Distributed photovoltaic low-carbon economic bearing capacity assessment method and system

Abstract

The invention discloses a distributed photovoltaic low-carbon economic bearing capacity assessment method and system, and the method comprises the steps: building a distributed photovoltaic low-carbon economic bearing capacity determinacy assessment model considering the low-carbon economic benefits with the optimization of the low-carbon economic benefits generated by distributed photovoltaic reduction as a target; a distributed photovoltaic output uncertainty evaluation model is constructed by adopting the distribution robust chance constraint of the Wasserstein distance; and processing nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, solving the converted mixed integer linear programming model by using a Cplex solver, and outputting an evaluation result of the distributed photovoltaic low-carbon economic bearing capacity. Unification of economic benefits and environmental benefits can be promoted, the bearing capacity of the power distribution network is enhanced, voltage fluctuation and system stability are relieved, the Wasserstein fuzzy set and the distribution robust opportunity constraint are adopted to deal with photovoltaic uncertainty, the McCormick relaxation technology is introduced to simplify the nonlinear problem, and the decision accuracy and the model practicability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of carrying capacity assessment, and in particular to a method and system for assessing the carrying capacity of a distributed photovoltaic low-carbon economy. Background Art

[0002] Distributed photovoltaics have developed rapidly in recent years due to their clean and low-carbon characteristics, effectively alleviating the pressure on fossil resources. On the one hand, the access of a large number of distributed photovoltaics also brings risks to the safe operation of the distribution network; on the other hand, increasing the installed capacity of distributed photovoltaics through photovoltaic reduction leads to an increase in the carbon emission cost per unit of electricity production. In order to solve the above problems and effectively balance the increase in the installed capacity of distributed photovoltaics with the low-carbon benefits of the entire life cycle, it is urgent to propose a distributed photovoltaic low-carbon economic carrying capacity assessment method for the entire life cycle to improve the distributed photovoltaic carrying capacity and distributed photovoltaic power generation of the distribution network.

[0003] In the traditional carrying capacity assessment process, domestic and foreign experts have conducted in-depth analysis on the concept of DG carrying capacity and formed a series of results. On the one hand, in response to the differentiated assessment needs of distribution networks, a DG carrying capacity characterization method of numerical accumulation and multidimensional space modeling is proposed. On the other hand, in response to the intermittent and volatile power generation of renewable energy, uncertainty processing methods such as stochastic programming, robust optimization and distributed robust optimization are proposed. Stochastic programming (SP) is a simple and practical uncertainty processing method that has been widely used in carrying capacity assessment. This method relies on known probability distributions and transforms the random variable optimization model of the original carrying capacity assessment into a deterministic model by sampling or analysis. It is worth noting that the actual probability characteristics of uncertain variables are often not completely obtained directly from historical data. At the same time, due to the need to generate a large number of scenarios to calculate the optimization results, the computational efficiency of SP is sometimes extremely low. In contrast, robust optimization (RO) and distributed robust optimization (DRO) have lower requirements for the probability information of uncertain factors and have higher computational efficiency by reformulating the carrying capacity model. However, the evaluation results obtained by the robust model are often too conservative and less economical. The robust model can make up for this shortcoming. In recent years, fuzzy sets based on Wasserstein distance have received widespread attention. It believes that the true probability distribution is contained in the Wasserstein sphere with a certain degree of confidence. If it is applied to the evaluation of distributed photovoltaic carrying capacity, better results will be achieved. In addition, in order to further improve the carrying capacity, domestic and foreign experts and scholars also mainly focus on the scenarios where node voltage deviation and electrical equipment capacity limit the carrying capacity, and use distributed photovoltaic active power reduction to increase the distributed photovoltaic access capacity.

[0004] In summary, existing research rarely pays attention to the full life cycle carbon benefits brought about by the improvement of distributed photovoltaic carrying capacity, and the carrying capacity assessment method that fully considers the complex coupling relationship between distributed photovoltaic power generation and carbon emission costs remains to be studied. Summary of the invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a distributed photovoltaic low-carbon economic carrying capacity assessment method and system, aiming to solve the problems that the existing technology fails to fully consider the complex coupling relationship between distributed photovoltaic power generation and carbon emission costs when evaluating distributed photovoltaic carrying capacity, and lacks a comprehensive assessment method for low-carbon benefits throughout the entire life cycle.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the present invention provides a method for evaluating the low-carbon economic carrying capacity of distributed photovoltaics, comprising: taking the optimization of the low-carbon economic benefits generated by distributed photovoltaic reduction as the goal, establishing a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaics that takes into account the low-carbon economic benefits; based on the deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaics, using the distributed robust chance constraint of the Wasserstein distance to construct a distributed photovoltaic output uncertainty evaluation model; processing the nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, and using the Cplex solver to solve the converted mixed integer linear programming model, and outputting the evaluation results of the low-carbon economic carrying capacity of distributed photovoltaics.

[0009] As a preferred embodiment of the distributed photovoltaic low-carbon economic carrying capacity evaluation method of the present invention, the establishment of a distributed photovoltaic low-carbon economic carrying capacity deterministic evaluation model taking into account the low-carbon economic benefits includes:

[0010] Considering the electricity sales revenue of the incremental distributed photovoltaic power generation, the carbon reduction benefits brought by the low-carbon effect, the initial investment cost of the incremental distributed photovoltaic capacity and the distributed photovoltaic operation and maintenance costs, with the goal of optimizing the low-carbon economic benefits generated by the reduction of distributed photovoltaic power generation, a deterministic evaluation model of the low-carbon economic carrying capacity of distributed photovoltaic power generation taking into account the low-carbon economic benefits is established, where the objective function is expressed as:

[0011] maxf=E G +E C -C inv -C ope

[0012]

[0013] Among them, E Gis the annual electricity sales revenue of the incremental distributed photovoltaic power generation, E C is the annual carbon reduction benefit generated by the low-carbon effect of distributed photovoltaics, C inv is the annual investment cost of distributed photovoltaic capacity increment, C ope is the annual operation and maintenance cost of the incremental distributed photovoltaic capacity, c1 is the electricity price per unit of electricity, d s is the number of days corresponding to season s, is the incremental power injected into the distribution network by the distributed photovoltaic power station at node j in season s at time t, c2 is the carbon price in the carbon trading market, is the carbon emission factor of the power grid at time t, c inc is the carbon emission per kilowatt-hour of distributed photovoltaic power generation, is the additional installed capacity of distributed photovoltaic at node j, ξ s,t is the output coefficient of distributed photovoltaic power generation at time t in season s, d is the discount rate, T pv is the time length of distributed photovoltaic capacity increment, c3 is the unit capacity investment cost of distributed photovoltaic, and c4 is the annual operation and maintenance cost of distributed photovoltaic unit capacity.

[0014] As a preferred scheme of the distributed photovoltaic low-carbon economic carrying capacity assessment method described in the present invention, wherein: the constraints of the distributed photovoltaic low-carbon economic carrying capacity deterministic assessment model include distributed photovoltaic power constraints, node voltage constraints, line capacity constraints, transformer capacity constraints, flow balance constraints, distributed photovoltaic reduction constraints, and distributed photovoltaic utilization constraints.

[0015] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity evaluation method of the present invention, the distributed photovoltaic output uncertainty evaluation model constructed by using the distributed robust chance constraint of Wasserstein distance includes:

[0016] Define the set of random variables Historical observation data of distributed photovoltaic The reference distribution is obtained based on the sampling set of historical data.

[0017] In order to express the gap between the true distribution and the reference distribution, Wasserstein distance is introduced and the true distribution P is set. * and the reference distribution For two arbitrary probability distributions, the corresponding Wasserstein distance is expressed as:

[0018]

[0019] Among them, d w(·) represents the Wasserstein distance, inf{·} represents the infimum function, and ξ is the function that follows the true distribution P * The set of random variables, Denotes dξ and The joint probability distribution of ||·|| represents the norm, where the first-order norm is used, dξ and are ξ and The differential of Ω 2 represents the support space of random variables;

[0020] Constructed with reference distribution Centered on ε pv Is a Wasserstein sphere with radius, that is:

[0021]

[0022] in, Represents a reference distribution As the center, ε pv Is the Wasserstein ball with radius, Γ represents the support P * All value spaces of .

[0023] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity evaluation method of the present invention, the distributed photovoltaic output uncertainty evaluation model constructed by using the distributed robust chance constraint of Wasserstein distance also includes:

[0024] The set of opportunity constraints considering node voltage is denoted as C, and the opportunity constraints are converted into conditional risk value constraints, which are expressed as:

[0025]

[0026] Among them, E(.) represents the expectation of the calculation expression, (.) + =max{.,0}, Indicates the variable β k The infimum of , x represents the decision variable, η is the energy utilization rate of distributed photovoltaic within the specified time scale;

[0027] The expression of the conditional risk value constraint is expressed as a maximum combination of two linear functions:

[0028]

[0029] Among them, a k,1 (x) = a k (x), a k,2 (x) = 0, b k,1 (x,β k)=b(x)-(1-η)β k , b k,2 (x,β k )=ηβ k ;

[0030] Then it is equivalently transformed into the following form of distributed robust chance constraint:

[0031]

[0032] Among them, β i is a random variable, ε pv is the radius of the Wasserstein sphere, λ i 、s i,l , γ i,l is the dual variable generated in the transformation process, γ i,l is a vector with the same dimension as U, ||·|| ∞ represents the infinity norm, L represents the number of dual norms, and C represents the penalty term of the dual variable.

[0033] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity evaluation method of the present invention, the distributed photovoltaic output uncertainty evaluation model constructed by using the distributed robust chance constraint of Wasserstein distance also includes:

[0034] When transforming the opportunity constraints considering node voltage into the standard form of constraint set C, the node voltage needs to be expressed as an explicit expression containing uncertain variables, and the power balance constraint needs to be transformed into the following form:

[0035] P i inj =(B in -B out ) j P ij =P i PV -P i L

[0036]

[0037] Among them, B in and B out is the association matrix of system nodes and branches. The former represents the relationship between power injection nodes and corresponding branches, and the latter represents the relationship between power outflow nodes and corresponding branches. i inj is the active power injected into node i, is the reactive power injected into node i, is the active power injection at the i-th node, and are the active load and reactive load at the i-th node respectively;

[0038] Let V = [V1, V2, …, V j ] T , but:

[0039] V=V ref -AΔV

[0040] =V ref -A[RΒ(P PV -P L )+XΒQ L ]

[0041] Combining the opportunity constraints of considering node voltage, we can get:

[0042]

[0043] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity assessment method of the present invention, the processing of nonlinear constraints in the distributed photovoltaic output uncertainty assessment model includes:

[0044] Due to the existence of nonlinear terms in distributed photovoltaic curtailment constraints and distributed photovoltaic power constraints make Then the distributed photovoltaic curtailment constraint is transformed into:

[0045]

[0046] Among them, W1 is the energy injected into the distribution network due to distributed photovoltaic reduction, W2 is the energy abandoned due to distributed photovoltaic reduction, and w s,j,t represents the weight of the jth distributed photovoltaic system at node s at time point t;

[0047] The distributed photovoltaic power constraint is transformed into:

[0048]

[0049] in, is the power injected into the distribution network by the distributed photovoltaic at node j in season s at time t;

[0050] Auxiliary variables are introduced in combination with McCormick relaxation, and the chance constraint is expressed as multiple inequality constraints via second-order cone relaxation.

[0051] In a second aspect, the present invention provides a distributed photovoltaic low-carbon economic carrying capacity assessment system, comprising:

[0052] A deterministic evaluation model building module is used to establish a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaic power generation that takes low-carbon economic benefits into account, with the goal of optimizing the low-carbon economic benefits generated by distributed photovoltaic reduction;

[0053] An uncertainty assessment model building module is used to construct a distributed photovoltaic output uncertainty assessment model based on the distributed photovoltaic low-carbon economic carrying capacity deterministic assessment model and using the distributed robust chance constraint of Wasserstein distance;

[0054] The evaluation result output module is used to process the nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, and use the Cplex solver to solve the converted mixed integer linear programming model to output the evaluation result of the distributed photovoltaic low-carbon economic carrying capacity.

[0055] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity assessment system of the present invention, the deterministic assessment model establishment module includes:

[0056] The objective function construction unit is used to consider the electricity sales revenue of the incremental distributed photovoltaic power generation, the carbon reduction benefits brought by the low-carbon effect, the initial investment cost of the incremental distributed photovoltaic capacity and the distributed photovoltaic operation and maintenance costs, and to establish a deterministic evaluation model of the distributed photovoltaic low-carbon economic carrying capacity taking into account the low-carbon economic benefits with the goal of optimizing the low-carbon economic benefits generated by the reduction of distributed photovoltaics;

[0057] The constraint condition construction unit is used for the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity. The constraints include distributed photovoltaic power constraints, node voltage constraints, line capacity constraints, transformer capacity constraints, power flow balance constraints, distributed photovoltaic reduction constraints, and distributed photovoltaic utilization constraints.

[0058] As a preferred solution of the distributed photovoltaic low-carbon economic carrying capacity assessment system of the present invention, the uncertainty assessment model establishment module includes:

[0059] Fuzzy set modeling unit, used to construct fuzzy sets based on Wasserstein distance;

[0060] The constraint improvement unit is used to record the set of opportunity constraints considering node voltage as C, and convert the opportunity constraints into conditional risk value constraints, and express the expression of the conditional risk value constraints by the maximum value combination of two linear functions, and then convert them into distributed robust opportunity constraints.

[0061] In a third aspect, the present invention provides a computing device, comprising:

[0062] Memory and processor;

[0063] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distributed photovoltaic low-carbon economic carrying capacity assessment method are implemented.

[0064] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distributed photovoltaic low-carbon economic carrying capacity assessment method.

[0065] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention provides a distributed photovoltaic low-carbon economic carrying capacity assessment method and system. By constructing a low-carbon economic carrying capacity assessment model, the distributed photovoltaic system's electricity sales revenue, carbon reduction revenue and its investment and operation and maintenance costs are cleverly integrated, which not only achieves the harmonious unity of economic benefits and environmental benefits, but also promotes the realization of sustainable development goals. In addition, by scientifically adjusting the photovoltaic installation capacity and reduction ratio, the distribution network's carrying capacity for distributed photovoltaics is significantly enhanced, effectively alleviating the difficulties such as voltage fluctuations and system stability degradation caused by high-proportion photovoltaic access, and ensuring the safe and stable operation of the power system. In the face of the inherent uncertainty challenges of photovoltaic power generation, the present invention adopts the method of Wasserstein fuzzy sets and distributed robust chance constraints to provide a more stable and reliable solution, greatly improving the accuracy and adaptability of decision-making. In order to enable this complex model to be more efficiently applied to actual projects, the McCormick relaxation technique is also introduced, which successfully converts the originally difficult-to-handle nonlinear problem into an easy-to-solve linear problem, greatly simplifies the calculation process, and ensures the practicability and operability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] In order to more clearly illustrate the technical solutions of 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 described below 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 work.

[0067] Figure 1 A schematic diagram of the overall process logic of a distributed photovoltaic low-carbon economic carrying capacity assessment method according to an embodiment of the present invention;

[0068] Figure 2 A diagram of IEEE33-node photovoltaic access status of a distributed photovoltaic low-carbon economic carrying capacity assessment method according to an embodiment of the present invention;

[0069] Figure 3A typical scenario carbon potential curve diagram of a method for evaluating the low-carbon economic carrying capacity of distributed photovoltaic power generation according to an embodiment of the present invention;

[0070] Figure 4 A typical scenario distributed photovoltaic curve diagram of a method for evaluating the low-carbon economic carrying capacity of distributed photovoltaics according to an embodiment of the present invention;

[0071] Figure 5 This is a schematic diagram of the impact of distributed photovoltaic output reduction on low-carbon economic benefits in a distributed photovoltaic low-carbon economic carrying capacity assessment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0072] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0073] Example 1

[0074] Reference Figure 1 As an embodiment of the present invention, a method for evaluating the low-carbon economic carrying capacity of distributed photovoltaics is provided. Figure 1 The specific steps shown include:

[0075] S100: With the goal of optimizing the low-carbon economic benefits generated by distributed photovoltaic reduction, a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaics taking into account the low-carbon economic benefits is established;

[0076] S200: Based on the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity, the distributed photovoltaic output uncertainty evaluation model is constructed using the distributed robust chance constraint of Wasserstein distance;

[0077] S300: Process the nonlinear constraints in the distributed photovoltaic output uncertainty assessment model, and use the Cplex solver to solve the converted mixed integer linear programming model to output the assessment results of the distributed photovoltaic low-carbon economic carrying capacity.

[0078] It should be noted that the present invention provides a distributed photovoltaic low-carbon economic carrying capacity assessment method and system. By constructing a low-carbon economic carrying capacity assessment model, it cleverly integrates the electricity sales revenue, carbon reduction revenue and investment and operation and maintenance costs of the distributed photovoltaic system, not only achieving the harmonious unity of economic benefits and environmental benefits, but also promoting the realization of sustainable development goals. In addition, by scientifically adjusting the photovoltaic installation capacity and reduction ratio, the distribution network's carrying capacity for distributed photovoltaics is significantly enhanced, effectively alleviating the difficulties such as voltage fluctuations and system stability reduction caused by high-proportion photovoltaic access, and ensuring the safe and stable operation of the power system. In the face of the inherent uncertainty challenges of photovoltaic power generation, the present invention adopts the method of Wasserstein fuzzy sets and distributed robust chance constraints to provide a more stable and reliable solution, greatly improving the accuracy and adaptability of decision-making. In order to enable this complex model to be more efficiently applied to actual projects, the McCormick relaxation technique is also introduced, which successfully converts the originally difficult-to-handle nonlinear problem into an easy-to-solve linear problem, greatly simplifies the calculation process, and ensures the practicality and operability of the model.

[0079] In the embodiment of the present application, the above step S100 aims to optimize the low-carbon economic benefits generated by the reduction of distributed photovoltaic power generation, and establishes a deterministic evaluation model of the low-carbon economic carrying capacity of distributed photovoltaic power generation taking into account the low-carbon economic benefits, including:

[0080] Specifically, the electricity sales revenue from the incremental distributed photovoltaic power generation, the carbon reduction benefits brought about by the low-carbon effect, the initial investment cost of the incremental distributed photovoltaic capacity and the distributed photovoltaic operation and maintenance costs are considered, and the low-carbon economic benefits generated by the reduction of distributed photovoltaic power generation are optimized. A deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaic power generation that takes into account the low-carbon economic benefits is established.

[0081] The objective function is expressed as:

[0082] maxf=E G +E C -C inv -C ope

[0083]

[0084] Among them, E G is the annual electricity sales revenue of the incremental distributed photovoltaic power generation, E C is the annual carbon reduction benefit generated by the low-carbon effect of distributed photovoltaics, C inv is the annual investment cost of distributed photovoltaic capacity increment, C ope is the annual operation and maintenance cost of the incremental distributed photovoltaic capacity, c1 is the electricity price per unit of electricity, d s is the number of days corresponding to season s, is the incremental power injected into the distribution network by the distributed photovoltaic power station at node j in season s at time t, c2 is the carbon price in the carbon trading market, is the carbon emission factor of the power grid at time t, c inc is the carbon emission per kilowatt-hour of distributed photovoltaic power generation, is the additional installed capacity of distributed photovoltaic at node j, ξ s,t is the output coefficient of distributed photovoltaic power generation at time t in season s, d is the discount rate, T pv is the time length of distributed photovoltaic capacity increment, c3 is the unit capacity investment cost of distributed photovoltaic, and c4 is the annual operation and maintenance cost of distributed photovoltaic unit capacity.

[0085] Specifically, the constraints of the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity include distributed photovoltaic power constraints, node voltage constraints, line capacity constraints, transformer capacity constraints, power flow balance constraints, distributed photovoltaic reduction constraints, and distributed photovoltaic utilization constraints;

[0086] The calculation of distributed photovoltaic power constraint is:

[0087]

[0088] in, is the power injected into the distribution network by the distributed photovoltaic at node j in season s at time t; s,t is the reduction ratio of distributed photovoltaic output at time t in season s;

[0089] The calculation of the node voltage constraint is:

[0090] V min ≤V s,i,t ≤V max

[0091] Among them, V min is the lower limit of node voltage; V s,i,t is the voltage of node i at time t in season s; V max is the upper limit of node voltage;

[0092] Among them, the calculation of line capacity constraint is:

[0093]

[0094] Among them, P s,ij,t is the active power flowing through branch ij at time t in season s; Q s,ij,t is the reactive power flowing through branch ij at time t in season s; is the maximum complex power allowed to flow through branch ij;

[0095] Among them, the calculation of transformer capacity constraint is:

[0096]

[0097] in, is the power of the DG directly connected to the transformer at time t; is the total load power supplied by the transformer at time t; S tra is the rated capacity of the transformer. According to the Electric Power Industry Standard of the People's Republic of China "Guidelines for Assessment of the Carrying Capacity of Distributed Generation Access to the Grid DL / T2041-2019", the reverse load rate constraint requirement is 80%.

[0098] The calculation of power flow balance constraint is:

[0099] V s,i,t -V s,j,t =r ij P s,ij,t +x ij Q s,ij,t

[0100]

[0101] Among them, r ij is the resistance of branch ij; x ij is the reactance of branch ij; P s,jk,t , are respectively the active power flowing through branch jk at time t in season s and the active power of load at node j at time t in season s; Q s,jk,t , They are the reactive power flowing through branch jk at time t in season s and the reactive power of load at node j at time t in season s;

[0102] The calculation of distributed photovoltaic reduction constraint is:

[0103]

[0104] W1-W2≥0

[0105] Among them, W1 is the energy injected into the distribution network due to distributed photovoltaic reduction; W2 is the energy abandoned due to distributed photovoltaic reduction;

[0106] Among them, the calculation of distributed photovoltaic utilization constraint is:

[0107]

[0108] Where η is the energy utilization rate of distributed photovoltaics within a specified time scale; E K is the actual power generation of distributed photovoltaic within the specified time scale; EN is the theoretical power generation of distributed photovoltaics;

[0109] It should be noted that the low-carbon economic carrying capacity of distributed photovoltaic refers to the distributed photovoltaic installation capacity that maximizes the low-carbon economic benefits generated by distributed photovoltaic reduction, taking into account the impact of distributed photovoltaic carbon emissions over the entire life cycle and the role of distributed photovoltaic output reduction in increasing distributed photovoltaic power generation, under the premise of safe operation of the distribution network. As the reduction ratio increases, although the electricity sales revenue and carbon reduction revenue brought by the distributed photovoltaic power generation gain increase, the initial investment and operation and maintenance costs required for the newly added distributed photovoltaic installation capacity will also increase. Therefore, it is necessary to comprehensively consider the benefits and costs of distributed photovoltaics and evaluate the distributed photovoltaic carrying capacity that optimizes the low-carbon economic benefits generated by distributed photovoltaic reduction.

[0110] It should be noted that the above step S100 can accurately quantify the contribution of distributed photovoltaic reduction to carbon emission reduction from both economic and environmental perspectives, ensuring that while increasing the installed capacity of distributed photovoltaics, the maximum low-carbon benefits over the entire life cycle are achieved. It can not only effectively balance the economy and environmental protection of the power system, but also provide a scientific basis for decision makers, while also enhancing the practicality and guiding significance of the model.

[0111] In the embodiment of the present application, the above step S200 is based on the distributed photovoltaic low-carbon economic carrying capacity deterministic evaluation model, and the distributed photovoltaic output uncertainty evaluation model is constructed using the distributed robust opportunity constraint of the Wasserstein distance, including:

[0112] (1) Fuzzy set modeling

[0113] In practical applications, we usually only have limited sample data, so it is impossible to know the true distribution of random variables. However, we can use historical data to construct a fuzzy set that contains the true distribution and use this fuzzy set to reflect the relevant information of the true distribution.

[0114] The construction of fuzzy sets is very important. A well-defined fuzzy set should have the following characteristics:

[0115] ①It can contain the true probability distribution with a certain probability;

[0116] ② The range of the fuzzy set should be as small as possible, and as the number of samples increases, the range of the fuzzy set can be further reduced to reduce the conservatism of the model;

[0117] ③The corresponding problem can be transformed into an easy-to-handle deterministic optimization problem, which is convenient for solving using numerical methods.

[0118] Wasserstein distance is a modeling method for fuzzy sets in distributed robust optimization. It is defined as a ball in the probability distribution space. This metric-based fuzzy set contains all empirical distributions or most likely distributions close to a given probability metric. By adjusting the radius of the fuzzy set, the degree of conservatism of the optimization problem can be controlled. If the radius is reduced to 0, the fuzzy set is reduced to a single-element set containing only empirical distributions. In this case, the distributed robust problem becomes a random optimization without ambiguity. A probability distribution fuzzy set based on Wasserstein distance is established to describe the uncertainty of distributed photovoltaic output.

[0119] The construction of fuzzy sets based on Wasserstein distance can be carried out as follows:

[0120] Define the set of random variables Historical observation data of distributed photovoltaic The reference distribution is obtained based on the sampling set of historical data. It is expressed as:

[0121]

[0122] in, for The Dirac function of is the lth historical observation data of distributed photovoltaic; L is the total number of samples;

[0123] In order to express the gap between the true distribution and the reference distribution, Wasserstein distance is introduced and the true distribution P is set. * and the reference distribution For two arbitrary probability distributions, the corresponding Wasserstein distance is expressed as:

[0124]

[0125] Among them, d w (·) represents the Wasserstein distance, inf{·} represents the infimum function, and ξ is the function that follows the true distribution P * The set of random variables, Denotes dξ and The joint probability distribution of ||·|| represents the norm, where the first-order norm is used, dξ and are ξ and The differential of Ω 2 represents the support space of random variables;

[0126] Constructed with reference distribution Centered on ε pv Is a Wasserstein sphere with radius, that is:

[0127]

[0128] in, Represents a reference distribution As the center, ε pv Is the Wasserstein ball with radius, Γ represents the support P * All value spaces of ε pv The choice of the size of has an important impact on the performance of the fuzzy set. pv The size of can make the Wasserstein ball contain the reference distribution with a certain confidence level, thereby controlling the conservatism of the optimization results.

[0129] Related literature proves that the radius of the Wasserstein sphere ε pv is the sample size L and the confidence level α pv The function of is as follows:

[0130]

[0131] Where P(·) is the probability function; S is the maximum Wasserstein distance between any pair of elements in the true distribution and the reference distribution.

[0132]

[0133] in, is the sample mean; μ is a variable greater than 0.

[0134] (2) Constraint Improvement

[0135] Considering that distributed photovoltaic output has strong uncertainty, it may cause the node voltage to exceed the limit, and usually exceed the voltage upper limit. Therefore, the opportunity constraint of the node voltage is considered, and the specific expression is:

[0136]

[0137] The meaning of the above expression is: under the worst probability distribution of distributed photovoltaic output, the probability of satisfying the node voltage constraint is greater than 1-ε;

[0138] The set of constraints is denoted as C, |C| = K, and the random variables are uniformly denoted by Represented by , the decision variable is represented by x, then each constraint in the constraint set C can be uniformly written in the following form:

[0139]

[0140] The expression on the left side of the inequality in brackets is about Linear, coefficient vector ai and scalar b i is related to the decision variable x;

[0141] Since the random variable The distribution of is unknown, and the chance constraint cannot be used directly for calculation. In this case, the original chance constraint can be converted into a distributed robust chance constraint in combination with historical data. The basic idea of ​​distributed robustness is to find the worst case among those distributions close to the sample distribution. This embodiment constructs a distributed robust chance constraint based on the above-mentioned Wasserstein distance, and the specific steps are as follows:

[0142] The set of opportunity constraints considering node voltage is denoted as C, and the opportunity constraints are transformed into conditional risk value constraints, which can be expressed as:

[0143]

[0144] Among them, E(.) represents the expectation of the calculation expression, (.) + =max{.,0}, Indicates the variable β k The infimum of , x represents the decision variable, η is the energy utilization rate of distributed photovoltaic within the specified time scale;

[0145] The expression of the conditional risk value constraint is expressed as the maximum value combination of two linear functions:

[0146]

[0147] Among them, a k,1 (x) = a k (x), a k,2 (x) = 0, b k,1 (x,β k )=b(x)-(1-η)β k , b k,2 (x,β k )=ηβ k ;

[0148] The distributed robustness constraint based on Wasserstein distance requires a rough setting of the set of random variables ξ, which has little effect on subsequent results. Generally, the polyhedral set E = {ξ|Cξ≤d} is taken. The norm is taken as l1 norm (the dual norm is l ∞ norm), the CVaR constraint can be equivalently transformed into the following form of distributed robustness chance constraint:

[0149]

[0150] Among them, β i is a random variable, ε pvis the radius of the Wasserstein sphere, λ i 、s i,l , γ i,l is the dual variable generated in the transformation process, γ i,l is a vector with the same dimension as U, ||·|| ∞ represents the infinity norm, L represents the number of dual norms, and C represents the penalty term of the dual variable.

[0151] Furthermore, when the opportunity constraint considering the node voltage is transformed into the standard form of the constraint set C, the node voltage needs to be expressed as an explicit expression containing uncertain variables, and the power balance constraint needs to be transformed into the following form:

[0152] P i inj =(B in -B out ) j P ij =P i PV -P i L

[0153]

[0154] Among them, B in and B out is the association matrix of system nodes and branches. The former represents the relationship between power injection nodes and corresponding branches, and the latter represents the relationship between power outflow nodes and corresponding branches. i inj is the active power injected into node i, is the reactive power injected into node i, is the active power injection at the i-th node, and are the active load and reactive load at the i-th node respectively;

[0155] make P=[P 01 ,P 12 ,…,P ij ] T , but:

[0156] P inj =(B in -B out )P=P PV -P L

[0157] P=[(B in -B out )-1 ] T (P PV -P L )

[0158] =Β(P PV -P L )

[0159] Similarly, we can get:

[0160] Q inj =(B in -B out )Q=Q L

[0161] Q=[(B in -B out ) -1 ] T Q L =ΒQ L

[0162] Let V = [V1, V2, …, V j ] T , but:

[0163] V=V ref -AΔV

[0164] =V ref -A[RΒ(P PV -P L )+XΒQ L ]

[0165] Considering the opportunity constraints of node voltage, we can get:

[0166]

[0167] It should be noted that based on the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity, the distributed photovoltaic output uncertainty is modeled using the distributed robust chance constraint based on Wasserstein distance. This method can mine the distribution information of random variables based on a limited number of historical data samples, and can control the conservativeness of the results by adjusting the model parameters, which can ensure the computational efficiency and does not require the accurate probability distribution of distributed photovoltaic output to be obtained in advance. The larger the sphere radius, the larger the distribution set of random variables, and the more conservative the distributed robust chance constraint model tends to be.

[0168] It should be noted that the above step S200 can effectively incorporate the randomness and volatility of photovoltaic output into the evaluation model. By introducing the distributed robust optimization method, it not only improves the model's ability to combat data uncertainty, but also ensures the stability and reliability of the evaluation results under different scenarios, thereby providing more scientific and reasonable decision-making support for the planning and operation of distributed photovoltaic systems.

[0169] In the embodiment of the present application, the above step S300 processes the nonlinear constraints in the distributed photovoltaic output uncertainty assessment model, and uses the Cplex solver to solve the converted mixed integer linear programming model, and outputs the assessment results of the distributed photovoltaic low-carbon economic carrying capacity, including:

[0170] Due to the existence of nonlinear terms in distributed photovoltaic curtailment constraints and distributed photovoltaic power constraints make Then the distributed photovoltaic reduction constraint is transformed into:

[0171]

[0172] Among them, W1 is the energy injected into the distribution network due to distributed photovoltaic reduction, W2 is the energy abandoned due to distributed photovoltaic reduction, and w s,j,t represents the weight of the jth distributed photovoltaic system at node s at time point t;

[0173] Distributed PV power constraints are transformed into:

[0174]

[0175] in, is the power injected into the distribution network by the distributed photovoltaic at node j in season s at time t;

[0176] Auxiliary variables are introduced in combination with McCormick relaxation, and the chance constraint is expressed as multiple inequality constraints through second-order cone relaxation. The constraints to be satisfied are as follows:

[0177]

[0178] β min ≤β s,t ≤β max

[0179]

[0180] It should be noted that the above step S300 can effectively solve the complex nonlinear relationship existing in the model. Through the application of linearization transformation and advanced solution technology, it not only improves the efficiency and accuracy of model solution, but also ensures the reliability and practicality of the calculation results, which provides strong technical support for the optimal configuration of distributed photovoltaic systems and the accurate assessment of low-carbon economic carrying capacity.

[0181] The above is a schematic scheme of a distributed photovoltaic low-carbon economic carrying capacity assessment method of this embodiment. It should be noted that the technical scheme of the distributed photovoltaic low-carbon economic carrying capacity assessment system and the technical scheme of the above-mentioned distributed photovoltaic low-carbon economic carrying capacity assessment method belong to the same concept. For details not described in detail in the technical scheme of the distributed photovoltaic low-carbon economic carrying capacity assessment system in this embodiment, please refer to the description of the technical scheme of the above-mentioned distributed photovoltaic low-carbon economic carrying capacity assessment method.

[0182] This embodiment also provides a distributed photovoltaic low-carbon economic carrying capacity assessment system, including:

[0183] A deterministic evaluation model building module is used to establish a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaic power generation that takes low-carbon economic benefits into account, with the goal of optimizing the low-carbon economic benefits generated by distributed photovoltaic reduction;

[0184] The uncertainty assessment model building module is used to build a distributed photovoltaic output uncertainty assessment model based on the deterministic assessment model of distributed photovoltaic low-carbon economic carrying capacity, using the distributed robust chance constraint of Wasserstein distance;

[0185] The evaluation result output module is used to process the nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, and use the Cplex solver to solve the converted mixed integer linear programming model to output the evaluation results of the distributed photovoltaic low-carbon economic carrying capacity.

[0186] Specifically, the deterministic evaluation model building module includes:

[0187] The objective function construction unit is used to consider the electricity sales revenue of the incremental distributed photovoltaic power generation, the carbon reduction benefits brought by the low-carbon effect, the initial investment cost of the incremental distributed photovoltaic capacity and the distributed photovoltaic operation and maintenance costs, and to establish a deterministic evaluation model of the distributed photovoltaic low-carbon economic carrying capacity taking into account the low-carbon economic benefits with the goal of optimizing the low-carbon economic benefits generated by the reduction of distributed photovoltaics;

[0188] The constraint condition construction unit is used for the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity. The constraints include distributed photovoltaic power constraints, node voltage constraints, line capacity constraints, transformer capacity constraints, power flow balance constraints, distributed photovoltaic reduction constraints, and distributed photovoltaic utilization constraints.

[0189] Specifically, the uncertainty assessment model building module includes:

[0190] Fuzzy set modeling unit, used to construct fuzzy sets based on Wasserstein distance;

[0191] The constraint improvement unit is used to record the set of opportunity constraints considering node voltage as C, and transform the opportunity constraints into conditional risk value constraints, and express the expression of the conditional risk value constraints by the maximum value combination of two linear functions, and then transform them into distributed robust opportunity constraints.

[0192] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0193] This embodiment also provides a computing device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a distributed photovoltaic low-carbon economic carrying capacity assessment method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0194] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0195] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0196] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0197] Example 2

[0198] Reference Figure 2 to Figure 5 This embodiment provides an application example of a distributed photovoltaic low-carbon economic carrying capacity assessment method and system to verify and illustrate the technical effects used in this method.

[0199] This example uses the IEEE 33-node distribution network, and the distributed photovoltaic access situation is as follows Figure 2 As shown in Figure 2, the carbon emission factor c of the power grid at different times dec The distributed photovoltaic timing characteristics on a typical day are as follows: Figure 3 and Figure 4 As shown, the corresponding days of each typical day and the relevant parameters of the economic cost of distributed photovoltaics are shown in Table 1, and the relevant parameters of the economic cost of distributed photovoltaics are shown in Table 2.

[0200] Table 1: Corresponding days of each typical day.

[0201] typical day spring summer Autumn winter Days 90 91 92 92

[0202] Table 2: Relevant parameters of distributed photovoltaic economic costs.

[0203]

[0204]

[0205] In order to explore the impact of distributed photovoltaic output reduction on the low-carbon economic benefits of distributed photovoltaics, the low-carbon economic benefits of distributed photovoltaics under different reduction ratios are calculated. The results are as follows: Figure 5 shown.

[0206] Depend on Figure 5It can be seen that there is an optimal distributed photovoltaic utilization rate that maximizes the low-carbon economic benefits of distributed photovoltaics. As the utilization rate of distributed photovoltaics continues to decrease, the low-carbon economic benefits of distributed photovoltaics first increase in a linear trend. This is because the low-carbon economic benefits brought by the growth of distributed photovoltaic power generation at this stage are greater than the reduction caused by the reduction of distributed photovoltaics; as the reduction ratio further decreases, the low-carbon economic benefits decline rapidly due to excessive pursuit of low carbon and neglect of the waste of resources caused by excessive reduction.

[0207] As can be seen from the above, the present invention, by constructing a low-carbon economic carrying capacity assessment model, cleverly integrates the electricity sales revenue, carbon reduction revenue and investment and operation and maintenance costs of the distributed photovoltaic system, not only achieving the harmonious unity of economic benefits and environmental benefits, but also promoting the realization of sustainable development goals. In addition, by scientifically adjusting the photovoltaic installation capacity and reduction ratio, the distribution network's carrying capacity for distributed photovoltaics is significantly enhanced, effectively alleviating the problems of voltage fluctuations and system stability degradation caused by high-proportion photovoltaic access, and ensuring the safe and stable operation of the power system. In the face of the inherent uncertainty challenges of photovoltaic power generation, the present invention adopts the method of Wasserstein fuzzy sets and distributed robust chance constraints to provide a more stable and reliable solution, greatly improving the accuracy and adaptability of decision-making. In order to enable this complex model to be more efficiently applied to actual projects, the McCormick relaxation technique is also introduced, which successfully converts the originally difficult-to-handle nonlinear problem into an easy-to-solve linear problem, greatly simplifies the calculation process, and ensures the practicality and operability of the model.

[0208] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0209] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0210] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0211] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0212] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0213] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0214] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for evaluating the carrying capacity of distributed photovoltaic low-carbon economy, characterized in that: include: With the goal of optimizing the low-carbon economic benefits generated by distributed photovoltaic reduction, a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaics taking into account the low-carbon economic benefits is established; Based on the deterministic evaluation model of distributed photovoltaic low-carbon economic carrying capacity, a distributed photovoltaic output uncertainty evaluation model is constructed using the distributed robust chance constraint of Wasserstein distance; The nonlinear constraints in the distributed photovoltaic output uncertainty assessment model are processed, and the converted mixed integer linear programming model is solved using the Cplex solver to output the assessment result of the distributed photovoltaic low-carbon economic carrying capacity.

2. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 1, characterized in that: The establishment of a deterministic evaluation model for distributed photovoltaic low-carbon economic carrying capacity taking into account low-carbon economic benefits includes: Considering the electricity sales revenue of the incremental distributed photovoltaic power generation, the carbon reduction benefits brought by the low-carbon effect, the initial investment cost of the incremental distributed photovoltaic capacity and the distributed photovoltaic operation and maintenance costs, with the goal of optimizing the low-carbon economic benefits generated by the reduction of distributed photovoltaic power generation, a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaic power generation taking into account the low-carbon economic benefits is established; The objective function is expressed as: max=E G +E C -C inv -C ope Among them, E G is the annual electricity sales revenue of the incremental distributed photovoltaic power generation, E C is the annual carbon reduction benefit generated by the low-carbon effect of distributed photovoltaics, C inv is the annual investment cost of distributed photovoltaic capacity increment, C ope is the annual operation and maintenance cost of the incremental distributed photovoltaic capacity, c1 is the electricity price per unit of electricity, d s is the number of days corresponding to season s, is the incremental power injected into the distribution network by the distributed photovoltaic power station at node j in season s at time t, c2 is the carbon price in the carbon trading market, is the carbon emission factor of the power grid at time t, c inc is the carbon emission per kilowatt-hour of distributed photovoltaic power generation, is the additional installed capacity of distributed photovoltaic at node j, ξ s,t is the output coefficient of distributed photovoltaic power generation at time t in season s, d is the discount rate, T pv is the time length of distributed photovoltaic capacity increment, c3 is the unit capacity investment cost of distributed photovoltaic, and c4 is the annual operation and maintenance cost of distributed photovoltaic unit capacity.

3. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 2, characterized in that: The constraints of the distributed photovoltaic low-carbon economic carrying capacity deterministic assessment model include distributed photovoltaic power constraints, node voltage constraints, line capacity constraints, transformer capacity constraints, power flow balance constraints, distributed photovoltaic reduction constraints, and distributed photovoltaic utilization constraints.

4. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 3, characterized in that: The distributed photovoltaic output uncertainty assessment model constructed by using the distributed robust chance constraint of Wasserstein distance includes: Define the set of random variables Historical observation data of distributed photovoltaic The reference distribution is obtained based on the sampling set of historical data. In order to express the gap between the true distribution and the reference distribution, Wasserstein distance is introduced and the true distribution P is set. * and the reference distribution For two arbitrary probability distributions, the corresponding Wasserstein distance is expressed as: Among them, d w (·) represents the Wasserstein distance, inf{·} represents the infimum function, and ξ is the function that follows the true distribution P * The set of random variables, Denotes dξ and The joint probability distribution of ||·|| represents the norm, where the first-order norm is used, dξ and are ξ and The differential of Ω 2 represents the support space of random variables; Constructed with reference distribution Centered on ε pv Is a Wasserstein sphere with radius, that is: in, Represents a reference distribution As the center, ε pv Is the Wasserstein ball with radius, Γ represents the support P * All value spaces of .

5. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 4, characterized in that: The distributed photovoltaic output uncertainty assessment model constructed by using the distributed robust chance constraint of Wasserstein distance also includes: The set of opportunity constraints considering node voltage is denoted as C, and the opportunity constraints are converted into conditional risk value constraints, which are expressed as: Among them, E(.) represents the expectation of the calculation expression, (.) + =max{.,0}, Indicates the variable β k The infimum of , x represents the decision variable, η is the energy utilization rate of distributed photovoltaic within the specified time scale; The expression of the conditional risk value constraint is expressed as a maximum combination of two linear functions: among them, a k,1 (x)=a k (x),a k,2 (x)=0,b k,1 (x,b k )=b(x)-(1-η)β k ,b k,2 (x,b k )=ηβ k ; Then it is equivalently transformed into the following form of distributed robust chance constraint: Among them, β i is a random variable, ε pv is the radius of the Wasserstein sphere, λ i 、s i,l , γ i,l is the dual variable generated in the transformation process, γ i,l is a vector with the same dimension as U, ||·|| ∞ represents the infinity norm, L represents the number of dual norms, and C represents the penalty term of the dual variable.

6. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 5, characterized in that: The distributed photovoltaic output uncertainty assessment model constructed by using the distributed robust chance constraint of Wasserstein distance also includes: When transforming the opportunity constraints considering node voltage into the standard form of constraint set C, the node voltage needs to be expressed as an explicit expression containing uncertain variables, and the power balance constraint needs to be transformed into the following form: Among them, B in and B out is the association matrix of system nodes and branches. The former represents the relationship between power injection nodes and corresponding branches, and the latter represents the relationship between power outflow nodes and corresponding branches. i inj is the active power injected into node i, is the reactive power injected into node i, is the active power injection at the i-th node, and are the active load and reactive load at the i-th node respectively; Let V = [V1, V2, …, V j ] T , but: V=V ref -AΔV =V ref -A[RΒ(P PV -P L )+XΒQ L ] Combining the opportunity constraints of considering node voltage, we can get:

7. The distributed photovoltaic low-carbon economic carrying capacity assessment method according to claim 6, characterized in that: The processing of nonlinear constraints in the distributed photovoltaic output uncertainty assessment model includes: Due to the existence of nonlinear terms in distributed photovoltaic curtailment constraints and distributed photovoltaic power constraints make Then the distributed photovoltaic curtailment constraint is transformed into: Among them, W1 is the energy injected into the distribution network due to distributed photovoltaic reduction, W2 is the energy abandoned due to distributed photovoltaic reduction, and w s,j,t represents the weight of the jth distributed photovoltaic system at node s at time point t; The distributed photovoltaic power constraint is transformed into: in, is the power injected into the distribution network by the distributed photovoltaic at node j in season s at time t; Auxiliary variables are introduced in combination with McCormick relaxation, and the chance constraint is expressed as multiple inequality constraints via second-order cone relaxation.

8. A system for evaluating the carrying capacity of distributed photovoltaic low-carbon economy, characterized in that: include: A deterministic evaluation model building module is used to establish a deterministic evaluation model for the low-carbon economic carrying capacity of distributed photovoltaic power generation that takes low-carbon economic benefits into account, with the goal of optimizing the low-carbon economic benefits generated by distributed photovoltaic reduction; An uncertainty assessment model building module is used to construct a distributed photovoltaic output uncertainty assessment model based on the distributed photovoltaic low-carbon economic carrying capacity deterministic assessment model and using the distributed robust chance constraint of Wasserstein distance; The evaluation result output module is used to process the nonlinear constraints in the distributed photovoltaic output uncertainty evaluation model, and use the Cplex solver to solve the converted mixed integer linear programming model to output the evaluation result of the distributed photovoltaic low-carbon economic carrying capacity.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the distributed photovoltaic low-carbon economic carrying capacity assessment method described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the distributed photovoltaic low-carbon economic carrying capacity assessment method as described in any one of claims 1 to 7.

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