This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol （NRGS） for an errors- in-variables （EIV） system corrupted with bounded noise. Following an identification framewo...This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol （NRGS） for an errors- in-variables （EIV） system corrupted with bounded noise. Following an identification framework for estimation of a perturbation model set, a worst-case v-gap error bound for the estimated nominal NRGS can be first determined from a priori and a posteriori information on the underlying EIV system. Then, an NRGS perturbation model set can be derived from a close relation between the v-gap metric of two models and H∞-norm of their NRGSs＇ difference. The obtained NRGS perturbation model set paves the way for robust controller design using an H∞ loop-shaping method because it is a standard form of the well-known NCF （normalized coprime factor） perturbation model set. Finally, a numerical simulation is used to demonstrate the effectiveness of the proposed identification method.展开更多
The performance of an aging structure is commonly evaluated under the framework of reliability analysis, where the uncertainties associated with the structural resistance and loads should be taken into account. In pra...The performance of an aging structure is commonly evaluated under the framework of reliability analysis, where the uncertainties associated with the structural resistance and loads should be taken into account. In practical engineering, the probability distribution of resistance deterioration is often inaccessible due to the limits of available data, although the statistical parameters such as mean value and standard deviation can be obtained by fitting or empirical judgments. As a result, an error of structural reliability may be introduced when an arbitrary probabilistic distribution is assumed for the resistance deterioration. With this regard, in this paper, the amount of reliability error posed by different choices of deterioration distribution is investigated, and a novel approach is proposed to evaluate the averaged structural reliability under incomplete deterioration information, which does not rely on the probabilistic weight of the candidate deterioration models. The reliability for an illustrative structure is computed parametrically for varying probabilistic models of deterioration and different resistance conditions, through which the reliability associated with different deterioration models is compared, and the application of the proposed method is illustrated.展开更多
The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitabili...The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market.This paper proposes two coherent models to address these challenges.Firstly,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation penalties.Secondly,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast errors.The role of the DEQ model is to guide the knowledgedriven bidding model.Notably,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational optimum.These two models have independent optimizations based on misaligned objectives.To address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding strategy.The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.61203119,61304153)the Key Program of Tianjin Natural Science Foundation,China(No.14JCZDJC36300)the Tianjin University of Technology and Education funded project(No.RC14-48)
文摘This paper proposes a novel method to quantify the error of a nominal normalized right graph symbol （NRGS） for an errors- in-variables （EIV） system corrupted with bounded noise. Following an identification framework for estimation of a perturbation model set, a worst-case v-gap error bound for the estimated nominal NRGS can be first determined from a priori and a posteriori information on the underlying EIV system. Then, an NRGS perturbation model set can be derived from a close relation between the v-gap metric of two models and H∞-norm of their NRGSs＇ difference. The obtained NRGS perturbation model set paves the way for robust controller design using an H∞ loop-shaping method because it is a standard form of the well-known NCF （normalized coprime factor） perturbation model set. Finally, a numerical simulation is used to demonstrate the effectiveness of the proposed identification method.
基金Project supported by the National Natural Science Foundation of China （No. 51578315） and the Major Projects Fund of Chinese Ministry of Transport （No. 201332849A090）
文摘The performance of an aging structure is commonly evaluated under the framework of reliability analysis, where the uncertainties associated with the structural resistance and loads should be taken into account. In practical engineering, the probability distribution of resistance deterioration is often inaccessible due to the limits of available data, although the statistical parameters such as mean value and standard deviation can be obtained by fitting or empirical judgments. As a result, an error of structural reliability may be introduced when an arbitrary probabilistic distribution is assumed for the resistance deterioration. With this regard, in this paper, the amount of reliability error posed by different choices of deterioration distribution is investigated, and a novel approach is proposed to evaluate the averaged structural reliability under incomplete deterioration information, which does not rely on the probabilistic weight of the candidate deterioration models. The reliability for an illustrative structure is computed parametrically for varying probabilistic models of deterioration and different resistance conditions, through which the reliability associated with different deterioration models is compared, and the application of the proposed method is illustrated.
基金supported by the U.S.Department of Energy's Office of Energy Efficiency and Renewable Energy(EERE)under the Solar Energy Technologies Office Award(No.DE-EE0009341)。
文摘The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market.This paper proposes two coherent models to address these challenges.Firstly,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation penalties.Secondly,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast errors.The role of the DEQ model is to guide the knowledgedriven bidding model.Notably,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational optimum.These two models have independent optimizations based on misaligned objectives.To address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding strategy.The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).
