Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electric...Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.展开更多
Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve pre...Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.展开更多
Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. ...Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.展开更多
Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by...Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by its high regulation price compared with coal power. Market reform is therefore of vital importance to promote the penetration of NGFE. The objective of this study is to analyze the impacts of market reform and the renewable electricity(RE) subsidy policy on the promotion of NGFE and RE. A dynamic game-theoretic model is developed to analyze the interaction among the NG supplier, the power sector and the power grid. Three scenarios are proposed with different policies, including a fixed regulation price of NG and electricity, real-time pricing(RTP) of NG and electricity, and subsidy targeted at RE. The results show that:(1) market reform can sharply decrease the NG price and consequently promote the development of NGFE and RE;(2) subsidy targeted at RE not only promotes the penetration of NGFE and RE, but also increases the utilization ratio of renewables significantly;(3) market reform and the subsidy also enhance consumers’ welfare by reducing their power consumption expenditure.展开更多
This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfish...This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.展开更多
With the development of smart meters,a realtime pricing(RTP)demand response is becoming possible for households in distribution networks.The power flow can be bidirectional in distribution networks which become smarte...With the development of smart meters,a realtime pricing(RTP)demand response is becoming possible for households in distribution networks.The power flow can be bidirectional in distribution networks which become smarter with distributed generators(DGs).It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use,so that it is more economical to use electricity generated by local distributed generators.Therefore,in order to curtail operating costs of distribution networks,this paper proposes a model of economic optimization conducted by distribution network operators.The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators.Further,based on price elasticity,the formulations of load demand considering RTP are proposed with economic optimization of distribution networks.The economic optimization problems are resolved by an interior point method.The case study shows that peak load demand can be reduced about 3.5%because the household RTP and electricity purchasing costs of distribution network operators can save 28.86£every hour.展开更多
A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side,and a generic generation mix of nuclear,hydro,coal,gas and oil-based generators ...A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side,and a generic generation mix of nuclear,hydro,coal,gas and oil-based generators representing the supply side,is investigated using agent-based simulations.The simulation includes a transactive load control in a real-time pricing electricity market.The study investigates the impacts of adding wind power and demand response(DR)on both greenhouse gas(GHG)emissions and generator cycling requirements.The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation.The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors.It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation.However,the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators,and especially on the capacity factor at which they operate.Therefore,if a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes,it will result in higher GHG emissions.The simulations show that DR can yield a small reduction in GHG emissions,but also lead to a smaller increase in emissions in circumstances when,for example,a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes.Nonetheless,DR is shown to enhance overall system operation,particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability.DR reduces the amount of generator cycling by an increased order of magnitude,thereby reducing wear and tear,improving generator efficiency,and avoiding the need for additional operating reserves.The effectiveness of DR for these uses depends on the participation of responsive loads,and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.展开更多
Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution netw...Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.展开更多
基金a phased achievement of Gansu Province’s Major Science and Technology Project(W22KJ2722005)“Research on Optimal Configuration and Operation Strategy of Energy Storage under“New Energy+Energy Storage”Mode”.
文摘Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.
基金National Natural Science Foundation of China(No.61701104)the “13th Five Year Plan” Research Foundation of Jilin Provincial Department of Education,China(No.JJKH2017018KJ)
文摘Real-time electricity price( RTEP) influence factor extraction is essential to forecasting accurate power system electricity prices. At present,new electricity price forecasting models have been studied to improve predictive accuracy,ignoring the extraction and analysis of RTEP influence factors. In this study,a correlation analysis method is proposed based on stochastic matrix theory.Firstly, an augmented matrix is formulated, including RTEP influence factor data and RTEP state data. Secondly, data correlation analysis results are obtained given the statistical characteristics of source data based on stochastic matrix theory.Mean spectral radius( MSR) is used as the measure of correlativity.Finally,the proposed method is evaluated in New England electricity markets and compared with the BP neural network forecasting method. Experimental results show that the extracted index system comprehensively generalizes RTEP influence factors,which play a significant role in improving RTEP forecasting accuracy.
文摘Under the background of smart grid’s real-time electricity prices theory, a real-time electricity prices and wireless communication smart meter was designed. The metering chip collects power consumption information. The real-time clock chip records current time. The communication between smart meter and system master station is achieved by the wireless communication module. The “freescale” micro controller unit displays power consumption information on screen. And the meter feedbacks the power consumption information to the system master station with time-scale and real-time electricity prices. It results that the information exchange between users and suppers can be realized by the smart meter. It fully reflects the demanding for communication of smart grid.
基金supported by Science Foundation of China University of Petroleum,Beijing(Nos.2462013YJRC015,2462014YJRC036)supported by Ministry of Education in China(MOE)Project of Humanities and Social Sciences(Project No.15YJC630195)
文摘Natural gas-fired electricity(NGFE) is expected to play a more important role in the future due to its characteristics of low pollution, high efficiency and flexibility. However, its development in China is impeded by its high regulation price compared with coal power. Market reform is therefore of vital importance to promote the penetration of NGFE. The objective of this study is to analyze the impacts of market reform and the renewable electricity(RE) subsidy policy on the promotion of NGFE and RE. A dynamic game-theoretic model is developed to analyze the interaction among the NG supplier, the power sector and the power grid. Three scenarios are proposed with different policies, including a fixed regulation price of NG and electricity, real-time pricing(RTP) of NG and electricity, and subsidy targeted at RE. The results show that:(1) market reform can sharply decrease the NG price and consequently promote the development of NGFE and RE;(2) subsidy targeted at RE not only promotes the penetration of NGFE and RE, but also increases the utilization ratio of renewables significantly;(3) market reform and the subsidy also enhance consumers’ welfare by reducing their power consumption expenditure.
基金supported by the European Union’s Horizon 2020 Research and Innovation Program through the SOCIALENERGY Project (No.731767)
文摘This paper contributes to the well-known challenge of active user participation in demand side management(DSM).In DSM, there is a need for modern pricing mechanisms that will be able to effectively incentivize selfishly behaving users in modifying their energy consumption pattern towards system-level goals like energy efficiency.Three generally desired properties of DSM algorithms are: user satisfaction, energy cost minimization and fairness.In this paper, a personalized real-time pricing(P-RTP) mechanism design framework is proposed that fairly allocates the energy cost reduction only to the users that provoke it.Thus, the proposed mechanism achieves significant reduction of the energy cost without sacrificing at all the welfare(user satisfaction)of electricity consumers.The business model that the proposed mechanism envisages is highly competitive flexibility market environments as well as energy cooperatives.
文摘With the development of smart meters,a realtime pricing(RTP)demand response is becoming possible for households in distribution networks.The power flow can be bidirectional in distribution networks which become smarter with distributed generators(DGs).It is expensive to import electricity from the generation far from load centers because of the cost of power loss and network use,so that it is more economical to use electricity generated by local distributed generators.Therefore,in order to curtail operating costs of distribution networks,this paper proposes a model of economic optimization conducted by distribution network operators.The electricity purchasing costs for distribution network operators are minimized by optimizing electric power from transmission systems and local distributed generators.Further,based on price elasticity,the formulations of load demand considering RTP are proposed with economic optimization of distribution networks.The economic optimization problems are resolved by an interior point method.The case study shows that peak load demand can be reduced about 3.5%because the household RTP and electricity purchasing costs of distribution network operators can save 28.86£every hour.
基金This work was supported by Pacific Institute for Climate Solutions(PICS)the Wind Energy Strategic Network(WESNet)and the US Department of Energy(DOE),Office of Electricity Delivery and Energy Reliability.
文摘A smart grid power system for a small region consisting of 1,000 residential homes with electric heating appliances from the demand side,and a generic generation mix of nuclear,hydro,coal,gas and oil-based generators representing the supply side,is investigated using agent-based simulations.The simulation includes a transactive load control in a real-time pricing electricity market.The study investigates the impacts of adding wind power and demand response(DR)on both greenhouse gas(GHG)emissions and generator cycling requirements.The results demonstrate and quantify the effectiveness of DR in mitigating the variability of renewable generation.The extent to which greenhouse gas emissions can be mitigated is found to be highly dependent on the mix of generators and their operational capacity factors.It is expected that the effects of demand response on electricity use can reduce dependency on fossil fuel-based electricity generation.However,the anticipated mitigation of GHG emissions is found to dependent on the number and efficiency of fossil fuel generators,and especially on the capacity factor at which they operate.Therefore,if a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes,it will result in higher GHG emissions.The simulations show that DR can yield a small reduction in GHG emissions,but also lead to a smaller increase in emissions in circumstances when,for example,a generator(the marginal seller)is forced to use less efficient fossil fuel power generation schemes.Nonetheless,DR is shown to enhance overall system operation,particularly by facilitating increased penetration of variable renewable electricity generation without jeopardizing grid operation reliability.DR reduces the amount of generator cycling by an increased order of magnitude,thereby reducing wear and tear,improving generator efficiency,and avoiding the need for additional operating reserves.The effectiveness of DR for these uses depends on the participation of responsive loads,and this study highlights the need to maintain a certain degree of diversity of loads to ensure they can provide adequate responsiveness to the changing grid conditions.
基金The National Key Research and Development Program of China(Basic Research Class 2017YFB0903000)Basic Theories and Methods of Analysis and Control of the Cyber Physical Systems for Power Grid,and the Natural Science Foundation of Zhejiang Province(LZ15E070001).
文摘Background:The increasing penetration of a massive number of plug-in electric vehicles(PEVs)and distributed generators(DGs)into current power distribution networks imposes obvious challenges on power distribution network operation.Methods:This paper presents an optimal temporal-spatial scheduling strategy of PEV charging demand in the presence of DGs.The solution is designed to ensure the reliable and secure operation of the active power distribution networks,the randomness introduced by PEVs and DGs can be managed through the appropriate scheduling of the PEV charging demand,as the PEVs can be considered as mobile energy storage units.Results:As a result,the charging demands of PEVs are optimally scheduled temporally and spatially,which can improve the DG utilization efficiency as well as reduce the charging cost under real-time pricing(RTP).Conclusions:The proposed scheduling strategy is evaluated through a series of simulations and the numerical results demonstrate the effectiveness and the benefits of the proposed solution.
