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| Probabilistic Available Transfer Capability Assessment in Power System Considering Conditional Value-at-Risk and Correlated Wind Power |
| Li Xue1, Li Jiaqi1,2, Zhang Rufeng1, Li Xiaojing2, Wang Mingxuan2 |
1. Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin 132012 China;
2. State Grid Jilin Electric Power Company Changchun 130021 China |
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Abstract To consider the impact of wind power output forecasting errors on the assessment of available transfer capability (ATC), this paper proposes a probabilistic ATC assessment method of power system considering wind power correlation and conditional value-at-risk (CVaR).
First of all, the correlation coefficient between wind farms is calculated through the historical wind power output data, and the correlation coefficient matrix among wind farms is obtained. Then, the probability distribution and Copula function of wind farm output prediction are used to construct a probability model of wind power output prediction error considering spatial correlation. According to the wind power output forecast error data and wind farm output prediction value that meet the correlation of actual wind farm output, the output prediction of wind farm is corrected, and then the revised wind power output forecast is used as the input for calculating interregional ATC.
After that, this paper proposes a bi-level optimal model considering the correlation of wind power output and CVaR for probabilistic ATC assessment. The lower level model aims to minimize the generation cost and risk under the base state. The upper level model aims at maximizing inter-regional ATC by taking power output under base state and extreme state as the interaction information between the upper and lower level models. On this basis, the Karush-Kuhn-Tucker (KKT) optimal conditions are used and the bi-level model is converted into a mathematical program with equilibrium constraints (MPEC) model. The MPEC model is further transformed into a mixed integer second-order cone programming form, and then the probabilistic ATC can be solved.
Finally, the PJM-5 bus test system and Jilin Western Power Grid are used to analyze the example. In the PJM-5 bus test system, when the confidence level is taken by 90%, as the correlation coefficient increases from 0.5 to 0.9, the correlation of wind farm output gradually increases, and the inter-regional probability ATC expectation first increases and then decreases; when the confidence level is taken at 70%, the inter-regional probability ATC expectation gradually decreases with the increase of the correlation coefficient, and the wind power prediction error scenarios under different correlation coefficients are different, resulting in different output of each generator set in the ground state, which in turn affects the probability ATC expectation. In the actual system of Jilin Western Power Grid, it can also be seen that the correlation coefficient and confidence level of wind power forecast error have an impact on the probability ATC expectation. It can be seen that in the ATC evaluation process of power system with a high proportion of wind power, it is necessary to consider the confidence level of total power generation cost and the influence of wind farm correlation on the ATC evaluation results.
The proposed wind power prediction error correlation modeling method corrects the wind farm output prediction value by obtaining the wind power output prediction error that conforms to the actual wind farm correlation. In the process of evaluating ATC, the confidence level should be reasonably set, and the prediction error caused by the uncertainty of wind power output should be reduced by taking into account the correlation of wind power forecast error, the risk cost of system operation should be reduced, and the calculation accuracy of ATC between regions should be improved.
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Received: 04 May 2022
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2023, Vol. 38 
