Electric vehicle split-phase guiding method for improving new energy consumption based on evolutionary game

Abstract

The invention belongs to the technical field of electric vehicle charging, and particularly relates to an electric vehicle split-phase charging guiding method, in particular to an electric vehicle split-phase guiding method for improving new energy consumption based on an evolutionary game. The invention relates to an electric vehicle split-phase guiding method for improving new energy consumption based on an evolutionary game, in particular to a three-layer model, an upper-layer optimization model establishes a charging network operator split-phase time-sharing charging electricity price model by taking the maximum net income of a charging network operator as a target; the middle-layer optimization model is an electric vehicle evolutionary game scheduling model established by taking the lowest total charging cost in an electric vehicle scheduling period as a target, and the lower-layer optimization model is a node marginal electricity price clearing model considering new energy consumption and established by taking the minimum sum of the power distribution network operation cost and the wind and light abandoning penalty. According to the electric vehicle split-phase guiding method for improving new energy consumption based on the evolutionary game, the revenue of a charging network operator is maximized, the charging cost of the electric vehicle and the total cost of a power distribution network are minimum, and the win-win situation of three parties is achieved.

Description

Technical Field

[0001] The present invention relates to a method for guiding the split-phase charging of an electric vehicle, and in particular to a method for guiding the split-phase charging of an electric vehicle for improving the consumption of new energy based on evolutionary game. Background Art

[0002] In the process of smart city development, environmental issues such as carbon emission pollution and oil consumption have become the main factors restricting sustainable development. In recent years, as a clean energy means of transportation, the penetration rate of electric vehicles in cities has continued to increase, and they have gradually replaced fossil fuel vehicles. The charging price will affect the charging behavior of electric vehicles. Setting a reasonable charging price and making the charging behavior of electric vehicles associated with the output of new energy can improve the distribution network's ability to absorb new energy.

[0003] Current research usually uses the time-of-use electricity price of the distribution network node as the charging price for electric vehicles, ignoring the revenue issue of the charging network operator. In addition, the single-phase access of new energy sources causes large differences between the phases of the distribution network, and many electric vehicles are currently connected in a single phase. The difference problem caused by the single-phase access of new energy sources can be solved by reasonably guiding the charging of electric vehicles.

[0004] Therefore, in order to guide electric vehicles to better absorb new energy, it is necessary to consider the operation of each phase of the new energy power generation and distribution network when determining the charging price, and determine the charging price of each phase of each charging station, which in turn affects the charging cost of electric vehicle users and the operating cost of the distribution network. There is a master-slave game relationship between electric vehicle users and charging network operators, and the process of guiding electric vehicle users is a gradual evolutionary process. Therefore, the evolutionary game is introduced to describe the changes in the charging strategy of electric vehicle users during the changes in electricity prices and new energy output, so as to determine the phase-by-phase guidance method of electric vehicles to improve the absorption of new energy. Summary of the invention

[0005] Aiming at the problem that the single-phase access of new energy to the distribution network results in large differences between phases and ignores the benefits of charging network operators, the present invention provides an electric vehicle phase guidance method for improving the consumption of new energy based on evolutionary game.

[0006] The technical solution adopted by the present invention is: an electric vehicle phase guidance method for improving the consumption of new energy based on evolutionary game, the method includes three-layer optimization model, the upper optimization model is the phase and time-sharing charging price model of the charging network operator, the middle optimization model is the electric vehicle phase charging scheduling model based on evolutionary game, and the lower optimization model is the node marginal price clearing model considering the consumption of new energy; in the upper optimization model, the charging network operator formulates different phase and time-sharing charging prices for the electric vehicle charging stations it manages, and passes them to the middle optimization model; in the middle optimization model, the electric vehicle user combines the phase and time-sharing charging prices transmitted by the upper optimization model through evolutionary game The optimal charging strategy is selected with the minimum payment function, and the phase-by-phase time-sharing charging load is obtained and passed to the lower optimization model and the upper optimization model respectively; according to the phase-by-phase time-sharing charging load passed by the middle optimization model, the lower optimization model establishes a node marginal price clearing model considering the new energy consumption with the minimum total cost of the distribution network operation cost and the penalty for wind and solar power abandonment to determine the phase-by-phase time-sharing electricity purchase price of the charging network operator, and passes it to the upper optimization model; the upper optimization model adjusts the phase-by-phase time-sharing charging price based on the phase-by-phase time-sharing charging load obtained by the middle optimization model and the phase-by-phase time-sharing electricity purchase price obtained by the lower optimization model to maximize the net profit of the charging network operator.

[0007] The specific process of establishing the three-layer optimization model for the above-mentioned electric vehicle phase guidance method for improving new energy consumption based on evolutionary game is as follows:

[0008] The upper optimization model establishes a phase-by-phase and time-by-phase charging price model for charging network operators with the goal of maximizing the net profit of charging network operators: , where: is the revenue of the charging network operator, I is the total number of charging stations within the jurisdiction of the charging network operator, is the charging price of phase y at the i-th charging station at time t, that is, the phase-by-phase and time-by-phase charging price, is the purchase price of electricity for phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-phase purchase price. is the charging load of phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-time charging load, is the time interval;

[0009] The specific steps for solving the evolutionary game of the middle-level optimization model are as follows:

[0010] Step 1: Assume that there are I charging stations, Z electric vehicles, and each vehicle has 3I charging strategies, each described as , where i is the charging station, i=1,2,…m…,I, y is the charging phase, y=A,B,C; at time t, select The ratio of the number of electric vehicles to the total number of electric vehicles is , , , then the electric vehicle population state can be expressed as X = [ x 1 − y ( t ) , ⋯ , x m − y ( t ) , ⋯ , x I − y ( t ) ] ;

[0011] If the electric vehicle chooses a charging strategy at time t , then the state of the electric car is ,otherwise ;

[0012] Step 2: Select all charging strategies at time t The payment function is established with the goal of minimizing the sum of the costs of electric vehicles. , , where: Select the charging strategy for time t The total cost of all electric vehicles, is the charging price of the y-phase at the i-th charging station at time t, Charging power for electric vehicles;

[0013] Step 3: The dynamic equation of the dynamic evolution process of the electric vehicle population state is: ∂ x m − y ∂ t = ∑ i − y = 1 − y I − y x i − y ( t ) ρ i − y , m − y [ U THIS, i − y t ] − x m − y ( t ) ∑ i − y = 1 − y I − y ρ m − y , i − y [ U This is − y t ] , where ρ i − y , m − y [ U THIS, i − y t ] The charging strategy at time t To charging strategy The conditional switching probability, ρ m − y , i − y [ U This is − y t ] The charging strategy at time t To charging strategy The conditional switching probability of

[0014] Step 4: The charging strategy modification protocol of the evolutionary game adopts the logit protocol, and the conditional conversion probability ρ i − y , m − y [ U THIS, i − y t ] = exp[ U THIS ， m − y t ] ∑ i − y = 1 − y I − y exp[ U THIS, i - y t ] , then the time-continuous dynamic evolution equation of the electric vehicle population state is: ∂ x m − y ∂ t = ρ i − y , m − y [ U THIS, i − y t ] − x m − y ( t ) = exp[ U THIS ， m − y t ] ∑ i − y = 1 − y I − y exp[ U THIS ， i − y t ] − x m − y ( t ) , discretize the above time-continuous dynamic evolution equation to obtain the time-discrete dynamic evolution equation of the electric vehicle population state: x m − y ( w + 1 ) = x m − y ( w ) + λ { ρ i − y , m − y [ U THIS, i − y t ] − x m − y ( w )} ;Where: w is the number of evolutionary game iterations; is the iteration step of the evolutionary game;

[0015] Step 5: Determine whether the payment functions of all charging strategies have reached the evolutionary convergence condition. , is the set convergence error, and it is considered that the evolution convergence condition has been reached, the solution is completed, and the electric vehicle population will no longer change the charging strategy;

[0016] Step 6: Output the final charging strategy of the electric vehicle population to obtain the phase-by-phase and time-by-phase charging load of each phase at each station;

[0017] Through the evolutionary game process of the above middle-level optimization model, the phase-by-phase and time-by-phase charging price can be realized. Phase-by-phase and time-by-time charging load The mapping process;

[0018] The lower optimization model is to establish a node marginal electricity price clearing model considering the consumption of new energy by minimizing the sum of the distribution network operation cost and the penalty for wind and solar abandonment: me f price = ∑ t ∈ T ∑ h ∈ L [ a g ( P h , t G ) 2 + b g P h , t G ] Δ t + ∑ t ∈ T ∑ K i ∈ P ( K ) b 0 P K i , t + ∑ t ∈ T ∑ y ∈ A , B , C c loss ( P c , t y − P C , t y ) Δ t , where , and is the generator cost coefficient, is the active output of the distributed generation at the generator node h at time t, and L is the set of generator nodes; is the power from the external grid at time t The amount of electricity purchased, A collection connected to an external power grid; The power generated by the wind power and photovoltaic units connected to the distribution network node in phase y; The actual power of the wind power and photovoltaic units connected to the distribution network node in phase y; is the penalty coefficient;

[0019] For the optimal power flow model of the distribution network, the active power equality constraint and the reactive power equality constraint are expressed as follows: , ; In the formula, , They are the y-phase branch at time t. Active power and reactive power; is the reactive power output of the distributed generation at the generator node h at time t; , are the active load and reactive load at node j of phase y at time t respectively; is the branch at time t The current flowing in the y phase; , Branch Resistance and reactance; is the set of end nodes of all branches whose head end is j; , They are the y-phase branch at time t. Active power and reactive power; is the charging load on the y phase connected to the node j in the distribution network at time t, and is also the charging load on the y phase of the i-th charging station at time t; J L is a set of distribution network nodes;

[0020] Solving the Lagrange multiplier of the active power equation constraint in the optimal power flow model of the distribution network is equivalent to solving the phase-by-phase node marginal electricity price of the distribution network. The phase-by-phase time-of-use electricity purchase price of the charging station is the node marginal electricity price of the node where it is located, which can realize the phase-by-phase time-of-use charging load Phase-by-phase and time-of-use electricity purchase price The mapping process.

[0021] The present invention proposes an electric vehicle phase guidance method for improving the consumption of new energy based on evolutionary game, thereby maximizing the benefits of charging network operators, minimizing the electric vehicle charging costs and the total distribution network costs, and achieving a win-win situation for all three parties. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a framework diagram for guiding phased charging of electric vehicles based on evolutionary game to improve the consumption of new energy. DETAILED DESCRIPTION

[0023] An electric vehicle phase guidance method for improving new energy consumption based on evolutionary game, the method includes three-layer optimization models, the upper optimization model is a phase and time-sharing charging price model of the charging network operator, the middle optimization model is an electric vehicle phase charging scheduling model based on evolutionary game, and the lower optimization model is a node marginal electricity price clearing model considering new energy consumption.

[0024] In the upper-level optimization model, the charging network operator formulates different phase-to-time charging prices for the electric vehicle charging stations it manages and passes them to the middle-level optimization model; in the middle-level optimization model, electric vehicle users select the optimal charging strategy through evolutionary game with the minimum payment function based on the phase-to-time charging prices passed by the upper-level optimization model, and at the same time obtain the phase-to-time charging load and pass it to the lower-level optimization model and the upper-level optimization model; according to the phase-to-time charging load passed by the middle-level optimization model, the lower-level optimization model establishes a node marginal price clearing model considering the consumption of new energy with the minimum total cost of the distribution network operation cost and the penalty for wind and solar power abandonment to determine the phase-to-time electricity purchase price of the charging network operator, and pass it to the upper-level optimization model; the upper-level optimization model adjusts the phase-to-time charging price based on the phase-to-time charging load obtained by the middle-level optimization model and the phase-to-time electricity purchase price obtained by the lower-level optimization model to maximize the net profit of the charging network operator.

[0025] For the upper optimization model:

[0026] Charging network operators set phase-based and time-based charging prices. Electric vehicle (EV) users change their charging behavior based on the prices, and users' feedback on prices will also affect the revenue of charging network operators.

[0027] Charging network operators earn the difference by purchasing electricity from distribution network operators and selling it to electric vehicle users. A charging network operator (CNO) phase-by-phase and time-by-phase charging price model is established with the goal of maximizing the net profit of charging network operators:

[0028]

[0029] Where: is the revenue of the charging network operator, I is the total number of charging stations within the jurisdiction of the charging network operator, is the charging price of phase y at the i-th charging station at time t, that is, the phase-by-phase and time-by-phase charging price, is the purchase price of electricity for phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-phase purchase price. is the charging load of phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-time charging load, For the time interval.

[0030] Phase-by-phase and time-of-use electricity price constraints

[0031]

[0032] Where: is the lower limit of charging price, The upper limit of charging price.

[0033] For the middle-level optimization model, the specific steps of solving the evolutionary game are as follows:

[0034] Step 1: Assume there are I charging stations, Z electric vehicles, and each vehicle has 3I charging strategies, each described as , where i is the charging station (i=1,2,…m…,I) and y is the charging phase (y=A, B, C). At time t, select The ratio of the number of electric vehicles to the total number of electric vehicles is , , , then the electric vehicle population state can be expressed as X = [ x 1 − y ( t ) , ⋯ , x m − y ( t ) , ⋯ , x I − y ( t ) ] .

[0035] For an electric vehicle, if it chooses a charging strategy at time t , then the state of the electric car is ,otherwise .

[0036] Step 2: Calculate the charging strategy for electric vehicles at time t The total payment function of

[0037] The total cost includes the charging cost of the electric vehicle. The payment function is established with the goal of minimizing the sum of the costs of electric vehicles. , as shown below:

[0038]

[0039] in: Select the charging strategy for time t The total cost of all electric vehicles, is the charging price of the y-phase at the i-th charging station at time t, which is determined by the charging network operator in the upper optimization model. The charging power for electric vehicles.

[0040] The payment functions of other charging strategies are similar to the above formula. The payment function of all charging strategies is .

[0041] Step 3: Calculate the conditional switching rate of the charging strategy;

[0042] During the dynamic evolution of electric vehicles choosing charging strategies, every time there is an opportunity to modify the charging strategy, the electric vehicles will compare the average benefits of choosing the current charging strategy with the average benefits of choosing other charging strategies, and modify the charging strategy at the conditional conversion rate ρ. The utility of the electric vehicle population is continuously optimized as the charging strategy changes, and eventually achieves evolutionary equilibrium. All electric vehicles modify their own charging strategies, and the kinetic equation of the dynamic evolution of the electric vehicle population state is shown below.

[0043] ∂ x m − y ∂ t = ∑ i − y = 1 − y I − y x i − y ( t ) ρ i − y , m − y [ U THIS, i − y t ] − x m − y ( t ) ∑ i − y = 1 − y I − y ρ m − y , i − y [ U This is − y t ]

[0044] In the formula, ρ i − y , m − y [ U THIS, i − y t ] The charging strategy at time t To charging strategy The conditional conversion probability, which means that at time t, the charging strategy Transformed into a charging strategy The proportion of electric vehicles is determined by the revised protocol of the chosen charging strategy; ρ m − y , i − y [ U This is − y t ] The charging strategy at time t To charging strategy The conditional conversion probability, which means that at time t, the charging strategy Transformed into a charging strategy The proportion of electric vehicles is determined by the revised protocol of the chosen charging strategy;

[0045] Step 4: Update the proportion of strategies adopted by individuals;

[0046] Since each individual in the electric vehicle population receives incomplete information, there is inevitably a random deviation in the evaluation of charging costs, which leads to a certain degree of randomness in the choice of charging strategy. Logit dynamically takes into account the individual perception error, the individual preferences and incomplete information of the decision-making subject, and makes decisions based on the principle of maximizing individual utility.

[0047] The charging strategy modification protocol of the evolutionary game adopts the logit protocol, and the conditional conversion probability

[0048] ρ i − y , m − y [ U THIS, i − y t ] = exp[ U THIS ， m − y t ] ∑ i − y = 1 − y I − y exp[ U THIS ， i − y t ]

[0049] Then the time-continuous dynamic evolution equation of the electric vehicle population is:

[0050] ∂ x m − y ∂ t = ρ i − y , m − y [ U THIS, i − y t ] − x m − y ( t ) = exp[ U THIS ， m − y t ] ∑ i − y = 1 − y I − y exp[ U THIS ， i − y t ] − x m − y ( t )

[0051] The above formula reveals the selection strategy within the electric vehicle population The evolution law of individual proportions describes the game behavior of electric vehicle users in actual decision-making scenarios.

[0052] The time-continuous dynamic evolution equation given by the above formula is discretized to obtain the final time-discrete dynamic evolution equation of the electric vehicle population.

[0053] x m − n ( w + 1 ) = x m − n ( w ) + λ { ρ i − y , m − n [ U THIS t ( X )] − x m − n ( w )} ;Where: w is the number of evolutionary game iterations; is the iteration step of the evolutionary game.

[0054] Step 5: Determine whether the payment functions of all charging strategies have reached the evolutionary convergence condition. , The convergence error is set, and it is considered that the evolutionary convergence condition has been met, the solution is completed, and the electric vehicle population will no longer change the charging strategy. This means that the evolutionary game of electric vehicle charging strategy has reached an equilibrium state, and the cost of the electric vehicle population is minimized.

[0055] Step 6: Output the final charging strategy of the electric vehicle population to obtain the phase-by-phase and time-by-phase charging load of each phase at each station.

[0056] Through the evolutionary game process of the above middle-level optimization model, the phase-by-phase and time-by-phase charging price can be realized. Phase-by-phase and time-by-time charging load The mapping process.

[0057] For the lower optimization model, a node marginal electricity price clearing model considering the consumption of new energy is established by minimizing the sum of the distribution network operation cost and the penalty for wind and solar power abandonment:

[0058] me f price = ∑ t ∈ T ∑ h ∈ L [ a g ( P h , t G ) 2 + b g P h , t G ] Δ t + ∑ t ∈ T ∑ K i ∈ P ( K ) b 0 P K i , t + ∑ t ∈ T ∑ y ∈ A , B , C c loss ( P c , t y − P C , t y ) Δ t

[0059] In the formula, is the total cost of the distribution network, , and is the generator cost coefficient, is the active output of the distributed generation at the generator node h at time t, and L is the set of generator nodes; is the power from the external grid at time t The amount of electricity purchased, A collection connected to an external power grid; The power generated by the wind power and photovoltaic units connected to the distribution network node in phase y; The actual power of the wind power and photovoltaic units connected to the distribution network node in phase y; is the penalty coefficient.

[0060] For the optimal power flow of the distribution network, first, any node should satisfy the active power equality constraint and the reactive power equality constraint balance, which can be expressed as follows:

[0061] , ,

[0062] In the formula, , They are the y-phase branch at time t. Active power and reactive power; is the reactive power output of the distributed generation at the generator node h at time t; , are the active load and reactive load at node j of phase y at time t respectively; is the branch at time t The current flowing in the y phase; , Branch Resistance and reactance; is the set of end nodes of all branches whose head end is j; , They are the y-phase branch at time t. Active power and reactive power; is the charging load on the y phase connected to the node j in the distribution network at time t, and is also the charging load on the y phase of the i-th charging station at time t; J L is the set of distribution network nodes.

[0063] Secondly, the lines of the radial distribution network should satisfy the voltage drop equation constraint, which can be expressed as

[0064]

[0065] In the formula, , They are the voltage of node s and the voltage of node j in the y-phase distribution network at time t respectively.

[0066] The power flow related variables should also meet the following boundary constraints:

[0067]

[0068]

[0069] In the formula, is the maximum current amplitude of the line; , are the upper and lower amplitude limits of the node voltage respectively;

[0070] At the same time, the constraints between line power, voltage and current amplitude are described by second-order cone inequality constraints, and the power flow problem is transformed into a second-order cone programming problem, as shown below.

[0071]

[0072] Solving the Lagrange multiplier of the active power equation constraint in the optimal power flow model of the distribution network is equivalent to solving the phase-by-phase node marginal electricity price of the distribution network. The phase-by-phase time-of-use electricity purchase price of the charging station is the node marginal electricity price of the node where it is located, which can realize the phase-by-phase time-of-use charging load Phase-by-phase and time-of-use electricity purchase price The mapping process.

Claims

1. An electric vehicle phase guidance method for improving new energy consumption based on evolutionary game theory is characterized by: The method includes three-layer optimization models, the upper optimization model is the phase-time charging price model of the charging network operator, the middle optimization model is the phase-time charging scheduling model of the electric vehicle based on evolutionary game, and the lower optimization model is the node marginal price clearing model considering the consumption of new energy; in the upper optimization model, the charging network operator formulates different phase-time charging prices for the electric vehicle charging stations it manages and passes them to the middle optimization model; in the middle optimization model, the electric vehicle user selects the optimal charging strategy through evolutionary game with the minimum payment function in combination with the phase-time charging prices passed by the upper optimization model. At the same time, the phase-by-phase time-sharing charging load is obtained and passed to the lower optimization model and the upper optimization model; according to the phase-by-phase time-sharing charging load passed by the middle optimization model, the lower optimization model establishes a node marginal price clearing model considering the consumption of new energy with the minimum total cost of the distribution network operation cost and the penalty for wind and solar power abandonment to determine the phase-by-phase time-sharing electricity purchase price of the charging network operator, and passes it to the upper optimization model; the upper optimization model adjusts the phase-by-phase time-sharing charging price based on the phase-by-phase time-sharing charging load obtained by the middle optimization model and the phase-by-phase time-sharing electricity purchase price obtained by the lower optimization model to maximize the net profit of the charging network operator.

2. The electric vehicle phase guidance method for improving new energy consumption based on evolutionary game according to claim 1 is characterized by: The specific process of establishing the three-layer optimization model is as follows: The upper optimization model establishes a phase-by-phase and time-by-phase charging price model for charging network operators with the goal of maximizing the net profit of charging network operators: , where: is the revenue of the charging network operator, I is the total number of charging stations within the jurisdiction of the charging network operator, is the charging price of phase y at the i-th charging station at time t, that is, the phase-by-phase and time-by-phase charging price, is the purchase price of electricity for phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-phase purchase price. is the charging load of phase y of the i-th charging station at time t, that is, the phase-by-phase and time-by-time charging load, is the time interval; The specific steps for solving the evolutionary game of the middle-level optimization model are as follows: Step 1: Assume that there are I charging stations, Z electric vehicles, and each vehicle has 3I charging strategies, each described as , where i is the charging station and y is the charging phase; at time t, select The ratio of the number of electric vehicles to the total number of electric vehicles is , , , then the electric vehicle population state can be expressed as ; If the electric vehicle chooses the charging strategy at time t , then the state of the electric car is ,otherwise ; Step 2: Select all charging strategies at time t The payment function is established with the goal of minimizing the sum of the costs of electric vehicles. , , where: Select the charging strategy for time t The total cost of all electric vehicles, is the charging price of the y-phase at the i-th charging station at time t, Charging power for electric vehicles; Step 3: The dynamic equation of the dynamic evolution process of the electric vehicle population state is: , where The charging strategy at time t To charging strategy The conditional switching probability, The charging strategy at time t To charging strategy The conditional switching probability of Step 4: The charging strategy modification protocol of the evolutionary game adopts the logit protocol, and the conditional conversion probability , then the time-continuous dynamic evolution equation of the electric vehicle population state is: , discretize the above time-continuous dynamic evolution equation to obtain the time-discrete dynamic evolution equation of the electric vehicle population state: ;Where: w is the number of evolutionary game iterations; is the iteration step of the evolutionary game; Step 5: Determine whether the payment functions of all charging strategies have reached the evolutionary convergence condition. , is the set convergence error, and it is considered that the evolution convergence condition has been reached, the solution is completed, and the electric vehicle population will no longer change the charging strategy; Step 6: Output the final charging strategy of the electric vehicle population to obtain the phase-by-phase and time-by-phase charging load of each phase at each station; Through the evolutionary game process of the above middle-level optimization model, the phase-by-phase and time-by-phase charging price can be realized. Phase-by-phase and time-by-time charging load The mapping process; The lower optimization model is to establish a node marginal electricity price clearing model considering the consumption of new energy by minimizing the sum of the distribution network operation cost and the penalty for wind and solar abandonment: In the formula, , and is the generator cost coefficient, is the active output of the distributed generation at the generator node h at time t, and L is the set of generator nodes; is the power from the external power grid at time t The amount of electricity purchased, A collection connected to an external power grid; The power generated by the wind power and photovoltaic units connected to the distribution network node in phase y; The actual power of the wind power and photovoltaic units connected to the distribution network node in phase y; is the penalty coefficient; For the optimal power flow model of the distribution network, the active power equality constraint and the reactive power equality constraint are expressed as follows: , ; In the formula, , They are the y-phase branch at time t. Active power and reactive power; is the reactive power output of the distributed generation at the generator node h at time t; , are the active load and reactive load at node j of phase y at time t respectively; is the branch at time t The current flowing in the y phase; , Branch Resistance and reactance; is the set of end nodes of all branches whose head end is j; , They are the y-phase branch at time t Active power and reactive power; is the charging load on the y phase connected to the node j in the distribution network at time t, and is also the charging load on the y phase of the i-th charging station at time t; J L is a set of distribution network nodes; Solving the Lagrange multiplier of the active power equation constraint in the optimal power flow model of the distribution network is equivalent to solving the phase-by-phase node marginal electricity price of the distribution network. The phase-by-phase time-of-use electricity purchase price of the charging station is the node marginal electricity price of the node where it is located, which can realize the phase-by-phase time-of-use charging load Phase-by-phase and time-of-use electricity purchase price The mapping process.

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