A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load cur...A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.展开更多
The State Council issued the Scheme for Reforming Electricity Pricing System m the second half of year 2003. It touches upon separating price of plant from that of network, sales price to network, transmission and dis...The State Council issued the Scheme for Reforming Electricity Pricing System m the second half of year 2003. It touches upon separating price of plant from that of network, sales price to network, transmission and distribution price, sales price and system principles in regard to electricity tariff mainly.展开更多
In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the...In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.展开更多
In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy sys...In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.展开更多
Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of ...Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.展开更多
With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This...With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning(DRL)algorithm considering privacy protection.The transaction process between the integrated energy service provider(IESP)and user aggregators(UAs)in RIES is modeled as a Stackelberg game.IESP serves as the leader in making retail prices,and different UAs serve as followers in optimizing their energy consumption strategies.Considering UAs’strategies are temporally coupled,a Markov decision process(MDP)is designed differently from existing studies.Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage.The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.展开更多
Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strate...Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.展开更多
This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data e...This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.展开更多
The reform in water pricing plays a critical role in agricultural production, which is believed to have great water savings potential. We consider eliminating irrigation subsidies as a simulation and conduct a compara...The reform in water pricing plays a critical role in agricultural production, which is believed to have great water savings potential. We consider eliminating irrigation subsidies as a simulation and conduct a comparative evaluation between the water parallel pricing system （WPPS） and the water pricing system （WPS）, which are incorporated into two computable general equilibrium （CGE） models, respectively. The results prove that, compared with WPPS, WPS would contribute higher capacities for water savings with more farming imports and less loss in farming output; households in rural and urban areas would benefit from more income and food consumption, which would be matched by increasing farming imports. A policy recommendation is that eliminating the irrigation subsidy should pay more concerns on alleviating the negative effects on farming outputs. Moreover, improvements in agricultural labor mobility and water demand elasticity are needed to enable more focus on the water conservation policy, particularly in WPS.展开更多
This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combin...This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.展开更多
The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric...The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.展开更多
Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy co...Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.展开更多
The China Basic Medical Insurance Program was created in 1999 with three objectives:equal accessibility,affordability,and quality.Today,it has become the biggest medical insurance program in the world,covering 95%of C...The China Basic Medical Insurance Program was created in 1999 with three objectives:equal accessibility,affordability,and quality.Today,it has become the biggest medical insurance program in the world,covering 95%of China's population.Since 2015,China's healthcare ecosystem has been reshaped by increasing innovation,which has in turn been driven by regulatory reform,enhancement of research and development capability,and capital market development.There has also been improved regulatory efficiency to reduce lags in launching drugs.In 2022,nearly 20%of novel active substances launched globally were from China.China has also risen to become the second biggest contributor to innovation in terms of pipelines.Using a“fast-follow”strategy,many locally developed innovative drugs can compete with products from multinational companies in their quality and pricing.However,China's pharmaceutical and biotechnology industry will continue to face challenges in pricing and reimbursement,as well as a shortened product lifecycle with rapid price erosion.The government has already accelerated the timeline for updating the drug reimbursement list and is willing to create a high-quality medical insurance program.However,some obstacles are hard to overcome,including reimbursement for advanced therapies,limited funding and an increasing burden of disease due to an aging population.This article reviews the trajectory of medical innovation in China,including the challenges.Looking forward,balancing affordability and innovation will be critical for China to continue the trajectory of growth.The article also offers some suggestions for future policy reform,including optimizing reimbursement efficiency with a focus on highquality solutions,enhancing the value assessment framework,payer repositioning from“value buyer”to“strategic buyer”,and developing alternative market access pathways for innovative drugs.展开更多
The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating int...The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.展开更多
A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically. The manufacturer is in charge o...A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically. The manufacturer is in charge of recollecting and re-disposal the used products. Demands of ultimate products and collecting quantity of used products are described as the function of prices and reference prices. A non-linear dynamic pricing model for this closed-loop supply chain is established. A numerical example is designed, and the results of this example verified the model’s validity to price for the operation of closed-loop supply chain system.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 essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In re...In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.展开更多
This paper presents a practical pricing model for backup reserve and wheeling, which attains a balanced strategy that ensures perceived benefits to both the buyer and the seller. The model and the associated computeri...This paper presents a practical pricing model for backup reserve and wheeling, which attains a balanced strategy that ensures perceived benefits to both the buyer and the seller. The model and the associated computerized algorithm deal collectively with diverse issues, including： （1） fulfilling local firm real （and reactive） power demand requirements, （2） fulfilling local power reserve requirements, （3） buying firm real （and reactive） power from the grid, （4） buying reserve power from the grid, （5） exporting firm real （and reactive） power demand to remote load centers via the grid, （6） exporting reserve power via the grid, （7） wheeling of firm power demand to remote owned sites using the grid, and （8） wheeling reserve power to remote owned sites using grid. Practical implementation features of the computerized algorithms are also discussed with an illustrative case example.展开更多
In order to meet the requirement of separating power plants from power network and that of the competition based power transaction in power market,the pricing decision support system for generation companies(GCPDSS)is...In order to meet the requirement of separating power plants from power network and that of the competition based power transaction in power market,the pricing decision support system for generation companies(GCPDSS)is built in electricity market.This paper introduces the conception of intelligent decision support system(IDSS)and puts emphasis on the systematical structural framework,work process,design principal,and fundamental function of GCPDSS.The system has the module to analyze the cost,to forecast the demand of power,to construct the pricing strategies,to manage the pricing risk,and to dispatch giving the pricing strategies.The case study illustrates that the friendly window-based user interface of the system enables the user to take full advantage of the capabilities of the system in order to make effective real-time decisions.展开更多
文摘A real-time pricing system of electricity is a system that charges different electricity prices for different hours of the day and for different days, and is effective for reducing the peak and flattening the load curve. In this paper, using a Markov decision process (MDP), we propose a modeling method and an optimal control method for real-time pricing systems. First, the outline of real-time pricing systems is explained. Next, a model of a set of customers is derived as a multi-agent MDP. Furthermore, the optimal control problem is formulated, and is reduced to a quadratic programming problem. Finally, a numerical simulation is presented.
文摘The State Council issued the Scheme for Reforming Electricity Pricing System m the second half of year 2003. It touches upon separating price of plant from that of network, sales price to network, transmission and distribution price, sales price and system principles in regard to electricity tariff mainly.
文摘In this paper, verification of real-time pricing systems of electricity is considered using a probabilistic Boolean network (PBN). In real-time pricing systems, electricity conservation is achieved by manipulating the electricity price at each time. A PBN is widely used as a model of complex systems, and is appropriate as a model of real-time pricing systems. Using the PBN-based model, real-time pricing systems can be quantitatively analyzed. In this paper, we propose a verification method of real-time pricing systems using the PBN-based model and the probabilistic model checker PRISM. First, the PBN-based model is derived. Next, the reachability problem, which is one of the typical verification problems, is formulated, and a solution method is derived. Finally, the effectiveness of the proposed method is presented by a numerical example.
基金supported by the Central Government Guides Local Science and Technology Development Fund Project(2023ZY0020)Key R&D and Achievement Transformation Project in InnerMongolia Autonomous Region(2022YFHH0019)+3 种基金the Fundamental Research Funds for Inner Mongolia University of Science&Technology(2022053)Natural Science Foundation of Inner Mongolia(2022LHQN05002)National Natural Science Foundation of China(52067018)Metallurgical Engineering First-Class Discipline Construction Project in Inner Mongolia University of Science and Technology,Control Science and Engineering Quality Improvement and Cultivation Discipline Project in Inner Mongolia University of Science and Technology。
文摘In this paper,a bilevel optimization model of an integrated energy operator(IEO)–load aggregator(LA)is constructed to address the coordinate optimization challenge of multiple stakeholder island integrated energy system(IIES).The upper level represents the integrated energy operator,and the lower level is the electricity-heatgas load aggregator.Owing to the benefit conflict between the upper and lower levels of the IIES,a dynamic pricing mechanism for coordinating the interests of the upper and lower levels is proposed,combined with factors such as the carbon emissions of the IIES,as well as the lower load interruption power.The price of selling energy can be dynamically adjusted to the lower LA in the mechanism,according to the information on carbon emissions and load interruption power.Mutual benefits and win-win situations are achieved between the upper and lower multistakeholders.Finally,CPLEX is used to iteratively solve the bilevel optimization model.The optimal solution is selected according to the joint optimal discrimination mechanism.Thesimulation results indicate that the sourceload coordinate operation can reduce the upper and lower operation costs.Using the proposed pricingmechanism,the carbon emissions and load interruption power of IEO-LA are reduced by 9.78%and 70.19%,respectively,and the capture power of the carbon capture equipment is improved by 36.24%.The validity of the proposed model and method is verified.
文摘Nodal pricing is a critical mechanism in electricity markets,utilized to determine the cost of power transmission to various nodes within a distribution network.As power systems evolve to incorporate higher levels of renewable energy and face increasing demand fluctuations,traditional nodal pricing models often fall short to meet these new challenges.This research introduces a novel enhanced nodal pricing mechanism for distribution networks,integrating advanced optimization techniques and hybrid models to overcome these limitations.The primary objective is to develop a model that not only improves pricing accuracy but also enhances operational efficiency and system reliability.This study leverages cutting-edge hybrid algorithms,combining elements of machine learning with conventional optimization methods,to achieve superior performance.Key findings demonstrate that the proposed hybrid nodal pricing model significantly reduces pricing errors and operational costs compared to conventional methods.Through extensive simulations and comparative analysis,the model exhibits enhanced performance under varying load conditions and increased levels of renewable energy integration.The results indicate a substantial improvement in pricing precision and network stability.This study contributes to the ongoing discourse on optimizing electricity market mechanisms and provides actionable insights for policymakers and utility operators.By addressing the complexities of modern power distribution systems,our research offers a robust solution that enhances the efficiency and reliability of power distribution networks,marking a significant advancement in the field.
文摘With deregulation of the energy market,the pricing strategy of energy sellers in a regional integrated energy system(RIES)can affect the interests of all participants in the market and the operation of the system.This paper proposes a pricing strategy for integrated energy service providers in RIES based on a deep reinforcement learning(DRL)algorithm considering privacy protection.The transaction process between the integrated energy service provider(IESP)and user aggregators(UAs)in RIES is modeled as a Stackelberg game.IESP serves as the leader in making retail prices,and different UAs serve as followers in optimizing their energy consumption strategies.Considering UAs’strategies are temporally coupled,a Markov decision process(MDP)is designed differently from existing studies.Case studies demonstrate that the proposed method is accurate and stable when solving a Stackelberg equilibrium without privacy leakage.The obtained pricing strategy avoids unreasonable pricing and guarantees the revenue of IESP and the energy demand of UAs.
基金sponsored in part by the National Natural Science Foundation of China(52167014)in part by the Science and Technology Commission of Shanghai Municipality(23XD1422000,23QB1400500).
文摘Due to the centralization of charging stations(CSs),CSs are organized as charging station alliances(CSAs)in the commercial competition.Under this situation,this paper studies the profit-oriented dynamic pricing strategy of CSAs.As the practicability basis,a privacy-protected bidirectional real-time information interaction framework is designed,under which the status of EVs is utilized as the reference for pricing,and the prices of CSs are the reference for charging decisions.Based on this framework,the decision-making models of EVs and CSs are established,in which the uncertainty caused by the information asymmetry between EVs and CSs and the bounded rationality of EV users are integrated.To solve the pricing decision model,the evolutionary game theory is adopted to describe the dynamic pricing game among CSAs,the equilibrium of which gives the optimal pricing strategy.Finally,the case study conducted in an urban area of Shanghai,China,validates the practicability of the framework and the effectiveness of the dynamic pricing strategy.
基金supported by the National Natural Science Foundation of China[grant numbers 12171158,12371474 and 12571510]Fundamental Research Funds for the Central Universities[grant number 2025ECNU-WLJC006].
文摘This work contributes to the theoretical foundation for pricing in data markets and offers practical insights for managing digital data exchanges in the era of big data.We propose a structured pricing model for data exchanges transitioning from quasi-public to marketoriented operations.To address the complex dynamics among data exchanges,suppliers,and consumers,the authors develop a threestage Stackelberg game framework.In this model,the data exchange acts as a leader setting transaction commission rates,suppliers are intermediate leaders determining unit prices,and consumers are followers making purchasing decisions.Two pricing strategies are examined:the Independent Pricing Approach(IPA)and the novel Perfectly Competitive Pricing Approach(PCPA),which accounts for competition among data providers.Using backward induction,the study derives subgame-perfect equilibria and proves the existence and uniqueness of Stackelberg equilibria under both approaches.Extensive numerical simulations are carried out in the model,demonstrating that PCPA enhances data demander utility,encourages supplier competition,increases transaction volume,and improves the overall profitability and sustainability of data exchanges.Social welfare analysis further confirms PCPA’s superiority in promoting efficient and fair data markets.
基金National Natural Science Foundation of China(41271547,41501604 and 41271546)the 57th China Postdoctoral Science Foundation(2015M571109)
文摘The reform in water pricing plays a critical role in agricultural production, which is believed to have great water savings potential. We consider eliminating irrigation subsidies as a simulation and conduct a comparative evaluation between the water parallel pricing system （WPPS） and the water pricing system （WPS）, which are incorporated into two computable general equilibrium （CGE） models, respectively. The results prove that, compared with WPPS, WPS would contribute higher capacities for water savings with more farming imports and less loss in farming output; households in rural and urban areas would benefit from more income and food consumption, which would be matched by increasing farming imports. A policy recommendation is that eliminating the irrigation subsidy should pay more concerns on alleviating the negative effects on farming outputs. Moreover, improvements in agricultural labor mobility and water demand elasticity are needed to enable more focus on the water conservation policy, particularly in WPS.
基金The National High Technology Research and Development Program of China (863 Program) (No. 2007AA11Z202)the National Key Technology R & D Program of China during the 11th Five-Year Plan Period(No. 2006BAJ18B03)the Fundamental Research Funds for the Central Universities (No. DUT10RC(3) 112)
文摘This paper considers the problem of time varying congestion pricing to determine optimal time-varying tolls at peak periods for a queuing network with the interactions between buses and private cars.Through the combined applications of the space-time expanded network（STEN） and the conventional network equilibrium modeling techniques,a multi-class,multi-mode and multi-criteria traffic network equilibrium model is developed.Travelers of different classes have distinctive value of times（VOTs）,and travelers from the same class perceive their travel disutility or generalized costs on a route according to different weights of travel time and travel costs.Moreover,the symmetric cost function model is extended to deal with the interactions between buses and private cars.It is found that there exists a uniform（anonymous） link toll pattern which can drive a multi-class,multi-mode and multi-criteria user equilibrium flow pattern to a system optimum when the system＇s objective function is measured in terms of money.It is also found that the marginal cost pricing models with a symmetric travel cost function do not reflect the interactions between traffic flows of different road sections,and the obtained congestion pricing toll is smaller than the real value.
基金supported by the Fundamental Research Funds for Central Universities of China(No.FRF-GF-18-009B,No.FRF-BD-18-001A)the 111 Project(Grant No.B12012).
文摘The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.
基金Supported by the National Key Research and Development Program of China(2018YFB1702300)the National Natural Science Foundation of China(61731012)。
文摘Owing to the fluctuant renewable generation and power demand,the energy surplus or deficit in nanogrids embodies differently across time.To stimulate local renewable energy consumption and minimize long-term energy costs,some issues still remain to be explored:when and how the energy demand and bidirectional trading prices are scheduled considering personal comfort preferences and environmental factors.For this purpose,the demand response and two-way pricing problems concurrently for nanogrids and a public monitoring entity(PME)are studied with exploiting the large potential thermal elastic ability of heating,ventilation and air-conditioning(HVAC)units.Different from nanogrids,in terms of minimizing time-average costs,PME aims to set reasonable prices and optimize profits by trading with nanogrids and the main grid bi-directionally.Such bilevel energy management problem is formulated as a stochastic form in a longterm horizon.Since there are uncertain system parameters,time-coupled queue constraints and the interplay of bilevel decision-making,it is challenging to solve the formulated problems.To this end,we derive a form of relaxation based on Lyapunov optimization technique to make the energy management problem tractable without forecasting the related system parameters.The transaction between nanogrids and PME is captured by a one-leader and multi-follower Stackelberg game framework.Then,theoretical analysis of the existence and uniqueness of Stackelberg equilibrium(SE)is developed based on the proposed game property.Following that,we devise an optimization algorithm to reach the SE with less information exchange.Numerical experiments validate the effectiveness of the proposed approach.
文摘The China Basic Medical Insurance Program was created in 1999 with three objectives:equal accessibility,affordability,and quality.Today,it has become the biggest medical insurance program in the world,covering 95%of China's population.Since 2015,China's healthcare ecosystem has been reshaped by increasing innovation,which has in turn been driven by regulatory reform,enhancement of research and development capability,and capital market development.There has also been improved regulatory efficiency to reduce lags in launching drugs.In 2022,nearly 20%of novel active substances launched globally were from China.China has also risen to become the second biggest contributor to innovation in terms of pipelines.Using a“fast-follow”strategy,many locally developed innovative drugs can compete with products from multinational companies in their quality and pricing.However,China's pharmaceutical and biotechnology industry will continue to face challenges in pricing and reimbursement,as well as a shortened product lifecycle with rapid price erosion.The government has already accelerated the timeline for updating the drug reimbursement list and is willing to create a high-quality medical insurance program.However,some obstacles are hard to overcome,including reimbursement for advanced therapies,limited funding and an increasing burden of disease due to an aging population.This article reviews the trajectory of medical innovation in China,including the challenges.Looking forward,balancing affordability and innovation will be critical for China to continue the trajectory of growth.The article also offers some suggestions for future policy reform,including optimizing reimbursement efficiency with a focus on highquality solutions,enhancing the value assessment framework,payer repositioning from“value buyer”to“strategic buyer”,and developing alternative market access pathways for innovative drugs.
基金funded by the National Natural Science Foundation of China(71931003)the Science and Technology Projects of Hunan Province and Changsha City(2018GK4002,2019CT5001,2019WK2011,2019GK5015,kq1907086).
文摘The two-way interaction between smart grid and customers will continuously play an important role in enhan-cing the overall efficiency of the green and low-carbon electric power industry and properly accommodating intermittent renewable energy resources.Thus far,the existing electricity pricing mechanisms hardly match the technical properties of smart grid;neither can they facilitate increasing end users participating in the electri-city market.In this paper,several relevant models and novel methods are proposed for pricing scheme design as well as to achieve optimal decision-makings for market participants,in which the mechanisms behind are com-patible with demand response operation of end users in the smart grid.The electric vehicles and prosumers are jointly considered by complying with the technical constraints and intrinsic economic interests.Based on the demand response of controllable loads,the real-time pricing,rewarding pricing and insurance pricing methods are proposed for the retailers and their bidding decisions for the wholesale market are also presented to increase the penetration level of renewable energy.The proposed demand response oriented electricity pricing scheme can provide some useful operational references on the cooperative operation of controllable loads and renewable energy through the feasible retail and wholesale market pricing methods,and thereby enhancing the development of the low-carbon energy system.
文摘A class of closed-loop supply chain system consisting of one manufacturer and one supplier is designed, in which re-distribution, remanufacturing and reuse are considered synthetically. The manufacturer is in charge of recollecting and re-disposal the used products. Demands of ultimate products and collecting quantity of used products are described as the function of prices and reference prices. A non-linear dynamic pricing model for this closed-loop supply chain is established. A numerical example is designed, and the results of this example verified the model’s validity to price for the operation of closed-loop supply chain system.
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Hierarchical Identification and Cross-Layer Correlation of Key Core Technologies from the Perspective of Industrial Chain Structure (National Social Science Fund of China, 24BTQ067)Chongqing Education Commission (CEC) Funding:Research on the Co-governance Mechanism of Patent Quality Based on the Dual-Filter Perspective(24SKGH213)Chongqing Graduate Education and Teaching Funding:Research on the Interdisciplinary Law of Intellectual Property and Optimization of Graduate Talent Training Mode(yjg213122)。
文摘In essence,the negotiation of license fees on standard essential patent(SEP)belongs to a kind of market be⁃havior,and the pricing right should be given to the market subjects under the requirements of patent law.In recent years,the frequent disputes on SEP license fees witnessed in the industrial and academic worlds,together with the lack of systematic supporting functions like FRAND,make SEP pricing excessively reliant on judicial judgment in practice.Fortunately,a variety of pricing methods have been proposed by theoretical research and practiced in judicial cases,which provide possible solutions for the license fee pricing of SEP from the operational level.In this paper,by focusing on the characteristics of the existing SEP pricing methods in the academic fields and judicial system,the dispute caused by license fees of SEP is clarified firstly,then by combining and interpreting twelve existing pricing methods of license fee of SEP with academic literature and judicial cases,four categories of methods are composed based on the application stages and calculation logic.Thirdly,the application barriers and dilemmas caused by the inherent limita⁃tions of the four categories of methods are analyzed,and the possible ways to put these methods into practice are ex⁃plored.Lastly,suggestions are presented from the aspects of preconditions for application,pricing stages,dispute reso⁃lution mechanisms,and comprehensive applications.The purpose of this paper is to provide enlightenment for getting back on track with the pricing right and further optimization of the pricing mechanism of license fees of SEP.
文摘This paper presents a practical pricing model for backup reserve and wheeling, which attains a balanced strategy that ensures perceived benefits to both the buyer and the seller. The model and the associated computerized algorithm deal collectively with diverse issues, including： （1） fulfilling local firm real （and reactive） power demand requirements, （2） fulfilling local power reserve requirements, （3） buying firm real （and reactive） power from the grid, （4） buying reserve power from the grid, （5） exporting firm real （and reactive） power demand to remote load centers via the grid, （6） exporting reserve power via the grid, （7） wheeling of firm power demand to remote owned sites using the grid, and （8） wheeling reserve power to remote owned sites using grid. Practical implementation features of the computerized algorithms are also discussed with an illustrative case example.
基金National Natural Science Foundation(No.60274048)Hebei Province Natural Science Foundation(No.2001ABB047)
文摘In order to meet the requirement of separating power plants from power network and that of the competition based power transaction in power market,the pricing decision support system for generation companies(GCPDSS)is built in electricity market.This paper introduces the conception of intelligent decision support system(IDSS)and puts emphasis on the systematical structural framework,work process,design principal,and fundamental function of GCPDSS.The system has the module to analyze the cost,to forecast the demand of power,to construct the pricing strategies,to manage the pricing risk,and to dispatch giving the pricing strategies.The case study illustrates that the friendly window-based user interface of the system enables the user to take full advantage of the capabilities of the system in order to make effective real-time decisions.
