Power distribution network operation optimization method and system considering electric vehicle demand response and carbon quota income

Abstract

The invention relates to the technical field of power distribution networks, and discloses a power distribution network operation optimization method and system considering electric vehicle demand response and carbon quota earning.The method comprises the steps that a load demand model of an electric vehicle is determined based on energy consumption change information of the electric vehicle in a trip chain, and the accuracy and reliability of the load demand are ensured; a power distribution network double-layer optimization model is constructed, minimization of the total system operation cost is achieved on the upper layer with the minimum total operation cost of the power distribution network as the optimization target, and minimization of the power distribution network loss is achieved on the lower layer with the minimum power distribution network loss as the second optimization target. Therefore, effective coordination between EV user charging and discharging behaviors and power distribution network operation efficiency is realized, the method is suitable for a power distribution network optimization operation scene containing a large-scale EV, system operation cost and network loss are remarkably reduced, and power distribution network operation reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network, 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 access of electric vehicles (EVs) has increased the randomness and volatility of grid loads, and has put forward higher requirements for grid stability and power supply reliability. As a mobile energy storage unit, the charging and discharging behavior of EVs is coordinated with the grid to optimize energy allocation, promote clean energy consumption and carbon emission reduction. Scientific scheduling guides EVs to charge and discharge in an orderly manner, alleviates the negative impact of their access to the grid, and improves the grid's ability to absorb renewable energy.

[0003] However, EV optimal dispatching involves multiple complex links, and it is necessary to comprehensively consider multiple factors such as the operating status of the distribution network and the charging needs of users, so as to achieve a balance of interests between EV users and the power system. Traditional dispatching methods are difficult to dynamically reflect the real-time grid load and user response characteristics, and they show great limitations in multi-objective and multi-constraint optimization problems, which easily leads to poor reliability of 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 taking into account electric vehicle demand response and carbon quota benefits, which solves the technical problem that traditional scheduling methods are difficult to dynamically reflect real-time power 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] Determine a load demand model of the electric vehicle according to 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;

[0007] 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 operation of the distribution network, and constructing an upper-level scheduling optimization model;

[0008] The upper-level scheduling optimization model is optimized and solved according to the load demand of the electric vehicle in the current period and the electricity price information to obtain a first scheduling optimization scheme; the first scheduling optimization scheme includes the charging power, the purchased electricity, the abandoned wind amount and the abandoned solar amount of the electric vehicle in each 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 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 by the steady-state balance of each node of the distribution network, and constructing 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 plan; the second scheduling optimization plan 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 consumption accessory of the electric vehicle;

[0013] Determining the load demand model of the electric vehicle according to the 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 road 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 according to the shortest path, the output power of each energy-consuming accessory 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] Determine a total operating cost function of the distribution network according to the carbon emission cost, charging cost, electricity purchase cost, wind abandonment cost and solar abandonment cost of the distribution network;

[0019] Taking the minimum total operating cost of the distribution network as the optimization target of the total operating cost function, determining a first objective function;

[0020] The first constraint condition of the first objective function is determined by the steady-state balance of operation of the distribution network, and the first constraint condition 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.

[0021] Preferably, the method further comprises:

[0022] The electricity price information is determined according to 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.

[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-level 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 target of the network loss calculation function, determining a second objective function;

[0026] The second constraint condition of the second objective function is determined by the steady-state balance of operation of each node of the distribution network, and the second constraint condition includes distribution network flow constraint, node safety operation limit constraint, node charging pile capacity limit constraint and distribution network EV quantity 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 of the electric vehicle in the current period and the electricity price information until the iteration converges to obtain the 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-layer 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 taking into account electric vehicle demand response and carbon quota benefits, comprising:

[0032] A load demand determination module, used to determine the load demand model of the electric vehicle according to 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;

[0033] An upper-level scheduling module is used to take the minimum 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] The upper-level scheduling solution module is used to optimize and solve the upper-level scheduling optimization model according to the load demand of the electric vehicle in the current period and the electricity price information to obtain a first scheduling optimization plan; the first scheduling optimization plan includes the charging power, power purchase, wind abandonment and solar abandonment of the electric vehicle in each period;

[0035] A power flow distribution determination 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, used to take the minimum network loss of the distribution network as the second optimization target, and determine the second constraint condition of the second optimization target by the steady-state balance of each node of the distribution network, so as to 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 plan; the second scheduling optimization plan 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] It can be seen from the above technical solutions that the present invention determines the load demand model of the electric vehicle based on the energy consumption change information of the electric vehicle in the travel chain, ensures the accuracy and reliability of the load demand, and constructs a two-layer optimization model of the distribution network. In the upper layer, the total operating cost of the distribution network is minimized as the optimization goal, thereby minimizing the total system operation cost. In 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. 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 drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[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 It is a 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 diagram of the structure 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 scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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 according to 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 operation 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 plan; the first scheduling optimization plan includes the charging power, power purchase amount, wind abandonment amount and solar abandonment amount of the electric vehicle in each time period; the power flow distribution of each node of the distribution network is updated according to the first scheduling optimization plan to obtain an initial power 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 power flow distribution to obtain a second scheduling optimization plan; the second scheduling optimization plan 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, laptop computers, smart phones, tablet computers, and the like.

[0050] The server 102 may be an independent physical server, or a server cluster or 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 example is used to illustrate, and the steps include the following steps S1 to S6. Among them:

[0052] Step S1, determining a load demand model of the electric vehicle according to 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.

[0053] Among them, the energy consumption change information of electric vehicles in the travel chain refers to the energy consumption change data caused by various factors such as road conditions, speed, and load during the driving process of electric vehicles. These data include but are not limited to the battery power consumption of electric vehicles, motor output power, and energy consumption of auxiliary equipment such as air conditioners. By collecting and analyzing this energy consumption change information, the energy consumption demand of electric vehicles at different times can be accurately grasped, thereby providing basic data support for the subsequent optimization of distribution network scheduling.

[0054] The load demand model is constructed based on this energy consumption change information through mathematical methods. It is a model that 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, and constructing an upper-level scheduling optimization model.

[0056] Among them, in order to ensure that the load of electric vehicles (EVs) can match the power generation output of renewable energy sources such as wind and solar energy in time, the embodiment of the present application adopts a strategy of minimizing 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 to achieve this optimization goal. Through such settings, an upper-level scheduling optimization model is constructed, which realizes efficient, economical and stable operation of the distribution network through intelligent scheduling and management.

[0057] Step S3, according to the load demand of electric vehicles in the current time period and the electricity price information, the upper-level scheduling optimization model is optimized and solved to obtain a first scheduling optimization plan; the first scheduling optimization plan includes the charging power, power purchase amount, wind abandonment amount and solar abandonment amount of electric vehicles 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] Among them, the first scheduling optimization scheme involved in the present invention includes in detail the arrangement of the charging and discharging power of electric vehicles in different time periods, as well as the amount of electricity required to be purchased in these time periods, while taking into account the wind and solar abandonment that may occur in the renewable energy power generation process, that is, the wind and solar energy that are not effectively utilized. By implementing this scheduling optimization scheme, the power flow distribution of 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-level 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, and constructing a lower-level scheduling optimization model.

[0063] Among them, the embodiment of the present application takes minimization of network loss in 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 plan; the second scheduling optimization plan 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. The upper layer takes the minimum total operating cost of the distribution network as the optimization goal to minimize the total system operation cost, and the lower layer takes the minimum network loss of the distribution network as the second optimization goal to minimize 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 scenarios of distribution networks containing large-scale EVs, significantly reduces the system operating costs and network losses, and improves the reliability of distribution network operation.

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

[0067] Among them, energy-consuming accessories refer to various components or equipment that consume energy during the driving and parking process of electric vehicles, including but not limited to battery packs, motors, air conditioners, lighting systems, audio systems, etc. The output power of these energy-consuming accessories will vary with the use status and environmental changes of the electric vehicle. For example, when the electric vehicle is driving at high speed, the output power of the motor will be relatively high; and when the electric vehicle is parked and the air conditioner is turned on, the energy consumption of the air conditioner will become dominant. By accurately measuring the output power of these energy-consuming accessories, the load demand model of electric vehicles can be further refined to more accurately reflect the energy consumption characteristics of electric vehicles in different scenarios.

[0068] Specifically, according to 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 according to the topological structure characteristics of the traffic network corresponding to the electric vehicle.

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

[0071] (1)

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

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

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

[0075] Among them, the Dijkstra algorithm is a classic algorithm for solving the shortest path of a single source. It finds the shortest path from the source node to all other nodes by gradually expanding the shortest path tree. When determining the shortest path for electric vehicles, the algorithm takes into account the weights in the path node adjacency matrix. These weights can represent factors such as the length of the path, driving time or energy consumption, thereby ensuring that the optimal path is found after comprehensive consideration of various factors.

[0076] Through step S101 and step S102, the embodiment of the present application can determine the shortest path of the electric vehicle in the travel chain based on the topological structure characteristics of the traffic road network corresponding to the electric vehicle, and then more accurately predict the load demand of the electric vehicle in 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 modes and driving habits, and provide more reliable data support for the dispatch optimization of the distribution network.

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

[0078] (2)

[0079] In the formula, express Time 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] In the formula, and are the expected and standard deviation of travel time respectively.

[0083] How long does it take to charge an EV? It can be expressed as:

[0084] (4)

[0085] In the formula, is the energy consumption of EV ( ); is the charging power of EV ( ); Charging efficiency for EVs; EV driving range.

[0086] Step S103: construct a load demand model of the electric vehicle according to 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] In the formula, Indicates that EV is 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 the air density; the EV front area and the 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 route section passed, and the section not passed 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] In the formula, is the probability of air conditioning on and off; T represents the ambient temperature; and Respectively represent the maximum thresholds for air conditioning cooling start and heating start; and represents the variance and mean of cooling activation; and represents the variance and mean of heating activation; and Respectively represent the minimum thresholds for cooling and heating startup. The minimum thresholds for cooling and heating startup constitute a comfort interval. When the temperature is within the comfort interval, 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] In the formula, 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 by the steady-state balance of the distribution network, and an upper-level scheduling optimization model is constructed, including:

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

[0104] Step S202: determine a first objective function by taking minimizing the total operating cost of the distribution network as the optimization target of the total operating cost function.

[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] In the formula, 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 during the time period; for The cost of electricity purchased during the time period; for The cost of wind curtailment during the period; for The cost of abandoned light in a certain period of time.

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

[0109] (15)

[0110] In the formula, is the average received basis ash percentage of the coal fired; This is the carbon emission factor converted from coal ash, and 5.1% is usually taken as the default value in calculations; The percentage of the efficiency of the control measure in removing carbon emissions 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] In the formula, 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 power purchase amount and the power purchase price of the distribution network at time t respectively; , They represent the wind curtailment and solar curtailment at time t respectively. and They represent the wind power abandonment penalty coefficient and the solar power abandonment penalty coefficient respectively.

[0118] Step S203, determining a first constraint condition of a 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 charging and discharging 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] In the formula, 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] In the formula, , 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] In the formula, 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] In the formula, is the predicted wind power.

[0131] The photovoltaic output limit constraint is:

[0132] (25)

[0133] In the formula, is the predicted photovoltaic power.

[0134] In some embodiments, in order to more effectively guide users to participate in demand response, it is crucial to reasonably formulate peak and valley time-of-use charging and discharging electricity prices. The electricity price model should not only reflect the economic advantages of electric vehicles, but also compete with the cost of fuel vehicles, thereby increasing users' acceptance of electric vehicles. In view of the shortcomings of the current orderly charging and discharging strategy, while keeping the original time-of-use electricity price unchanged, the charging and discharging period is optimized, and the existing orderly charging and discharging strategy is improved. While ensuring the stable operation of the power grid, the cost of electric vehicle users participating in vehicle-to-grid (V2G) is reduced as much as possible.

[0135] The embodiment of the present application also includes: determining electricity price information according to 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, and the elasticity coefficient and the electricity quantity and electricity price elasticity matrix are respectively shown as follows:

[0137] (26)

[0138] (27)

[0139] In the formula, Indicates the amount of electricity; Indicates the price of electricity; and They 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 price 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 finally the 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 price 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 the demand for electricity. By reasonably setting the upper and lower limits of electricity prices, the stable operation of the power grid and the economic interests of electric vehicle users can be ensured while guiding users to charge and discharge in an orderly manner. When formulating the 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 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 the embodiment of the present application, the valley electricity price in the peak-valley time-of-use electricity price formulated by the power department is used as the lower limit of the EV charging electricity price. The model is as follows:

[0144] (28)

[0145] In the formula, for valley electricity prices; is the peak electricity price; To equalize electricity prices; 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] In the formula, 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] In the formula, is the annual mileage of the car; for oil prices; The fuel consumption per 100 kilometers of the car; For the useful life.

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

[0153] (31)

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

[0155] (32)

[0156] (33)

[0157] In the formula, 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 driving distance per kilowatt-hour; The price of battery recycling; is the average charge of the EV battery.

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

[0159] (34)

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

[0161] Discharge price lower limit:

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

[0163] (35)

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

[0165] (36)

[0166] In the formula, 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] In the formula, , Respectively represent the battery charging and discharging efficiency.

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

[0170] (38)

[0171] Solving for this yields:

[0172] (39)

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

[0174] The upper limit of the 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 set by the power department is used as the upper limit of the discharge electricity price.

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

[0177] In some embodiments, 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 a lower-level scheduling optimization model is constructed, including:

[0178] Step S501: determining a network loss calculation function of the distribution network according to 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 target 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] In the formula, is the initial network loss value in period t; for all branches of the network; For Node and The conductance between for Time period node Voltage; for Time period node and The phase angle difference between for Time period node Voltage; It is the network loss value increased after EV is connected to the grid; is the total charging power of the 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 electric vehicles in the distribution network during period t, and T is the total period.

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

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

[0188] (43)

[0189] In the formula, , for Injection node within the time period The active power and reactive power of For Node and The electromagnetic strength between.

[0190] The node safety operation limit constraints are:

[0191] (44)

[0192] In the formula, , Node The maximum and minimum allowed voltages.

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

[0194] (45)

[0195] In the formula, Accessible Node The maximum number of EVs; , They are Time period at node The number of EVs being charged and discharged.

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

[0197] (46)

[0198] In the formula, , 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 the electric vehicle 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] HO-SFOA hybrid search algorithm (Hybrid Squirrel and Fruit Fly Optimization Algorithm) is a hybrid algorithm that combines the squirrel search algorithm (SSA) and the fruit fly optimization algorithm (FOA). This combination aims to take advantage of the respective advantages of the two 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 the iteration converges 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 position 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, return to step (3) to continue iterating and obtain the final scheduling result.

[0221] In some embodiments, 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:

[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 the iteration converges 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, its fitness value is calculated 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 position 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, return 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 the system is similar to the implementation solution recorded in the above method. Therefore, the specific limitations in one or more distribution network operation optimization system embodiments taking into account electric vehicle demand response and carbon quota benefits provided below can be referred to 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 taking 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 according to 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 scheduling module 200 is used to take the minimum total operating cost of the distribution network as the first optimization goal, determine the first constraint condition of the first optimization goal based on the steady-state balance of the distribution network, and construct an upper scheduling optimization model;

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

[0246] A power flow distribution determination module 400, 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 used to take the minimum network loss of the distribution network as the second optimization target, determine the second constraint condition of the second optimization target by the steady-state balance of each node of 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 plan; the second scheduling optimization plan 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 consumption component of the electric vehicle. The load demand determination module 100 is used to:

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

[0251] Determine the shortest path for the electric vehicle 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 scheduling module 200 is used to:

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

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

[0256] The first constraint of the first objective function is determined by 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: an electricity price dynamic determination module, which is used to determine 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.

[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 network loss of the distribution network as the optimization target of the network loss calculation function, a second objective function is determined;

[0261] The second constraint of the second objective function is determined by 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 quantity 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 of electric vehicles in the current period and the electricity price information until the iteration converges to obtain the 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 can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, electronic device and computer storage medium 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 inherent to these processes, methods, products or apparatus.

[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, a program segment or a part of a code, and a part of a module, a program segment or a code includes 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 from 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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 separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0271] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0272] If the integrated unit is implemented in the form of 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 is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for executing all or part of the steps of the method described in each embodiment of the present invention through a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (full name in English: Read-Only Memory, English abbreviation: ROM), random access memory (full name in English: Random Access Memory, English abbreviation: RAM), disk or optical disk and other media that can store program codes.

[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 the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. 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 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: Determine a load demand model of the electric vehicle according to 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; 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 operation of the distribution network, and constructing an upper-level scheduling optimization model; The upper-level scheduling optimization model is optimized and solved according to the load demand of the electric vehicle in the current period and the electricity price information to obtain a first scheduling optimization scheme; the first scheduling optimization scheme includes the charging power, the purchased electricity, the abandoned wind amount and the abandoned solar amount of the electric vehicle in each 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 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 by the steady-state balance of each node of the distribution network, and constructing a lower-level scheduling optimization model; The lower-level scheduling optimization model is optimized and solved according to the initial power flow distribution to obtain a second scheduling optimization plan; the second scheduling optimization plan 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 consumption accessory of the electric vehicle; Determining the load demand model of the electric vehicle according to the 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 road 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 according to the shortest path, the output power of each energy-consuming accessory 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: 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, to construct an upper-level scheduling optimization model, including: Determine a total operating cost function of the distribution network according to the carbon emission cost, charging cost, electricity purchase cost, wind abandonment cost and solar abandonment cost of the distribution network; Taking the minimum total operating cost of the distribution network as the optimization target of the total operating cost function, determining a first objective function; The first constraint condition of the first objective function is determined by the steady-state balance of operation of the distribution network, and the first constraint condition 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.

4. 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-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.

5. 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 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-level scheduling optimization model is constructed, including: Determining a network loss calculation function of the distribution network according to power flow distribution information of each node of the distribution network; Taking minimizing the network loss of the distribution network as the optimization target of the network loss calculation function, determining a second objective function; The second constraint condition of the second objective function is determined by the steady-state balance of operation of each node of the distribution network, and the second constraint condition includes distribution network flow constraint, node safety operation limit constraint, node charging pile capacity limit constraint and distribution network EV quantity limit constraint.

6. 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 5, characterized in that: The step of optimizing and solving the upper-layer scheduling optimization model according to the load demand and electricity price information of the electric vehicle in the current period to obtain a first scheduling optimization solution includes: Based on the HO-SFOA hybrid search algorithm, the upper-level scheduling optimization model is optimized and solved according to the load demand of the electric vehicle in the current period and the electricity price information until the iteration converges to obtain the first scheduling optimization solution.

7. 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 5, 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-layer 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.

8. 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, used to determine the load demand model of the electric vehicle according to 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; An upper-level scheduling module is used to take the minimum 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; The upper-level scheduling solution module is used to optimize and solve the upper-level scheduling optimization model according to the load demand of the electric vehicle in the current period and the electricity price information to obtain a first scheduling optimization plan; the first scheduling optimization plan includes the charging power, power purchase, wind abandonment and solar abandonment of the electric vehicle in each period; A power flow distribution determination 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; A lower-layer scheduling module, used to take the minimum network loss of the distribution network as the second optimization target, and determine the second constraint condition of the second optimization target by the steady-state balance of each node of the distribution network, so as to construct a lower-layer scheduling optimization model; 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 plan; the second scheduling optimization plan includes the number of electric vehicles at each node of the distribution network in each time period.

9. 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 7.

10. 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 7 are implemented.

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