This paper intends to describe how to design an optimized and effective strategic information system （SIS） by using the existing resources under the new market circumstances in China. Some feasible methods are thus ...This paper intends to describe how to design an optimized and effective strategic information system （SIS） by using the existing resources under the new market circumstances in China. Some feasible methods are thus proposed, which are about the design of SIS by detailed analysis of the principle requirements. The paper also puts forward the architecture and configuration of SIS in China in general.展开更多
Many countries often find it extremely hard to do business in the Middle East, including the United Arab Emirates （UAE）. The Middle East is a dramatically different environment to do business, The reason is that Ara...Many countries often find it extremely hard to do business in the Middle East, including the United Arab Emirates （UAE）. The Middle East is a dramatically different environment to do business, The reason is that Arab culture is markedly different from those in other countries. From analyzing the culture, social institutions and informal trade barriers about UAE, some trouble spots and countermeasures can be found. To successfully do business in UAE, businessmen must understand Arab ways and culture, and also learn about sociotal structures including social classes, the religion, language, attitudes toward strangers and so on.展开更多
The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilie...The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilience of power systems towards extreme events.To quantify the temporal and spatial flexibility of EVs for charging and discharging,a novel dynamic traffic assignment(DTA)problem is proposed.The DTA problem is based on a link transmission model(LTM)with extended charging links,depicting the interaction between EVs and power systems.It models the charging rates as continuous variables by an energy boundary model.To consider the evacuation requirements of TNs and the uncertainties of traffic conditions,the DTA problem is extended to a two-stage distributionally robust version.It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs.The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers.Case studies are performed on two test networks.The effectiveness is verified by the numerical results,e.g.,reducing the load shedding amount without increasing the unmet traffic demand.展开更多
Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC ...Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy management.However,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased operation.This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques.First,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management.Second,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management.Mathematical programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation schemes.Finally,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.展开更多
To enhance the resilience of power systems with offshore wind farms(OWFs),a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers(CDCs)responding to uncertain spatial and temporal imp...To enhance the resilience of power systems with offshore wind farms(OWFs),a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers(CDCs)responding to uncertain spatial and temporal impacts induced by hurricanes.The total life simulation(TLS)is adopted to project the local weather conditions at transmission lines and OWFs,before,during,and after the hurricane.The static power curve of wind turbines(WTs)is used to capture the output of OWFs,and the fragility analysis of transmission-line components is used to formulate the time-varying failure rates of transmission lines.A novel distributionally robust ambiguity set is constructed with a discrete support set,where the impacts of hurricanes are depicted by these supports.To minimize load sheddings and dropping workloads,the spatial and temporal demand response capabilities of CDCs according to task migration and delay tolerance are incorporated into resilient management.The flexibilities of CDC’s power consumption are integrated into a two-stage distributionally robust optimization problem with conditional value at risk(CVaR).Based on Lagrange duality,this problem is reformulated into its deterministic counterpart and solved by a novel decomposition method with hybrid cuts,admitting fewer iterations and a faster convergence rate.The effectiveness of the proposed resilient management strategy is verified through case studies conducted on the modified IEEERTS 24 system,which includes 4 data centers and 5 offshore wind farms.展开更多
Wind power curve modeling is essential in the analysis and control of wind turbines(WTs),and data preprocessing is a critical step in accurate curve modeling.As traditional methods do not sufficiently consider WT mode...Wind power curve modeling is essential in the analysis and control of wind turbines(WTs),and data preprocessing is a critical step in accurate curve modeling.As traditional methods do not sufficiently consider WT models,this paper proposes a new data cleaning method for wind power curve modeling.In this method,a model-data hybrid-driven(MDHD)outlier detection method is constructed,and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model.Simultaneously,because the MDHD outlier detection method considers multiple types of operating data of WTs,anomaly detection results require further analysis.Accordingly,an expert system is developed in which a knowledgebase and an inference engine are designed based on the coupling relationships of different operating data.Finally,abnormal data are eliminated and the power curve modeling is completed.The proposed and traditional methods are compared in numerical cases,and the superiority of the proposed method is demonstrated.展开更多
As a classically known mitogen,fibroblast growth factor 1(FGF1)has been found to exert other pleiotropic functions such as metabolic regulation and myocardial protection.Here,we show that serum levels of FGF1 were dec...As a classically known mitogen,fibroblast growth factor 1(FGF1)has been found to exert other pleiotropic functions such as metabolic regulation and myocardial protection.Here,we show that serum levels of FGF1 were decreased and positively correlated with fraction shortening in diabetic cardiomyopathy(DCM)patients,indicating that FGF1 is a potential therapeutic target for DCM.We found that treatment with a FGF1 variant(FGF1^(△HBS))with reduced proliferative potency prevented diabetes-induced cardiac injury and remodeling and restored cardiac function.RNA-Seq results obtained from the cardiac tissues of db/db mice showed significant increase in the expression levels of anti-oxidative genes and decrease of Nur77 by FGF1AHBS treatment.Both in vivo and in vitro studies indicate that FGF1^(△HBS) exerted these beneficial effects by markedly reducing mitochondrial fragmentation,reactive oxygen species(ROS)generation and cytochrome c leakage and enhancing mitochondrial respiration rate and β-oxidation in a 5;AMP-activated protein kinase(AMPK)/Nur77-dependent manner,all of which were not observed in the AMPK null mice.The favorable metabolic activity and reduced proliferative properties of FGF1^(△HBS) testify to its promising potential for use in the treatment of DCM and other metabolic disorders.展开更多
In this paper,a flexible management method is proposed for an active distribution system(ADS)with distributed energy resources(DERs)integrated,where DERs can provide spinning reserves to transmission networks.This met...In this paper,a flexible management method is proposed for an active distribution system(ADS)with distributed energy resources(DERs)integrated,where DERs can provide spinning reserves to transmission networks.This method,based on the information-gap decision-making theory(IGDT)theory,could be of use to the ADS operator(ADSO)from either the opportunistic or robust perspective when reserve is called by the independent system operator(ISO).Two IGDT uncertainty models are employed to depict the characteristics of reserve uncertainty in centralized and decentralized control frameworks.The reactive power of each DER is managed by the ADSO in the immunity functions,which are reformulated as bi-level biobjective optimization problems.A hybrid multi-objective differential evolutional algorithm(MODE)is proposed to solve the optimization problems.The relationship between the uncertainty levels and robust/opportunistic limits is revealed by the Pareto fronts obtained by MODE.Effectiveness of the proposed method is demonstrated based on simulation results of a 33-bus and 123-bus test system.展开更多
Increased penetration of electric vehicles(EVs)is expected to impact power system performance in adverse ways,e.g.,overloading,uncertain power quality,and increased voltage fluctuation,particularly at the distribution...Increased penetration of electric vehicles(EVs)is expected to impact power system performance in adverse ways,e.g.,overloading,uncertain power quality,and increased voltage fluctuation,particularly at the distribution level.Most EV charging control strategies that have been proposed only benefit the grid or EV users.A centralized EV charging strategy based on bilevel optimization is proposed in this paper with the objectives of deriving benefits for the grid and EV users simultaneously.The proposed strategy involves distributing the EV charging load more beneficially across both spatial and temporal levels.In the spatial problem,the whole fleet of EVs is controlled to minimize load variance as spatial coordination,with total charging rate and energy needed as the constraint.While in the temporal problem,EVs in each aggregator are controlled to minimize the charging cost or maximize the EV user’s degree of satisfaction with each aggregator’s charging rate and energy needed as the constraint.The proposed bi-level charging strategy is transformed to a single-stage optimization problem and solved using the classical optimization method.The impacts of uncontrolled charging on the grid and EV users are studied using the Monte Carlo Simulation(MCS)method.Then,the effectiveness of the proposed charging strategy is demonstrated via results obtained in the MCS.展开更多
To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The C...To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The CLSC is used to depict the battery-swapping-charging process between the battery charging stations(BCSs)and battery swapping stations(BSSs).The arrival,departure and swapping service of electric vehicles(EVs)at BSSs is modeled as distinct queues based on the network calculus theory.The depleted batteries(DBs)and well-charging batteries(WBs)based interaction among BCSs and BSSs is formulated as a Stackelberg game.In the game,one BCS acts as the leader and the BSSs act as the followers.The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs,supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service(QoS)needed for battery swapping.The existence of Stackelberg equilibriums(SEs)of the proposed game is proved.A differential evaluation based hybrid algorithm is proposed to compute an SE.The effectiveness of proposed method has been demonstrated by the simulation results,guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.展开更多
Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation...Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power grid.The proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,simultaneously.Afterwards,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling problem.Finally,simulation studies verify the effectiveness of the proposed multi-objective operation method.展开更多
As battery technology matures,the battery energy storage system(BESS)becomes a promising candidate for addressing renewable energy uncertainties.BESS degradation is one of key factors in BESS operations,which is usual...As battery technology matures,the battery energy storage system(BESS)becomes a promising candidate for addressing renewable energy uncertainties.BESS degradation is one of key factors in BESS operations,which is usually considered in the planning stage.However,BESS degradations are directly affected by the depth of discharge(DoD),which is closely related to the BESS daily schedule.Specifically,the BESS life losses may be different when providing the same amount of energy under a distinct DoD.Therefore,it is necessary to develop a model to consider the effect of daily discharge on BESS degradation.In this paper,a model quantifying the nonlinear impact of DoD on BESS life loss is proposed.By adopting the chance-constrained goal programming,the degradation in day-ahead unit commitment is formulated as a multi-objective optimization problem.To facilitate an efficient solution,the model is converted into a mixed integer linear programming problem.The effectiveness of the proposed method is verified in a modified IEEE 39-bus system.展开更多
To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirement...To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirements for both residential and parking loads are managed simultaneously,i.e.,electric and thermal loads for residence,and charging power and gas filling requirements for parking vehicles.The proposed model is formulated as a two-stage joint chance-constrained programming,where the first stage is a day-ahead operation problem that provides the hourly generation,conversion,and storage towards the minimization of operation cost considering the forecasting error of photovoltaic output and load demand.Meanwhile,the second stage is an on-line scheduling which adjusts the energy scheme in hourly time-scale considering the uncertainty.Simulations have demonstrated the validity of the proposed method,i.e.,collecting the flexibilities of thermal system,gas system,and parking vehicles to facilitate the operation of electrical networks.Sensitivity analysis shows that the proposed scheme can achieve a compromise between the operation efficiency and service quality.展开更多
文摘This paper intends to describe how to design an optimized and effective strategic information system （SIS） by using the existing resources under the new market circumstances in China. Some feasible methods are thus proposed, which are about the design of SIS by detailed analysis of the principle requirements. The paper also puts forward the architecture and configuration of SIS in China in general.
文摘Many countries often find it extremely hard to do business in the Middle East, including the United Arab Emirates （UAE）. The Middle East is a dramatically different environment to do business, The reason is that Arab culture is markedly different from those in other countries. From analyzing the culture, social institutions and informal trade barriers about UAE, some trouble spots and countermeasures can be found. To successfully do business in UAE, businessmen must understand Arab ways and culture, and also learn about sociotal structures including social classes, the religion, language, attitudes toward strangers and so on.
文摘The rapid development of electric vehicles(EVs)is strengthening the bi-directional interactions between electric power networks(EPNs)and transportation networks(TNs)while providing opportunities to enhance the resilience of power systems towards extreme events.To quantify the temporal and spatial flexibility of EVs for charging and discharging,a novel dynamic traffic assignment(DTA)problem is proposed.The DTA problem is based on a link transmission model(LTM)with extended charging links,depicting the interaction between EVs and power systems.It models the charging rates as continuous variables by an energy boundary model.To consider the evacuation requirements of TNs and the uncertainties of traffic conditions,the DTA problem is extended to a two-stage distributionally robust version.It is further incorporated into a two-stage distributionally robust unit commitment problem to balance the enhancement of EPNs and the performance of TNs.The problem is reformulated into a mixed-integer linear programming problem and solved by off-the-shelf commercial solvers.Case studies are performed on two test networks.The effectiveness is verified by the numerical results,e.g.,reducing the load shedding amount without increasing the unmet traffic demand.
基金supported in part by the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS21002in part by the National Natural Science Foundation of China under Grant 52061635102in part by Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110583.
文摘Microgrids(MGs)are playing a fundamental role in the transition of energy systems towards a low carbon future due to the advantages of a highly efficient network architecture for flexible integration of various DC/AC loads,distributed renewable energy sources,and energy storage systems,as well as a more resilient and economical on/off-grid control,operation,and energy management.However,MGs,as newcomers to the utility grid,are also facing challenges due to economic deregulation of energy systems,restructuring of generation,and marketbased operation.This paper comprehensively summarizes the published research works in the areas of MGs and related energy management modelling and solution techniques.First,MGs and energy storage systems are classified into multiple branches and typical combinations as the backbone of MG energy management.Second,energy management models under exogenous and endogenous uncertainties are summarized and extended to transactive energy management.Mathematical programming,adaptive dynamic programming,and deep reinforcement learning-based solution methods are investigated accordingly,together with their implementation schemes.Finally,problems for future energy management systems with dynamics-captured critical component models,stability constraints,resilience awareness,market operation,and emerging computational techniques are discussed.
基金the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources under Grant LAPS21002the State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment under Grant SGHNFZ00FBYJJS2100047.
文摘To enhance the resilience of power systems with offshore wind farms(OWFs),a proactive scheduling scheme is proposed to unlock the flexibility of cloud data centers(CDCs)responding to uncertain spatial and temporal impacts induced by hurricanes.The total life simulation(TLS)is adopted to project the local weather conditions at transmission lines and OWFs,before,during,and after the hurricane.The static power curve of wind turbines(WTs)is used to capture the output of OWFs,and the fragility analysis of transmission-line components is used to formulate the time-varying failure rates of transmission lines.A novel distributionally robust ambiguity set is constructed with a discrete support set,where the impacts of hurricanes are depicted by these supports.To minimize load sheddings and dropping workloads,the spatial and temporal demand response capabilities of CDCs according to task migration and delay tolerance are incorporated into resilient management.The flexibilities of CDC’s power consumption are integrated into a two-stage distributionally robust optimization problem with conditional value at risk(CVaR).Based on Lagrange duality,this problem is reformulated into its deterministic counterpart and solved by a novel decomposition method with hybrid cuts,admitting fewer iterations and a faster convergence rate.The effectiveness of the proposed resilient management strategy is verified through case studies conducted on the modified IEEERTS 24 system,which includes 4 data centers and 5 offshore wind farms.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515110547)Open Fund of State Key Laboratory of Operation and Control of Renewable Energy and Storage Systems(China Electric Power Research Institute)(No.NYB51202101982)。
文摘Wind power curve modeling is essential in the analysis and control of wind turbines(WTs),and data preprocessing is a critical step in accurate curve modeling.As traditional methods do not sufficiently consider WT models,this paper proposes a new data cleaning method for wind power curve modeling.In this method,a model-data hybrid-driven(MDHD)outlier detection method is constructed,and an adaptive update rule for major parameters in the detection algorithm is designed based on the WT model.Simultaneously,because the MDHD outlier detection method considers multiple types of operating data of WTs,anomaly detection results require further analysis.Accordingly,an expert system is developed in which a knowledgebase and an inference engine are designed based on the coupling relationships of different operating data.Finally,abnormal data are eliminated and the power curve modeling is completed.The proposed and traditional methods are compared in numerical cases,and the superiority of the proposed method is demonstrated.
基金This work was supported by Grants from National Key R&D Program of China(2017YFA0506000)(to X.L.and Z.H.)Natural Science Foundation of China(81874323,92057122 and 81903532 to Z.H.and D.W.)+1 种基金CAMS Innovation Fund for Medical Sciences(2019-12M-5-028 to X.L)Zhejiang Provincial Natural Science Foundation(LY18H070002 to Y.W).
文摘As a classically known mitogen,fibroblast growth factor 1(FGF1)has been found to exert other pleiotropic functions such as metabolic regulation and myocardial protection.Here,we show that serum levels of FGF1 were decreased and positively correlated with fraction shortening in diabetic cardiomyopathy(DCM)patients,indicating that FGF1 is a potential therapeutic target for DCM.We found that treatment with a FGF1 variant(FGF1^(△HBS))with reduced proliferative potency prevented diabetes-induced cardiac injury and remodeling and restored cardiac function.RNA-Seq results obtained from the cardiac tissues of db/db mice showed significant increase in the expression levels of anti-oxidative genes and decrease of Nur77 by FGF1AHBS treatment.Both in vivo and in vitro studies indicate that FGF1^(△HBS) exerted these beneficial effects by markedly reducing mitochondrial fragmentation,reactive oxygen species(ROS)generation and cytochrome c leakage and enhancing mitochondrial respiration rate and β-oxidation in a 5;AMP-activated protein kinase(AMPK)/Nur77-dependent manner,all of which were not observed in the AMPK null mice.The favorable metabolic activity and reduced proliferative properties of FGF1^(△HBS) testify to its promising potential for use in the treatment of DCM and other metabolic disorders.
基金supported by the Fundamental Research Funds for the Central Universities(No.2014XS09)Chinese Scholarship Council of the Ministry of Education.
文摘In this paper,a flexible management method is proposed for an active distribution system(ADS)with distributed energy resources(DERs)integrated,where DERs can provide spinning reserves to transmission networks.This method,based on the information-gap decision-making theory(IGDT)theory,could be of use to the ADS operator(ADSO)from either the opportunistic or robust perspective when reserve is called by the independent system operator(ISO).Two IGDT uncertainty models are employed to depict the characteristics of reserve uncertainty in centralized and decentralized control frameworks.The reactive power of each DER is managed by the ADSO in the immunity functions,which are reformulated as bi-level biobjective optimization problems.A hybrid multi-objective differential evolutional algorithm(MODE)is proposed to solve the optimization problems.The relationship between the uncertainty levels and robust/opportunistic limits is revealed by the Pareto fronts obtained by MODE.Effectiveness of the proposed method is demonstrated based on simulation results of a 33-bus and 123-bus test system.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2014XS09).
文摘Increased penetration of electric vehicles(EVs)is expected to impact power system performance in adverse ways,e.g.,overloading,uncertain power quality,and increased voltage fluctuation,particularly at the distribution level.Most EV charging control strategies that have been proposed only benefit the grid or EV users.A centralized EV charging strategy based on bilevel optimization is proposed in this paper with the objectives of deriving benefits for the grid and EV users simultaneously.The proposed strategy involves distributing the EV charging load more beneficially across both spatial and temporal levels.In the spatial problem,the whole fleet of EVs is controlled to minimize load variance as spatial coordination,with total charging rate and energy needed as the constraint.While in the temporal problem,EVs in each aggregator are controlled to minimize the charging cost or maximize the EV user’s degree of satisfaction with each aggregator’s charging rate and energy needed as the constraint.The proposed bi-level charging strategy is transformed to a single-stage optimization problem and solved using the classical optimization method.The impacts of uncontrolled charging on the grid and EV users are studied using the Monte Carlo Simulation(MCS)method.Then,the effectiveness of the proposed charging strategy is demonstrated via results obtained in the MCS.
基金This work was supported by the Fundamental Research Funds for the Central Universities(No.2014XS09)the China Scholarship Council of the Ministry of Education.
文摘To realize optimal day-ahead operation of battery swapping and charging systems(BSCSs),a closed loop supply chain(CLSC)based management scheme is proposed,where the game theory is adopted for benefits allocation.The CLSC is used to depict the battery-swapping-charging process between the battery charging stations(BCSs)and battery swapping stations(BSSs).The arrival,departure and swapping service of electric vehicles(EVs)at BSSs is modeled as distinct queues based on the network calculus theory.The depleted batteries(DBs)and well-charging batteries(WBs)based interaction among BCSs and BSSs is formulated as a Stackelberg game.In the game,one BCS acts as the leader and the BSSs act as the followers.The BCS sets optimized prices to maximize its utility and the BSSs optimally demand WBs,supply DBs and provide battery swapping services to maximize their own utilities while guaranteeing the quality of service(QoS)needed for battery swapping.The existence of Stackelberg equilibriums(SEs)of the proposed game is proved.A differential evaluation based hybrid algorithm is proposed to compute an SE.The effectiveness of proposed method has been demonstrated by the simulation results,guaranteeing the QoS and balancing benefits among the BCS and BSSs while maximizing social welfare.
基金This work was supported by the Key Scientific and Technological Research Project of State Grid Corporation of China(No.5400-202022113A-0-0-00).
文摘Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV users.This paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power grid.The proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,simultaneously.Afterwards,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling problem.Finally,simulation studies verify the effectiveness of the proposed multi-objective operation method.
文摘As battery technology matures,the battery energy storage system(BESS)becomes a promising candidate for addressing renewable energy uncertainties.BESS degradation is one of key factors in BESS operations,which is usually considered in the planning stage.However,BESS degradations are directly affected by the depth of discharge(DoD),which is closely related to the BESS daily schedule.Specifically,the BESS life losses may be different when providing the same amount of energy under a distinct DoD.Therefore,it is necessary to develop a model to consider the effect of daily discharge on BESS degradation.In this paper,a model quantifying the nonlinear impact of DoD on BESS life loss is proposed.By adopting the chance-constrained goal programming,the degradation in day-ahead unit commitment is formulated as a multi-objective optimization problem.To facilitate an efficient solution,the model is converted into a mixed integer linear programming problem.The effectiveness of the proposed method is verified in a modified IEEE 39-bus system.
文摘To enhance the flexible interactions among multiple energy carriers,i.e.,electricity,thermal power and gas,a coordinated programming method for multi-energy microgrid(MEMG)system is proposed.Various energy requirements for both residential and parking loads are managed simultaneously,i.e.,electric and thermal loads for residence,and charging power and gas filling requirements for parking vehicles.The proposed model is formulated as a two-stage joint chance-constrained programming,where the first stage is a day-ahead operation problem that provides the hourly generation,conversion,and storage towards the minimization of operation cost considering the forecasting error of photovoltaic output and load demand.Meanwhile,the second stage is an on-line scheduling which adjusts the energy scheme in hourly time-scale considering the uncertainty.Simulations have demonstrated the validity of the proposed method,i.e.,collecting the flexibilities of thermal system,gas system,and parking vehicles to facilitate the operation of electrical networks.Sensitivity analysis shows that the proposed scheme can achieve a compromise between the operation efficiency and service quality.
