Receiving and transmitting self-balanced charging network operator optimal operation scheduling method

Abstract

The invention discloses a receiving and transmitting self-balanced charging network operator optimal operation scheduling method. The method comprises the following steps: 1) obtaining network parameters and operation parameters of a traffic system, network parameters and operation parameters of a power system, and operation parameters of a charging station; 2) based on the acquired network parameters and operation parameters, taking traffic system operation constraints as constraints, taking price signals as regulation and control objects, and taking maximization of revenue of the charging network operator as a target function, establishing a charging network operator optimal operation scheduling model with transmitting and receiving self-balance; 3) establishing an optimal operation scheduling model of the power system based on the acquired network parameters, operation parameters and scene data by taking the operation constraint of the power system as a constraint, taking the output of the distributed power supply as a regulation and control object and taking the minimum operation cost of the power system as a target function; and 4) designing a charging network operator-power system interaction algorithm to solve the model to obtain a receiving and transmitting self-balanced charging network operator optimal operation scheduling strategy.

Description

Technical Field

[0001] This invention belongs to the field of electric-transportation integration and vehicle-to-grid interaction, specifically involving an optimal operation scheduling method for charging network operators with self-balancing transmission and reception. Background Technology

[0002] With the widespread adoption of electric vehicles, their charging demand places enormous pressure on the safe operation of power distribution networks and the smooth flow of traffic. To guide electric vehicle charging behavior, existing technologies generally employ dynamic pricing strategies implemented by charging network operators and power distribution system operators, such as charging service fees based on time and space differences. While these methods incentivize users to change their charging times and locations through price signals and have achieved some success in optimizing network operation and alleviating congestion, their core function is to collect fees unilaterally from users.

[0003] This one-way charging mechanism has inherent flaws: it shifts almost all system adjustment costs to users, forcing them to either pay higher fees or endure longer travel times, significantly increasing user travel costs and raising issues of low public acceptance and social equity. While some studies have attempted to limit user spending by setting price caps or designing a minimum necessary rate, this may weaken the guidance effect on user behavior and impact operator revenue. Other literature proposes subsidies from operators to compliant users; while beneficial to users, this directly increases the financial burden on system operators, lacking commercial sustainability for charging network operators whose fundamental goal is profit. Therefore, the existing technological system presents an irreconcilable contradiction between the three core objectives of operator revenue, system security, and user burden. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned technical problems by proposing an optimal operation scheduling method for charging network operators that achieves self-balancing transmission and reception. The core of this invention lies in constructing a two-way adjustment mechanism: charging network operators charge a reasonable service fee to some users charging at key nodes, while simultaneously providing an equivalent charging subsidy to another group of users charging at surplus nodes in the system. This achieves self-balancing and circulation of the fund pool within the user group. This means that operators can achieve precise guidance of charging demand without additional investment or sacrificing their reasonable profits.

[0005] Technical Solution: This invention proposes an optimal operation scheduling method for charging network operators with self-balancing transmit and receive capabilities. The method includes the following steps:

[0006] 1) Obtain network and operating parameters of the transportation system, the power system, and the charging station;

[0007] 2) Based on the acquired network parameters and operating parameters, taking traffic system operating constraints as constraints, price signals as the control object, and maximizing the revenue of charging network operators as the objective function, an optimal operation scheduling model for charging network operators with self-balancing transmission and reception is established.

[0008] 3) Based on the acquired network parameters and operating parameters, with power system operating constraints as constraints, distributed power generation output as the control object, and minimizing power system operating costs as the objective function, an optimal power system operation and scheduling model is established.

[0009] 4) Design an interaction algorithm between the charging network operator and the power system to solve the optimal operation and scheduling model of the charging network operator with self-balancing transmission and reception in step 2) and the model in step 3) to obtain the optimal operation and scheduling strategy of the charging network operator with self-balancing transmission and reception.

[0010] Furthermore, in step 1), the relevant network parameters and operating parameters include:

[0011] a. The network parameters of the transportation system include the coupling parameters of each road segment and path in the transportation system, the free travel time of each road segment in the transportation system, and the segment capacity;

[0012] b. The operational parameters of the transportation system include the unit travel time cost for users, daily travel demand forecast data of the transportation system, and the electric vehicle penetration rate;

[0013] c. The operating parameters of the charging station include the charging station capacity, maximum waiting time, charging power, distributed photovoltaic installed capacity, and price signal upper limit;

[0014] d. Network parameters of the power system include the resistance and reactance of transmission lines, the topology of transmission lines, the upper and lower limits of node voltage, the upper and lower limits of distributed generation output, the generation parameters of distributed generation, and the upper limit of line transmission power.

[0015] e. The operating parameters of the power system include daily load forecast data.

[0016] Furthermore, in step 2), the optimal operation scheduling model for a self-balancing charging network operator is as follows:

[0017] (1)

[0018] + = , ∩ (2)

[0019] = , ∩ (3)

[0020] (4)

[0021] ∩ (5)

[0022] (6)

[0023] (7)

[0024] (8)

[0025] (9)

[0026] (10)

[0027] (11)

[0028] (12)

[0029] (13)

[0030] (14)

[0031] (15)

[0032] (16)

[0033] (17)

[0034] In the formula, This represents the objective function of the charging network operator; the subscript 'a' indicates the charging station number. Indicates a collection of charging stations; This represents the price signal applied to charging station a. A positive value indicates that a service fee is levied on electric vehicle users, while a negative value indicates that a charging subsidy is issued to electric vehicle users. Indicates the benchmark electricity price; Indicates the amount of electricity charged per vehicle; This indicates the number of electric vehicles heading to charging station a; This indicates the unit price at which charging station A purchases electricity from the power distribution network; This indicates that charging station A purchases electricity from the power distribution network; This represents the distributed photovoltaic power generation within charging station a; This refers to a collection of charging stations equipped with distributed photovoltaic systems. This refers to a collection of charging stations that have not been equipped with distributed photovoltaic systems. This indicates the maximum power that each charging station is allowed to draw from the power distribution network; This represents the power generation capacity of the distributed photovoltaic system in charging station a; This indicates the traffic flow on road segment l; Represents the set of origin and destination points for transportation demand; Indicates the path number; This represents the set of paths for fuel-powered vehicles between their start and end points; This represents the number of fuel-powered vehicles that choose path k. These are the indication parameters for the path and road segment of a fuel-powered vehicle. If road segment l is located on path k, then... Select 1; otherwise select 0. This represents the set of electric vehicle paths between the start and end points. This represents the number of electric vehicles that choose path k. These are the indication parameters for the path and road segment of the electric vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. Indicates a set of traffic segments; This indicates the path of the electric vehicle and the indication parameters of the charging station. If charging station 'a' is located on path 'k', then... Select 1 if the value is 1, otherwise select 0. This represents the demand for fuel-powered vehicle travel between the starting point r and the destination s; This represents the demand for electric vehicle travel between the starting point r and the destination s; Indicates the travel time on road segment l; This indicates the free travel time for road segment l; This indicates the traffic capacity of road segment a; This indicates the charging time at charging station a; This indicates the charging power of the charging station; This indicates the queuing time at charging station a when it is fully loaded; This indicates the maximum service capacity of charging station a; Indicates cost per unit of time; This represents the toll cost that a fuel-powered vehicle must pay when choosing route k; This represents the toll cost that an electric vehicle must pay when choosing route k; and These represent the minimum cost of traveling by gasoline-powered vehicle and the minimum cost of traveling by electric vehicle, respectively, between rs. Indicates the upper limit of the price signal;

[0035] Equation (1) represents the objective function of the charging network operator, including the revenue from providing charging services and the expenditure cost of purchasing electricity from the grid; Equation (2) represents the power balance equation of a charging station equipped with distributed photovoltaic power; Equation (3) represents the power balance equation of a charging station without distributed photovoltaic power; Equation (4) represents the upper limit constraint of the charging station's purchase of electricity from the grid; Equation (5) represents the power generation constraint of distributed photovoltaic power; Equation (6) represents the traffic flow conservation constraint of road segments in the traffic system; Equation (7) represents the traffic flow conservation constraint of charging stations; Equation (8) represents the demand conservation constraint of fuel-powered vehicles; Equation (9) represents the demand conservation constraint of electric vehicles; Equation (10) represents the demand conservation constraint of electric vehicles; Equation (1) represents the nonlinear relationship between traffic flow and travel time on a road segment; Equation (11) represents the nonlinear relationship between the number of vehicles queuing at a charging station and the queuing time at a charging station; Equation (12) defines the travel cost of fuel vehicles; Equation (13) defines the travel cost of electric vehicles, including charging costs and time costs; Equation (14) represents the Wardrop user equilibrium criterion for fuel vehicles; Equation (15) represents the Wardrop user equilibrium criterion for electric vehicles; Equation (16) defines the self-balancing constraint of charging and receiving, requiring that the total amount of additional charging service fees collected and the total amount of charging subsidies issued offset each other; Equation (17) represents the upper limit constraint of price signals.

[0036] Furthermore, in step 3), the constructed power system operation model is represented as follows:

[0037] (18)

[0038] (19)

[0039] (20)

[0040] (twenty one)

[0041] (twenty two)

[0042] (twenty three)

[0043] (twenty four)

[0044] (25)

[0045] (26)

[0046] (27)

[0047] In the formula, This represents the objective function for the operation of the power system; the subscripts i, j, and h represent power grid nodes. Represents the set of nodes in a distribution network; This represents the set of child nodes connected to the root node. , , These are the three generation parameters of the distributed power source located at node i; This represents the active power output of the distributed power source at node i; and These represent the unit price of electricity purchased from the main grid and the amount of electricity purchased from the main grid, respectively. This represents the set of all child nodes connected to node j; and These represent the active power and reactive power flowing through line ij, respectively. and These are the active power and reactive power flowing through line jh, respectively; and These are the resistance and reactance of line ij, respectively; Let be the voltage amplitude at node i; Let be the voltage amplitude at node j; Indicates the voltage amplitude at the checkpoint node; and These are the upper and lower limits of the voltage amplitude at node i, respectively; The apparent power limit of line ij; and These represent the active power and reactive power generated by the power source at node i, respectively. and These represent the active load and reactive load at node i, respectively. , These are the upper and lower limits of the active power output of distributed generation sources, respectively. , These are the upper and lower limits of the reactive power output of distributed power sources, respectively. This represents the set of charging stations powered by power system node j; This represents the normal active load at node j; This indicates the power value that charging station a needs to adjust; sign represents the sign function. If its independent variable is a non-negative value, the sign function takes the value of 1; if its independent variable is a negative value, the sign function takes the value of 0.

[0048] Equation (18) represents the objective function of the power system operation; Equation (19) represents the active power balance constraint of the power system; Equation (20) represents the reactive power balance constraint of the power system; Equation (21) represents Ohm's law of the power system; Equation (22) represents the capacity constraint of the power line power of the power system; Equation (23) represents the upper and lower limit constraints of the node voltage; Equation (24) represents the upper and lower limit constraints of the active power output of distributed generation; Equation (25) represents the upper and lower limit constraints of the reactive power output of distributed generation; Equation (26) defines the active load of the node; Equation (27) represents the upper and lower limits of the power adjustment amount of each charging station, requiring that the adjustment amount shall not exceed its original purchased power.

[0049] Furthermore, in step 4), the designed charging network operator-power system interaction algorithm is as follows:

[0050] (4.1) Initialization ;

[0051] (4.2), in a fixed position In this case, solve the optimal pricing model for charging network operators to obtain... ;

[0052] (4.3) In fixed In this case, solve the power system operation model to calculate ;

[0053] if Then let If the current scheduling scheme meets the power system operation constraints, return to (5.2); otherwise, terminate the loop step.

[0054] Furthermore, the present invention proposes a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the aforementioned self-balancing charging network operator optimal operation scheduling method.

[0055] Furthermore, the present invention proposes a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the aforementioned self-balancing charging network operator optimal operation scheduling method.

[0056] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0057] This invention proposes an optimal operation scheduling method for charging network operators that achieves self-balancing transmission and reception. This method ensures that the pricing strategy always complies with the safety constraints of the distribution network, thereby achieving multi-objective collaborative optimization that reduces user burden, guarantees operator revenue, and ensures system safety and stability. Attached Figure Description

[0058] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0059] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading this invention, any modifications of the invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0060] like Figure 1 As shown, this invention proposes an optimal operation scheduling method for charging network operators with self-balancing transmit and receive capabilities. This method includes the following steps:

[0061] 1) Obtain network and operating parameters of the transportation system, the power system, and the charging station;

[0062] 2) Based on the acquired network parameters and operating parameters, taking traffic system operating constraints as constraints, price signals as the control object, and maximizing the revenue of charging network operators as the objective function, an optimal operation scheduling model for charging network operators with self-balancing transmission and reception is established.

[0063] 3) Based on the acquired network parameters and operating parameters, with power system operating constraints as constraints, distributed power generation output as the control object, and minimizing power system operating costs as the objective function, an optimal power system operation and scheduling model is established.

[0064] 4) Design an interaction algorithm between the charging network operator and the power system to solve the optimal operation and scheduling model of the charging network operator with self-balancing transmission and reception in step 2) and the model in step 3) to obtain the optimal operation and scheduling strategy of the charging network operator with self-balancing transmission and reception.

[0065] Furthermore, in step 1), the relevant network parameters and operating parameters include:

[0066] a. The network parameters of the transportation system include the coupling parameters of each road segment and path in the transportation system, the free travel time of each road segment in the transportation system, and the segment capacity;

[0067] b. The operational parameters of the transportation system include the unit travel time cost for users, daily travel demand forecast data of the transportation system, and the electric vehicle penetration rate;

[0068] c. The operating parameters of the charging station include the charging station capacity, maximum waiting time, charging power, distributed photovoltaic installed capacity, and price signal upper limit;

[0069] d. Network parameters of the power system include the resistance and reactance of transmission lines, the topology of transmission lines, the upper and lower limits of node voltage, the upper and lower limits of distributed generation output, the generation parameters of distributed generation, and the upper limit of line transmission power.

[0070] e. The operating parameters of the power system include daily load forecast data.

[0071] Furthermore, in step 2), the optimal operation scheduling model for a self-balancing charging network operator is as follows:

[0072] (1)

[0073] + = , ∩ (2)

[0074] = , ∩ (3)

[0075] (4)

[0076] ∩ (5)

[0077] (6)

[0078] (7)

[0079] (8)

[0080] (9)

[0081] (10)

[0082] (11)

[0083] (12)

[0084] (13)

[0085] (14)

[0086] (15)

[0087] (16)

[0088] (17)

[0089] In the formula, This represents the objective function of the charging network operator; the subscript 'a' indicates the charging station number. Indicates a collection of charging stations; This represents the price signal applied to charging station a. A positive value indicates that a service fee is levied on electric vehicle users, while a negative value indicates that a charging subsidy is issued to electric vehicle users. Indicates the benchmark electricity price; Indicates the amount of electricity charged per vehicle; This indicates the number of electric vehicles heading to charging station a; This indicates the unit price at which charging station A purchases electricity from the power distribution network; This indicates that charging station A purchases electricity from the power distribution network; This represents the distributed photovoltaic power generation within charging station a; This refers to a collection of charging stations equipped with distributed photovoltaic systems. This refers to a collection of charging stations that have not been equipped with distributed photovoltaic systems. This indicates the maximum power that each charging station is allowed to draw from the power distribution network; This represents the power generation capacity of the distributed photovoltaic system in charging station a; This indicates the traffic flow on road segment l; Represents the set of origin and destination points for transportation demand; Indicates the path number; This represents the set of paths for fuel-powered vehicles between their start and end points; This represents the number of fuel-powered vehicles that choose path k. These are the indication parameters for the path and road segment of a fuel-powered vehicle. If road segment l is located on path k, then... Select 1; otherwise select 0. This represents the set of electric vehicle paths between the start and end points. This represents the number of electric vehicles that choose path k. These are the indication parameters for the path and road segment of the electric vehicle. If road segment l is located on path k, then... Select 1 if the value is 1, otherwise select 0. Indicates a set of traffic segments; This indicates the path of the electric vehicle and the indication parameters of the charging station. If charging station 'a' is located on path 'k', then... Select 1 if the value is 1, otherwise select 0. This represents the demand for fuel-powered vehicle travel between the starting point r and the destination s; This represents the demand for electric vehicle travel between the starting point r and the destination s; Indicates the travel time on road segment l; This indicates the free travel time for road segment l; This indicates the traffic capacity of road segment a; This indicates the charging time at charging station a; This indicates the charging power of the charging station; This indicates the queuing time at charging station a when it is fully loaded; This indicates the maximum service capacity of charging station a; Indicates cost per unit of time; This represents the toll cost that a fuel-powered vehicle must pay when choosing route k; This represents the toll cost that an electric vehicle must pay when choosing route k; and These represent the minimum cost of traveling by gasoline-powered vehicle and the minimum cost of traveling by electric vehicle, respectively, between rs. Indicates the upper limit of the price signal;

[0090] Equation (1) represents the objective function of the charging network operator, including the revenue from providing charging services and the expenditure cost of purchasing electricity from the grid; Equation (2) represents the power balance equation of a charging station equipped with distributed photovoltaic power; Equation (3) represents the power balance equation of a charging station without distributed photovoltaic power; Equation (4) represents the upper limit constraint of the charging station's purchase of electricity from the grid; Equation (5) represents the power generation constraint of distributed photovoltaic power; Equation (6) represents the traffic flow conservation constraint of road segments in the traffic system; Equation (7) represents the traffic flow conservation constraint of charging stations; Equation (8) represents the demand conservation constraint of fuel-powered vehicles; Equation (9) represents the demand conservation constraint of electric vehicles; Equation (10) represents the demand conservation constraint of electric vehicles; Equation (1) represents the nonlinear relationship between traffic flow and travel time on a road segment; Equation (11) represents the nonlinear relationship between the number of vehicles queuing at a charging station and the queuing time at a charging station; Equation (12) defines the travel cost of fuel vehicles; Equation (13) defines the travel cost of electric vehicles, including charging costs and time costs; Equation (14) represents the Wardrop user equilibrium criterion for fuel vehicles; Equation (15) represents the Wardrop user equilibrium criterion for electric vehicles; Equation (16) defines the self-balancing constraint of charging and receiving, requiring that the total amount of additional charging service fees collected and the total amount of charging subsidies issued offset each other; Equation (17) represents the upper limit constraint of price signals.

[0091] Furthermore, in step 3), the constructed power system operation model is represented as follows:

[0092] (18)

[0093] (19)

[0094] (20)

[0095] (twenty one)

[0096] (twenty two)

[0097] (twenty three)

[0098] (twenty four)

[0099] (25)

[0100] (26)

[0101] (27)

[0102] In the formula, This represents the objective function for the operation of the power system; the subscripts i, j, and h represent power grid nodes. Represents the set of nodes in a distribution network; This represents the set of child nodes connected to the root node. , , These are the three generation parameters of the distributed power source located at node i; This represents the active power output of the distributed power source at node i; and These represent the unit price of electricity purchased from the main grid and the amount of electricity purchased from the main grid, respectively. This represents the set of all child nodes connected to node j; and These represent the active power and reactive power flowing through line ij, respectively. and These are the active power and reactive power flowing through line jh, respectively; and These are the resistance and reactance of line ij, respectively; Let be the voltage amplitude at node i; Let be the voltage amplitude at node j; Indicates the voltage amplitude at the checkpoint node; and These are the upper and lower limits of the voltage amplitude at node i, respectively; The apparent power limit of line ij; and These represent the active power and reactive power generated by the power source at node i, respectively. and These represent the active load and reactive load at node i, respectively. , These are the upper and lower limits of the active power output of distributed generation sources, respectively. , These are the upper and lower limits of the reactive power output of distributed power sources, respectively. This represents the set of charging stations powered by power system node j; This represents the normal active load at node j; This indicates the power value that charging station a needs to adjust; sign represents the sign function. If its independent variable is a non-negative value, the sign function takes the value of 1; if its independent variable is a negative value, the sign function takes the value of 0.

[0103] Equation (18) represents the objective function of the power system operation; Equation (19) represents the active power balance constraint of the power system; Equation (20) represents the reactive power balance constraint of the power system; Equation (21) represents Ohm's law of the power system; Equation (22) represents the capacity constraint of the power line power of the power system; Equation (23) represents the upper and lower limit constraints of the node voltage; Equation (24) represents the upper and lower limit constraints of the active power output of distributed generation; Equation (25) represents the upper and lower limit constraints of the reactive power output of distributed generation; Equation (26) defines the active load of the node; Equation (27) represents the upper and lower limits of the power adjustment amount of each charging station, requiring that the adjustment amount shall not exceed its original purchased power.

[0104] Furthermore, in step 4), the designed charging network operator-power system interaction algorithm is as follows:

[0105] (4.1) Initialization ;

[0106] (4.2), in a fixed position In this case, solve the optimal pricing model for charging network operators to obtain... ;

[0107] (4.3) In fixed In this case, solve the power system operation model to calculate ;

[0108] if Then let If the current scheduling scheme meets the power system operation constraints, return to (4.2); otherwise, terminate the loop step.

[0109] Furthermore, the present invention proposes a computer device comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the aforementioned self-balancing charging network operator optimal operation scheduling method.

[0110] Furthermore, the present invention proposes a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of the aforementioned self-balancing charging network operator optimal operation scheduling method.

[0111] Table 1 Comparison of results under different control schemes

[0112]

[0113] Table 1 summarizes the comparative performance of two pricing schemes (the self-balancing control strategy proposed in this invention and the single strategy) in coordinating the economic interests of charging network operators and electric vehicle users. The test was conducted on a coupled Sioux-Falls transportation network and an IEEE-118 node distribution network. Before the implementation of control measures, the operator's profit was only $620. Significant differences emerged after the implementation of the price incentive scheme: the self-balancing control scheme increased user spending by only $20 while achieving a $600 increase in operator profit; whereas the single control scheme, although bringing a $1794 profit increase, required an additional $1212 in user spending as a cost. This means that under the single tax control scheme, for every $1 increase in operator profit, users incur an additional $0.68 in expenditure, resulting in a poor cost-benefit ratio and demonstrating significant stakeholder conflict. This imbalance stems from the single tax control scheme's reliance on a unilateral pricing strategy to regulate charging behavior. In contrast, the self-balancing control scheme, through an innovative dual-track mechanism of parallel taxation and subsidies, forms an internal balance system, ultimately achieving a self-balancing characteristic where the marginal expenditure impact is reduced to only $0.03 in user spending for every $1 increase in profit. The above results indicate that the self-balancing control scheme has significant advantages in balancing operator revenue and user economic burden.

[0114] The above description merely illustrates the embodiments of the present invention, and while the description is relatively specific and detailed, it should not be construed as limiting the scope of the invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A method for optimal operation scheduling of a transceiver self-balanced charging network operator, characterized in that, The method comprises the following steps: 1) obtaining network parameters and operation parameters of the traffic system, network parameters and operation parameters of the power system, and operation parameters of the charging station; 2) based on the obtained network parameters and operation parameters, taking the traffic system operation constraints as the constraint conditions, taking the price signal as the regulation object, and taking the maximization of the charging network operator's revenue as the objective function, a charging network operator optimal operation scheduling model for self-balancing is established; 3) based on the obtained network parameters and operation parameters, taking the power system operation constraints as the constraint conditions, taking the distributed power output as the regulation object, and taking the minimization of the power system operation cost as the objective function, a power system optimal operation scheduling model is established; 4) designing a charging network operator-power system interaction algorithm, solving the charging network operator optimal operation scheduling model for self-balancing in step 2) and the model in step 3), and obtaining the charging network operator optimal operation scheduling strategy for self-balancing.

2. The optimal operation scheduling method of a transceiving self-balanced charging network operator according to claim 1, characterized in that, In the step 1), the related network parameters and operation parameters include: a. the network parameters of the traffic system include the coupling parameters of each road section and path of the traffic system, the free travel time and road capacity of each road section in the traffic system; b. the operation parameters of the traffic system include the unit travel time cost of the trip user, the daily trip demand prediction data of the traffic system, and the electric vehicle penetration rate; c. the operation parameters of the charging station include the charging station capacity, the maximum waiting time, the charging power, the distributed photovoltaic installed capacity, and the price signal upper limit; d. the network parameters of the power system include the resistance and reactance of the power transmission line, the power transmission line topology, the node voltage upper and lower limits, the distributed power output upper and lower limits, the distributed power generation parameters, and the line transmission power upper limit; e. the operation parameters of the power system include the daily load prediction data of the power system.

3. The optimal operation scheduling method of a transceiving self-balanced charging network operator according to claim 1, characterized in that, In the step 2), the charging network operator optimal operation scheduling model for self-balancing is as follows: (1) + = ， ∩ (2) = ， ∩ (3) (4) ∩ (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) wherein, represents the objective function of the charging network operator; subscript a represents the charging station number; represents the charging station set; represents the price signal applied to the charging station a, which takes a positive value to represent charging service fees charged to electric vehicle users, and takes a negative value to represent charging subsidies issued to electric vehicle users; represents the benchmark electricity price; represents the single-vehicle charging amount; represents the number of electric vehicles going to the charging station a; represents the single-vehicle charging amount; represents the single-vehicle charging amount; represents the distributed photovoltaic power generation amount inside the charging station a; represents the charging station set equipped with distributed photovoltaics; represents the charging station set not equipped with distributed photovoltaics; represents the upper limit of power allowed to be obtained from the power distribution network by each charging station; represents the distributed photovoltaic power generation capacity of the charging station a; represents the traffic flow on the road segment l; represents the origin-destination set of traffic travel demand; represents the path number; represents the set of oil vehicle paths between the origin and destination; represents the number of oil vehicles selecting the path k; represents the indication parameter of the path and road segment of the oil vehicle, and if the road segment l is located on the path k, then takes 1; otherwise, takes 0; represents the set of electric vehicle paths between the origin and destination; represents the number of electric vehicles selecting the path k; represents the indication parameter of the path and road segment of the electric vehicle, and if the road segment l is located on the path k, then takes 1, otherwise, takes 0; represents the set of traffic road segments; represents the indication parameter of the path and charging station of the electric vehicle, and if the charging station a is located on the path k, then takes 1, otherwise, takes 0; represents the oil vehicle travel demand between the origin r and the destination s; represents the electric vehicle travel demand between the origin r and the destination s; represents the travel time on the road segment l; represents the free travel time of the road segment l; represents the travel capacity of the road segment a; represents the charging time inside the charging station a; charging power of the charging station; queueing time of charging station a at full load; upper limit of service capacity of charging station a; unit time cost; passage cost paid by the fuel vehicle when choosing path k; passage cost paid by the electric vehicle when choosing path k; and lowest fuel vehicle travel cost and lowest electric vehicle travel cost between rs, respectively, upper limit of price signal; Formula (1) represents the objective function of the charging network operator, including the income from providing charging services and the cost of purchasing electricity from the power grid; formula (2) represents the power balance equation of the charging station equipped with distributed photovoltaics; formula (3) represents the power balance equation of the charging station without distributed photovoltaics; formula (4) represents the upper limit constraint of the charging station purchasing electricity from the power grid; formula (5) represents the power generation constraint of the distributed photovoltaics; formula (6) represents the traffic system link flow conservation constraint; formula (7) represents the charging station flow conservation constraint; formula (8) represents the fuel vehicle trip demand conservation constraint; formula (9) represents the electric vehicle trip demand conservation constraint; formula (10) represents the nonlinear relationship between the link flow and the link travel time; formula (11) represents the nonlinear relationship between the charging station queuing vehicles and the charging station queuing time; formula (12) defines the travel cost of the fuel vehicle; formula (13) defines the travel cost of the electric vehicle, including the charging cost and the time cost; formula (14) represents the Wardrop user equilibrium criterion of the fuel vehicle; formula (15) represents the Wardrop user equilibrium criterion of the electric vehicle; formula (16) defines the send-receive balance constraint, requiring that the total amount of additional charging service fees and the total amount of charging subsidies issued offset each other; and formula (17) represents the upper limit constraint of the price signal.

4. The optimal operation scheduling method of a transceiving self-balanced charging network operator according to claim 3, characterized in that, In the step 3), the power system operation model constructed is represented as follows: (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) wherein, represents the objective function of power system operation; subscripts i, j, h represent the nodes of power grid; represents the set of nodes of distribution network; represents the set of child nodes connected to the root node; , , are three generation parameters of the distributed power supply located at node i; represents the active power output value of the distributed power supply at node i; and respectively represent the unit price of power purchase from the main grid and the power purchase amount from the main grid; represents the set of all child nodes connected to node j; and respectively represent the active power and the reactive power flowing through line ij; and respectively represent the active power and the reactive power flowing through line jh; and respectively represent the resistance and the reactance of line ij; is the voltage amplitude of node i; is the voltage amplitude of node j; represents the voltage amplitude of the boundary node; and respectively represent the upper limit and the lower limit of the voltage amplitude of node i; is the upper limit of the apparent power of line ij; and respectively represent the active power and the reactive power generated by the power supply at node i; and respectively represent the active load and the reactive load at node i; , respectively represent the upper limit and the lower limit of the active power output of the distributed power supply; , respectively represent the upper limit and the lower limit of the reactive power output of the distributed power supply; represents the set of charging stations supplied by power system node j; represents the conventional active load at node j; represents the power value required to be adjusted by charging station a; sign represents a sign function, which takes the value 1 if its argument is a non-negative value, and takes the value 0 if its argument is a negative value; Formula (18) represents the objective function of the power system operation; formula (19) represents the active power balance constraint of the power system; formula (20) represents the reactive power balance constraint of the power system; formula (21) represents the Ohm's law of the power system; formula (22) represents the capacity constraint of the power system line power; formula (23) represents the upper and lower limit constraints of the node voltage; formula (24) represents the upper and lower limit constraints of the active power output of the distributed power supply; formula (25) represents the upper and lower limit constraints of the reactive power output of the distributed power supply; formula (26) defines the node active load; and formula (27) represents the upper and lower limit of the power adjustment amount of each charging station, requiring that the adjustment amount cannot be higher than the original power purchase amount.

5. The optimal operation scheduling method of a transceiving self-balanced charging network operator according to claim 4, characterized in that, In the step 4), the designed charging network operator-power system interaction algorithm is as follows: (4.1), initialization ; (4.2), in the case of fixed solving the optimal pricing model of the charging network operator to obtain ; (4.3) in the case of fixing the power system operation model to calculate ; If , let , return (4.2); otherwise, it means that the current dispatching scheme satisfies the power system operation constraints, and terminate the loop step.

6. A computer device, comprising: The computer device includes a processor, a memory, and a computer program stored on the memory and executable on the processor, and the computer program is executed by the processor to implement the steps of the charging network operator optimal operation scheduling method of claim 1-5.

7. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the charging network operator optimal operation scheduling method of claim 1-5.

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