The Experts below are selected from a list of 1884 Experts worldwide ranked by ideXlab platform
Zhaoyang Dong - One of the best experts on this subject based on the ideXlab platform.
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reliability based min max regret stochastic optimization model for Capacity Market with renewable energy and practice in china
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
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Reliability Based Min–Max Regret Stochastic Optimization Model for Capacity Market With Renewable Energy and Practice in China
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min-max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min-max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
Runzhao Lu - One of the best experts on this subject based on the ideXlab platform.
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reliability based min max regret stochastic optimization model for Capacity Market with renewable energy and practice in china
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
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Reliability Based Min–Max Regret Stochastic Optimization Model for Capacity Market With Renewable Energy and Practice in China
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min-max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min-max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
Rui Bo - One of the best experts on this subject based on the ideXlab platform.
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reliability based min max regret stochastic optimization model for Capacity Market with renewable energy and practice in china
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
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Reliability Based Min–Max Regret Stochastic Optimization Model for Capacity Market With Renewable Energy and Practice in China
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min-max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min-max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
Tao Ding - One of the best experts on this subject based on the ideXlab platform.
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reliability based min max regret stochastic optimization model for Capacity Market with renewable energy and practice in china
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min–max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min–max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
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Reliability Based Min–Max Regret Stochastic Optimization Model for Capacity Market With Renewable Energy and Practice in China
IEEE Transactions on Sustainable Energy, 2019Co-Authors: Runzhao Lu, Tao Ding, Rui Bo, Zhaoyang DongAbstract:Capacity Market is a long-term clearing model to coordinate the traditional thermal generation and the renewable energy generation, which minimizes the total Capacity cost of traditional generation, while satisfying the operational constraints and reliability requirement. Furthermore, to address the uncertainties in the long-term optimal decision, min-max regret is employed to find the optimal solution under the worst regret, generating several representable scenarios that can help generation companies to understand how these scenarios would impact on the future generation planning. Finally, a decomposition method is proposed to solve this reliability based min-max regret stochastic optimization model by bisection. The test results on one regional grid in China show the effectiveness of the proposed model.
Felix F. Wu - One of the best experts on this subject based on the ideXlab platform.
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HICSS - Financing Long-Term Generation Capacity in a Reference Price Oriented Capacity Market
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Felix F. WuAbstract:Many power Markets around the world have been facing inadequacy with generation Capacity investment to meet the growing demand. Among various frameworks directed towards this problem, Capacity Markets have emerged in major eastern US power Markets. In this paper, a prototype Capacity Market is discussed which is consistent with the trend of convergence of Market design. Based on this, the critical role of the reference Capacity price is brought up, followed by a detailed explanation of its economic rationale and concerns. Noted with the necessity of a systematic pricing scheme to determine the value of the reference Capacity price, a pricing model based on the general Black-Scholes contingent claim framework is proposed. In this model, the Capacity value is treated as a path-dependent derivative with electricity prices and natural gas prices as underlyings. Numerical study is conducted to prove model validity with a lattice approach adopted.
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Financing Long-Term Generation Capacity in a Reference Price Oriented Capacity Market
2010 43rd Hawaii International Conference on System Sciences, 2010Co-Authors: Felix F. WuAbstract:Many power Markets around the world have been facing inadequacy with generation Capacity investment to meet the growing demand. Among various frameworks directed towards this problem, Capacity Markets have emerged in major eastern US power Markets. In this paper, a prototype Capacity Market is discussed which is consistent with the trend of convergence of Market design. Based on this, the critical role of the reference Capacity price is brought up, followed by a detailed explanation of its economic rationale and concerns. Noted with the necessity of a systematic pricing scheme to determine the value of the reference Capacity price, a pricing model based on the general Black-Scholes contingent claim framework is proposed. In this model, the Capacity value is treated as a path-dependent derivative with electricity prices and natural gas prices as underlyings. Numerical study is conducted to prove model validity with a lattice approach adopted.
