Optical storage and charging multi-type equipment collaborative power distribution network scheduling method

Abstract

The invention discloses an optical storage and charging multi-type equipment collaborative power distribution network scheduling method, which belongs to the technical field of power distribution network scheduling, and comprises the following steps: S1, collecting historical operation data of a power distribution network based on a topological structure of the power distribution network, and extracting change characteristics of the historical operation data; s2, constructing an objective function and constraint conditions according to the change characteristics, and integrating the objective function and the constraint conditions to obtain a power flow model; s3, collecting real-time operation data of the power distribution network, and converting constraint conditions of the power flow model based on the real-time operation data; s4, solving the power flow model after constraint condition conversion based on an interior point method to obtain a power distribution network operation parameter set; s5, determining an optimal power distribution network operation parameter based on the power distribution network operation parameter set and a compensation gap calculated according to a compensation gap formula; s6, the power distribution network is adjusted to run in the optimal running state according to the optimal power distribution network running parameters; the method overcomes the problem of poor operation stability of the corresponding power distribution network due to the fact that the power flow change characteristics of the power distribution network are not considered in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network dispatching, and in particular to a distribution network dispatching method for coordinated distribution of multiple types of photovoltaic storage and charging equipment. Background Art

[0002] With the development of new energy technologies, the penetration rate of distributed photovoltaics in distribution networks is increasing, which has caused the peak-to-valley difference of distribution networks to continue to increase, especially during the peak of photovoltaic power generation at noon, which will also cause power quality problems such as voltage over-limit. Although it can be alleviated by reducing photovoltaic power generation, it will also waste a lot of resources. Therefore, some scholars have proposed to dispatch the distribution network according to the existing elastic resources of the distribution network to achieve storage when there is surplus electricity and replenishment when there is a shortage of electricity. However, these elastic resources are always highly uncertain due to various reasons, resulting in dynamically changing flow characteristics in the distribution network, resulting in power quality problems such as voltage over-limit even if the distribution network operates according to the corresponding dispatching plan.

[0003] Chinese patent, publication number: CN116706919A, publication date: September 5, 2023, discloses a method for solving dynamic optimization power flow problems for new energy power systems, including: step 1, obtaining a calculation model of the power system including calculation variables of node voltage phase angle and amplitude; step 2, using the PLAM method to linearize the node voltage phase angle and amplitude, dividing the power system into several regions or partitions, each region contains several nodes, and by calculating the equivalent load impedance and equivalent transmission admittance of the nodes in the region, the voltage phase angle and amplitude of all nodes in the region are expressed as a linear function of the phase angle and amplitude of a node in the region, and then the linear functions of all regions are concatenated. Step 3: Establish a DOPF model according to the constraints of the power system, including the constraints of the generator, the constraints of the line capacity, the constraints of the transformer and the steady-state constraints; Step 4: Use a supercomputing server to solve the DOPF, and use an optimization algorithm to solve the DOPF model; Step 5: Dispatch and manage the power system according to the solution results in step 4, including real-time monitoring and control of the operating status of the power system, and adjust and optimize the operation of the power system as needed; However, the invention does not take into account the flow change characteristics of the distribution network, resulting in poor operating stability of the corresponding power system. Summary of the invention

[0004] The purpose of the present invention is to address the problem that the existing technology does not take into account the power flow change characteristics of the distribution network, resulting in poor operating stability of the corresponding distribution network; a method for coordinating distribution network scheduling of multiple types of photovoltaic storage and charging equipment is proposed, the power flow change characteristics of the distribution network are extracted through the distribution network topology structure and historical operation data, and a power flow model is constructed according to the power flow change characteristics, and then the power flow model is iteratively solved according to the real-time operation data of the distribution network and the interior point method to obtain a distribution network operation parameter set, and in the process of iteratively solving the power flow model, the compensation gap is calculated along with the number of iterations, and the optimal distribution network operation parameters are determined in the distribution network operation parameter set based on the compensation gap, and finally, the distribution network is adjusted to the optimal operating state according to the optimal distribution network operation parameters calculated based on the power flow change characteristics of the distribution network, thereby significantly improving the stability of the distribution network.

[0005] In a first aspect, a technical solution provided in an embodiment of the present invention is a method for coordinating a distribution network with multiple types of photovoltaic storage and charging devices, comprising the following steps: S1. Collecting historical operation data of the distribution network based on the topological structure of the distribution network, and extracting the change characteristics of the historical operation data; S2. constructing and integrating the objective function and constraint conditions according to the change characteristics to obtain a power flow model; S3, collecting real-time operation data of the distribution network, and converting the constraint conditions of the power flow model based on the real-time operation data; S4, solving the power flow model that has completed the constraint condition conversion based on the interior point method to obtain the distribution network operation parameter set; S5. Determine optimal distribution network operating parameters based on the distribution network operating parameter set and the compensation gap calculated according to the compensation gap formula; S6. The distribution network is adjusted to operate in an optimal operating state according to the optimal distribution network operating parameters.

[0006] In this solution, based on the topological structure and historical operation data of the distribution network, the power flow change characteristics of the distribution network are extracted, the potential problems of the corresponding distribution network are clarified, and the target function and constraint conditions are constructed and integrated according to the power flow change characteristics for the potential problems of the distribution network to obtain a power flow model. Then, the constraint conditions of the power flow model are converted according to the real-time operation data of the distribution network, so that the power flow model is more in line with the real-time state of the distribution network. After the constraint conditions of the power flow model are converted, the power flow model is solved based on the interior point method to obtain the distribution network operation parameter set, so as to achieve efficient and accurate processing of large-scale regular data of the distribution network, and determine the optimal distribution network operation parameters in the distribution network operation parameter set that best match the current state of the distribution network according to the compensation gap. Finally, the distribution network is adjusted to the optimal operation state according to the optimal distribution network operation parameters calculated based on the power flow change characteristics of the distribution network, thereby significantly improving the stability of the distribution network.

[0007] Preferably, in S1, the specific process of extracting the change characteristics of the historical operation data is: S11, dividing the historical operation data into at least a voltage set, a current set and an electric vehicle data set according to a distribution network topology structure corresponding to the historical operation data; S12, determining voltage dynamic characteristics based on the voltage change trend of the voltage concentration, and determining current dynamic characteristics based on the current change trend of the current concentration; Simultaneously, determining the dynamic characteristics of the electric vehicle based on the data change trend in the electric vehicle dataset; S13. Integrate the voltage dynamic characteristics, the current dynamic characteristics and the electric vehicle dynamic characteristics to obtain the change characteristics of the historical operation data; wherein the change characteristics of the historical operation data represent the dynamic flow characteristics of the corresponding distribution network.

[0008] In this scheme, the corresponding dynamic characteristics are extracted according to the voltage and current data of different nodes in the distribution network, which can clarify the power fluctuation characteristics of different nodes in the distribution network, and at the same time clarify the power support capacity of different nodes. Secondly, the dynamic characteristics of electric vehicles are determined according to the data change trend of the electric vehicle data set, which can clarify the reserve scheduling capacity of different nodes and elastic available resources, and provide support for the subsequent construction of the power flow model to analyze the distribution network operation parameters.

[0009] Preferably, in S2, the objective function constructed is specifically: In the formula, f loss is the quadratic function of node voltage, N l is the total number of lines in the distribution network, n is the number of phases, a represents phase A in the three-phase circuit, is the kth line phase and The admittance between phases, is the i-th node Phase voltage, is the jth node Phase voltage, is the i-th node Phase voltage, is the jth node Phase voltage, real is the operator for taking the real part of a complex number, * is the conjugate operator, f u is the three-phase voltage unbalance, N n is the total number of nodes, U i,a is the A phase voltage of the ith node, U i,b is the B-phase voltage of the ith node, U i,c is the C phase voltage of the ith node, e j240° is the rotation factor that represents the phase voltage of the corresponding phase rotated 240 degrees counterclockwise, ej120° is the rotation factor that represents the phase voltage of the corresponding phase rotated 120 degrees counterclockwise, f ne is the neutral line voltage, U i,ne is the voltage of the neutral line corresponding to the i-th node.

[0010] In this scheme, three objective functions are constructed based on three indicators of the distribution network operation status, namely node voltage, three-phase voltage imbalance and neutral line voltage, which serve as the basis of the subsequent power flow model objective function, accurately evaluate the changes in the indicators, and then obtain the accurate operation status of the distribution network.

[0011] Preferably, in S2, the constraints constructed are specifically: Where U is the voltage amplitude at node i Lower limit, is the voltage amplitude of node i Upper limit, is the load active power, is the load reactive power, is the node i Harmony Current between phases, is the node i Phase voltage, is the node i Phase voltage, is the node i and node j The admittance between phases, is the node j Phase voltage, * is the conjugate operator, n is the number of phases, N n is the total number of nodes, a is phase A in the three-phase circuit, is the rated capacity of the converter, is the rated current of the converter, SOC i,t is the initial charge of the i-th electric vehicle, SOC i,e is the expected charging capacity of the ith electric vehicle, C is the maximum charging capacity of the battery, P i,t is the charging power of the i-th electric vehicle at time t, η is the charging efficiency, v i,t is the charging indicator variable of the i-th electric vehicle at time t, Δt is the charging interval, T c is the total charging time, and t0 is the starting time of charging.

[0012] Preferably, in S2, the specific process of integrating the objective function and the constraint conditions to obtain the power flow model is: The weight of the objective function is set according to the topological structure of the distribution network to obtain a new objective function, which is as follows: In the formula, ω loss is the node voltage quadratic function f loss The optimization weight, F loss is the node voltage quadratic function f loss Initial value before optimization, ω u is the three-phase voltage unbalance degree f u The optimization weight, F u is the three-phase voltage unbalance degree f u Initial value before optimization, ω ne is the neutral line voltage f ne The optimization weight, F ne is the neutral line voltage f ne Initial value before optimization.

[0013] Preferably, in S3, the specific process of converting the power flow model constraint conditions based on the real-time operation data is: S31. Building a distribution network model in a simulation platform based on real-time operation data and arranging the power flow model in the simulation platform; S32, the simulation platform performs a power flow calculation according to the distribution network model and the power flow model to obtain reactive power, active power, node voltage and node admittance matrix; S33. Calculate equivalent current according to the reactive power and active power, and convert power flow model constraints into solution constraints based on the equivalent current, node voltage and node admittance matrix.

[0014] In this solution, a power flow calculation is performed based on the real-time operation data and the power flow model to obtain the current operation state of the distribution network, specifically the digital embodiment of the reactive power, active power, node voltage and node admittance matrix, and the reactive power and active power are equivalent to the current to obtain the equivalent current, and the adjustable resources of the current state of the corresponding distribution network are clarified, and the power flow model constraints are converted into a simplified model for solving the constraints based on the equivalent current, node voltage and node admittance matrix.

[0015] Preferably, in S4, the specific process of solving the power flow model that has completed the constraint condition conversion based on the interior point method to obtain the distribution network operation parameter set is: S41, converting the converted constraint condition into an equality constraint based on a slack variable addition formula, and converting the objective function of the power flow model into a barrier function based on a barrier adjustment formula; S42, performing Lagrangian transformation according to the equality constraint and the barrier function to obtain a Lagrangian function; S43, inputting the real-time operation data into a Lagrangian function to perform Newton-Lagrangian iterative calculation to obtain a distribution network operation parameter set.

[0016] In this scheme, by adding slack variables to the constraints, they are transformed into equality constraints, which makes it easier to describe the interior point method and improve its convergence. Secondly, the objective function is transformed into a barrier function to ensure that the corresponding iteration point always remains in the feasible domain during the subsequent iterative solution process.

[0017] Preferably, in S43, when the real-time operation data is input into the Lagrangian function for Newton-Lagrangian iterative calculation, whether the Newton-Lagrangian iterative calculation is completed is judged based on the current number of iterations and a preset iteration threshold. If the current number of iterations is less than the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is not completed; if the current number of iterations is greater than or equal to the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is completed.

[0018] In this scheme, the preset iteration threshold can prevent the calculation process of the Lagrangian function from falling into an infinite loop process, resulting in a waste of computing resources and potential numerical problems, such as numerical instability and calculation overflow.

[0019] Preferably, in S5, each group of distribution network operating parameters in the distribution network operating parameter set corresponds to a separate compensation gap, and when the compensation gap is less than a preset compensation gap threshold, the distribution network operating parameter corresponding to the compensation gap is marked as the optimal distribution network operating parameter.

[0020] In this solution, by supplementing the gap and presetting the compensation gap threshold, it is possible to ensure that the distribution network state corresponding to the optimal distribution network operation parameters is close to the ideal operation state of the distribution network, and maximize the resource utilization of the distribution network.

[0021] Preferably, in S6, the specific process of adjusting the distribution network to the optimal operating state according to the optimal distribution network operating parameters is: S61, determining a node scheduling action according to the optimal distribution network operation parameters and the real-time operation data of the distribution network, and arranging the node scheduling actions to obtain a scheduling strategy; S62. The distribution network is adjusted to an optimal operating state according to the dispatching strategy.

[0022] The present invention has at least the following substantial effects: The present application extracts the power flow change characteristics of the distribution network based on the topological structure and historical operation data of the distribution network, clarifies the potential problems of the corresponding distribution network, and constructs and integrates the objective function and constraint conditions for the potential problems of the distribution network according to the power flow change characteristics to obtain a power flow model. Then, the constraint conditions of the power flow model are converted according to the real-time operation data of the distribution network, so that the power flow model is more in line with the real-time state of the distribution network. After the constraint conditions of the power flow model are converted, the power flow model is solved based on the interior point method to obtain a distribution network operation parameter set, so as to achieve efficient and accurate processing of large-scale regular data of the distribution network, and determine the optimal distribution network operation parameters in the distribution network operation parameter set that best match the current state of the distribution network according to the compensation gap. Finally, the distribution network is adjusted to operate in the optimal operating state according to the optimal distribution network operation parameters calculated based on the power flow change characteristics of the distribution network, thereby significantly improving the stability of the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Other features, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings. The drawings are only for the purpose of illustrating preferred embodiments and are not to be considered as limiting the present invention. Also, the same reference symbols are used throughout the drawings to represent the same parts.

[0024] Figure 1 This is a flow chart of a method for coordinating distribution network dispatching of multiple types of photovoltaic storage and charging equipment according to the present invention; Figure 2 Schematic diagram of voltage constraint conditions. DETAILED DESCRIPTION

[0025] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific implementation method described herein is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0027] Embodiment 1: like Figure 1 As shown, this embodiment provides a method for coordinating distribution network scheduling of multiple types of photovoltaic storage and charging devices, including the following steps: S1, collecting historical operation data of the distribution network based on the topological structure of the distribution network, and extracting the change characteristics of the historical operation data.

[0028] S11, dividing the historical operation data into at least a voltage set, a current set and an electric vehicle data set according to a distribution network topology structure corresponding to the historical operation data; S12, determining voltage dynamic characteristics based on the voltage change trend of the voltage concentration, and determining current dynamic characteristics based on the current change trend of the current concentration; Simultaneously, determining the dynamic characteristics of the electric vehicle based on the data change trend in the electric vehicle dataset; S13. Integrate the voltage dynamic characteristics, the current dynamic characteristics and the electric vehicle dynamic characteristics to obtain the change characteristics of the historical operation data; wherein the change characteristics of the historical operation data represent the dynamic flow characteristics of the corresponding distribution network.

[0029] In this embodiment, the voltage and current in the historical operation data are marked and integrated according to the nodes in the distribution network to obtain a voltage set and a current set, and the voltage and current related to the electric vehicle are extracted from the voltage set and the current set to form an electric vehicle data set; then, the corresponding curve graph is drawn according to the time scale change of the voltage and current data to analyze the dynamic characteristics, and the correlation analysis is performed according to the node markings of the voltage and current to determine the mutual connection between different nodes, and finally the dynamic characteristics and the mutual connection are sorted out to obtain the change characteristics of the historical operation data.

[0030] S2. Construct an objective function and constraint conditions according to the change characteristics and integrate them to obtain a power flow model.

[0031] In one embodiment, the objective function is specifically: In the formula, f loss is the node voltage, N l is the total number of lines in the distribution network, n is the number of phases, a represents phase A in the three-phase circuit, is the kth line phase and The admittance between phases, is the i-th node Phase voltage, is the jth node Phase voltage, is the i-th node Phase voltage, is the jth node Phase voltage, real is the operator for taking the real part of a complex number, * is the conjugate operator, fu is the three-phase voltage unbalance, N n is the total number of nodes, U i,a is the A phase voltage of the ith node, U i,b is the B-phase voltage of the ith node, U i,c is the C phase voltage of the ith node, e j240° is the rotation factor that represents the phase voltage of the corresponding phase rotated 240 degrees counterclockwise, e j120° is the rotation factor that represents the phase voltage of the corresponding phase rotated 120 degrees counterclockwise, f ne is the neutral line voltage, U i,ne is the voltage of the neutral line corresponding to the ith node, where the node voltage f loss Corresponding to the active power of the distribution network.

[0032] In one embodiment, the constraint conditions are specifically: Where U is the voltage amplitude at node i Lower limit, is the voltage amplitude of node i Upper limit, is the load active power, is the load reactive power, is the node i Harmony Current between phases, is the node i Phase voltage, is the node i Phase voltage, is the node i and node j The admittance between phases, is the node j Phase voltage, * is the conjugate operator, n is the number of phases, N n is the total number of nodes, a is phase A in the three-phase circuit, is the rated capacity of the converter, is the rated current of the converter, SOC i,t is the initial charge of the i-th electric vehicle, SOC i,e is the expected charging capacity of the ith electric vehicle, C is the maximum charging capacity of the battery, P i,t is the charging power of the i-th electric vehicle at time t, η is the charging efficiency, v i,t is the charging indicator variable of the i-th electric vehicle at time t, Δt is the charging interval, T c is the total charging time, and t0 is the starting time of charging.

[0033] In one embodiment, in order to unify the objective functions into the same objective function, the weight of the objective function is set according to the topology of the distribution network to obtain a new objective function, and the new objective function is as follows: In the formula, ω loss is the node voltage quadratic function f loss The optimization weight, F loss is the node voltage quadratic function f loss Initial value before optimization, ω u is the three-phase voltage unbalance degree f u The optimization weight, F u is the three-phase voltage unbalance degree f u Initial value before optimization, ω ne is the neutral line voltage f ne The optimization weight, F ne is the neutral line voltage f ne Initial value before optimization.

[0034] S3. Collecting real-time operation data of the distribution network, and converting constraint conditions of the power flow model based on the real-time operation data.

[0035] S31. Building a distribution network model in a simulation platform based on real-time operation data and arranging the power flow model in the simulation platform; S32, the simulation platform performs a power flow calculation according to the distribution network model and the power flow model to obtain reactive power, active power, node voltage and node admittance matrix; S33. Calculate equivalent current according to the reactive power and active power, and convert power flow model constraints into solution constraints based on the equivalent current, node voltage and node admittance matrix.

[0036] In this embodiment, since the constraint condition of the power flow model is a nonlinear constraint, the power flow model is usually difficult to solve. Therefore, according to the current state of the distribution network, the distribution network model built based on the real-time operation data of the distribution network and the power flow model are used to perform a power flow calculation in the OpenDSS simulation platform to obtain reactive power, active power, node voltage and node admittance matrix, and the equivalent current is calculated according to the reactive power output of the energy storage AC device in the distribution network and the active power of the electric vehicle charging and discharging in the V2G mode, that is, the equivalent current is calculated according to the reactive power and active power. Finally, based on the equivalent current, node voltage and node admittance matrix, the power flow constraint and capacity constraint in the power flow model constraint condition are converted into solution constraint conditions. In addition, since the variation range of the voltage phase angle of each node in the distribution network is generally not large, the voltage constraint condition in the power flow model constraint condition is approximately expressed as a trapezoidal area and converted into a voltage linear inequality, such as Figure 2The blue shaded part.

[0037] The solution constraint conditions are specifically: In the formula, After a power flow calculation based on the current state of the distribution network, the Harmony The current between phases is a fixed value. The subscript x represents the real part of the corresponding data after the rectangular coordinate decomposition, and the subscript y represents the imaginary part of the corresponding data after the rectangular coordinate decomposition. After a power flow calculation based on the current state of the distribution network, the Phase voltage, also a fixed value, Represents the node i and node j Admittance between phases The real part of the conductivity after rectangular coordinate decomposition is: Represents the node i and node j Admittance between phases The imaginary part of the susceptance after rectangular coordinate decomposition; The voltage linear inequality is divided into three groups according to the three-phase lines of the distribution network, namely, the A-phase voltage linear inequality, the B-phase voltage linear inequality and the C-phase voltage linear inequality. The three groups of voltage linear inequalities are the same except for the phase angle, wherein the A-phase line phase angle is 0°, the B-phase line phase angle is 240°, and the C-phase line phase angle is 120°. Taking the B-phase voltage linear inequality as an example, the B-phase voltage linear inequality is specifically: U i,b,y -tan(240°-δ / 2)U i,b,x ≤0; U i,b,y -tan(240°+δ / 2)U i,b,x ≥0; Where U i,b,x is the real part of the phase B voltage at node i, U i,b,y is the imaginary part of the B-phase voltage at node i, and δ is the voltage phase angle at node i.

[0038] S4. Solve the power flow model that has completed the constraint condition conversion based on the interior point method to obtain the distribution network operation parameter set.

[0039] S41, converting the converted constraint condition into an equality constraint based on a slack variable addition formula, and converting the objective function of the power flow model into a barrier function based on a barrier adjustment formula; S42, performing Lagrangian transformation according to the equality constraint and the barrier function to obtain a Lagrangian function; S43, inputting the real-time operation data into a Lagrangian function to perform Newton-Lagrangian iterative calculation to obtain a distribution network operation parameter set.

[0040] In one embodiment, when the real-time running data is input into the Lagrangian function for Newton-Lagrangian iterative calculation, whether the Newton-Lagrangian iterative calculation is completed is judged based on the current number of iterations and a preset iteration threshold. If the current number of iterations is less than the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is not completed. If the current number of iterations is greater than or equal to the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is completed.

[0041] In this embodiment, the inequality constraints in the converted constraint conditions are converted into equality constraints by adding slack variables u and l to the inequality constraints. The slack variable adding formula is specifically: g(x)=0; In the formula, h(x) represents the inequality constraint, g(x) means no change is made to the equality constraint, h is the lower limit of the inequality constraint, is the upper limit of the inequality constraint. The number of slack variables u and l is the same as the number of inequality constraints. The slack variable u corresponds to the upper limit of the transformed inequality constraint, and the slack variable l corresponds to the lower limit of the transformed inequality constraint. u>0, l>0; Then, an obstacle constant μ greater than zero is introduced into the objective function to convert the objective function into an obstacle function. The corresponding obstacle adjustment formula is as follows: Where r is the number of inequality constraints, l i and u i is the i-th slack variable.

[0042] When the slack variables are added and the barrier constants are introduced, the Lagrange multiplier method is used to convert the power flow model into an unconstrained problem to obtain the Lagrangian function. The Lagrangian function is specifically: Wherein, y, z, and w are all Lagrange multipliers, the number of y is the same as the number of equality constraints, the number of z and w is the same as the number of inequality constraints before the power flow model constraint condition conversion, z>0, w<0, and in addition, the necessary condition for the existence of the minimum value of the Lagrangian function is that the partial derivatives of the Lagrangian function L with respect to the variable x, the slack variables u and l, and the Lagrangian multipliers y, z, and w are 0, and these partial derivatives constitute a set of nonlinear equations, which are specifically: In the equation system, e is the unit matrix, L = diag (l1, ..., l r ), U=diag(u1,…,u r ), Z=diag(z1,…,z r ), W=diag(w1,…,w r ); After the power flow model is transformed into the Lagrangian function, the Newton method is used to iteratively solve the linear equations. According to the iteration rule of variables, the variable values ​​of the (k+1)th iteration are as follows: x (k+1) =x (k) +α p Δx; y (k+1) =y (k) +α d Δy; u (k+1) =u (k) +α p Δu; w (k+1) =w (k) +α d Δw; l (k+1) = l (k) +α p Δl; z (k+1) =z (k) +α d Δz; In the formula, α p With α d is the correction step size of each iteration, and Δx, Δy, Δu, Δw, Δl, and Δz are the correction amounts of the kth iteration.

[0043] S5. Determine optimal distribution network operating parameters based on the distribution network operating parameter set and the compensation gap calculated according to the compensation gap formula.

[0044] In one embodiment, each group of distribution network operating parameters in the distribution network operating parameter set corresponds to a separate compensation gap. When the compensation gap is less than a preset compensation gap threshold, the distribution network operating parameter corresponding to the compensation gap is marked as the optimal distribution network operating parameter.

[0045] In this embodiment, when the barrier constant μ decreases, and will decrease accordingly. When the barrier constant μ approaches 0, the barrier function It will tend more and more to the original function minf(x), so in the above-mentioned Newton method iterative calculation process, it is necessary to add a formula that makes the barrier constant μ approach 0, so the calculation formula of the compensation gap Gap is defined as: Gap = l T zu T w; According to the calculation formula of the compensation gap Gap, the compensation gap Gap will gradually approach 0 during the iteration process, so the calculation formula of the barrier constant μ can be defined as: In the formula, σ is the central parameter with an empirical value of 0.1, and r is the number of inequality constraints; Secondly, according to the Newton-Rafale iterative calculation process and the calculation formula of the compensation gap Gap, it can be known that the compensation gap is continuously updated along with the iterative process, so the compensation gap corresponds to the correction amount one by one, so each set of distribution network operation parameters in the distribution network operation parameter set corresponds to a separate compensation gap.

[0046] S6. The distribution network is adjusted to operate in an optimal operating state according to the optimal distribution network operating parameters.

[0047] S61, determining a node scheduling action according to the optimal distribution network operation parameters and the real-time operation data of the distribution network, and arranging the node scheduling actions to obtain a scheduling strategy; S62. The distribution network is adjusted to an optimal operating state according to the dispatching strategy.

[0048] This embodiment has at least the following substantial effects: This embodiment extracts the power flow change characteristics of the distribution network based on the topological structure and historical operation data of the distribution network, clarifies the potential problems of the corresponding distribution network, and constructs and integrates the objective function and constraint conditions according to the power flow change characteristics for the potential problems of the distribution network to obtain a power flow model. Then, the constraint conditions of the power flow model are converted according to the real-time operation data of the distribution network, so that the power flow model is more in line with the real-time state of the distribution network. After the constraint conditions of the power flow model are converted, the power flow model is solved based on the interior point method to obtain the distribution network operation parameter set, so as to achieve efficient and accurate processing of large-scale regular data of the distribution network, and determine the optimal distribution network operation parameters in the distribution network operation parameter set that best match the current state of the distribution network according to the compensation gap, and continuously update the barrier constant added to the power flow model according to the interior point method solution process while updating the compensation gap, until the barrier constant approaches zero, so as to avoid the appearance of infeasible distribution network operation parameters due to over-optimization. Finally, the distribution network is adjusted to operate in the optimal operation state according to the optimal distribution network operation parameters calculated based on the power flow change characteristics of the distribution network, thereby significantly improving the stability of the distribution network.

[0049] The above specific embodiments are preferred embodiments of the present invention, and are not intended to limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to the present specific embodiments. All equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for coordinating distribution network dispatching of multiple types of photovoltaic storage and charging equipment, characterized in that: The method comprises the following steps: S1. Collecting historical operation data of the distribution network based on the topological structure of the distribution network, and extracting the change characteristics of the historical operation data; S2. constructing and integrating the objective function and constraint conditions according to the change characteristics to obtain a power flow model; S3, collecting real-time operation data of the distribution network, and converting the constraint conditions of the power flow model based on the real-time operation data; S4, solving the power flow model that has completed the constraint condition conversion based on the interior point method to obtain the distribution network operation parameter set; S5. Determine the optimal distribution network operation parameters based on the distribution network operation parameter set and the compensation gap calculated according to the compensation gap formula; S6. The distribution network is adjusted to operate in an optimal operating state according to the optimal distribution network operating parameters.

2. According to claim 1, a method for coordinating distribution network dispatching of multiple types of photovoltaic storage and charging equipment is characterized in that: In S1, the specific process of extracting the change characteristics of the historical operation data is: S11, dividing the historical operation data into at least a voltage set, a current set and an electric vehicle data set according to a distribution network topology structure corresponding to the historical operation data; S12, determining voltage dynamic characteristics based on the voltage change trend of the voltage concentration, and determining current dynamic characteristics based on the current change trend of the current concentration; Simultaneously, determining the dynamic characteristics of the electric vehicle based on the data change trend in the electric vehicle dataset; S13. Integrate the voltage dynamic characteristics, the current dynamic characteristics and the electric vehicle dynamic characteristics to obtain the change characteristics of the historical operation data; wherein the change characteristics of the historical operation data represent the dynamic flow characteristics of the corresponding distribution network.

3. According to claim 1, a method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment, characterized in that: In S2, the constructed objective function is specifically: In the formula, f loss is the node voltage, N l is the total number of lines in the distribution network, n is the number of phases, a represents phase A in the three-phase circuit, is the kth line phase and The admittance between the phases, is the i-th node Phase voltage, is the jth node Phase voltage, is the i-th node Phase voltage, is the jth node Phase voltage, real is the operator for taking the real part of a complex number, * is the conjugate operator, f u is the three-phase voltage unbalance, N n is the total number of nodes, U i,a is the phase A voltage of the ith node, U i,b is the B-phase voltage of the ith node, U i,c is the C-phase voltage of the ith node, e j240° is the rotation factor that represents the phase voltage of the corresponding phase rotated 240 degrees counterclockwise, e j120° is the rotation factor that represents the phase voltage of the corresponding phase rotated 120 degrees counterclockwise, f ne is the neutral line voltage, U i,ne is the voltage of the neutral line corresponding to the i-th node.

4. A method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 3, characterized in that: In S2, the constraints constructed are specifically: Where U is the voltage amplitude at node i Lower limit, is the voltage amplitude of node i Upper limit, is the load active power, is the load reactive power, is the node i Harmony Current between phases, is the node i Phase voltage, is the node i Phase voltage, is the node i and node j The admittance between the phases, is the node j Phase voltage, * is the conjugate operator, n is the number of phases, N n is the total number of nodes, a is phase A in the three-phase circuit, is the rated capacity of the converter, is the rated current of the converter, SOC i,t is the initial charge of the i-th electric vehicle, SOC i,e is the expected charging capacity of the ith electric vehicle, C is the maximum charging capacity of the battery, P i,t is the charging power of the i-th electric vehicle at time t, η is the charging efficiency, v i,t is the charging indicator variable of the i-th electric vehicle at time t, Δt is the charging interval, T c is the total charging time, and t0 is the starting time of charging.

5. A method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 4, characterized in that: In S2, the specific process of integrating the objective function and the constraint conditions to obtain the power flow model is: The weight of the objective function is set according to the topological structure of the distribution network to obtain a new objective function, which is as follows: In the formula, ω loss is the node voltage quadratic function f loss The optimization weight, F loss is the node voltage quadratic function f loss Initial value before optimization, ω u is the three-phase voltage unbalance degree f u The optimization weight, F u is the three-phase voltage unbalance degree f u Initial value before optimization, ω ne is the neutral line voltage f ne The optimization weight, F ne is the neutral line voltage f ne Initial value before optimization.

6. A method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 1, characterized in that: In S3, the specific process of converting the power flow model constraint conditions based on real-time operation data is as follows: S31. Building a distribution network model in a simulation platform based on real-time operation data and arranging the power flow model in the simulation platform; S32, the simulation platform performs a power flow calculation according to the distribution network model and the power flow model to obtain reactive power, active power, node voltage and node admittance matrix; S33. Calculate equivalent current according to the reactive power and active power, and convert power flow model constraints into solution constraints based on the equivalent current, node voltage and node admittance matrix.

7. The method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 1 is characterized in that: In S4, the specific process of solving the power flow model that has completed the constraint condition conversion based on the interior point method to obtain the distribution network operation parameter set is: S41, converting the converted constraint condition into an equality constraint based on a slack variable addition formula, and converting the objective function of the power flow model into a barrier function based on a barrier adjustment formula; S42, performing Lagrangian transformation according to the equality constraint and the barrier function to obtain a Lagrangian function; S43, inputting the real-time operation data into a Lagrangian function to perform Newton-Lagrangian iterative calculation to obtain a distribution network operation parameter set.

8. A method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 7, characterized in that: In S43, when the real-time operation data is input into the Lagrangian function for Newton-Lagrangian iterative calculation, whether the Newton-Lagrangian iterative calculation is completed is judged based on the current number of iterations and the preset iteration threshold. If the current number of iterations is less than the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is not completed. If the current number of iterations is greater than or equal to the iteration threshold, it is judged that the Newton-Lagrangian iterative calculation is completed.

9. The method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 1 is characterized in that: In S5, each group of distribution network operating parameters in the distribution network operating parameter set corresponds to a separate compensation gap, and when the compensation gap is less than a preset compensation gap threshold, the distribution network operating parameter corresponding to the compensation gap is marked as the optimal distribution network operating parameter.

10. The method for dispatching a distribution network with multiple types of photovoltaic storage and charging equipment according to claim 1, characterized in that: In S6, the specific process of adjusting the distribution network to the optimal operating state according to the optimal distribution network operating parameters is as follows: S61, determining a node scheduling action according to the optimal distribution network operation parameters and the real-time operation data of the distribution network, and arranging the node scheduling actions to obtain a scheduling strategy; S62. The distribution network is adjusted to an optimal operating state according to the dispatching strategy.

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