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Dr. Jilin Cai
Southeast University

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0 Electrical Engineering
0 Renewable Energy
0 Power System Planning
0 power system optimal scheduling
0 capacity credit

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Original research paper
Published: 27 January 2021 in IET Generation, Transmission & Distribution
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A method of capacity value evaluation for wind farms considering the correlation between wind power and load is presented. The paper starts with defining the metric of capacity value called capacity credit, and its basic evaluation process. Then the core part of capacity credit evaluation, which is the reliability assessment of power systems, is focused on. In this core part, two limitations of the frequently used cross entropy based importance sampling method are analysed. To solve the problems, an improved method is proposed by using truncated Gaussian mixture model as the proposal distribution of the cross entropy based importance sampling methods. This improved method is adopted to speed up the reliability assessment of composite power systems in the capacity credit evaluation. Finally, several numerical tests are designed and performed on the IEEE‐RTS 79 and IEEE‐RTS 96 test systems. The results show that the improved method is faster than traditional cross entropy based importance sampling methods when assessing the reliability of power system. Besides, the efficiency of the improved method is almost impervious to the correlation of load and wind power output, which ensures its applicability in different scenarios.

ACS Style

Jilin Cai; Qingshan Xu. Capacity value evaluation of wind farms considering the correlation between wind power output and load. IET Generation, Transmission & Distribution 2021, 1 .

AMA Style

Jilin Cai, Qingshan Xu. Capacity value evaluation of wind farms considering the correlation between wind power output and load. IET Generation, Transmission & Distribution. 2021; ():1.

Chicago/Turabian Style

Jilin Cai; Qingshan Xu. 2021. "Capacity value evaluation of wind farms considering the correlation between wind power output and load." IET Generation, Transmission & Distribution , no. : 1.

Journal article
Published: 13 November 2020 in Sustainable Energy Technologies and Assessments
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Capacity credit (CC) evaluation is a conception used in power system planning, which quantifies the contribution of various generating resources to the power system reliability. CC evaluation is essentially an iterative process of solving equations, where the reliability index of the power system is computed in every round of iteration. Therefore, fast and accurate evaluation of CC requires both a competent reliability assessment method and a compatible iterative method for equation solving. From this perspective, this paper presents a new methodology of CC evaluation for wind energy, by combining an improved cross-entropy-based importance sampling (ICE-IS) method for reliability assessment and a robust secant method for equation solving. The ICE-IS method is able to substantially accelerate the computation of reliability indices, especially when the wind power output and the system load are highly correlated. The robust secant method provides an unbiased estimate of the real CC value and ensures fast convergence, despite the error in the reliability index computed by ICE-IS in each round of iteration. Numerical tests are designed based on the historical data of two actual wind farms to prove the correctness of the proposed methodology. Besides, the results are discussed to analyze the impact of different factors on the CC of wind energy.

ACS Style

Jilin Cai; Qingshan Xu. Capacity credit evaluation of wind energy using a robust secant method incorporating improved importance sampling. Sustainable Energy Technologies and Assessments 2020, 43, 100892 .

AMA Style

Jilin Cai, Qingshan Xu. Capacity credit evaluation of wind energy using a robust secant method incorporating improved importance sampling. Sustainable Energy Technologies and Assessments. 2020; 43 ():100892.

Chicago/Turabian Style

Jilin Cai; Qingshan Xu. 2020. "Capacity credit evaluation of wind energy using a robust secant method incorporating improved importance sampling." Sustainable Energy Technologies and Assessments 43, no. : 100892.

Journal article
Published: 16 August 2019 in Electric Power Systems Research
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Microgrid cluster (MGC) formed by interconnected multiple microgrids (MGs) is beneficial to the enhancement of system economy and supply reliability. To overcome the drawbacks of traditional centralized control scheme, such as complicated communication network and significant optimization delay, an optimal dispatch strategy is proposed in this paper based on multi-agent system (MAS). Firstly, a tri-layer MAS architecture is established, where flexible and coordinated control of MGC system is realized via agents’ cooperation. Then, a multi-time scale optimization approach combined with alternating direction method of multipliers (ADMM) is put forward for MGC energy management, considering the coupling relationship between different time scales. Through day-ahead scheduling, day-in rolling and real-time dispatch, the influence of uncertain factors on MGC’s stable and economic operation can be eliminated at the most extent. Finally, the effectiveness and feasibility of the proposed approach are verified by the results of case study.

ACS Style

Lijuan Chen; Xu Zhu; Jilin Cai; Xiaohui Xu; Haixuan Liu. Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS. Electric Power Systems Research 2019, 177, 105976 .

AMA Style

Lijuan Chen, Xu Zhu, Jilin Cai, Xiaohui Xu, Haixuan Liu. Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS. Electric Power Systems Research. 2019; 177 ():105976.

Chicago/Turabian Style

Lijuan Chen; Xu Zhu; Jilin Cai; Xiaohui Xu; Haixuan Liu. 2019. "Multi-time scale coordinated optimal dispatch of microgrid cluster based on MAS." Electric Power Systems Research 177, no. : 105976.

Journal article
Published: 08 January 2019 in Applied Sciences
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This paper concentrates on the capacity credit (CC) evaluation of wind energy, where a new method for constructing the joint distribution of wind speed and load is proposed. The method is based on the skew-normal mixture model (SNMM) and D-vine copulas, which is used to model the marginal distribution and the correlation structure, respectively. Then a cross entropy based importance sampling (CE-IS) is improved to enhance the efficiency of the power system reliability assessment, which is a crucial part of the CC evaluation. After that, the proposed methods are adopted to combine with the secant method to develop a complete algorithm to calculate the CC of wind energy. Numerical tests are designed and carried out based on the IEEE-RTS 79 system and wind speed data obtained from four wind farms in Northwest China. In order to show the superiority of SNMM and D-vine copula, the goodness-of-fit is quantified by different statistics. Besides, the improved CE-IS method is validated by comparison with Monte Carlo sampling (MCS) and traditional CE-IS in the efficiency of reliability assessment. Finally, the proved methods are combined with the secant method to calculate the CC of four wind farms, which can provide information for wind farm planning.

ACS Style

Jilin Cai; Qingshan Xu; Minjian Cao; Yongbiao Yang. Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling. Applied Sciences 2019, 9, 199 .

AMA Style

Jilin Cai, Qingshan Xu, Minjian Cao, Yongbiao Yang. Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling. Applied Sciences. 2019; 9 (1):199.

Chicago/Turabian Style

Jilin Cai; Qingshan Xu; Minjian Cao; Yongbiao Yang. 2019. "Capacity Credit Evaluation of Correlated Wind Resources Using Vine Copula and Improved Importance Sampling." Applied Sciences 9, no. 1: 199.