This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomne...This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.展开更多
This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fa...This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.展开更多
The study of physical systems endowed with a position-dependent mass (PDM) remains a fundamental issue of quantum mechanics. In this paper we use a new approach, recently developed by us for building the quantum kinet...The study of physical systems endowed with a position-dependent mass (PDM) remains a fundamental issue of quantum mechanics. In this paper we use a new approach, recently developed by us for building the quantum kinetic energy operator (KEO) within the Schrodinger equation, in order to construct a new class of exactly solvable models with a position varying mass, presenting a harmonic-oscillator-like spectrum. To do so we utilize the formalism of supersymmetric quantum mechanics (SUSY QM) along with the shape invariance condition. Recent outcomes of non-Hermitian quantum mechanics are also taken into account.展开更多
Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually...Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually, and are therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present a fully automated fractionation detection algorithm for patients with VT which expands on previously defined fractionation features and which substantially decreases associated study times. Paced electrogram signals were collected from six patients during electrophysiologic study according to a modified paced electrogram fractionation analysis protocol. Data were exported and analyzed offline using custom written software. Electrograms from right ventricular pacing catheter were used as reference. Surface electrograms, along with ventricular geometry and relative catheter locations, were used to identify physiological interference and physiologically irrelevant features. A total of 264 electrograms, collected from a roving catheter, were manually and automatically annotated for fractionation as defined by three features: conduction time (CT), electrogram duration (ED), and number of deflections (ND). Of these, 60 were selected manually to have no discernable features and were successfully discarded by our algorithm;yielding a specificity of 100%. Of the remaining 204, 16 were erroneously discarded by our algorithm;yielding a sensitivity of 92.16%. A comparison between annotations showed correlations of 0.98, 0.97, and 0.94 for AL, ED, and ND respectively.展开更多
This study proposes an optimized model of a micro-energy network(MEN)that includes electricity and natural gas with integrated solar,wind,and energy storage systems(ESSs).The proposed model is based on energy hubs(EHs...This study proposes an optimized model of a micro-energy network(MEN)that includes electricity and natural gas with integrated solar,wind,and energy storage systems(ESSs).The proposed model is based on energy hubs(EHs)and it aims to minimize operation costs and greenhouse emissions.The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects.A general algebraic modeling system(GAMS)is used to solve the optimal operation problem in the MEN.The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons.It also yields significant reductions in operation costs and emissions.The proposed model can contribute to future research by providing a more efficient network model(as compared with the traditional electricity supply system)to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.展开更多
District energy systems(DESs)have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings.For DFISs,the operational flexibility could be maintained by ...District energy systems(DESs)have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings.For DFISs,the operational flexibility could be maintained by energy conversion and storage facilities.This paper proposes a hierarchical optimization framework for leveraging and aggregating the DES flexibility to provide contingency reserves.To characterize and quantify the flexibility in individual DESs,the concept of available reserve profile,which is measured by a set of indices,is established.A two-stage robust optimization(RO)model is developed for calculating the indices,which considers the uncertainties associated with wind power and ambient temperature.The lower stage of the two-stage model is managed by district energy system operators(DESOs)which submit reserve profiles to the district energy system coordinator(DESC)at the upper stage,which is responsible for the coordination process.Correspondingly,information privacy is preserved using a coordinated data-sharing strategy.Using reserve profiles submitted by multiple DESOs,the DESC applies the proposed coordination model to provide a certain reserve capacity schedule to DESs,which satisfies the stated objectives.The coordination model is formulated and solved based on the special ordered set(SOS)technique and particle swarm optimization(PSO)algorithm.A test system is developed to illustrate the technical viability and economic feasibility of the proposed technique.展开更多
Residential buildings are one of the major contributors to climate change due to their significant impacts on global energy consumption.Hence,most countries have introduced regulations to minimize energy use in reside...Residential buildings are one of the major contributors to climate change due to their significant impacts on global energy consumption.Hence,most countries have introduced regulations to minimize energy use in residential buildings.To date,the focus of these regulations has mainly been on operational energy while excluding embodied energy.In recent years,extensive studies have highlighted the necessity of minimizing both embodied energy and operational energy by applying the life-cycle energy assessment(LCEA)approach.However,the absence of a standardized framework and calculation methodology for the analysis of embodied energy has reportedly led to variations in the LCEA results.Retrospective research endeavoured to explore the causes of variations,with a limited focus on calculating embodied impacts.Despite the undertaken attempts,there is still a need to investigate the key parameters causing variations in LCEA results by examining methodological approaches of the current studies toward quantifications of embodied and operational energies.This paper aims to address three primary questions:‘what is the current trend of methodological approach for applying LCEA in residential buildings?’;‘what are the key parameters causing variations in LCEA results?’;and‘how can the continued variations in the application of LCEA in residential buildings be overcome?’.To this end,40 LCEA studies representing 157 cases of residential buildings across 16 countries have been critically reviewed.The findings reveal four principal categories of parameters that potentially contribute to the varying results of LCEAs:system boundary definition,calculation methods,geographical context,and interpretation of results.This paper also proposes a conceptual framework to minimize variations in LCEA studies by standardizing the process of conducting LCEAs.展开更多
This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combin...This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combined cooling,heating,and power system(CCHP)and photovoltaic(PV)systems are not currently allowed.However,with the Chinese supply-side power grid reform,the permissions for connections between DESs and utilities are gradually evolving.By performing building simulation and using mixed integer linear programming(MILP),a real-time updated operation strategy of a DES is established.Then,considering the DES from Tianjin Eco-city as a case study,a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors:economic efficiency,energy consumption,and CO2 emission.The results show that the updated strategy can reduce 29.12%of electricity time-of-use cost,10.11%of total fuel consumption,and 18.40%of CO2 emission during the cooling season.Besides,a method of“rolling load forecasting”for DES by using Support vector regression machine(SVR)is proposed and discussed.The testing shows that the Mean Absolute Percentage Error(MAPE)is below 7.5%.And when the training sample is large,the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.展开更多
The energy storage system(ESS) is becoming an important component in power systems to mitigate the adverse impact of intermittent renewable energy resources and improve power grid reliability and efficiency.However,st...The energy storage system(ESS) is becoming an important component in power systems to mitigate the adverse impact of intermittent renewable energy resources and improve power grid reliability and efficiency.However,storage devices driven by different technologies can have specific grid impacts.This special section is dedicated to reflecting the展开更多
As parameter independent yet simple techniques,the energy operator(EO)and its variants have received considerable attention in the field of bearing fault feature detection.However,the performances of these improved EO...As parameter independent yet simple techniques,the energy operator(EO)and its variants have received considerable attention in the field of bearing fault feature detection.However,the performances of these improved EO techniques are subjected to the limited number of EOs,and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction.As a result,the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises.To address these issues,this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively.Specifically,the proposed strategy is conducted through the following three steps.First,a multi-dimensional information matrix(MDIM)is constructed by performing the higher order energy operator(HOEO)on the analysis signal iteratively.MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region.Second,an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses.Third,the intrinsic manifolds are weighted to recover the fault-related transients.Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods,including HOEOs,the weighting HOEO fusion,the fast Kurtogram,and the empirical mode decomposition.展开更多
Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, no...Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoderdecoder model is proposed, called empirical mode decomposition-attention-long short-term memory(EMD-Att-LSTM).EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data,leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator(AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error(RMSE) and mean absolute percentage error(MAPE).展开更多
基金supported by National Natural Science Foundation of China(61100159,61233007)National High Technology Research and Development Program of China(863 Program)(2011AA040103)+2 种基金Foundation of Chinese Academy of Sciences(KGCX2-EW-104)Financial Support of the Strategic Priority Research Program of Chinese Academy of Sciences(XDA06021100)the Cross-disciplinary Collaborative Teams Program for Science,Technology and Innovation,of Chinese Academy of Sciences-Network and System Technologies for Security Monitoring and Information Interaction in Smart Grid Energy Management System for Micro-smart Grid
文摘This paper focuses on the energy optimal operation problem of microgrids(MGs) under stochastic environment.The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources.Therefore,it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system.This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs.The goal is to minimize the expected operation cost of MGs.The uncertainties are modeled based on autoregressive moving average(ARMA) model to expose the effects of physical world on cyber world.Through the comparison of the simulation results with deterministic method,it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.
基金supported in part by the National Natural Science Foundation of China(52177042)Natural Science Foundation of Hebei Province(E2020502031)+1 种基金the Fundamental Research Funds for the Central Universities(2017MS151),Suzhou Social Developing Innovation Project of Science and Technology(SS202134)the Top Youth Talent Support Program of Hebei Province([2018]-27).
文摘This paper proposed a new diagnosis model for the stator inter-turn short circuit fault in synchronous generators.Different from the past methods focused on the current or voltage signals to diagnose the electrical fault,the sta-tor vibration signal analysis based on ACMD(adaptive chirp mode decomposition)and DEO3S(demodulation energy operator of symmetrical differencing)was adopted to extract the fault feature.Firstly,FT(Fourier trans-form)is applied to the vibration signal to obtain the instantaneous frequency,and PE(permutation entropy)is calculated to select the proper weighting coefficients.Then,the signal is decomposed by ACMD,with the instan-taneous frequency and weighting coefficient acquired in the former step to obtain the optimal mode.Finally,DEO3S is operated to get the envelope spectrum which is able to strengthen the characteristic frequencies of the stator inter-turn short circuit fault.The study on the simulating signal and the real experiment data indicates the effectiveness of the proposed method for the stator inter-turn short circuit fault in synchronous generators.In addition,the comparison with other methods shows the superiority of the proposed model.
基金The authors gratefully acknowledge Qassim University,represented by the Deanship of Scienti c Research,on the material support for this research under the number(1671-ALRASSCAC-2016-1-12-S)during the academic year 1437 AH/2016 AD.
文摘The study of physical systems endowed with a position-dependent mass (PDM) remains a fundamental issue of quantum mechanics. In this paper we use a new approach, recently developed by us for building the quantum kinetic energy operator (KEO) within the Schrodinger equation, in order to construct a new class of exactly solvable models with a position varying mass, presenting a harmonic-oscillator-like spectrum. To do so we utilize the formalism of supersymmetric quantum mechanics (SUSY QM) along with the shape invariance condition. Recent outcomes of non-Hermitian quantum mechanics are also taken into account.
文摘Catheter ablation therapy has become a key intervention in treatment of ventriculartachycardia (VT). However, current fractionation mapping methods used to isolate the ablation targets in VT patients are done manually, and are therefore time consuming. They also have limited success rates (50% recurrence rate within 2 years). We present a fully automated fractionation detection algorithm for patients with VT which expands on previously defined fractionation features and which substantially decreases associated study times. Paced electrogram signals were collected from six patients during electrophysiologic study according to a modified paced electrogram fractionation analysis protocol. Data were exported and analyzed offline using custom written software. Electrograms from right ventricular pacing catheter were used as reference. Surface electrograms, along with ventricular geometry and relative catheter locations, were used to identify physiological interference and physiologically irrelevant features. A total of 264 electrograms, collected from a roving catheter, were manually and automatically annotated for fractionation as defined by three features: conduction time (CT), electrogram duration (ED), and number of deflections (ND). Of these, 60 were selected manually to have no discernable features and were successfully discarded by our algorithm;yielding a specificity of 100%. Of the remaining 204, 16 were erroneously discarded by our algorithm;yielding a sensitivity of 92.16%. A comparison between annotations showed correlations of 0.98, 0.97, and 0.94 for AL, ED, and ND respectively.
基金This work was supported by the National Natural Science Foundation of China(No.51777077)Thai Nguyen University of Technology(TNUT),Thai Nguyen,Vietnam.
文摘This study proposes an optimized model of a micro-energy network(MEN)that includes electricity and natural gas with integrated solar,wind,and energy storage systems(ESSs).The proposed model is based on energy hubs(EHs)and it aims to minimize operation costs and greenhouse emissions.The research is motivated by the increasing use of renewable energies and ESSs for secure energy supply while reducing operation costs and environment effects.A general algebraic modeling system(GAMS)is used to solve the optimal operation problem in the MEN.The results demonstrate that an optimal MEN formed by multiple EHs can provide appropriate and flexible responses to fluctuations in electricity prices and adjustments between time periods and seasons.It also yields significant reductions in operation costs and emissions.The proposed model can contribute to future research by providing a more efficient network model(as compared with the traditional electricity supply system)to scale down the environmental and economic impacts of electricity storage and supply systems on MEN operation.
基金supported by the National Natural Science Foundation of China under grant 52022016China Postdoctoral Science Foundation under grant 2021M693711.
文摘District energy systems(DESs)have become a popular form of satisfying comprehensive energy demands for different types of loads in multiple local buildings.For DFISs,the operational flexibility could be maintained by energy conversion and storage facilities.This paper proposes a hierarchical optimization framework for leveraging and aggregating the DES flexibility to provide contingency reserves.To characterize and quantify the flexibility in individual DESs,the concept of available reserve profile,which is measured by a set of indices,is established.A two-stage robust optimization(RO)model is developed for calculating the indices,which considers the uncertainties associated with wind power and ambient temperature.The lower stage of the two-stage model is managed by district energy system operators(DESOs)which submit reserve profiles to the district energy system coordinator(DESC)at the upper stage,which is responsible for the coordination process.Correspondingly,information privacy is preserved using a coordinated data-sharing strategy.Using reserve profiles submitted by multiple DESOs,the DESC applies the proposed coordination model to provide a certain reserve capacity schedule to DESs,which satisfies the stated objectives.The coordination model is formulated and solved based on the special ordered set(SOS)technique and particle swarm optimization(PSO)algorithm.A test system is developed to illustrate the technical viability and economic feasibility of the proposed technique.
文摘Residential buildings are one of the major contributors to climate change due to their significant impacts on global energy consumption.Hence,most countries have introduced regulations to minimize energy use in residential buildings.To date,the focus of these regulations has mainly been on operational energy while excluding embodied energy.In recent years,extensive studies have highlighted the necessity of minimizing both embodied energy and operational energy by applying the life-cycle energy assessment(LCEA)approach.However,the absence of a standardized framework and calculation methodology for the analysis of embodied energy has reportedly led to variations in the LCEA results.Retrospective research endeavoured to explore the causes of variations,with a limited focus on calculating embodied impacts.Despite the undertaken attempts,there is still a need to investigate the key parameters causing variations in LCEA results by examining methodological approaches of the current studies toward quantifications of embodied and operational energies.This paper aims to address three primary questions:‘what is the current trend of methodological approach for applying LCEA in residential buildings?’;‘what are the key parameters causing variations in LCEA results?’;and‘how can the continued variations in the application of LCEA in residential buildings be overcome?’.To this end,40 LCEA studies representing 157 cases of residential buildings across 16 countries have been critically reviewed.The findings reveal four principal categories of parameters that potentially contribute to the varying results of LCEAs:system boundary definition,calculation methods,geographical context,and interpretation of results.This paper also proposes a conceptual framework to minimize variations in LCEA studies by standardizing the process of conducting LCEAs.
基金This study was supported by Scientific Research Project of Science and technology Commission of Shanghai Municipality(Grant No.18DZ1202700).
文摘This study focuses on the development and analysis of a real-time updated operations strategy of a distributed energy system(DES).Owing to the relevant Chinese policy of electrical transmission and distribution,combined cooling,heating,and power system(CCHP)and photovoltaic(PV)systems are not currently allowed.However,with the Chinese supply-side power grid reform,the permissions for connections between DESs and utilities are gradually evolving.By performing building simulation and using mixed integer linear programming(MILP),a real-time updated operation strategy of a DES is established.Then,considering the DES from Tianjin Eco-city as a case study,a comparative analysis between this updated strategy and the current operation strategy is performed by evaluating three factors:economic efficiency,energy consumption,and CO2 emission.The results show that the updated strategy can reduce 29.12%of electricity time-of-use cost,10.11%of total fuel consumption,and 18.40%of CO2 emission during the cooling season.Besides,a method of“rolling load forecasting”for DES by using Support vector regression machine(SVR)is proposed and discussed.The testing shows that the Mean Absolute Percentage Error(MAPE)is below 7.5%.And when the training sample is large,the particle swarm optimization algorithm can be used to shorten the modeling time of the air conditioning load forecasting model.
文摘The energy storage system(ESS) is becoming an important component in power systems to mitigate the adverse impact of intermittent renewable energy resources and improve power grid reliability and efficiency.However,storage devices driven by different technologies can have specific grid impacts.This special section is dedicated to reflecting the
基金supported by the National Natural Science Foundation of China (Grant Nos.52172406 and 51875376)the China Postdoctoral Science Foundation (Grant Nos.2022T150552 and 2021M702752)the Suzhou Prospective Research Program,China (Grant No.SYG202111)。
文摘As parameter independent yet simple techniques,the energy operator(EO)and its variants have received considerable attention in the field of bearing fault feature detection.However,the performances of these improved EO techniques are subjected to the limited number of EOs,and they cannot reflect the non-linearity of the machinery dynamic systems and affect the noise reduction.As a result,the fault-related transients strengthened by these improved EO techniques are still subject to contamination of strong noises.To address these issues,this paper presents a novel EO fusion strategy for enhancing the bearing fault feature nonlinearly and effectively.Specifically,the proposed strategy is conducted through the following three steps.First,a multi-dimensional information matrix(MDIM)is constructed by performing the higher order energy operator(HOEO)on the analysis signal iteratively.MDIM is regarded as the fusion source of the proposed strategy with the properties of improving the signal-to-interference ratio and suppressing the noise in the low-frequency region.Second,an enhanced manifold learning algorithm is performed on the normalized MDIM to extract the intrinsic manifolds correlated with the fault-related impulses.Third,the intrinsic manifolds are weighted to recover the fault-related transients.Simulation studies and experimental verifications confirm that the proposed strategy is more effective for enhancing the bearing fault feature than the existing methods,including HOEOs,the weighting HOEO fusion,the fast Kurtogram,and the empirical mode decomposition.
文摘Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoderdecoder model is proposed, called empirical mode decomposition-attention-long short-term memory(EMD-Att-LSTM).EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data,leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator(AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error(RMSE) and mean absolute percentage error(MAPE).
