seconds 33) . If we consider 40 units taking 32.5984 seconds, then a 42-unit Surrogate-Assisted Adaptive Bat Algorithm would exceed 32.5984 seconds 34) . Finally, if 40 units necessitate 5.0169 seconds, a 42-unit Improved Bat Algorithm would require more than 5.0169 seconds 35) .…”

Section: Test Systemmentioning

confidence: 99%

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

Rifki Rahman Nur Ikhsan,

Raharjo,

Rahmat

2024

Evergreen

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The economic dispatch process in power markets aims to find the optimal generation schedule based on cost-based bids from generators. The lambda iteration method is commonly used but faces computational challenges for large-scale systems. To address this, a novel approach called the vectorized lambda iteration method is proposed. This method utilizes vectorization techniques to reduce computation time while maintaining accuracy. By parallelizing the computation of schedules and Lagrange multipliers, it achieves faster convergence and enables real-time decision-making. The method improves efficiency, competitiveness, and adaptability in power markets, ensuring reliable operation. Software and hardware optimizations further enhance performance. The results demonstrate a significant reduction in computation time and the lowest cost across three test systems. The vectorized lambda iteration method offers an efficient solution for power system operation and decision-making, with the potential for optimization in practical applications. The proposed vectorized variant that we introduced is capable of reducing computation time by 99% compared to conventional methods, while also achieving a 2% cost reduction.

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Section: Test Systemmentioning

confidence: 99%

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

Rifki Rahman Nur Ikhsan,

Raharjo,

Rahmat

2024

Evergreen

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“…The bat optimization algorithm was inspired by bat life. The echolocation ability of bats helps them locate prey [29]. The bat makes loud noises and receives echoes from its surroundings to find prey [30].…”

Section: Bat Algorithm (Ba)mentioning

confidence: 99%

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

Ehteram,

Banadkooki

2023

Water

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Groundwater level (GLW) prediction is essential for monitoring water resources. Our study introduces a novel model called convolutional neural network (CNN)–long short-term memory neural network (LSTM)–Multiple linear regression (MLR) for groundwater level prediction. We combine two deep learning models with the MLR model to predict GWL and overcome the limitations of the MLR model. The current paper has several innovations. Our study develops an advanced hybrid model for predicting groundwater levels (GWLs). The study also presents a novel feature selection method for selecting optimal input scenarios. Finally, an advanced method is developed to examine the impact of inputs and model parameters on output uncertainty. The current paper introduces the gannet optimization algorithm (GOA) for choosing the optimal input scenario. A CNN-LSTM-MLR model (CLM), CNN, LSTM, MLR model, CNN-MLR model (CNM), LSTM-MLR model (LSM), and CNN-LSTM model (CNL) were built to predict one-month-ahead GWLs using climate data and lagged GWL data. Output uncertainty was also decomposed into parameter uncertainty (PU) and input uncertainty (IU) using the analysis of variance (ANOVA) method. Based on our findings, the CLM model can successfully predict GWLs, reduce the uncertainty of CNN, LSTM, and MLR models, and extract spatial and temporal features. Based on the study’s findings, the combination of linear models and deep learning models can improve the performance of linear models in predicting outcomes. The GOA method can also contribute to feature selection and input selection. The study findings indicated that the CLM model improved the training Nash–Sutcliffe efficiency coefficient (NSE) of the CNL, LSM, CNM, LSTM, CNN, and MLR models by 6.12%, 9.12%, 12%, 18%, 22%, and 30%, respectively. The width intervals (WIs) of the CLM, CNL, LSM, and CNM models were 0.03, 0.04, 0.07, and, 0.12, respectively, based on IU. The WIs of the CLM, CNL, LSM, and CNM models were 0.05, 0.06, 0.09, and 0.14, respectively, based on PU. Our study proposes the CLM model as a reliable model for predicting GWLs in different basins.

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“…The variations made to the structure of PSO to realize derived versions and its hybridization with other algorithms to avoid premature convergence for well-constructed constrained ELD problems have been summarized comprehensively. A robust learning mechanism-assisted grey wolf optimization algorithm [18] and a surrogate-assisted adaptive bat algorithm [19] successfully tackled large-scale economic load dispatch considering VPL effects. Similarly, in [20], to resolve the large-scale power dynamic economic dispatch of large-scale thermal power units while taking VPL effects and ramp-rate limits into consideration, a two-stage based multi-gradient PSO (MG-PSO) method was proposed.…”

Section: Literature Reviewmentioning

confidence: 99%

On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems

et al. 2023

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Recently, different metaheuristic techniques, their variants, and hybrid forms have been extensively used to solve economic load dispatch (ELD) problems with and without valve point loading (VPL) effects. Due to the randomization involved in these metaheuristic techniques, one has to perform extensive runs for each experiment to get an optimal solution. The process may sometimes become laborious and time-consuming to converge to an optimal solution. On the other hand, advanced calculus-based techniques, being deterministic, perform iteration systematically and come up with the same solution on each run of the experiment. Since ELD problems are constrained optimization problems, we are proposing the constrained (deterministic) optimization algorithm for their solutions. Various 13–unit, 38-unit, and 40-unit thermal test systems are considered. Valve point loading (VPL) effects are also considered in some cases. Computer-based numerical results depict that the constrained optimization algorithm shows evidence of being almost as competitive in a total fuel cost as the metaheuristic optimization techniques, especially for the less-constrained ELD problems but with far reduced computation time. This finding validates the application of the constrained optimization technique to solve the economic dispatch problem.

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A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch

Cited by 11 publications

References 50 publications

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

Vectorized Lambda Iteration Method for Swift Economic Dispatch Analysis

A Developed Multiple Linear Regression (MLR) Model for Monthly Groundwater Level Prediction

On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems

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