A distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty

Abstract

The application discloses a kind of distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty, constructs photovoltaic output uncertainty probability distribution set based on distribution robust optimization algorithm, and generates typical scene and extreme scene accordingly.Face different scenes and construct a distributed photovoltaic and energy storage collaborative planning second-order cone model, with the economic cost of photovoltaic and energy storage and distribution network voltage deviation as optimization target, and embedded considering photovoltaic output uncertainty short-circuit current constraint of grid-connected point.Through short-circuit current checking result constructs cut plane, and proposes a C&CG algorithm to solve the model.The application constructs uncertainty probability distribution set, realizes distributed photovoltaic and energy storage bundling planning in combination with scene occurrence probability, improves the accessible capacity of photovoltaic and stabilizes distribution network voltage, and the model takes the minimum total economic cost and voltage deviation as optimization target and satisfies short-circuit current constraint, and the planning scheme has the advantages of economic optimality and voltage stability and strong credibility.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrical engineering, in particular to a distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty. BACKGROUND

[0002] The carrying capacity of a distribution network refers to the maximum capacity of distributed renewable energy that the distribution network can accommodate under the premise of meeting the safety operation indicators (such as short-circuit current and voltage deviation) without exceeding the limit. Currently, the network structure of the distribution network is relatively weak, and the local load distribution is uneven, resulting in overload on some lines and causing energy imbalance in the system, thereby limiting the access capacity of renewable energy. Therefore, it is urgent to study methods to improve the carrying capacity of high-penetration distribution networks to maximize the access scale of renewable energy.

[0003] The main impact of distributed photovoltaic grid connection on the power grid is the randomness and volatility of its output. By configuring energy storage, the volatility can be smoothed in time to improve the power quality of power supply. However, current researches mainly optimize the voltage and economic cost of the distribution network by configuring photovoltaic and energy storage systems respectively, ignoring the synergistic benefits of the two. At the same time, the uncertainty of photovoltaic output leads to the uncertainty of voltage sag at each node after the access of distributed photovoltaic, and the uncertainty characteristics of short-circuit current and voltage deviation have not been fully considered in existing researches, resulting in conservative planning results, reducing the economic benefits of the distribution network, and limiting the accessible capacity of distributed photovoltaic. SUMMARY

[0004] The purpose of the present application is to provide a distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty to achieve the purpose of a distributed photovoltaic and energy storage collaborative planning scheme considering photovoltaic output uncertainty and embedded short-circuit current constraints in order to solve the problems in the prior art.

[0005] To achieve the above purpose, the present application adopts the following technical scheme: a distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty, comprising the following steps:

[0006] (1) For photovoltaic output uncertainty, a distribution robust optimization algorithm is used to process photovoltaic output historical data to construct a photovoltaic output uncertainty probability distribution set, thereby generating a photovoltaic output curve with the most severe fluctuations as an extreme scenario and generating a photovoltaic output curve with the largest probability of occurrence as a typical scenario set;

[0007] (2) After generating the typical scenario and the extreme scenario, the short-circuit current level of the distributed photovoltaic and energy storage grid connection point is calculated based on the equivalent voltage source method, and it is embedded into the model to construct a second-order cone model of distributed photovoltaic and energy storage collaborative planning with embedded short-circuit current constraints, including planning cost, operation cost and distribution network voltage deviation amount;

[0008] (3) Using short-circuit current constraints as cut sets, a column sum constraint generation algorithm based on short-circuit current cut sets is proposed to solve the second-order cone model of the distributed photovoltaic and energy storage collaborative planning. The algorithm includes a solution loop. First, the main problem is solved based on the second-order cone model. Then, the sub-problems are calculated based on the preliminary planning scheme. Cut sets of the accessible capacity of photovoltaic and energy storage are constructed through short-circuit current constraints. The algorithm is iteratively solved until the second-order cone model of the distributed photovoltaic and energy storage collaborative planning converges, and the optimal planning decision of distributed photovoltaic and energy storage is obtained.

[0009] Furthermore, the Bruker optimization algorithm is used to process historical photovoltaic data and construct a set of uncertain probability distributions for photovoltaic output. This generates the scenario with the most drastic fluctuations in photovoltaic output as the extreme scenario, and generates the photovoltaic output curve with the highest probability of occurrence as the typical scenario set S, where S = {S1, S2, ..., S...}. l}, where l is the number of typical scenarios; the probability of each typical scenario occurring throughout the year is N. n , 1≤n≤l; finally, the probability distribution set P of typical photovoltaic scenarios is obtained. 0 =[P1 0 P2 0 …,P l 0 The original probability distribution is P. n 0 =N n / Z, where Z is the total historical output for the whole year;

[0010] Based on L 1- The paradigm characterizes the actual probability distribution set Ω of photovoltaics; P s γ1 represents the actual probability distribution of the s-th typical scenario; γ1 represents the uncertainty interval of photovoltaic output.

[0011]

[0012] Since the most drastic fluctuation in photovoltaic (PV) output occurs at the extreme point of the feasible region, two 0-1 variables, α and β, are used to characterize the upper and lower boundaries of PV output; when α = 1, the output reaches its maximum fluctuation, and when β = 1, the output reaches its minimum fluctuation. PV This is the actual power output curve of the photovoltaic system, P. PV,f Forecast curve of photovoltaic power output;

[0013] P PV =P PV,f +αγ1-βγ1

[0014]

[0015] The photovoltaic output fluctuation variable has a limitation condition; because the two variables α and β have mutual exclusivity, where Г is the photovoltaic uncertainty, which limits the number of photovoltaics simultaneously reaching the fluctuation limit, the greater Г is, the more intense the photovoltaic output fluctuation is, and the more extreme the scenario is.

[0016] Further, based on the historical scenarios of photovoltaic output, a photovoltaic output uncertainty probability distribution set is constructed, and a second-order cone (SOP) model of distributed photovoltaic and energy storage collaborative planning for different scenarios is constructed; the annual comprehensive cost and the voltage deviation amount of the distribution network are minimized as the objective function for photovoltaic and energy storage bundling planning, and the specific form of the objective function is:

[0017]

[0018] Wherein, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily power purchase cost in the operation stage; σ is the number of operation days in a year; C3 is the deviation amount of the voltage of the distribution network node and the source point;

[0019] Further, the calculation formulas of C1, C2 and C3 are as follows:

[0020]

[0021] Wherein, ρ pinv , ρ einv is the unit energy storage power capacity investment cost and the unit energy storage power capacity investment cost; ρ pbuv is the time-of-use price of the main network; C ess,om is the unit energy storage power capacity maintenance cost; C PV,inv is the unit photovoltaic power capacity investment cost; δ is the discount rate; y is the service life of the energy storage; E ess , P ess is the energy storage capacity and energy storage power to be decided; E PV is the photovoltaic capacity to be decided; P g，i is the power purchase amount of the grid node i from the main network; U1 is the source point voltage; U i is the voltage of the grid node i; N is the total number of grid nodes;

[0022] The constraint conditions include:

[0023] 1) Photovoltaic and energy storage capacity constraint; according to the nature of the distribution network, the accessible photovoltaic and energy storage capacity limit is set:

[0024]

[0025] Wherein, E ess,j,min , E ess,j,max is the minimum and maximum values of the energy storage capacity accessible to node j, and E ess,j is the photovoltaic capacity accessible to node j; EPV,j,min E PV,j,max Let E be the minimum and maximum photovoltaic capacity that node j can access. PV,j Let be the photovoltaic capacity that node j can access; E is the set of potential energy storage nodes.

[0026] 2) Energy storage charge and discharge constraints; design charge and discharge constraints and energy storage SOC balance constraints based on the conditions of the energy storage device itself:

[0027]

[0028] In the formula, P dch,min (t), P dch,max (t) represents the minimum and maximum energy storage discharge power at time t, P dch (t) represents the energy storage discharge power at time t; P ch,min (t), P ch,max (t) represents the minimum and maximum energy storage charging power at time t, P ch (t) represents the energy storage charging power at time t; u dch (t), u ch (t) represents the energy storage discharge / charge state at time t, restricting energy storage charging and discharging from occurring simultaneously; SOC ess,min SOC ess,max η represents the minimum and maximum energy storage capacity. dch η ch Energy storage discharge / charge efficiency; P dch (t), P ch (t) represents the discharge / charge power of the energy storage at time t; SOC ess (t) represents the energy storage capacity at time t;

[0029] 3) Power flow constraints; Under the distribution network structure, the planning must satisfy the balance of active and reactive power. The specific calculation formula is as follows:

[0030]

[0031] In the formula, P ij Q ij Let P(j) represent the active / reactive power of line ij; P(j) and C(j) represent the sets of candidate nodes for photovoltaic and energy storage, respectively; P jk Q jk The active / reactive power of line jk; r is the square of the current on line ij; ij x ij P represents the resistance and reactance of line ij; j load Q j loadP PV,j , P ess,j is the photovoltaic and energy storage output of node j; N is the set of distribution network nodes;

[0032] 4) Distribution network branch current constraints;

[0033]

[0034] wherein, are the lower and upper limits of the current of branch ij, respectively;

[0035] 5) Distribution network node voltage constraints;

[0036]

[0037] wherein, are the lower and upper limits of the voltage of node j, respectively, is the voltage of node j;

[0038] 6) Second-order cone constraints; since the model contains some nonlinear constraints, it is converted into second-order cone constraints, and the specific calculation formula is as follows:

[0039]

[0040] Further, the short-circuit current level of the distributed photovoltaic and energy storage grid-connected point is calculated based on the equivalent voltage source method, and is embedded in the model for safety checking; through the short-circuit conductance of the photovoltaic and energy storage and the admittance matrix of the system, the photovoltaic port voltage U tj，PV (w) and the energy storage port voltage U tBESS (w) at the jth node in the wth time are calculated, and the specific formula is as follows:

[0041]

[0042] wherein, c max , c min is the maximum and minimum value of the voltage coefficient; ΔI wtj is the fault component of the steady-state value of the photovoltaic short-circuit current of node j at time t, wherein, ΔI wtj (0) = 0; G w is the conductance coefficient; G j is the conductance of node j; G ij is the conductance of line ij; ΔI wBESS is the fault component of the steady-state value of the battery energy storage short-circuit current of node j, wherein, ΔI wBESS (0) = 0; m is the selected energy storage access node; E is the set of selected energy storage access nodes; G mj is the conductance of line mj; according to the obtained port voltage, the short-circuit currents of the photovoltaic and the energy storage are calculated, respectively

[0043]

[0044] where I N is the nominal current; U w is the wth port voltage; I kq (w) is the wth photovoltaic short-circuit current reactive component; I kp (w) is the wth photovoltaic short-circuit current active component; K Lvrt is the photovoltaic low-voltage ride-through reactive current coefficient; K qlim is the photovoltaic reactive component maximum value; K i is the maximum overload coefficient of the photovoltaic current; P0 is the rated power; where the superscript j in is the imaginary unit, and arctan(·) is the inverse tangent function; after obtaining the wth short-circuit current component, the fault component of the grid-connected point j photovoltaic output short-circuit current steady-state value is calculated as follows:

[0045]

[0046] After obtaining the photovoltaic short-circuit current steady-state component, the energy storage injection short-circuit current is calculated, wherein the influence of the charge and discharge state of the energy storage on the short-circuit current is considered. In the discharge state, the short-circuit current of the energy storage is calculated as shown below:

[0047]

[0048] where j represents the imaginary unit, U PCC is the voltage of the energy storage coupling point; I qref is the reference value of the reactive current; in the charging state, the output short-circuit current is calculated according to the following formula, wherein P ref is taken as positive:

[0049]

[0050] wherein is the short-circuit current of the energy storage in the charging or discharging state, S N is the rated capacity of the energy storage converter; I N is the rated current of the energy storage converter; I max is the maximum output current of the energy storage; P ref is the active power reference value before the fault; Q ref is the reactive power reference value before the fault; K L is the low-voltage ride-through reactive current coefficient; U k is the voltage of the energy storage grid-connected point after the fault; U L is the voltage threshold for entering the low-voltage ride-through state; U N is the rated voltage of the energy storage; therefore, the fault component of the jth energy storage output short-circuit current steady-state value is:

[0051]

[0052] iteratively until U tj,PV and U kBESS The difference between the two times is less than the threshold value, the iteration is ended, the short-circuit current and the port voltage of the grid-connected point are obtained; wherein the initial value of the short-circuit current of the grid-connected point is calculated by the equivalent voltage source method recommended by GB / T 15544; the initial value of the short-circuit current of the photovoltaic is calculated by the conductance of the photovoltaic module and the nominal voltage, and the specific formula is as follows

[0053]

[0054] G wPV,j =E PV G PV

[0055] In the formula, I PV，j is the short-circuit current of the photovoltaic at the node j; c is the voltage coefficient; U n is the nominal voltage; G wPV,j is the equivalent short-circuit conductance of the photovoltaic connected to the grid-connected point j; G ij is the modulus of the i-th row and the j-th column of the admittance matrix of the grid node; ΔI wtj (w) is the current increment of the j-th photovoltaic power station before and after the fault, which is determined by iterative calculation; E PV is the photovoltaic capacity to be decided; G PV is the per-unit value of the conductance of the photovoltaic unit;

[0056] When the energy storage is connected to the distribution network, the internal bus line is equivalent to an equivalent impedance, and each energy storage unit is equivalent to an equivalent energy storage unit. The equivalent energy storage unit and the equivalent impedance are connected in series, and the short-circuit current output by the equivalent energy storage unit is the sum of the output currents of each energy storage unit. The calculation formula of the initial value is as follows:

[0057]

[0058] G wBESS,j =E BESS G BESS

[0059] In the formula, I BESS，j is the short-circuit current of the energy storage at the node j; G wBESS,j is the equivalent short-circuit conductance of the energy storage connected to the grid-connected point j; ΔI wBESSj is the current increment of the j-th energy storage before and after the fault, which is determined by iterative calculation; E BESS is the energy storage capacity to be decided at the grid-connected point; G BESSis the conductance unit of the energy storage unit; the short-circuit current of the distributed photovoltaic and energy storage grid-connected node j includes the short-circuit currents injected by the photovoltaic, the energy storage and the transformer; wherein the short-circuit current injected by the transformer is calculated by its short-circuit capacity and impedance percentage;

[0060] I SCC,j = I PV,j + I BESS,j + I R,j

[0061]

[0062] wherein, I SCC，j is the short-circuit current of the grid-connected node j; I R,j is the short-circuit current contributed by the transformer accessed by the grid-connected node j; S CC，R is the short-circuit capacity of the transformer; U k % is the impedance percentage of the transformer;

[0063] Based on the short-circuit protection principle, the short-circuit current of the distributed photovoltaic and energy storage access during fault should exceed the rated breaking current allowed by the circuit breaker, so that the circuit breaker can quickly cut off the line, achieving the purpose of circuit breaking protection; at the same time, the short-circuit current during fault should not exceed the breaking current of the circuit breaker, so as to ensure that the circuit breaker can work normally and cut off the circuit during short-circuit; therefore, based on the requirement of short-circuit protection of the circuit breaker, the short-circuit current constraint is as follows:

[0064] I ncl ≤ I SCC,j ≤ I z

[0065] wherein, I ncl is the rated breaking current of the circuit breaker of the grid-connected node; I z is the breaking current of the circuit breaker of the grid-connected node.

[0066] Further, a column and constraint generation algorithm using short-circuit current cutset is proposed to solve the distributed photovoltaic and energy storage collaborative planning second-order cone model, which contains a solving loop, first solves the main problem based on the second-order cone model, then calculates the sub-problem based on the planning scheme solved by the main problem, constructs the cutset of the accessible capacity of photovoltaic and energy storage through the short-circuit current limit, and iteratively solves until the distributed photovoltaic and energy storage collaborative planning second-order cone model converges, and the specific solving steps are as follows:

[0067] 1) Photovoltaic uncertainty characterization: a distribution robust optimization algorithm is used to construct a photovoltaic output probability distribution set Ω;

[0068] 2) Set the initial value: set the iteration convergence threshold ∈, and initialize the iteration number k = 1;

[0069] 3) Master problem solving: solving economic cost optimization problem F, P under extreme scenarios s is the actual probability distribution of the s-th typical scenario; the linearized master problem objective function is:

[0070]

[0071] where C1 is the annual comprehensive cost in the planning phase, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation phase; σ is the number of operation days in a year; C3 is the deviation amount of the distribution network node voltage and the source point voltage

[0072] The objective function value F1 is obtained by solving the master problem * and the distributed photovoltaic and energy storage planning scheme X * The specific content is as shown below, and the lower bound LP = F1 is updated * ;

[0073] X * = {x PV , x ess , x buy}

[0074] In the formula, x PV represents photovoltaic decision variables, including access nodes and access capacity; x ess represents energy storage decision variables, including access nodes and access capacity; x buy represents main grid electricity purchase decision variables;

[0075] 4) Sub-problem solving: based on scheme X * Solving the sub-problem F2, the objective function value F2 * is obtained, and the short-circuit current level I * of the photovoltaic and energy storage grid-connected point is iteratively calculated SCC,j ;

[0076]

[0077] I * SCC,j = I * PV,j + I * BESS,j + I * R,j

[0078] In the formula, ΔE ess represents the energy storage capacity to be adjusted; I * PV,j and I * BESS,j are the short-circuit current levels of the photovoltaic and energy storage grid-connected points under scheme X * ; I *R,j Representation scheme X * Down transformer short-circuit current level;

[0079] 5) Short-circuit current check: judge whether the short-circuit current I * SCC,j meets the requirements, if the short-circuit current meets the short-circuit protection requirements of the circuit breaker, update the upper bound UP=F1 * +F2 * ; otherwise, increase the energy storage capacity constraint considering the short-circuit current constraint and add it to the main problem using the cut plane method, and return to step 3 to solve again; the cut set formula is as follows:

[0080]

[0081] P PV =P PV,f +αγ1

[0082] 6) Convergence judgment: compare the difference between the upper and lower bounds, if the difference is less than the threshold, end the iteration and output the planning scheme X * ; otherwise, let k=k+1, and iterate until the model converges.

[0083] The distributed photovoltaic and energy storage collaborative planning model of the application optimizes the economic cost and voltage deviation of the distribution network, and embeds the short-circuit current constraint, proposes a distributed photovoltaic and energy storage bundling planning configuration method considering photovoltaic output uncertainty, considers the economy and safety of the distribution network for different scenarios, is suitable for distributed photovoltaic and energy storage collaborative planning configuration application, and has rationality.

[0084] Compared with the prior art, the application has the following beneficial effects:

[0085] 1. The distributed photovoltaic and energy storage bundling planning method considering photovoltaic output uncertainty can improve the effectiveness and representativeness of scene selection, and make reasonable photovoltaic and energy storage bundling configuration decisions;

[0086] 2. Considering the photovoltaic output uncertainty problem, a photovoltaic and energy storage grid-connected point short-circuit current calculation method based on the equivalent voltage source method is adopted, and it is embedded in the model, and the short-circuit current constraint is fully considered in combination with the power flow change, the calculation efficiency is improved without losing calculation accuracy;

[0087] 3. For the problem of limited distribution network carrying capacity, the distributed photovoltaic and energy storage are bundled and planned to increase the grid-connected point short-circuit current and stabilize the distribution network voltage, so that the system can safely and stably operate with the minimum economic cost, and the carrying capacity of the distribution network is improved. BRIEF DESCRIPTION OF DRAWINGS

[0088] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0089] Figure 1 Here is a flowchart of an improved C&CG algorithm proposed in this invention;

[0090] Figure 2 This is a framework diagram of the solution model of this invention. Detailed Implementation

[0091] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Unless otherwise specified, the features of the following embodiments and implementation methods can be combined with each other.

[0092] like Figure 2 As shown, the steps of a distributed photovoltaic energy storage bundling planning method that takes into account photovoltaic uncertainties according to the present invention include:

[0093] First, historical photovoltaic (PV) output data is processed using the Bruker bar optimization algorithm, and a set of uncertain probability distributions for PV output is constructed. The scenario with the most drastic fluctuations in PV output is generated as the extreme scenario, and the PV output curve with the highest probability of occurrence is generated as the typical scenario set. The short-circuit current level at the grid connection point of distributed PV and energy storage is calculated based on the equivalent voltage source method and embedded into the model, constructing an economically optimal second-order cone programming model with embedded short-circuit current constraints. Using the short-circuit current constraints as cut sets, an improved column sum constraint generation algorithm (C&CG) is proposed to solve the model to obtain the optimal planning scheme for distributed PV and energy storage. The specific steps of this embodiment are as follows:

[0094] (1) The Bloom bar optimization algorithm is used to process the historical photovoltaic data and construct a photovoltaic output uncertainty probability distribution set (the photovoltaic output uncertainty caused by factors such as light intensity and cloud conditions). This generates the scenario with the most drastic photovoltaic output fluctuation as the extreme scenario, and the photovoltaic output curve with the highest probability of occurrence as the typical scenario. A typical scenario set S is established, S = {S1, S2, ..., S...} l}, where l is the number of typical scenarios. The probability of each typical scenario occurring throughout the year is N. n (1≤n≤l). The probability distribution set P for typical photovoltaic scenarios is finally obtained. 0 =[P1 0 P2 0 …,P k 0 The original probability distribution is P. n 0 =Nn Z, where Z is the total number of annual historical output, and 365 is taken herein.

[0095] (a) Based on L 1- The paradigm depicts the actual probability distribution set Ω of photovoltaic. P s is the actual probability distribution of the s-th typical scene; γ1 is the uncertainty interval of photovoltaic output. As γ1 increases, the fluctuation range of photovoltaic output depicted by the probability distribution set also increases, and the robustness of the model is improved.

[0096]

[0097] (b) Since the scenes with the most severe photovoltaic output fluctuation are taken at the extreme points of the feasible region, the upper and lower boundaries of the photovoltaic output are depicted by two 0-1 variables α and β. When α = 1, the output reaches the maximum fluctuation, and when β = 1, the output reaches the minimum fluctuation. P PV is the actual output curve of photovoltaic, P PV,f is the predicted output curve of photovoltaic.

[0098] P PV = P PV,f + αγ1- βγ1

[0099]

[0100] The photovoltaic output fluctuation variable has a limit condition. The two variables α and β have mutual exclusivity, where Г is the uncertainty of photovoltaic, which limits the number of photovoltaics that can be accessed to reach the fluctuation limit at the same time. The greater Г is, the more severe the photovoltaic output fluctuation is, and the more extreme the scene is.

[0101] (2) Based on the constructed probability distribution uncertainty set of wind and light output, a second-order cone model for distributed photovoltaic and energy storage collaborative planning for different scenes is proposed:

[0102]

[0103] In the formula, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily power purchase cost in the operation stage; σ is the number of operation days in a year, which is taken as 365 in the embodiment, and can be selected according to actual conditions, and is not limited; C3 is the deviation amount of the voltage of the distribution network node and the voltage of the source point.

[0104] In an embodiment, the model is:

[0105] (a) The objective function is selected to be the minimum of the annual comprehensive cost and the voltage deviation amount of the distribution network, and the calculation formula is as follows:

[0106]

[0107] Where, ρpinv , p einv is the unit investment cost of energy storage power capacity and the unit investment cost of energy storage energy capacity; p pbuv is the time-of-use price of the main grid; C ess,om is the unit maintenance cost of energy storage power capacity; C PV,inv is the unit investment cost of photovoltaic power capacity; d is the discount rate; y is the service life of the energy storage; E ess , P ess is the energy storage capacity to be decided and the energy storage power; E PV is the photovoltaic capacity to be decided; P g，i is the electricity purchase quantity of node i from the main grid; U1 is the source point voltage; U i is the voltage of node i; and N is the total number of nodes.

[0108] (b) The constraint conditions include:

[0109] 1) Photovoltaic and energy storage capacity constraint. The accessible photovoltaic and energy storage capacity limit is set according to the nature of the distribution network.

[0110]

[0111] In the formula, E ess,j,min , E ess,j,max is the minimum and maximum values of the energy storage capacity accessible to node j, E ess,j is the photovoltaic capacity accessible to node j; E PV,j,min , E PV,j,max is the minimum and maximum values of the photovoltaic capacity accessible to node j, E PV,j is the photovoltaic capacity accessible to node j; and E is the set of energy storage nodes to be selected.

[0112] 2) Energy storage charging and discharging constraint. The charging and discharging constraint and the energy storage SOC balance constraint are designed according to the conditions of the energy storage device itself.

[0113]

[0114] In the formula, P dch,min (t), P dch,max (t) is the minimum and maximum values of the energy storage discharging power at time t, P dch (t) is the energy storage discharging power at time t; P ch,min (t), P ch,max (t) is the minimum and maximum values of the energy storage charging power at time t, P ch (t) is the energy storage charging power at time t; u dch (t), u ch (t) is the energy storage discharging / charging state at time t, which limits that the energy storage charging and discharging cannot be performed at the same time; SOC ess,min , SOC ess,maxη represents the minimum and maximum energy storage capacity. dch η ch Energy storage discharge / charge efficiency; P dch (t), P ch (t) represents the discharge / charge power of the energy storage at time t; SOC ess (t) represents the energy storage capacity at time t.

[0115] 3) Power flow constraints. Under the distribution network structure, the planning must satisfy the balance between active and reactive power. The specific calculation formula is as follows:

[0116]

[0117] In the formula, P ij Q ij Let P(j) represent the active / reactive power of line ij; P(j) and C(j) represent the sets of candidate nodes for photovoltaic and energy storage, respectively; P jk Q jk The active / reactive power of line jk; r is the square of the current on line ij; ij x ij P represents the resistance and reactance of line ij; j load Q j load P represents the active / reactive power of the load at node j; PV,j P ess,j Let N be the photovoltaic power output and energy storage power output of node j; N is the set of distribution network nodes.

[0118] 4) Current constraints of distribution network branches.

[0119]

[0120] In the formula, These are the lower and upper limits of the current in branch ij, respectively.

[0121] 5) Voltage constraints at distribution network nodes.

[0122]

[0123] In the formula, Let be the lower and upper voltage limits for node j, respectively. Let be the voltage at node j.

[0124] 6) Second-order cone constraint. Because the model contains some nonlinear constraints, they are transformed into second-order cone constraints. The specific calculation formula is as follows:

[0125]

[0126] (3) The short-circuit current level of the distributed photovoltaic and energy storage grid-connected point is calculated based on the equivalent voltage source method, and is embedded in the model for safety check. Through the short-circuit conductance of the light and energy storage and the admittance matrix of the system, the light port voltage U tj，PV (w) and the energy storage port voltage U tBESS (w) are calculated, and the specific formula is as follows:

[0127]

[0128] In the formula, c max , c min are the maximum and minimum values of the voltage coefficient; ΔI wtj is the fault component of the steady-state value of the photovoltaic short-circuit current of node j at time t, wherein ΔI wtj (0) = 0; G w is the conductance coefficient; G j is the conductance of node j; G ij is the conductance of line ij; ΔI wBESS is the fault component of the steady-state value of the battery energy storage short-circuit current of node j, wherein ΔI wBESS (0) = 0; m is the selected energy storage access node; E is the selected energy storage access node set; G mj is the conductance of line mj; according to the obtained port voltage, the short-circuit currents of the photovoltaic and the energy storage are calculated respectively

[0129]

[0130] In the formula, I N is the nominal current; U w is the wth port voltage; I kq (w) is the wth photovoltaic short-circuit current reactive component; I kp (w) is the wth photovoltaic short-circuit current active component; I kq 2 (w) is the square of the wth photovoltaic short-circuit current reactive component; K Lvrt is the photovoltaic low-voltage ride-through reactive current coefficient; K qlim is the maximum value of the photovoltaic reactive component; K i is the maximum overload coefficient of the photovoltaic current; P0 is the rated power; The superscript j in the formula is an imaginary unit, and arctan(·) is the inverse tangent function. After obtaining the wth short-circuit current component, the fault component of the steady-state value of the photovoltaic output short-circuit current of the grid-connected point j is calculated:

[0131]

[0132] After obtaining the steady-state component of the short-circuit current of the photovoltaic, the short-circuit current of the energy storage is calculated, wherein the influence of the charge and discharge state of the energy storage on the short-circuit current is considered, and the short-circuit current of the energy storage in the discharge state is calculated as shown below.

[0133]

[0134] wherein j represents an imaginary unit, is the short-circuit current of the energy storage in the charging or discharging state, U PCC is the voltage of the coupling point of the energy storage; I qref is the reference value of the reactive current; I kq is the active component of the short-circuit current of the photovoltaic; I kp is the active component of the short-circuit current of the photovoltaic. In the charging state, the output short-circuit current is calculated according to the following formula, wherein P ref is taken as positive:

[0135]

[0136] wherein S N is the rated capacity of the energy storage converter; I N is the rated current of the energy storage converter; I max is the maximum output current of the energy storage; P ref is the reference value of the active power before the fault; Q ref is the reference value of the reactive power before the fault; K L is the low-voltage ride-through reactive current coefficient; U k is the voltage of the grid-connected point of the energy storage after the fault; U L is the voltage threshold for entering the low-voltage ride-through state; U N is the rated voltage of the energy storage. Therefore, the fault component of the short-circuit current of the energy storage at the grid-connected point j is:

[0137]

[0138] iterating until U tj,PV and U kBESS are both less than the threshold value, ending the iteration, and obtaining the short-circuit current and the port voltage of the grid-connected point. The equivalent voltage source method recommended in GB / T 15544 is used to calculate the initial value of the short-circuit current of the grid-connected point. The initial value of the short-circuit current of the photovoltaic is calculated from the conductance and the nominal voltage of the photovoltaic component, and the specific formula is as shown below

[0139]

[0140] G wPV,j = E PV G PV

[0141] wherein I PV，jIs the short-circuit current of the photovoltaic at node j; c is the voltage coefficient; U n is the nominal voltage; G wPV,j is the equivalent short-circuit conductance of the photovoltaic connected at the grid node j; ΔI ij is the modulus of the admittance matrix of the grid node at the ith row and the jth column; ΔI wtj (w) is the current increment of the jth photovoltaic power station before and after the wth fault, which is determined by iterative calculation; E PV is the photovoltaic capacity to be decided; G PV is the per-unit value of the conductance of the photovoltaic unit; P is the set of photovoltaic nodes to be selected.

[0142] When the energy storage is connected to the distribution network, the internal bus line is equivalent to an equivalent impedance, and each energy storage unit is equivalent to an equivalent energy storage unit. The equivalent energy storage unit and the equivalent impedance are connected in series, and the short-circuit current output by the equivalent energy storage unit is the sum of the output currents of each energy storage unit. The calculation formula of the initial value is as follows:

[0143]

[0144] G wBESS,j = E BESS G BESS

[0145] In the formula, I BESS，j is the short-circuit current of the energy storage at node j; G wBESS,j is the equivalent short-circuit conductance of the energy storage connected at the grid node j; ΔI wBESSj is the current increment of the jth energy storage before and after the fault, which is determined by iterative calculation; E BESS is the energy storage capacity to be decided at the grid node; G BESS is the per-unit value of the conductance of the energy storage unit. The short-circuit current of the distributed photovoltaic and energy storage grid-connected node j includes the short-circuit currents injected by the photovoltaic, the energy storage and the transformer. Among them, the short-circuit current injected by the transformer is calculated by its short-circuit capacity and impedance percentage.

[0146] I SCC,j = I PV,j + I BESS,j + I R,j

[0147]

[0148] In the formula, I SCC，j is the short-circuit current of the grid node j; I R,j is the short-circuit current contributed by the transformer connected at the grid node j; S CC，R is the short-circuit capacity of the transformer; U k is the impedance percentage of the transformer.

[0149] Based on the short-circuit protection principle, the short-circuit current of the distributed photovoltaic energy storage access at the time of fault should exceed the rated breaking current allowed by the circuit breaker, so that the circuit breaker can quickly cut off the line to achieve the purpose of circuit protection. At the same time, the short-circuit current at the time of fault should not exceed the breaking current of the circuit breaker, so as to ensure that the circuit breaker can work normally and cut off the circuit at the time of short circuit. Therefore, based on the requirements of the short-circuit protection of the circuit breaker, the short-circuit current constraint is as follows:

[0150] I ncl ≤I SCC,j ≤I z

[0151] I ncl is the rated breaking current of the circuit breaker at the grid connection point; I z is the breaking current of the circuit breaker at the grid connection point.

[0152] (4) A C&CG algorithm using the short-circuit current cut set is proposed to solve the second-order cone model of the distributed photovoltaic and energy storage collaborative planning, which includes a solving loop. First, the main problem is solved based on the second-order cone model, and then the sub-problem is calculated based on the preliminary planning scheme. The cut set of the accessible capacity of photovoltaic and energy storage is constructed through the short-circuit current limit, and the iteration is solved until the model converges.

[0153] The specific solving process of the model is shown in Figure 1 .

[0154] 1) Uncertainty characterization of photovoltaic: a distributed robust optimization algorithm is used to construct the probability distribution set Ω of photovoltaic output.

[0155] 2) Set initial value: set the iteration convergence threshold ∈, and initialize the iteration number k = 1.

[0156] 3) Solve the main problem: solve the economic cost optimization problem F under extreme conditions, P s is the actual probability distribution of the s-th typical scenario. The objective function of the linearized main problem is:

[0157]

[0158] Through the solution of the main problem, the objective function value F1 * and the distributed photovoltaic and energy storage planning scheme X * are obtained, the specific content is shown below, and the lower bound LP = F1 * is updated.

[0159] X * = {x PV , x ess , x buy}

[0160] I PVx ess x buy x

[0161] 4) Sub-problem solving: based on scheme X * Solving sub-problem F2, get objective function value F2 * , and the short-circuit current level I * of photovoltaic and energy storage grid-connected point is calculated by the formula above. SCC,j .

[0162]

[0163] I * SCC,j = I * PV,j + I * BESS,j + I * R,j

[0164] where ΔE ess represents the energy storage capacity to be adjusted; I * PV,j and I * BESS,j are the short-circuit current levels of photovoltaic and energy storage grid-connected points under scheme X * ; I * R,j represents the short-circuit current level of transformer under scheme X * .

[0165] 5) Short-circuit current checking: judge whether the short-circuit current I * SCC,j meets the requirements. If the short-circuit current meets the short-circuit protection requirements of the circuit breaker, update the upper bound UP = F1 * + F2 * ; otherwise, add the energy storage capacity constraint considering the short-circuit current constraint to the main problem by using the cutting plane method, and return to step 3 to solve again. The cutting plane formula is as follows.

[0166]

[0167] P PV = P PV,f + αγ1

[0168] 6) Convergence judgment: compare the difference between the upper and lower bounds. If the difference is less than the threshold value, end the iteration and output the planning scheme X * ; otherwise, let k = k + 1 and iterate until the model converges.

[0169] The foregoing description of the embodiments has been presented for the purpose of illustration and description. It is not intended to be exhaustive or to limit the application to the precise form disclosed. Modifications and variations are possible in light of the above teachings or can be acquired from practice of the application. As well, the application has been described above with the aid of functional building blocks illustrating the principles of operation at a conceptual level. These building blocks have been recognized to be more functional than structural. The actual implementation can always depend on the specific application and design restrictions and, therefore, should not be interpreted as a critical or essential block or function that the application is organized around. The components described or claimed herein can be implemented in hardware, software, firmware or any combination thereof.

Claims

1. A method for bundled planning of distributed photovoltaic energy storage that takes into account photovoltaic uncertainties, characterized in that, Includes the following steps: (1) To address the uncertainty of photovoltaic power output, the split-Brow bar optimization algorithm is used to process the historical data of photovoltaic power output and construct a set of uncertain probability distributions of photovoltaic power output. This generates the scenario with the most drastic fluctuations in photovoltaic power output as an extreme scenario and generates the photovoltaic power output curve with the highest probability of occurrence as a set of typical scenarios. (2) After generating typical and extreme scenarios, the short-circuit current level of the grid connection point of distributed photovoltaic and energy storage is calculated based on the equivalent voltage source method, and embedded into the model to construct a second-order cone model for collaborative planning of distributed photovoltaic and energy storage with embedded short-circuit current constraints, including planning cost, operating cost and distribution network voltage deviation. (3) Using short-circuit current constraints as cut sets, a column sum constraint generation algorithm based on short-circuit current cut sets is proposed to solve the second-order cone model of the distributed photovoltaic and energy storage collaborative planning. The algorithm includes a solution loop. First, the main problem is solved based on the second-order cone model. Then, the sub-problems are calculated based on the preliminary planning scheme. Cut sets of the accessible capacity of photovoltaic and energy storage are constructed through short-circuit current constraints. The algorithm is iteratively solved until the second-order cone model of the distributed photovoltaic and energy storage collaborative planning converges, and the optimal planning decision of distributed photovoltaic and energy storage is obtained.

2. The method for planning and configuring distributed photovoltaic and energy storage bundled together, taking into account the uncertainty of photovoltaic output, as described in claim 1, is characterized in that... The Bloomberg bar optimization algorithm is used to process historical photovoltaic data and construct a set of uncertain probability distributions for photovoltaic power output. This generates the scenario with the most drastic fluctuations in photovoltaic power output as the extreme scenario, and generates the photovoltaic power output curve with the highest probability of occurrence as the typical scenario set S, where S = {S1, S2, ..., S...}. l }, where l is the number of typical scenarios; the probability of each typical scenario occurring throughout the year is N. n , 1≤n≤l; finally, the probability distribution set P of typical photovoltaic scenarios is obtained. 0 =[P1 0 P2 0 …,P l 0 The original probability distribution is P. n 0 =N n / Z, where Z is the total historical output for the whole year; Based on L 1- The paradigm characterizes the actual probability distribution set Ω of photovoltaics; P s γ1 represents the actual probability distribution of the s-th typical scenario; γ1 represents the uncertainty interval of photovoltaic output. Since the most drastic fluctuation in photovoltaic (PV) output occurs at the extreme point of the feasible region, two 0-1 variables, α and β, are used to characterize the upper and lower boundaries of PV output; when α = 1, the output reaches its maximum fluctuation, and when β = 1, the output reaches its minimum fluctuation. PV This is the actual power output curve of the photovoltaic system, P. PV,f Forecast curve of photovoltaic power output; P PV =P PV,f +αγ1-βγ1 There are constraints on the variable of photovoltaic power output fluctuation. Since the two variables α and β are mutually exclusive, Γ is the photovoltaic uncertainty, which limits the number of photovoltaic grids that can be connected at the same time to reach the fluctuation limit. The larger Γ is, the more violent the photovoltaic power output fluctuation and the more extreme the scenario.

3. The method for planning and configuring distributed photovoltaic and energy storage bundled together, taking into account the uncertainty of photovoltaic output, as described in claim 1, is characterized in that... Based on historical scenarios of photovoltaic (PV) output, an uncertain probability distribution set of PV output is constructed, and then a second-order cone (SOP) model for distributed PV and energy storage collaborative planning is built for different scenarios. The objective function is to minimize the annual comprehensive cost and the distribution network voltage deviation, and the specific form of the objective function is as follows: Wherein, C1 is the annual comprehensive cost during the planning phase, including construction and operation and maintenance costs; C2 is the daily electricity purchase cost during the operation phase; σ is the number of operating days in a year; and C3 is the deviation between the voltage at the distribution network nodes and the voltage at the source points.

4. The method for planning and configuring distributed photovoltaic and energy storage bundled together, taking into account the uncertainty of photovoltaic output, as described in claim 3, is characterized in that... The calculation formulas for C1, C2, and C3 are as follows: Where, ρ pinv , ρ einv The costs are the investment costs per unit of energy storage power capacity and per unit of energy storage capacity; ρ pbuv Main grid time-of-use pricing; C ess,om Maintenance cost per unit of energy storage capacity; C PV,inv δ represents the investment cost per unit photovoltaic power capacity; δ is the discount rate; y is the lifespan of the energy storage; E ess ,P ess For the energy storage capacity and energy storage power to be decided; E PV The photovoltaic capacity to be decided; P g，i Node i purchases electricity from the main grid; U1 is the source voltage; U i Where is the voltage at grid node i; N is the total number of grid nodes; The constraints include: 1) Photovoltaic and energy storage capacity constraints; limits on the capacity of accessible photovoltaic and energy storage systems are set based on the characteristics of the distribution network: In the formula, E ess,j,min E ess,j,max Let E be the minimum and maximum energy storage capacity that node j can access. ess,j E represents the photovoltaic capacity that node j can access; PV,j,min E PV,j,max Let E be the minimum and maximum photovoltaic capacity that node j can access. PV,j Let be the photovoltaic capacity that node j can connect to; E is the set of potential energy storage nodes. 2) Energy storage charge and discharge constraints; design charge and discharge constraints and energy storage SOC balance constraints based on the conditions of the energy storage device itself: In the formula, P dch,min (t), P dch,max (t) represents the minimum and maximum energy storage discharge power at time t, P dch (t) represents the energy storage discharge power at time t; P ch,min (t), P ch,max (t) represents the minimum and maximum energy storage charging power at time t, P ch (t) represents the energy storage charging power at time t; u dch (t), u ch (t) represents the energy storage discharge / charge state at time t, restricting energy storage charging and discharging from occurring simultaneously; SOC ess,min SOC ess,max η represents the minimum and maximum energy storage capacity. dch η ch Energy storage discharge / charge efficiency; P dch (t), P ch (t) represents the discharge / charge power of the energy storage at time t; SOC ess (t) represents the energy storage capacity at time t; 3) Power flow constraints; Under the distribution network structure, the planning must satisfy the balance of active and reactive power. The specific calculation formula is as follows: In the formula, P ij Q ij Let P(j) represent the active / reactive power of line ij; P(j) and C(j) represent the sets of candidate nodes for photovoltaic and energy storage, respectively; P jk Q jk The active / reactive power of line jk; r is the square of the current on line ij; ij x ij P represents the resistance and reactance of line ij; j load Q j load P represents the active / reactive power of the load at node j; PV,j P ess,j Let N represent the photovoltaic and energy storage outputs of node j; N is the set of distribution network nodes. 4) Distribution network branch current constraints; In the formula, These are the lower and upper limits of the current in branch ij, respectively; 5) Voltage constraints at distribution network nodes; In the formula, Let be the lower and upper voltage limits for node j, respectively. Let be the voltage at node j; 6) Second-order cone constraint; Because the model contains some nonlinear constraints, they are converted into second-order cone constraints. The specific calculation formula is as follows:

5. The method for planning and configuring distributed photovoltaic and energy storage bundled together, taking into account the uncertainty of photovoltaic output, as described in claim 1, is characterized in that... The short-circuit current level at the grid connection point of distributed photovoltaic and energy storage is calculated based on the equivalent voltage source method, and then embedded into the model for safety verification. The photovoltaic port voltage U at node j (w-th time) is calculated using the short-circuit conductance of the photovoltaic and energy storage systems and the system admittance matrix. tj，PV (w) and energy storage port voltage U tBESS (w), the specific formula is shown below: In the formula, c max c min These are the maximum and minimum values ​​of the voltage coefficient; ΔI wtj Let ΔI be the fault component of the steady-state value of the photovoltaic short-circuit current at node j and time t, where ΔI wtj (0) = 0; G w G is the electrical conductivity. j For the conductance at node j; G ij Let ΔI be the conductance of the line ij; wBESS Let ΔI be the fault component of the steady-state value of the battery energy storage short-circuit current at node j, where ΔI wBESS (0) = 0; m is the candidate energy storage access node; E is the set of candidate energy storage access nodes; G mj Given the line conductance mj; calculate the short-circuit currents of the photovoltaic and energy storage systems based on the obtained port voltages. In the formula, I N It is the nominal current; U w It is the port voltage of the wth time; I kq (w) is the reactive component of the photovoltaic short-circuit current during the wth iteration; I kp (w) is the active component of the photovoltaic short-circuit current during the w-th iteration; K Lvrt It is the photovoltaic low-voltage ride-through reactive current coefficient; K qlim It is the maximum value of the photovoltaic reactive power component; K i P0 is the maximum overload factor of the photovoltaic current; P0 is the rated power. In this context, the superscript j represents the imaginary unit, and arctan(·) is the arctangent function. After obtaining the w-th short-circuit current component, the fault component of the steady-state value of the photovoltaic output short-circuit current at grid-connected point j is calculated: After obtaining the steady-state component of the photovoltaic short-circuit current, the short-circuit current injected into the energy storage is calculated. The influence of the charging and discharging state of the energy storage on the short-circuit current is considered. Under the discharging state, the short-circuit current of the energy storage is calculated as follows: In the formula, j represents the imaginary unit, U PCC It is the voltage at the energy storage coupling point; I qref This is a reference value for reactive current; during charging, the output short-circuit current is calculated using the following formula, where P... ref The inflow into the system is considered positive: In the formula, It is the short-circuit current of energy stored during charging or discharging, S N It is the rated capacity of the energy storage converter; I N It is the rated current of the energy storage converter; I max It is the maximum output current of the energy storage; P ref This is the reference value of active power before the fault; Q ref This is the reference value for reactive power before the fault; K L It is the low-voltage ride-through reactive current coefficient; U k It is the voltage at the energy storage grid connection point after the fault; U L It is the voltage threshold for entering the low-voltage ride-through state; U N Let be the rated voltage of the energy storage; therefore, the fault component of the steady-state value of the short-circuit current of the j-th energy storage output is: Iterate continuously until U tj,PV and U kBESS The iteration ends when the difference between the two iterations is less than the threshold, yielding the short-circuit current and port voltage at the grid connection point. The initial value of the short-circuit current at the grid connection point is calculated using the equivalent voltage source method recommended in GB / T 15544. The initial value of the photovoltaic short-circuit current is calculated from the photovoltaic module's conductance and nominal voltage, as shown in the following formula: G wPV,j =E PV G PV In the formula, I PV，j U is the short-circuit current of the photovoltaic system at node j; c is the voltage coefficient; U n It is the nominal voltage; G wPV,j G is the equivalent short-circuit conductance of the photovoltaic system connected to the grid at point j; ij It is the magnitude of the grid node admittance matrix in the i-th row and j-th column; ΔI wtj (w) represents the current increment before and after the w-th and j-th photovoltaic power station fault, determined by iterative calculation; E PV Photovoltaic capacity to be decided; G PV This represents the per-unit value of the photovoltaic unit's conductivity; When energy storage is connected to the distribution network, the internal busbar is equivalent to an equivalent impedance, and each energy storage unit is equivalent to an equivalent energy storage unit. The equivalent energy storage unit and the equivalent impedance are connected in series. The short-circuit current output by the equivalent energy storage unit is the sum of the output currents of all energy storage units. The initial value is calculated using the following formula: G wBESS,j =E BESS G BESS In the formula, I BESS，j It is the short-circuit current of the energy stored at node j; G wBESS,j The equivalent short-circuit conductance of the energy storage connected to the grid at point j; ΔI wBESSj The current increment before and after the j-th energy storage fault is determined by iterative calculation; E BESS The energy storage capacity to be decided at this grid connection point; G BESS The per-unit conductance of the energy storage unit is given; the short-circuit current of the distributed photovoltaic and energy storage grid-connected node j includes the short-circuit current injected by the photovoltaic, energy storage and transformer; among which, the short-circuit current injected by the transformer is calculated by its short-circuit capacity and impedance percentage. I SCC,j =I PV,j +I BESS,j +I R,j In the formula, I SCC，j It is the sum of short-circuit currents at grid connection point j; I R,j It is the short-circuit current contributed by the transformer connected to the grid at point j; S CC，R U represents the transformer's short-circuit capacity. k % represents the percentage of transformer impedance; Based on the principle of short-circuit protection, the short-circuit current of distributed photovoltaic energy storage during a fault should exceed the rated breaking current of the circuit breaker, thereby enabling the circuit breaker to quickly disconnect the line and achieve the purpose of circuit breaker protection. Simultaneously, the short-circuit current during a fault must not exceed the breaking current of the circuit breaker to ensure that the circuit breaker can operate normally and disconnect the circuit during a short circuit. Therefore, based on the requirements of circuit breaker short-circuit protection, short-circuit current constraints are imposed: I ncl ≤I SCC,j ≤I z In the formula, I ncl The rated breaking current of the circuit breaker at the grid connection point; I z This refers to the breaking current of the circuit breaker at the grid connection point.

6. The method for planning and configuring distributed photovoltaic and energy storage bundled together, taking into account the uncertainty of photovoltaic output, as described in claim 1, is characterized in that... A column sum and constraint generation algorithm based on short-circuit current cut sets is proposed to solve the second-order cone model for the collaborative planning of distributed photovoltaic and energy storage. This algorithm includes a solution loop: first, the main problem is solved based on the second-order cone model; then, subproblems are calculated based on the planning scheme solved by the main problem; cut sets of the accessible capacity of photovoltaic and energy storage are constructed through short-circuit current constraints; and the solution is iteratively solved until the second-order cone model for the collaborative planning of distributed photovoltaic and energy storage converges. The specific solution steps are as follows: 1) Characterization of photovoltaic uncertainty: A photovoltaic output probability distribution set Ω is constructed using the sub-Bruker bar optimization algorithm; 2) Set initial values: Set the iteration convergence threshold ∈ and initialize the number of iterations k = 1; 3) Solving the main problem: Solving the economic cost optimization problem F and P under extreme conditions. s Let be the actual probability distribution of the s-th typical scenario; the linearized objective function of the main problem is: Where C1 is the annual comprehensive cost during the planning phase, including construction and operation and maintenance costs; C2 is the daily electricity purchase cost during the operation phase; σ is the number of operating days per year; and C3 is the deviation between the voltage at the distribution network nodes and the voltage at the source points. The objective function value F1 is obtained by solving the main problem. * Distributed photovoltaic and energy storage planning scheme X * The specific details are shown below, and the lower bound LP = F1 is updated. * ; X * ={x PV ,x ess ,x buy } In the formula, x PV The variables representing photovoltaic decision-making include the number of nodes connected to the grid and the grid capacity; x ess This represents the energy storage decision variables, including the access node and the access capacity; x buy Indicates the main grid electricity purchase decision variable; 4) Subproblem solving: Based on scheme X * Solve subproblem F2 to obtain the objective function value F2. * Simultaneously, iteratively calculate the short-circuit current level I at the grid connection points of photovoltaic and energy storage. * SCC,j ; I * SCC,j =I * PV,j +I * BESS,j +I * R,j In the formula, ΔE ess Indicates the energy storage capacity that needs to be adjusted; I * PV,j and I * BESS,j Scheme X * The short-circuit current level at the grid connection point of photovoltaic and energy storage; I * R,j Scheme X * The short-circuit current level of the transformer; 5) Short-circuit current check: Determine the short-circuit current I * SCC,j If the short-circuit current meets the requirements of the circuit breaker's short-circuit protection, update the upper limit UP = F1. * +F2 * Conversely, by using the cutting plane method that considers short-circuit current constraints to add energy storage capacity constraints to the main problem, we return to step 3 and solve it again; the cut set formula is shown below: P PV =P PV,f +αγ1 6) Convergence check: Compare the difference between the upper and lower bounds to see if it is less than the threshold. If it is less than the convergence threshold, end the iteration and output the planning scheme X. * Conversely, let k = k + 1, and iterate until the model converges.

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