An active and reactive power coordinated optimization scheduling method, system and device for electric vehicle load capacity improvement and a medium

Abstract

The present application relates to the technical field of active and reactive power coordinated optimization scheduling of power distribution network, and particularly relates to a method, system, device and medium for active and reactive power coordinated optimization scheduling of power distribution network for electric vehicle carrying capacity improvement. A power interaction constraint model of photovoltaic, static var generator, energy storage device, flexible interconnection soft switch, temperature control load, electric vehicle and power distribution network is constructed; a minimum active regulation cost and minimum load balancing degree are taken as upper layer targets, and a minimum voltage out-of-limit risk and a maximum number of electric vehicles accepted by the power distribution network are taken as lower layer core targets, so as to construct an active and reactive power decoupling scheduling model; an improved sparrow search algorithm combining sine and cosine search and Cauchy mutation is adopted to perform upper and lower layer interactive iteration solution, and a multi-element flexible resource active and reactive power regulation plan is output, so as to improve the carrying capacity of electric vehicle charging load under the premise of ensuring the safe operation of the power distribution network.

Description

Technical Field

[0001] This invention relates to the field of active and reactive power coordinated optimization scheduling technology for distribution networks, and in particular to a method, system, equipment, and medium for coordinated optimization scheduling of active and reactive power in distribution networks aimed at improving the carrying capacity of electric vehicles. Background Technology

[0002] With the continuous expansion of electric vehicle (EV) access, disorderly charging behavior has led to power quality problems such as voltage exceeding limits, line overload, and heavy overload of distribution transformers in the distribution network. The capacity of the distribution network to bear the EV charging load has been compromised. At the same time, diverse flexible resources such as distributed photovoltaic (PV), energy storage devices, static var generators (SVG), soft-open-point (SOP) flexible interconnection, and temperature-controlled loads have been widely deployed in the distribution network, possessing the potential for flexible regulation in both active and reactive power dimensions. How to fully utilize the complementary characteristics of diverse flexible resources to improve the EV carrying capacity of the distribution network is an urgent problem to be solved.

[0003] Regarding the improvement of electric vehicle carrying capacity in distribution networks, existing research mainly focuses on modeling charging behavior characteristics and assessing carrying capacity, with relatively simple control methods and insufficient consideration of the effects of joint control of multiple resources in the "source-grid-load-storage" links. In terms of coordinated control of multiple flexible resources, existing research primarily focuses on optimizing the active power output of single-type distributed power sources or energy storage, or only improves node voltage from the perspective of reactive power compensation, lacking a systematic solution for coordinated optimization of both active and reactive power dimensions, making it difficult to unleash the overall control potential of multiple resources. Furthermore, the coordinated optimization problem of active and reactive power in distribution networks is a large-scale, multi-objective, mixed-integer nonlinear programming problem, and traditional analytical methods and single intelligent optimization algorithms have significant limitations in terms of solution accuracy and convergence stability. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention is proposed.

[0005] Therefore, the problem to be solved by this invention is how to address the insufficient active and reactive power coordinated regulation capability of existing power distribution networks with multiple flexible resources, the difficulty in improving the carrying capacity of electric vehicles, and the problem that intelligent algorithms are prone to getting trapped in local optima in large-scale multi-objective nonlinear optimization problems.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: In a first aspect, embodiments of the present invention provide a method for coordinated and optimized scheduling of active and reactive power in a power distribution network to improve the carrying capacity of electric vehicles, comprising, Based on the differences in active and reactive power regulation of various flexible resources, power interaction constraint models between various resources and the distribution network are constructed respectively. Based on the aforementioned power interaction constraint model, with the minimum active power regulation cost and load balance as the upper-level optimization objectives, and the voltage safety level and the maximum absorption capacity of the distribution network for electric vehicle charging load as the lower-level core optimization objectives, a hierarchical decoupled scheduling model for active and reactive power is constructed. An improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation is used to perform interactive iterative solutions between the upper and lower layers of the active and reactive power decoupled scheduling model, and output a multi-dimensional flexible resource active and reactive power control plan.

[0007] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, the step of constructing power interaction constraint models for various resources and power distribution networks includes determining the upper and lower limits of adjustable reactive power of photovoltaics based on the proportion of photovoltaic active power output to the rated capacity of inverters, thereby obtaining photovoltaic reactive power interaction constraints. Based on the range of reactive power that the static var generator can continuously absorb or emit, the upper and lower limits of the reactive power of the static var generator are determined, and the reactive power interaction constraint of the static var generator is obtained. Based on the state of charge and rated capacity of the energy storage device, the range of active power and the upper and lower limits of reactive power for charging and discharging of the energy storage device are determined, and the active and reactive power interaction constraints of the energy storage device are obtained. Based on the upper limit of the allowable current carrying capacity of the flexible interconnection soft switch, the active power transmission range and the upper and lower limits of the reactive power of the flexible interconnection soft switch are determined, and the active and reactive power interaction constraints of the flexible interconnection soft switch are obtained. Based on the thermal comfort requirements of the temperature-controlled load, the range of active power and upper and lower limits of reactive power of the temperature-controlled load are determined, and the active and reactive power interaction constraints of the temperature-controlled load are obtained. The Monte Carlo method is used to randomly sample the starting charging time, off-grid time, and daily mileage of electric vehicles to determine the initial state of charge and allowable charging time window of each electric vehicle when it is connected to the grid. Then, the range of active power and the upper and lower limits of reactive power in the orderly charging stage are determined, and the active and reactive power interaction constraints of electric vehicles are obtained.

[0008] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, the construction of the hierarchical decoupled scheduling model for active and reactive power includes: using the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles as control variables, and using the minimum operating cost of active power regulation of multiple resources, the minimum dispersion of active power distribution of each node and the minimum daily peak-valley difference as the upper-level objective function to construct an upper-level active power optimization scheduling model; Using the reactive power of photovoltaics, static var generators, flexible interconnection soft switches, energy storage devices, temperature-controlled loads, and electric vehicles as control variables, and taking the minimum voltage over-limit risk index, the minimum reactive power regulation cost, the minimum network loss, and the maximum number of electric vehicles that the distribution network can accommodate as the lower-level objective functions, a lower-level reactive power optimization scheduling model is constructed. The active power scheduling results from the upper layer are passed to the lower layer as fixed boundary conditions, and the reactive power scheduling results and fitness values ​​from the lower layer are returned to the upper layer, thus completing the data transfer between the upper and lower layers.

[0009] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, the constraints of the upper-level active power optimization scheduling model include: transmission capacity constraints of flexible interconnection soft switches, charging and discharging power constraints of energy storage devices and consistency constraints of the first and last charge states of the scheduling cycle, comfort constraints of temperature-controlled loads, charging time constraints within the range from the start charging time of the electric vehicle to the desired travel time, and power guarantee constraints that the battery capacity of the electric vehicle at the desired travel time is not less than the power required for the desired travel. The constraints of the lower-level reactive power optimization scheduling model include multi-time distribution network power flow constraints based on branch power flow equations, upper and lower limits of node voltage amplitude constraints, branch current thermal stability constraints, and distribution transformer load rate constraints.

[0010] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, the solution of the hierarchical decoupled scheduling model of active and reactive power distribution networks includes: in the upper active power optimization scheduling stage, the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles is randomly encoded into the initial position matrix of the population within their respective control ranges, the fitness value of each individual is calculated, the global optimal solution and the worst solution are determined, and the active power control results and fitness information are transmitted to the lower reactive power optimization scheduling stage; In the lower-level reactive power optimization and scheduling stage, after receiving the active power boundary transmitted from the upper level, the reactive power of photovoltaic, static var generator, flexible interconnect soft switch, energy storage equipment, temperature control load and electric vehicle is randomly encoded into the initial position matrix of the population. The position of the discoverer is updated using sine and cosine search. Random perturbation following the standard Cauchy distribution is introduced into the current best individual. The position update and fitness evaluation are performed iteratively until the lower-level objective function converges or the maximum number of iterations is reached. The reactive power regulation result and fitness value are then returned to the upper level. After receiving feedback from the lower layer, the upper layer updates the population position and recalculates the fitness value. The upper and lower layers repeat the interactive solution until the upper layer objective function converges or reaches the maximum number of iterations, and outputs a multi-dimensional flexible resource active and reactive power control plan.

[0011] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, the method of updating the location of the discoverer using sine and cosine search includes introducing a control factor that decreases linearly with the number of iterations. When the control factor is large in the early stage of the search, the discoverer is driven to perform a large-scale location update in the solution space by combining sine and cosine random parameters, thereby improving the diversity of population search. As the number of iterations increases, the control factor gradually decreases, and the range of the discoverer's location update shrinks towards the neighborhood of the current best individual in order to improve the local fine search capability; When the warning value exceeds the safety threshold during the search process, the discoverer's location is adjusted to a relatively safe area; when the warning value is below the safety threshold, the discoverer's location is updated to a direction with better fitness.

[0012] As a preferred embodiment of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in this invention, wherein: the introduction of a random disturbance following a standard Cauchy distribution to the current optimal individual includes, in each iteration, applying a random disturbance following a standard Cauchy distribution to the position vector of the current optimal individual with a variation intensity factor that decreases with the number of iterations, to obtain a new individual position vector; Calculate the fitness value of the new individual. If the fitness value of the new individual is better than that of the current best individual, replace the current best individual with the new individual; otherwise, retain the current best individual.

[0013] Secondly, embodiments of the present invention provide a distribution network active and reactive power coordinated optimization scheduling system for improving the carrying capacity of electric vehicles, which includes an interactive model construction module for establishing power interaction constraint models between various resources and the distribution network based on the differences in active and reactive power regulation of multiple flexible resources. The hierarchical scheduling model construction module is used to construct an active and reactive power hierarchical decoupled optimization scheduling model based on the power interaction constraint model. The optimization solution module is used to perform interactive iterative solution of the active and reactive power hierarchical decoupled optimization scheduling model by adopting an improved swarm intelligence algorithm that integrates a global exploration diversity enhancement mechanism and an elite individual local escape strategy, and outputs a multi-dimensional flexible resource active and reactive power coordination and control plan.

[0014] Thirdly, embodiments of the present invention provide a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, they implement the steps of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in the first aspect of the present invention.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, they implement the steps of the active and reactive power coordinated optimization scheduling method for improving the carrying capacity of electric vehicles in the distribution network as described in the first aspect of the present invention.

[0016] The beneficial effects of this invention are as follows: By establishing power interaction constraint models for photovoltaics, static var generators, energy storage devices, flexible interconnection soft switches, temperature-controlled loads, and electric vehicles respectively, the actual adjustable boundaries of various resources in each scheduling period are accurately characterized, avoiding infeasible solutions due to boundary setting distortion in subsequent optimizations; On this basis, the maximum number of electric vehicles absorbed by the distribution network is used as the lower-level objective function rather than the constraint condition, so that the regulation of reactive power resources changes from passively satisfying voltage constraints to actively improving the carrying capacity limit, solving the problem that existing methods are difficult to systematically improve the carrying capacity of electric vehicles in the distribution network because they only focus on single resource or single-dimensional regulation; Furthermore, an improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation is adopted. The former maintains the population's ability to explore a wide range in the early stage of the search by adaptively decreasing control factors, while the latter uses the heavy-tailed characteristics of Cauchy distribution to perturb the current best individual to escape local extrema, making the algorithm more stable in large-scale multi-objective mixed integer nonlinear programming problems; Overall, under the premise of satisfying the safety constraints of node voltage amplitude, branch current thermal stability, and transformer load rate, the coordinated regulation of active and reactive power of multiple flexible resources is achieved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart for a distribution network active and reactive power coordinated optimization scheduling method aimed at improving the carrying capacity of electric vehicles; Figure 2 A computer equipment diagram for a distribution network active and reactive power coordinated optimization scheduling method aimed at improving the carrying capacity of electric vehicles; Figure 3 A flowchart for solving the active and reactive power coordinated optimization scheduling model of the distribution network for improving the carrying capacity of electric vehicles; Figure 4 A complete solution flowchart for the active and reactive power coordinated optimization scheduling method of distribution network for improving the carrying capacity of electric vehicles. Detailed Implementation

[0019] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0020] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0021] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0022] Example 1 Reference Figure 1 This is the first embodiment of the present invention, which provides a method for coordinated optimization scheduling of active and reactive power in power distribution networks to improve the carrying capacity of electric vehicles, including: S100: Based on the differences in active and reactive power regulation of various flexible resources, power interaction constraint models between various resources and the distribution network are constructed respectively.

[0023] S200: Based on the power interaction constraint model, with the minimum active power regulation cost and load balance as the upper-level optimization objectives, and the voltage safety level and the maximum absorption capacity of the distribution network for electric vehicle charging load as the lower-level core optimization objectives, a hierarchical decoupled scheduling model for active and reactive power is constructed.

[0024] S300: An improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation is used to perform interactive iterative solutions on the active and reactive power hierarchical decoupled scheduling model, and output a multi-dimensional flexible resource active and reactive power control plan.

[0025] It should be noted that disorderly charging of electric vehicles causes problems such as node voltage exceeding limits, line overload, and transformer overload in the power distribution network. Furthermore, the characteristics of various flexible resources, such as photovoltaics, static var generators, energy storage devices, flexible interconnection soft switches, and temperature-controlled loads, differ in both active and reactive power regulation. If a single-layer unified optimization is adopted, the high dimensionality of decision variables and complex constraints make it a large-scale mixed-integer nonlinear programming problem, making it difficult to obtain a stable solution using traditional methods. At the same time, existing research often focuses only on the regulation of a single resource or a single dimension, failing to fully leverage the overall regulation potential of multiple resources, thus limiting the improvement of the carrying capacity of electric vehicles.

[0026] To address the aforementioned issues, S100 establishes interactive constraint models for active and reactive power for photovoltaics, static var generators, energy storage devices, flexible interconnection soft switches, temperature-controlled loads, and electric vehicles, clarifying the control boundaries of various resources. S200 constructs a hierarchical decoupled scheduling model for active and reactive power, with the upper-level objectives of minimizing active power control costs and load balancing, and the lower-level core objectives of minimizing voltage exceedance risks and maximizing the number of electric vehicles that the distribution network can accommodate. This transforms the high-dimensional, strongly coupled unified optimization problem into a hierarchical problem solvable layer by layer. S300 employs an improved sparrow search algorithm that integrates sine and cosine search with Cauchy mutation for iterative solving between the upper and lower layers, ensuring the safe operation of the distribution network while enhancing the carrying capacity of electric vehicles.

[0027] Example 2 Reference Figures 1-4 This is the second embodiment of the present invention. Figure 3 This is a complete solution flowchart of the method proposed in this invention. The flowchart illustrates the following: S100 stage: constructing power interaction constraint models for six types of resources (photovoltaics, static var generators, energy storage devices, flexible interconnection soft switches, temperature-controlled loads, and electric vehicles) and the distribution network; S200 stage: constructing upper-level active power optimization scheduling models, lower-level reactive power optimization scheduling models, and determining the data transmission methods between the upper and lower levels; S300 stage: employing an improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation to perform iterative solutions for the upper and lower levels. The flowchart also shows two loop judgment nodes: lower-level convergence judgment and upper-level convergence judgment. (Refer to...) Figure 4 The complete solution process of this embodiment is as follows: First, in step S100, a power interaction constraint model of six types of resources and the distribution network is constructed to provide feasible domain boundaries for subsequent optimization; then, in step S200, an active and reactive power hierarchical decoupled optimization scheduling model is constructed, and an upper-level active power scheduling model (B1) and a lower-level reactive power scheduling model (B2) are established respectively, and the data transmission method between the two layers is determined (B3); finally, in step S300, an improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation is used to perform interactive iterative solution between the upper and lower layers. After the lower layer completes one round of reactive power iteration, it is judged whether it has converged or reached the maximum number of iterations. If not, it returns to C2 to re-execute reactive power optimization. If so, the result is returned to the upper layer; the upper layer updates the population accordingly and also judges whether it has converged or reached the maximum number of iterations. If not, it returns to C1 to re-execute active power optimization. If so, it outputs a multi-dimensional flexible resource active and reactive power coordination and control plan.

[0028] In this embodiment, step S100 constructs power interaction constraint models between various resources and the distribution network based on the differences in active and reactive power regulation of multiple flexible resources, including the following A1 steps: A1: Construct power interaction constraint models for various resources and the distribution network, including determining the upper and lower limits of adjustable reactive power of photovoltaics based on the proportion of photovoltaic active power output to the rated capacity of the inverter, and obtaining photovoltaic reactive power interaction constraints. Based on the range of reactive power that the static var generator can continuously absorb or emit, the upper and lower limits of the reactive power of the static var generator are determined, and the reactive power interaction constraint of the static var generator is obtained. Based on the state of charge and rated capacity of the energy storage device, the range of active power and the upper and lower limits of reactive power for charging and discharging of the energy storage device are determined, and the active and reactive power interaction constraints of the energy storage device are obtained. Based on the upper limit of the allowable current carrying capacity of the flexible interconnection soft switch, the active power transmission range and the upper and lower limits of the reactive power of the flexible interconnection soft switch are determined, and the active and reactive power interaction constraints of the flexible interconnection soft switch are obtained. Based on the thermal comfort requirements of the temperature-controlled load, the range of active power and upper and lower limits of reactive power of the temperature-controlled load are determined, and the active and reactive power interaction constraints of the temperature-controlled load are obtained. The Monte Carlo method is used to randomly sample the starting charging time, off-grid time, and daily mileage of electric vehicles to determine the initial state of charge and allowable charging time window of each electric vehicle when it is connected to the grid. Then, the range of active power and the upper and lower limits of reactive power in the orderly charging stage are determined, and the active and reactive power interaction constraints of electric vehicles are obtained.

[0029] Specifically, while a photovoltaic (PV) inverter supplies active power to the distribution network, its remaining capacity can be used for reactive power regulation. The reactive power output of the photovoltaic is simultaneously limited by the active power output at the current moment and the rated capacity of the inverter, achieving continuous regulation between the maximum reactive power output and the maximum reactive power absorption. The reactive power constraint of the i-th photovoltaic at time t is shown in formula (1): (1) In formula (1), Let be the reactive power of the i-th photovoltaic cell at time t. Let be the active power of the i-th photovoltaic cell at time t. Let be the rated capacity of the i-th photovoltaic cell at time t. and Let be the minimum and maximum reactive power output of the i-th photovoltaic (PV) unit, respectively. Within any scheduling period t, the upper limit of the adjustable reactive power of the PV unit is determined by subtracting the square of the active power output from the square of the rated capacity and taking the square root. When the active power output of the PV unit is close to the rated capacity, its available reactive power adjustment range is compressed to near zero. When the active power output is low, the PV inverter has a large reactive power margin and can provide strong reactive power support to the distribution network.

[0030] A static var generator (SVM) interacts with the reactive power of the power distribution network. The SVM can continuously absorb or release reactive power.

[0031] (2) Among them, Time of the first The reactive power of a static var generator; , These are the minimum and maximum reactive power of the static var generator, respectively.

[0032] By establishing the reactive power boundary of photovoltaic power in each scheduling period through formula (1), the value of photovoltaic reactive power variable in subsequent upper-level active power scheduling and lower-level reactive power scheduling is provided as a constraint basis, so that the optimization model can fully explore the reactive power regulation potential of photovoltaic power without violating the physical limitations of the inverter, and realize the coordinated utilization of active and reactive power resources of photovoltaic "source side".

[0033] The Monte Carlo method was used to randomly sample the initial charging time, off-grid time, and daily mileage of electric vehicles. The unordered charging model for electric vehicles included four cases: ① The starting charging time and the time of disconnection from the grid for electric vehicles approximately follow a log-normal distribution.

[0034] (3) (4) in, and The starting charging time and off-grid time for electric vehicles; and Expectations for the initial charging time and off-grid time of electric vehicles; and The standard deviation of the starting charging time and the off-grid time for electric vehicles.

[0035] ② Daily driving range of electric vehicles. The daily driving range of electric vehicles determines the initial battery capacity of electric vehicles. The daily driving range of electric vehicles approximately follows a log-normal distribution.

[0036] (5) in, This refers to the daily mileage of electric vehicles. Expected daily driving range for electric vehicles; This represents the standard deviation of the daily mileage of electric vehicles.

[0037] ③ Battery capacity of electric vehicles. The battery capacity of electric vehicles before and after driving is shown in formula (6).

[0038] (6) in, and The battery capacity of the i-th electric vehicle at the end of its last charge and the battery capacity after the last trip. Let be the battery capacity of the i-th electric vehicle; Let be the maximum driving range of the i-th electric vehicle.

[0039] ④ Electric vehicle charging time: The time required to charge an electric vehicle is the time interval between charging the initial battery capacity to the desired battery capacity.

[0040] (7) in, The time required to charge the i-th electric vehicle; Let be the desired battery capacity of the i-th electric vehicle; The charging power of the i-th electric vehicle; Let be the charging efficiency of the i-th electric vehicle.

[0041] In this embodiment, step S200 is based on the power interaction constraint model, with the minimum active power regulation cost and load balance as the upper-level optimization objectives, and the voltage safety level and the maximum absorption capacity of the distribution network for electric vehicle charging load as the lower-level core optimization objectives, to construct a hierarchical decoupled scheduling model for active and reactive power, including the following steps B1-B3: B1: Construct a hierarchical decoupled scheduling model for active and reactive power, including using the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles as control variables, and using the minimum operating cost of active power regulation of multiple resources, the minimum dispersion of active power distribution of each node and the minimum daily peak-valley difference as the upper-level objective function to construct an upper-level active power optimization scheduling model. Using the reactive power of photovoltaics, static var generators, flexible interconnection soft switches, energy storage devices, temperature-controlled loads, and electric vehicles as control variables, and taking the minimum voltage over-limit risk index, the minimum reactive power regulation cost, the minimum network loss, and the maximum number of electric vehicles that the distribution network can accommodate as the lower-level objective functions, a lower-level reactive power optimization scheduling model is constructed. The active power scheduling results from the upper layer are passed to the lower layer as fixed boundary conditions, and the reactive power scheduling results and fitness values ​​from the lower layer are returned to the upper layer, thus completing the data transfer between the upper and lower layers.

[0042] Specifically, the upper-level active power optimization scheduling model uses the SOP, energy storage equipment, temperature control load and electric vehicle active power within the scheduling period as control variables, and constructs a comprehensive objective function from three dimensions: active power regulation cost, load balance degree and daily peak-valley difference, as shown in formula (8): (8) In formula (8), In order to effectively control costs, For load balancing, The daily peak-to-valley difference, along with three sub-objective functions corresponding to the economic, uniform, and stable requirements of active power dispatching in the distribution network, are integrated into a unified upper-level objective through a weighted summation method. Active power regulation cost. The active operating cost coefficient of SOP, energy storage equipment, temperature control load and electric vehicle is measured by the sum of the products of their active power in each time period, as shown in formula (9): (9) In formula (9), T is the total scheduling period; , , and Let be the unit active power operating cost coefficients for the i-th SOP at time t, the energy storage device, the temperature control load, and the electric vehicle, respectively. , , and Let be the active power of the i-th SOP at time t, the energy storage device, the temperature control load, and the electric vehicle, respectively. , , and These represent the total number of SOPs, energy storage devices, temperature-controlled loads, and electric vehicles.

[0043] Load balancing It is used to measure the dispersion of active power distribution at each node in a distribution network, as shown in formula (10): (10) In formula (10), Let N be the active power of node k at time t, and N be the number of nodes in the distribution network. The smaller the value, the more uniform the active power distribution at each node, the more reasonable the power flow distribution of the distribution network, and the lower the risk of local overload on the feeders. Daily peak-valley difference It is used to measure the fluctuation range of active power load in a distribution network within a dispatching cycle. It is determined by the difference between the maximum and minimum active power at each time period at the feeder head within the dispatching cycle. The calculation method is shown in formula (11) and formula (12).

[0044] (11) (12) In formula (11), and These are the maximum and minimum active power loads of the distribution network during the scheduling period, respectively; in formula (12), This represents the active power of the load at time t at the feeder's head end. The smaller the daily peak-to-valley difference, the smoother the load curve, and the lower the system's peak-shaving pressure.

[0045] The constraints of the upper-level active power optimization scheduling model cover the active power control boundary constraints of each resource. The SOP operation constraints are determined by formula (13): (13) in, For the first Each SOP allows for a maximum capacity limit; and for Time of the first The reactive power and active power of each SOP; and They are respectively Time of the first The upper and lower limits of active power transmitted by each SOP; and They are respectively Time of the first The upper and lower limits of reactive power for each SOP.

[0046] The active power transmitted by the SOP must meet the upper and lower limit constraints, and the sum of the squares of the active power and reactive power shall not exceed the square of the upper limit of the allowable current carrying capacity. The charging and discharging active power of the energy storage device must meet the upper and lower limit constraints of the power in formula (14); (14) in, and The upper limit of the active and reactive power of the energy storage device i during charging and discharging at time t; Let be the rated capacity of energy storage device i at time t; and Let t be the lower and upper limits of the active power of energy storage device i during charging and discharging. and Let t be the lower and upper limits of the reactive power absorbed and emitted by the energy storage device i at time t.

[0047] Meanwhile, energy storage devices must also meet energy state transition constraints and power balance constraints at the beginning and end of the scheduling cycle, as shown in formula (15): (15) In formula (15), and Let be the battery capacity of energy storage device i at time t and time t+1, respectively. and Let be the charging efficiency and discharging efficiency of energy storage device i at time t, respectively. and These represent the upper and lower limits of the battery capacity of the energy storage device. and These represent the battery capacities at the start and end of the scheduling process, respectively. This constraint ensures that the state of charge of the energy storage device does not exceed the limit during the scheduling cycle, and that the energy level remains consistent before and after the scheduling. The operating constraints for temperature-controlled loads are determined by formulas (16) and (17). The operating power of temperature-controlled loads is closely related to human body temperature requirements. Without affecting indoor temperature needs, power consumption can be partially or completely reduced at certain times by controlling the temperature. Human comfort requirements for the indoor environment can be calculated using the predicted average vote count index.

[0048] (16) in, For the first Temperature control load The predicted average number of votes at any given time; for The desired indoor temperature at all times; The rate of human metabolism; For clothing thermal resistance. According to "Ergonomics of Thermal Environment: Analysis, Measurement, and Interpretation of Thermal Comfort Through Calculation of PMV and PDD Indices and Local Thermal Comfort Criteria", it can be seen that... .

[0049] (17) in, and They are respectively Time of the first The actual operating power of each temperature-controlled load; air density; The specific heat capacity of air; Indoor air volume; for The indoor temperature at any given time; The tangent of the power factor angle of the temperature-controlled load can be obtained from its power factor; and They are respectively Time of the first The maximum and minimum active power of each temperature-controlled load.

[0050] Formula (16) transforms indoor temperature demand into the operating boundary of the temperature control load by using the predicted mean vote (PMV) index, ensuring that thermal comfort is within the allowable range. Formula (17) limits the actual operating power of the temperature control load between the maximum and minimum active power, and establishes a dynamic relationship between indoor temperature changes and operating power. The active power regulation constraints of electric vehicles are determined by formulas (18) to (20), and include electric vehicle charging power constraints and electric vehicle charging state constraints.

[0051] ① Electric vehicle charging power constraints: (18) in, Let t be the reactive power of the i-th electric vehicle at time t; and Let be the upper and lower limits of the active power of the i-th electric vehicle at time t; and Let be the upper and lower limits of the reactive power of the i-th electric vehicle at time t.

[0052] ② Electric vehicle charging state constraints: (19) in, Let be the charging state variable of the i-th electric vehicle, when When electric vehicles are charging, At that time, the electric vehicle is not charging; The starting charging time of the i-th electric vehicle is obtained by simulating formula (17) using the Monte Carlo method; Let be the expected travel time of the i-th electric vehicle. This constraint states that the electric vehicle is allowed to charge if its charging start time is less than its expected travel time, and not allowed to charge otherwise.

[0053] ③ Electric vehicle battery capacity constraints: (20) in, The unit time interval; and Let be the battery capacity of the i-th electric vehicle at time t and time t+1, where The battery capacity at the start of charging the i-th electric vehicle is determined by the randomness of the daily mileage. It can be regarded as a random variable obtained by mapping the probability distribution of mileage. The initial state of charge of the electric vehicle at the start of charging is determined by the randomness of the daily mileage. It can be simulated by the Monte Carlo method to obtain formula (7). and These are the upper and lower limits of the battery capacity of the i-th electric vehicle at time t; and Let t represent the battery capacity when the i-th electric vehicle leaves at time t and the expected electric capacity. The final constraint ensures the normal travel of the electric vehicle.

[0054] Formula (18) limits the range of active power values ​​for each electric vehicle during each scheduling period. Formula (19) specifies that electric vehicles are only allowed to charge between the start of charging and the desired travel time through the charging state variable. Formula (20) constrains the time-series transfer relationship of the electric vehicle battery capacity and ensures that the battery capacity of the electric vehicle at the off-grid time is not lower than the amount of electricity required for the desired travel.

[0055] B2: The constraints of the upper-level active power optimization scheduling model include the transmission capacity constraints of flexible interconnection soft switches, the charging and discharging power constraints of energy storage devices and the consistency constraints of the first and last charge states of the scheduling cycle, the comfort constraints of temperature-controlled loads, the charging period constraints within the range from the start charging time of electric vehicles to the desired travel time, and the power guarantee constraints that the battery capacity of electric vehicles at the desired travel time is not less than the power required for the desired travel.

[0056] Specifically, after receiving the active power boundary conditions transmitted from the upper layer, the lower-level reactive power optimization scheduling model uses the reactive power of photovoltaics, static-var-generators (SVG), SOPs, energy storage devices, temperature-controlled loads, and electric vehicles as control variables. It constructs a comprehensive objective function from four dimensions: voltage safety level, reactive power regulation economy, network loss, and electric vehicle carrying capacity, as shown in formula (21). (twenty one) In formula (21), This is a voltage over-limit risk indicator. To adjust reactive power costs, For network loss, To achieve the target load-bearing capacity for electric vehicles, four sub-objectives are interconnected and constrained. These are then weighted and summed to form a unified lower-level objective function. Voltage over-limit risk indicator. The following formulas are used to quantify the severity of the deviation of the voltage of each node from the safe operating range, as shown in formulas (22) and (23): (twenty two) (twenty three) In formula (22), The total number of nodes; for Time Node Severity index of voltage exceeding limit; , and They are respectively Time Node Upper and lower limits of voltage amplitude and reference voltage; for Time Node The voltage. When the node voltage exceeds the safe range... The ratio of the difference between the current voltage and the over-limit boundary to the reference voltage is taken. When the node voltage is within the safe range, The value is zero, thus transforming the discrete limit violation judgment into a continuously differentiable limit violation risk quantification index, which facilitates optimization and solution.

[0057] Reactive power adjustment costs The reactive power operating cost is measured by the sum of the products of various resource reactive power coefficients and their reactive power, covering SOP, photovoltaics, static var generators, energy storage devices, temperature-controlled loads and electric vehicles, as shown in formula (24): (twenty four) In formula (24), , , , , and They are respectively Reactive power costs of SOPs, photovoltaics, static var generators, energy storage devices, temperature-controlled loads, and electric vehicles; , , , , and They are respectively The reactive power operating cost coefficient for the i-th SOP, photovoltaic, static var generator, energy storage device, temperature-controlled load and electric vehicle; , , , , and They are respectively The reactive power of the i-th SOP, photovoltaic, static var generator, energy storage device, temperature-controlled load and electric vehicle; and These represent the number of photovoltaic and static var generators, respectively.

[0058] Network loss The loss is measured by the sum of the active and reactive power losses generated by each branch in the line resistance, as shown in formula (25): (25) In formula (25), The total number of branch roads, and Let be the active power and reactive power of branch j at time t, respectively. Let J be the resistance of branch j. Let j be the voltage at the beginning of branch j. By minimizing... This enables the multi-resource reactive power control scheme to improve voltage distribution while reducing line losses.

[0059] Electric vehicle load capacity target The maximum number of electric vehicles that the distribution network can accommodate under all operating constraints is defined as shown in formula (26): (26) In formula (26), This refers to the number of electric vehicles connected to the distribution network. Unlike approaches that treat electric vehicle carrying capacity merely as a post-event evaluation indicator, this scheme incorporates it into the lower-level objective function. Through the coordinated regulation of reactive resources, it proactively increases the upper limit of the distribution network's capacity to accept electric vehicle charging loads while meeting voltage and power flow security constraints.

[0060] The constraints of the lower-level reactive power optimization scheduling model are described by the Distflow branch power flow model, which describes the multi-time period power flow of the distribution network, as shown in formula (27): (27) in, , and for Time Branch Branch roads and injection node The active power; , and for Time Branch Branch roads and injection node reactive power; and They are respectively Time Branch Branch roads The current; and The first node in the distribution network is The set of end nodes of the branch and the end node are The set of first nodes of the branch paths; and for Time Node and the voltage at node j; for branch road Resistance and reactance; and branch road Resistance and reactance; and for Active and reactive power at the head end at any given time; and for Time Node The active and reactive loads.

[0061] Formula (27) includes the active and reactive power balance equations for each branch, as well as the relationship equations between node voltage and branch current. The Distflow branch model fully incorporates the physical characteristics of the distribution network into the optimization constraints, ensuring that the scheduling results meet the electrical operation rules. The node voltage constraints are shown in Formula (28): (28) in, for Time Node The voltage; and They are respectively Time Node Upper and lower limits of voltage amplitude; Ensure that the voltage amplitude of each node operates within the upper and lower limits to prevent new voltage over-limit problems caused by reactive power regulation of multiple resources. The branch current constraint is shown in formula (29): (29) in, for Time Branch Current on; , branch road The maximum and minimum values ​​of the current.

[0062] Limit the current amplitude of each branch circuit to not exceed the thermal stability limit to avoid overheating and damage to the line. The load factor constraint of the distribution transformer is shown in formula (30): (30) in, The rated capacity of the distribution transformer This is the overload factor.

[0063] The apparent power of the transformer is limited by the overload factor to prevent the transformer from operating under overload.

[0064] B3: The constraints of the lower-level reactive power optimization scheduling model include multi-time distribution network power flow constraints based on branch power flow equations, upper and lower limits of node voltage amplitude constraints, branch current thermal stability constraints, and distribution transformer load rate constraints.

[0065] Specifically, the upper-level active power optimization scheduling model and the lower-level reactive power optimization scheduling model achieve collaborative iteration through data transfer. After completing one active power population iteration, the upper-level model transfers the active power regulation results of SOP, energy storage devices, temperature-controlled loads, and electric vehicles to the lower-level model as fixed boundary parameters. The lower-level model then uses these parameters to determine the active power injection amount for each node and solves the lower-level objective function within the adjustable range of reactive power.

[0066] After completing one round of reactive power optimization, the lower layer returns the reactive power control results of photovoltaics, static var generators, SOPs, energy storage devices, temperature-controlled loads, and electric vehicles, as well as the fitness value of the lower layer's objective function, to the upper layer. The upper layer evaluates the comprehensive benefits of the current active power control scheme based on the fitness information fed back from the lower layer, updates the population position accordingly, and enters the next round of active power iteration.

[0067] By using an interactive method where the upper layer unidirectionally transmits the active power boundary to the lower layer and the lower layer feeds back the reactive power result and fitness value to the upper layer, the high-dimensional and strongly coupled unified optimization problem of active and reactive power in the distribution network is decomposed into layered sub-problems that can be solved iteratively layer by layer. This reduces the dimensionality of a single solution while taking into account the coordination between the active and reactive power dimensions, ensuring the consistency of the solutions between the upper and lower layers and the feasibility of the overall scheduling scheme.

[0068] In this embodiment, step S300 employs an improved sparrow search algorithm that integrates sine and cosine search with Cauchy mutation to perform iterative solving of the active and reactive power hierarchical decoupled scheduling model, outputting a multi-dimensional flexible resource active and reactive power control plan, including the following steps C1-C3: C1: When solving the hierarchical decoupled scheduling model of active and reactive power in the distribution network, the following steps are taken: In the upper active power optimization scheduling stage, the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles is randomly encoded into the initial position matrix of the population within their respective control ranges, the fitness value of each individual is calculated, the global optimal solution and the worst solution are determined, and the active power control results and fitness information are transmitted to the lower reactive power optimization scheduling stage. In the lower-level reactive power optimization and scheduling stage, after receiving the active power boundary transmitted from the upper level, the reactive power of photovoltaic, static var generator, flexible interconnect soft switch, energy storage equipment, temperature control load and electric vehicle is randomly encoded into the initial position matrix of the population. The position of the discoverer is updated using sine and cosine search. Random perturbation following the standard Cauchy distribution is introduced into the current best individual. The position update and fitness evaluation are performed iteratively until the lower-level objective function converges or the maximum number of iterations is reached. The reactive power regulation result and fitness value are then returned to the upper level. After receiving feedback from the lower layer, the upper layer updates the population position and recalculates the fitness value. The upper and lower layers repeat the interactive solution until the upper layer objective function converges or reaches the maximum number of iterations, and outputs a multi-dimensional flexible resource active and reactive power control plan.

[0069] Specifically, before solving the active and reactive power hierarchical decoupled scheduling model, the control variables of the upper active power optimization scheduling model and the lower reactive power optimization scheduling model need to be encoded to construct the position representation of individuals in the population in the sparrow search algorithm (SSA).

[0070] For the upper-level active power optimization scheduling model, the control variables are the SOP, energy storage equipment, temperature-controlled load, and active power of electric vehicles within the scheduling period. The active power column vectors of each resource are shown in formulas (31) to (34): (31) (32) (33) (34) (35) Each column vector contains the active power value of the corresponding resource in each time period within the scheduling cycle. The vector dimension is determined by the product of the number of resources and the number of scheduling time periods. The active power column vectors of each resource are concatenated column by column to form the overall matrix of upper-level control variables. As shown in formula (35). In formula (35), This is the overall matrix formed by encoding all control variables in the upper-level active power optimization scheduling model. , , and These are column vectors representing the active power of SOP, the active power of energy storage devices, the active power of temperature-controlled loads, and the active power of electric vehicles, respectively. The position of each sparrow in the upper population is represented by a matrix. The population position matrix is ​​represented by a single line in formula (36): (36) In formula (36), n is the number of sparrows in the population. Each row of the population position matrix corresponds to the position of a sparrow individual in the upper-level control variable space, that is, the active power control scheme represented by that individual. When the upper-level population is initialized, each row of the matrix is ​​randomly sampled within the constraint range of the active power of each resource to ensure that the initial population covers a sufficiently large search space. Based on the population position matrix, the fitness value of each individual is calculated by substituting it into the upper-level objective function (8) to form the upper-level fitness matrix, as shown in formula (37): (37) in, The i-th row represents the fitness value of the i-th sparrow individual in the upper population.

[0071] For the lower-level reactive power optimization scheduling model, the control variables are the reactive power of photovoltaics, static var generators, SOPs, energy storage devices, temperature-controlled loads, and electric vehicles during the scheduling period. The reactive power column vectors of each resource are shown in formulas (38) to (43): (38) (39) (40) (41) (42) (43) The structure of each column vector is constructed in the same way as the active power column vector of the upper layer, and the dimension is determined by the number of corresponding resources and the number of scheduling periods. The reactive power column vectors of each resource are concatenated column by column to form the overall matrix of the lower-level control variables. As shown in formula (44): (44) In formula (44), , , , , and These are column vectors representing the reactive power of SOP, photovoltaic reactive power, static var generator reactive power, energy storage equipment reactive power, temperature-controlled load reactive power, and electric vehicle reactive power, respectively. The lower-level population position matrix is ​​shown in formula (45): (45) The fitness matrix of the lower layer is shown in formula (46): (46) in, The i-th row represents the fitness value of the i-th sparrow individual in the lower-level population. After receiving the active power boundary conditions from the upper-level population, the lower-level population initialization randomly samples the population position matrix within the constraints of reactive power of each resource to ensure that the initial population is uniformly distributed within the feasible region, providing a reasonable starting point for subsequent improvement iterations of sine and cosine search and Cauchy mutation.

[0072] C2: Update the location of the discoverer using sine and cosine search, including introducing a control factor that decreases linearly with the number of iterations. When the control factor is large in the early stage of the search, the sine and cosine random parameters are combined to drive the discoverer to perform a large-scale location update in the solution space, thereby improving the diversity of population search. As the number of iterations increases, the control factor gradually decreases, and the range of the discoverer's location update shrinks towards the neighborhood of the current best individual in order to improve the local fine search capability; When the warning value exceeds the safety threshold during the search process, the discoverer's location is adjusted to a relatively safe area; when the warning value is below the safety threshold, the discoverer's location is updated to a direction with better fitness.

[0073] Specifically, in the lower-level reactive power optimization and scheduling stage, the improved sparrow search algorithm divides the population into three roles: discoverers, followers, and vigilants. Each type of individual collaborates to search the solution space through different position update rules. Discoverers are responsible for exploring potential good solution areas over a large area and guiding the population's search direction. Followers adjust their positions based on the discoverers' search results in order to obtain better solutions. Vigilants guide the population to adjust to safe areas when they perceive search risks, thus forming a collaboration between global exploration and local development.

[0074] The discovery position update rule in the standard sparrow search algorithm is shown in formula (47): (47) In formula (47), Let be the position value of the i-th sparrow in the j-th dimension during the t-th iteration. This represents the current iteration number. A random number within the interval [0,1]. This is a warning value. Let be the maximum number of iterations, Q be a random number following a normal distribution, and L be a d×1 dimensional matrix of all 1s. This is a safe value. When At this time, the current search state is relatively stable, and the discoverer guides the population along an exponentially decaying path to search in directions with better fitness; when At this time, the search process carries potential risks, and the discoverer moves to a relatively safe area to avoid traps. However, the standard discoverer update relies solely on exponentially decaying paths, resulting in insufficient search diversity. In distribution network active and reactive power coordination optimization problems with high control variable dimensions, it is prone to premature convergence to local optima.

[0075] Therefore, a sine and cosine search is introduced to improve the discovery location update. The improved discovery location update rule is shown in formula (48): (48) Control factor The calculation method is shown in formula (49): (49) In formula (49), a is the initial search magnitude constant, and T is the maximum number of iterations; , , A random number within the interval [0,1]; This represents the globally optimal individual position in the t-th generation of the population. (Control factor) It decreases linearly with the number of iterations, especially in the early stages of the search. The relatively large sine and cosine functions drive the discoverers to make large-scale positional jumps within the solution space, thus fully preserving population diversity; as the iteration progresses... As the range of the discoverer's position is gradually reduced, it shrinks towards the neighborhood of the current best individual. The search gradually shifts from global exploration to local refinement, balancing the breadth of the search and the convergence accuracy of the algorithm. The follower's position update is shown in formula (50): (50) in the formula Let be the position of the sparrow with the best fitness in the t-th iteration. The position of the sparrow with the worst adaptability. The matrix is ​​d×1 and its elements are randomly selected as -1 or 1; the position of the watchdog is updated as shown in formula (51): (51) In the formula, β is the step size parameter, and K is the sparrow's movement direction parameter, which takes the value of 1 or -1. To avoid extremely small constants with a denominator of zero, This represents the current fitness value of the individual sparrow. , These are the current best and worst fitness values, respectively.

[0076] C3: Introduce a random perturbation following a standard Cauchy distribution to the current best individual, including applying a random perturbation following a standard Cauchy distribution to the position vector of the current best individual in each iteration with a variation intensity factor that decreases with the number of iterations, to obtain a new individual position vector; Calculate the fitness value of the new individual. If the fitness value of the new individual is better than that of the current best individual, replace the current best individual with the new individual; otherwise, retain the current best individual.

[0077] Specifically, to further enhance the algorithm's ability to escape local optima during the lower-level reactive power optimization scheduling solution, a Cauchy mutation perturbation is introduced into the current best individual in the population in each iteration. The position vector of the new individual after the Cauchy mutation is shown in formula (52): (52) Variation intensity factor The calculation method is shown in formula (53): (53) In formula (52), This represents the position vector of the new individual after Cauchy mutation. This represents the current optimal individual position vector. Let be a random variable that follows a standard Cauchy distribution. In formula (53), The initial mutation intensity coefficient decreases monotonically with the number of iterations. In the early stage of the search, the mutation intensity is relatively large, and the new individuals can deviate from the current optimal position within a wide range, enhancing the ability to escape local extrema. In the later stage of the search, the mutation intensity decreases, and the perturbation range shrinks to the vicinity of the current optimal solution, which helps to conduct a fine search for the region of excellent solutions.

[0078] The Cauchy distribution has a heavy-tailed characteristic, and the disturbance it generates is concentrated near zero with a high probability. At the same time, it has a certain probability of producing large-amplitude jumps. This allows the mutated new individual to perform a fine search near the current optimal solution while having a certain probability of large-scale deviation. This effectively avoids the complex local extremum traps in the large-scale multi-objective nonlinear programming problem of active and reactive power coordination optimization of distribution networks.

[0079] After completing the Cauchy mutation, the new individual is substituted into the lower-level objective function formula (21) to calculate its fitness value. If the fitness value of the new individual is better than that of the current best individual, the new individual replaces the current best individual; otherwise, the original best individual is retained to ensure that the overall fitness of the population does not degenerate. The lower level repeats the position update, fitness assessment and Cauchy mutation operations until the lower-level objective function converges or reaches the maximum number of iterations. The reactive power results of photovoltaic, static var generator, SOP, energy storage equipment, temperature control load and electric vehicle and the lower-level fitness values ​​are returned to the upper level. The upper level completes the current round of population position update based on the feedback fitness information and transmits the active power regulation results to the lower level again. The upper and lower layers alternate in this iterative process until the objective function formula (8) of the upper layer converges or reaches the maximum number of iterations. Finally, the coordinated control plan of active and reactive power of multiple flexible resources within the scheduling cycle is output. Under the premise of ensuring that the voltage of each node of the distribution network meets the constraints of formula (28), the current of each branch meets the constraints of formula (29), and the load rate of the distribution transformer meets the constraints of formula (30), the coordinated minimization of active power control cost, load balance, daily peak-valley difference, voltage over-limit risk, reactive power control cost and network loss is achieved, and the number of electric vehicle charging loads that the distribution network can accept is maximized.

[0080] Example 3 Reference Figure 2 This is the third embodiment of the present invention.

[0081] The above is a schematic scheme of a distribution network active and reactive power coordinated optimization scheduling method for improving the carrying capacity of electric vehicles. It should be noted that the technical solution of this distribution network active and reactive power coordinated optimization scheduling system for improving the carrying capacity of electric vehicles belongs to the same concept as the technical solution of the aforementioned distribution network active and reactive power coordinated optimization scheduling method for improving the carrying capacity of electric vehicles. Details not described in detail in this embodiment of the distribution network active and reactive power coordinated optimization scheduling system for improving the carrying capacity of electric vehicles can be found in the description of the aforementioned distribution network active and reactive power coordinated optimization scheduling method for improving the carrying capacity of electric vehicles.

[0082] This embodiment also provides a distribution network active and reactive power coordinated optimization scheduling system for improving the carrying capacity of electric vehicles, including: The interactive model building module is used to establish power interaction constraint models between various resources and the distribution network based on the differences in active and reactive power regulation of multiple flexible resources. The hierarchical scheduling model construction module is used to build an active and reactive power hierarchical decoupled optimization scheduling model based on the power interaction constraint model. The optimization solution module is used to perform interactive iterative solution of the active and reactive power hierarchical decoupled optimization scheduling model by adopting an improved swarm intelligence algorithm that integrates a global exploration diversity enhancement mechanism and an elite individual local escape strategy, and outputs a multi-dimensional flexible resource active and reactive power coordination and control plan.

[0083] This embodiment also provides an electronic device applicable to the coordinated optimization scheduling of active and reactive power in distribution networks aimed at improving the carrying capacity of electric vehicles. The device includes a memory and a processor. The memory stores computer-executable instructions, and the processor executes the computer-executable instructions to implement the coordinated optimization scheduling method for active and reactive power in distribution networks aimed at improving the carrying capacity of electric vehicles as proposed in the above embodiment.

[0084] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as proposed in the above embodiments.

[0085] The storage medium proposed in this embodiment and the method for coordinated optimization scheduling of active and reactive power distribution networks to improve the carrying capacity of electric vehicles proposed in the above embodiments belong to the same inventive concept. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0086] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0087] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for coordinated and optimized scheduling of active and reactive power in power distribution networks to improve the carrying capacity of electric vehicles, characterized in that: This includes constructing power interaction constraint models between various resources and the distribution network based on the differences in active and reactive power regulation of diverse flexible resources; Based on the aforementioned power interaction constraint model, with the minimum active power regulation cost and load balance as the upper-level optimization objectives, and the voltage safety level and the maximum absorption capacity of the distribution network for electric vehicle charging load as the lower-level core optimization objectives, a hierarchical decoupled scheduling model for active and reactive power is constructed. An improved sparrow search algorithm that integrates sine and cosine search and Cauchy mutation is used to perform interactive iterative solutions between the upper and lower layers of the active and reactive power decoupled scheduling model, and output a multi-dimensional flexible resource active and reactive power control plan.

2. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 1, is characterized in that: The construction of power interaction constraint models for various resources and the distribution network includes determining the upper and lower limits of adjustable reactive power of photovoltaics based on the proportion of photovoltaic active power output to the rated capacity of the inverter, thereby obtaining photovoltaic reactive power interaction constraints. Based on the range of reactive power that the static var generator can continuously absorb or emit, the upper and lower limits of the reactive power of the static var generator are determined, and the reactive power interaction constraint of the static var generator is obtained. Based on the state of charge and rated capacity of the energy storage device, the range of active power and the upper and lower limits of reactive power for charging and discharging of the energy storage device are determined, and the active and reactive power interaction constraints of the energy storage device are obtained. Based on the upper limit of the allowable current carrying capacity of the flexible interconnection soft switch, the active power transmission range and the upper and lower limits of the reactive power of the flexible interconnection soft switch are determined, and the active and reactive power interaction constraints of the flexible interconnection soft switch are obtained. Based on the thermal comfort requirements of the temperature-controlled load, the range of active power and upper and lower limits of reactive power of the temperature-controlled load are determined, and the active and reactive power interaction constraints of the temperature-controlled load are obtained. The Monte Carlo method is used to randomly sample the starting charging time, off-grid time, and daily mileage of electric vehicles to determine the initial state of charge and allowable charging time window of each electric vehicle when it is connected to the grid. Then, the range of active power and the upper and lower limits of reactive power in the orderly charging stage are determined, and the active and reactive power interaction constraints of electric vehicles are obtained.

3. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 2, is characterized in that: The construction of the active and reactive power hierarchical decoupled scheduling model includes: using the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles as control variables, and using the minimum operating cost of multi-resource active power regulation, the minimum dispersion of active power distribution of each node and the minimum daily peak-valley difference as the upper-level objective function to construct an upper-level active power optimization scheduling model. Using the reactive power of photovoltaics, static var generators, flexible interconnection soft switches, energy storage devices, temperature-controlled loads, and electric vehicles as control variables, and taking the minimum voltage over-limit risk index, the minimum reactive power regulation cost, the minimum network loss, and the maximum number of electric vehicles that the distribution network can accommodate as the lower-level objective functions, a lower-level reactive power optimization scheduling model is constructed. The active power scheduling results from the upper layer are passed to the lower layer as fixed boundary conditions, and the reactive power scheduling results and fitness values ​​from the lower layer are returned to the upper layer, thus completing the data transfer between the upper and lower layers.

4. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 3, is characterized in that: The constraints of the upper-level active power optimization scheduling model include: transmission capacity constraints of flexible interconnection soft switches, charging and discharging power constraints of energy storage devices and consistency constraints of the first and last charge states of the scheduling cycle, comfort constraints of temperature-controlled loads, charging time constraints within the range from the start charging time of electric vehicles to the desired travel time, and power guarantee constraints that the battery capacity of electric vehicles at the desired travel time is not less than the power required for the desired travel. The constraints of the lower-level reactive power optimization scheduling model include multi-time distribution network power flow constraints based on branch power flow equations, upper and lower limits of node voltage amplitude constraints, branch current thermal stability constraints, and distribution transformer load rate constraints.

5. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 4, is characterized in that: When solving the hierarchical decoupled scheduling model of active and reactive power in the distribution network, the following steps are taken: in the upper active power optimization scheduling stage, the active power of flexible interconnected soft switches, energy storage devices, temperature-controlled loads and electric vehicles is randomly encoded into the initial position matrix of the population within their respective control ranges, the fitness value of each individual is calculated, the global optimal solution and the worst solution are determined, and the active power control results and fitness information are transmitted to the lower reactive power optimization scheduling stage. In the lower-level reactive power optimization and scheduling stage, after receiving the active power boundary transmitted from the upper level, the reactive power of photovoltaic, static var generator, flexible interconnect soft switch, energy storage equipment, temperature control load and electric vehicle is randomly encoded into the initial position matrix of the population. The position of the discoverer is updated using sine and cosine search. Random perturbation following the standard Cauchy distribution is introduced into the current best individual. The position update and fitness evaluation are performed iteratively until the lower-level objective function converges or the maximum number of iterations is reached. The reactive power regulation result and fitness value are then returned to the upper level. After receiving feedback from the lower layer, the upper layer updates the population position and recalculates the fitness value. The upper and lower layers repeat the interactive solution until the upper layer objective function converges or reaches the maximum number of iterations, and outputs a multi-dimensional flexible resource active and reactive power control plan.

6. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 5, is characterized in that: The method of updating the location of the discoverer using sine and cosine search includes introducing a control factor that decreases linearly with the number of iterations. When the control factor is large in the early stage of the search, the sine and cosine random parameters are combined to drive the discoverer to perform a large-scale location update in the solution space, thereby improving the diversity of population search. As the number of iterations increases, the control factor gradually decreases, and the range of the discoverer's location update shrinks towards the neighborhood of the current best individual in order to improve the local fine search capability; When the warning value exceeds the safety threshold during the search process, the discoverer's location is adjusted to a relatively safe area; When the warning value is lower than the safety threshold, the location of the discoverer is updated in the direction of better fitness.

7. The active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles, as described in claim 6, is characterized in that: The step of introducing a random perturbation following a standard Cauchy distribution into the current best individual includes: in each iteration, applying a random perturbation following a standard Cauchy distribution to the position vector of the current best individual with a variation intensity factor that decreases with the number of iterations, to obtain a new individual position vector; calculating the fitness value of the new individual; if the fitness value of the new individual is better than the fitness value of the current best individual, replacing the current best individual with the new individual; otherwise, retaining the current best individual.

8. A distribution network active and reactive power coordinated optimization scheduling system for improving the carrying capacity of electric vehicles, based on the distribution network active and reactive power coordinated optimization scheduling method for improving the carrying capacity of electric vehicles as described in any one of claims 1 to 7, characterized in that: It also includes an interactive model building module, which is used to establish power interaction constraint models between various resources and the distribution network based on the differences in active and reactive power regulation of multiple flexible resources. The hierarchical scheduling model construction module is used to construct an active and reactive power hierarchical decoupled optimization scheduling model based on the power interaction constraint model. The optimization solution module is used to perform upper and lower layer interactive iterative solution of the active and reactive power hierarchical decoupled optimization scheduling model by adopting an improved swarm intelligence algorithm that integrates a global exploration diversity enhancement mechanism and an elite individual local escape strategy, and outputs a multi-dimensional flexible resource active and reactive power coordination and control plan.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles 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 by the processor, it implements the steps of the active and reactive power coordinated optimization scheduling method for power distribution networks aimed at improving the carrying capacity of electric vehicles as described in any one of claims 1 to 7.

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