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Yongning Zhao
School of Engineering, Cardiff University, Cardiff CF24 3AA, UK

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Journal article
Published: 15 March 2021 in Applied Energy
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Compressor stations play a crucial role in natural gas networks to maintain required pressure levels for transporting gas. Centrifugal compressors commonly used in high pressure gas transmission networks could be driven by gas turbines or electric motors. Including compressor units powered via different fuels in a compressor station allows switching between fuels required by compressors to achieve a set outlet pressure and flow throughput. In this paper, an optimisation model of gas network was developed considering reasonably detailed representation of a compressor station to investigate flexibility provision from the compressor station to the power system. The model was formulated as a mixed integer linear programming (MILP) problem by linearising the nonlinear equations governing gas flow along pipes and compressor power consumption. The model was tested on the gas transmission system in South Wales, UK. The operation of compressors was optimised in response to gas and electricity price subject to meeting operational limits of the gas network. The results showed that the compressors can provide flexibility to the power system through shifting their electricity energy consumption in time or switching between gas- and electric-driven compressor units. It was found that the allowable range for variation of linepack plays a key role in the magnitude and duration of flexibility provision from compressor units.

ACS Style

Yongning Zhao; Xiandong Xu; Meysam Qadrdan; Jianzhong Wu. Optimal operation of compressor units in gas networks to provide flexibility to power systems. Applied Energy 2021, 290, 116740 .

AMA Style

Yongning Zhao, Xiandong Xu, Meysam Qadrdan, Jianzhong Wu. Optimal operation of compressor units in gas networks to provide flexibility to power systems. Applied Energy. 2021; 290 ():116740.

Chicago/Turabian Style

Yongning Zhao; Xiandong Xu; Meysam Qadrdan; Jianzhong Wu. 2021. "Optimal operation of compressor units in gas networks to provide flexibility to power systems." Applied Energy 290, no. : 116740.

Journal article
Published: 18 June 2019 in The Journal of Engineering
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Wind power forecasting (WPF) is crucial in helping schedule and trade wind power generation at various spatial and temporal scales. With increasing number of wind farms over a region, research focus of WPF methods has been recently moved onto exploring spatial correlation among wind farms to benefit forecasting. In this study, a spatio-temporal Markov chain model is proposed for very-short-term WPF by extending the traditional discrete-time Markov chain and incorporating off-site reference information to improve forecasting accuracy of regional wind farms. Not only are the transitions between the power output states of the target wind farm itself considered in the forecasting model, but also the transitions from the output states of reference wind farms to that of the target wind farm are introduced. The forecasting results derived from multiple spatio-temporal Markov chains regarding different reference wind farms over the same region are optimally weighted using sparse optimisation to generate forecasts of the target wind farm. The proposed method is validated by comparing with both local and spatio-temporal WPF methods, using a real-world dataset.

ACS Style

Yongning Zhao; Lin Ye; Zheng Wang; Linlin Wu; Bingxu Zhai; Haibo Lan; Shihui Yang. Spatio‐temporal Markov chain model for very‐short‐term wind power forecasting. The Journal of Engineering 2019, 2019, 5018 -5022.

AMA Style

Yongning Zhao, Lin Ye, Zheng Wang, Linlin Wu, Bingxu Zhai, Haibo Lan, Shihui Yang. Spatio‐temporal Markov chain model for very‐short‐term wind power forecasting. The Journal of Engineering. 2019; 2019 (18):5018-5022.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Zheng Wang; Linlin Wu; Bingxu Zhai; Haibo Lan; Shihui Yang. 2019. "Spatio‐temporal Markov chain model for very‐short‐term wind power forecasting." The Journal of Engineering 2019, no. 18: 5018-5022.

Journal article
Published: 01 May 2019 in IEEE Transactions on Power Systems
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Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods.

ACS Style

Lin Ye; Cihang Zhang; Yong Tang; Wu Zhi Zhong; Yongning Zhao; Peng Lu; Bing Xu Zhai; Hai Bo Lan; Zhi Li; Ying Qu; Bohao Sun; Huadong Sun; Boyu He. Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration. IEEE Transactions on Power Systems 2019, 34, 4617 -4629.

AMA Style

Lin Ye, Cihang Zhang, Yong Tang, Wu Zhi Zhong, Yongning Zhao, Peng Lu, Bing Xu Zhai, Hai Bo Lan, Zhi Li, Ying Qu, Bohao Sun, Huadong Sun, Boyu He. Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration. IEEE Transactions on Power Systems. 2019; 34 (6):4617-4629.

Chicago/Turabian Style

Lin Ye; Cihang Zhang; Yong Tang; Wu Zhi Zhong; Yongning Zhao; Peng Lu; Bing Xu Zhai; Hai Bo Lan; Zhi Li; Ying Qu; Bohao Sun; Huadong Sun; Boyu He. 2019. "Hierarchical Model Predictive Control Strategy Based on Dynamic Active Power Dispatch for Wind Power Cluster Integration." IEEE Transactions on Power Systems 34, no. 6: 4617-4629.

Journal article
Published: 30 January 2019 in Computers & Electrical Engineering
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With the large-scale wind power penetration, probabilistic power flow plays an important role in power system uncertainty analysis. This paper proposes a novel Gaussian Mixture Model to fit the probability density distribution of short-term wind power forecasting errors with the multimodal and asymmetric characteristics. Cumulants are used to calculate mean value and deviation of state variables for each random combination result of Gaussian components. Probabilistic power flow is acquired by summing up all the Gaussian probability density functions with weights counted by the product of Gaussian components in each random combination. Parallel probabilistic power flow computation by use of the Gaussian Mixture Model and cumulants could simplify the calculation procedure in large scale of integrated wind power network. Case studies are carried out in modified IEEE 57-bus test system to verify advantages of the novel approach. Results show that the computational efficiency and accuracy are well improved in the proposed method.

ACS Style

Lin Ye; Yali Zhang; Cihang Zhang; Peng Lu; Yongning Zhao; Boyu He. Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network. Computers & Electrical Engineering 2019, 74, 117 -129.

AMA Style

Lin Ye, Yali Zhang, Cihang Zhang, Peng Lu, Yongning Zhao, Boyu He. Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network. Computers & Electrical Engineering. 2019; 74 ():117-129.

Chicago/Turabian Style

Lin Ye; Yali Zhang; Cihang Zhang; Peng Lu; Yongning Zhao; Boyu He. 2019. "Combined Gaussian Mixture Model and cumulants for probabilistic power flow calculation of integrated wind power network." Computers & Electrical Engineering 74, no. : 117-129.

Journal article
Published: 13 October 2018 in Renewable Energy
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With the development of large-scale wind power integration, wind curtailment appears around the world, especially in China. It is essential to perform the assessment on capability of wind power accommodation (ACWPA) by calculating the maximum admissible wind power which plays an important role in system planning and operation. This paper proposes a long-term assessment on the maximum level of wind power installed capacity in future years based on peak power regulation, with consideration of potential wind curtailment. Meanwhile, a short-term assessment based on wind power forecasting is developed through day-ahead unit commitment to get admissible zone of wind power in grid operation. In particular, the extreme wind variation scenario (EWVS) calculated by quadratic programming (QP) is applied to optimize upper limit of admissible zone. Case studies are carried out to analyze wind power characteristics in a province in Southern China. Results show that the proposed approaches can effectively and accurately evaluate the capability of wind power accommodation in regional power grids.

ACS Style

Lin Ye; Cihang Zhang; Hui Xue; Jiachen Li; Peng Lu; Yongning Zhao. Study of assessment on capability of wind power accommodation in regional power grids. Renewable Energy 2018, 133, 647 -662.

AMA Style

Lin Ye, Cihang Zhang, Hui Xue, Jiachen Li, Peng Lu, Yongning Zhao. Study of assessment on capability of wind power accommodation in regional power grids. Renewable Energy. 2018; 133 ():647-662.

Chicago/Turabian Style

Lin Ye; Cihang Zhang; Hui Xue; Jiachen Li; Peng Lu; Yongning Zhao. 2018. "Study of assessment on capability of wind power accommodation in regional power grids." Renewable Energy 133, no. : 647-662.

Journal article
Published: 21 March 2018 in Energies
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Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models.

ACS Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies 2018, 11, 697 .

AMA Style

Peng Lu, Lin Ye, Bohao Sun, Cihang Zhang, Yongning Zhao, Jingzhu Teng. A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA. Energies. 2018; 11 (4):697.

Chicago/Turabian Style

Peng Lu; Lin Ye; Bohao Sun; Cihang Zhang; Yongning Zhao; Jingzhu Teng. 2018. "A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA." Energies 11, no. 4: 697.

Journal article
Published: 17 January 2018 in IEEE Transactions on Power Systems
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The ever-increasing number of wind farms has brought challenges and opportunities in wind power forecasting techniques to take advantage of interdependencies between hundreds of spatially distributed wind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vector Autoregressive (SC-VAR) model is introduced to obtain sparse model structures in a spatio-temporal wind power forecasting framework by reformulating the original VAR model into a constrained Mixed Integer Non-Linear Programming (MINLP) problem. It allows controlling the sparsity of the coefficient matrices in direct manner. However this original SC-VAR is difficult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to benefit model building and forecasting, the original SC-VAR is modified and a Correlation-Constrained SC-VAR (CCSC-VAR) is proposed based on spatial correlation information about wind farms. Our approach is evaluated based on a case study of very-short-term forecasting for 25 wind farms in Denmark. Comparison is performed with a set of traditional local methods and spatio-temporal methods. Results show the proposed CCSC-VAR has better overall performance than both the original SC-VAR and other benchmark methods, taking into account all evaluation indicators, including sparsity-control ability, sparsity, accuracy and efficiency.

ACS Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems 2018, 33, 5029 -5040.

AMA Style

Yongning Zhao, Lin Ye, Pierre Pinson, Yong Tang, Peng Lu. Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting. IEEE Transactions on Power Systems. 2018; 33 (5):5029-5040.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Pierre Pinson; Yong Tang; Peng Lu. 2018. "Correlation-Constrained and Sparsity-Controlled Vector Autoregressive Model for Spatio-Temporal Wind Power Forecasting." IEEE Transactions on Power Systems 33, no. 5: 5029-5040.

Journal article
Published: 19 June 2017 in IEEE Transactions on Sustainable Energy
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Regardless of the rapid development of wind power capacity installation around the world, wind curtailment is a severe problem to be solved. Wind curtailment can cause abundant outliers and change the original characteristics of operation data in wind farms. Power curve cannot be accurately modeled with these outliers and consequently wind power forecasting as well as other applications in power system will be negatively affected. In this paper, the characteristics of the outliers caused by wind curtailment are analyzed. Then, a data-driven outlier elimination approach combining quartile method and density-based clustering method is proposed. First, the quartile method is used twice for eliminating sparse outliers. Then density-based spatial clustering of applications with noise method is applied to eliminate stacked outliers. A case study is carried out by modeling the power curves of a wind farm and 20 wind turbines in this wind farm. The accuracy of power curve modeling is significantly improved and the elimination procedure can be completed in a very short time, indicating that the proposed methods are effective and efficient for eliminating outliers. The performance of the methods is insensitive to their parameters and can be directly used in different cases without tuning parameters, both for wind turbines and wind farms.

ACS Style

Yongning Zhao; Lin Ye; Weisheng Wang; Huadong Sun; Yuntao Ju; Yong Tang. Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment. IEEE Transactions on Sustainable Energy 2017, 9, 95 -105.

AMA Style

Yongning Zhao, Lin Ye, Weisheng Wang, Huadong Sun, Yuntao Ju, Yong Tang. Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment. IEEE Transactions on Sustainable Energy. 2017; 9 (1):95-105.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Weisheng Wang; Huadong Sun; Yuntao Ju; Yong Tang. 2017. "Data-Driven Correction Approach to Refine Power Curve of Wind Farm Under Wind Curtailment." IEEE Transactions on Sustainable Energy 9, no. 1: 95-105.

Journal article
Published: 01 February 2017 in Renewable Energy
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ACS Style

Lin Ye; Yongning Zhao; Cheng Zeng; Cihang Zhang. Short-term wind power prediction based on spatial model. Renewable Energy 2017, 101, 1067 -1074.

AMA Style

Lin Ye, Yongning Zhao, Cheng Zeng, Cihang Zhang. Short-term wind power prediction based on spatial model. Renewable Energy. 2017; 101 ():1067-1074.

Chicago/Turabian Style

Lin Ye; Yongning Zhao; Cheng Zeng; Cihang Zhang. 2017. "Short-term wind power prediction based on spatial model." Renewable Energy 101, no. : 1067-1074.

Journal article
Published: 01 September 2016 in Applied Energy
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ACS Style

Yongning Zhao; Lin Ye; Zhi Li; Xuri Song; Yansheng Lang; Jian Su. A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy 2016, 177, 793 -803.

AMA Style

Yongning Zhao, Lin Ye, Zhi Li, Xuri Song, Yansheng Lang, Jian Su. A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy. 2016; 177 ():793-803.

Chicago/Turabian Style

Yongning Zhao; Lin Ye; Zhi Li; Xuri Song; Yansheng Lang; Jian Su. 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting." Applied Energy 177, no. : 793-803.