With the gradual integration of vehi-cle-to-grid technology in electric vehicles(EVs),the in-teraction between transportation and distribution networks has become increasingly critical,intensifying the demand for powe...With the gradual integration of vehi-cle-to-grid technology in electric vehicles(EVs),the in-teraction between transportation and distribution networks has become increasingly critical,intensifying the demand for power grid communication and transforming the power grid into a cyber-physical-transportation system.In response to these challenges,this paper proposes a pricing and control strategy for battery changing stations(BCSs)across multiple markets.Firstly,a Bayesian adaptive spline surface based sensitivity analysis method is employed to quantify the impact of pricing on road congestion rates.In the intraday market,a dynamic pricing strategy,guided by sensitivity analysis,is designed to influence EV traffic flow with minimal price adjustments.This optimizes BCS revenue in energy and reserve capacity markets while alleviating traffic congestion and reducing the communication burden.In the real-time market,a game-based subjective and objective evaluation method is developed to assess the response characteristics of BCSs considering factors such as communication delays,regulation capacity,and market revenue,enabling an equitable allocation of frequency regulation tasks among BCSs.Additionally,this method ensures fair compensation to balance the financial impact of price changes across multiple BCSs.Simulation results validate the effectiveness of the proposed method.展开更多
With the development of the carbon markets(CMs)and electricity markets(EMs),discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies(GenCos).Moti...With the development of the carbon markets(CMs)and electricity markets(EMs),discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies(GenCos).Motivated by the carbon option and Black-Scholes(B-S)model,GenCos are given the right but not the obligation to trade carbon emission allowances(CEAs)and use instruments to hedge against price risks.To model the strategic behaviors of GenCos that capitalize on these cross-market and cross-time opportunities,a multi-market trading strategy that incorporates option-jointed daily trading and reinforcement learning-jointed weekly continuous trading are modeled.The daily trading is built with a bi-level structure,where a profit-oriented bidding model that jointly considers both the optimal CEA holding shares and the best bidding curves is developed at the upper level.At the lower level,in addition to market clearing models of the day-ahead EM and auction-based CM,a B-S model that considers carbon trading asynchronism and option pricing is constructed.Then,by expanding the daily trading,the weekly continuous trading is modeled and solved using reinforcement learning.Binary expansion and strike-to-spot price ratio are utilized to address the nonlinearity.Finally,case studies on an IEEE 30-bus system are conducted to validate the effectiveness of the proposed trading strategy.Results show that the proposed trading strategy can increase GenCo profits by influencing market prices and leveraging carbon options.展开更多
基金supported in part by the National Natural Science Foundation of China(No.62293500 and No.62293504)in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province(No.KYCX23_1044).
文摘With the gradual integration of vehi-cle-to-grid technology in electric vehicles(EVs),the in-teraction between transportation and distribution networks has become increasingly critical,intensifying the demand for power grid communication and transforming the power grid into a cyber-physical-transportation system.In response to these challenges,this paper proposes a pricing and control strategy for battery changing stations(BCSs)across multiple markets.Firstly,a Bayesian adaptive spline surface based sensitivity analysis method is employed to quantify the impact of pricing on road congestion rates.In the intraday market,a dynamic pricing strategy,guided by sensitivity analysis,is designed to influence EV traffic flow with minimal price adjustments.This optimizes BCS revenue in energy and reserve capacity markets while alleviating traffic congestion and reducing the communication burden.In the real-time market,a game-based subjective and objective evaluation method is developed to assess the response characteristics of BCSs considering factors such as communication delays,regulation capacity,and market revenue,enabling an equitable allocation of frequency regulation tasks among BCSs.Additionally,this method ensures fair compensation to balance the financial impact of price changes across multiple BCSs.Simulation results validate the effectiveness of the proposed method.
基金supported by the National Science Foundation of Jiangsu Province(No.BK20232026).
文摘With the development of the carbon markets(CMs)and electricity markets(EMs),discrepancies in prices between the two markets and between two time periods offer profit opportunities for generation companies(GenCos).Motivated by the carbon option and Black-Scholes(B-S)model,GenCos are given the right but not the obligation to trade carbon emission allowances(CEAs)and use instruments to hedge against price risks.To model the strategic behaviors of GenCos that capitalize on these cross-market and cross-time opportunities,a multi-market trading strategy that incorporates option-jointed daily trading and reinforcement learning-jointed weekly continuous trading are modeled.The daily trading is built with a bi-level structure,where a profit-oriented bidding model that jointly considers both the optimal CEA holding shares and the best bidding curves is developed at the upper level.At the lower level,in addition to market clearing models of the day-ahead EM and auction-based CM,a B-S model that considers carbon trading asynchronism and option pricing is constructed.Then,by expanding the daily trading,the weekly continuous trading is modeled and solved using reinforcement learning.Binary expansion and strike-to-spot price ratio are utilized to address the nonlinearity.Finally,case studies on an IEEE 30-bus system are conducted to validate the effectiveness of the proposed trading strategy.Results show that the proposed trading strategy can increase GenCo profits by influencing market prices and leveraging carbon options.
