Electricity-carbon combined market pricing strategy considering electric vehicle

Abstract

The invention provides an electricity-carbon combined market pricing strategy considering an electric vehicle. On the premise that a large number of electric vehicles are connected to a power grid for charging, in order to achieve the benefit maximization of the electricity-carbon combined market and balance the relation of all benefit subjects of the electricity-carbon combined market, a bidding model based on a complete dynamic information game and a sequential game is established. The strategy comprises the following steps: collecting electricity-carbon joint market data; defining an electricity-carbon combined market income optimization target; constructing a market bidding game mechanism; optimizing a bidding game strategy by using an alternating direction multiplier method (ADMM) to obtain pricing under an ideal condition; the decision deviation of the game in the ideal scene is obtained by combining the actual cost; pricing is continuously adjusted in combination with user charging cost, and decision deviation is reduced; and when the decision deviation reaches a reasonable range, the optimal pricing is obtained.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization, and in particular to an electricity-carbon joint market pricing strategy considering electric vehicles. Background Art

[0002] With the continuous advancement of low-carbon technology, the implementation of numerous supportive policies has accelerated the development of the electric vehicle (EV) industry. As an important component of low-carbon technology, the number of EVs is growing rapidly. However, the uncertainty of EV charging poses a challenge to the stability of power system loads and may weaken the profits of the power market. In addition, disordered charging behavior may lead to inefficient energy allocation and waste of intermittent renewable energy, which in turn affects the expected carbon emission reduction effect of EVs.

[0003] The temporal and spatial randomness and volatility of EV charging demand pose a challenge to the power market, while the pricing strategy of the power market effectively optimizes the operation of the power system and improves the stability and economy of the power grid by regulating the charging behavior of users. The reasonable planning of EV charging behavior depends on whether the interests of various stakeholders in the power market can be balanced. On the one hand, each stakeholder has its own optimization goal, and on the other hand, the decisions of each stakeholder influence each other. Therefore, the game theory of comprehensive multi-subject interest balance has become a powerful pricing tool. At present, there are two main directions in the research of game theory on the balance of interests of various stakeholders in the power market: the first is to establish a cooperative game model, in which the various stakeholders in the power market comprehensively consider the existing resources and constraints, and make reasonable pricing strategies with the optimization goal of maximizing the interests of the alliance of each subject; the second is to establish a non-cooperative game model, in which each stakeholder has its own interest maximization as the optimization goal, participates in the pricing strategy optimization and charging behavior planning process alone, and makes decisions that are beneficial to itself. The above methods have good results in solving the pricing optimization and charging behavior planning problems of a single power market. However, these methods only consider the maximization of the economic interests of various entities, without effectively optimizing the additional carbon emissions generated by EVs during the charging process, and ignoring the demands of various stakeholders for carbon emission reduction.

[0004] Currently, carbon emission rights trading, as a quantitative indicator, allows stakeholders to buy and sell in the market to meet their needs for reducing carbon emissions. This mechanism mainly reduces carbon emissions by increasing the use of renewable energy and reducing the proportion of coal-fired power generation. However, the low energy density and instability of renewable energy, combined with the increase in the number of electric vehicle (EV) users, may lead to a surge in charging demand for the power grid during peak hours. This surge in demand may not only reduce the efficiency of power generation facilities and grid assets, but also lead to a waste of power resources, which in turn has a negative impact on shared charging services for electric vehicles.

[0005] Therefore, it is particularly urgent to develop a strategy that can simultaneously ensure the profitability of the electricity market and the effect of carbon emission reduction, couple the electricity market and the carbon trading market, and effectively plan EV charging behavior. Summary of the invention

[0006] In view of the above-mentioned deficiencies in current electricity market pricing and EV scheduling, the present invention provides an electricity-carbon joint market pricing strategy considering EVs, comprising the following steps:

[0007] S1: Electricity-carbon joint market data collection, including the number, charging power and distribution location of charging piles of PV charging station operators (referred to as operators) and private charging facility aggregators (referred to as aggregators); the number, distribution location, battery status, EV driving speed and expected power of EVs that need to be charged.

[0008] S2: Determine the optimization target of electricity-carbon joint market pricing. In the electricity market and carbon trading market, operators and aggregators aim to maximize their own profits. The profit of operators in the electricity market is the difference between charging income and electricity purchase costs and abandoned light costs, and the profit of aggregators is the difference between charging income and electricity purchase costs. The carbon trading market implements a tiered carbon trading mechanism, and its profit is the difference between the carbon emission quota purchased by EV and the free carbon emission quota.

[0009] S3: Construct a market pricing game framework. Operators or aggregators reduce their charging fees to attract more customers while affecting the revenue of competitors. To this end, the two parties will engage in a price-based competitive game. In this process, all bidding information is open to the public, and the quotations are made in an orderly manner. A complete information dynamic game model can be constructed to explain this bidding process.

[0010] S4: Solve for the ideal pricing and use the alternating direction method of multipliers (ADMM) to optimize the bidding game strategy to find the pricing of operators and aggregators and the ideal number of EV access when the equilibrium solution is achieved.

[0011] S5: Define decision bias. Combined with the actual distribution location, power consumption and other information of EV, with the goal of minimizing EV charging costs, the charging facilities actually selected by EV users will differ from S4. The decision bias is the difference between the ideal access volume and actual access volume of charging facilities of operators and aggregators, and the difference between the ideal carbon trading volume and the actual carbon trading volume.

[0012] S6: The ideal pricing is modified based on the charging cost, and a charging cost matrix is ​​constructed. Each EV user selects the charging facility with the lowest cost for charging. The decision deviation under the ideal pricing is calculated, and the ideal pricing is adjusted to control the decision deviation within a smaller range, thereby obtaining the optimal pricing based on the EV cost.

[0013] According to one aspect of the present invention, the charging choices of EV users are significantly affected by the strategies of operators and aggregators, and these decisions are made in a certain order. Based on this, we can regard the price correction process as a dynamic sequential game model.

[0014] The traditional electricity market pricing mechanism is mainly focused on a single market and fails to fully consider the impact of various stakeholders in the carbon trading market, especially their expectations for carbon emission reduction. In addition, EVs are directly connected to the grid for charging and discharging activities, and their uncertainty may have an impact on the pricing strategy of the electricity market. Therefore, the charging and discharging behavior of EVs should also be considered an important consideration in the pricing strategy.

[0015] This paper proposes an innovative pricing strategy that optimizes the total benefits of all stakeholders in the electricity-carbon joint market by integrating the electricity market and the carbon trading market. This strategy not only takes into account the charging behavior of EVs, but also meets the needs of carbon emission reduction to a certain extent, while balancing the interests of EV users, operators and aggregators, and promoting win-win cooperation among all stakeholders. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 This is a flow chart of a combined electricity-carbon market pricing strategy considering EVs as described in the present invention.

[0018] Figure 2 The flowchart of the ADMM of the present invention for solving the complete dynamic information game. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Embodiment 1

[0021] Please refer to Figure 1 The figure shows a pricing strategy for the electricity-carbon joint market considering EVs. In step S2, the optimization target of the electricity-carbon joint market pricing needs to be determined. First, the total electricity-carbon joint market revenue of operators and aggregators is determined. The revenue of the electricity market is defined based on the electricity purchase cost and charging conditions. By building a ladder carbon trading mechanism, the carbon trading right is used as a quantitative indicator for buying and selling. Then, the revenue of the carbon trading market is determined based on the trading mechanism, which includes:

[0022] Determine the total revenue function of operators and aggregators, which is the sum of electricity market revenue and carbon trading market revenue. The calculation formula is as follows:

[0023] maxf pv,t =f pv-bm,t +f pv-ct,t

[0024] maxf pvp,t =f pvp-bm,t +f pvp-ct,t

[0025] Where: f pv,t , f pvp,t are the total revenues of operators and aggregators respectively; f pv-bm,t , f pvp-bm,t are the net benefits of operators and aggregators in the electricity market respectively; pv-ct,t , f pvp-ct,t are the carbon emission benefits of operators and aggregators respectively.

[0026] Determine the operator's revenue function. The net benefit of the operator in the electricity market is the revenue of the charging facility minus the sum of the power purchase cost and the cost of abandoned light. The calculation formula is as follows:

[0027] f pv-bm,t =f PVBev,t -f PVCpe,t -f PVCapv,t

[0028]

[0029] Where: f PVBev,t is the revenue of charging facilities, f PVCpe,t is the electricity purchase cost, f PVCapv,t is the cost of abandoned light, c pv,t , b pv,t are EV charging price and operator electricity purchase price respectively; P pv,t , P pv,pe,t are the active output and purchased power of the operator’s photovoltaic units at time t; P ev,i,tFor electric vehicles V i Charging power at time t; n pv,t is the number of EVs connected by the operator, and u is the penalty price coefficient for abandoned light.

[0030] Determine the aggregator's revenue function. The net benefit of the aggregator in the electricity market is the revenue of the charging facility minus the cost of purchasing electricity. The calculation formula is as follows:

[0031] f pvp-bm,t =f PVPBev,t -f PVPCpe,t

[0032]

[0033] Where: f PVPBev,t is the revenue of charging facilities, f PVPCpe,t is the electricity purchase cost, c pvp,t , b pvp,t are EV charging price and aggregator electricity purchase price respectively; P pvp,pe,t P is the power purchased at time t; ev,i,t For EVV i Charging power at time t; n pvp,t The number of EVs connected to aggregators.

[0034] Build a tiered carbon trading mechanism to quantify carbon emission rights trading. The larger the carbon trading volume, the higher the carbon price. The calculation formula is as follows:

[0035]

[0036] Where: C′ c is the tiered carbon trading price; C c is the benchmark carbon price; α, β are the carbon trading price reward and penalty coefficients respectively; v is the length of the carbon trading interval, E f,t Free quota for carbon emissions; E pv-p,t , E pvp-p,t are the carbon emissions of the operator and aggregator at time t respectively; λ is the carbon emission amount allocated per unit of purchased power.

[0037] To determine the carbon trading market revenue of operators and aggregators under the tiered carbon trading mechanism, the calculation formula is as follows:

[0038] f pv-ct,t =C c '(E pv-p,t -E f,t )

[0039] f pvp-ct,t =C c '(E pvp-p,t -E f,t )

[0040] In step S3, the price game mechanism of the electricity-carbon joint market includes describing the pricing problem, analyzing the price game mechanism and proving the existence of the game equilibrium solution. In this embodiment, the specific operations are:

[0041] The relationship between the price adjustment of operators and aggregators and the number of EV charging during the bidding process is described by the following formula:

[0042] n pv,t,a+1 =γ1n pv,t,a +(1-γ1)n pvp,t,a

[0043] n pvp,t,a+1 =γ2n pvp,t,a +(1-γ2)n pv,t,a

[0044]

[0045] Where: n pv,t,a and n pvp,t is the number of cars charged in the ath iteration of the operator and aggregator during period t; γ1 and γ2 are the user's sensitivity coefficients to the price adjustment of the operator and aggregator; Q pv and Q pvp is the number of charging stations owned by operators and aggregators. pv and σ pvp is the price adjustment coefficient of the operator and aggregator, and is the external price adjustment factor for operators and aggregators.

[0046] The price game mechanism first determines the acceptable pricing range for operators and aggregators, and on this basis, combines their respective preferences and other relevant factors to formulate an optimal bidding strategy to maximize revenue and calculate the ideal number of EV access. The bidding relationship formula is as follows:

[0047] c pv,t-max =(1+χ1)b pv,t

[0048] c pvp,t-max =(1+χ2)b pvp,t

[0049] c pv,t ~U(α pv +β pv b pv,t ,α pv +β pv c pv,t-max )

[0050] c pvp,t ~U(αpvp +β pvp b pvp,t ,α pvp +β pvp c pvp,t-max )

[0051] Where: χ1 and χ2 are the minimum profit pricing coefficients acceptable to operators and aggregators; c pv,t-max and c pvp,t-max is the maximum value of the price that the operator and the aggregator can accept; U(·) represents uniform distribution; α pv , α pvp and β pv , β pvp are the constant term and linear term coefficient of the pricing strategy.

[0052] Prove the existence of the game equilibrium solution. From the objective function of maximizing revenue and the relationship between the number of charging cars and the price, we know that the revenue is a quadratic function of the charging price. To verify its concavity, we calculate c pv,t and c pvp,t The second-order partial derivative of , the result is as follows:

[0053]

[0054] From the above formula, we can see that the second-order derivatives of the operator and aggregator's revenues are both negative, indicating that the function is a concave function and there is a maximum revenue. By taking the first-order derivative of the revenue function and finding the pricing when the function reaches the maximum value, if the pricing is within the acceptable quotation range, the equilibrium solution of the game can be obtained.

[0055] In step S4, the optimization objective in the game mechanism is solved using the ADMM algorithm, such as Figure 2 The ADMM solution flow chart shown includes:

[0056] S41: Perform strong convex transformation on the optimization objective. From the second-order partial derivative of the optimization objective function, it can be seen that the function is strongly concave, and ADMM is mainly used to solve strong convex problems. Therefore, the function is transformed into a strong convex problem through dual transformation. The transformation process is as follows:

[0057] maxf pv,t →minF pv,t ,maxf pvp,t →minF pvp,t

[0058] Where: F pv,t and F pvp,t Convex optimization functions for operators and aggregators.

[0059] S42: Construct an augmented Lagrangian function. The original problem and the dual problem have the same optimal solution. Therefore, the optimal solution of the original problem can be solved by constructing an augmented Lagrangian function of the dual problem. The constructor is as follows:

[0060]

[0061] Where: represents the Lagrange multiplier, ρ1, ρ2 represent the penalty coefficients.

[0062] S43: Iteratively update the function variable c pv 、c pv , λ1, λ2, when the original residual and the dual residual are less than the corresponding convergence gap, the function is judged to be converged and the optimal pricing strategy in the ideal scenario is obtained. Otherwise, iterative updates are continued until the convergence condition or the maximum number of iterations is reached. The variable update rules and convergence conditions are as follows:

[0063]

[0064]

[0065] Where: φ P ,φ D represents the convergence gap between the primal and dual residuals.

[0066] In step S5, after solving the ideal electricity-carbon joint market pricing strategy in S43, it is necessary to combine comprehensive factors such as EV distribution location, charging distance cost, user preference, etc. to determine the actual EV charging quantity and carbon emissions of operators and aggregators, so as to determine the decision deviation. In this embodiment, the specific operations are:

[0067] Define decision bias, and the calculation formula is as follows:

[0068]

[0069] Where: N ηpv 、N ηpvp and E ηpv 、E ηpvp The deviation of the number of connected battery vehicles and carbon emission decision of operators and aggregators; pv 、n pvp and E pv 、E pvp is the optimal EV access quantity and optimal carbon trading volume of operators and aggregators obtained through dynamic game with complete information; S pv , S pvp and S Epv , S Epvp Actual EV access numbers and carbon trading volumes for operators and aggregators.

[0070] Calculate the EV charging cost and get the cost matrix. The calculation formula is as follows:

[0071]

[0072]

[0073] Where: C i,j The cost for the i-th user to go to the j-th charging facility to charge; D i,j , and ω C is the distance cost, charging cost and their corresponding weights; E n and N um The number of EVs with charging needs and the number of shared charging facilities.

[0074] Finally, the cost matrix needs to be normalized from 0 to 1. In each row, the lowest charging cost is marked as 1, and the rest are marked as 0, indicating that all EVs go to the charging facility with the lowest cost for charging. The actual number of accesses S of operators and aggregators is calculated through the cost matrix. pv and S pvp , and count the carbon trading volume S owned by the user Epv and S Epvp , the decision deviation N is calculated ηpv 、N ηpvp and E ηpv 、E ηpvp .

[0075] In step S6, the ideal pricing correction process of the electricity-carbon joint market includes defining the convergence gap of decision deviation, price correction strategy, solving the sequential game problem using the best response method, and obtaining the optimal pricing combined with EV cost. The specific operations are:

[0076] The scope of decision deviation is limited, and the convergence gap ε is defined (ε is a small positive number); operators and aggregators adjust pricing to guide EV users to charge through prices, avoiding charging congestion in EV concentrated areas, while charging facilities in marginal areas are idle, resulting in the inability to achieve the equilibrium solution of the perfect information dynamic game under ideal conditions. When the decision deviation is less than ε, the entities in the electricity-carbon market have completed the allocation process. The formula for ε is defined as follows:

[0077] N ηpv , N ηpvp , E ηpv , E ηpvp <ε

[0078] The price correction strategy of operators and aggregators is calculated as follows:

[0079] c pv =c pv (1-θ pv ),c pvp =c pvp (1-θ pvp )

[0080]

[0081] Where: θ pv and θ pvp is the price correction factor for operators and aggregators.

[0082] The optimal response method is used to solve the sequential game in the price correction process. The solution process includes:

[0083] Establish a decision set, which includes the decision of whether the operator and aggregator access EV charging, and the decision of whether the EV user chooses shared charging. The decision set is as follows:

[0084]

[0085] Where: S p and S ev is the decision set of charging facilities and EV users, and To make decisions on whether to connect EVs to shared charging facilities. and Decision-making for EV users on whether to choose shared charging.

[0086] Adjust charging prices. Operators and aggregators adjust their charging prices and continuously make adjustments in the decision set S. p Find the optimal decision in the decision set S and guide EV users to ev Find the optimal decision in , when the decision deviation is less than ε, the decision set is no longer updated, and the sequential game equilibrium solution is obtained.

[0087] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A pricing strategy for the electricity-carbon joint market considering electric vehicles, characterized in that: The following steps are involved: S1: Electricity_carbon joint market data collection; S2: Determine the optimization target of electricity-carbon joint market pricing. In the electricity market and carbon trading market, operators and aggregators aim to maximize their own profits; S3: Construct a market pricing game framework, where operators or aggregators reduce their charging fees to attract more customers while affecting the revenue of competitors; a complete information dynamic game model can be constructed to explain this bidding process; S4: Solve for the ideal pricing, use the alternating direction multiplier method to optimize the bidding game strategy, and find the pricing of operators and aggregators and the ideal number of EV access when the equilibrium solution is achieved; S5: Define decision bias, which is the difference between the ideal access volume and the actual access volume of charging facilities of operators and aggregators, and the difference between the ideal carbon trading volume and the actual carbon trading volume; S6: The ideal pricing is modified based on the charging cost, and a charging cost matrix is ​​constructed. Each EV user selects the charging facility with the lowest cost for charging. The decision deviation under the ideal pricing is calculated, and the ideal pricing is adjusted to control the decision deviation within a smaller range, thereby obtaining the optimal pricing based on the EV cost.

2. The electricity-carbon joint market pricing strategy considering electric vehicles according to claim 1 is characterized in that: The pricing optimization goal in step S2 is: maxf pv,t =f pv-bm,t +f pv-ct,t maxf pvp,t =f pvp-bm,t +f pvp-ct,t Where: f pv,t , f pvp,t are the total revenues of operators and aggregators respectively; f pv-bm,t , f pvp-bm,t are the net benefits of operators and aggregators in the electricity market respectively; pv-ct,t , f pvp-ct,t are the carbon emission benefits of operators and aggregators respectively.

3. The electricity-carbon joint market pricing strategy considering electric vehicles according to claim 1 is characterized in that: The ladder carbon trading model in step S2 is: Where: C' c is the tiered carbon trading price; C c is the benchmark carbon price; α, β are the carbon trading price reward and penalty coefficients respectively; v is the length of the carbon trading interval, E f,t Free quota for carbon emissions; E pv-p,t , E pvp-p,t are the carbon emissions of the operator and aggregator at time t respectively; λ is the carbon emission amount allocated per unit of purchased power.

4. The electricity-carbon joint market pricing strategy considering electric vehicles according to claim 1 is characterized in that: The market pricing game mechanism in step S3 is a complete information dynamic game model, including a description of the pricing problem and an analysis of the pricing game mechanism; Among them, the pricing problem describes the relationship between the price adjustment of operators and aggregators and the number of EV charging during the pricing adjustment process, which is expressed by the following formula: n pv,t,a+1 =γ1n pv,t,a +(1-γ1)n pvp,t,a n pvp,t,a+1 =γ2n pvp,t,a +(1-γ2)n pv,t,a Where: n pv,t,a and n pvp,t is the number of cars charged in the ath iteration of the operator and aggregator during period t; γ1 and γ2 are the user's sensitivity coefficients to the price adjustment of the operator and aggregator; Q pv and Q pvp is the number of charging stations owned by operators and aggregators; pv and σ pvp is the price adjustment coefficient of the operator and aggregator, and is the external price adjustment factor for operators and aggregators; The pricing game mechanism first determines the acceptable pricing range for operators and aggregators, and on this basis, combines their respective preferences and other relevant factors to formulate an optimal bidding strategy to maximize revenue and calculate the ideal number of EV access. The bidding relationship formula is as follows: c pv,t-max =(1+χ1)b pv,t c pvp,t-max =(1+χ2)b pvp,t c pv,t ~U(a pv +b pv b pv,t ,a pv +b pv c pv,t-max ) c pvp,t ~U(a pvp +b pvp b pvp,t ,a pvp +b pvp c pvp,t-max ) Where: χ1 and χ2 are the minimum profit pricing coefficients acceptable to operators and aggregators; c pv,t-max and c pvp,t-max is the maximum value of the price that the operator and the aggregator can accept; U(·) represents uniform distribution; α pv , α pvp and β pv , β pvp are the constant term and linear term coefficient of the pricing strategy.

5. The electricity-carbon joint market pricing strategy considering electric vehicles according to claim 1 is characterized in that: The decision deviation in step S5 is the deviation between the actual charging selection behavior of EV users in real life scenarios and the game equilibrium solution obtained by the alternating direction multiplier method, and the calculation formula is as follows: Where: N ηpv 、N ηpvp and E ηpv 、E ηpvp The deviation of the number of connected battery vehicles and carbon emission decision of operators and aggregators; pv 、n pvp and E pv 、E pvp is the optimal EV access quantity and optimal carbon trading volume of operators and aggregators obtained through dynamic game with complete information; S pv , S pvp and S Epv , S Epvp Actual EV access numbers and carbon trading volumes for operators and aggregators.

6. The electricity-carbon joint market pricing strategy considering electric vehicles according to claim 1 is characterized in that: The ideal pricing correction process in step S6 includes defining a convergence gap of the decision deviation, continuously reducing the decision deviation through a pricing correction strategy, so that the decision deviation is within the convergence gap interval, and obtaining the optimal pricing. The specific steps are as follows: Define the convergence gap ε to limit the range of decision deviation. The formula is defined as follows: N ηpv ,N ηpvp ,E ηpv ,E ηpvp <e Combined with decision-making bias, the price correction strategy for operators and aggregators is formulated, and the formula is defined as follows: c pv =c pv (1-θ pv ),c pvp =c pvp (1-θ pvp ) Where: θ pv and θ pvp is the price correction factor for operators and aggregators.

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