The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitabili...The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market.This paper proposes two coherent models to address these challenges.Firstly,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation penalties.Secondly,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast errors.The role of the DEQ model is to guide the knowledgedriven bidding model.Notably,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational optimum.These two models have independent optimizations based on misaligned objectives.To address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding strategy.The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).展开更多
In addition to renewable energy sources and market prices uncertainties, the regulation commands issued by the superior market operator are unpredictable factors within the virtual power plant (VPP) bidding range. To ...In addition to renewable energy sources and market prices uncertainties, the regulation commands issued by the superior market operator are unpredictable factors within the virtual power plant (VPP) bidding range. To avoid branch power flow outage and voltage violation under uncertain regulation commands, a tri-level robust optimization-based day-ahead energy and regulation service bidding strategy for a generalized VPP, considering various distributed energy resources is proposed, in which the VPP bidding strategy, worst scenario estimation approach and regulation service scheduling method are formulated at three optimization layers, respectively. Then, the proposed tri-level model is transformed into an equivalent, single-level, mixed integer, second-order cone programming problem with rigid proof. Numerical simulations illustrate the effectiveness and superiority of the proposed approach by comparison with other prevailing methods in recent literature.展开更多
With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smo...With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smooth power output and enhance grid stability.Coordinating multiple PV-ESS plants is essential to maintain system reliability,balance stochastic renewable outputs with real-time load demands,and leverage time-varying electricity prices for economic benefits.In this paper,a learning-based joint bidding framework is proposed to maximise the aggregated profit of PV–ESS plants.First,a multi-PV-ESS model is built to emulate the coordinated operation of PV and ESS units in the power grid,aiming to maximise PV power revenues while considering penalty payments for power shortages,real-time load demands and dynamic power prices.Then,the joint bidding operations of PV-ESS plants are formulated as a Markov decision process,and a deep reinforcement learning algorithm is developed to learn optimal bidding strategies that adapt to load dynamics and price fluctuations.Extensive case studies on distribution systems of different scales,including the IEEE 33-bus and 69-bus systems,are conducted to demonstrate the effectiveness of the proposed method.展开更多
Virtual power plant(VPP)aggregates large amounts of distributed energy and controllable loads.The comprehensive consideration of carbon emissions and electricity transactions has great significance in improving the VP...Virtual power plant(VPP)aggregates large amounts of distributed energy and controllable loads.The comprehensive consideration of carbon emissions and electricity transactions has great significance in improving the VPP operation’s economic efficiency.In this paper,the bidding strategy of the VPP by considering the carbon-electricity integration trading in an auxiliary service(AS)market is studied.First of all,the basic structure and operating features of the VPP are briefly introduced.Then,the bidding strategy model of carbon-electricity integration trading in an auxiliary service market is proposed and the corresponding objective function and the constraint conditions are also analyzed.Furthermore,the GAMS solver is utilized to give the optimal solution of the bidding strategy model.Finally,the effectiveness of the bidding strategy of a VPP based on the consideration of carbon-electricity integration trading is verified through simulation cases.展开更多
The active distribution network(ADN)is able to manage distributed generators(DGs),active loads and storage facilities actively.It is also capable of purchasing electricity from main grid and providing ancillary servic...The active distribution network(ADN)is able to manage distributed generators(DGs),active loads and storage facilities actively.It is also capable of purchasing electricity from main grid and providing ancillary services through a flexible dispatching mode.A competitive market environment is beneficial for the exploration of ADN’s activeness in optimizing dispatch and bidding strategy.In a bilateral electricity market,the decision variables such as bid volume and price can influence the market clearing price(MCP).The MCP can also have impacts on the dispatch strategy of ADN at the same time.This paper proposes a bilevel coordinate dispatch model with fully consideration of the information interaction between main grid and ADN.Simulation results on a typical ADN validate the feasibility of the proposed method.A balanced proportion between energy market and ancillary services market can be achieved.展开更多
Photovoltaic(PV)and battery energy storage systems(BESSs)are key components in the energy market and crucial contributors to carbon emission reduction targets.These systems can not only provide energy but can also gen...Photovoltaic(PV)and battery energy storage systems(BESSs)are key components in the energy market and crucial contributors to carbon emission reduction targets.These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading.This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets,with a specific focus on carbon reduction benefits.A two-stage bidding framework that optimizes the profit of PV and BESSs is presented.In the first stage,the day-ahead energy market takes into account potential real-time forecast deviations.In the second stage,the real-time balancing market uses a rolling optimization method to account for multiple uncertainties.Notably,a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control(AGC).This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market.This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error(ACE)occurs.The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.展开更多
The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity market.Within this context,this paper proposes a bi-level stochastic optimization model for the j...The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity market.Within this context,this paper proposes a bi-level stochastic optimization model for the joint operation of a coordinated wind power plant(WPP)and natural gas generating(NGG)-power to gas(P2G)suppliers participating in the day-ahead(DA)market and real-time(RT)market as well as providing real-time auxiliary services.The coordinated supplier’s payoff is maximized in the upper level with consideration of the uncertainties of WPP output capacity and RT electricity price,while the social welfare of the grid is maximized in the lower level.Simulation results demonstrate the effectiveness of the proposed bidding model of the coordinated WPPs and NGG-P2G suppliers by examining its bidding behaviors and benefits with comparisons of four other bidding models.展开更多
The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the suffic...The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.展开更多
The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.Wh...The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.展开更多
In a SIPV model,when the commission proportion is not certain,but related with bargain price,generally,it is a linear function of the bargain price,this paper gives bidders'equilibrium bidding strategies in the fi...In a SIPV model,when the commission proportion is not certain,but related with bargain price,generally,it is a linear function of the bargain price,this paper gives bidders'equilibrium bidding strategies in the first-and secondprice auctions.We find that the equilibrium strategies in second-price auction are dominant strategies.For seller or auction house,whether the fixed proportion or the unfixed proportion is good is not only related with constant item and the linear coefficient of the linear function,the size of the fixed commission proportion,but also related with the value of the item auctioned.So,in the practical auctions,the seller and the auction house negotiated with each other to decide the commission rules for their own advantage.展开更多
The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such a...The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.展开更多
The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantifi...The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.展开更多
In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each...In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.展开更多
With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental frie...With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.展开更多
To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method ...To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method to present a new scoring function that converts each bid into a score.Twokinds of multi-attribute auction models are introduced in terms of scoring rules and bidding objectivefunctions.Equilibrium bidding strategies,procurer's revenue comparisons and optimal auction designare characterized in these two models.Finally,this article discusses some improvement of robustnessof our models,with respect to the assumptions.展开更多
Developing the electricity market at the distribution level can facilitate the energy transactions in distribution networks with a high penetration level of distributed energy resources(DERs)and microgrids(MGs).Howeve...Developing the electricity market at the distribution level can facilitate the energy transactions in distribution networks with a high penetration level of distributed energy resources(DERs)and microgrids(MGs).However,the lack of comprehensive information about the marginal production cost of competitors leads to uncertainties in the optimal bidding strategy of participants.The electricity demand within the network and the price in the wholesale electricity market are two other sources of the uncertainties.In this paper,a day-ahead-market-based framework for managing the energy transactions among MGs and other participants in distribution networks is introduced.A game-theory-based method is presented to model the competition and determine the optimal bidding strategy of participants in the market.Robust optimization technique is employed to capture the uncertainties in the marginal cost of competitors.Additionally,the uncertainties in demand are modeled using a scenario-based stochastic approach.The results ob-tained from case studies reveal the merit of considering competition modeling and uncertainties.展开更多
The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome t...The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.展开更多
In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable t...In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.展开更多
As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of...As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.展开更多
The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment,w...The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment,where the low cost renewable energy generation is not reflected in the high bidding price of high cost conventional energy generation.This study addresses this issue by proposing a bi-level optimization based two-stage market clearing model that considers the bidding strategies of market players,and guarantees the accommodation of renewable energy generation.The first stage implements a dual-market clearing mechanism that includes a unified market for trading the power generations of both renewable energy and conventional energy units,and a subsidy market reserved exclusively for conventional generation units.A re-adjustment clearing mechanism is then proposed in the second stage to accommodate the power generation of remaining renewable energy units after first stage energy allocations.Each stage of the proposed model is further described as a bi-level market equilibrium problem and is solved using a co-evolutionary algorithm.Finally,numerical results involving an improved IEEE 39-bus system dem-onstrate that the proposed two-stage model meets the basic requirements of incentive compatibility and individual rationality.It can facilitate the rational allocation of resources,promote the economical operation of electric power grids,and enhance social welfare.展开更多
基金supported by the U.S.Department of Energy's Office of Energy Efficiency and Renewable Energy(EERE)under the Solar Energy Technologies Office Award(No.DE-EE0009341)。
文摘The hybrid photovoltaic(PV)-battery energy storage system(BESS)plant(HPP)can gain revenue by performing energy arbitrage in low-carbon power systems.However,multiple operational uncertainties challenge the profitability and reliability of HPP in the day-ahead market.This paper proposes two coherent models to address these challenges.Firstly,a knowledge-driven penalty-based bidding(PBB)model for HPP is established,considering forecast errors of PV generation,market prices,and under-generation penalties.Secondly,a data-driven dynamic error quantification(DEQ)model is used to capture the variational pattern of the distribution of forecast errors.The role of the DEQ model is to guide the knowledgedriven bidding model.Notably,the DEQ model aims at the statistical optimum,but the knowledge-driven PBB model aims at the operational optimum.These two models have independent optimizations based on misaligned objectives.To address this,the knowledge-data-complementary learning(KDCL)framework is proposed to align data-driven performance with knowledge-driven objectives,thereby enhancing the overall performance of the bidding strategy.A tailored algorithm is proposed to solve the bidding strategy.The proposed bidding strategy is validated by using data from the National Renewable Energy Laboratory(NREL)and the New York Independent System Operator(NYISO).
基金supported by a Science and Technology Project of State Grid Corporation of China“Research on urban power grid dispatching technology for large-scale electric vehicles integration”(No.5108-202119040A-0-0-00).
文摘In addition to renewable energy sources and market prices uncertainties, the regulation commands issued by the superior market operator are unpredictable factors within the virtual power plant (VPP) bidding range. To avoid branch power flow outage and voltage violation under uncertain regulation commands, a tri-level robust optimization-based day-ahead energy and regulation service bidding strategy for a generalized VPP, considering various distributed energy resources is proposed, in which the VPP bidding strategy, worst scenario estimation approach and regulation service scheduling method are formulated at three optimization layers, respectively. Then, the proposed tri-level model is transformed into an equivalent, single-level, mixed integer, second-order cone programming problem with rigid proof. Numerical simulations illustrate the effectiveness and superiority of the proposed approach by comparison with other prevailing methods in recent literature.
文摘With the rapid development of clean energy,photovoltaic(PV)power plants have gained increasing attention.However,the inherent intermittency of PV generation requires the integration of energy storage system(ESS)to smooth power output and enhance grid stability.Coordinating multiple PV-ESS plants is essential to maintain system reliability,balance stochastic renewable outputs with real-time load demands,and leverage time-varying electricity prices for economic benefits.In this paper,a learning-based joint bidding framework is proposed to maximise the aggregated profit of PV–ESS plants.First,a multi-PV-ESS model is built to emulate the coordinated operation of PV and ESS units in the power grid,aiming to maximise PV power revenues while considering penalty payments for power shortages,real-time load demands and dynamic power prices.Then,the joint bidding operations of PV-ESS plants are formulated as a Markov decision process,and a deep reinforcement learning algorithm is developed to learn optimal bidding strategies that adapt to load dynamics and price fluctuations.Extensive case studies on distribution systems of different scales,including the IEEE 33-bus and 69-bus systems,are conducted to demonstrate the effectiveness of the proposed method.
文摘Virtual power plant(VPP)aggregates large amounts of distributed energy and controllable loads.The comprehensive consideration of carbon emissions and electricity transactions has great significance in improving the VPP operation’s economic efficiency.In this paper,the bidding strategy of the VPP by considering the carbon-electricity integration trading in an auxiliary service(AS)market is studied.First of all,the basic structure and operating features of the VPP are briefly introduced.Then,the bidding strategy model of carbon-electricity integration trading in an auxiliary service market is proposed and the corresponding objective function and the constraint conditions are also analyzed.Furthermore,the GAMS solver is utilized to give the optimal solution of the bidding strategy model.Finally,the effectiveness of the bidding strategy of a VPP based on the consideration of carbon-electricity integration trading is verified through simulation cases.
基金This work was supported by the National High Technology Research and Development Program of China(No.2014AA051902)State Grid Science&Technology Project(No.5217L0140009).
文摘The active distribution network(ADN)is able to manage distributed generators(DGs),active loads and storage facilities actively.It is also capable of purchasing electricity from main grid and providing ancillary services through a flexible dispatching mode.A competitive market environment is beneficial for the exploration of ADN’s activeness in optimizing dispatch and bidding strategy.In a bilateral electricity market,the decision variables such as bid volume and price can influence the market clearing price(MCP).The MCP can also have impacts on the dispatch strategy of ADN at the same time.This paper proposes a bilevel coordinate dispatch model with fully consideration of the information interaction between main grid and ADN.Simulation results on a typical ADN validate the feasibility of the proposed method.A balanced proportion between energy market and ancillary services market can be achieved.
基金supported by the Jilin Province Science and Technology Development Plan Project(No.20220203163SF).
文摘Photovoltaic(PV)and battery energy storage systems(BESSs)are key components in the energy market and crucial contributors to carbon emission reduction targets.These systems can not only provide energy but can also generate considerable revenue by providing frequency regulation services and participating in carbon trading.This study proposes a bidding strategy for PV and BESSs operating in joint energy and frequency regulation markets,with a specific focus on carbon reduction benefits.A two-stage bidding framework that optimizes the profit of PV and BESSs is presented.In the first stage,the day-ahead energy market takes into account potential real-time forecast deviations.In the second stage,the real-time balancing market uses a rolling optimization method to account for multiple uncertainties.Notably,a real-time frequency regulation control method is proposed for the participation of PV and BESSs in automatic generation control(AGC).This is particularly relevant given the uncertainty of grid frequency fluctuations in the optimization model of the real-time balancing market.This control method dynamically assigns the frequency regulation amount undertaken by the PV and BESSs according to the control interval in which the area control error(ACE)occurs.The case study results demonstrate that the proposed bidding strategy not only enables the PV and BESSs to effectively participate in the grid frequency regulation response but also yields considerable carbon emission reduction benefits and effectively improves the system operation economy.
基金This work was jointly supported by The Hong Kong Polytechnic University,Shenzhen Polytechnic,the National Natural Science Foundation of China(52077075)the National Natural Science Foundation of China(Grant No.72001058)National Natural Science Foundation of China(Grant No.72171155)。
文摘The synergies between electricity and other energy resources could promote energy utilization efficiency in electricity market.Within this context,this paper proposes a bi-level stochastic optimization model for the joint operation of a coordinated wind power plant(WPP)and natural gas generating(NGG)-power to gas(P2G)suppliers participating in the day-ahead(DA)market and real-time(RT)market as well as providing real-time auxiliary services.The coordinated supplier’s payoff is maximized in the upper level with consideration of the uncertainties of WPP output capacity and RT electricity price,while the social welfare of the grid is maximized in the lower level.Simulation results demonstrate the effectiveness of the proposed bidding model of the coordinated WPPs and NGG-P2G suppliers by examining its bidding behaviors and benefits with comparisons of four other bidding models.
基金This work was supported by the National Natural Science Foundation of China(No.U1866206).
文摘The power market is a typical imperfectly competitive market where power suppliers gain higher profits through strategic bidding behaviors.Most existing studies assume that a power supplier is accessible to the sufficient market information to derive an optimal bidding strategy.However,this assumption may not be true in reality,particularly when a power market is newly launched.To help power suppliers bid with the limited information,a modified continuous action reinforcement learning automata algorithm is proposed.This algorithm introduces the discretization and Dyna structure into continuous action reinforcement learning automata algorithm for easy implementation in a repeated game.Simulation results verify the effectiveness of the proposed learning algorithm.
基金supported by National Natural Science Foundation of China(52407126).
文摘The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
基金Supported by the National Natural Science Foun-dation of China(70071012)
文摘In a SIPV model,when the commission proportion is not certain,but related with bargain price,generally,it is a linear function of the bargain price,this paper gives bidders'equilibrium bidding strategies in the first-and secondprice auctions.We find that the equilibrium strategies in second-price auction are dominant strategies.For seller or auction house,whether the fixed proportion or the unfixed proportion is good is not only related with constant item and the linear coefficient of the linear function,the size of the fixed commission proportion,but also related with the value of the item auctioned.So,in the practical auctions,the seller and the auction house negotiated with each other to decide the commission rules for their own advantage.
文摘The bidding strategies of power suppliers to maximize their interests is of great importance.The proposed bilevel optimization model with coalitions of power suppliers takes restraint factors into consideration,such as operating cost reduction,potential cooperation,other competitors’bidding behavior,and network constraints.The upper model describes the coalition relationship between suppliers,and the lower model represents the independent system operator’s optimization without network loss(WNL)or considering network loss(CNL).Then,a novel algorithm,the evolutionary game theory algorithm(EGA)based on a hybrid particle swarm optimization and improved firefly algorithm(HPSOIFA),is proposed to solve the bi-level optimization model.The bidding behavior of the power suppliers in equilibrium with a dynamic power market is encoded as one species,with the EGA automatically predicting a plausible adaptation process for the others.Individual behavior changes are employed by the HPSOIFA to enhance the ability of global exploration and local exploitation.A novel improved firefly algorithm(IFA)is combined with a chaotic sequence theory to escape from the local optimum.In addition,the Shapley value is applied to the profit distribution of power suppliers’cooperation.The simulation,adopting the standard IEEE-30 bus system,demonstrates the effectiveness of the proposed method for solving the bi-level optimization problem.
基金the H2020 project CROSSBOW-CROSS Border management of variable renewable energies and storage units enabling a transnational wholesale market(No.773430)this work was supported in part by the Croatian Science Foundation under the project IMPACT-Implementation of Peer-to-Pecr Advanced Concept for Electricity Trading(No.UIP-2017-05-4068).
文摘The paper analyses the coordinated hydro-wind power generation considering joint bidding in the electricity market.The impact of mutual bidding strategies on market prices,traded volumes,and revenues has been quantified.The coordination assumes that hydro power generation is scheduled mainly to compensate the differences between actual and planned wind power outputs.The potential of this coordination in achieving and utilizing of market power is explored.The market equilibrium of asymmetric generation companies is analyzed using a game theory approach.The assumed market situation is imperfect competition and non-cooperative game.A nu-merical approximation of the asymmetric supply function equilibrium is used to model this game.An introduced novelty is the application of an asymmetric supply function equilibrium approximation for coordinated hydro-wind power generation.The model is tested using real input data from the Croatian power system.
基金This work was supported in part by the US Department of Energy(DOE),Office of Electricity and Office of Energy Efficiency and Renewable Energy under contract DE-AC05-00OR22725in part by CURENT,an Engineering Research Center funded by US National Science Foundation(NSF)and DOE under NSF award EEC-1041877in part by NSF award ECCS-1809458.
文摘In this paper,a day-ahead electricity market bidding problem with multiple strategic generation company(GEN-CO)bidders is studied.The problem is formulated as a Markov game model,where GENCO bidders interact with each other to develop their optimal day-ahead bidding strategies.Considering unobservable information in the problem,a model-free and data-driven approach,known as multi-agent deep deterministic policy gradient(MADDPG),is applied for approximating the Nash equilibrium(NE)in the above Markov game.The MAD-DPG algorithm has the advantage of generalization due to the automatic feature extraction ability of the deep neural networks.The algorithm is tested on an IEEE 30-bus system with three competitive GENCO bidders in both an uncongested case and a congested case.Comparisons with a truthful bidding strategy and state-of-the-art deep reinforcement learning methods including deep Q network and deep deterministic policy gradient(DDPG)demonstrate that the applied MADDPG algorithm can find a superior bidding strategy for all the market participants with increased profit gains.In addition,the comparison with a conventional-model-based method shows that the MADDPG algorithm has higher computational efficiency,which is feasible for real-world applications.
基金This work was partially supported by the National Key R&D Program of China(2018YFB0905000)National Natural Science Foundation of China(51761135015,51877189)+1 种基金the Fundamental Research Funds for the Central Universities(2018QNA4015)The work of C.Wan was supported by the Hundred Talents Program of Zhejiang University.
文摘With the increasingly serious climate change and energy crisis,photovoltaic(PV)generation,as one of the most important renewable energy resources,has experienced dramatic growth worldwide due to its environmental friendliness.How-ever,the uncertainty and intermittency of PV bring inevitable challenges to power systems.With the rapid development of distributed PV and the continuous evolution of the electricity market,increasingly high penetration levels of distributed PV generation have led to a series of problems in power system operations,such as voltage fluctuation,frequency deviation,etc.The market participation of distributed PV needs to be solved.Reasonable market participation form,market mechanism and bidding strategies are vital to the development of distributed PV in the electricity market.This paper comprehensively reviews the development and impacts of distributed PV in the electricity market and discusses the relevant market modes and bidding strategies in detail.
基金supported by the Foundation for the Author of National Excellent Doctoral Dissertation of China under Grant No. 200159National Natural Science Foundation of China under Grant No. 70571014
文摘To eliminate computational problems involved in evaluating multi-attribute bids with differentmeasures,this article first normalizes each individual component of a bid,and then makes use ofthe weighted product method to present a new scoring function that converts each bid into a score.Twokinds of multi-attribute auction models are introduced in terms of scoring rules and bidding objectivefunctions.Equilibrium bidding strategies,procurer's revenue comparisons and optimal auction designare characterized in these two models.Finally,this article discusses some improvement of robustnessof our models,with respect to the assumptions.
文摘Developing the electricity market at the distribution level can facilitate the energy transactions in distribution networks with a high penetration level of distributed energy resources(DERs)and microgrids(MGs).However,the lack of comprehensive information about the marginal production cost of competitors leads to uncertainties in the optimal bidding strategy of participants.The electricity demand within the network and the price in the wholesale electricity market are two other sources of the uncertainties.In this paper,a day-ahead-market-based framework for managing the energy transactions among MGs and other participants in distribution networks is introduced.A game-theory-based method is presented to model the competition and determine the optimal bidding strategy of participants in the market.Robust optimization technique is employed to capture the uncertainties in the marginal cost of competitors.Additionally,the uncertainties in demand are modeled using a scenario-based stochastic approach.The results ob-tained from case studies reveal the merit of considering competition modeling and uncertainties.
基金supported by National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption,2018YFB0904200)Eponymous Complement S&T Program of State Grid Corporation of China(SGLNDKOOKJJS1800266)。
文摘The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.
基金This work was supported by the National Science Foundation of China(Grant 2014CB249200)the National Natural Science Foundation of China(Grant 61873162)the Shanghai Pujiang Program(Grant 18PJ1405500).
文摘In this paper,we design a new bidding algorithm by employing a deep reinforcement learning approach.Firms use the proposed algorithm to estimate conjectural variation of the other firms and then employ this variable to generate the optimal bidding strategy so as to pursue maximal profits.With this algorithm,electricity generation firms can improve the accuracy of conjectural variations of competitors by dynamically learning in an electricity market with incomplete information.Electricity market will reach an equilibrium point when electricity firms adopt the proposed bidding algorithm for a repeated game of power trading.The simulation examples illustrate the overall energy efficiency of power network will increase by 9.90%as the market clearing price decreasing when all companies use the algorithm.The simulation examples also show that the power demand elasticity has a positive effect on the convergence of learning process.
基金This work was supported by the National Key Research and Development Program of China (No. 2017YFB0902200)Key Technology Project of State Grid Corporation of China (No. 5228001700CW)the State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources (No. LAPS20002).
文摘As a dispatchable renewable energy technology, the fast ramping capability of concentrating solar power (CSP) can be exploited to provide regulation services. However, frequent adjustments in real-time power output of CSP, which stems out of strategies offered by ill-designed market, may affect the durability and the profitability of the CSP plant, especially when it provides fast regulation services in a real-time operation. We propose the coordinated operation of a CSP plant and wind farm by exploiting their complementarity in accuracy and durability for providing frequency regulation. The coordinated operation can respond to regulation signals effectively and achieve a better performance than conventional thermal generators. We further propose an optimal bidding strategy for both energy and frequency regulations for the coordinated operation of CSP plant and wind farm in day-ahead market (DAM). The validity of the coordinated operation model and the proposed bidding strategy is verified by a case study including a base case and sensitivity analyses on several impacting factors in electricity markets.
基金supported by the National Natural Science Foundation of China 51937005the Natural Science Foundation of Guangdong Province 2019A1515010689.
文摘The existing electricity market mechanisms designed to promote the consumption of renewable energy generation complicate network participation in market transactions owing to an unfair market competition environment,where the low cost renewable energy generation is not reflected in the high bidding price of high cost conventional energy generation.This study addresses this issue by proposing a bi-level optimization based two-stage market clearing model that considers the bidding strategies of market players,and guarantees the accommodation of renewable energy generation.The first stage implements a dual-market clearing mechanism that includes a unified market for trading the power generations of both renewable energy and conventional energy units,and a subsidy market reserved exclusively for conventional generation units.A re-adjustment clearing mechanism is then proposed in the second stage to accommodate the power generation of remaining renewable energy units after first stage energy allocations.Each stage of the proposed model is further described as a bi-level market equilibrium problem and is solved using a co-evolutionary algorithm.Finally,numerical results involving an improved IEEE 39-bus system dem-onstrate that the proposed two-stage model meets the basic requirements of incentive compatibility and individual rationality.It can facilitate the rational allocation of resources,promote the economical operation of electric power grids,and enhance social welfare.
