Distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty

Abstract

The invention discloses a distributed photovoltaic energy storage bundling planning method considering photovoltaic uncertainty, and the method comprises the steps: constructing a photovoltaic output uncertainty probability distribution set based on a distribution robust optimization algorithm, and generating a typical scene and an extreme scene according to the photovoltaic output uncertainty probability distribution set; a distributed photovoltaic and energy storage collaborative planning second-order cone model is constructed for different scenes, the economic cost of photovoltaic and energy storage and the voltage deviation of a power distribution network are taken as optimization targets, and a grid-connected point short-circuit current constraint considering the photovoltaic output uncertainty is embedded. Constructing a cutting plane through a short-circuit current checking result, and proposing a Camp; and solving the model through a CG algorithm. According to the method, the uncertain probability distribution set is constructed, distributed photovoltaic and energy storage bundling planning is realized in combination with the scene occurrence probability, the photovoltaic accessible capacity is improved, the voltage of the power distribution network is stabilized, the model takes the minimum total economic cost and the minimum voltage deviation value as optimization targets and meets the short-circuit current constraint, and the method is suitable for large-scale popularization and application. The planning scheme has the advantages of optimal economical efficiency, stable voltage and high credibility.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a distributed photovoltaic energy storage bundling planning method taking photovoltaic uncertainty into account. Background Art

[0003] The carrying capacity of a distribution network refers to the maximum capacity of distributed renewable energy that can be connected to the distribution network under the premise that the safety operation indicators (such as short-circuit current and voltage deviation) are met and not exceeded during the planning stage. At present, the grid structure of the distribution network is relatively weak, and the local load distribution is uneven, resulting in overload on some lines and energy imbalance in the system, thus limiting the access capacity of renewable energy. Therefore, it is urgent to study methods to improve the carrying capacity of high-penetration distribution networks in order to maximize the scale of renewable energy access.

[0004] The main impact of distributed photovoltaic grid connection on the power grid lies in the randomness and volatility of its output. By configuring energy storage, fluctuations can be smoothed in time and the quality of power supply can be improved. However, most current studies optimize the voltage and economic costs of the distribution network by configuring photovoltaic and energy storage systems separately, ignoring the synergistic benefits of the two. At the same time, the uncertainty of photovoltaic output leads to uncertainty in the voltage sag of each node after connecting to distributed photovoltaics. The uncertainty characteristics of short-circuit current and voltage deviation have not been fully considered in existing studies, resulting in conservative planning results, reducing the economic benefits of the distribution network and limiting the accessible capacity of distributed photovoltaics. Summary of the invention

[0005] The purpose of the present invention is to address the problems existing in the prior art and provide a distributed photovoltaic energy storage bundling planning method that takes photovoltaic uncertainty into account, so as to achieve the purpose of a distributed photovoltaic and energy storage collaborative planning scheme with an embedded short-circuit current constraint that takes into account photovoltaic output uncertainty.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a distributed photovoltaic energy storage bundling planning method taking photovoltaic uncertainty into account, comprising the following steps:

[0007] (1) Aiming at the uncertainty of PV output, the distributed robust optimization algorithm is used to process the historical data of PV output and construct a probability distribution set of PV output uncertainty, thereby generating the scenario with the most drastic PV output fluctuation as the extreme scenario and the PV output curve with the highest probability of occurrence as the typical scenario set;

[0008] (2) After generating typical and extreme scenarios, 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 embedded into the model to construct a distributed photovoltaic and energy storage collaborative planning second-order cone model with embedded short-circuit current constraints, including planning cost, operating cost and distribution network voltage deviation;

[0009] (3) Using the short-circuit current constraint as a cut set, a column and constraint generation algorithm using the short-circuit current cut set is proposed to solve the second-order cone model of the coordinated planning of distributed photovoltaic and energy storage; the algorithm includes a solution loop, first solving the main problem based on the second-order cone model, then calculating the sub-problem based on the preliminary planning scheme, constructing the cut set of the accessible capacity of photovoltaic and energy storage through the short-circuit current constraint, and iterating the solution until the second-order cone model of the coordinated planning of distributed photovoltaic and energy storage converges, and obtaining the optimal planning decision of distributed photovoltaic and energy storage.

[0010] Furthermore, the distributed robust optimization algorithm is used to process the photovoltaic historical data and construct the photovoltaic output uncertainty probability distribution set, so as to generate the most drastic photovoltaic output fluctuation scenario as the extreme scenario, and generate the photovoltaic output curve with the highest probability of occurrence as the typical scenario set S, 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;

[0011] Based on L 1- The paradigm describes the actual probability distribution set of photovoltaics Ω; P s is the actual probability distribution of the sth typical scenario; γ1 is the uncertainty interval of photovoltaic output;

[0012]

[0013] Since the most drastic PV output fluctuation scenario is obtained at the extreme point of the feasible domain, the upper and lower boundaries of PV output are characterized by two 0-1 variables α and β; when α = 1, the output reaches the maximum fluctuation value, and when β = 1, the output reaches the minimum fluctuation value; P PV is the actual photovoltaic output curve, P PV,f It is the photovoltaic output prediction curve;

[0014] P PV =P PV,f +αγ1-βγ1

[0015]

[0016] There are restrictions on the photovoltaic output fluctuation variable; because the two variables α and β are mutually exclusive, where Г is the photovoltaic uncertainty, which limits the number of connected photovoltaics that reach the fluctuation limit at the same time. The larger Г is, the more severe the photovoltaic output fluctuation is and the more extreme the scenario is.

[0017] Furthermore, based on the historical scenarios of photovoltaic output, an uncertain probability distribution set of photovoltaic output is constructed, and then a second-order cone (SOP) model for the coordinated planning of distributed photovoltaic and energy storage for different scenarios is constructed; the photovoltaic and energy storage bundling planning is carried out with the minimum annual comprehensive cost and distribution network voltage deviation as the objective function. The specific form of the objective function is:

[0018]

[0019] Among them, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation stage; σ is the number of operating days in the whole year; C3 is the deviation between the node voltage and the source point voltage of the distribution network;

[0020] Furthermore, the calculation formulas of C1, C2 and C3 are as follows:

[0021]

[0022] Among them, ρ pinv , ρ einv is the investment cost per unit energy storage power capacity and the investment cost per unit energy storage capacity; ρ pbuv The main grid time-of-use electricity price; C ess,om is the maintenance cost per unit energy storage power capacity; C PV,inv is the unit photovoltaic power capacity investment cost; δ is the discount rate; y is the service life of energy storage; E ess ,P ess E is the energy storage capacity and power to be decided; PV is the photovoltaic capacity to be decided; P g，i is the amount of electricity purchased by grid node i from the main grid; U1 is the source voltage; U i is the voltage of grid node i; N is the total number of grid nodes;

[0023] The constraints include:

[0024] 1) PV and energy storage capacity constraints: Set the accessible PV and energy storage capacity limits based on the nature of the distribution network:

[0025]

[0026] In the formula, E ess,j,min , E ess,j,max is the minimum and maximum energy storage capacity that node j can access, E ess,j is the photovoltaic capacity accessible to node j; EPV,j,min , E PV,j,max is the minimum and maximum photovoltaic capacity that can be connected to node j, E PV,j is the photovoltaic capacity that can be connected to node j; E is the set of energy storage nodes to be selected;

[0027] 2) Energy storage charging and discharging constraints: Design charging and discharging constraints and energy storage SOC balance constraints based on the conditions of the energy storage equipment itself:

[0028]

[0029] Where P dch,min (t), P dch,max (t) is the minimum and maximum value of the energy storage discharge power at time t, P dch (t) is the energy storage discharge power at time t; P ch,min (t), P ch,max (t) is the minimum and maximum 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 discharge / charge state at time t, which limits the energy storage charge and discharge to not be performed simultaneously; SOC ess,min , SOC ess,max is the minimum and maximum value of energy storage capacity; η dch , η ch is the energy storage discharge / charge efficiency; P dch (t), P ch (t) is the discharge / charge power of the energy storage at time t; SOC ess (t) is the energy storage capacity at time t;

[0030] 3) Power flow constraints: Under the distribution network structure, the planning must meet the balance of active power and reactive power. The specific calculation formula is:

[0031]

[0032] Where P ij , Q ij is the active / reactive power of line ij; P(j), C(j) are the candidate node sets for photovoltaic and energy storage respectively; P jk , Q jk is the active / reactive power of line jk; is the square of the current on line ij; r ij , x ij is the resistance and reactance of line ij; P j load , Q j loadis the load active / reactive power of node j; P PV,j , P ess,j is the photovoltaic output and energy storage output of node j; N is the set of distribution network nodes;

[0033] 4) Current constraints on distribution network branches;

[0034]

[0035] In the formula, are the lower and upper limits of the current of branch ij respectively;

[0036] 5) Distribution network node voltage constraints;

[0037]

[0038] In the formula, are the lower and upper limits of the voltage at node j, is the voltage at node j;

[0039] 6) Second-order cone constraint: Since the model contains some nonlinear constraints, they are converted into second-order cone constraints. The specific calculation formula is as follows:

[0040]

[0041] Furthermore, 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 is embedded in the model for safety verification; the photovoltaic port voltage U at the wth node j is calculated through the short-circuit conductance of photovoltaic and energy storage and the admittance matrix of the system. tj，PV (w) and energy storage port voltage U tBESS (w), the specific formula is as follows:

[0042]

[0043] In the formula, 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 at node j at time t, where ΔI wtj (0) = 0; G w is the conductivity 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 at node j, where ΔI wBESS (0) = 0; m is the energy storage access node to be selected; E is the set of energy storage access nodes to be selected; G mj is the line mj conductance; according to the obtained port voltage, the short-circuit current of photovoltaic and energy storage is calculated respectively

[0044]

[0045] In the formula, I N is the nominal current; U w is the wth port voltage; I kq (w) is the reactive component of the w-th photovoltaic short-circuit current; I kp (w) is the active component of the w-th photovoltaic short-circuit current; K Lvrt K is the photovoltaic low voltage ride-through reactive current coefficient; qlim is the maximum value of the photovoltaic reactive component; K i is the maximum overload factor of the photovoltaic current; P0 is the rated power; The superscript j in is an imaginary unit, and arctan(·) is the inverse tangent 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 the grid-connected point j is calculated:

[0046]

[0047] After obtaining the steady-state component of the photovoltaic short-circuit current, the short-circuit current injected by the energy storage is calculated, wherein the influence of the charging and discharging state of the energy storage on the short-circuit current is considered. In the discharge state, the short-circuit current calculation of the energy storage is as follows:

[0048]

[0049] In the formula, j represents the imaginary unit, U PCC is the voltage at the energy storage coupling point; I qref is the reference value of reactive current; in the charging state, the output short-circuit current is calculated as follows: ref Taking the inflow into the system as positive:

[0050]

[0051] In the formula, is the short-circuit current of energy storage in 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 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 connection 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 steady-state value of the short-circuit current output of the jth energy storage is:

[0052]

[0053] Continue iterating until U tj,PV and U kBESS The difference between the two times is less than the threshold, the iteration ends, and the short-circuit current and port voltage of the grid-connected point are obtained; the equivalent voltage source method recommended by 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 photovoltaic short-circuit current is calculated by the conductance and nominal voltage of the photovoltaic module. The specific formula is as follows

[0054]

[0055] G wPV,j =E PV G PV

[0056] In the formula, I PV，j is the short-circuit current of the photovoltaic at node j; c is the voltage coefficient; U n is the nominal voltage; G wPV,j G is the photovoltaic equivalent short-circuit conductance connected to the grid point j; ij is the modulus of the i-th row and j-th column of the grid node admittance matrix; ΔI wtj (w) is the current increment before and after the w-th j-th PV power station fault, 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;

[0057] When energy storage is connected to the distribution network, the internal bus line is equivalent to an equivalent impedance, 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 for its initial value is as follows:

[0058]

[0059] G wBESS,j =E BESS G BESS

[0060] In the formula, I BESS，j is the short-circuit current of energy stored at node j; G wBESS,j is the equivalent short-circuit conductance of the energy storage connected to the grid point j; ΔI wBESSj is the current increment before and after the jth energy storage fault, determined by iterative calculation; E BESS G is the energy storage capacity to be decided at the grid connection point; BESSis 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 current injected by 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;

[0061] I SCC,j =I PV,j +I BESS,j +I R,j

[0062]

[0063] In the formula, I SCC，j is the comprehensive short-circuit current of the grid connection point j; I R,j is the short-circuit current contributed by the transformer connected to the grid point j; S CC，R is the short-circuit capacity of the transformer; U k % is the transformer impedance percentage;

[0064] Based on the short-circuit protection principle, the short-circuit current of the distributed photovoltaic energy storage connected during a fault should exceed the rated breaking current allowed by the circuit breaker, so that the circuit breaker can quickly cut off the line and achieve the purpose of circuit breaking protection; at the same time, the short-circuit current during a fault cannot exceed the breaking current of the circuit breaker to ensure that the circuit breaker can work normally and cut off the circuit during a short circuit; therefore, based on the requirements of the circuit breaker short-circuit protection, short-circuit current constraints are performed:

[0065] I ncl ≤I SCC,j ≤I z

[0066] In the formula, I ncl I is the rated breaking current of the grid-connected circuit breaker; z It is the breaking current of the grid-connected circuit breaker.

[0067] Furthermore, a column and constraint generation algorithm using short-circuit current cut sets is proposed to solve the second-order cone model of distributed photovoltaic and energy storage collaborative planning. The algorithm includes a solution cycle. First, the main problem is solved based on the second-order cone model. Then, the sub-problem is calculated based on the planning scheme solved by the main problem. The cut sets of photovoltaic and energy storage accessible capacity are constructed through short-circuit current restrictions. The solution is iteratively solved until the second-order cone model of distributed photovoltaic and energy storage collaborative planning converges. The specific solution steps are as follows:

[0068] 1) Photovoltaic uncertainty characterization: The distributed blue bar optimization algorithm is used to construct the photovoltaic output probability distribution set Ω;

[0069] 2) Set initial value: set the iteration convergence threshold ∈ and initialize the number of iterations k = 1;

[0070] 3) Solving the main problem: Solving the economic cost optimization problem F, P under extreme circumstances s is the actual probability distribution of the sth typical scenario; the linearized objective function of the main problem is:

[0071]

[0072] Among them, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation stage; σ is the number of operating days in the whole year; C3 is the deviation between the node voltage and the source voltage of the distribution network

[0073] By solving the main problem, we can get the objective function value F1 * and distributed photovoltaic and energy storage planning solutionsX * , the specific content is as follows, and the lower bound LP=F1 is updated * ;

[0074] X * ={x PV ,x ess ,x buy}

[0075] 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 the decision variable for purchasing electricity from the main network;

[0076] 4) Sub-problem solving: based on solution X * Solve subproblem F2 and get the objective function value F2 * , and iteratively calculate the short-circuit current level I of the photovoltaic and energy storage grid connection points * SCC,j ;

[0077]

[0078] I * SCC,j =I * PV,j +I * BESS,j +I * R,j

[0079] In the formula, ΔE ess Indicates the energy storage capacity that needs to be adjusted; I * PV,j and I * BESS,j Plan X * The short-circuit current level of the photovoltaic and energy storage grid connection points; I *R,j Indicates Scheme X * Lower transformer short-circuit current level;

[0080] 5) Short-circuit current verification: Determine the short-circuit current I * SCC,j Whether it 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 * On the contrary, the cutting plane method considering the short-circuit current constraint is used to increase the energy storage capacity constraint and add it to the main problem, and then return to step 3 to solve it again; the cut set formula is as follows:

[0081]

[0082] P PV =P PV,f +αγ1

[0083] 6) Convergence judgment: Compare whether the difference between the upper and lower bounds is less than the threshold. If it is less than the convergence threshold ∈, the iteration ends and the planning scheme X is output. * ; Otherwise, let k = k + 1 and iterate until the model converges.

[0084] The distributed photovoltaic and energy storage collaborative planning model of the present invention optimizes the economic cost and voltage deviation of the distribution network, and embeds short-circuit current constraints, and proposes a distributed photovoltaic and energy storage bundling planning and configuration method that takes into account the uncertainty of photovoltaic output. It considers the economy and safety of the distribution network for different scenarios, is suitable for distributed photovoltaic and energy storage collaborative planning and configuration applications, and is reasonable.

[0085] Compared with the prior art, the present invention has the following beneficial effects:

[0086] 1. The distributed photovoltaic and energy storage bundling planning method that takes into account the uncertainty of photovoltaic output can improve the effectiveness and representativeness of scenario selection and make reasonable photovoltaic and energy storage bundling configuration decisions;

[0087] 2. Considering the uncertainty of photovoltaic output, a method for calculating the short-circuit current of photovoltaic and energy storage grid connection points based on the equivalent voltage source method is adopted and embedded in the model. The short-circuit current constraint is fully considered in combination with the power flow changes to improve the calculation efficiency without losing the calculation accuracy.

[0088] 3. In order to address the problem of limited distribution network carrying capacity, distributed photovoltaic and energy storage are bundled to help increase the short-circuit current at the grid connection point and stabilize the distribution network voltage, thereby ensuring the safe and stable operation of the system at the lowest economic cost and improving the distribution network carrying capacity. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0090] Figure 1 This is a flow chart of an improved C&CG algorithm proposed by the present invention;

[0091] Figure 2 It is a framework diagram of the solution model of the present invention. DETAILED DESCRIPTION

[0092] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. In the absence of conflict, the features of the following embodiments and implementations can be combined with each other.

[0093] like Figure 2 As shown, the steps of a distributed photovoltaic energy storage bundling planning method taking photovoltaic uncertainty into account according to the present invention include:

[0094] First, the historical data of photovoltaic output is processed based on the distributed robust optimization algorithm, and a set of uncertain probability distributions of photovoltaic output is constructed. The scenario with the most drastic fluctuations in photovoltaic output is generated as an extreme scenario, and the photovoltaic output curve with the highest probability of occurrence is generated as a typical scenario set. Based on the equivalent voltage source method, the short-circuit current level of the distributed photovoltaic and energy storage grid-connected points is calculated, and embedded in the model to construct an economically optimal second-order cone programming model with embedded short-circuit current constraints. Using the short-circuit current constraint as a cut set, an improved column and constraint generation algorithm (C&CG) is proposed to solve the model to obtain the optimal planning scheme for distributed photovoltaics and energy storage. The specific steps of this embodiment are as follows:

[0095] (1) The distributed robust optimization algorithm is used to process the photovoltaic historical data and construct the photovoltaic output uncertainty probability distribution set (the photovoltaic output uncertainty caused by factors such as light intensity and cloud conditions), so as to generate the scene with the most drastic photovoltaic output fluctuation as the extreme scene, and the photovoltaic output curve with the highest probability as the typical scene and establish the typical scene set S, 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 k 0 ], the original probability distribution is P n 0 =Nn / Z, where Z is the total historical output for the whole year, and this article takes 365.

[0096] (a) Based on L 1- The paradigm characterizes the actual probability distribution set Ω of photovoltaic. s is the actual probability distribution of the sth typical scenario; γ1 is the uncertainty interval of photovoltaic output. As γ1 increases, the fluctuation range of photovoltaic output described by the probability distribution set also increases, and the robustness of the model is improved.

[0097]

[0098] (b) Since the most drastic PV output fluctuation scenario is obtained at the extreme point of the feasible region, the upper and lower boundaries of PV output are characterized by two 0-1 variables α and β. When α = 1, the output reaches the maximum fluctuation value, and when β = 1, the output reaches the minimum fluctuation value. PV is the actual photovoltaic output curve, P PV,f It is the photovoltaic output prediction curve.

[0099] P PV =P PV,f +αγ1-βγ1

[0100]

[0101] There are restrictions on the photovoltaic output fluctuation variable. Because the two variables α and β are mutually exclusive, Г is the photovoltaic uncertainty, which limits the number of connected photovoltaics that reach the fluctuation limit at the same time. The larger Г is, the more severe the photovoltaic output fluctuation is and the more extreme the scenario is.

[0102] (2) Based on the constructed uncertainty set of wind and solar power output probability distribution, a second-order cone model for the coordinated planning of distributed photovoltaic and energy storage for different scenarios is proposed:

[0103]

[0104] Wherein, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation stage; σ is the number of operating days in a year, which is 365 in this embodiment and can be selected according to actual conditions without limitation; C3 is the deviation between the node voltage and the source point voltage of the distribution network.

[0105] In one embodiment, the model is:

[0106] (a) The objective function is to minimize the annual comprehensive cost and the voltage deviation of the distribution network. The calculation formula is as follows:

[0107]

[0108] Among them, ρpinv , ρ einv is the investment cost per unit energy storage power capacity and the investment cost per unit energy storage capacity; ρ pbuv The main grid time-of-use electricity price; C ess,om is the maintenance cost per unit energy storage power capacity; C PV,inv is the unit photovoltaic power capacity investment cost; δ is the discount rate; y is the service life of energy storage; E ess ,P ess is the energy storage capacity and power to be decided; E PV is the photovoltaic capacity to be decided; P g，i is the amount of electricity purchased by node i from the main grid; U1 is the source voltage; U i is the voltage of node i; N is the total number of nodes.

[0109] (b) Constraints include:

[0110] 1) PV and energy storage capacity constraints: Set the limits on accessible PV and energy storage capacity based on the nature of the distribution network.

[0111]

[0112] In the formula, E ess,j,min , E ess,j,max is the minimum and maximum energy storage capacity that node j can access, E ess,j is the photovoltaic capacity accessible to node j; E PV,j,min , E PV,j,max is the minimum and maximum photovoltaic capacity that can be connected to node j, E PV,j is the photovoltaic capacity that can be connected to node j; E is the set of energy storage nodes to be selected.

[0113] 2) Energy storage charging and discharging constraints. Design charging and discharging constraints and energy storage SOC balance constraints based on the conditions of the energy storage equipment itself.

[0114]

[0115] Where P dch,min (t), P dch,max (t) is the minimum and maximum value of the energy storage discharge power at time t, P dch (t) is the energy storage discharge power at time t; P ch,min (t), P ch,max (t) is the minimum and maximum 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 discharge / charge state at time t, which limits the energy storage charge and discharge to not be performed simultaneously; SOC ess,min , SOC ess,maxis the minimum and maximum value of energy storage capacity; η dch , η ch is the energy storage discharge / charge efficiency; P dch (t), P ch (t) is the discharge / charge power of the energy storage at time t; SOC ess (t) is the energy storage capacity at time t.

[0116] 3) Power flow constraints. The planning under the distribution network structure must meet the balance of active power and reactive power. The specific calculation formula is:

[0117]

[0118] Where P ij , Q ij is the active / reactive power of line ij; P(j), C(j) are the candidate node sets for photovoltaic and energy storage respectively; P jk , Q jk is the active / reactive power of line jk; is the square of the current on line ij; r ij , x ij is the resistance and reactance of line ij; P j load , Q j load is the load active / reactive power of node j; P PV,j , P ess,j is the photovoltaic output and energy storage output of node j; N is the set of distribution network nodes.

[0119] 4) Current constraints in distribution network branches.

[0120]

[0121] In the formula, They are the lower and upper limits of the current in branch ij respectively.

[0122] 5) Distribution network node voltage constraints.

[0123]

[0124] In the formula, are the lower and upper limits of the voltage at node j, is the voltage at node j.

[0125] 6) Second-order cone constraint. Since the model contains some nonlinear constraints, they are converted into second-order cone constraints. The specific calculation formula is as follows:

[0126]

[0127] (3) 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 is embedded in the model for safety verification. The photovoltaic port voltage U at the wth grid connection point j is calculated through the short-circuit conductance of the photovoltaic and energy storage and the admittance matrix of the system. tj，PV (w) and energy storage port voltage U tBESS (w), the specific formula is as follows:

[0128]

[0129] In the formula, 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 at node j at time t, where ΔI wtj (0) = 0; G w is the conductivity 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 at node j, where ΔI wBESS (0) = 0; m is the energy storage access node to be selected; E is the set of energy storage access nodes to be selected; G mj is the line mj conductance; according to the obtained port voltage, the short-circuit current of photovoltaic and energy storage is calculated respectively

[0130]

[0131] In the formula, I N is the nominal current; U w is the wth port voltage; I kq (w) is the reactive component of the w-th photovoltaic short-circuit current; I kp (w) is the active component of the w-th photovoltaic short-circuit current; I kq 2 (w) is the square of the reactive component of the w-th photovoltaic short-circuit current; K Lvrt K is the photovoltaic low voltage ride-through reactive current coefficient; qlim is the maximum value of the photovoltaic reactive component; K i is the maximum overload factor of the photovoltaic current; P0 is the rated power; The superscript j in is an imaginary unit, and arctan(·) is the inverse tangent function. After obtaining the w-th short-circuit current component, calculate the fault component of the steady-state value of the photovoltaic output short-circuit current at the grid-connected point j:

[0132]

[0133] After obtaining the steady-state component of the photovoltaic short-circuit current, the short-circuit current injected by the energy storage is calculated, wherein the influence of the charging and discharging 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.

[0134]

[0135] In the formula, j represents the imaginary unit, is the short-circuit current of energy storage in charging or discharging state, U PCC is the voltage at the energy storage coupling point; I qref is the reference value of reactive current; I kq is the reactive component of the photovoltaic short-circuit current; I kp is the active component of the photovoltaic short-circuit current. In the charging state, the output short-circuit current is calculated as follows: ref Taking the inflow into the system as positive:

[0136]

[0137] In the formula, 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 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 connection 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 steady-state value of the short-circuit current output of the energy storage at the grid connection point j is:

[0138]

[0139] Continue iterating until U tj,PV and U kBESS The difference between the two times is less than the threshold, the iteration ends, and the short-circuit current and port voltage of the grid-connected point are obtained. Among them, the equivalent voltage source method recommended by 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 photovoltaic short-circuit current is calculated from the conductance and nominal voltage of the photovoltaic module. The specific formula is as follows:

[0140]

[0141] G wPV,j =E PV G PV

[0142] In the formula, 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 G is the photovoltaic equivalent short-circuit conductance connected to the grid point j; ij is the modulus of the i-th row and j-th column of the grid node admittance matrix; ΔI wtj (w) is the current increment before and after the w-th j-th PV power station fault, 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.

[0143] When energy storage is connected to the distribution network, the internal bus line is equivalent to an equivalent impedance, 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 for its initial value is as follows:

[0144]

[0145] G wBESS,j =E BESS G BESS

[0146] In the formula, I BESS，j is the short-circuit current of energy stored at node j; G wBESS,j is the equivalent short-circuit conductance of the energy storage connected to the grid point j; ΔI wBESSj is the current increment before and after the jth energy storage fault, determined by iterative calculation; E BESS G is the energy storage capacity to be decided at the grid connection point; BESS is the per unit 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 current injected by photovoltaic, energy storage and transformer. Among them, the short-circuit current injected by the transformer is calculated by its short-circuit capacity and impedance percentage.

[0147] I SCC,j =I PV,j +I BESS,j +I R,j

[0148]

[0149] In the formula, I SCC，j is the comprehensive short-circuit current of the grid connection point j; I R,j is the short-circuit current contributed by the transformer connected to the grid point j; S CC，R is the short-circuit capacity of the transformer; U k % is the transformer impedance percentage.

[0150] Based on the short-circuit protection principle, the short-circuit current of the distributed photovoltaic energy storage connected to the fault should exceed the rated breaking current allowed by the circuit breaker, so that the circuit breaker can quickly cut off the line and achieve the purpose of circuit breaking protection. At the same time, the short-circuit current during the fault cannot exceed the breaking current of the circuit breaker to ensure that the circuit breaker can work normally and cut off the circuit during a short circuit. Therefore, based on the requirements of the circuit breaker short-circuit protection, the short-circuit current constraint is performed:

[0151] I ncl ≤I SCC,j ≤I z

[0152] In the formula, I ncl I is the rated breaking current of the grid-connected circuit breaker; z It is the breaking current of the grid-connected circuit breaker.

[0153] (4) A C&CG algorithm using short-circuit current cut sets is proposed to solve the second-order cone model of 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. The cut sets of photovoltaic and energy storage accessible capacities are constructed through short-circuit current restrictions. The solution is iteratively solved until the model converges.

[0154] The specific solution process of the model is as follows Figure 1 shown.

[0155] 1) Photovoltaic uncertainty characterization: The distributed blue-rod optimization algorithm is used to construct the photovoltaic output probability distribution set Ω.

[0156] 2) Set initial value: Set the iterative convergence threshold ∈ and initialize the number of iterations k = 1.

[0157] 3) Solving the main problem: Solving the economic cost optimization problem F, P under extreme circumstances s is the actual probability distribution of the sth typical scenario. The linearized objective function of the main problem is:

[0158]

[0159] By solving the main problem, we can get the objective function value F1 * and distributed photovoltaic and energy storage planning solutions X * , the specific content is as follows, and the lower bound LP=F1 is updated * .

[0160] X * ={x PV ,x ess ,x buy}

[0161] In the formula, x PVrepresents 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 the main grid electricity purchase decision variable.

[0162] 4) Sub-problem solving: based on solution X * Solve subproblem F2 and get the objective function value F2 * At the same time, the short-circuit current level I of the photovoltaic and energy storage grid connection points is iteratively calculated by the formula mentioned above * SCC,j .

[0163]

[0164] I * SCC,j =I * PV,j +I * BESS,j +I * R,j

[0165] In the formula, ΔE ess Indicates the energy storage capacity that needs to be adjusted; I * PV,j and I * BESS,j Plan X * The short-circuit current level of the photovoltaic and energy storage grid connection points; I * R,j Indicates Scheme X * The short-circuit current level of the lower transformer.

[0166] 5) Short-circuit current verification: Determine the short-circuit current I * SCC,j Whether it 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, the cutting plane method considering the short-circuit current constraint is used to increase the energy storage capacity constraint and add it to the main problem, and then return to step 3 to solve it again. The cut set formula is shown below.

[0167]

[0168] P PV =P PV,f +αγ1

[0169] 6) Convergence judgment: Compare whether the difference between the upper and lower bounds is less than the threshold. If it is less than the convergence threshold ∈, the iteration ends and the planning scheme X is output. * ; Otherwise, let k = k + 1 and iterate until the model converges.

[0170] The above description of the embodiments is to facilitate the understanding and application of the present invention by those skilled in the art. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative work. Therefore, the present invention is not limited to the above embodiments, and improvements and modifications made to the present invention by those skilled in the art based on the disclosure of the present invention should be within the scope of protection of the present invention.

Claims

1. A distributed photovoltaic energy storage bundling planning method taking into account photovoltaic uncertainty, characterized in that: The steps include: (1) Aiming at the uncertainty of PV output, the distributed robust optimization algorithm is used to process the historical data of PV output and construct a probability distribution set of PV output uncertainty, thereby generating the scenario with the most drastic PV output fluctuation as the extreme scenario and the PV output curve with the highest probability of occurrence as the typical scenario set; (2) After generating typical and extreme scenarios, 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 embedded into the model to construct a distributed photovoltaic and energy storage collaborative planning second-order cone model with embedded short-circuit current constraints, including planning cost, operating cost and distribution network voltage deviation; (3) Using the short-circuit current constraint as a cut set, a column and constraint generation algorithm using the short-circuit current cut set is proposed to solve the second-order cone model of the coordinated planning of distributed photovoltaic and energy storage; the algorithm includes a solution loop, first solving the main problem based on the second-order cone model, then calculating the sub-problem based on the preliminary planning scheme, constructing the cut set of the accessible capacity of photovoltaic and energy storage through the short-circuit current constraint, and iterating the solution until the second-order cone model of the coordinated planning of distributed photovoltaic and energy storage converges, and obtaining the optimal planning decision of distributed photovoltaic and energy storage.

2. The distributed photovoltaic and energy storage bundling planning and configuration method taking into account the uncertainty of photovoltaic output according to claim 1 is characterized in that: The distributed robust optimization algorithm is used to process the photovoltaic historical data and construct the photovoltaic output uncertainty probability distribution set, so as to generate the most drastic photovoltaic output fluctuation scenario as the extreme scenario and generate the photovoltaic output curve with the highest probability as the typical scenario set S, 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 describes the actual probability distribution set of photovoltaics Ω; P s is the actual probability distribution of the sth typical scenario; γ1 is the uncertainty interval of photovoltaic output; Since the most drastic PV output fluctuation scenario is obtained at the extreme point of the feasible domain, the upper and lower boundaries of PV output are characterized by two 0-1 variables α and β; when α = 1, the output reaches the maximum fluctuation value, and when β = 1, the output reaches the minimum fluctuation value; P PV is the actual photovoltaic output curve, P PV,f It is the photovoltaic output prediction curve; P PV =P PV,f +αγ1-βγ1 There are restrictions on the photovoltaic output fluctuation variable; because the two variables α and β are mutually exclusive, where Г is the photovoltaic uncertainty, which limits the number of connected photovoltaics that reach the fluctuation limit at the same time. The larger Г is, the more severe the photovoltaic output fluctuation is and the more extreme the scenario is.

3. The distributed photovoltaic and energy storage bundling planning and configuration method taking into account the uncertainty of photovoltaic output according to claim 1 is characterized in that: Based on the historical scenarios of photovoltaic output, an uncertain probability distribution set of photovoltaic output is constructed, and then a second-order cone (SOP) model for distributed photovoltaic and energy storage collaborative planning for different scenarios is constructed; photovoltaic and energy storage bundling planning is carried out with the minimum annual comprehensive cost and distribution network voltage deviation as the objective function. The specific form of the objective function is: Among them, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation stage; σ is the number of operating days in the whole year; C3 is the deviation between the node voltage and the source point voltage of the distribution network.

4. The distributed photovoltaic and energy storage bundling planning and configuration method taking into account the uncertainty of photovoltaic output according to claim 3 is characterized in that: The calculation formulas of C1, C2 and C3 are as follows: Among them, ρ pinv , ρ einv is the investment cost per unit energy storage power capacity and the investment cost per unit energy storage capacity; ρ pbuv The main grid time-of-use electricity price; C ess,om is the maintenance cost per unit energy storage power capacity; C PV,inv is the unit photovoltaic power capacity investment cost; δ is the discount rate; y is the service life of energy storage; E ess ,P ess is the energy storage capacity and power to be decided; E PV is the photovoltaic capacity to be decided; P g，i is the amount of electricity purchased by grid node i from the main grid; U1 is the source voltage; U i is the voltage of grid node i; N is the total number of grid nodes; The constraints include: 1) PV and energy storage capacity constraints: Set the accessible PV and energy storage capacity limits based on the nature of the distribution network: In the formula, E ess,j,min , E ess,j,max is the minimum and maximum energy storage capacity that node j can access, E ess,j is the photovoltaic capacity accessible to node j; E PV,j,min , E PV,j,max is the minimum and maximum photovoltaic capacity that can be connected to node j, E PV,j is the photovoltaic capacity that can be connected to node j; E is the set of energy storage nodes to be selected; 2) Energy storage charging and discharging constraints: Design charging and discharging constraints and energy storage SOC balance constraints based on the conditions of the energy storage equipment itself: Where P dch,min (t), P dch,max (t) is the minimum and maximum value of the energy storage discharge power at time t, P dch (t) is the energy storage discharge power at time t; P ch,min (t), P ch,max (t) is the minimum and maximum 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 discharge / charge state at time t, which limits the energy storage charge and discharge to not be performed simultaneously; SOC ess,min , SOC ess,max is the minimum and maximum value of energy storage capacity; η dch , η ch is the energy storage discharge / charge efficiency; P dch (t), P ch (t) is the discharge / charge power of the energy storage at time t; SOC ess (t) is the energy storage capacity at time t; 3) Power flow constraints: Under the distribution network structure, the planning must meet the balance of active power and reactive power. The specific calculation formula is: Where P ij , Q ij is the active / reactive power of line ij; P(j), C(j) are the candidate node sets for photovoltaic and energy storage respectively; P jk , Q jk is the active / reactive power of line jk; is the square of the current on line ij; r ij , x ij is the resistance and reactance of line ij; P j load , Q j load is the load active / reactive power of node j; P PV,j , P ess,j is the photovoltaic output and energy storage output of node j; N is the set of distribution network nodes; 4) Current constraints on distribution network branches; In the formula, are the lower and upper limits of the current of branch ij respectively; 5) Distribution network node voltage constraints; In the formula, are the lower and upper limits of the voltage at node j, is the voltage at node j; 6) Second-order cone constraint: Since the model contains some nonlinear constraints, they are converted into second-order cone constraints. The specific calculation formula is as follows:

5. The distributed photovoltaic and energy storage bundling planning and configuration method taking into account the uncertainty of photovoltaic output according to claim 1 is characterized in that: 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 is embedded in the model for safety verification; the photovoltaic port voltage U at the wth node j is calculated through the short-circuit conductance of photovoltaic and energy storage and the admittance matrix of the system tj，PV (w) and energy storage port voltage U tBESS (w), the specific formula is as follows: In the formula, 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 at node j at time t, where ΔI wtj (0) = 0; G w is the conductivity 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 at node j, where ΔI wBESS (0) = 0; m is the energy storage access node to be selected; E is the set of energy storage access nodes to be selected; G mj is the line mj conductance; according to the obtained port voltage, the short-circuit current of photovoltaic and energy storage is calculated respectively In the formula, I N is the nominal current; U w is the w-th port voltage; I kq (w) is the reactive component of the w-th photovoltaic short-circuit current; I kp (w) is the active component of the w-th photovoltaic short-circuit current; K Lvrt K is the photovoltaic low voltage ride-through reactive current coefficient; qlim is the maximum value of the photovoltaic reactive component; K i is the maximum overload factor of the photovoltaic current; P0 is the rated power; The superscript j in is an imaginary unit, and arctan(·) is the inverse tangent 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 the grid-connected point j is calculated: After obtaining the steady-state component of the photovoltaic short-circuit current, the short-circuit current injected by the energy storage is calculated, wherein the influence of the charging and discharging state of the energy storage on the short-circuit current is considered. In the discharge state, the short-circuit current calculation of the energy storage is as follows: In the formula, j represents the imaginary unit, U PCC is the voltage at the energy storage coupling point; I qref is the reference value of reactive current; in the charging state, the output short-circuit current is calculated as follows: ref Taking the inflow into the system as positive: In the formula, is the short-circuit current of energy storage in 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 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 connection 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 steady-state value of the short-circuit current output of the jth energy storage is: Continue iterating until U tj,PV and U kBESS The difference between the two times is less than the threshold, the iteration ends, and the short-circuit current and port voltage of the grid-connected point are obtained; the equivalent voltage source method recommended by 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 photovoltaic short-circuit current is calculated by the conductance and nominal voltage of the photovoltaic module, and the specific formula is as follows: G wPV,j =E PV G PV In the formula, I PV，j is the short-circuit current of the photovoltaic at node j; c is the voltage coefficient; U n is the nominal voltage; G wPV,j G is the photovoltaic equivalent short-circuit conductance connected to the grid point j; ij is the modulus of the i-th row and j-th column of the grid node admittance matrix; ΔI wtj (w) is the current increment before and after the w-th j-th PV power station fault, 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; When energy storage is connected to the distribution network, the internal bus line is equivalent to an equivalent impedance, 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 for its initial value is as follows: G wBESS,j =E BESS G BESS In the formula, I BESS，j is the short-circuit current of energy stored at node j; G wBESS,j is the equivalent short-circuit conductance of the energy storage connected to the grid point j; ΔI wBESSj is the current increment before and after the jth energy storage fault, determined by iterative calculation; E BESS G is the energy storage capacity to be decided at the grid connection point; 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 current injected by 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 is the comprehensive short-circuit current of the grid connection point j; I R,j is the short-circuit current contributed by the transformer connected to the grid point j; S CC，R is the short-circuit capacity of the transformer; U k % is the transformer impedance percentage; Based on the short-circuit protection principle, the short-circuit current of the distributed photovoltaic energy storage connected during a fault should exceed the rated breaking current allowed by the circuit breaker, so that the circuit breaker can quickly cut off the line and achieve the purpose of circuit breaking protection; at the same time, the short-circuit current during a fault cannot exceed the breaking current of the circuit breaker to ensure that the circuit breaker can work normally and cut off the circuit during a short circuit; therefore, based on the requirements of the circuit breaker short-circuit protection, short-circuit current constraints are performed: I ncl ≤I SCC,j ≤I z In the formula, I ncl I is the rated breaking current of the grid-connected circuit breaker; z It is the breaking current of the circuit breaker at the grid connection point.

6. The distributed photovoltaic and energy storage bundling planning and configuration method taking into account the uncertainty of photovoltaic output according to claim 1 is characterized in that: A column and constraint generation algorithm using short-circuit current cut sets is proposed to solve the second-order cone model of distributed photovoltaic and energy storage collaborative planning. The algorithm includes a solution cycle. First, the main problem is solved based on the second-order cone model. Then, the sub-problem is calculated based on the planning scheme solved by the main problem. The cut sets of photovoltaic and energy storage accessible capacity are constructed through short-circuit current restrictions. The solution is iteratively solved until the second-order cone model of distributed photovoltaic and energy storage collaborative planning converges. The specific solution steps are as follows: 1) Photovoltaic uncertainty characterization: The distributed blue bar optimization algorithm is used to construct the photovoltaic output probability distribution set Ω; 2) Set initial value: 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, P under extreme circumstances s is the actual probability distribution of the sth typical scenario; the linearized objective function of the main problem is: Among them, C1 is the annual comprehensive cost in the planning stage, including construction cost and operation and maintenance cost; C2 is the daily electricity purchase cost in the operation stage; σ is the number of operating days in the whole year; C3 is the deviation between the node voltage and the source voltage of the distribution network By solving the main problem, we can get the objective function value F1 * and distributed photovoltaic and energy storage planning solutionsX * , the specific content is as follows, and the lower bound LP=F1 is updated * ; X * ={x PV ,x ess ,x buy } 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 the decision variable for purchasing electricity from the main network; 4) Sub-problem solving: based on solution X * Solve subproblem F2 and get the objective function value F2 * , and iteratively calculate the short-circuit current level I of the photovoltaic and energy storage grid connection points * 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 Plan X * The short-circuit current level of the photovoltaic and energy storage grid connection points; I * R,j Indicates Scheme X * Lower transformer short-circuit current level; 5) Short-circuit current verification: Determine the short-circuit current I * SCC,j Whether it 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 * On the contrary, the cutting plane method considering the short-circuit current constraint is used to increase the energy storage capacity constraint and add it to the main problem, and then return to step 3 to solve it again; the cut set formula is as follows: P PV =P PV,f +αγ1 6) Convergence judgment: Compare whether the difference between the upper and lower bounds is less than the threshold. If it is less than the convergence threshold ∈, the iteration ends and the planning scheme X is output. * ; Otherwise, let k = k + 1 and iterate until the model converges.

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