Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration

Ji, Bin; Yuan, Xiao; Li, Xianshan; Huang, Yuehua; Li, Wenwu

doi:10.1016/j.enconman.2014.07.060

Cited by 75 publications

(39 citation statements)

References 39 publications

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“…In [13], a method combining Chance-Constrained Programming (CCP) with a Quantum-Inspired Binary Gravitational Search Algorithm (QBGSA) was proposed. The UC is determined for different confidence levels and prediction errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

et al. 2018

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Insular power systems represent an asset and an excellent starting point for the development and analysis of innovative tools and technologies. The integration of renewable energy resources that has taken place in several islands in the south of Europe, particularly in Portugal, has brought more uncertainty to production management. In this work, an innovative scheduling model is proposed, which considers the integration of wind and solar resources in an insular power system in Portugal, with a strong conventional generation basis. This study aims to show the benefits of increasing the integration of renewable energy resources in this insular power system, and the objectives are related to minimizing the time for which conventional generation is in operation, maximizing profits, reducing production costs, and consequently, reducing greenhouse gas emissions.

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Section: Literature Reviewmentioning

confidence: 99%

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

et al. 2018

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“…The binary variables denoting the on/off status of units are generated based on (10)- (12) while the PEV variables are updated based on the initialisation values or updated values from the previous iteration according to (13)- (16).…”

Section: ) Generate New Solutionsmentioning

confidence: 99%

“…Step 4 are utilized to update the probability in BPSO according to (12) and solutions by SaDE based on (16). The process goes back to Step 2 until the maximum iteration number is achieved.…”

Section: ) Algorithm Updatesmentioning

confidence: 99%

“…These methods however require a large number of iterations to ensure the algorithm convergence, which may reduce their computational efficiency. Hybrid algorithm frameworks combine binary meta-heuristic methods such as binary particle swarm optimisation (BPSO) [ 14], quantum-inspired particle swarm algorithm (QPSO) [15] and binary gravitational search algorithm (BGSA) [ 16 ] with the conventional Lagrangian relaxation method to trade off the accuracy and convergence speed. However, a large number of plug-in electric vehicles (PEVs) introduced into the power system end users and their integration with the grid through both grid-to-vehicle (V2G) and vehicle-to-grid (V2G) has brought even more remarkable challenges to the power system operators.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles

Yang

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…Meanwhile the newly installed wind power capacity was 23.4 GW and the total capacity reached 168.7 GW with the growth rate of 14.0% for China [1]. To promote the quantity application of wind energy, electricity generated from wind power needs to be connected to the grid so as to be transferred to areas that consume lots of electric energy [2,3]. Furthermore, during the process of wind energy being transformed into electricity, accurate control should be conducted so that higher generation efficiency can be obtained [4,5].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

Kang

Tan

Yuan

et al. 2017

Advances in Meteorology

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

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Application of quantum-inspired binary gravitational search algorithm for thermal unit commitment with wind power integration

Cited by 75 publications

References 39 publications

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

Scheduling Model for Renewable Energy Sources Integration in an Insular Power System

A hybrid meta-heuristic method for unit commitment considering flexible charging and discharging of plug-in electric vehicles

Short-Term Wind Speed Prediction Using EEMD-LSSVM Model

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