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CN-121984124-A - Active power distribution network flexibility improving method and system based on intelligent soft switch optimization

CN121984124ACN 121984124 ACN121984124 ACN 121984124ACN-121984124-A

Abstract

The invention discloses an active power distribution network flexibility improving method and system based on intelligent soft switch optimization, firstly, a network flexibility evaluating frame is constructed, and then, based on the obtained flexibility evaluation index, constructing a deterministic planning model of intelligent soft switch optimal configuration. Based on a deterministic planning model, a scene-based maximum and minimum regret method is introduced, an intelligent soft switch optimal configuration robust planning model is constructed, and finally model solving and verification are carried out. The method defines the maximum osmotic capacity range of renewable energy as a flexibility index, can be used for more efficiently adapting to the uncertainty of a new energy high-permeability scene, considers the economy and flexibility of the power distribution network through double-objective optimization, adopts a scene-based robust method to solve, can cover all the considered renewable energy output fluctuation scenes, ensures that the obtained scheme has feasibility under various fluctuation working conditions, and remarkably enhances the running stability and anti-interference capability of the power distribution network.

Inventors

  • CHEN LE
  • Tang Zao
  • ZENG PINGLIANG
  • CHEN CHAO
  • XIE CHENG

Assignees

  • 杭州电子科技大学

Dates

Publication Date
20260505
Application Date
20260122

Claims (10)

  1. 1. An active power distribution network flexibility improving method based on intelligent soft switch optimization is characterized by comprising the following steps: step (1) constructing a network flexibility assessment framework: Setting the output power and the related permeability coefficient of the wind driven generator under the minimum and maximum allowable permeability conditions by taking the renewable energy permeability range as a flexibility evaluation index of the power distribution network, maximizing the sum of the maximum and minimum allowable power generation capacity difference values of all wind power units as a flexibility objective function, and reflecting the regulation capability of the power distribution network to wind power change; step (2) establishing a deterministic programming model of intelligent soft switch optimization configuration: based on flexibility evaluation indexes obtained by a network flexibility evaluation framework, constructing a double-target deterministic programming model with minimized total construction cost and maximized flexibility of the power distribution network, and converting a non-convex mixed integer nonlinear programming model into a mixed integer second-order cone programming model; Step (3) constructing a scene-based intelligent soft switch optimal configuration robust planning model: Based on a deterministic planning model, a scene-based maximum and minimum regret method is introduced for uncertainty of wind power output fluctuation, and an intelligent soft switch optimal configuration robust planning model is constructed, so that the obtained solution has feasibility in all wind power generation scenes and achieves near-optimal performance; And (4) solving and verifying the model, namely selecting an IEEE33 node system as a test power distribution network, processing double-target conflict by adopting an enhanced constraint method after configuring a test environment, outputting a pareto optimal solution, analyzing the trade-off relation between the cost and the flexibility, performing robust constraint verification, and finally verifying the safety and the feasibility of the model by displaying the maximum/minimum allowable penetration capacity of a fan under each pareto solution.
  2. 2. The method for improving the flexibility of the active power distribution network based on intelligent soft switch optimization according to claim 1, wherein the network flexibility assessment framework uses the size of a penetration capacity range which can be stably accepted after a wind turbine generator is connected into the power distribution network as a core quantization basis of the power distribution network flexibility; Firstly, determining a penetration capacity range of a wind turbine, namely a minimum-maximum allowable output power interval of the wind turbine, wherein the specific operation is as follows, the actual output power and the output predicted power of the wind turbine in a wind power generation scene Viewed as consistent, by calculating permeation parameters related to allowable permeation levels of wind turbines , The output power of the generator under the minimum and maximum allowable penetration conditions is obtained respectively, and the relation between the output power and the predicted power is shown in the formulas (1) and (2): (1) (2) wherein: And The output power of the wind driven generator under the minimum and maximum allowable penetration conditions is respectively; And Respectively minimum and maximum relative permeability coefficients; The method comprises the steps of collecting all bus assemblies provided with wind driven generators; rated power of the wind driven generator arranged for the ith node; The flexibility objective function of the network flexibility evaluation framework is the maximum allowable output range of the wind turbine, namely, the sum of the difference values of the maximum allowable power generation amount and the minimum allowable power generation amount of all the wind turbines mounted on the bus is maximized, and the formula is as follows: (3) In the formula, Is a flexible objective function.
  3. 3. The method for improving the flexibility of the active power distribution network based on intelligent soft switch optimization according to claim 2 is characterized in that related parameters, wind power output parameters, intelligent soft switch performance parameters and economic parameters of the power distribution network in a deterministic planning model are processed into determined quantities, the model uses the minimization of the total construction cost of the power distribution network and the maximization of the flexibility of the power distribution network as double objective functions, the two functions together form an optimization object of a double objective optimization method, and in order to unify the optimization types of functions in the double objective optimization method, a negative sign is added to the flexibility objective function, and the flexibility objective function is added to the total construction cost objective function Merging to obtain a model objective function; the objective function of minimizing the total construction cost of the distribution network is as follows: (5) (6) (7) (8) (9) (10) (11) (12) In the formula, 、 And Respectively equal annual investment cost, total running cost and total average loss cost of an intelligent soft Switch (SOP); The service life of SOP; Initial investment cost for SOP; a candidate installation position set for SOP; The installation capacity for SOP at line ij; Cost rate for annual operation; And The maximum and minimum power consumption costs, respectively; And Maximum and minimum power consumption, respectively; the price of the electric energy loss is; And The maximum and minimum currents of the lines ij respectively; Is a line set; The resistance of the line ij; 8760 is the number of hours of a year, which is an annual loss factor; The objective function for maximizing the flexibility of the distribution network is as follows: (13)。
  4. 4. The method for improving flexibility of an active power distribution network based on intelligent soft switch optimization according to claim 3, wherein the deterministic planning model of intelligent soft switch optimization configuration comprises a power distribution network reconstruction model based on optimal power flow and an intelligent soft switch model, wherein the power distribution network reconstruction model of the optimal power flow provides a compliance operation rule of a power distribution network physical operation constraint framework and network topology adjustment, and the intelligent soft switch model provides equipment technical characteristics of the intelligent soft switch.
  5. 5. The method for improving the flexibility of the active power distribution network based on intelligent soft switching optimization according to claim 4, wherein the power distribution network reconstruction model based on the optimal power flow is specifically as follows: under the working conditions of the two penetration capacities, an optimal power flow optimization equation is adopted to construct the reconstruction problem of the power distribution network, and the reconstruction constraint of the power distribution network limits the reconstruction feasible range of the power distribution network; the power distribution network reconstruction model is used for adapting wind power penetration by adjusting topology, and the system operation constraint is a core component of the optimal power flow optimization equation, and the formula is as follows: (36) (37) (38) (39) In the formula, And A voltage lower limit and a voltage upper limit allowed by the voltage respectively; And The voltage amplitude of the node i in the corresponding scene; long-term allowable current-carrying capacity for line ij; And The current amplitude value flowing through the line ij under the corresponding scene; The method is a set of all nodes in the power grid; Which is the set of all lines in the system.
  6. 6. The method for improving the flexibility of the active power distribution network based on intelligent soft switch optimization according to claim 5 is characterized in that the intelligent soft switch model is established by standardizing the operation logic and configuration scheme of the intelligent soft switch according to clear boundary rules, namely intelligent soft switch operation and configuration constraint, wherein the intelligent soft switch operation constraint, intelligent soft switch capacity constraint and intelligent soft switch installation position and capacity constraint are included, and the specific constraint is as follows: intelligent soft switch operation constraint For the intelligent soft switch connected on the line ij, the power injection mode of the intelligent soft switch is represented by a formula (40) and a formula (41), the intelligent soft switch model is applicable to each operation working condition of the power distribution network, the physical meaning of the intelligent soft switch model is that the sum of the power injected into nodes i and j at two ends and the power loss of two converters inside the SOP is 0, formulas (42) and (43) describe a power loss calculation method of the SOP converter, the formulas (40) and (41) are power balance constraint of the intelligent soft switch, and the formulas (42) and (43) are converter power loss constraint of the intelligent soft switch, and the formulas are as follows: (40) (41) (42) (43) In the formula, 、 、 And Under the scenes of minimum allowable penetration capacity and maximum allowable penetration capacity respectively, the intelligent soft switch generates internal active power loss at the node i side and the node j side converters; the loss coefficient of the intelligent soft switching converter connected at the node i is used as the loss coefficient; The intelligent soft switch capacity constraint limits the intelligent soft switch active and reactive output upper limit, and provides a safety boundary for intelligent soft switch model capacity optimization; the limits of active and reactive power injected or absorbed by the intelligent soft switching converter are expressed as follows: (44) (45) the installation position and capacity constraint formula of the intelligent soft switch are expressed as follows: (46) (47) In the formula, The number of standard modules of the intelligent soft switch installed at the candidate position ij is expressed as an integer decision variable; The rated capacity of a single standard module is the intelligent soft switch.
  7. 7. The method for improving flexibility of an active power distribution network based on intelligent soft switch optimization according to claim 6, wherein the deterministic programming model of intelligent soft switch optimization configuration is converted into a mixed integer second order cone programming model: The deterministic programming model of the optimal configuration of the intelligent soft switch is a non-convex mixed integer nonlinear programming problem, the non-convex mixed integer nonlinear programming problem is converted into a mixed integer second order cone programming model, the problem is reconstructed into a linear objective function which is minimized under linear equality constraint and convex cone constraint, and specifically, node power balance constraint, branch voltage safety constraint, radial network topology constraint, main transformer substation busbar voltage constraint, power balance constraint of the intelligent soft switch and installation position and capacity constraint of the intelligent soft switch are linear constraint, linear equality constraint which is directly reserved into mixed integer second order cone programming is directly reserved, and branch current safety constraint, converter power loss constraint of the intelligent soft switch and intelligent soft switch capacity constraint conversion results are shown as follows: The conversion result of the branch current safety constraint is as follows: (48) (49) the conversion result of the converter power loss constraint of the intelligent soft switch is as follows: The absolute value of SOP active power is split into two linear inequalities, and the two inequalities are converted into linear constraint of second order cone constraint: (50) (51) the conversion result of the intelligent soft switch capacity constraint is as follows: (52) (54)。
  8. 8. The method for improving flexibility of active power distribution network based on intelligent soft switch optimization according to claim 7, wherein the scene-based intelligent soft switch optimal configuration robust planning model adopts a group of scenes for uncertainty of wind power output The method is characterized by comprising the steps of describing, in a model, the installation position and capacity of an intelligent soft switch, the network topology change state and the allowable penetration level of a wind turbine generator, wherein the installation position and capacity, the network topology change state and the allowable penetration level are called design variables, other variables are called control variables, introducing a scene-based maximum and minimum regret method based on a deterministic planning model, and constructing a scene-based intelligent soft switch optimal configuration robust planning model by taking cost and flexibility as objective functions, wherein the specific operation is as follows: the robust optimization of the cost and flexibility corresponding to the dual objective function of the model includes a robust remorse minimization objective function corresponding to the cost objective as shown in equation (56) and a robust remorse minimization objective function corresponding to the flexibility objective as shown in equation (57): (56) (57) wherein s is a scene index, and represents an uncertainty scene of possible wind power output fluctuation conditions; A set of all uncertainty scenarios considered; And The solution is the reference optimal solution; And The value actually realized in the scene s after the robust scheme is adopted; Taking the maximum remorse value in all uncertain scenes; And Minimizing parameters for maximum remorse values of cost targets and flexibility targets in uncertain scenes; In the maximum and minimum regret method, the regret value with the aim of cost minimization represents the maximum deviation degree of the cost target value relative to the optimal solution of the uncertainty scene under the robust scheme, the regret value with the aim of flexibility maximization represents the maximum deviation value of the flexibility target value relative to the optimal solution of the uncertainty scene under the robust scheme, and the corresponding optimal solution And By solving a deterministic programming model in each uncertainty scenario Lower the optimal value in the scene obtained after optimization, and form cost function Flexibility function Then in an uncertainty scenario Next, an optimal target value is generated from a set of robust design variable values.
  9. 9. The method for improving the flexibility of the active power distribution network based on intelligent soft switch optimization according to claim 8 is characterized in that an uncertainty scene of wind power output is defined according to a preset scene set in a solving process, the installation position and rated capacity of a wind turbine generator are defined, then a deterministic planning model of intelligent soft switch optimization configuration is independently solved for each wind power scene, a cost optimal solution and a flexibility optimal solution under each scene are obtained and used as references for calculating remorse values, a double-target robust planning model is constructed based on a maximum and minimum remorse value method, remorse values of the cost and the flexibility remorse values are minimized to be core targets, a mixed integer second order cone planning algorithm solving model is adopted to ensure solving efficiency and global optimality, finally solving results are substituted into all wind power scenes, whether the operating parameters meet constraint requirements is verified, the maximum remorse value under the worst scene is minimized, robustness verification is completed, and finally an optimal SOP configuration scheme adapting wind power uncertainty is output.
  10. 10. An active power distribution network flexibility improving system based on intelligent soft switch optimization is characterized by comprising the following modules: The flexibility evaluation framework construction module is used for constructing a network flexibility evaluation framework; Setting the output power and the related permeability coefficient of the wind driven generator under the minimum and maximum allowable permeability conditions by taking the renewable energy permeability range as a flexibility evaluation index of the power distribution network, maximizing the sum of the maximum and minimum allowable power generation capacity difference values of all wind power units as a flexibility objective function, and reflecting the regulation capability of the power distribution network to wind power change; the deterministic programming model construction module is used for constructing a deterministic programming model of intelligent soft switch optimization configuration; based on flexibility evaluation indexes obtained by a network flexibility evaluation framework, constructing a double-target deterministic programming model with minimized total construction cost and maximized flexibility of the power distribution network, and converting a non-convex mixed integer nonlinear programming model into a mixed integer second-order cone programming model; The robust planning model construction module is used for constructing an intelligent soft switch optimal configuration robust planning model based on a scene; Based on a deterministic planning model, a scene-based maximum and minimum regret method is introduced for uncertainty of wind power output fluctuation, and an intelligent soft switch optimal configuration robust planning model is constructed, so that the obtained solution has feasibility in all wind power generation scenes and achieves near-optimal performance; The solving module is used for solving the intelligent soft switch optimal configuration robust planning model based on the scene; Aiming at each wind power scene, independently solving a deterministic planning model of intelligent soft switch optimal configuration, obtaining a cost optimal solution and a flexibility optimal solution under each scene, taking the cost optimal solution and the flexibility optimal solution as references for calculating remorse values, constructing a double-target robust planning model based on a maximum and minimum remorse value method, adopting a mixed integer second order cone planning algorithm to solve the model, substituting the solving result into all wind power scenes, and outputting an optimal SOP configuration scheme adapting to wind power uncertainty.

Description

Active power distribution network flexibility improving method and system based on intelligent soft switch optimization Technical Field The invention belongs to the technical field of active planning and operation optimization of a power distribution network, and particularly relates to an active power distribution network flexibility improving method and system based on intelligent soft switch optimization in a wind power generation scene. Background Balancing power generation and power consumption in real time is a core sustainability requirement of power grid operation, but the requirement is impacted by the intermittence, volatility and other uncertainty factors of renewable energy power generation. The ability of the grid to cope with system uncertainty, power generation and power usage to adapt to different operating conditions, referred to as the flexibility of the grid, is one of the key requirements of current power systems. One of the core features of the current active power distribution network (ADN) is the richness of multi-side controllable resources, and energy storage systems, demand Response (DR) resources (such as adjustable loads), demand management based on supervision policies and electricity price mechanisms, distributed Generation (DG) technology and the like can be used as supply sources of power grid flexibility. Because the power distribution side high-proportion Renewable Energy (RES) is connected into the power distribution side high-proportion Renewable Energy (RES), the power distribution side high-proportion renewable energy is connected into the power distribution side high-proportion renewable energy. In the existing power distribution network flexibility improving method, the maximum admitting capacity or the processing reduction minimization is defined as a power distribution network flexibility index to cope with uncertainty caused by renewable energy source intermittence, but the two methods for improving the power distribution network flexibility can reduce the benefits of DG operators, and secondly, the method for improving the power distribution network flexibility by adopting continuous flexible resources of a network side also has some problems, taking an intelligent soft Switch (SOP) as an example, the installation position/capacity of the SOP in the researches is fixedly preset, but actual decisions are influenced by multi-dimensional factors such as investment capacity, budget, construction period, policy guidance and the like, and all technical/operation factors are difficult to cover in modeling to realize accurate decisions. Disclosure of Invention Aiming at the defects existing in the prior art, the invention provides an active power distribution network flexibility improving method and system based on intelligent soft switch optimization. The specific technical scheme is as follows: in a first aspect, an embodiment of the present application provides a method for improving flexibility of an active power distribution network based on intelligent soft switching optimization, including the following steps: step (1) constructing a network flexibility assessment framework: And taking the renewable energy source permeability capacity range as a flexibility evaluation index of the power distribution network, setting the output power and related permeability coefficient of the wind driven generator under the minimum and maximum allowable permeability conditions, maximizing the sum of the maximum and minimum allowable power generation capacity difference values of all the wind power generation sets as a flexibility objective function, and reflecting the regulation capability of the power distribution network to wind power change. Step (2) establishing a deterministic programming model of intelligent soft switch optimization configuration: and constructing a double-target deterministic programming model with minimized total construction cost and maximized flexibility of the power distribution network based on flexibility evaluation indexes obtained by a network flexibility evaluation framework, and converting the non-convex mixed integer nonlinear programming model into a mixed integer second-order cone programming model. Step (3) constructing a scene-based intelligent soft switch optimal configuration robust planning model: based on a deterministic planning model of intelligent soft switch optimal configuration, a scene-based maximum and minimum regret method is introduced for uncertainty of wind power output fluctuation, and an intelligent soft switch optimal configuration robust planning model is constructed, so that the obtained solution has feasibility in all wind power generation scenes, and achieves near-optimal performance. And (4) solving and verifying an intelligent soft switch optimal configuration robust planning model based on a scene, namely selecting an IEEE33 node system as a test power distribution network, processing double-target conflict by adoptin