Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations...Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.展开更多
In 2020,China made a solemn commitment to the world:striving to achieve carbon peak before 2030 and carbon neutrality before 2060.The energy industry is the main source of carbon emissions and the key path to achievin...In 2020,China made a solemn commitment to the world:striving to achieve carbon peak before 2030 and carbon neutrality before 2060.The energy industry is the main source of carbon emissions and the key path to achieving the“dual carbon”goals.The revolution in energy production and consumption has already sparked a wave.However,the energy transition still faces challenges such as a high proportion of fossil fuel usage,multiple constraints on clean energy supply,urgent request to improve the carrying capacity and flexible regulation capability of the power system,and rising energy costs for the entire society.To cope with these difficulties and challenges,it is necessary to balance safety and stability,economic efficiency,and clean and low-carbon development three aspects;strengthen energy technology innovation;and deepen institutional and market reform and innovation.Therefore,the editorial department of Global Energy Interconnection has planned the special issue of“Energy Transition Technology for Emission Peak and Carbon Neutrality”.展开更多
With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provi...With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.展开更多
This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a sing...This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.展开更多
Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model ...Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs,considering the topology,power balance,loss,and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation,this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization,which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.展开更多
With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in...With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.展开更多
This paper presents an adaptive Under Frequency Load Shedding scheme based on Wide Area Measurement System. Due to the lack of enough adaptability to the operation state of the system, the traditional successive appro...This paper presents an adaptive Under Frequency Load Shedding scheme based on Wide Area Measurement System. Due to the lack of enough adaptability to the operation state of the system, the traditional successive approximation under frequency load shedding method will cause excessive cut or undercut problems inevitably. This method consists first in a comprehensive weight index including load characteristics and inertias of generators. Then active-power deficit calculation based on the Low-order Frequency Response Model, concerning the effect of voltage was put forward. Finally, a dynamic correction of the load shedding amount was proposed to modify the scheme. This approach was applied to IEEE-39 system and the simulation results indicated that the proposed method was effective in reducing the load shedding amount as well as the frequency recovery time.展开更多
In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their c...In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.展开更多
To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,comp...To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.展开更多
The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy base...The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.展开更多
It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the ...It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.展开更多
To utilize electricity in a clean and integrated manner,a zero-carbon hydro-photovoltaic(PV)-pumped hydro storage(PHS)integrated power system is studied,considering the uncertainties of PV and load demand.It is a chal...To utilize electricity in a clean and integrated manner,a zero-carbon hydro-photovoltaic(PV)-pumped hydro storage(PHS)integrated power system is studied,considering the uncertainties of PV and load demand.It is a challenge for operators to develop a dynamic dispatch mechanism for such a system,and traditional dispatch methods are difficult to adapt to random changes in the actual environment.Therefore,this study proposes a real-time dynamic dispatch strategy considering economic operation and complementary regulatory ability.First,the dynamic dispatch of a hydro-PV-PHS integrated power system is presented as a multi-objective optimization problem and the weight factor between different goals is effectively calculated using information entropy.Afterwards,the dispatch model is converted into the Markov decision process,where the dynamic dispatch decision is formulated as a reinforcement learning framework.Then,a deep deterministic policy gradient(DDPG)is deployed towards the online decision for dispatch in continuous action spaces.Finally,a case study is applied to evaluate the performance of the proposed method based on a real hydroPV-PHS integrated power system in China.Simulations show that the system agent reduces the power volatility of supply by 26.7%after hydropower regulating and further relieves power fluctuation at the point of common coupling(PCC)to the upperlevel grid by 3.28%after PHS participation.The comparison results verify the effectiveness of the proposed method.展开更多
As an integrated carrier of energy production,transmission,distribution,conversion,storage,and utilization,multiple energy systems(MESs)have significant low-carbon potential.This paper proposes a hierarchical distribu...As an integrated carrier of energy production,transmission,distribution,conversion,storage,and utilization,multiple energy systems(MESs)have significant low-carbon potential.This paper proposes a hierarchical distributed dispatch model of MESs considering carbon trading,which is composed of the lower autonomous operation level of each MES and the upper coordinated control level.Different carbon emission sources are considered,including combined heat and power(CHP)units,gas boilers,and power to gas(P2G)devices.The transactive control(TC)mechanism is used to solve the model by introducing a virtual price signal.In the case study based on a 3-MES system,the effectiveness of the proposed distributed method is proved by comparison with a centralized algorithm.Meanwhile,the impacts of different carbon prices on MESs with different resource endowments are analyzed from the aspects of scheduling results,carbon emissions,clean energy consumption rate,and comprehensive operating costs.展开更多
Decarbonisation of power systems is essential for realising carbon neutrality,in which the economic cost caused by carbon needs to be qualified.Based on the formulation of locational marginal price(LMP),this paper pro...Decarbonisation of power systems is essential for realising carbon neutrality,in which the economic cost caused by carbon needs to be qualified.Based on the formulation of locational marginal price(LMP),this paper proposes a locational marginal electricity-carbon price(EC-LMP)model to reveal carbon-related costs caused by power consumers.A carbon-priceintegrated optimal power flow(C-OPF)is then developed to maximise economic efficiency of the power system considering the costs of electricity and carbon.Case studies are presented to demonstrate the new formulation and results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy.展开更多
The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advan...The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.展开更多
The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elastici...The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers.The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions.The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price.Then,the sensitivity of incentive price on retailers’benefits is analyzed and the optimal incentive price is obtained according to the DR model.The case study verifies the effectiveness of the proposed model.展开更多
With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this groun...With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this ground,An AC-DC hybrid DER system becomes necessary for effective management and control over DER.This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system.The impact of these new features on system configuration planning is analyzed with respect to its flexible networking,rich operation control modes,and tight sourcenetwork-load-storage coupling.Then,based on a review of the existing research,problems and technical difficulties are figured out in terms of converter modeling,steady-state analysis,power flow calculation,operating scenarios management,and optimization model solution.In light of the problems and difficulties,a framework for the configuration optimization of AC-DC hybrid DER systems is proposed.At last,the paper provides a prospect of key technologies from six aspects including morphology forecasting,coupling interaction analysis,uncertainty modeling,operation simulation,optimization model solving algorithm and comprehensive scheme evaluation.展开更多
Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and ov...Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.展开更多
基金supported by National Natural Science Foundation of China(U2066209)。
文摘Energy storage systems(ESSs)operate as independent market participants and collaborate with photovoltaic(PV)generation units to enhance the flexible power supply capabilities of PV units.However,the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood.This study introduces a dual-timescale dynamics model that integrates a spot market clearing(SMC)model into a system dynamics(SD)model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems(IESSs)with that of an ESS integrated within a PV(PV-ESS).Furthermore,this study aims to ascertain the optimal allocation of the PV-ESS.First,SD and SMC models were set up.Second,the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem,a dual-timescale model was constructed.Finally,a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation.Additionally,negative electricity prices were considered during clearing processes.The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS,helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.
文摘In 2020,China made a solemn commitment to the world:striving to achieve carbon peak before 2030 and carbon neutrality before 2060.The energy industry is the main source of carbon emissions and the key path to achieving the“dual carbon”goals.The revolution in energy production and consumption has already sparked a wave.However,the energy transition still faces challenges such as a high proportion of fossil fuel usage,multiple constraints on clean energy supply,urgent request to improve the carrying capacity and flexible regulation capability of the power system,and rising energy costs for the entire society.To cope with these difficulties and challenges,it is necessary to balance safety and stability,economic efficiency,and clean and low-carbon development three aspects;strengthen energy technology innovation;and deepen institutional and market reform and innovation.Therefore,the editorial department of Global Energy Interconnection has planned the special issue of“Energy Transition Technology for Emission Peak and Carbon Neutrality”.
基金supported by the National Natural Science Foundation of China under Grant 52077146.
文摘With the construction of the power Internet of Things(IoT),communication between smart devices in urban distribution networks has been gradually moving towards high speed,high compatibility,and low latency,which provides reliable support for reconfiguration optimization in urban distribution networks.Thus,this study proposed a deep reinforcement learning based multi-level dynamic reconfiguration method for urban distribution networks in a cloud-edge collaboration architecture to obtain a real-time optimal multi-level dynamic reconfiguration solution.First,the multi-level dynamic reconfiguration method was discussed,which included feeder-,transformer-,and substation-levels.Subsequently,the multi-agent system was combined with the cloud-edge collaboration architecture to build a deep reinforcement learning model for multi-level dynamic reconfiguration in an urban distribution network.The cloud-edge collaboration architecture can effectively support the multi-agent system to conduct“centralized training and decentralized execution”operation modes and improve the learning efficiency of the model.Thereafter,for a multi-agent system,this study adopted a combination of offline and online learning to endow the model with the ability to realize automatic optimization and updation of the strategy.In the offline learning phase,a Q-learning-based multi-agent conservative Q-learning(MACQL)algorithm was proposed to stabilize the learning results and reduce the risk of the next online learning phase.In the online learning phase,a multi-agent deep deterministic policy gradient(MADDPG)algorithm based on policy gradients was proposed to explore the action space and update the experience pool.Finally,the effectiveness of the proposed method was verified through a simulation analysis of a real-world 445-node system.
文摘This paper presents an adaptive gain-scheduled backstepping control(AGSBC) scheme for the balance control of an underactuated mechanical power-line inspection(PLI) robotic system with two degrees of freedom and a single control input.First, a nonlinear dynamic model of the balance adjustment process of the PLI robot is constructed, and then the model is linearized at a nominal equilibrium point to overcome the computational infeasibility of the conventional backstepping technique. Second, to solve generalized stabilization control issue for underactuated systems with multiple equilibrium points,an equilibrium manifold linearized model is developed using a scheduling variable, and then a gain-scheduled backstepping control(GSBC) scheme for expanding the operational area of the controlled system is constructed. Finally, an adaptive mechanism is proposed to counteract the impact of external disturbances. The robust stability of the closed-loop system is ensured by Lyapunov theorem. Simulation results demonstrate the effectiveness and high performance of the proposed scheme compared with other control schemes.
基金supported by the National Key Research and Development Program of China(2017YFB0903300).
文摘Recently,power electronic transformers(PETs)have received widespread attention owing to their flexible networking,diverse operating modes,and abundant control objects.In this study,we established a steady-state model of PETs and applied it to the power flow calculation of AC-DC hybrid systems with PETs,considering the topology,power balance,loss,and control characteristics of multi-port PETs.To address new problems caused by the introduction of the PET port and control equations to the power flow calculation,this study proposes an iterative method of AC-DC mixed power flow decoupling based on step optimization,which can achieve AC-DC decoupling and effectively improve convergence.The results show that the proposed algorithm improves the iterative method and overcomes the overcorrection and initial value sensitivity problems of conventional iterative algorithms.
文摘With the problem of global energy shortage and people’s awareness of energy saving, electric vehicles receive world-wide attention from government to business. Then the load of the power grid will rapidly increase in a short term, and a series of effects will bring to the power grid operation, management, production and planning. With the large-scale penetration of electric vehicles and distributed energy gradually increased, if they can be effectively controlled and regulated, they can play the roles of load shifting, stabling intermittent renewable energy sources, providing emergency power supply and so on. Otherwise they may have a negative impact, which calls for a good interaction of electric vehicles and power grid. Analyzed the status of the current study on the interaction between the electric vehicles and the power grid, this paper builds the material basis, information architecture and the corresponding control method for the interaction from the aspect of the energy and information exchanging, and then discusses the key issues, which makes a useful exploration for the further research.
文摘This paper presents an adaptive Under Frequency Load Shedding scheme based on Wide Area Measurement System. Due to the lack of enough adaptability to the operation state of the system, the traditional successive approximation under frequency load shedding method will cause excessive cut or undercut problems inevitably. This method consists first in a comprehensive weight index including load characteristics and inertias of generators. Then active-power deficit calculation based on the Low-order Frequency Response Model, concerning the effect of voltage was put forward. Finally, a dynamic correction of the load shedding amount was proposed to modify the scheme. This approach was applied to IEEE-39 system and the simulation results indicated that the proposed method was effective in reducing the load shedding amount as well as the frequency recovery time.
基金supported by the National Natural Science Foundation of China(No.52077146)the Sichuan Science and Technology Program(No.2023YFSY0032).
文摘In the competitive energy market,energy retailers are facing the uncertainties of both energy price and demand,which requires them to formulate reasonable energy purchasing and selling strategies for improving their competitiveness in this market.Particularly,the attractive multi-energy retail packages are the key for retailers to increase their benefit.Therefore,combined with incentive means and price signals,five types of multi-energy retail packages such as peak-valley time-of-use(TOU)price package and day-night bundled price package are designed in this paper for retailers.The iterative interactions between retailers and end-users are modeled using a bi-level model of stochastic optimization based on multi-leader multi-follower(MLMF)Stackelberg game,in which retailers are leaders and end-users are followers.Retailers make decisions to maximize the profit considering the conditional value at risk(CVaR)while end-users optimize the satisfaction of both energy comfort and economy.Besides,a distributed algorithm is proposed to obtain the Nash equilibrium of above MLMF Stackelberg game model while the particle swarm optimization(PSO)algorithm and CPLEX solver are applied to solve the optimization model for each participant(retailer or end-user).Numeral results show that the designed retail packages can increase the overall profit of retailers,and the overall satisfaction of industrial users is the highest while that of residential users is the lowest after game interaction.
基金supported in part by State Grid Corporation of China Project“Research on high penetrated renewable energy oriented intelligent identification for curtailment impacts and aid decision-making for promoting consumption in regional power grids”(No.5108-202135035A-0-0-00).
文摘To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.
基金supported by the National Natural Science Foundation of China(No.52077146)Sichuan Science and Technology Program(No.2023NSFSC1945)。
文摘The increasing integration of intermittent renewable energy sources(RESs)poses great challenges to active distribution networks(ADNs),such as frequent voltage fluctuations.This paper proposes a novel ADN strategy based on multiagent deep reinforcement learning(MADRL),which harnesses the regulating function of switch state transitions for the realtime voltage regulation and loss minimization.After deploying the calculated optimal switch topologies,the distribution network operator will dynamically adjust the distributed energy resources(DERs)to enhance the operation performance of ADNs based on the policies trained by the MADRL algorithm.Owing to the model-free characteristics and the generalization of deep reinforcement learning,the proposed strategy can still achieve optimization objectives even when applied to similar but unseen environments.Additionally,integrating parameter sharing(PS)and prioritized experience replay(PER)mechanisms substantially improves the strategic performance and scalability.This framework has been tested on modified IEEE 33-bus,IEEE 118-bus,and three-phase unbalanced 123-bus systems.The results demonstrate the significant real-time regulation capabilities of the proposed strategy.
基金This work was supported by the National Key R&D Program of China(Grant No.2019YFE0123600)National Science Foundation of China(Grant No.52077146)Young Elite Scientists Sponsorship Program by CSEE(Grant No.CESS-YESS-2019027).
文摘It is expected that multiple virtual power plants(multi-VPPs)will join and participate in the future local energy market(LEM).The trading behaviors of these VPPs needs to be carefully studied in order to maximize the benefits brought to the local energy market operator(LEMO)and each VPP.We propose a bounded rationality-based trading model of multiVPPs in the local energy market by using a dynamic game approach with different trading targets.Three types of power bidding models for VPPs are first set up with different trading targets.In the dynamic game process,VPPs can also improve the degree of rationality and then find the most suitable target for different requirements by evolutionary learning after considering the opponents’bidding strategies and its own clustered resources.LEMO would decide the electricity buying/selling price in the LEM.Furthermore,the proposed dynamic game model is solved by a hybrid method consisting of an improved particle swarm optimization(IPSO)algorithm and conventional largescale optimization.Finally,case studies are conducted to show the performance of the proposed model and solution approach,which may provide some insights for VPPs to participate in the LEM in real-world complex scenarios.
基金supported by the National Key R&D Program of China under Grant 2018YFB0905200.
文摘To utilize electricity in a clean and integrated manner,a zero-carbon hydro-photovoltaic(PV)-pumped hydro storage(PHS)integrated power system is studied,considering the uncertainties of PV and load demand.It is a challenge for operators to develop a dynamic dispatch mechanism for such a system,and traditional dispatch methods are difficult to adapt to random changes in the actual environment.Therefore,this study proposes a real-time dynamic dispatch strategy considering economic operation and complementary regulatory ability.First,the dynamic dispatch of a hydro-PV-PHS integrated power system is presented as a multi-objective optimization problem and the weight factor between different goals is effectively calculated using information entropy.Afterwards,the dispatch model is converted into the Markov decision process,where the dynamic dispatch decision is formulated as a reinforcement learning framework.Then,a deep deterministic policy gradient(DDPG)is deployed towards the online decision for dispatch in continuous action spaces.Finally,a case study is applied to evaluate the performance of the proposed method based on a real hydroPV-PHS integrated power system in China.Simulations show that the system agent reduces the power volatility of supply by 26.7%after hydropower regulating and further relieves power fluctuation at the point of common coupling(PCC)to the upperlevel grid by 3.28%after PHS participation.The comparison results verify the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China (U2166211).
文摘As an integrated carrier of energy production,transmission,distribution,conversion,storage,and utilization,multiple energy systems(MESs)have significant low-carbon potential.This paper proposes a hierarchical distributed dispatch model of MESs considering carbon trading,which is composed of the lower autonomous operation level of each MES and the upper coordinated control level.Different carbon emission sources are considered,including combined heat and power(CHP)units,gas boilers,and power to gas(P2G)devices.The transactive control(TC)mechanism is used to solve the model by introducing a virtual price signal.In the case study based on a 3-MES system,the effectiveness of the proposed distributed method is proved by comparison with a centralized algorithm.Meanwhile,the impacts of different carbon prices on MESs with different resource endowments are analyzed from the aspects of scheduling results,carbon emissions,clean energy consumption rate,and comprehensive operating costs.
基金supported by the National Natural Science Foundation of China(U2166211).
文摘Decarbonisation of power systems is essential for realising carbon neutrality,in which the economic cost caused by carbon needs to be qualified.Based on the formulation of locational marginal price(LMP),this paper proposes a locational marginal electricity-carbon price(EC-LMP)model to reveal carbon-related costs caused by power consumers.A carbon-priceintegrated optimal power flow(C-OPF)is then developed to maximise economic efficiency of the power system considering the costs of electricity and carbon.Case studies are presented to demonstrate the new formulation and results demonstrate the efficacy of the EC-LMP-based C-OPF on decarbonisation and economy.
基金This work was supported by National High Technology Research and Development Program of China under Grant 2014AA051901(Key Technology Research and Demonstration for Active Distribution Grid).
文摘The approach to planning,design and operation of distribution networks have significantly changed due to the proliferation of distributed energy resources(DERs)together with load growth,energy storage technology advancements and increased consumer expectations.Planning of active distribution systems(ADS)has been a very hot topic in the 21st Century.A large number of studies have been done on ADS planning.This paper reviews the state of the art of current ADS planning.Firstly,the influences of DERs on the ADS planning are addressed.Secondly,the characteristics and objectives of ADS planning are summarized.Then,up to date planning model and some related research are highlighted in different areas such as forecasting load and distributed generation,mathematical model of ADS planning and solution algorithms.Finally,the paper explores some directions of future research on ADS planning including planning collaboratively with all elements combined in ADS,taking into account of joint planning in secondary system,coordinating goals among different layers,integrating detailed operation simulations and regular performance based reviews into planning,and developing advanced planning tools.
基金supported in part by the National Natural Science Foundation of China(No.51807127)in part by the Fundamental Research Funds for the Central Universities of China opment Program of China(No.2018YFB0905200).(No.YJ201654)in part by the National Key Research and Development Program of China(No.2018YFB0905200).
文摘The change of customer behaviors and the fluctuation of spot prices can affect the benefits of electricity retailers.To address this issue,an incentive-based demand response(DR)model involving the utility and elasticity of customers is proposed for maximizing the benefits of retailers.The benefits will increase by triggering an incentive price to influence customer behaviors to change their demand consumptions.The optimal reduction of customers is obtained by their own profit optimization model with a certain incentive price.Then,the sensitivity of incentive price on retailers’benefits is analyzed and the optimal incentive price is obtained according to the DR model.The case study verifies the effectiveness of the proposed model.
基金This work was supported by the National Key R&D Program of China(2017YFB0903300).
文摘With the highly-extensive integration of distributed renewable energy resources(DER)into the grid,the power distribution system has changed greatly in the structure,function and operating characteristics.On this ground,An AC-DC hybrid DER system becomes necessary for effective management and control over DER.This paper first summarizes the physical characteristics and morphological evolution of AC-DC hybrid DER system.The impact of these new features on system configuration planning is analyzed with respect to its flexible networking,rich operation control modes,and tight sourcenetwork-load-storage coupling.Then,based on a review of the existing research,problems and technical difficulties are figured out in terms of converter modeling,steady-state analysis,power flow calculation,operating scenarios management,and optimization model solution.In light of the problems and difficulties,a framework for the configuration optimization of AC-DC hybrid DER systems is proposed.At last,the paper provides a prospect of key technologies from six aspects including morphology forecasting,coupling interaction analysis,uncertainty modeling,operation simulation,optimization model solving algorithm and comprehensive scheme evaluation.
基金This work was supported by the New Century Higher Education Teaching Reform Project of Sichuan University under Grant SCU8007and the Inter-disciplinary Training Project for Talents of Sichuan University under grant SCUKG056.
文摘Over the past decades,both agriculture and power systems have faced serious problems,such as the power supply shortage in agriculture,and difficulties of clean energy consump-tion in the power system.To address and overcome these issues,this paper proposes an idea to combine smart agriculture and clean energy consumption,use surplus clean energy to supply agriculture production,and utilize smart agriculture to support power system with clean energy penetration.A comprehensive review has been conducted to first depict the roadmap of coupling a agriculture-clean energy system,analyze their feasibilities and advantages.The recent technologies and bottlenecks are summa-rized and evaluated for the development of a combined system consisting of smart agriculture production and clean energy consumption.Several case studies are introduced to explore the mutual benefits of agriculture-clean energy systems in both the energy and food industries.
