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Risk-averse Robust Interval Economic Dispatch for Power Systems with Large-scale Wind Power Integration

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摘要 This paper presents a robust interval economic dispatch(RIED)model for power systems with large-scale wind power integration.Differing from existing interval optimization(IO)approaches that merely rely on the upper and lower boundaries of random variables,the distribution information retained in the historical data is introduced to the IO method in this paper.Based on the available probability distribution function(PDF),wind power curtailment and load shedding are quantified as the operational risk and incorporated into the decision-making process.In this model,we need not rely on the forecasted value of wind power,which is randomly fluctuating and quite unpredictable.Furthermore,when the PDFs of wind power are taken into account,the resulting dispatch solution makes a good tradeoff between the generation cost and the operational risk.Finally,the RIED model yields an optimal dispatch solution for thermal units and the allowable intervals of wind power for the wind farms,which efficiently mitigates the uncertainty in wind power generation and provides more practical suggestions for system operators.Simulation studies are conducted on a modified IEEE-118 bus system and the results verify the effectiveness of the proposed RIED model.
出处 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2024年第1期105-116,共12页 中国电机工程学会电力与能源系统学报(英文)
基金 supported by the National Natural Science Foundation of China(51937005) the Natural Science Foundation of Guangdong Province(2019A1515010689) the Oversea Study Program of Guangzhou Elite Project(GEP).
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  • 1Zhen Yan, Zeng Lingkang, Chen Xi, Li Xiangzhen and Liu Jianming State Grid Information & Telecommunication Company Ltd.,.Architecture of Power Internet of Things[J].Electricity,2011,22(5):10-15. 被引量:5
  • 2王守相,武志峰,王成山.计及不确定性的电力系统直流潮流的区间算法[J].电力系统自动化,2007,31(5):18-22. 被引量:30
  • 3Morales J M,Perez R J.Point estimate schemes to solve the probabilistic power flow[J].IEEE Trans. on Power Systems,2013,28(2):1550-1559.
  • 4Hajian M,Rosehart W D,Zareipour H.Probabilistic power flow by Monte Carlo simulation with Latin supercube sampling[J].IEEE Trans. on Power Systems,2007,22(4):1594-1601.
  • 5Miranda V,Saralva J T.Fuzzy modelling of power system optimal load flow[J].IEEE Trans. on Power Systems,1992,7(2):843-849.
  • 6Zhang H,Li P.Probabilistic analysis for optimal power ?ow under uncertainty[J].IET Generation,Transmission & Distribution,2010,4(5):553-561.
  • 7Yu H,Rosehart W D.An optimal power flow algorithm to achieve robust operation considering load and renewable generation uncertainties[J].IEEE Trans. on Power Systems,2012,27(4):1808-1817.
  • 8Lee J,Kim J H,Joo S K.Stochastic method for the operation of a power system with wind generators and superconducting magnetic energy storages(SMESs) [J].IEEE Transactions on Applied Superconductivity,2011,21(2):2144-2148.
  • 9Zhang Hui,Li P.Chance constrained programming for optimal power flow under uncertainty[J].IEEE Trans. on Power Systems,2011,26(4):2417-2424.
  • 10Wang Y,Xia Q,Kang C Q.Unit commitment with volatile node injections by interval optimization[J].IEEE Trans. on Power Systems,2011,26(3):1705-1713.

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