Distribution network operation optimization method and system considering electric vehicle demand response and carbon quota benefits

Abstract

The present invention relates to the technical field of distribution networks, and discloses a distribution network operation optimization method and system that takes into account electric vehicle demand response and carbon quota benefits. The method determines a load demand model for electric vehicles based on energy consumption change information of the electric vehicles in the travel chain, ensuring the accuracy and reliability of the load demand. By constructing a two-layer optimization model for the distribution network, the upper layer takes minimizing the total operating cost of the distribution network as the optimization goal, thereby minimizing the total system operating cost. The lower layer takes minimizing the network loss of the distribution network as the second optimization goal, thereby minimizing the network loss of the distribution network. This effectively coordinates the charging and discharging behavior of EV users with the operating efficiency of the distribution network. The method is suitable for optimized operation scenarios of distribution networks containing large-scale EVs, significantly reduces system operating costs and network losses, and improves the reliability of distribution network operation.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution networks, and in particular to a distribution network operation optimization method and system taking into account electric vehicle demand response and carbon quota benefits. Background Art

[0002] The large-scale integration of electric vehicles (EVs) has increased the randomness and volatility of grid loads, placing higher demands on grid stability and power supply reliability. As mobile energy storage units, EV charging and discharging behaviors coordinate with the grid, optimizing energy allocation, promoting clean energy consumption, and reducing carbon emissions. Scientific scheduling guides the orderly charging and discharging of EVs, mitigates the negative impact of their grid integration, and enhances the grid's ability to absorb renewable energy.

[0003] However, EV optimal scheduling involves multiple complex steps, requiring comprehensive consideration of factors such as the distribution network's operating status and user charging needs to achieve a balance of interests between EV users and the power system. Traditional scheduling methods struggle to dynamically reflect real-time grid load and user response characteristics, and exhibit significant limitations in multi-objective, multi-constraint optimization problems, which can easily lead to poor reliability in distribution network operation optimization. Summary of the Invention

[0004] In view of this, the present invention provides a distribution network operation optimization method and system that takes into account electric vehicle demand response and carbon quota benefits, which solves the technical problems that traditional scheduling methods are difficult to dynamically reflect real-time grid load and user response characteristics, and show great limitations in multi-objective and multi-constraint optimization problems, which easily leads to poor reliability of distribution network operation optimization.

[0005] A first aspect of the present invention provides a distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits, comprising:

[0006] Determining a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in the travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period;

[0007] Taking minimizing the total operating cost of the distribution network as a first optimization objective, and determining a first constraint condition of the first optimization objective based on the steady-state balance of the distribution network, to construct an upper-level scheduling optimization model;

[0008] The upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current time period to obtain a first scheduling optimization plan; the first scheduling optimization plan includes the charging power, power purchase amount, wind curtailment amount, and solar curtailment amount of the electric vehicle in each time period;

[0009] updating the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution;

[0010] Taking minimizing the network loss of the distribution network as the second optimization objective, and determining the second constraint condition of the second optimization objective based on the steady-state balance of each node of the distribution network, to construct a lower-level scheduling optimization model;

[0011] The lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization scheme; the second scheduling optimization scheme includes the number of electric vehicles at each node of the distribution network in each time period.

[0012] Preferably, the energy consumption change information includes the output power of each energy-consuming accessory of the electric vehicle;

[0013] The step of determining a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in the travel chain includes:

[0014] Determining a path node adjacency matrix according to the topological structure characteristics of the traffic network corresponding to the electric vehicle;

[0015] Determining the shortest path of the electric vehicle according to the path node adjacency matrix;

[0016] A load demand model of the electric vehicle is constructed based on the shortest path, the output power of each energy-consuming component of the electric vehicle, and the battery capacity.

[0017] Preferably, the first optimization objective is to minimize the total operating cost of the distribution network, and the first constraint condition of the first optimization objective is determined by the steady-state balance of the distribution network, and the upper-level scheduling optimization model is constructed, including:

[0018] Determining a total operating cost function of the distribution network based on the carbon emission cost, charging cost, electricity purchase cost, wind curtailment cost, and solar curtailment cost of the distribution network;

[0019] Determining a first objective function by taking minimizing the total operating cost of the distribution network as the optimization goal of the total operating cost function;

[0020] The first constraint condition of the first objective function is determined based on the steady-state balance of the distribution network. The first constraint condition includes a power balance constraint, an electric vehicle quantity limit constraint, a charging and discharging demand limit constraint, a wind power output limit constraint, and a photovoltaic output limit constraint.

[0021] Preferably, the method further comprises:

[0022] The electricity price information is determined according to a peak-valley time-of-use charging and discharging electricity price model; wherein the peak-valley time-of-use charging and discharging electricity price model is used to describe the upper and lower limits of the peak-valley time-of-use charging electricity price, as well as the upper and lower limits of the peak-valley time-of-use discharging electricity price.

[0023] Preferably, the second optimization objective is to minimize the network loss of the distribution network, and the second constraint condition of the second optimization objective is determined by the steady-state balance of each node of the distribution network, and the lower-layer scheduling optimization model is constructed, including:

[0024] Determining a network loss calculation function of the distribution network according to power flow distribution information of each node of the distribution network;

[0025] Taking minimizing the network loss of the distribution network as the optimization goal of the network loss calculation function, determining a second objective function;

[0026] The second constraint condition of the second objective function is determined based on the steady-state balance of operation of each node in the distribution network. The second constraint condition includes a distribution network flow constraint, a node safety operation limit constraint, a node charging pile capacity limit constraint, and a distribution network EV number limit constraint.

[0027] Preferably, the upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current period to obtain a first scheduling optimization solution, including:

[0028] Based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current period until the iteration converges to obtain a first scheduling optimization solution.

[0029] Preferably, the lower-layer scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization solution, including:

[0030] Based on the HO-SFOA hybrid search algorithm, the lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution until the iteration converges to obtain a second scheduling optimization solution.

[0031] In a second aspect, the present invention further provides a distribution network operation optimization system that takes into account electric vehicle demand response and carbon quota benefits, comprising:

[0032] A load demand determination module, configured to determine a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in a travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period;

[0033] An upper-level scheduling module is configured to take minimizing the total operating cost of the distribution network as a first optimization objective, determine a first constraint condition of the first optimization objective based on the steady-state balance of the distribution network, and construct an upper-level scheduling optimization model;

[0034] an upper-level scheduling solution module, configured to optimize and solve the upper-level scheduling optimization model according to the load demand and electricity price information of the electric vehicle in the current time period to obtain a first scheduling optimization solution; the first scheduling optimization solution includes the charging power, power purchase amount, wind curtailment amount, and solar curtailment amount of the electric vehicle in each time period;

[0035] a power flow distribution determining module, configured to update the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution;

[0036] a lower-layer scheduling module, configured to take minimizing the network loss of the distribution network as a second optimization objective, determine a second constraint condition of the second optimization objective based on the steady-state balance of each node of the distribution network, and construct a lower-layer scheduling optimization model;

[0037] The lower-level scheduling solution module is used to optimize and solve the lower-level scheduling optimization model according to the initial power flow distribution to obtain a second scheduling optimization solution; the second scheduling optimization solution includes the number of electric vehicles at each node of the distribution network in each time period.

[0038] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits as described in the first aspect.

[0039] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the steps of the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits as described in the first aspect.

[0040] As can be seen from the above technical solutions, the present invention determines the load demand model of electric vehicles based on the energy consumption change information of electric vehicles in the travel chain, ensures the accuracy and reliability of the load demand, and constructs a two-layer optimization model for the distribution network. At the upper layer, the total operating cost of the distribution network is minimized as the optimization goal, thereby minimizing the total system operating cost. At the lower layer, the network loss of the distribution network is minimized as the second optimization goal, thereby minimizing the network loss of the distribution network. This effectively coordinates the charging and discharging behavior of EV users with the operating efficiency of the distribution network. This model is suitable for optimized operation scenarios of distribution networks containing large-scale EVs, significantly reduces system operating costs and network losses, and improves the reliability of distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0042] Figure 1 An application environment for a distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits provided by an embodiment of the present invention;

[0043] Figure 2 A flow chart of a distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits provided by an embodiment of the present invention;

[0044] Figure 3 Schematic diagram of road topology;

[0045] Figure 4 A schematic diagram of the structure of a distribution network operation optimization system taking into account electric vehicle demand response and carbon quota benefits provided by an embodiment of the present invention;

[0046] Figure 5 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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 making creative efforts shall fall within the scope of protection of the present invention.

[0048] The distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits provided in the embodiment of the present application can be applied to Figure 1In the application environment shown, the terminal 101 communicates with the server 102 via a network. The data storage system can store data that the server 102 needs to process. The data storage system can be integrated on the server 102 or placed on the cloud or other network servers. The terminal 101 or the server 102 determines the load demand model of the electric vehicle based on the energy consumption change information of the electric vehicle in the travel chain; wherein, the load demand model is used to determine the load demand of the electric vehicle in each time period; the total operating cost of the distribution network is minimized as the first optimization goal, and the first constraint condition of the first optimization goal is determined by the steady-state balance of the operation of the distribution network, and an upper-level scheduling optimization model is constructed; the upper-level scheduling optimization model is optimized and solved according to the load demand of the electric vehicle in the current time period and the electricity price information to obtain a first scheduling optimization scheme; the first scheduling optimization scheme includes the charging power, power purchase amount, wind curtailment amount and solar curtailment amount of the electric vehicle in each time period; the flow distribution of each node of the distribution network is updated according to the first scheduling optimization scheme to obtain an initial flow distribution; the network loss of the distribution network is minimized as the second optimization goal, and the second constraint condition of the second optimization goal is determined by the steady-state balance of the operation of each node of the distribution network to construct a lower-level scheduling optimization model; the lower-level scheduling optimization model is optimized and solved according to the initial flow distribution to obtain a second scheduling optimization scheme; the second scheduling optimization scheme includes the number of electric vehicles at each node of the distribution network in each time period.

[0049] The terminal 101 may be, but is not limited to, various personal computers, laptops, smart phones, tablet computers, and the like.

[0050] The server 102 may be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0051] like Figure 2 As shown, the embodiment of the present application provides a distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits, and the method is applied to Figure 1 The terminal 101 or the server 102 in the embodiment is used as an example to illustrate the method, which includes the following steps S1 to S6.

[0052] Step S1: Determine a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in the travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period.

[0053] Energy consumption changes for electric vehicles during the travel chain refer to data on energy consumption changes during driving, caused by various factors such as road conditions, speed, and load. This data includes, but is not limited to, battery power consumption, motor output power, and energy consumption of auxiliary equipment such as air conditioners. By collecting and analyzing this energy consumption change information, we can accurately understand the energy consumption needs of electric vehicles at different times, providing basic data support for subsequent distribution network scheduling optimization.

[0054] The load demand model is constructed based on this energy consumption change information through mathematical methods. It can predict the load demand of electric vehicles in different time periods.

[0055] Step S2: Taking the minimization of the total operating cost of the distribution network as the first optimization objective, and determining the first constraint condition of the first optimization objective based on the steady-state balance of the distribution network, an upper-level scheduling optimization model is constructed.

[0056] To ensure that the load of electric vehicles (EVs) can be time-matched with the power generation output of renewable energy sources such as wind and solar energy, the embodiments of this application adopt a strategy that minimizes the total operating cost of the distribution network as the primary optimization goal. On this basis, the steady-state balance of the distribution network operation is further determined as the primary constraint for achieving this optimization goal. Through this setting, an upper-level scheduling optimization model is constructed, which achieves efficient, economical and stable operation of the distribution network through intelligent scheduling and management.

[0057] Step S3: The upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current time period to obtain a first scheduling optimization scheme; the first scheduling optimization scheme includes the charging power, power purchase amount, wind curtailment amount, and solar curtailment amount of the electric vehicle in each time period.

[0058] Step S4: Update the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution.

[0059] The first scheduling optimization scheme involved in this invention details the charging and discharging power schedules for electric vehicles during different time periods, as well as the amount of electricity required to be purchased during these periods. It also accounts for the potential wind and solar curtailment that can occur during renewable energy generation, i.e., the inefficient use of wind and solar energy. By implementing this scheduling optimization scheme, the power flow distribution at each node in the distribution network can be updated and optimized, thereby obtaining an initial power flow distribution state.

[0060] Among them, EV charging power directly affects the load level and node voltage of the distribution network.

[0061] The power purchased by the distribution network refers to the power purchased from the upper power grid to meet local load demand and EV charging needs.

[0062] Step S5: Taking the minimization of the network loss of the distribution network as the second optimization objective, and determining the second constraint condition of the second optimization objective based on the steady-state balance of each node in the distribution network, a lower-level scheduling optimization model is constructed.

[0063] Among them, the embodiment of the present application takes minimizing the network loss of the distribution network as the second optimization goal, and takes the operating steady-state balance of each node in the distribution network as the second constraint condition for determining the second optimization goal, thereby constructing a lower-level scheduling optimization model.

[0064] Step S6: searching and solving the lower-level scheduling optimization model according to the initial power flow distribution to obtain a second scheduling optimization scheme; the second scheduling optimization scheme includes the number of electric vehicles at each node of the distribution network in each time period.

[0065] It should be noted that the embodiment of the present application determines the load demand model of electric vehicles based on the energy consumption change information of electric vehicles in the travel chain, ensures the accuracy and reliability of the load demand, and constructs a two-layer optimization model of the distribution network. At the upper layer, the total operating cost of the distribution network is minimized as the optimization goal, thereby minimizing the total system operating cost. At the lower layer, the network loss of the distribution network is minimized as the second optimization goal, thereby minimizing the network loss of the distribution network, thereby achieving effective coordination between the charging and discharging behavior of EV users and the operating efficiency of the distribution network. It is suitable for the optimized operation scenario of the distribution network containing large-scale EVs, significantly reducing the system operating cost and network loss, and improving the reliability of the distribution network operation.

[0066] In some embodiments, the energy consumption change information includes the output power of each energy-consuming accessory of the electric vehicle.

[0067] Energy-consuming accessories refer to the various components or devices that consume energy while an electric vehicle is driving and docked, including but not limited to battery packs, motors, air conditioners, lighting systems, and audio systems. The output power of these energy-consuming accessories varies depending on the vehicle's usage and environmental conditions. For example, when an electric vehicle is traveling at high speeds, the motor's output power is relatively high; whereas, when the electric vehicle is docked and the air conditioner is on, the air conditioner's energy consumption becomes dominant. By accurately measuring the output power of these energy-consuming accessories, we can further refine the electric vehicle's load demand model, more accurately reflecting the energy consumption characteristics of the electric vehicle in different scenarios.

[0068] Specifically, based on the energy consumption change information of electric vehicles in the travel chain, the load demand model of electric vehicles is determined, including:

[0069] Step S101: Determine a path node adjacency matrix based on the topological structure characteristics of the traffic network corresponding to the electric vehicle.

[0070] Among them, the principle of graph theory can be used to describe the topological structure characteristics of the traffic network corresponding to electric vehicles. Figure 3 Take the topology shown in the figure as an example. The path node adjacency matrix is constructed as follows:

[0071] (1)

[0072] Where D is the path node adjacency matrix, For nodes The weight of the path between them.

[0073] Step S102: Determine the shortest path of the electric vehicle according to the path node adjacency matrix.

[0074] For example, assuming that users are rational, the embodiment of the present application can use Dijkstra algorithm to obtain the node The shortest path between .

[0075] The Dijkstra algorithm is a classic algorithm for solving single-source shortest paths. It finds the shortest path from a source node to all other nodes by gradually expanding the shortest path tree. When determining the shortest path for electric vehicles, the algorithm considers the weights in the path node adjacency matrix. These weights can represent factors such as path length, travel time, or energy consumption, ensuring that the optimal path is found after considering various factors.

[0076] Through steps S101 and S102, the present embodiment can determine the shortest path for an electric vehicle in a travel chain based on the topological structure of the traffic network corresponding to the electric vehicle, thereby more accurately predicting the load demand of the electric vehicle at each time period. This load demand prediction based on actual road conditions and driving paths can better reflect the actual power consumption of electric vehicles compared to traditional prediction methods based on fixed charging patterns and driving habits, providing more reliable data support for the scheduling optimization of the distribution network.

[0077] In order to describe the driving characteristics of EVs, the OD (Origin-Destination) matrix is used to obtain the starting and ending points of EVs. The OD probability matrix is shown as follows:

[0078] (2)

[0079] Where, express Time period slave node To Node The probability of traveling; For slave nodes To Node The number of EVs.

[0080] EV travel time approximately follows a normal distribution, and the corresponding probability density function is:

[0081] (3)

[0082] Where, and are the expected and standard deviation of travel time, respectively.

[0083] EV charging time It can be expressed as:

[0084] (4)

[0085] Where, is the energy consumption of EV ( ); is the charging power of EV ( ); Charging efficiency for EVs; is the EV mileage.

[0086] Step S103: constructing a load demand model of the electric vehicle based on the shortest path, the output power of each energy-consuming component of the electric vehicle, and the battery capacity.

[0087] For example, the energy consumption accessories in the embodiment of the present application are mainly tire power and air conditioning power, and the tire power is calculated as:

[0088] (5)

[0089] (6)

[0090] (7)

[0091] (8)

[0092] Where, Indicates EV driving on the road Tire power; For EV quality; is the acceleration due to gravity; is the rolling resistance coefficient, and is the road resistance parameter; is the air density, and They represent air density; EV front area and vehicle aerodynamic drag coefficient respectively; For the road Driving speed; is the motor power; and represent the transmission system efficiency and motor efficiency respectively; Indicates the loss SOC of the section passed through, and the section not passed through is 0; is the battery capacity of the EV; is the battery efficiency of the EV; Indicates the distance of the road section during the k-trip; Indicates the remaining SOC; is the number of nodes; is the total charge.

[0093] The energy consumption model of air conditioning start-stop is as follows:

[0094] (9)

[0095] (10)

[0096] (11)

[0097] Where, is the probability of the air conditioner turning on or off; T represents the ambient temperature; and Respectively represent the maximum thresholds for air conditioning cooling startup and heating startup; and represents the variance and mean of cooling activation; and represents the variance and mean of heating activation; and The minimum thresholds for cooling and heating activation, respectively, constitute the comfort range. When the temperature is within the comfort range, the probability of the air conditioner turning on is 0.

[0098] Therefore, based on the above energy consumption model, the load demand model of electric vehicles is as follows:

[0099] (12)

[0100] (13)

[0101] Where, Indicates the route section loss SOC; and Respectively represent the heating power and cooling power of the air conditioner; is the battery capacity of the EV; is the battery efficiency of the EV; Indicates the distance of the road section during the k-trip; Indicates the remaining SOC.

[0102] In some embodiments, the first optimization objective is to minimize the total operating cost of the distribution network, and the first constraint condition of the first optimization objective is determined based on the steady-state balance of the distribution network. An upper-level scheduling optimization model is constructed, including:

[0103] Step S201: Determine the total operating cost function of the distribution network based on the carbon emission cost, charging cost, electricity purchase cost, wind power curtailment cost, and solar power curtailment cost of the distribution network.

[0104] Step S202: Taking the minimum total operating cost of the distribution network as the optimization goal of the total operating cost function, a first objective function is determined.

[0105] Among them, the first objective function takes the minimum total operating cost of the distribution network as the optimization goal, and the first objective function is:

[0106] (14)

[0107] Where, is the scheduling period; is the penalty price coefficient for carbon emissions; is the carbon emissions of the parent power grid; For carbon quota income; For EV Charging cost for the time period; for The cost of electricity purchased during the time period; for The cost of curtailing wind power during the period; for The cost of abandoned solar power during the period.

[0108] Carbon emissions from the upstream power grid It can be calculated by the following formula:

[0109] (15)

[0110] Where, is the average received basis ash percentage of the coal fired; This is the carbon emission coefficient converted from coal ash, and 5.1% is usually taken as the default value in calculations; The percentage of carbon emission removal efficiency of the control measure is usually taken as 99% by default in the calculation; 、 、 is the coefficient of the coal consumption characteristic curve of the thermal power unit in the upper power grid, is the active power.

[0111] The calculation formulas for carbon quota income, charging costs, electricity purchase costs, wind curtailment costs, and solar curtailment costs are as follows:

[0112] (16)

[0113] (17)

[0114] (18)

[0115] (19)

[0116] (20)

[0117] Where, and represents the carbon quota price and carbon quota of electric vehicles at time t; and They represent the charging power and charging price at time t respectively; and They represent the amount of electricity purchased and the price of electricity purchased from the distribution network at time t respectively; , They represent the amount of wind and solar power abandoned at time t, and They represent the wind power curtailment penalty coefficient and the solar power curtailment penalty coefficient respectively.

[0118] Step S203: determining a first constraint condition of the first objective function based on the steady-state balance of the distribution network, wherein the first constraint condition includes a power balance constraint, an electric vehicle quantity limit constraint, a charge and discharge demand limit constraint, a wind power output limit constraint, and a photovoltaic output limit constraint.

[0119] Specifically, the power balance constraint is:

[0120] (twenty one)

[0121] Where, For photovoltaic Output during the time period; For wind farms Wind power during the period; For distribution network Network loss during the time period; 、 Respectively in The number of EVs charged and discharged during the time period; is the discharge power, is the load power.

[0122] The limit on the number of electric vehicles is:

[0123] (twenty two)

[0124] Where, 、 Respectively in The maximum number of EVs that can be charged or discharged during a time period.

[0125] The charge and discharge demand limit constraints are:

[0126] (twenty three)

[0127] Where, is the average charging time; is the total number of EVs that can be charged; is the total number of EVs that can be discharged, is the discharge duration, For charging time.

[0128] The wind power output limit constraint is:

[0129] (twenty four)

[0130] Where, is the predicted wind power.

[0131] The photovoltaic output limit constraint is:

[0132] (25)

[0133] Where, is the predicted photovoltaic power.

[0134] In some embodiments, rationally formulating peak-valley time-of-use charging and discharging pricing is crucial to more effectively guide user participation in demand response. The pricing model must not only reflect the economic advantages of electric vehicles but also compete with the cost of fuel-powered vehicles to increase user acceptance of electric vehicles. To address the shortcomings of the current orderly charging and discharging strategy, while maintaining the original time-of-use electricity price, the charging and discharging period is optimized and the existing orderly charging and discharging strategy is improved. This ensures stable grid operation while minimizing the cost of participating in vehicle-to-grid (V2G) services for electric vehicle users.

[0135] The embodiment of the present application also includes: determining electricity price information based on a peak-valley time-sharing charging and discharging electricity price model; wherein the peak-valley time-sharing charging and discharging electricity price model is used to describe the upper and lower limits of the peak-valley time-sharing charging electricity price, as well as the upper and lower limits of the peak-valley time-sharing discharging electricity price.

[0136] For example, an electricity quantity and electricity price elasticity matrix is formulated, wherein the change in electricity price leading to the change in electricity demand is called electricity quantity and electricity price elasticity. The elasticity coefficient and the electricity quantity and electricity price elasticity matrix are respectively shown as follows:

[0137] (26)

[0138] (27)

[0139] Where, Indicates the amount of electricity; Indicates the electricity price; and Represent the relative increments of electricity quantity and electricity price respectively; is the total study period; is the self-elastic coefficient; E is the elastic matrix; is the cross elastic coefficient.

[0140] The peak-valley time-of-use charging and discharging pricing model is constructed based on the electricity quantity and price elasticity matrix. Through the electricity quantity and price elasticity matrix, the direct and indirect impact of electricity price changes on demand can be quantified, and ultimately a peak-valley electricity price that balances supply and demand and reduces system costs can be formulated. Specifically, the peak-valley time-of-use charging and discharging pricing model determines the charging and discharging prices during peak and valley hours by analyzing the impact of electricity price changes in different time periods on electricity demand. By reasonably setting the upper and lower limits of electricity prices, it is possible to guide users to charge and discharge in an orderly manner while ensuring the stable operation of the power grid and the economic interests of electric vehicle users. When formulating peak-valley time-of-use charging and discharging prices, factors such as the charging and discharging characteristics of electric vehicles, the load demand of the power grid, and the output of renewable energy sources must also be considered to achieve the overall goal of optimizing the operation of the distribution network.

[0141] Among them, the peak-valley time-of-use charging and discharging electricity price model is used to describe the upper and lower limits of the peak-valley time-of-use charging electricity price, as well as the upper and lower limits of the peak-valley time-of-use discharging electricity price.

[0142] Charging electricity price setting:

[0143] In this embodiment of the application, the valley electricity price in the peak-valley time-of-use electricity price set by the power department is used as the lower limit of the EV charging electricity price. The model is as follows:

[0144] (28)

[0145] Where, is the valley electricity price; is the peak electricity price; To provide a flat electricity price; It is the off-peak electricity price period; This is the peak electricity price period.

[0146] The process of setting the upper limit of charging electricity price is as follows:

[0147] If EVs are to replace fuel vehicles, they must ensure that, under certain conditions, the cost of using EVs is lower than that of fuel vehicles. Calculated by the following formula:

[0148] (29)

[0149] Where, The purchase price of a bare car for a fuel vehicle; is the total fuel cost; is the total maintenance cost. Calculated by formula (30):

[0150] (30)

[0151] Where, is the annual mileage of the car; for oil prices; The fuel consumption per 100 kilometers of the car; For the service life.

[0152] EV usage fees Calculated by the following formula:

[0153] (31)

[0154] Where, Cost of purchasing the bare EV vehicle; is the total charging cost; For battery replacement costs; Revenue from battery recycling. and Calculated by formula (32) and formula (33) respectively:

[0155] (32)

[0156] (33)

[0157] Where, The number of days that EV participates in V2G in a year; is the average driving distance of EV per trip; is the number of trips per day; is the distance traveled per kilowatt-hour; Battery recycling price; is the average charge of the EV battery.

[0158] In summary, the upper limit of EV charging electricity price is defined Cost of using fuel vehicles EV usage fees The difference between them, that is:

[0159] (34)

[0160] The process of setting EV discharge electricity prices is as follows:

[0161] Discharge electricity price lower limit:

[0162] The daily charging cost of an EV is:

[0163] (35)

[0164] Backfeed during peak hours, the user's daily income is:

[0165] (36)

[0166] Where, For users' daily income, The average maximum power consumption for charging a single EV; is the discharge electricity price; For battery loss; The remaining power of the EV battery. It can be calculated by the following formula:

[0167] (37)

[0168] Where, 、 Represent the battery charging and discharging efficiency respectively.

[0169] Therefore, EV users will only choose to discharge their EVs into the grid when the benefits of discharging them into the grid are greater than the charging costs, that is:

[0170] (38)

[0171] The solution is:

[0172] (39)

[0173] This price is the lower limit of the discharge electricity price .

[0174] The upper limit of discharge electricity price is:

[0175] EV discharge can only be carried out during peak electricity price periods, and the electricity price should not exceed the sales price of the power grid at that moment. Therefore, the peak electricity price in the time-of-use electricity price formulated by the power department is used as the upper limit of the discharge electricity price.

[0176] It can be understood that the proposed peak-valley time-of-use charging and discharging pricing model sets upper and lower limits for peak-valley time-of-use charging prices, as well as upper and lower limits for peak-valley time-of-use discharging prices. Based on these parameters, peak charging prices, valley charging prices, peak discharging prices, and valley discharging prices can be accurately determined. Therefore, by designing a dynamic peak-valley time-of-use pricing mechanism, this mechanism aims to encourage users to actively participate in demand response activities by flexibly adjusting charging and discharging prices, thereby helping to smooth out grid load fluctuations. At the same time, this mechanism can also improve the economic benefits of electric vehicle (EV) users, achieving a win-win situation for all parties.

[0177] In some embodiments, minimizing the network loss of the distribution network is used as the second optimization objective, and the second constraint condition of the second optimization objective is determined by the steady-state balance of each node in the distribution network. A lower-layer scheduling optimization model is constructed, including:

[0178] Step S501: Determine a network loss calculation function of the distribution network according to the power flow distribution information of each node of the distribution network.

[0179] Step S502: Taking minimizing the network loss of the distribution network as the optimization goal of the network loss calculation function, a second objective function is determined.

[0180] Among them, the second objective function is:

[0181] (40)

[0182] (41)

[0183] (42)

[0184] Where, is the initial network loss value in period t; For all branches of the network; For nodes and The conductance between for Time period node voltage; for Time period node and The phase angle difference between for Time period node voltage; The increased network loss after EV is connected to the grid; is the total charging power of EV; is the equivalent resistance; U is the grid voltage; where,

[0185] ,in, is the charging power of the mth EV at time t, M is the number of EVs in the distribution network during period t, and T is the total period.

[0186] Step S503: Determine the second constraint of the second objective function based on the steady-state balance of each node in the distribution network. The second constraint includes the distribution network flow constraint, the node safety operation limit constraint, the node charging pile capacity limit constraint, and the distribution network EV number limit constraint.

[0187] Among them, the distribution network flow constraint is:

[0188] (43)

[0189] Where, 、 for Injection node within the period The active power and reactive power of For nodes and The electromagnetic intensity between.

[0190] The node safe operation limit constraints are:

[0191] (44)

[0192] Where, 、 Node The maximum and minimum allowed voltages.

[0193] The node charging pile capacity limit constraint is:

[0194] (45)

[0195] Where, Accessible Node The maximum number of EVs; 、 They are Time period at node The number of EVs charging and discharging.

[0196] The limit constraint on the number of EVs in the distribution network is:

[0197] (46)

[0198] Where, 、 In the distribution network The number of EVs charged and discharged during the time period.

[0199] In some embodiments, the upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of electric vehicles in the current period to obtain a first scheduling optimization solution, including:

[0200] Based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand of electric vehicles in the current period and the electricity price information until the iteration converges to obtain the first scheduling optimization solution.

[0201] The HO-SFOA hybrid search algorithm (Hybrid Squirrel and Fruit Fly Optimization Algorithm) combines the Squirrel Search Algorithm (SSA) and the Fruit Fly Optimization Algorithm (FOA). This combination aims to leverage the strengths of both algorithms to improve global search capabilities and local search accuracy.

[0202] Squirrel Optimization Algorithm (SSA): Inspired by the food storage behavior of North American red squirrels, this algorithm searches for the optimal solution by simulating the process of squirrels finding, storing, and transferring food.

[0203] Fruit Fly Optimization Algorithm (FOA): Based on the foraging behavior of fruit flies, it searches for food sources through smell and vision, and updates the population position to find the optimal solution.

[0204] Specifically, based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand of electric vehicles in the current period and the electricity price information until iterative convergence to obtain the first scheduling optimization solution, including:

[0205] (1) A set of initial solutions is randomly generated as a population, where each individual represents a possible scheduling solution.

[0206] (2) Set the HO-SFOA algorithm parameters and initialize the population;

[0207] (3) For each individual, its fitness value is calculated according to the objective function. The fitness function is the first objective function, which is to minimize the total operating cost, including charging cost, electricity purchase cost, wind curtailment cost, solar curtailment cost, etc.

[0208] (4) Hybrid search strategy:

[0209] Squirrel optimization stage:

[0210] Update the location of each individual to simulate the food storage behavior of squirrels in different seasons.

[0211] Adjust the new position of the individual according to the current position of the individual and the historical optimal position.

[0212] Fruit fly optimization stage:

[0213] In each iteration, a portion of individuals are randomly selected to perform fruit fly optimization operations.

[0214] Using the fruit fly's olfactory and visual mechanisms, the positions of these individuals are updated to explore new potential solution spaces.

[0215] (5) Hybridization and mutation:

[0216] In each generation, some individuals are hybridized and mutated to increase population diversity and prevent premature convergence.

[0217] (6) Update the global optimal solution:

[0218] Compare the fitness values of all individuals, update and record the current global optimal solution.

[0219] (7) Termination condition judgment:

[0220] If the maximum number of iterations is reached or there is no significant improvement after multiple generations, the algorithm is terminated. Otherwise, it returns to step (3) to continue iterating and obtain the final scheduling result.

[0221] In some embodiments, the lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization solution, including:

[0222] Based on the HO-SFOA hybrid search algorithm, the lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution until the iteration converges to obtain the second scheduling optimization solution.

[0223] Specifically, based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand of electric vehicles in the current period and the electricity price information until iterative convergence to obtain the first scheduling optimization solution, including:

[0224] (1) A set of initial solutions is randomly generated as a population, where each individual represents a possible scheduling solution.

[0225] (2) Set the HO-SFOA algorithm parameters and initialize the population;

[0226] (3) For each individual, calculate its fitness value according to the objective function. The fitness function is the second objective function, which is to minimize the network loss.

[0227] (4) Hybrid search strategy:

[0228] Squirrel optimization stage:

[0229] Update the location of each individual to simulate the food storage behavior of squirrels in different seasons.

[0230] Adjust the new position of the individual according to the current position of the individual and the historical optimal position.

[0231] Fruit fly optimization stage:

[0232] In each iteration, a portion of individuals are randomly selected to perform fruit fly optimization operations.

[0233] Using the fruit fly's olfactory and visual mechanisms, the positions of these individuals are updated to explore new potential solution spaces.

[0234] (5) Hybridization and mutation:

[0235] In each generation, some individuals are hybridized and mutated to increase population diversity and prevent premature convergence.

[0236] (6) Update the global optimal solution:

[0237] Compare the fitness values of all individuals, update and record the current global optimal solution.

[0238] (7) Termination condition judgment:

[0239] If the maximum number of iterations is reached or there is no significant improvement after multiple generations, the algorithm is terminated. Otherwise, it returns to step (3) to continue iterating and obtain the final scheduling result.

[0240] Based on the same inventive concept, an embodiment of the present application also provides a distribution network operation optimization system taking into account electric vehicle demand response and carbon quota benefits for implementing the above-mentioned distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits.

[0241] The implementation solution to the problem provided by this system is similar to the implementation solution described in the above method. Therefore, the specific limitations in one or more embodiments of the distribution network operation optimization system taking into account electric vehicle demand response and carbon quota benefits provided below can be found in the above limitations on the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits, and will not be repeated here.

[0242] like Figure 4 As shown, the embodiment of the present application provides a distribution network operation optimization system that takes into account electric vehicle demand response and carbon quota benefits, including:

[0243] The load demand determination module 100 is used to determine the load demand model of the electric vehicle based on the energy consumption change information of the electric vehicle in the travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period;

[0244] The upper-layer scheduling module 200 is configured to take minimizing the total operating cost of the distribution network as a first optimization objective, determine a first constraint condition of the first optimization objective based on the steady-state balance of the distribution network, and construct an upper-layer scheduling optimization model;

[0245] The upper-level scheduling solution module 300 is used to optimize and solve the upper-level scheduling optimization model based on the load demand and electricity price information of the electric vehicles in the current time period to obtain a first scheduling optimization solution; the first scheduling optimization solution includes the charging power, power purchase, wind curtailment, and solar curtailment of the electric vehicles in each time period;

[0246] A power flow distribution determination module 400 is configured to update the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution;

[0247] The lower-layer scheduling module 500 is configured to take minimizing the network loss of the distribution network as the second optimization objective, determine the second constraint condition of the second optimization objective based on the steady-state balance of each node in the distribution network, and construct a lower-layer scheduling optimization model;

[0248] The lower-level scheduling solution module 600 is used to optimize and solve the lower-level scheduling optimization model according to the initial power flow distribution to obtain a second scheduling optimization solution; the second scheduling optimization solution includes the number of electric vehicles at each node of the distribution network in each time period.

[0249] In some embodiments, the energy consumption change information includes the output power of each energy-consuming component of the electric vehicle. The load demand determination module 100 is used to:

[0250] According to the topological structure characteristics of the traffic network corresponding to electric vehicles, the path node adjacency matrix is determined;

[0251] Determine the shortest path for electric vehicles based on the path node adjacency matrix;

[0252] The load demand model of electric vehicles is constructed based on the shortest path, the output power of each energy-consuming component of the electric vehicle, and the battery capacity.

[0253] In some embodiments, the upper layer scheduling module 200 is configured to:

[0254] Determine the total operating cost function of the distribution network based on its carbon emission costs, charging costs, electricity purchase costs, wind curtailment costs, and solar curtailment costs;

[0255] Taking the minimum total operating cost of the distribution network as the optimization goal of the total operating cost function, determining the first objective function;

[0256] The first constraint of the first objective function is determined based on the steady-state balance of the distribution network. The first constraint includes power balance constraint, electric vehicle quantity limit constraint, charging and discharging demand limit constraint, wind power output limit constraint, and photovoltaic output limit constraint.

[0257] In some embodiments, the system also includes: a dynamic electricity price determination module, which is used to determine electricity price information based on the peak-valley time-of-use charging and discharging electricity price model; wherein the peak-valley time-of-use charging and discharging electricity price model is used to describe the upper and lower limits of the peak-valley time-of-use charging electricity price, as well as the upper and lower limits of the peak-valley time-of-use discharging electricity price.

[0258] In some embodiments, the lower layer scheduling module 500 is used to:

[0259] Determine the network loss calculation function of the distribution network according to the power flow distribution information of each node of the distribution network;

[0260] Taking the minimization of the distribution network loss as the optimization goal of the network loss calculation function, a second objective function is determined;

[0261] The second constraint of the second objective function is determined based on the steady-state balance of each node in the distribution network. The second constraint includes the distribution network flow constraint, the node safety operation limit constraint, the node charging pile capacity limit constraint and the distribution network EV number limit constraint.

[0262] In some embodiments, the upper-level scheduling solution module 300 is used to search and solve the upper-level scheduling optimization model based on the HO-SFOA hybrid search algorithm according to the load demand and electricity price information of electric vehicles in the current period until the iteration converges to obtain a first scheduling optimization solution.

[0263] In some embodiments, the lower-layer scheduling solution module 600 is used to search and solve the lower-layer scheduling optimization model based on the HO-SFOA hybrid search algorithm according to the initial power flow distribution until the iteration converges to obtain a second scheduling optimization solution.

[0264] like Figure 5 As shown, an embodiment of the present application provides an electronic device, the electronic device 10 includes a memory 20 and a processor 30, the memory 20 stores a computer program, and when the computer program is executed by the processor 30, the processor 30 executes the steps of the distribution network operation optimization method taking into account the electric vehicle demand response and carbon quota benefits in the above embodiment.

[0265] An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the steps of the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits as in the above-mentioned embodiment are implemented.

[0266] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, and computer storage media can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0267] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0268] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, program segment or part of the code, and the module, program segment or part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0269] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0270] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0271] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0272] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present invention via a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0273] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits, characterized in that: include: Determining a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in the travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period; Taking the minimization of the total operating cost of the distribution network as the first optimization objective and determining the first constraint condition of the first optimization objective based on the steady-state balance of the distribution network, an upper-level scheduling optimization model is constructed, including: Determine the total operating cost function of the distribution network based on its carbon emission costs, charging costs, electricity purchase costs, wind curtailment costs, and solar curtailment costs; Taking the minimum total operating cost of the distribution network as the optimization goal of the total operating cost function, determining the first objective function; Among them, the first objective function is: ； Where, is the scheduling period; is the penalty price coefficient for carbon emissions; is the carbon emissions of the parent power grid; For carbon quota income; For EV Charging cost for the time period; for The cost of electricity purchased during the time period; for The cost of curtailing wind power during the period; for The cost of curtailed solar power during the time period; Determining a first constraint of the first objective function based on the steady-state balance of the distribution network, the first constraint including a power balance constraint, an electric vehicle quantity limit constraint, a charge and discharge demand limit constraint, a wind power output limit constraint, and a photovoltaic output limit constraint; The upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current time period to obtain a first scheduling optimization plan; the first scheduling optimization plan includes the charging power, power purchase amount, wind curtailment amount, and solar curtailment amount of the electric vehicle in each time period; updating the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution; Taking minimizing the network loss of the distribution network as the second optimization objective, and determining the second constraint condition of the second optimization objective based on the steady-state balance of each node of the distribution network, a lower-level scheduling optimization model is constructed, including: Determine the network loss calculation function of the distribution network based on the power flow distribution information of each node in the distribution network; Taking the minimization of the distribution network loss as the optimization goal of the network loss calculation function, a second objective function is determined; Among them, the second objective function is: ； ； ； Where, is the initial network loss value in period t; For all branches of the network; For nodes and The conductance between for Time period node voltage; for Time period node and The phase angle difference between for Time period node voltage; The increased network loss after EV is connected to the grid; is the total charging power of EV; is the equivalent resistance; U is the grid voltage; where, ,in, is the charging power of the mth EV at time t, and M is the number of EVs in the distribution network during period t; The second constraint of the second objective function is determined based on the steady-state balance of each node in the distribution network. The second constraint includes the distribution network power flow constraint, the node safety operation limit constraint, the node charging pile capacity limit constraint, and the distribution network EV number limit constraint. The lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization scheme; the second scheduling optimization scheme includes the number of electric vehicles at each node of the distribution network in each time period.

2. The distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits according to claim 1 is characterized in that: The energy consumption change information includes the output power of each energy-consuming accessory of the electric vehicle; The step of determining a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in the travel chain includes: Determining a path node adjacency matrix according to the topological structure characteristics of the traffic network corresponding to the electric vehicle; Determining the shortest path of the electric vehicle according to the path node adjacency matrix; A load demand model of the electric vehicle is constructed based on the shortest path, the output power of each energy-consuming component of the electric vehicle, and the battery capacity.

3. The distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits according to claim 1 is characterized in that: Also includes: The electricity price information is determined according to a peak-valley time-of-use charging and discharging electricity price model; wherein the peak-valley time-of-use charging and discharging electricity price model is used to describe the upper and lower limits of the peak-valley time-of-use charging electricity price, as well as the upper and lower limits of the peak-valley time-of-use discharging electricity price.

4. The distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits according to any one of claims 1 to 3, characterized in that: The upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current period to obtain a first scheduling optimization solution, including: Based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand and electricity price information of the electric vehicle in the current period until the iteration converges to obtain a first scheduling optimization solution.

5. The distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits according to any one of claims 1 to 3, characterized in that: The lower-layer scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization solution, including: Based on the HO-SFOA hybrid search algorithm, the lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution until the iteration converges to obtain a second scheduling optimization solution.

6. A distribution network operation optimization system taking into account electric vehicle demand response and carbon quota benefits, characterized in that: include: A load demand determination module, configured to determine a load demand model of the electric vehicle based on energy consumption change information of the electric vehicle in a travel chain; wherein the load demand model is used to determine the load demand of the electric vehicle in each time period; The upper-layer scheduling module is configured to take minimizing the total operating cost of the distribution network as a first optimization objective, determine a first constraint condition for the first optimization objective based on the steady-state balance of the distribution network, and construct an upper-layer scheduling optimization model, including: Determine the total operating cost function of the distribution network based on its carbon emission costs, charging costs, electricity purchase costs, wind curtailment costs, and solar curtailment costs; Taking the minimum total operating cost of the distribution network as the optimization goal of the total operating cost function, determining the first objective function; Among them, the first objective function is: ； Where, is the scheduling period; is the penalty price coefficient for carbon emissions; is the carbon emissions of the parent power grid; For carbon quota income; For EV Charging cost for the time period; for The cost of electricity purchased during the time period; for The cost of curtailing wind power during the period; for The cost of curtailed solar power during the time period; Determining a first constraint of the first objective function based on the steady-state balance of the distribution network, the first constraint including a power balance constraint, an electric vehicle quantity limit constraint, a charge and discharge demand limit constraint, a wind power output limit constraint, and a photovoltaic output limit constraint; an upper-level scheduling solution module, configured to optimize and solve the upper-level scheduling optimization model according to the load demand and electricity price information of the electric vehicle in the current time period to obtain a first scheduling optimization solution; the first scheduling optimization solution includes the charging power, power purchase amount, wind curtailment amount, and solar curtailment amount of the electric vehicle in each time period; a power flow distribution determining module, configured to update the power flow distribution of each node of the distribution network according to the first scheduling optimization scheme to obtain an initial power flow distribution; The lower-layer scheduling module is configured to take minimizing the network loss of the distribution network as the second optimization objective, determine the second constraint condition of the second optimization objective based on the steady-state balance of each node of the distribution network, and construct a lower-layer scheduling optimization model, including: Determine the network loss calculation function of the distribution network according to the power flow distribution information of each node of the distribution network; Taking the minimization of the distribution network loss as the optimization goal of the network loss calculation function, a second objective function is determined; Among them, the second objective function is: ； ； ； Where, is the initial network loss value in period t; For all branches of the network; For nodes and The conductance between for Time period node voltage; for Time period node and The phase angle difference between for Time period node voltage; The increased network loss after EV is connected to the grid; is the total charging power of EV; is the equivalent resistance; U is the grid voltage; where, ,in, is the charging power of the mth EV at time t, and M is the number of EVs in the distribution network during period t; The second constraint of the second objective function is determined based on the steady-state balance of each node in the distribution network. The second constraint includes the distribution network power flow constraint, the node safety operation limit constraint, the node charging pile capacity limit constraint, and the distribution network EV number limit constraint. The lower-level scheduling solution module is used to optimize and solve the lower-level scheduling optimization model according to the initial power flow distribution to obtain a second scheduling optimization solution; the second scheduling optimization solution includes the number of electric vehicles at each node of the distribution network in each time period.

7. An electronic device, characterized in that: The electronic device includes a memory and a processor, wherein a computer program is stored in the memory. When the computer program is executed by the processor, the processor executes the steps of the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the steps of the distribution network operation optimization method taking into account electric vehicle demand response and carbon quota benefits as described in any one of claims 1 to 5 are implemented.

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