A power distribution network carrying capacity improvement method and system considering distributed power supply access

Abstract

The application discloses a power distribution network carrying capacity improvement method and system considering distributed power access, and belongs to the technical field of power grid new energy. The method comprises the following steps: constructing a power distribution network carrying capacity improvement model; generating random scene samples of distributed power output and load; inputting the random scene samples and corresponding optimal reactive compensation device installation positions into the power distribution network carrying capacity improvement model to obtain an initialization population; optimizing and solving the initialization population meeting the constraint conditions of the power distribution network carrying capacity improvement model to obtain an optimal solution set, and screening an optimal solution realizing maximum distributed power access capacity and minimum investment and operation cost from the optimal solution set; and obtaining an SVC optimal compensation capacity and an incentive demand response optimal scheduling strategy based on the optimal solution. The application can maximize the distributed power receiving capacity and optimize the investment and operation cost under the premise of comprehensively guaranteeing the safe and stable operation of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the field of new energy technology for power grids, and in particular relates to a method and system for improving the carrying capacity of distribution networks considering the access of distributed power sources. Background Technology

[0002] The large-scale integration of distributed generation has profoundly altered the traditional unidirectional power flow distribution characteristics of distribution networks, easily triggering a series of safety and stability issues such as node voltage exceeding limits, line current carrying capacity overload, and transformer reverse overload. Simultaneously, the inherent randomness, volatility, and intermittency of photovoltaic and wind power output, coupled with the uncertainty of load-side electricity consumption, pose severe challenges to the planning, design, and operation control of distribution networks. The distributed generation carrying capacity of a distribution network refers to the maximum capacity of distributed generation that the distribution network can accommodate while meeting safety operation constraints, power quality requirements, and power supply reliability standards. Its assessment and improvement technologies have become a research hotspot and core engineering requirement in the field of distribution networks under the background of new power system construction.

[0003] Currently, scholars and engineering institutions both domestically and internationally have conducted extensive research on the assessment and improvement of distributed generation capacity in distribution networks. Existing technologies primarily utilize reactive power optimization as a core method to improve the operating characteristics of distribution networks, thereby enhancing the capacity to accommodate distributed generation. For example, Chinese invention patent application CN116207776A discloses a method and device for improving grid capacity considering distributed photovoltaic (PV) power. This scheme acquires real-time data from grid equipment, buses, and lines, performs abnormal data correction and normalization preprocessing, calculates operating parameters such as voltage deviation and voltage fluctuation after distributed PV integration, and classifies grid capacity levels through weighted assignment after voltage fluctuation, short-circuit current, and harmonic verification. With the optimization objectives of minimizing grid voltage deviation and network loss, an improved gray wolf algorithm is used to complete reactive power optimization, thereby improving grid capacity by suppressing voltage fluctuation and reducing network loss. This scheme provides a feasible path for the assessment and optimization of distribution network capacity in distributed PV integration scenarios, realizing quantitative classification and reactive power optimization based on real-time operating data, and to a certain extent solving the voltage fluctuation problem caused by distributed PV integration.

[0004] This scheme only focuses on the load-bearing capacity optimization design for a single type of distributed photovoltaic power source, without covering the access scenarios of other mainstream types of distributed power sources such as wind power. Furthermore, its optimization process is based on real-time deterministic operating data, without quantitative modeling and multi-scenario adaptation for the uncertainty of distributed power output and load fluctuations. It cannot capture the nonlinear impact of random source-load fluctuations on the operating characteristics of the distribution network, which may lead to conservative load-bearing capacity assessment results. The optimization scheme is not robust enough in source-load fluctuation scenarios, and may even lead to engineering problems such as reactive power compensation strategy failure and voltage exceeding limits. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for improving the carrying capacity of a distribution network that considers the access of distributed power sources, which partially solves or alleviates the above-mentioned deficiencies in the prior art, and can maximize the capacity to accept distributed power sources and optimize investment and operating costs while fully ensuring the safe and stable operation of the distribution network.

[0006] To solve the aforementioned technical problems, the present invention specifically adopts the following technical solution: A first aspect of the present invention is to provide a method for improving the carrying capacity of a distribution network considering the integration of distributed power sources, comprising: Based on distribution network topology data and load data, a distribution network carrying capacity improvement model is constructed; Generate random scenario samples of distributed power output and load, and calculate the optimal installation location of reactive power compensation device for each sample. The random scenario samples and the corresponding optimal reactive power compensation device installation locations are input into the power distribution network carrying capacity improvement model to obtain the initial population. The initial population that meets the constraints of the distribution network carrying capacity improvement model is optimized to obtain the optimal solution set, and the optimal solution that simultaneously achieves the maximum distributed power generation access capacity and the minimum investment and operation cost is selected from the optimal solution set. Based on the optimized solution, the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response are obtained.

[0007] Furthermore, the power distribution network carrying capacity enhancement model is as follows:

[0008] in, f 1 represents the total capacity of the connected distributed power sources; f 2 represents the total investment and operating costs; f cost To cover additional reactive power compensation investment costs; f loss The cost of active power loss during system operation; C IL To compensate for system demand response costs; S N C is the set of all nodes; DG，i S represents the distributed power supply capacity that node i can access; c For the additional reactive power compensation set; ρ Q The unit capacity price for additional reactive power compensation; τ is the discount rate; y is the service life of the reactive power compensation; Q c，k For the k-th additional reactive power compensation capacity; r c,k The fixed installation investment cost for the kth additional reactive power compensation; ρ P Price per unit of active power loss; S l Z is the set of branches;l I is the impedance of branch l; l C is the current flowing through branch l; IL i,t The interruption compensation fee received by the user; △P IL i,t α represents the load reduction for user i. i β i This represents the interruption compensation coefficient.

[0009] Furthermore, methods for generating random scenario samples of distributed power output and load include: The steps for generating samples for distributed photovoltaic power output uncertainty scenarios include: A probability distribution model for light intensity is constructed, wherein the probability distribution model for light intensity is as follows: ; Among them, f r (r) is the probability density function of the light intensity r, and r and r max These represent the radiation value and maximum radiation value received by the photovoltaic power generation system, respectively. G It is the Gamma function. a and b It is the shape parameter of the distribution; The stratified sampling of light radiation values ​​was performed using the Latin hypercube sampling method to generate multiple sets of random samples of light radiation intensity. Based on the sampled solar radiation values, the active power output of the corresponding sample is calculated; the formula for the output power utilization of a distributed photovoltaic power generation system is as follows:

[0010] Calculate; where P DG (r) represents the real-time active power output of the distributed photovoltaic power generation system, M represents the number of photovoltaic panels, A represents the total area of ​​the photovoltaic panels, and η represents the operating efficiency. m Let A be the working efficiency of the m-th photovoltaic panel. m Let m be the area of ​​the m-th photovoltaic panel; The steps for generating samples for scenarios with uncertain wind power output include: Construct a wind speed probability distribution model, wherein the wind speed probability distribution model is as follows: , ; Where f(v) is the probability density function of wind speed v, k and c are the shape and scale parameters respectively, v is the wind speed, and σ w , These represent the variance and mean of the wind speed, respectively; E w For the output of wind power generation; The wind speed v was stratified and sampled using the Latin hypercube sampling method to generate multiple sets of random wind speed samples. Based on the sampled wind speed, the wind power output of the corresponding sample is calculated using a piecewise function of wind turbine output; the formula for utilizing the output power of wind power generation is as follows:

[0011] Calculate; where P WTG (v) represents the real-time active power output of wind power generation. ci v cr v co These are the cut-in, rated, and cut-out wind speeds for wind power generation, respectively; P r This refers to the rated power of wind power generation. The steps for generating samples for power distribution network load uncertainty scenarios include: A probability distribution model of the distribution network load is constructed, and the probability distribution model of the distribution network load is as follows: ; Among them, f r (P L The active power P injected into the power grid by the load L The probability density function, P L Q L , representing the active power and reactive power injected into the power grid by the load, respectively; μL and σL are the mean and standard deviation of the active power, respectively; The active power injected into the power grid by load is sampled in a stratified manner using the Latin hypercube sampling method to generate multiple sets of random samples of active power injected into the power grid by load. Based on the assumption of a constant power factor, using the formula:

[0012] Calculate the reactive load corresponding to the active load sample; where φ is the power factor angle.

[0013] Furthermore, using the formula:

[0014] Calculate the sensitivity of candidate nodes for network loss and reactive power correction in a single scenario; where ΔP loss P is the increment of the total active power loss of the system. loss Let be the total active power loss of the system; u be the node input power matrix; Δu be the node power increment, (Δu) T Transpose the node power increment; P loss / u represents the first-order network loss sensitivity; 2 Ploss / ( u u) represents the second-order network loss sensitivity; U i U j These are the voltages at node i and node j, respectively; G ij B ij These are the elements of the nodal admittance matrix; δ ij Let i be the voltage phase angle difference between nodes i and j; i and j are the node numbers, i and j = 1 to n.

[0015] Furthermore, the constraints of the power distribution network carrying capacity improvement model include: Distributed power generation output constraints: ; in, P DG，i For nodes i The current output active power of the distributed power source connected to the upstream is... i ∈ S N ; P DG，i，max For nodes i The upper limit of the active power output of the distributed power source; Node voltages will be subject to constraints: ; in, U i For nodes i voltage amplitude, i ∈ S N ; U i，max and U i，min These are the upper and lower limits of the voltage amplitude at node i, respectively; Line carrying capacity opportunity constraints: ; in, I l，max For the line l Maximum allowable current carrying capacity; S l For branch set; Transformer reverse load rate opportunity constraint:

[0016] in, P L，i For nodes i The equivalent load output; S T，max Let T be the maximum transmission capacity of transformer T, where T∈S T ,in, S T A collection of transformers; l max This represents the maximum reverse load rate of the transformer. Current balance constraints:

[0017] in, Q L，i For nodes i Reactive power under load, i ∈ S N ; Q C，i For nodes i Additional reactive power compensation; i ij For nodes i and j Inter-voltage phase angle; G ij B ij These are the elements of the nodal admittance matrix; Incentive-based demand response constraints:

[0018] in, R For user satisfaction; R min The minimum value of user satisfaction; Δ P L,t for t Load change at any time; |Δ P L,t | represents the absolute value of the load change, used to measure the intensity of the fluctuation; P L,t for t The workload of the moment; Static var compensator constraints:

[0019] in, Q SVC i , Q SVC min , Q SVC max The first i The reactive power output and its upper and lower limits of a static var compensator.

[0020] Furthermore, after generating the initial population, the initial population is optimized. The optimization steps include: The initial population is divided into several subpopulations according to the variable type; Using the formula:

[0021] The initial individuals are generated by boundary shrinking of the individuals in the subpopulation; where X (0) i For the first i An initial individual, i =1,2,..., S size , S size This represents the initial population size. r i Let A be a random number uniformly distributed in [0,1] for the i-th initial individual; A and B are the sets of lower and upper bound values ​​of the control variable, respectively. If individuals in the optimized initial population do not meet the constraints, then the formula is used:

[0022] Individuals that do not meet the constraints are shrunk and brought together before being re-input into the power grid carrying capacity improvement model; where X (0) s Let X be the s-th infeasible initial individual in the initial population. (0) s-1 For each feasible initial individual that has passed the constraint check, α is the contraction coefficient.

[0023] Furthermore, the steps for optimizing the initial population that satisfies the constraints of the distribution network carrying capacity improvement model include: The new individual generation step involves selection, crossover, and mutation operations on each subpopulation in the initial population to generate new individuals. P x '; The population cooperation step involves selecting the individual with the highest fitness value from other subpopulations, decoding it, and then interacting with new individuals within the current subpopulation. P x Together they constitute the control variables P '' x And calculate the new individual P x 'Fitness; The decision step is to determine whether the maximum number of generations k has been reached. max If the result is positive, the optimization process is terminated and the optimal solution set is obtained; otherwise, the new individual generation step, the population cooperation step, and the judgment step are repeated.

[0024] Furthermore, the steps for selecting the optimal solution from the set of optimal solutions include: The satisfaction level of the optimal solution to be evaluated in the optimal solution set is calculated using a slightly smaller satisfaction function and a slightly larger satisfaction function; the slightly smaller satisfaction function is: ; The skewed satisfaction function is: ; Where, μ m For the fuzzy satisfaction level of the m-th optimization objective, f m Let be the actual calculated value of the m-th objective function in the optimal solution to be evaluated. f m max and f m min The first m The maximum and minimum values ​​of the objective function; Using the formula:

[0025] Calculate the standardized satisfaction level of the optimal solution to be evaluated in the optimal solution set; where, z j For the th in the optimal solution set j Standardized satisfaction level of the optimal solution. P jm This is the satisfaction probability matrix. C This represents the number of optimal solutions to be evaluated in the optimal solution set. m The index of the objective function. E m For information entropy, w m The weights of the objective function, M To optimize the number of objective functions, w m For the first m The weights of each objective function, m j m For the th in the optimal solution set j The first optimal solution to be evaluated m The satisfaction level of each objective function; The optimal solution with the highest standardized overall satisfaction is selected as the final global optimal solution.

[0026] The present invention also provides a distribution network carrying capacity enhancement system considering distributed power source access, comprising: The model building module is used to build a distribution network carrying capacity improvement model based on distribution network topology data and load data; The sample generation module is used to generate random scenario samples of distributed power output and load, and calculate the optimal installation location of reactive power compensation device for each sample. The initialization population acquisition module is used to input random scenario samples and the corresponding optimal reactive power compensation device installation locations into the power distribution network carrying capacity improvement model to obtain the initialization population; The optimal solution acquisition module is used to find the optimal solution set by optimizing the initial population that meets the constraints of the distribution network carrying capacity improvement model, and then select the optimal solution set from the optimal solution set to simultaneously achieve the maximum distributed power supply access capacity and the minimum investment and operation cost. The strategy formulation module is used to obtain the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response based on the optimized solution.

[0027] Beneficial effects: This invention addresses the challenges of fitting solar output using a Beta distribution to represent irradiance, wind output using a Weibull distribution to represent wind speed, and load using a normal distribution to represent fluctuation characteristics. This accurately recreates the random fluctuation patterns of these three core variables, making it suitable for engineering applications involving multiple types of distributed power sources, including solar and wind power. It employs Latin hypercube sampling to generate at least 500 sets of random source-load scenarios. Through stratified sampling logic, it achieves complete coverage of the entire source-load fluctuation range with fewer sampling attempts, balancing simulation accuracy and computational efficiency. From reactive power compensation node selection and initial population constraint verification to optimization iteration, the entire process is based on full-scenario samples. The final solution meets safety constraints in most probabilistic scenarios, avoiding overly conservative approaches in deterministic scenarios and addressing the insufficient robustness of single-scenario solutions. The carrying capacity assessment and optimization results perfectly align with the actual operating characteristics of the distribution network.

[0028] This invention uses second-order network loss reactive power correction sensitivity as the core indicator for site selection. It also quantifies the linear and nonlinear superposition effects of reactive power injection on network losses. In scenarios with high proportions of distributed power supply access and significant source-load fluctuations, the calculation accuracy is far higher than traditional first-order sensitivity, accurately locating the node with the highest reactive power regulation efficiency. Based on each group of random source-load scenarios, node sensitivity is calculated individually. Then, the frequency of node occurrence, average sensitivity, and comprehensive score are statistically analyzed across all scenarios. Finally, nodes with high regulation efficiency across all scenarios are selected, completely avoiding the limitations of single-scenario site selection and ensuring the regulation effect of the reactive power compensation scheme under all operating conditions. By pre-determining the optimal installation node, the high-dimensional site selection and capacity optimization problem is decoupled into a low-dimensional problem of first site selection and then capacity optimization, significantly reducing the search space of subsequent multi-objective optimization algorithms, reducing ineffective iterations, and accelerating algorithm convergence. Simultaneously, it ensures optimal regulation effect per unit compensation capacity, achieving the maximum distributed power supply carrying capacity improvement with minimal reactive power compensation investment.

[0029] This invention proposes an improved strategy of uniform distribution generation and shrinking and converging correction of infeasible individuals. Linear uniform interpolation ensures the initial population covers the entire feasible region, and a shrinkage coefficient quickly corrects infeasible individuals into feasible ones, guaranteeing that the initial population satisfies all safety constraints. This reduces invalid iterations from the source and significantly improves the algorithm's convergence speed. The algorithm splits the variable type into three independent subpopulations, each evolving independently. Then, through inter-population collaboration, a complete control variable is constructed, decomposing the complex high-dimensional optimization problem into three low-dimensional subproblems. This avoids mutual interference between different types of variables, resulting in stronger global optimization capabilities and effectively circumventing the traditional algorithm's tendency to get trapped in local optima. Furthermore, the improved genetic algorithm is embedded into the NSGA-II multi-objective collaborative optimization algorithm. Through non-dominated sorting, elite retention, and crowding calculation, a complete Pareto optimal solution set is obtained, providing a comprehensive and objective basis for the final decision and completely avoiding the subjective bias of manual weighting. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. The elements or parts in the drawings are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0031] Figure 1 This is a flowchart of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the process of generating an initial population, optimizing the initial population, calculating the optimal solution set, and selecting the optimal solution in Embodiment 1 of the present invention. Detailed Implementation

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0033] In this document, suffixes such as "module," "part," or "unit" used to denote elements are used only for the purpose of illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0034] In this document, the terms "upper," "lower," "inner," "outer," "front," "rear," "one end," and "the other end," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the present invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0035] In this document, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, a direct connection, or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0036] In this document, "and / or" includes any and all combinations of one or more of the listed related items.

[0037] In this article, "multiple" means two or more, that is, it includes two, three, four, five, etc.

[0038] Example 1: like Figure 1 , Figure 2 As shown, this embodiment provides a method for improving the carrying capacity of a distribution network considering the access of distributed power sources. The specific steps include: S1 constructs a distribution network carrying capacity improvement model based on distribution network topology data and load data.

[0039] Distribution network topology data is the physical basis for power flow calculation, network loss calculation, and security constraint verification. Specifically, it includes: basic distribution network topology information such as the total number of nodes, node types, and branch topology connections; branch electrical parameters such as resistance R, reactance X, conductance G, susceptance B, and rated current carrying capacity of each line, rated capacity, turns ratio, short-circuit impedance, and no-load loss of transformers, as well as the node admittance matrix generated based on the above parameters; and boundary parameters of power grid equipment such as the rated breaking current of circuit breakers at each node, rated bus voltage, candidate nodes for SVC device installation, and thermal stability limits of transformers.

[0040] Distribution network load baseline data is the core input for load uncertainty modeling, demand response cost calculation, and power flow balance constraint construction. Specifically, it includes: historical time-series load data, such as hourly / minute-level active and reactive load historical operating data of each node in the distribution network, used to fit the probability distribution characteristics of the load; load statistical characteristic parameters, such as the mean and standard deviation of active load at each node, and the constant power factor of the load, which are the core parameters for subsequent Latin hypercube sampling to generate random load scenarios; and demand response supporting data, such as the list of users participating in incentive-based demand response, the maximum load reduction amount acceptable to users, the minimum electricity satisfaction threshold, and the user interruption compensation coefficient, used for demand response cost modeling and constraint construction.

[0041] Specifically, the distribution network carrying capacity enhancement model based on distribution network topology data and load data is as follows:

[0042] in, f 1 represents the total capacity of the connected distributed power sources; f 2 represents the total investment and operating costs; f cost To cover additional reactive power compensation investment costs; f loss The cost of active power loss during system operation; C IL To compensate for system demand response costs; S N C is the set of all nodes; DG，i S represents the distributed power supply capacity that node i can access; c For the additional reactive power compensation set; ρ Q The unit capacity price for additional reactive power compensation; τ is the discount rate; y is the service life of the reactive power compensation; Q c，k For the k-th additional reactive power compensation capacity; r c,k The fixed installation investment cost for the kth additional reactive power compensation; ρ P Price per unit of active power loss; S l Z is the set of branches; l I is the impedance of branch l; l C is the current flowing through branch l; IL i,t The interruption compensation fee received by the user; △P IL i,t α represents the load reduction for user i. i β i This represents the interruption compensation coefficient.

[0043] The constraints are rigid boundaries for the safe and stable operation of the distribution network, and also define the feasible region for model optimization. All optimization variables must satisfy these constraints. Specifically, the constraints of the distribution network carrying capacity improvement model described in this embodiment include: Distributed power generation output constraints: ; in, P DG，i For nodes i The current output active power of the distributed power source connected to the upstream is... i ∈ S N ; P DG，i，max For nodes i The upper limit of the active power output of the distributed power source; Node voltages will be subject to constraints: ; in, U i For nodes i voltage amplitude, i ∈ S N ; U i，max and U i，min These are the upper and lower limits of the voltage amplitude at node i, respectively; Line carrying capacity opportunity constraints: ; in, I l，max For the line l Maximum allowable current carrying capacity; S l For branch set; Transformer reverse load rate opportunity constraint:

[0044] in, P L，i For nodes i The equivalent load output; S T，max Let T be the maximum transmission capacity of transformer T, where T∈ S T ,in, S T A collection of transformers; l max This represents the maximum reverse load rate of the transformer. Current balance constraints:

[0045] in, Q L，i For nodes i Reactive power under load,i ∈ S N ; Q C，i For nodes i Additional reactive power compensation; i ij For nodes i and j Inter-voltage phase angle; G ij B ij These are the elements of the nodal admittance matrix; Incentive-based demand response constraints:

[0046] in, R For user satisfaction; R min The minimum value of user satisfaction; Δ P L,t for t Load change at any time; |Δ P L,t | represents the absolute value of the load change, used to measure the intensity of the fluctuation; P L,t for t The workload of the moment; Static var compensator constraints:

[0047] in, Q SVC i , Q SVC min , Q SVC max The first i The reactive power output and its upper and lower limits of a static var compensator.

[0048] S2 generates random scenario samples of distributed power output and load, and calculates the optimal installation location of reactive power compensation device for each sample.

[0049] The purpose of this step is to address the limitations of existing technologies that are based on single-scenario optimization, to capture the nonlinear impact of distributed power generation output and random load fluctuations on network loss sensitivity, and to identify the optimal installation node for robust reactive power compensation across all scenarios. This not only provides a comprehensive input basis for subsequent distribution network capacity optimization but also significantly reduces the search dimension of subsequent optimization algorithms, improves solution efficiency, and enhances the engineering practicality of the solution.

[0050] Specifically, in this embodiment, the Latin hypercube sampling (LHS) method is used to generate random scenario samples covering the full fluctuation of source and load conditions, generating no fewer than 500 valid scenarios. Compared to traditional Monte Carlo random sampling, LHS employs stratified sampling logic, which can ensure scenario coverage and simulation accuracy with fewer sampling times, perfectly meeting the needs of power distribution network source-load uncertainty modeling.

[0051] This step is further divided into four sub-steps: distributed photovoltaic power output sample generation, distributed wind power output sample generation, distribution network load sample generation, and source-load joint scenario integration. The complete process is as follows: The steps for generating samples for distributed photovoltaic power output uncertainty scenarios include: A probability distribution model for light intensity is constructed, wherein the probability distribution model for light intensity is as follows: ; Among them, f r (r) is the probability density function of the light intensity r, and r and r max These represent the radiation value and maximum radiation value received by the photovoltaic power generation system, respectively. G It is the Gamma function. a and b It is the shape parameter of the distribution; The stratified sampling of light radiation values ​​was performed using the Latin hypercube sampling method to generate multiple sets of random samples of light radiation intensity. Based on the sampled solar radiation values, the active power output of the corresponding sample is calculated; the formula for the output power utilization of a distributed photovoltaic power generation system is as follows:

[0052] Calculate; where P DG (r) represents the real-time active power output of the distributed photovoltaic power generation system, M represents the number of photovoltaic panels, A represents the total area of ​​the photovoltaic panels, and η represents the operating efficiency. m Let A be the working efficiency of the m-th photovoltaic panel. m Let m be the area of ​​the m-th photovoltaic panel; The steps for generating samples for scenarios with uncertain wind power output include: Construct a wind speed probability distribution model, wherein the wind speed probability distribution model is as follows: , ; Where f(v) is the probability density function of wind speed v, k and c are the shape and scale parameters respectively, v is the wind speed, and σ w , These represent the variance and mean of the wind speed, respectively; Ew For the output of wind power generation; The wind speed v was stratified and sampled using the Latin hypercube sampling method to generate multiple sets of random wind speed samples. Based on the sampled wind speed, the wind power output of the corresponding sample is calculated using a piecewise function of wind turbine output; the formula for utilizing the output power of wind power generation is as follows:

[0053] Calculate; where P WTG (v) represents the real-time active power output of wind power generation. ci v cr v co These are the cut-in, rated, and cut-out wind speeds for wind power generation, respectively; P r This refers to the rated power of wind power generation. The steps for generating samples for power distribution network load uncertainty scenarios include: A probability distribution model of the distribution network load is constructed, and the probability distribution model of the distribution network load is as follows:

[0054] Among them, f r (P L The active power P injected into the power grid by the load L The probability density function, P L Q L , representing the active power and reactive power injected into the power grid by the load, respectively; μL and σL are the mean and standard deviation of the active power, respectively; The active power injected into the power grid by load is sampled in a stratified manner using the Latin hypercube sampling method to generate multiple sets of random samples of active power injected into the power grid by load. Based on the assumption of a constant power factor, using the formula:

[0055] Calculate the reactive load corresponding to the active load sample; where φ is the power factor angle.

[0056] Distributed photovoltaic power output samples, wind power output samples, and distribution network load samples under the same sampling sequence number are paired to form a complete set of joint random scenario samples. The above sampling and calculation process is repeated until no less than 500 sets of valid source-load joint random scenario samples are generated to meet the accuracy requirements of subsequent multi-scenario analysis.

[0057] In this step, based on each group of source-load random scenario samples, the optimal candidate node for a single scenario is calculated using the second-order network loss reactive power correction sensitivity method. Finally, through multi-scenario cross-screening, the optimal installation location of the reactive power compensation device with robustness across all scenarios is obtained. The specific steps include: S21 calculates the initial power flow full result for random scenario samples.

[0058] Based on the current single-source-load random scenario sample to be calculated, the distribution network topology, line parameters, and node admittance matrix, the forward-backward substitution method commonly used in distribution networks is adopted to perform power flow calculation, and the initial full power flow results under this scenario are obtained, including: the voltage amplitude of each node, the voltage phase angle difference between nodes, the elements of the node admittance matrix, the total active power loss of the system, and the total reactive power load of the system.

[0059] S22 selects load nodes and distributed generation access nodes in the distribution network as candidate nodes.

[0060] Load nodes and distributed power source access nodes in the distribution network are selected as candidate nodes, while balancing nodes and nodes without regulation potential are excluded to narrow the calculation scope and better fit the engineering reality of reactive power compensation in the distribution network.

[0061] S23 calculates the network loss reactive power correction sensitivity of candidate nodes in a single scenario based on the initial power flow full result.

[0062] In this embodiment, the formula is used:

[0063] Calculate the sensitivity of candidate nodes for network loss and reactive power correction in a single scenario; where ΔP loss P is the increment of the total active power loss of the system. loss Let be the total active power loss of the system; u be the node input power matrix; Δu be the node power increment, (Δu) T Transpose the node power increment; P loss / u represents the first-order network loss sensitivity; 2 P loss / ( u u) represents the second-order network loss sensitivity; U i U j These are the voltages at node i and node j, respectively; G ij B ij These are the elements of the nodal admittance matrix; δ ij Let i be the voltage phase angle difference between nodes i and j; i and j are the node numbers, i and j = 1 to n.

[0064] Using 1% of the total reactive load of the system as the unit reactive power increment Δu ensures the effectiveness of the sensitivity calculation, conforms to the engineering norms of reactive power regulation in distribution networks, and ensures the comparability of sensitivity calculation results under different scenarios.

[0065] S24 sorts the candidate nodes from largest to smallest according to their reactive power compensation sensitivity, and selects the top-ranked nodes as the optimal reactive power compensation candidate nodes for the single scenario corresponding to the random scenario sample.

[0066] S25 calculates the frequency of occurrence, average sensitivity, and comprehensive sensitivity score of all candidate nodes in the full-scene sample, and selects candidate nodes as the optimal installation location for reactive power compensation devices based on the scores.

[0067] Repeat steps S21 to S245 to complete the calculation of no less than 500 sets of source-load random scenario samples, and obtain the initial optimal node set corresponding to each set of samples; count the occurrence frequency, average sensitivity, and comprehensive sensitivity score of all candidate nodes in the full scenario samples, and select the nodes that are in the high sensitivity ranking and have the best comprehensive reactive power regulation effect in most probability scenarios as the optimal installation position of the final reactive power compensation device.

[0068] S3 inputs random scenario samples and the corresponding optimal reactive power compensation device installation locations into the power distribution network carrying capacity improvement model to obtain the initial population.

[0069] The purpose of this step is to generate an initial population that meets the full-scenario robust reactive power compensation node and source-load random scenario samples output in step 2, satisfies the full-dimensional security constraints of the distribution network, covers the entire feasible domain, and has high individual quality. This provides an efficient and compliant starting point for subsequent multi-objective collaborative optimization and solves the pain points of traditional genetic algorithms, such as a high proportion of infeasible individuals in the initial population, many invalid iterations, and slow convergence speed.

[0070] In this embodiment, after generating the initial population, the initial population is optimized. The optimization steps include: S31 splits the initial population into several subpopulations according to the variable type.

[0071] Specifically, in this embodiment, the initial population is divided into 3 sub-populations, including: The distributed generation access capacity subpopulation, whose control variable is the rated access capacity of distributed generation at each node of the distribution network, is represented by the vector form X1=[ P DG,1 , P DG,2 ,..., P DG,i ], where i is the total number of nodes in the distribution network, P DG,i This represents the rated capacity of the distributed power sources that node i can access.

[0072] An additional reactive power compensation quantum population is added, whose control variable is the SVC (Static Var Compensator) compensation capacity of the optimal reactive power compensation node, in vector form X2=[QC,1 Q C,2 Q C,k ], where k is the total number of optimal reactive power compensation installation nodes, Q C,k This represents the optimal compensation capacity for the SVC at the k-th node.

[0073] The quantum population for incentive-driven demand response load change has its control variable being the load reduction amount of each participating node before and after the demand response is implemented, expressed in vector form as X3=[△ P 1, △ P 1, ..., △ P n ], where n is the total number of nodes participating in incentive-based demand response, ΔP n This represents the load reduction amount for the nth node.

[0074] S32 uses the formula:

[0075] The initial individuals are generated by boundary shrinking of the individuals in the subpopulation; where X (0) i For the first i An initial individual, i =1,2,..., S size , S size This represents the initial population size. r i For the first i An initial set of individuals is a random number that follows a uniform distribution on [0,1]; A and B are the sets of lower and upper bound values ​​for the control variables, respectively.

[0076] The above formula uses r i The uniform randomness of [0,1] allows the initial individuals to be evenly distributed within the complete feasible interval of [A,B], avoiding the problems of individual clustering and duplication that occur in traditional random generation, and improving the diversity of the initial population and the coverage of the feasible solution space.

[0077] To ensure that the generated initial individuals meet the constraints of the full-dimensional safe operation of the distribution network, the source-load random scenario samples generated in step S2 are used to perform multi-scenario chance constraint verification on each initial individual. The constraint conditions are detailed in step S1 and will not be repeated here.

[0078] In this embodiment, if individuals in the optimized initial population do not meet the constraints, then the formula is used:

[0079] Individuals that do not meet the constraints are shrunk and brought together before being re-input into the power grid carrying capacity improvement model; where X (0)s Let X be the s-th infeasible initial individual in the initial population. (0) s-1 For each feasible initial individual that has passed the constraint check, α is the contraction coefficient.

[0080] This formula gradually reduces the proportion of infeasible individuals and increases the proportion of feasible individuals by continuously reducing the weight of α, eventually ensuring that the corrected individuals converge into the feasible region, guaranteeing that the initial population is 100% feasible. For the corrected individuals, a multi-scenario opportunity constraint check is performed again. If the constraint is met, the individual is determined to be feasible; otherwise, the α value is halved for further shrinkage and correction until the check is passed.

[0081] S4 optimizes the initial population that meets the constraints of the distribution network carrying capacity improvement model, obtains the optimal solution set, and selects the optimal solution from the optimal solution set that simultaneously achieves the maximum distributed power supply access capacity and the minimum investment and operating cost.

[0082] This step embeds a genetic algorithm that improves the initial population generation strategy into the NSGA-II multi-objective collaborative optimization algorithm. It efficiently solves the bi-objective optimization model through the strategies of independent evolution of subpopulations and cooperation among populations, obtaining the Pareto optimal solution set. Then, the information entropy fuzzy satisfaction method is used to select the unique engineering optimal solution from the solution set that simultaneously takes into account the maximization of carrying capacity and the optimization of economy, thus solving the problem of non-dominated solution set decision-making in multi-objective optimization.

[0083] In this embodiment, the specific steps for optimizing the initial population that satisfies the constraints of the distribution network carrying capacity improvement model include: The S411 new individual generation step involves selecting, crossing over, and mutating each subpopulation in the initial population to generate a new individual Px'.

[0084] To reduce the difficulty of solving complex optimization problems, this embodiment uses independent genetic algorithms to evolve three subpopulations with different physical properties, avoiding mutual interference between variables of different types and improving search efficiency.

[0085] The selection operation employs Tournament Selection, which randomly selects several individuals from the current subpopulation and chooses the individual with the best fitness as the parent generation to preserve superior genes and prevent population degradation.

[0086] The crossover operation uses simulated binary crossover (SBX) to crossover and recombine the control variables of the parent individuals to generate new offspring individuals, simulating gene recombination in biological evolution and ensuring population diversity.

[0087] The mutation operation employs multinomial mutation, which applies a small-probability random perturbation to the control variables of the newly generated offspring individuals, thus preventing the algorithm from getting trapped in local optima and expanding the search space.

[0088] After selection, crossover, and mutation are performed on each subpopulation, a new individual Px' corresponding to that subpopulation is generated.

[0089] The S412 population cooperation step involves selecting the individual with the highest fitness value from other subpopulations, decoding it, and then interacting with new individuals within the current subpopulation. P x Together they constitute the control variables P '' x And calculate the new individual P x 'Adaptability'.

[0090] Since the control variables of the three subpopulations are interrelated, individual evolution cannot assess the merits of a single new individual. Therefore, it is necessary to construct complete control variables through interpopulation collaboration.

[0091] For new individuals in the current subpopulation P x 'Select the individual with the highest fitness value from the other two subpopulations, decode it, and then...' P x 'Together they constitute the complete control variables of the system' P '' x It covers all optimization variables, including distributed power supply capacity, SVC compensation capacity, and demand response load changes; it includes complete control variables. P '' x Combined with random scenario samples, perform power flow calculations for the distribution network and verify the results. P '' x Does the constraint condition satisfy? If P '' x Satisfying all constraints, based on the power flow calculation results, calculate the two objective function values: the total distributed power supply capacity corresponding to the complete control variable f1; and the total investment and operating cost corresponding to the complete control variable f2.

[0092] Based on the rules of NSGA-II, for new individuals P x 'Perform non-dominated ranking and crowding calculations to ultimately determine the fitness of new individuals.'

[0093] S413 Judgment Step: Determine whether the maximum number of generations k has been reached. max If the result is positive, the optimization process is terminated and the optimal solution set is obtained; otherwise, the new individual generation step, the population cooperation step, and the judgment step are repeated.

[0094] Determine if the current iteration number has reached the preset maximum number of generations k. max If k is reached max If k is not reached, the optimization iteration process is terminated, and the Pareto optimal solution set in the current population is output; if k is not reached... max The newly generated individuals are then merged with the parent population, and the optimal S individual is selected through the NSGA-II elite preservation strategy. size Each individual becomes the parent population of the next generation, and the process returns to step S411 to continue iterative optimization.

[0095] After obtaining the Pareto optimal solution set, it is necessary to select the unique engineering optimal solution that simultaneously takes into account both objectives from the Pareto optimal solution set using the objective information entropy weight method and the fuzzy satisfaction function, so as to solve the decision-making problem of multi-objective optimization.

[0096] Specifically, the steps for selecting the optimal solution from the set of optimal solutions include: S421 calculates the satisfaction level of the optimal solution to be evaluated in the optimal solution set using a slightly smaller satisfaction function and a slightly larger satisfaction function; the slightly smaller satisfaction function is: ; The skewed satisfaction function is: ; Where, μ m For the fuzzy satisfaction level of the m-th optimization objective, f m Let be the actual calculated value of the m-th objective function in the optimal solution to be evaluated. f m max and f m min The first m The maximum and minimum values ​​of each objective function.

[0097] This step optimizes two objectives with completely different directions and dimensions, mapping them uniformly to the fuzzy satisfaction level in the [0,1] interval, eliminating the difference in dimensions, and quantifying the degree of fit of each solution to a single objective.

[0098] S422 uses the formula:

[0099] Calculate the standardized satisfaction level of the optimal solution to be evaluated in the optimal solution set; where, z j For the th in the optimal solution set j Standardized satisfaction level of the optimal solution. P jm This is the satisfaction probability matrix. CThis represents the number of optimal solutions to be evaluated in the optimal solution set. m The index of the objective function. E m For information entropy, w m The weights of the objective function, M To optimize the number of objective functions, w m For the first m The weights of each objective function, m j m For the th in the optimal solution set j The first optimal solution to be evaluated m The satisfaction level of each objective function.

[0100] S423 selects the optimal solution with the highest standardized overall satisfaction as the final global optimal solution.

[0101] Standardized overall satisfaction with all optimal solutions z j Sort by size from largest to smallest, then select... z j The solution corresponding to the maximum value is taken as the final global optimal solution.

[0102] S5 obtains the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response based on the optimized solution.

[0103] The purpose of this step is to transform the mathematically optimal solution output in step S4 into an SVC device selection and installation strategy and an incentive-based demand response scheduling execution scheme that power grid companies can directly implement. At the same time, it verifies the synergistic optimization effect of the strategy to ensure that the dual core objectives of maximizing the capacity of distributed power generation access and minimizing investment and operating costs in the distribution network are ultimately achieved.

[0104] The selection and installation strategy for SVC devices includes: 1. Transforming the baseline SVC compensation capacity value from the optimal solution into an engineering strategy that can be directly used for equipment selection, installation, and operation. Simultaneously, considering the continuously adjustable characteristics of SVC, dynamic operating rules are formulated. 2. Since the optimization results are usually consecutive decimals, while actual SVC equipment has a fixed series of rated capacities, engineering adjustments are necessary to ensure the solution is feasible. 3. SVC is a continuously adjustable dynamic reactive power compensation device; dynamic operating rules need to be formulated based on real-time fluctuations in the source load to ensure optimal reactive power support under all operating conditions.

[0105] The incentive-based demand response scheduling execution scheme includes: 1. Time-series mapping and time-segmentation of load reduction. Demand response is implemented in time segments, requiring the mapping of the baseline load reduction value to specific runtime segments. 2. Optimizing the allocation of the total load reduction amount across time segments to specific participating users, minimizing demand response compensation costs while meeting load reduction requirements. 3. Establishing clear scheduling execution rules to ensure the operability of the scheme and user satisfaction.

[0106] The optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response can be formulated using conventional methods, which will not be elaborated here.

[0107] Example 2: This embodiment also provides a distribution network capacity enhancement system considering distributed power source access, including: The model building module is used to build a distribution network carrying capacity improvement model based on distribution network topology data and load data; The sample generation module is used to generate random scenario samples of distributed power output and load, and calculate the optimal installation location of reactive power compensation device for each sample. The initialization population acquisition module is used to input random scenario samples and the corresponding optimal reactive power compensation device installation locations into the power distribution network carrying capacity improvement model to obtain the initialization population; The optimal solution acquisition module is used to find the optimal solution set by optimizing the initial population that meets the constraints of the distribution network carrying capacity improvement model, and then select the optimal solution set from the optimal solution set to simultaneously achieve the maximum distributed power supply access capacity and the minimum investment and operation cost. The strategy formulation module is used to obtain the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response based on the optimized solution.

[0108] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0109] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. 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 is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a computer terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0110] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for improving the carrying capacity of a distribution network considering the integration of distributed generation sources, characterized in that, include: Based on distribution network topology data and load data, a distribution network carrying capacity improvement model is constructed; Generate random scenario samples of distributed power output and load, and calculate the optimal installation location of reactive power compensation device for each sample. The random scenario samples and the corresponding optimal reactive power compensation device installation locations are input into the power distribution network carrying capacity improvement model to obtain the initial population. The initial population that meets the constraints of the distribution network carrying capacity improvement model is optimized to obtain the optimal solution set, and the optimal solution that simultaneously achieves the maximum distributed power generation access capacity and the minimum investment and operation cost is selected from the optimal solution set. Based on the optimized solution, the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response are obtained.

2. The method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, The power distribution network carrying capacity enhancement model is as follows: in, f 1 represents the total capacity of the connected distributed power sources; f 2 represents the total investment and operating costs; f cost To cover additional reactive power compensation investment costs; f loss The cost of active power loss during system operation; C IL To compensate for system demand response costs; S N C is the set of all nodes. DG，i S represents the distributed power supply capacity that node i can access; c For the additional reactive power compensation set; ρ Q The unit capacity price for additional reactive power compensation; τ is the discount rate; y is the service life of the reactive power compensation; Q c，k For the k-th additional reactive power compensation capacity; r c,k The fixed installation investment cost for the kth additional reactive power compensation; ρ P Price per unit of active power loss; S l Z is the set of branches; l I is the impedance of branch l; l C is the current flowing through branch l; IL i,t The interruption compensation fee received by the user; △P IL i,t α represents the load reduction for user i. i β i This represents the interruption compensation coefficient.

3. The method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, Methods for generating random scenario samples of distributed power generation output and load include: The steps for generating samples for distributed photovoltaic power output uncertainty scenarios include: A probability distribution model for light intensity is constructed, wherein the probability distribution model for light intensity is as follows: Among them, f r (r) is the probability density function of the light intensity r, and r and r max These represent the radiation value and maximum radiation value received by the photovoltaic power generation system, respectively. Г It is the Gamma function. a and b It is the shape parameter of the distribution; The stratified sampling of light radiation values ​​was performed using the Latin hypercube sampling method to generate multiple sets of random samples of light radiation intensity. Based on the sampled solar radiation values, the active power output of the corresponding sample is calculated; the formula for the output power utilization of a distributed photovoltaic power generation system is as follows: Calculate; where P DG (r) represents the real-time active power output of the distributed photovoltaic power generation system, M represents the number of photovoltaic panels, A represents the total area of ​​the photovoltaic panels, and η represents the operating efficiency. m Let A be the working efficiency of the m-th photovoltaic panel. m Let m be the area of ​​the m-th photovoltaic panel; The steps for generating samples for scenarios with uncertain wind power output include: Construct a wind speed probability distribution model, wherein the wind speed probability distribution model is as follows: ， ； Where f(v) is the probability density function of wind speed v, k and c are the shape and scale parameters respectively, v is the wind speed, and σ w , These represent the variance and mean of the wind speed, respectively; E w For the output of wind power generation; The wind speed v was stratified and sampled using the Latin hypercube sampling method to generate multiple sets of random wind speed samples. Based on the sampled wind speed, the wind power output of the corresponding sample is calculated using a piecewise function of wind turbine output; the formula for utilizing the output power of wind power generation is as follows: Calculate; where P WTG (v) represents the real-time active power output of wind power generation. ci v cr v co These are the cut-in, rated, and cut-out wind speeds for wind power generation, respectively; P r This refers to the rated power of wind power generation. The steps for generating samples for power distribution network load uncertainty scenarios include: A probability distribution model of the distribution network load is constructed, and the probability distribution model of the distribution network load is as follows: Among them, f r (P L The active power P injected into the power grid by the load L The probability density function, P L Q L These represent the active and reactive power injected into the power grid by the load, respectively; μ L σ L These are the mean and standard deviation of the active power, respectively. The active power injected into the power grid by load is sampled in a stratified manner using the Latin hypercube sampling method to generate multiple sets of random samples of active power injected into the power grid by load. Based on the assumption of a constant power factor, using the formula: Calculate the reactive load corresponding to the active load sample; where φ is the power factor angle.

4. The method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, The steps for calculating the optimal installation location of the reactive power compensation device include: Calculate the initial full power flow result for random scenario samples; Load nodes and distributed generation access nodes in the distribution network are selected as candidate nodes. The sensitivity of network loss reactive power correction for candidate nodes in a single scenario is calculated based on the initial power flow full result. Candidate nodes are sorted from largest to smallest according to their reactive power compensation sensitivity, and the top-ranked nodes are selected as the optimal reactive power compensation candidate nodes for the single scenario corresponding to the random scenario sample. The frequency of occurrence, average sensitivity, and comprehensive sensitivity score of all candidate nodes in the full-scene sample are statistically analyzed, and candidate nodes are selected as the optimal installation locations for reactive power compensation devices based on the scores.

5. A method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 4, characterized in that, Using the formula: Calculate the sensitivity of candidate nodes for network loss and reactive power correction in a single scenario; where ΔP loss P is the increment of the total active power loss of the system. loss Let be the total active power loss of the system; u be the node input power matrix; Δu be the node power increment, (Δu) T Transpose the node power increment; P loss / u represents the first-order network loss sensitivity; 2 P loss / ( u u) represents the second-order network loss sensitivity; U i U j These are the voltages at node i and node j, respectively; G ij B ij These are the elements of the nodal admittance matrix; δ ij Let i be the voltage phase angle difference between nodes i and j; i and j are the node numbers, i and j = 1 to n.

6. A method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, The constraints of the power distribution network carrying capacity improvement model include: Distributed power generation output constraints: ； in, P DG，i For nodes i The current output active power of the distributed power source connected to the upstream is... i ∈ S N ; P DG，i，max For nodes i The upper limit of the active power output of the distributed power source; Node voltages will be subject to constraints: ； in, U i For nodes i voltage amplitude, i ∈ S N ; U i，max and U i，min These are the upper and lower limits of the voltage amplitude at node i, respectively; Line carrying capacity opportunity constraints: ； in, I l，max For the line l Maximum allowable current carrying capacity; S l For branch set; Transformer reverse load rate opportunity constraint: in, P L，i For nodes i The equivalent load output; S T，max Let T be the maximum transmission capacity of transformer T, where T∈ S T ,in, S T A collection of transformers; λ max This represents the maximum reverse load rate of the transformer. Current balance constraints: in, Q L，i For nodes i Reactive power under load, i ∈ S N ; Q C，i For nodes i Additional reactive power compensation; θ ij For nodes i and j Inter-voltage phase angle; G ij B ij These are the elements of the nodal admittance matrix; Incentive-based demand response constraints: in, R For user satisfaction; R min The minimum value of user satisfaction; Δ P L,t for t Load change at any time; |Δ P L,t | represents the absolute value of the load change, used to measure the intensity of the fluctuation; P L,t for t The workload of the moment; Static var compensator constraints: in, Q SVC i , Q SVC min , Q SVC max The first i The reactive power output and its upper and lower limits of a static var compensator.

7. A method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, After generating the initial population, the initial population is optimized. The optimization steps include: The initial population is divided into several subpopulations according to the variable type; Using the formula: The initial individuals are generated by boundary shrinking of the individuals in the subpopulation; where X (0) i For the first i An initial individual, i =1,2,..., S size , S size This represents the initial population size. r i Let A be a random number uniformly distributed in [0,1] for the i-th initial individual; A and B are the sets of lower and upper bound values ​​of the control variable, respectively. If individuals in the optimized initial population do not meet the constraints, then the formula is used: Individuals that do not meet the constraints are shrunk and brought together before being re-input into the power grid carrying capacity improvement model; where X (0) s Let X be the s-th infeasible initial individual in the initial population. (0) s-1 For each feasible initial individual that has passed the constraint check, α is the contraction coefficient.

8. A method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 7, characterized in that, The steps for finding the optimal solution for the initial population that satisfies the constraints of the distribution network carrying capacity improvement model include: The new individual generation step involves selection, crossover, and mutation operations on each subpopulation in the initial population to generate new individuals. P x '; The population cooperation step involves selecting the individual with the highest fitness value from other subpopulations, decoding it, and then interacting with new individuals within the current subpopulation. P x Together they constitute the control variables P '' x And calculate the new individual P x 'Fitness; The decision step is to determine whether the maximum number of generations k has been reached. max If the result is positive, the optimization process is terminated and the optimal solution set is obtained; otherwise, the new individual generation step, the population cooperation step, and the judgment step are repeated.

9. A method for improving the carrying capacity of a distribution network considering distributed power source access according to claim 1, characterized in that, The steps for selecting the optimal solution from the set of optimal solutions include: The satisfaction level of the optimal solution to be evaluated in the optimal solution set is calculated using a slightly smaller satisfaction function and a slightly larger satisfaction function; the slightly smaller satisfaction function is: ； The aforementioned large-scale satisfaction function is: ； Where, μ m For the fuzzy satisfaction level of the m-th optimization objective, f m Let be the actual calculated value of the m-th objective function in the optimal solution to be evaluated. f m max and f m min The first m The maximum and minimum values ​​of the objective function; Using the formula: Calculate the standardized satisfaction level of the optimal solution to be evaluated in the optimal solution set; where, z j For the th in the optimal solution set j Standardized satisfaction level of the optimal solution. P jm This is the satisfaction probability matrix. C This represents the number of optimal solutions to be evaluated in the optimal solution set. m The index of the objective function. E m For information entropy, w m The weights of the objective function, M To optimize the number of objective functions, w m For the first m The weights of each objective function, μ j m For the th in the optimal solution set j The first optimal solution to be evaluated m The satisfaction level of each objective function; The optimal solution with the highest standardized overall satisfaction is selected as the final global optimal solution.

10. A distribution network capacity enhancement system considering distributed generation access, characterized in that, include: The model building module is used to build a distribution network carrying capacity improvement model based on distribution network topology data and load data; The sample generation module is used to generate random scenario samples of distributed power output and load, and calculate the optimal installation location of reactive power compensation device for each sample. The initialization population acquisition module is used to input random scenario samples and the corresponding optimal reactive power compensation device installation locations into the power distribution network carrying capacity improvement model to obtain the initialization population; The optimal solution acquisition module is used to find the optimal solution set by optimizing the initial population that meets the constraints of the distribution network carrying capacity improvement model, and then select the optimal solution set from the optimal solution set to simultaneously achieve the maximum distributed power source access capacity and the minimum investment and operation cost. The strategy formulation module is used to obtain the optimal compensation capacity of SVC and the optimal scheduling strategy for incentive-based demand response based on the optimized solution.

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