With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of ...With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of demand response(DR)resources has become a valuable solution to this problem.However,existing research indicates that problems on flexibility prediction of DR resources have not been investigated.This study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate prediction.The prediction can generate the size and maintenance time of the aggregated flexibility.The accuracy of the flexibility prediction results was verified through simulations of case studies.The simulation results show that under different maintenance times,the size of the flexibility changed.The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.展开更多
The utilization of renewable energy in sending-end power grids is increasing rapidly,which brings difficulties to voltage control.This paper proposes a coordinated voltage control strategy based on model predictive co...The utilization of renewable energy in sending-end power grids is increasing rapidly,which brings difficulties to voltage control.This paper proposes a coordinated voltage control strategy based on model predictive control(MPC)for the renewable energy power plants of wind and solar power connected to a weak sending-end power grid(WSPG).Wind turbine generators(WTGs),photovoltaic arrays(PVAs),and a static synchronous compensator are coordinated to maintain voltage within a feasible range during operation.This results in the full use of the reactive power capability of WTGs and PVAs.In addition,the impact of the active power outputs of WTGs and PVAs on voltage control are considered because of the high R/X ratio of a collector system.An analytical method is used for calculating sensitivity coefficients to improve computation efficiency.A renewable energy power plant with 80 WTGs and 20 PVAs connected to a WSPG is used to verify the proposed voltage control strategy.Case studies show that the coordinated voltage control strategy can achieve good voltage control performance,which improves the voltage quality of the entire power plant.展开更多
Denmark’ goal of being independent of fossil energy sources in 2050 puts forward great demands on all energy subsystems (electricity, heat, gas and transport, etc.) to be operated in a holistic manner. The Danish exp...Denmark’ goal of being independent of fossil energy sources in 2050 puts forward great demands on all energy subsystems (electricity, heat, gas and transport, etc.) to be operated in a holistic manner. The Danish experience and challenges of wind power integration and the development of district heating systems are summarized in this paper. How to optimally use the cross-sectoral flexibility by intelligent control (model predictive control-based) of the key coupling components in an integrated heat and power system including electrical heat pumps in the demand side, and thermal storage applications in buildings is investigated.展开更多
With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and ...With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.展开更多
The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution n...The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.展开更多
The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of...The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.展开更多
As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based ...As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.展开更多
A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC s...A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator(TSO),and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system.The optimal power flow(OPF)of the TS is relaxed by using the semidefinite programming(SDP)relaxation while the branch flow model is used to model the WF collection system.In the DARPC strategy,the large-scale strongly-coupled optimization problem is decomposed by using the ADMM,which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality.The boundary information is exchanged between the regional TS controller and WF controllers.Compared with the conventional OPF method of the TS with WFs,the optimality and accuracy of the system operation can be improved.Moreover,the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller.The protection of the information privacy can be enhanced.A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines(WTs)is used to validate the proposed DARPC strategy.展开更多
There is increasing interest in the evaluation of wind turbine control capabilities for providing grid support.Power hardware in the loop(PHIL)simulation is an advanced method that can be used for studying the interac...There is increasing interest in the evaluation of wind turbine control capabilities for providing grid support.Power hardware in the loop(PHIL)simulation is an advanced method that can be used for studying the interaction of hardware with the power network,as the scaled-down actual wind turbine is connected with a simulated system through an amplifier.Special consideration must be made in the design of the PHIL platform to ensure that the system is stable and yields accurate results.This paper presents a method for stabilizing the PHIL interface and improving the accuracy of PHIL simulation in a real-time application.The method factors in both the power and voltage scaling level,and a phase compensation scheme.It uses the reactive power control capability of the wind turbine inverter to eliminate the phase shift imposed by the feedback current filter.This is accomplished with no negative impact on the dynamic behavior of the wind turbine.The PHIL simulation results demonstrate the effectiveness of the proposed stability analysis method and phase compensation scheme.The strength of the platform is demonstrated by extending the simulation method to wind turbine control validation.展开更多
As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective securi...As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective security-constrained optimal power flow(RO-PCSCOPF)model is proposed to improve power system reliability under N−k outages.Both the short-term emergency limit(STL)and the long-term operating limit(LTL)of the post-contingency power flow on the branch are considered.Compared with the existing robust corrective SCOPF model that only considers STL or LTL,the proposed ROPCSCOPF model can achieve a more reliable generation dispatch solution.In addition,this paper also summarizes and compares the solution methods for solving the N−k SCOPF problem.The computational efficiency of the classical Benders decomposition(BD)method,robust optimization(RO)method,and line outage distribution factor(LODF)method are investigated on the IEEE 24-bus Reliability Test System and 118-bus system.Simulation results show that the BD method has the worst computation performance.The RO method and the LODF method have comparable performance.However,the LODF method can only be used for the preventive SCOPF and not for the corrective SCOPF.The RO method can be used for both.展开更多
The current status of wind power and the energy infrastructure in Denmark is reviewed in this paper.The reasons for why Denmark is a world leader in wind power are outlined.The Danish government is aiming to achieve 1...The current status of wind power and the energy infrastructure in Denmark is reviewed in this paper.The reasons for why Denmark is a world leader in wind power are outlined.The Danish government is aiming to achieve 100%renewable energy generation by 2050.A major challenge is balancing load and generation.In addition,the current and future solutions of enhancing wind power penetration through optimal use of cross-energy sector flexibility,so-called indirect electric energy storage options,are investigated.A conclusion is drawn with a summary of experiences and lessons learned in Denmark related to wind power development.展开更多
Excessive consumption of fossil fuels in the industry sector has caused hij»h operating costs and severe environmental pollution,advocating a cost-effective and sustainable substitute for fossil fuels.I'his p...Excessive consumption of fossil fuels in the industry sector has caused hij»h operating costs and severe environmental pollution,advocating a cost-effective and sustainable substitute for fossil fuels.I'his paper proposes an enhanced utilization mechanism of biomass-to-syngas(B2S)to provide various types of steam flows in industrial multi-energy systems(MESs).In this mechanism,the available generations from renewable energy sources(RESs)can be harvested to assist in the biomass gasification in a B2S gasifier for enhancing the syngas yield and its calorific value.A thermodynamic interaction model for B2S is formulated to capture gasification temperature dynamics under high-temperature steam injections and optimally control the thermochemical behaviors of biomass drying,pyrolysis,and gasification.A B2S based energy hub framework with its multienergy coupling matrix is formulated for mapping the input hiomass-wind-solar energy into electricity,syngas,and various types of’steam carriers to satisfy industrial energy cieniands.A hierarchical multi-timeframe dispatch scheme is developed for the energy-efficient conversion and utilization of multi-energy carriers to minimize the system operation costs.Comparative studies are implemented to demonstrate the superior performance of the proposed methodology on system operational economy and sustainability.展开更多
To achieve active control of the AC voltage magnitude of wind power plant(WPP)collector network and improve the fault ride-through(FRT)capability,an FRT scheme based on feed forward DC voltage control is presented for...To achieve active control of the AC voltage magnitude of wind power plant(WPP)collector network and improve the fault ride-through(FRT)capability,an FRT scheme based on feed forward DC voltage control is presented for voltage source converter-high voltage direct current(VSC-HVDC)connected offshore WPPs.During steady state operation,an open loop AC voltage control is implemented at the WPP-side VSC of the HVDC system so that any possible control interactions between WPP-side VSC and VSC of wind turbine are minimized.Whereas during any grid fault,a dynamic AC voltage reference is made according to both the DC voltage error and AC active current from the WPP collector system,thus ensuring fast and robust FRT of the VSC-HVDC-connected offshore WPPs.Under the unbalanced fault condition in the host power system,the resulting oscillatory DC voltage is directly used in the VSC AC voltage controller at the WPP side so that the WPP collector system voltage also reflects the unbalance in the main grid.Time domain simulations are performed to verify the efficacy of the FRT scheme based on the proposed feed forward DC voltage control.Simulation results show satisfactory FRT responses of the VSC-HVDC-connected offshore WPP under balanced and unbalanced faults in the host power system,as is shown under a serious fault in the WPP collector network.展开更多
A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavi...A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.展开更多
Buildings have both high as well as flexible energy demands and play an important role in the energy internet solution.The buildings’energy flexibility(BEF)is a widely recognized concept;however,how to unlock its pot...Buildings have both high as well as flexible energy demands and play an important role in the energy internet solution.The buildings’energy flexibility(BEF)is a widely recognized concept;however,how to unlock its potential is a relatively new research topic.In this paper,the authors provide an overview of the latest research related to BEF.An introduction to BEF is provided,methods developed for identifying and characterizing BEF are presented,and several key influencing factors are identified.The overview also covers various aggregation methods to scale up BEF impacts and service-oriented solutions for enabling BEF applications in different energy sectors.This work lays the groundwork for designing and developing seamless integration strategies for BEF use in both present and future energy systems.展开更多
Modern power systems,employing an increasing number of converter-based renewable energy sources(RES)and decreasing the usage of conventional power plants,are leading to lower levels of short-circuit power and rotation...Modern power systems,employing an increasing number of converter-based renewable energy sources(RES)and decreasing the usage of conventional power plants,are leading to lower levels of short-circuit power and rotational inertia.A solution to this is the employment of synchronous condensers in the grid,in order to provide sufficient short-circuit power.This results in the increase of the short-circuit ratio(SCR)at transmission system busbars serving as points of interconnection(POI)to renewable generation.Evaluation of the required capacity and grid-location of the synchronous condensers,is inherently a mixed integer nonlinear optimization problem,which could not be done on manual basis considering each type of machine and all bus-bars.This study therefore proposes a method of optimal allocation of synchronous condensers in a hypothetic future scenario of a transmission system fed by renewable generation.Total cost of synchronous condenser installations in the system is minimized and the SCRs at the POIs of central renewable power plants are strengthened.The method has potential for application on larger grids,aiding grid-integration of RES.展开更多
The concern of the environment and energy sustainability requests a crucial target of CO_(2)abatement and results in a relatively high penetration of renewable energy generation in the transmission system.For maintain...The concern of the environment and energy sustainability requests a crucial target of CO_(2)abatement and results in a relatively high penetration of renewable energy generation in the transmission system.For maintaining system reliability and security,the transmission company(TRANSCO)has to make strategic planning to handle the uncertainty challenges from the intermittent renewable energy resources.In this paper,a stochastic multi-period multi-objective transmission planning(MPMOTP)model is proposed to reduce correlated uncertainties from renewable energy generation,conventional generation,demand-side variations,market price volatility,and transmission configuration.Three objectives,i.e.social CO_(2)reduction benefit,energy purchase and network expansion cost and power delivery profit,are optimized simultaneously by a developed two-phase multi-objective particle swarm optimization(MOPSO)method.The feasibility and effectiveness of the proposed uncertainty-averse MPMOTP model have been verified by the IEEE 24-bus test system.展开更多
Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal alloc...Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.展开更多
In the modern power system, both local and centralized reactive power control strategies for photovoltaic(PV) plants, are proposed and compared. While local control improves the network security, it lacks the optimiza...In the modern power system, both local and centralized reactive power control strategies for photovoltaic(PV) plants, are proposed and compared. While local control improves the network security, it lacks the optimization benefits from centralized control strategies.Therefore, this paper considers the coordination of the two control strategies, depending on external impact from the weather system and consumer behavior, in a low voltage(LV) distribution feeder. Through modeling and simulation in an established real-time cyber-physical simulation platform, the LV network is evaluated with both local and centralized control. A set of boundaries for coordinating between the two strategies are identified, which can help network operators decide suitable control in different operating situations. Furthermore, the cyber-physical simulation platform, is used to study the impact of physical perturbations, i.e. changes in irradiance and consumption,and cyber disturbances, in form of communication channel noise, is evaluated for the control strategies. Results show how small and large disturbances in the cyber system affect the centralized control strategy optimizer performance.展开更多
基金This work was supported by the National Natural Science Foundation of China(51877078 and 52061635102)the Beijing Nova Program(Z201100006820106).
文摘With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency stability.The flexibility of demand response(DR)resources has become a valuable solution to this problem.However,existing research indicates that problems on flexibility prediction of DR resources have not been investigated.This study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate prediction.The prediction can generate the size and maintenance time of the aggregated flexibility.The accuracy of the flexibility prediction results was verified through simulations of case studies.The simulation results show that under different maintenance times,the size of the flexibility changed.The proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.
基金supported by National Natural Science Foundation Joint Key Project of China(2016YFB0900900).
文摘The utilization of renewable energy in sending-end power grids is increasing rapidly,which brings difficulties to voltage control.This paper proposes a coordinated voltage control strategy based on model predictive control(MPC)for the renewable energy power plants of wind and solar power connected to a weak sending-end power grid(WSPG).Wind turbine generators(WTGs),photovoltaic arrays(PVAs),and a static synchronous compensator are coordinated to maintain voltage within a feasible range during operation.This results in the full use of the reactive power capability of WTGs and PVAs.In addition,the impact of the active power outputs of WTGs and PVAs on voltage control are considered because of the high R/X ratio of a collector system.An analytical method is used for calculating sensitivity coefficients to improve computation efficiency.A renewable energy power plant with 80 WTGs and 20 PVAs connected to a WSPG is used to verify the proposed voltage control strategy.Case studies show that the coordinated voltage control strategy can achieve good voltage control performance,which improves the voltage quality of the entire power plant.
基金Danish Agency for Science, Technology and Innovation (No. 6144-00037)Danish InnovationFunding (No. 5185-00005A)
文摘Denmark’ goal of being independent of fossil energy sources in 2050 puts forward great demands on all energy subsystems (electricity, heat, gas and transport, etc.) to be operated in a holistic manner. The Danish experience and challenges of wind power integration and the development of district heating systems are summarized in this paper. How to optimally use the cross-sectoral flexibility by intelligent control (model predictive control-based) of the key coupling components in an integrated heat and power system including electrical heat pumps in the demand side, and thermal storage applications in buildings is investigated.
基金supported partly by the National Key R&D Program of China(2018YFA0702200)the Science and Technology Project of State Grid Shandong Electric Power Company(520604190002)。
文摘With the development of carbon electricity,achieving a low-carbon economy has become a prevailing and inevitable trend.Improving low-carbon expansion generation planning is critical for carbon emission mitigation and a lowcarbon economy.In this paper,a two-layer low-carbon expansion generation planning approach considering the uncertainty of renewable energy at multiple time scales is proposed.First,renewable energy sequences considering the uncertainty in multiple time scales are generated based on the Copula function and the probability distribution of renewable energy.Second,a two-layer generation planning model considering carbon trading and carbon capture technology is established.Specifically,the upper layer model optimizes the investment decision considering the uncertainty at a monthly scale,and the lower layer one optimizes the scheduling considering the peak shaving at an hourly scale and the flexibility at a 15-minute scale.Finally,the results of different influence factors on low-carbon generation expansion planning are compared in a provincial power grid,which demonstrate the effectiveness of the proposed model.
基金financed by the TNO Early Research Program on Energy Storage and Conversion(ERP ECS)through the SOSENS projectpartly supported by the Danish iPower project(http://www.ipowernet.dk/)funded by the Danish Agency for Research and Innovation(No.0603-00435B)
文摘The increasing number of distributed energy resources connected to power systems raises operational challenges for the network operator, such as introducing grid congestion and voltage deviations in the distribution network level, as well as increasing balancing needs at the whole system level. Control and coordination of a large number of distributed energy assets requires innovative approaches. Transactive control has received much attention due to its decentralized decision-making and transparent characteristics. This paper introduces the concept and main features of transactive control, followed by a literature review and demonstration projects that apply to transactive control. Cases are then presented to illustrate the transactive control framework. At the end, discussions and research directions are presented, for applying transactive control to operating power systems, characterized by a high penetration of distributed energy resources.
基金supported in part by the National Natural Science Foundation of China(No.51877078)the Fundamental Research Funds for the Central Universities(No.2018MS012)
文摘The increasing number of photovoltaic(PV)generation and electric vehicles(EVs)on the load side has necessitated an aggregator(Agg)in power system operation.In this paper,an Agg is used to manage the energy profiles of PV generation and EVs.However,the daily management of the Agg is challenged by uncertain PV fluctuations.To address this problem,a robust multi-time scale energy management strategy for the Agg is proposed.In a day-ahead phase,robust optimization is developed to determine the power schedule.In a real-time phase,a rolling horizon-based convex optimization model is established to track the day-ahead power schedule based on the flexibilities of the EVs.A case study indicates a good scheduling performance under an uncertain PV output.Through the convexification,the solving efficiency of the real-time operation model is improved,and the over-charging and over-discharging problems of EVs can be suppressed to a certain extent.Moreover,the power deviation between day-ahead and real-time scheduling is controllable when the EV dispatching capacity is sufficient.The strategy can ensure the flexibility of the Agg for real-time operation.
基金This work was supported by the National Natural Science Foundation of China(No.51877078)the State Key Laboratory of Smart Grid Protection and Operation Control Open Project(No.SGNR0000KJJS1907535)the Beijing Nova Program(No.Z201100006820106)。
文摘As typical prosumers,commercial buildings equipped with electric vehicle(EV)charging piles and solar photovoltaic panels require an effective energy management method.However,the conventional optimization-model-based building energy management system faces significant challenges regarding prediction and calculation in online execution.To address this issue,a long short-term memory(LSTM)recurrent neural network(RNN)based machine learning algorithm is proposed in this paper to schedule the charging and discharging of numerous EVs in commercial-building prosumers.Under the proposed system control structure,the LSTM algorithm can be separated into offline and online stages.At the offline stage,the LSTM is used to map states(inputs)to decisions(outputs)based on the network training.At the online stage,once the current state is input,the LSTM can quickly generate a solution without any additional prediction.A preliminary data processing rule and an additional output filtering procedure are designed to improve the decision performance of LSTM network.The simulation results demonstrate that the LSTM algorithm can generate near-optimal solutions in milliseconds and significantly reduce the prediction and calculation pressures compared with the conventional optimization algorithm.
基金supported in part by Technical University of Denmark(DTU)in part by China Scholarship Council(No.201806130202)。
文摘A distributed active and reactive power control(DARPC)strategy based on the alternating direction method of multipliers(ADMM)is proposed for regional AC transmission system(TS)with wind farms(WFs).The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator(TSO),and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system.The optimal power flow(OPF)of the TS is relaxed by using the semidefinite programming(SDP)relaxation while the branch flow model is used to model the WF collection system.In the DARPC strategy,the large-scale strongly-coupled optimization problem is decomposed by using the ADMM,which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality.The boundary information is exchanged between the regional TS controller and WF controllers.Compared with the conventional OPF method of the TS with WFs,the optimality and accuracy of the system operation can be improved.Moreover,the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller.The protection of the information privacy can be enhanced.A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines(WTs)is used to validate the proposed DARPC strategy.
基金supported in part by the National Basic Research Program of China(973 Program)under Grant 2012CB215105.
文摘There is increasing interest in the evaluation of wind turbine control capabilities for providing grid support.Power hardware in the loop(PHIL)simulation is an advanced method that can be used for studying the interaction of hardware with the power network,as the scaled-down actual wind turbine is connected with a simulated system through an amplifier.Special consideration must be made in the design of the PHIL platform to ensure that the system is stable and yields accurate results.This paper presents a method for stabilizing the PHIL interface and improving the accuracy of PHIL simulation in a real-time application.The method factors in both the power and voltage scaling level,and a phase compensation scheme.It uses the reactive power control capability of the wind turbine inverter to eliminate the phase shift imposed by the feedback current filter.This is accomplished with no negative impact on the dynamic behavior of the wind turbine.The PHIL simulation results demonstrate the effectiveness of the proposed stability analysis method and phase compensation scheme.The strength of the platform is demonstrated by extending the simulation method to wind turbine control validation.
基金This work was supported by the Education Department of Guangdong Province:New and Integrated Energy System Theory and Technology Research Group(No.2016KCXTD022)National Natural Science Foundation of China(No.51907031)+2 种基金Guangdong Basic and Applied Basic Research Foundation(Guangdong-Guangxi Joint Foundation)(No.2021A1515410009)China Scholarship CouncilBrunel University London BRIEF Funding。
文摘As extreme weather events have become more frequent in recent years,improving the resilience and reliability of power systems has become an important area of concern.In this paper,a robust preventive-corrective security-constrained optimal power flow(RO-PCSCOPF)model is proposed to improve power system reliability under N−k outages.Both the short-term emergency limit(STL)and the long-term operating limit(LTL)of the post-contingency power flow on the branch are considered.Compared with the existing robust corrective SCOPF model that only considers STL or LTL,the proposed ROPCSCOPF model can achieve a more reliable generation dispatch solution.In addition,this paper also summarizes and compares the solution methods for solving the N−k SCOPF problem.The computational efficiency of the classical Benders decomposition(BD)method,robust optimization(RO)method,and line outage distribution factor(LODF)method are investigated on the IEEE 24-bus Reliability Test System and 118-bus system.Simulation results show that the BD method has the worst computation performance.The RO method and the LODF method have comparable performance.However,the LODF method can only be used for the preventive SCOPF and not for the corrective SCOPF.The RO method can be used for both.
文摘The current status of wind power and the energy infrastructure in Denmark is reviewed in this paper.The reasons for why Denmark is a world leader in wind power are outlined.The Danish government is aiming to achieve 100%renewable energy generation by 2050.A major challenge is balancing load and generation.In addition,the current and future solutions of enhancing wind power penetration through optimal use of cross-energy sector flexibility,so-called indirect electric energy storage options,are investigated.A conclusion is drawn with a summary of experiences and lessons learned in Denmark related to wind power development.
基金supported by the National Natural Science Foundation of China(51877072)the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(LAPS20005)the Huxiang Young Talents Programme of Hunan Province(2019RS2018).
文摘Excessive consumption of fossil fuels in the industry sector has caused hij»h operating costs and severe environmental pollution,advocating a cost-effective and sustainable substitute for fossil fuels.I'his paper proposes an enhanced utilization mechanism of biomass-to-syngas(B2S)to provide various types of steam flows in industrial multi-energy systems(MESs).In this mechanism,the available generations from renewable energy sources(RESs)can be harvested to assist in the biomass gasification in a B2S gasifier for enhancing the syngas yield and its calorific value.A thermodynamic interaction model for B2S is formulated to capture gasification temperature dynamics under high-temperature steam injections and optimally control the thermochemical behaviors of biomass drying,pyrolysis,and gasification.A B2S based energy hub framework with its multienergy coupling matrix is formulated for mapping the input hiomass-wind-solar energy into electricity,syngas,and various types of’steam carriers to satisfy industrial energy cieniands.A hierarchical multi-timeframe dispatch scheme is developed for the energy-efficient conversion and utilization of multi-energy carriers to minimize the system operation costs.Comparative studies are implemented to demonstrate the superior performance of the proposed methodology on system operational economy and sustainability.
文摘To achieve active control of the AC voltage magnitude of wind power plant(WPP)collector network and improve the fault ride-through(FRT)capability,an FRT scheme based on feed forward DC voltage control is presented for voltage source converter-high voltage direct current(VSC-HVDC)connected offshore WPPs.During steady state operation,an open loop AC voltage control is implemented at the WPP-side VSC of the HVDC system so that any possible control interactions between WPP-side VSC and VSC of wind turbine are minimized.Whereas during any grid fault,a dynamic AC voltage reference is made according to both the DC voltage error and AC active current from the WPP collector system,thus ensuring fast and robust FRT of the VSC-HVDC-connected offshore WPPs.Under the unbalanced fault condition in the host power system,the resulting oscillatory DC voltage is directly used in the VSC AC voltage controller at the WPP side so that the WPP collector system voltage also reflects the unbalance in the main grid.Time domain simulations are performed to verify the efficacy of the FRT scheme based on the proposed feed forward DC voltage control.Simulation results show satisfactory FRT responses of the VSC-HVDC-connected offshore WPP under balanced and unbalanced faults in the host power system,as is shown under a serious fault in the WPP collector network.
基金supported by the Sichuan Science and Technology Program(No.2020JDJQ0037)。
文摘A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.
文摘Buildings have both high as well as flexible energy demands and play an important role in the energy internet solution.The buildings’energy flexibility(BEF)is a widely recognized concept;however,how to unlock its potential is a relatively new research topic.In this paper,the authors provide an overview of the latest research related to BEF.An introduction to BEF is provided,methods developed for identifying and characterizing BEF are presented,and several key influencing factors are identified.The overview also covers various aggregation methods to scale up BEF impacts and service-oriented solutions for enabling BEF applications in different energy sectors.This work lays the groundwork for designing and developing seamless integration strategies for BEF use in both present and future energy systems.
文摘Modern power systems,employing an increasing number of converter-based renewable energy sources(RES)and decreasing the usage of conventional power plants,are leading to lower levels of short-circuit power and rotational inertia.A solution to this is the employment of synchronous condensers in the grid,in order to provide sufficient short-circuit power.This results in the increase of the short-circuit ratio(SCR)at transmission system busbars serving as points of interconnection(POI)to renewable generation.Evaluation of the required capacity and grid-location of the synchronous condensers,is inherently a mixed integer nonlinear optimization problem,which could not be done on manual basis considering each type of machine and all bus-bars.This study therefore proposes a method of optimal allocation of synchronous condensers in a hypothetic future scenario of a transmission system fed by renewable generation.Total cost of synchronous condenser installations in the system is minimized and the SCRs at the POIs of central renewable power plants are strengthened.The method has potential for application on larger grids,aiding grid-integration of RES.
基金The authors gratefully acknowledge the finan-cial supports of Danish national project iPower and the great contri-butions of Danish Energy Association and DONG Energy involved in this task.
文摘The concern of the environment and energy sustainability requests a crucial target of CO_(2)abatement and results in a relatively high penetration of renewable energy generation in the transmission system.For maintaining system reliability and security,the transmission company(TRANSCO)has to make strategic planning to handle the uncertainty challenges from the intermittent renewable energy resources.In this paper,a stochastic multi-period multi-objective transmission planning(MPMOTP)model is proposed to reduce correlated uncertainties from renewable energy generation,conventional generation,demand-side variations,market price volatility,and transmission configuration.Three objectives,i.e.social CO_(2)reduction benefit,energy purchase and network expansion cost and power delivery profit,are optimized simultaneously by a developed two-phase multi-objective particle swarm optimization(MOPSO)method.The feasibility and effectiveness of the proposed uncertainty-averse MPMOTP model have been verified by the IEEE 24-bus test system.
基金This work was supported by the Science and Technology Project of State Grid Corporation of China“Research on resilience technology and application foundation of intelligent distribution network based on integrated energy system”(No.52060019001H).
文摘Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.
文摘In the modern power system, both local and centralized reactive power control strategies for photovoltaic(PV) plants, are proposed and compared. While local control improves the network security, it lacks the optimization benefits from centralized control strategies.Therefore, this paper considers the coordination of the two control strategies, depending on external impact from the weather system and consumer behavior, in a low voltage(LV) distribution feeder. Through modeling and simulation in an established real-time cyber-physical simulation platform, the LV network is evaluated with both local and centralized control. A set of boundaries for coordinating between the two strategies are identified, which can help network operators decide suitable control in different operating situations. Furthermore, the cyber-physical simulation platform, is used to study the impact of physical perturbations, i.e. changes in irradiance and consumption,and cyber disturbances, in form of communication channel noise, is evaluated for the control strategies. Results show how small and large disturbances in the cyber system affect the centralized control strategy optimizer performance.
