Planning method considering flexible access of electric vehicle to power distribution network

Abstract

The invention discloses a planning method considering flexible access of an electric vehicle to a power distribution network, and the method specifically comprises the steps: building a model of flexibility resource adjustment capability and / or calling cost according to the characteristics of flexibility resources; wherein the flexible resources comprise stored energy, loads and electric vehicles; on the basis, taking the minimum flexible demand and the optimal economical efficiency as targets, adding a real-time flexible supply and demand balance constraint, and establishing a flexible resource optimization scheduling model; and solving the established flexible resource optimization scheduling model by adopting a genetic algorithm introduced with a particle swarm algorithm to obtain an optimal flexible resource scheduling result. According to the invention, quantitative analysis of the flexibility of the electric vehicle is carried out, the upper and lower standby capability of the electric vehicle is described, the upper and lower standby capability is used as the adjustable power upper and lower limits of the demand side flexibility resource during power distribution network planning, and the flexible resource scheduling method is provided.

Description

Technical Field

[0001] The present invention relates to a planning method for considering the flexible access of electric vehicles to the distribution network, belonging to the technical field of distribution network planning. Background Technique

[0002] Under the "dual carbon" goal, the proportion of photovoltaic power in the distribution network is gradually increasing, and the volatility of photovoltaic output has caused a certain degree of impact on the stable operation of the distribution network. The load on the demand side of the power system also increases with the improvement of the electrification level. Under the new power system, the resources on the demand side will become grid-side friendly interactive resources, including electric vehicles, heating ventilation and air conditioning (HVAC) systems, etc. However, these resources need to be aggregated through virtual power plant technology to adjust the power load resources on the demand side, so as to improve the flexibility of the power grid.

[0003] Due to the characteristics of easy transmission and transformation of electric energy and the fact that it is cleaner than other forms of energy utilization, the power system plays an increasingly important role in the energy system. The uncertain factors in the power system are generally divided into random factors that can be simulated by statistical data, fuzzy factors that cannot be accurately predicted due to lack of information, and subjective uncertain factors that are difficult to quantitatively analyze by mathematical methods, covering distributed power generation output, power load, multi-load characteristics, economic parameters, etc. Traditional distribution network planning plans for distribution network grid structure, power source location selection, etc. based on deterministic power source and load scenarios, but lacks the planning of flexible resources on the demand side of the distribution network. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: to provide a planning method for considering the flexible access of electric vehicles to the distribution network, aiming at minimizing flexibility demand and optimizing economy, adding real-time flexibility supply-demand balance constraints, and establishing a flexibility optimization scheduling model for solution.

[0005] The present invention adopts the following technical solutions to solve the above technical problems:

[0006] A planning method for considering the flexible access of electric vehicles to the distribution network includes the following steps:

[0007] Step 1, according to the characteristics of flexible resources, establish a model of the regulation ability and / or call cost of flexible resources; the flexible resources include energy storage, load, and electric vehicles;

[0008] Step 2, on the basis of Step 1, aiming at minimizing flexibility demand and optimizing economy, add real-time flexibility supply-demand balance constraints, and establish a flexible resource optimization scheduling model;

[0009] Step 3, use a genetic algorithm introduced with a particle swarm algorithm to solve the flexible resource optimization scheduling model established in Step 2 to obtain the optimal flexible resource scheduling result.

[0010] Compared with the prior art, the present invention adopting the above technical solution has the following technical effects:

[0011] The present invention takes into account the uncertainties of energy storage charging and discharging, demand-side management, and vehicle-to-grid (V2G) of electric vehicles, and establishes an optimization scheduling model for flexible resources; aiming at minimizing flexible demand and optimizing economy, real-time flexible supply-demand balance constraints are added. The method of the present invention can enhance the advantages of the flexibility of the distribution network, well describe the flexibility of the distribution network, and can perform quantitative characterization. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 is a schematic flow chart of a planning method for considering the flexible access of electric vehicles to the distribution network according to the present invention;

[0013] Figure 2 is the solution process of the optimization scheduling model for flexible resources according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] The following describes in detail the embodiments of the present invention, and the examples of the embodiments are shown in the drawings. The embodiments described below by referring to the drawings are exemplary and are only used to explain the present invention, and cannot be construed as limiting the present invention.

[0015] As Figure 1 shown, it is a schematic flow chart of a planning method for considering the flexible access of electric vehicles to the distribution network proposed by the present invention, and the specific steps are as follows:

[0016] S1. According to the characteristics of flexible resources, establish a model of the regulation capacity and / or call cost of flexible resources; wherein, the flexible resources include energy storage, load, and electric vehicles.

[0017] (1) The cost model of calling energy storage as a flexible resource is as follows:

[0018] C ESS = C ESS,O + C ESS,P

[0019]

[0020]

[0021] In the formula, C ESS is the operating cost of the energy storage device, C ESS,O is the operating loss cost paid for the energy storage device itself, C ESS,P is the sum of the charging cost, power purchase cost, and subsidy cost of the energy storage, T is the number of time instants, N ESS is the number of energy storages, P ESSC,i (t), P ESSD,i(t) is the charging and discharging power of the i-th energy storage at time t, e E (t) is the time-of-use electricity purchase price at time t, e S is the unit subsidy price for the power grid to purchase electricity from the energy storage, τ is the duration of the scheduling period, e ESS is the energy storage purchase cost, M ESS is the number of charge-discharge cycle life times of the energy storage

[0022] (2) The load response demand at the load side and the load cut off due to system faults, etc. are all load uncertainties. The load-side flexibility demand of the present invention mainly considers the latter, and its load shedding amount is determined by the real-time operating state of the distribution network. When the system loses some load, the flexibility demand is downward. The load regulation ability model is as follows:

[0023] F L (t) = P LOAD - P L,F (t)

[0024] F L (t) = P LOAD L OLP

[0025]

[0026] In the formula, F L (t) is the system load, P LOAD is the total system load, P L,F (t) is the load shedding amount at time t, L OLP is the load shedding probability, is the downward flexibility demand.

[0027] (3) The regulation ability of electric vehicles and the cost model of calling electric vehicles as flexibility resources are as follows:

[0028] 1) The expressions for the upward or downward flexible regulation ability of electric vehicles are:

[0029]

[0030] In the formula, flexS DG,t,+ and flexS DG,t,- are the upward and downward regulation abilities of a single electric vehicle at time t respectively; P DG,c (t) and P DG,d (t) are the charging power and discharging power of the electric vehicle battery at time t respectively; SOC DG,max and SOC DG,min are the upper and lower limits of the electric vehicle battery power respectively; SOC DG (t) is the power stored in the electric vehicle battery at time t, τ is the duration of the scheduling period;

[0031] 2) The cost expression for calling electric vehicles as flexibility resources is as follows:

[0032]

[0033] In the formula, C DG is the total cost of providing flexible regulation capabilities by calling all dispatchable electric vehicles, N DG is the number of electric vehicles, and H C is the total dispatch time; represents the action of the nth electric vehicle at time t. When , it means the electric vehicle is in an off-grid state. When , it means the electric vehicle discharges to the power grid according to the dispatch demand. When , it means the electric vehicle charges from the power grid according to the dispatch demand; P DG is the instantaneous power of the electric vehicle, τ is the duration of the dispatch period, and e E (t) is the time-of-use electricity purchase price at time t.

[0034] S2. On the basis of S1, with the goal of minimizing flexibility demand and optimizing economy, a real-time flexibility supply-demand balance constraint is added to establish a flexibility resource optimal dispatch model.

[0035] 1) The objective function of the flexibility resource optimal dispatch model is as follows:

[0036]

[0037] In the formula, C1(X) is the operation cost of flexibility resources, Δt is the total dispatch duration, T is the number of time instants, N ESS is the number of energy storages, e E (t) is the time-of-use electricity purchase price at time t, C ESS is the capacity cost coefficient of the energy storage, M ESS is the number of charge-discharge cycle lives of the energy storage, P ESSC,i (t), P ESSD,i (t) are the charging and discharging powers of the ith energy storage at time t respectively, C sp is the subsidy price given by the power grid for the energy storage to discharge, N CL is the number of interruptible loads, is the contract price signed by the interruptible load, is the interruption amount of the jth interruptible load at time t.

[0038] 2) The constraint conditions for flexibility resource dispatch are as follows:

[0039] (1) Power balance constraint

[0040]

[0041]

[0042] Wherein, is the active power of the connecting line between the distribution network and the infinite power grid, and N DG is the number of electric vehicles, is the active power of the electric vehicle, and N ESS is the number of energy storages, is the charging and discharging time of the energy storage, is the power of the i-th energy storage at time t, N is the number of nodes, and P l,t is the active power of the node, and P loss is the power loss of the system, is the reactive power of the distribution network, is the reactive power of the electric vehicle, and N CL is the number of interruptible loads, is the reactive power of the interruptible load, and Q l,t is the reactive power of the node;

[0043] (2) Interruptible load quantity constraint

[0044]

[0045] Wherein, and are respectively the lower limit and the upper limit of the interruptible load, is the interruption amount of the j-th interruptible load at time t;

[0046] (3) Interruptible load interruption time constraint

[0047]

[0048] Wherein, is the maximum value of the interruptible time stipulated in the interruptible load contract, is the interruption time of the interruptible load;

[0049] (4) Energy storage state of charge constraint

[0050] SOC min ≤SOC≤SOC max

[0051] Wherein, SOC min and SOC max are respectively the lower limit and the upper limit of the energy storage SOC, and SOC is the state of charge of the energy storage;

[0052] (5) Real-time flexibility balance constraint

[0053]

[0054] In the formula, is the real-time flexibility balance demand rate, is the real-time flexibility balance allowable demand rate.

[0055] S3. Use the genetic algorithm introducing the particle swarm optimization algorithm to solve the flexibility resource optimization scheduling model established in S2, and obtain the optimal flexibility resource scheduling result.

[0056] By improving the crossover probability and mutation probability of the GA algorithm, introducing the crossover and mutation operations of the improved GA algorithm into the PSO algorithm, using the elite strategy to expand the sample space and select the best, finally obtain the optimization of the GAPSO algorithm with better performance than the GA algorithm and the PSO algorithm. The steps are as follows:

[0057] (1) Input original data such as network data, electric vehicle load, photovoltaic output, energy storage device parameters, etc.;

[0058] (2) Set parameters such as the particle swarm acceleration factor, inertia factor, convergence accuracy, maximum number of iterations, boundary values of position and velocity, etc., initialize the individual optimal value and the global optimal value, initialize the position and velocity of the particles, and form the initial population;

[0059] (3) Sort the individuals and perform the operations of crossover and mutation;

[0060] (4) Update the velocity and position of the particles and check the constraint conditions;

[0061] (5) Calculate the flexibility demand and the flexibility resource supply capacity, and calculate the fitness according to the objective function formula;

[0062] (6) Perform global search and update the individual optimal value and the global optimal value;

[0063] (7) Judge whether the convergence accuracy or the maximum number of iterations is reached. If satisfied, go to (8); if not, go to (3);

[0064] (8) Output the optimal flexibility resource scheduling result, calculate the flexibility evaluation index, and the specific algorithm flow is as Figure 2 shown.

[0065] According to the flexibility supply-demand balance in the planning operation simulation of the distribution network, select appropriate measurement indexes and grid operation flexibility evaluation indexes for evaluation.

[0066] 1. Real-time flexibility supply-demand balance measurement index:

[0067] Considering the balance of flexibility supply and demand in the planning and operation simulation of the distribution network, a set of indicators for measuring the real-time balance of flexibility supply and demand needs to be established. The present invention proposes two time-varying indicators, namely the net load volatility and the maximum allowable net load volatility, to evaluate the system's ability to support uncertain fluctuations and the flexibility in the direction and time dimensions.

[0068] The real-time flexibility demand rate refers to the ratio of the flexibility demand to the net load per unit time, which characterizes the intensity of the flexibility demand, as shown in the following formula:

[0069]

[0070] In the formula, FL t is the flexibility demand of the system at time t, and NL t is the net load at time t;

[0071] Flexible scheduling resources should, while taking into account economy, quickly and timely respond to the flexibility demand of the system to achieve real-time flexibility supply and demand balance. In the distribution network, the main sources of flexibility demand lie in the large-scale access of distributed power sources, the growing electric vehicle load, and the original system load, which can be defined as the change of the net load jointly composed of these three on a fixed time scale.

[0072] FL t = NL t+1 - NL t , t = 1,..., T

[0073] In the formula, T is the number of moments divided according to a certain time scale.

[0074] The real-time flexibility balance allowable demand rate is the ability of the system to respond to the flexibility demand, and the formula is as follows:

[0075]

[0076] In the formula, N flexS is the number of flexibility resources, is the flexible regulation ability of the kth flexibility resource at time t; when it means that the flexibility resources and demand are balanced at this time; otherwise, it means that the flexibility balance is not satisfied.

[0077] 2. Evaluation indicators for the flexibility of grid operation:

[0078] The line load rate refers to the ratio of the actual value transmitted by the line at a certain moment to the maximum allowable transmission capacity, which describes the flexibility of the grid to withstand the net load fluctuation.

[0079]

[0080] In the formula, is the load rate of the g-th distribution line at time g; P max,lg is the maximum transmission capacity of line g; is the actual transmitted power of line g at time t; It shows that the line has sufficient capacity margin and certain flexibility. It shows that the line is blocked and lacks flexibility, and the line needs to be upgraded.

[0081] The spatio-temporal distribution uniformity of the line load rate is positively correlated with the ability of the power grid framework structure to withstand uncertain factors, and the higher the reliability of the system operation. The maximum value of the line balance index corresponding to all operating modes of the distribution network is used as the index F to measure the flexibility abundance of the framework operation. NS That is

[0082] F NS = max(E1, E2)

[0083] In the formula, E1 and E2 respectively represent the range and standard deviation of the load rates of each line in the distribution network.

[0084] The present invention designs two scheduling strategies: a distributed scheduling strategy and a centralized scheduling strategy. Under the distributed scheduling strategy, each flexibility resource and its own target of action cooperate respectively. When centralized scheduling, all flexibility resources act on the overall net load of the system, and the effects of the two strategies in improving the flexibility of the distribution network are compared.

[0085] Distributed scheduling strategy:

[0086] Adopt a strategy of coordinating photovoltaic-storage and electric vehicle-storage, optimize the real-time charge and discharge power of the energy storage and the real-time interruption power of the interruptible load, and adopt different control targets for the photovoltaic-storage station, electric vehicle-storage station, and interruptible load respectively, so as to meet with the smallest economic cost:

[0087] (1) The energy storage of the photovoltaic-storage station minimizes the flexibility demand rate of the net load composed of the photovoltaic station and the system load;

[0088] (2) The energy storage of the electric vehicle-storage station minimizes the flexibility demand rate of the charging load of electric vehicles accessing the grid. When the energy storage is charging, it cooperates with the overall net load of the system to minimize its flexibility demand rate;

[0089] (3) The interruption of the interruptible load minimizes the flexibility demand rate of the overall net load of the system.

[0090] Centralized scheduling strategy:

[0091] When the energy storage is bundled with the photovoltaic / electric vehicle station, the power of the energy storage and its acting body is merged into the DC bus, and then connected to the AC network through a DC / AC converter.

[0092] Therefore, when optimizing the siting of DC power supplies and loads during the later-stage planning process and selecting the overall scheduling strategy for the scheduling plan, the PV energy storage station or the electric vehicle - energy storage station is regarded as the overall entity to be planned. When selecting the distributed scheduling strategy, the PV station, the energy storage station, and the electric vehicle station are regarded as independent nodes to be planned respectively.

[0093] The comparison of the overall flexibility evaluation indicators of the two strategy schemes is shown in Table 1:

[0094] Table 1 Comparison of Overall Flexibility Evaluation Indicators

[0095] Before scheduling Overall scheduling strategy Distributed scheduling strategy Average flexibility demand degree 6.452 3.376 2.512 Average flexibility allowed demand degree / 142.351 147.745 Average flexibility abundance degree / 138.334 139.522

[0096] From the real-time net load volatility, the maximum allowable volatility, and the source-load-storage flexibility supply-demand balance indicators over the entire time scale, the advantages and disadvantages of the traditional resource scheduling scheme and the unified flexibility resource scheduling scheme are compared, which proves the advantages of the proposed scheduling scheme of the invention in improving the flexibility of the distribution network. It is proved that the real-time flexibility supply-demand balance indicator can be well applied to optimize the scheduling, and the source-load-storage flexibility supply-demand balance indicator can well describe the flexibility of the distribution network and can be quantitatively characterized.

[0097] Based on the same inventive concept, an embodiment of the present application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the aforementioned planning method for considering the flexible access of electric vehicles to the distribution network are implemented.

[0098] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the aforementioned planning method for considering the flexible access of electric vehicles to the distribution network are implemented.

[0099] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0100] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 a block or multiple blocks.

[0101] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 a block or multiple blocks.

[0102] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 a block or multiple blocks.

[0103] The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention cannot be limited thereby. Any modification made on the basis of the technical solution according to the technical idea proposed by the present invention falls within the protection scope of the present invention.

Claims

1. A planning method considering the flexible access of electric vehicles to the distribution network, characterized in that, It includes the following steps: Step 1: Establish a model for the regulation capacity and / or call cost of flexible resources according to the characteristics of flexible resources. The flexible resources include energy storage, load, and electric vehicles; Step 2: On the basis of Step 1, with the goal of minimizing flexible demand and optimizing economy, add real-time flexible supply-demand balance constraints to establish an optimal scheduling model for flexible resources; Step 3: Use a genetic algorithm introduced with a particle swarm algorithm to solve the optimal scheduling model for flexible resources established in Step 2 to obtain the optimal flexible resource scheduling result.

2. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, characterized in that In Step 1, the cost model for calling energy storage as a flexible resource is as follows: C ESS = C ESS,O + C ESS,P Where, C ESS is the operating cost of the energy storage device, C ESS,O is the operating loss cost for paying the energy storage device itself, C ESS,P is the sum of the charging cost, electricity purchase cost and subsidy cost of the energy storage, T is the number of time instants, N ESS is the number of energy storages, P ESSC,i (t), P ESSD,i (t) are the charging and discharging powers of the i-th energy storage at time t respectively, e E (t) is the time-of-use unit price of electricity purchase at time t, e S is the unit subsidy price for the power grid to purchase electricity from the energy storage, τ is the duration of the dispatching period, e ESS is the purchase cost of the energy storage, M ESS is the number of charge-discharge cycle lives of the energy storage.

3. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, characterized in that, In Step 1, the regulation capacity model of the load is as follows: F L f(t) = P LOAD -P L,F f(t) F L (t) = P LOAD L OLP Where, F L (t) is the system load, P LOAD is the total system load, P L,F (t) is the load shedding amount at time t, L OLP is the probability of load shedding, is the downward flexibility requirement.

4. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, wherein In Step 1, the regulation capacity of electric vehicles and the cost model for calling electric vehicles as flexible resources are as follows: (1) The expression for the upward or downward flexible regulation capacity of electric vehicles is: where flexS DG,t,+ and flexS DG,t,- are the upward and downward regulation capabilities of a single electric vehicle at time t, respectively; P DG,c (t) and P DG,d (t) are the charging power and discharging power of the electric vehicle battery at time t, respectively; SOC DG,max and SOC DG,min are the upper and lower limits of the electric vehicle battery charge, respectively; SOC DG (t) is the amount of electricity stored in the electric vehicle battery at time t, and τ is the duration of the scheduling period; (2) The cost expression for calling electric vehicles as flexible resources is: Where, C DG is the total cost of providing flexible regulation capabilities for calling all dispatchable electric vehicles, N DG is the number of electric vehicles, H C is the total scheduling time; represents the action of the nth electric vehicle at time t. When , it means the electric vehicle is in an off-grid state. When , it means the electric vehicle discharges to the power grid according to the scheduling demand. When , it means the electric vehicle charges from the power grid according to the scheduling demand; P DG is the instantaneous power of the electric vehicle, τ is the duration of the scheduling period, and e E (t) is the time-of-use electricity purchase unit price at time t.

5. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, wherein In Step 2, the objective function of the optimal scheduling model for flexible resources is: Where, C1(X) is the operating cost of flexible resources, Δt is the total scheduling duration, T is the number of time instants, N ESS is the number of energy storages, e E (t) is the time-of-use electricity purchase price at time t, C ESS is the capacity cost coefficient of the energy storage, M ESS is the number of charge-discharge cycles of the energy storage, P ESSC,i (t), P ESSD,i (t) are the charging and discharging powers of the i-th energy storage at time t, respectively, C sp is the subsidy price given by the power grid for the energy storage to discharge, N CL is the number of interruptible loads, is the contract price signed by the interruptible load, is the interruption amount of the j-th interruptible load at time t.

6. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, characterized in that In Step 2, the specific flexible supply-demand balance constraints are as follows: (1) Power balance constraint Where, P t G is the active power of the connection line between the distribution network and the infinite power grid, N DG is the number of electric vehicles, is the active power of the electric vehicle, N ESS is the number of energy storages, is the charge and discharge time of the energy storage, is the power of the i-th energy storage at time t, N is the number of nodes, P l,t is the active power of the node, P loss is the power loss of the system, is the reactive power of the distribution network, is the reactive power of the electric vehicle, N CL is the number of interruptible loads, is the reactive power of the interruptible load, Q l,t is the reactive power of the node; (2) Interruptible load quantity constraint wherein, and are respectively the lower limit and the upper limit of the interruptible load, is the interruption amount of the j-th interruptible load at the t-th moment; (3) Interruptible load interruption time constraint wherein, is the maximum value of the interruptible time specified in the interruptible load contract, is the interrupt time of the interruptible load; (4) Energy storage state of charge constraint SOC min ≤SOC≤SOC max where SOC min and SOC max are the lower and upper limits of the energy storage SOC respectively, and SOC is the state of charge of the energy storage; (5) Real-time flexibility balance constraint In the formula, is the real-time flexibility balance demand rate, is the real-time flexibility balance allowable demand rate.

7. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 6, characterized in that In the real-time flexibility balance constraint, the real-time flexibility balance demand rate is the ratio of flexible demand and net load per unit time, and the formula is as follows: where FL t is the flexibility requirement at time t, and NL t is the net load at time t; The real-time flexibility balance allowable demand rate is the ability of the system to respond to flexible demand, and the formula is as follows: Where N flexS is the number of flexibility resources, is the flexible regulation capacity of the k-th flexibility resource at time t; when it means that the real-time flexibility balance constraint is satisfied; otherwise it means that the constraint is not satisfied.

8. The planning method for considering the flexible access of electric vehicles to the distribution network according to claim 1, characterized in that The specific process of Step 3 is as follows: Step 3.1: Set parameters such as particle swarm acceleration factor, inertia factor, convergence accuracy, maximum number of iterations, and boundary values of position and velocity, initialize the individual optimal value and the global optimal value, and initialize the position and velocity of the particles to form an initial population; Step 3.2: Sort the individuals and perform crossover and mutation operations; Step 3.3: Update the velocity and position of the particles and check the constraint conditions of flexible resources; Step 3.4: Calculate the flexible demand and flexible resource supply capacity, and calculate the fitness according to the objective function of the optimal scheduling model for flexible resources; Step 3.5: Perform global search and update the individual optimal value and the global optimal value; Step 3.6: Determine whether the convergence accuracy or the maximum number of iterations is reached. If the convergence accuracy or the maximum number of iterations is reached, output the optimal flexible resource scheduling result; otherwise, return to Step 3.2 to continue the iteration.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the planning method for considering the flexible access of electric vehicles to the distribution network as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the planning method for considering the flexible access of electric vehicles to the distribution network as described in any one of claims 1 to 8.

Images