Distribution network distributed photovoltaic bearing capacity assessment method, system, equipment and medium

Abstract

The invention discloses a power distribution network distributed photovoltaic bearing capacity assessment method, system and device and a medium, and the method comprises the steps: constructing a source load fuzzy set according to a credibility theory based on the historical source load data of a power distribution network; based on the source load fuzzy set, a distributed photovoltaic bearing capacity evaluation model is constructed according to distributed robust optimization, the distributed photovoltaic bearing capacity evaluation model comprises a first optimization objective function and a second optimization objective function, the first optimization objective function is constructed based on the fuzzy expectation of the distributed photovoltaic installed capacity in the maximized power distribution network, and the second optimization objective function is constructed based on the fuzzy expectation of the distributed photovoltaic installed capacity in the maximized power distribution network. The second optimization objective function is constructed based on the fuzzy expectation for minimizing the operation cost of the distributed unit in the power distribution network; and solving the distributed photovoltaic bearing capacity evaluation model to obtain the accessible distributed photovoltaic installed capacity of the power distribution network. The evaluation result obtained by the distributed photovoltaic bearing capacity evaluation model constructed based on distributed robust optimization can effectively balance robustness and economy.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a method, system, device and medium for evaluating the carrying capacity of distributed photovoltaic in a distribution network. Background Art

[0002] With the large-scale access of distributed photovoltaic, the original structure of the power system is undergoing profound changes. In the traditional power system, large centralized power plants mainly transmit electricity to each user through transmission lines. However, in the new power system, the distribution system has changed from a traditional receiving-end network to a complex active network. This change not only alters the operation mode of the power system but also poses unprecedented challenges to the safe and stable operation of the regional power grid. Moreover, due to the randomness and volatility of distributed photovoltaic, its output is affected by various factors such as weather and climate, so its power generation is unstable, which brings great difficulties to the dispatching and operation of the regional power grid. Among them, volatility is the main reason for the current inability of distributed photovoltaic to be connected to the grid on a large scale and the high curtailment rate. As the installed capacity of distributed photovoltaic increases, the probability of substation transformers failing increases, and the probability of large-scale power outages increases. Therefore, how to evaluate the carrying capacity of distributed photovoltaic with source-load uncertainty is an urgent problem to be solved currently.

[0003] In the prior art, there is a method of constructing a polyhedron set of distributed photovoltaic output, establishing a robust evaluation model for the carrying capacity of distributed photovoltaic, and using the column and constraint generation algorithm for solution. However, the robust evaluation model constructed only utilizes the information of the most adverse scenario and does not utilize the distribution information of the scenarios, and the evaluation result is relatively conservative. Summary of the Invention

[0004] To make up for the deficiencies existing in the prior art, the present invention provides a method, system, device and medium for evaluating the carrying capacity of distributed photovoltaic in a distribution network.

[0005] In a first aspect, an embodiment of the present invention provides a method for evaluating the carrying capacity of distributed photovoltaic in a distribution network, including:

[0006] Based on the historical source-load data of the distribution network, constructing a source-load fuzzy set according to the credibility theory, wherein the historical source-load data of the distribution network includes historical distributed photovoltaic output data and historical load data, and the source-load fuzzy set includes a distributed photovoltaic fuzzy set and a load fuzzy set;

[0007] Based on the source-load fuzzy set, a distributed photovoltaic carrying capacity evaluation model is constructed according to distributionally robust optimization. Among them, the distributed photovoltaic carrying capacity evaluation model includes a first optimization objective function and a second optimization objective function. The first optimization objective function is constructed based on maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, and the second optimization objective function is constructed based on minimizing the fuzzy expectation of the operation cost of the distributed units in the distribution network;

[0008] Solve the distributed photovoltaic carrying capacity evaluation model to obtain the distributed photovoltaic installed capacity that can be connected to the distribution network.

[0009] Preferably, constructing the source-load fuzzy set according to the credibility theory based on the historical source-load data of the distribution network includes:

[0010] Based on the historical source-load data of the distribution network, a sample scenario set is constructed. Among them, the sample scenario set includes several sample scenarios, and each sample scenario includes a set of historical distributed photovoltaic output data and a set of historical load data;

[0011] Perform fuzzy processing on the sample scenario set to obtain the first triangular fuzzy number of each set of historical distributed photovoltaic output data and the second triangular fuzzy number of each set of historical load data in each sample scenario;

[0012] Perform fuzzy expectation calculation on each first triangular fuzzy number and each second triangular fuzzy number respectively to obtain the first fuzzy expectation of each set of historical distributed photovoltaic output data and the second fuzzy expectation of each set of historical load data in each sample scenario;

[0013] Replace each set of historical distributed photovoltaic output data in each sample scenario with the corresponding first fuzzy expectation to obtain a distributed photovoltaic fuzzy set;

[0014] Replace each set of historical load data in each sample scenario with the corresponding second fuzzy expectation to obtain a load fuzzy set.

[0015] Preferably, constructing the distributed photovoltaic carrying capacity evaluation model according to distributionally robust optimization based on the source-load fuzzy set includes:

[0016] Based on the source-load fuzzy set, with the goal of maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, a first optimization objective function is constructed. Among them, the decision variables of the first optimization objective function include the distributed photovoltaic grid connection position and the distributed photovoltaic grid connection capacity;

[0017] Based on the source-load fuzzy set, with the goal of minimizing the fuzzy expectation of the operating cost of distributed units in the distribution network, a second optimization objective function is constructed, where the decision variables of the second optimization objective function include the output power of distributed units in the distribution network;

[0018] Based on the first optimization objective function and the second optimization objective function, a distributed photovoltaic carrying capacity evaluation model is obtained.

[0019] Preferably, the constructing of the first optimization objective function based on the source-load fuzzy set with the goal of maximizing the fuzzy expectation of the installed capacity of distributed photovoltaics in the distribution network includes:

[0020] The first optimization objective function is represented by the following formula:

[0021]

[0022] where L 1 represents the first optimization objective function, N r represents the set of alternative nodes for distributed photovoltaic grid connection, u i represents the Boolean variable indicating whether node i in the distribution network is connected to distributed photovoltaics, G i represents the installed capacity of distributed photovoltaics connected to node i in the distribution network.

[0023] Preferably, the constructing of the second optimization objective function based on the source-load fuzzy set with the goal of minimizing the fuzzy expectation of the operating cost of distributed units in the distribution network includes:

[0024] The second optimization objective function is represented by the following formula:

[0025]

[0026] where L 2 represents the second optimization objective function, T represents the daily operating time, represents the time-of-use electricity price at time t, P t in represents the active power at time t, C loss represents the network loss electricity price, represents the loss power of line l at time t, L represents the set of lines in the distribution network, C BG represents the gas turbine power generation cost, represents the active output power of the i-th gas turbine at time t, N BG represents the number of gas turbines, C PV represents the photovoltaic unit power generation cost, represents the active output power of the i-th photovoltaic unit at time t, N PV represents the number of photovoltaic units, C ESSDenotes the maintenance cost of the energy storage system, Denotes the active output power of the i-th energy storage system at time t, N ESS Denotes the number of energy storage systems.

[0027] Preferably, based on the first optimization objective function and the second optimization objective function, a distributed photovoltaic carrying capacity evaluation model is obtained, including:

[0028] Combining the first optimization objective function and the second optimization objective function to obtain a third optimization objective function, and using preset constraint conditions to constrain the third optimization objective function to obtain a distributed photovoltaic carrying capacity evaluation model;

[0029] Among them, the preset constraint conditions include distributed photovoltaic operation constraint conditions, distributed energy storage operation constraint conditions, gas turbine operation constraint conditions, distribution network power flow constraint conditions and distribution network safe operation constraint conditions. The following formula is used to represent the third optimization objective function:

[0030] L 3 = L 1 + L 2

[0031] Among them, L 3 Denotes the third optimization objective function or the objective function of the distributed photovoltaic carrying capacity evaluation model, L 1 Denotes the first optimization objective function, L 2 Denotes the second optimization objective function.

[0032] Preferably, solving the distributed photovoltaic carrying capacity evaluation model to obtain the installable capacity of distributed photovoltaics that can be connected to the distribution network includes:

[0033] Using the column and constraint generation algorithm and combining with the duality theorem to solve the distributed photovoltaic carrying capacity evaluation model to obtain the installable capacity of distributed photovoltaics that can be connected to the distribution network.

[0034] In a second aspect, an embodiment of the present invention provides a distributed photovoltaic carrying capacity evaluation system for a distribution network, including:

[0035] A fuzzy set construction module, configured to construct a source-load fuzzy set based on the historical source-load data of the distribution network according to the credibility theory. Among them, the historical source-load data of the distribution network includes historical distributed photovoltaic output data and historical load data, and the source-load fuzzy set includes a distributed photovoltaic fuzzy set and a load fuzzy set;

[0036] An evaluation model construction module, configured to construct a distributed photovoltaic carrying capacity evaluation model based on the source-load fuzzy set according to distributionally robust optimization. The distributed photovoltaic carrying capacity evaluation model includes a first optimization objective function and a second optimization objective function. The first optimization objective function is constructed based on maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, and the second optimization objective function is constructed based on minimizing the fuzzy expectation of the operating cost of the distributed units in the distribution network.

[0037] A photovoltaic capacity evaluation module, configured to solve the distributed photovoltaic carrying capacity evaluation model to obtain the distributed photovoltaic installed capacity that can be accessed by the distribution network.

[0038] In a third aspect, an embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the above-mentioned method for evaluating the distributed photovoltaic carrying capacity of the distribution network is implemented.

[0039] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which includes a stored computer program. When the computer program runs, it controls the device where the computer-readable storage medium is located to execute the above-mentioned method for evaluating the distributed photovoltaic carrying capacity of the distribution network.

[0040] Compared with the prior art, the method, system, device, and medium for evaluating the distributed photovoltaic carrying capacity of the distribution network in the embodiments of the present invention have the following beneficial effects: constructing a source-load fuzzy set based on credibility theory can not only make full use of the historical data of the distribution network but also better represent the uncertainty of the source-load; the evaluation results obtained from the distributed photovoltaic carrying capacity evaluation model constructed based on distributionally robust optimization can effectively balance robustness and economy; solving the model based on the column and constraint generation algorithm and the duality theorem greatly improves the solving efficiency. Description of the Drawings

[0041] Figure 1 is a schematic flowchart of a method for evaluating the distributed photovoltaic carrying capacity of the distribution network in an embodiment of the present invention;

[0042] Figure 2 is a schematic flowchart of constructing a source-load fuzzy set in an embodiment of the present invention;

[0043] Figure 3 is a schematic flowchart of constructing a distributed photovoltaic carrying capacity evaluation model in an embodiment of the present invention;

[0044] Figure 4 is a schematic structural diagram of a system for evaluating the distributed photovoltaic carrying capacity of the distribution network in an embodiment of the present invention;

[0045] Figure 5 It is a schematic diagram of the structure of a terminal device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0046] The specific implementation of the present invention is further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0047] In the description of the present invention, it should be understood that the terms "first" and "second" etc. are used in the present invention to distinguish different objects rather than to describe a specific order.

[0048] In the description of the present invention, it should be noted that, unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as those commonly understood by those skilled in the art. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood by specific circumstances.

[0049] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the carrying capacity of distributed photovoltaic power distribution networks, comprising the steps of:

[0050] S1. Based on the historical source-load data of the distribution network, the source-load fuzzy set is constructed according to the credibility theory;

[0051] In order to better describe the uncertainty of distributed photovoltaic output and load fluctuation, the present invention describes the uncertainty of source and load by establishing a fuzzy number rewriting of the historical source and load data of the distribution network based on the credibility theory. Specifically, the historical source and load data of the distribution network include historical distributed photovoltaic output data and historical load data, and the source and load fuzzy set includes distributed photovoltaic fuzzy set and load fuzzy set.

[0052] It should be noted that the credibility theory applied in the present invention includes:

[0053] Definition 1. Fuzzy variables: From a credibility array (Φ, P, C r ) is a function that maps to R. Where Φ is a non-empty set, P is the power set of Φ, and C r is a credibility measure.

[0054] Definition 2, membership function: If ξ is defined in (Φ, P, C r ), then its membership function μ(x) is:

[0055] μ(x)=2*Cr{ξ=x}∧1

[0056] Among them, ∧ is the minimization operator, which means choosing the minimum value between the two.

[0057] Theorem 1, Credibility Inversion Theorem:

[0058]

[0059] Theorem 2, Expectation of Fuzzy Variable:

[0060]

[0061] Theorem 3. Suppose ξ is a fuzzy variable with a finite mean. Then for any real numbers λ 1 and λ 2 , we have:

[0062] E[λ 1 ξ + λ 2 = λ 1 E[ξ] + λ 2

[0063] Theorem 4. Suppose ξ and η are a pair of independent triangular fuzzy numbers, where ξ = (a 1 , a 2 , a 3 ), η = (b 1 , b 2 , b 3 ). Then ξ + η is also a triangular fuzzy number, and we have:

[0064] ξ + η = (a 1 + b 1 , a 2 + b 2 , a 3 + b 3 )

[0065] Theorem 5. Suppose ξ is a triangular fuzzy number with a triple (a, b, c), and λ is a real number. Then we have:

[0066]

[0067] As Figure 2 shown, step S1 includes:

[0068] S101. Based on the historical source-load data of the distribution network, construct a sample scenario set;

[0069] The sample scenario set includes several sample scenarios, and each sample scenario includes a set of historical distributed photovoltaic output data and a set of historical load data. It should be noted that the sample scenario set is actually also a set of several groups of historical observation data of two random variables, distributed photovoltaic output and load.

[0070] S102. Fuzzify the sample scenario set to obtain the first triangular fuzzy numbers of each group of historical distributed PV output data and the second triangular fuzzy numbers of each group of historical load data in each sample scenario;

[0071] Specifically, in this embodiment, the SPSS data processing software is used to fuzzify the sample scenario set to obtain the first triangular fuzzy numbers of each group of historical distributed PV output data and the second triangular fuzzy numbers of each group of historical load data in each sample scenario.

[0072] S103. Calculate the fuzzy expectations for each first triangular fuzzy number and each second triangular fuzzy number respectively to obtain the first fuzzy expectations of each group of historical distributed PV output data and the second fuzzy expectations of each group of historical load data in each sample scenario;

[0073] Calculate the fuzzy expectations for each first triangular fuzzy number and each second triangular fuzzy number respectively according to Theorem 5 above to obtain the first fuzzy expectations of each group of historical distributed PV output data and the second fuzzy expectations of each group of historical load data in each sample scenario.

[0074] S104. Replace each group of historical distributed PV output data in each sample scenario with the corresponding first fuzzy expectation to obtain the distributed PV fuzzy set;

[0075] S105. Replace each group of historical load data in each sample scenario with the corresponding second fuzzy expectation to obtain the load fuzzy set.

[0076] The present invention constructs the source-load fuzzy set based on the credibility theory, which can not only make full use of the historical data of the distribution network, but also better represent the uncertainty of the source-load.

[0077] S2. Based on the source-load fuzzy set, construct a distributed PV carrying capacity evaluation model according to the distributionally robust optimization;

[0078] Specifically, the distributed PV carrying capacity evaluation model includes a first optimization objective function and a second optimization objective function. The first optimization objective function is constructed based on maximizing the fuzzy expectation of the distributed PV installed capacity in the distribution network, and the second optimization objective function is constructed based on minimizing the fuzzy expectation of the operation cost of the distributed units in the distribution network.

[0079] As Figure 3 shown, step S2 includes:

[0080] S201. Based on the source-load fuzzy set, construct a first optimization objective function with the goal of maximizing the fuzzy expectation of the distributed PV installed capacity in the distribution network;

[0081] The decision variables of the first optimization objective function include the distributed PV grid connection location and the distributed PV grid connection capacity. Specifically, the first optimization objective function is characterized by the following formula:

[0082]

[0083] Among them, L 1 represents the first optimization objective function, N r represents the set of alternative nodes for distributed PV grid connection, u i represents the Boolean variable indicating whether node i in the distribution network is connected to distributed PV, G i represents the installed capacity of distributed PV connected to node i in the distribution network. Among them, when u i takes the value of 1, it indicates that node i in the distribution network is the centralized confluence and grid connection point of distributed PV; when u i takes the value of 0, it indicates that node i in the distribution network is not the centralized confluence and grid connection point of distributed PV.

[0084] S202. Based on the source-load fuzzy set, with the goal of minimizing the fuzzy expectation of the operation cost of distributed units in the distribution network, construct the second optimization objective function;

[0085] The decision variables of the second optimization objective function include the output power of distributed units in the distribution network. Specifically, the second optimization objective function is characterized by the following formula:

[0086]

[0087] Among them, L 2 represents the second optimization objective function, T represents the daily operation time, represents the time-of-use electricity price at time t, P t in represents the active power at time t, C loss represents the network loss electricity price, represents the loss power of line l at time t, L represents the set of lines in the distribution network, C BG represents the gas turbine power generation cost, represents the active output power of the i-th gas turbine at time t, N BG represents the number of gas turbines, C PV represents the PV unit power generation cost, represents the active output power of the i-th PV unit at time t, N PV represents the number of PV units, C ESS represents the energy storage system maintenance cost, represents the active output power of the i-th energy storage system at time t, N ESS represents the number of energy storage systems.

[0088] S203. Based on the first optimization objective function and the second optimization objective function, a distributed photovoltaic carrying capacity evaluation model is obtained.

[0089] The first optimization objective function and the second optimization objective function are combined to obtain a third optimization objective function, and the third optimization objective function is constrained by preset constraint conditions to obtain a distributed photovoltaic carrying capacity evaluation model.

[0090] Specifically, the third optimization objective function is represented by the following formula:

[0091] L 3 = L 1 + L 2

[0092] Among them, L 3 represents the third optimization objective function or the objective function of the distributed photovoltaic carrying capacity evaluation model, L 1 represents the first optimization objective function, and L 2 represents the second optimization objective function.

[0093] Furthermore, the preset constraint conditions include distributed photovoltaic operation constraint conditions, distributed energy storage operation constraint conditions, gas turbine operation constraint conditions, distribution network power flow constraint conditions, and distribution network safe operation constraint conditions.

[0094] The following specifically explains the preset constraint conditions of the distributed photovoltaic carrying capacity evaluation model:

[0095] 1) Distributed photovoltaic operation constraint conditions

[0096] The distributed photovoltaic is connected to the grid through an inverter. During operation, its active power output cannot exceed its configured capacity, and generally adopts maximum power point tracking control. The actual maximum active power output is affected by light and temperature, and the reactive power output is limited by the active power output and power factor. Specifically, the distributed photovoltaic operation constraint conditions are represented by the following formula:

[0097]

[0098] Among them, represents the maximum output power that the distributed photovoltaic unit at node i in the distribution network can actually reach at time t, φ represents the power factor angle corresponding to the minimum power factor allowed during the operation of the distributed photovoltaic unit, represents the reactive power output of the i-th photovoltaic unit at time t.

[0099] 2) Distributed energy storage operation constraint conditions

[0100] The distributed energy storage needs to meet the charge and discharge power constraint and the state of charge constraint during operation. Specifically, the distributed energy storage operation constraint conditions are represented by the following formula:

[0101]

[0102] Among them, represents the active output power of the i-th energy storage system at time t, represents the reactive output power of the i-th energy storage system at time t, with charging being positive and discharging being negative; represents the energy storage power configured at node i in the distribution network, SOC min represents the minimum state of charge, SOC max represents the maximum state of charge, SOC i,t represents the state of charge of the i-th energy storage system at time t, and η represents the charge-discharge efficiency of the energy storage system, represents the energy storage capacity configured at node i in the distribution network.

[0103] 3) Gas turbine operation constraint conditions

[0104] The output power of the gas turbine needs to satisfy the unit output range constraint and the ramp power constraint. Specifically, the following formula is used to represent the gas turbine operation constraint conditions:

[0105]

[0106] Among them, P i BG,max represents the upper limit of the active output power of the i-th gas turbine, P i BG,min represents the lower limit of the active output power of the i-th gas turbine, represents the reactive output power of the i-th gas turbine at time t, represents the upper limit of the reactive output power of the i-th gas turbine, represents the lower limit of the reactive output power of the i-th gas turbine, P i BG,d represents the maximum slope rate of the i-th gas turbine, P i BG,u represents the maximum ramp rate of the i-th gas turbine.

[0107] 4) Distribution network power flow constraint conditions

[0108] In this embodiment, the DistFlow model is used to describe the distribution network power flow relationship. Assuming that N d is the set of all nodes in the distribution network, then the following formula is used to represent the distribution network power flow constraint conditions:

[0109]

[0110] Among them, node i is the node adjacent to node j on the path from node j to the root node, that is, the parent node; P ij represents the active power on line i-j, Q ij represents the reactive power on line i-j, w(j) represents the set of nodes adjacent to node j but not on the path from node j to the root node, I ij represents the current on line i-j, r ij represents the resistance on line i-j, x ij represents the reactance on line i-j, P j represents the injected active power at node j, Q j represents the injected reactive power at node j, U i represents the voltage amplitude of node i.

[0111] According to the above power flow constraint conditions of the distribution network, the loss power can be expressed as:

[0112]

[0113] Among them, I ij,t represents the current on line i-j at time t.

[0114] 5) Distribution network safe operation constraint conditions

[0115] To ensure the safe and stable operation of the distribution network, the operating voltage of each node should meet the safety limit:

[0116] U max >U i >U min

[0117] Among them, U max represents the maximum node voltage, U min represents the minimum node voltage.

[0118] To avoid line overload, the line current should meet the line capacity limit:

[0119] I max >|I ij

[0120] Among them, I max represents the maximum line current.

[0121] To ensure the thermal stability of the transformer, the reverse load rate constraint needs to be considered when evaluating the bearing capacity:

[0122]

[0123] Among them, λ represents the reverse load rate, λ max represents the reverse load rate limit, P DDenote the output of distributed power sources as \(P\). L Denote the equivalent electricity load at the same moment, that is, the load minus the output of other power sources except distributed power sources, as \(S\). e Denote the actual operating limit of the transformer or line.

[0124] It can be understood that the safety operation constraint conditions of the distribution network include node voltage constraint, line current constraint, and inverse load rate constraint.

[0125] The evaluation results obtained by the distributed photovoltaic carrying capacity evaluation model constructed based on distributionally robust optimization in the present invention can effectively balance robustness and economy.

[0126] S3. Solve the distributed photovoltaic carrying capacity evaluation model to obtain the installed capacity of distributed photovoltaics that can be connected to the distribution network.

[0127] Specifically, use the column and constraint generation algorithm combined with the duality theorem to solve the distributed photovoltaic carrying capacity evaluation model to obtain the installed capacity of distributed photovoltaics that can be connected to the distribution network.

[0128] The following specifically describes the solution process of the distributed photovoltaic carrying capacity evaluation model:

[0129] Based on the column and constraint generation algorithm, decompose the distributed photovoltaic carrying capacity evaluation model into a master problem MP and a subproblem SP.

[0130] The MP problem can be expressed as:

[0131]

[0132] s.t. \(y\in S\) y

[0133] where \(y\) represents the decision variable of the MP problem, that is, the decision variable of the first optimization objective function, and \(S\) y represents the feasible region of \(y\), and \(P\) m represents the installed capacity of distributed photovoltaics that can be connected to the distribution network, and \(\mu\) represents the objective function value of the SP problem, that is, the second optimization objective function value.

[0134] After each iteration, add a cutting plane and a constraint on the decision variable of the newly added second optimization objective function to the MP problem:

[0135] \(\mu\geq b\) T \(x\) l , \(l = 1,\cdots,k\)

[0136] \(Ax\) l \(\leq g(y,\xi\) l ), \(l = 1,\cdots,k\)

[0137] \(x\) l \(\in S\) x, l = 1, …, k

[0138] Among them, b represents the coefficient vector of the second optimization objective function, x represents the decision variable of the second optimization objective function, k represents the number of iterations, A represents the constant coefficient matrix of the decision variable constraints of the second optimization objective function, g(·) represents the variable coefficient matrix of the decision variable constraints of the second optimization objective function related to the decision variable and random variable of the first optimization objective function, ξ represents the random variable, including distributed photovoltaic output and load, and S x represents the feasible region of x.

[0139] The SP problem is solved with the solution of the MP problem as the known input, optimized under the given planning scheme, and pursues the lowest operating cost under the worst-case scenario distribution. Its model can be simplified as:

[0140]

[0141] s.t. Ax ≤ g(y, ξ)

[0142] After the SP problem is solved in the k-th iteration, a new set of decision variables x of the second optimization objective function is added to the MP problem k+1 , and at the same time, a new cutting plane and constraint will be provided for the MP problem:

[0143] μ ≥ b T x k+1

[0144] Ax k+1 ≤ g(y, ξ k )

[0145] Among them, ξ k represents the scenario with the highest operating cost under the worst-case scenario distribution obtained by solving the SP problem in the k-th iteration.

[0146] The SP problem is a min-max problem with uncertain variables and is difficult to solve directly. In this embodiment, the Lagrangian dual method can be used to transform the original model into a deterministic optimization problem for solution. The transformed model is:

[0147]

[0148] Among them, γ and represent the Lagrangian operator, ξ s represents the value of the random variable in scenario s, ξ min represents the scenario where the random variable takes the minimum value, and ξ max represents the scenario where the random variable takes the maximum value.

[0149] For easy understanding, the solution process of the distributed photovoltaic carrying capacity assessment model includes the steps:

[0150] 1) Input the historical distributed PV output data and historical load data to construct a sample scenario set;

[0151] 2) Initialize the algorithm, let k = 0, UB = +∞, LB = -∞. At this time, the decision variables in the MP problem model only include those of the first optimization objective function;

[0152] 3) Solve the MP problem to obtain the planning scheme for the k - th iteration, and at the same time update LB = max{LB, P m (y k ) + μ k};

[0153] 4) Construct a fuzzy set based on credibility theory according to the planning scheme;

[0154] 5) Substitute y k as a known quantity into the SP problem, solve the SP problem to obtain the value ξ k of the random variable under the worst - case scenario distribution, and at the same time update UB = min{UB, P m (y k ) + b T x(y k )}, create a new set of decision variables x k+1 of the second optimization objective function in MP, and add the constraint:

[0155] μ≥b T x k+1

[0156] Ax k+1 ≤g(y, ξ k )

[0157] 6) Judge whether to end the iteration and output the optimal solution when |UB - LB|≤δ, otherwise k = k + 1, and return to step 3).

[0158] The present invention solves the model based on the column - and - constraint generation algorithm and the duality theorem, which greatly improves the solving efficiency.

[0159] An evaluation method for the distributed PV carrying capacity of a distribution network according to an embodiment of the present invention constructs a source - load fuzzy set based on credibility theory, which can not only make full use of the historical data of the distribution network but also better represent the uncertainty of the source - load; the evaluation results obtained from the distributed PV carrying capacity evaluation model constructed based on distributionally robust optimization can effectively balance robustness and economy; solving the model based on the column - and - constraint generation algorithm and the duality theorem greatly improves the solving efficiency.

[0160] Based on the above - mentioned evaluation method for the distributed PV carrying capacity of a distribution network, as Figure 4As shown in the figure, an embodiment of the present invention provides a distributed photovoltaic carrying capacity evaluation system for a distribution network, including:

[0161] A fuzzy set construction module 1, configured to construct a source-load fuzzy set based on the historical source-load data of the distribution network according to the credibility theory. The historical source-load data of the distribution network includes historical distributed photovoltaic output data and historical load data. The source-load fuzzy set includes a distributed photovoltaic fuzzy set and a load fuzzy set;

[0162] An evaluation model construction module 2, configured to construct a distributed photovoltaic carrying capacity evaluation model based on the source-load fuzzy set according to distributionally robust optimization. The distributed photovoltaic carrying capacity evaluation model includes a first optimization objective function and a second optimization objective function. The first optimization objective function is constructed based on maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, and the second optimization objective function is constructed based on minimizing the fuzzy expectation of the operating cost of the distributed units in the distribution network;

[0163] A photovoltaic capacity evaluation module 3, configured to solve the distributed photovoltaic carrying capacity evaluation model to obtain the distributed photovoltaic installed capacity that can be accessed by the distribution network.

[0164] It should be noted that each module in the above-mentioned distributed photovoltaic carrying capacity evaluation system for a distribution network can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above-mentioned modules. For the specific limitations of a distributed photovoltaic carrying capacity evaluation system for a distribution network, refer to the limitations of a distributed photovoltaic carrying capacity evaluation method for a distribution network in the above text. The two have the same functions and effects and will not be elaborated here.

[0165] An embodiment of the present invention also provides a terminal device, which includes:

[0166] A processor, a memory, and a bus;

[0167] The bus is used to connect the processor and the memory;

[0168] The memory is used to store operation instructions;

[0169] The processor is configured to execute the operations corresponding to the above-mentioned distributed photovoltaic carrying capacity evaluation method of the present invention by calling the operation instructions.

[0170] In an alternative embodiment, a terminal device is provided, as Figure 5 shown. Figure 5The terminal device 5000 shown includes: a processor 5001 and a memory 5003. Among them, the processor 5001 and the memory 5003 are connected, such as connected through a bus 5002. Optionally, the terminal device 5000 may further include a transceiver 5004. It should be noted that in practical applications, the transceiver 5004 is not limited to one, and the structure of the terminal device 5000 does not constitute a limitation to the embodiments of the present invention.

[0171] The processor 5001 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logic blocks, modules and circuits described in connection with the disclosure of the present invention. The processor 5001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0172] The bus 5002 may include a path for transmitting information between the above components. The bus 5002 may be a PCI bus or an EISA bus, etc. The bus 5002 may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 5 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0173] The memory 5003 may be a ROM or other types of static storage devices that can store static information and instructions, a RAM or other types of dynamic storage devices that can store information and instructions, or an EEPROM, a CD-ROM or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0174] The memory 5003 is used to store the application program code for implementing the solution of the present invention and is controlled by the processor 5001 to execute. The processor 5001 is used to execute the application program code stored in the memory 5003 to implement the content shown in any of the foregoing method embodiments.

[0175] Among them, the terminal device includes but is not limited to: mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc. and fixed terminals such as digital TVs, desktop computers, etc.

[0176] An embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the above-mentioned method for evaluating the bearing capacity of distributed photovoltaic in a distribution network of the present invention.

[0177] Another embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When it runs on a computer, it enables the computer to execute the corresponding content in the foregoing method embodiments.

[0178] In addition, an embodiment of the present invention further proposes a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the above method.

[0179] In summary, for the method, system, device and medium for evaluating the bearing capacity of distributed photovoltaic in a distribution network according to the embodiments of the present invention, a source-load fuzzy set is constructed based on the credibility theory, which can not only make full use of the historical data of the distribution network, but also better represent the uncertainty of the source and load; the evaluation results obtained by the distributed photovoltaic bearing capacity evaluation model constructed based on distributionally robust optimization can effectively balance robustness and economy; the model is solved based on the column and constraint generation algorithm and the duality theorem, which greatly improves the solution efficiency.

[0180] Each embodiment in this specification is described in a progressive manner. For parts that are the same or similar in each embodiment, they can be referred to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment. It should be noted that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this specification.

[0181] The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and replacements can still be made, and these improvements and replacements should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the carrying capacity of distributed photovoltaic power distribution networks, characterized in that: include: Based on the historical source-load data of the distribution network, a source-load fuzzy set is constructed according to the credibility theory, wherein the historical source-load data of the distribution network includes historical distributed photovoltaic output data and historical load data, and the source-load fuzzy set includes distributed photovoltaic fuzzy set and load fuzzy set; Based on the source-load fuzzy set, a distributed photovoltaic carrying capacity assessment model is constructed according to distributed blue-rod optimization, wherein the distributed photovoltaic carrying capacity assessment model includes a first optimization objective function and a second optimization objective function, the first optimization objective function is constructed based on the fuzzy expectation of maximizing the distributed photovoltaic installed capacity in the distribution network, and the second optimization objective function is constructed based on the fuzzy expectation of minimizing the operating cost of the distributed units in the distribution network; The distributed photovoltaic carrying capacity assessment model is solved to obtain the distributed photovoltaic installed capacity that can be connected to the distribution network.

2. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 1, characterized in that: The source-load fuzzy set is constructed based on the historical source-load data of the distribution network according to the credibility theory, including: Based on the historical source and load data of the distribution network, a sample scenario set is constructed, wherein the sample scenario set includes a plurality of sample scenarios, and each sample scenario includes a set of historical distributed photovoltaic output data and a set of historical load data; Performing fuzzy processing on the sample scene set to obtain a first triangular fuzzy number of each group of the historical distributed photovoltaic output data and a second triangular fuzzy number of each group of the historical load data in each of the sample scenes; Performing fuzzy expectation calculation on each of the first triangular fuzzy numbers and each of the second triangular fuzzy numbers respectively, to obtain a first fuzzy expectation of each group of the historical distributed photovoltaic output data and a second fuzzy expectation of each group of the historical load data in each of the sample scenarios; Replacing each set of the historical distributed photovoltaic output data in each of the sample scenarios with the corresponding first fuzzy expectation to obtain a distributed photovoltaic fuzzy set; Each group of the historical load data in each of the sample scenarios is replaced with the corresponding second fuzzy expectation to obtain a load fuzzy set.

3. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 1, characterized in that: The distributed photovoltaic carrying capacity evaluation model is constructed based on the source-load fuzzy set according to distributed robust optimization, including: Based on the source-load fuzzy set, a first optimization objective function is constructed with the goal of maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, wherein the decision variables of the first optimization objective function include the distributed photovoltaic grid-connected location and the distributed photovoltaic grid-connected capacity; Based on the source-load fuzzy set, a second optimization objective function is constructed with the goal of minimizing the fuzzy expectation of the operating cost of the distributed units in the distribution network, wherein the decision variables of the second optimization objective function include the output power of the distributed units in the distribution network; Based on the first optimization objective function and the second optimization objective function, a distributed photovoltaic carrying capacity assessment model is obtained.

4. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 3, characterized in that: Based on the source-load fuzzy set, the first optimization objective function is constructed with the goal of maximizing the fuzzy expectation of the distributed photovoltaic installed capacity in the distribution network, including: The following formula is used to characterize the first optimization objective function: Among them, L1 represents the first optimization objective function, N r represents the set of candidate nodes for distributed photovoltaic grid connection, u i A Boolean variable indicating whether node i in the distribution network is connected to distributed photovoltaics, G i Represents the distributed photovoltaic installed capacity connected to node i in the distribution network.

5. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 3, characterized in that: The second optimization objective function is constructed based on the source-load fuzzy set with the goal of minimizing the fuzzy expectation of the operating cost of the distributed units in the distribution network, including: The second optimization objective function is characterized by the following formula: Among them, L2 represents the second optimization objective function, T represents the daily operation, represents the time-of-use electricity price during period t, P t in represents the active power in period t, C loss represents the grid loss electricity price, represents the power loss of line l during period t, L represents the line set in the distribution network, C BG represents the gas turbine power generation cost, represents the active output power of the i-th gas turbine in period t, N BG represents the number of gas turbines, C PV represents the power generation cost of the photovoltaic unit, represents the active output power of the ith photovoltaic unit in period t, N PV Indicates the number of photovoltaic units, C ESS represents the maintenance cost of the energy storage system, represents the active output power of the i-th energy storage system in period t, N ESS Indicates the number of energy storage systems.

6. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 3, characterized in that: The distributed photovoltaic carrying capacity evaluation model is obtained based on the first optimization objective function and the second optimization objective function, including: The first optimization objective function and the second optimization objective function are combined to obtain a third optimization objective function, and the third optimization objective function is constrained by a preset constraint condition to obtain a distributed photovoltaic carrying capacity assessment model; The preset constraints include distributed photovoltaic operation constraints, distributed energy storage operation constraints, gas turbine operation constraints, distribution network flow constraints and distribution network safety operation constraints. The third optimization objective function is characterized by the following formula: L3=L1+L2 Among them, L3 represents the third optimization objective function or the objective function of the distributed photovoltaic carrying capacity assessment model, L1 represents the first optimization objective function, and L2 represents the second optimization objective function.

7. The method for evaluating the distributed photovoltaic carrying capacity of a distribution network according to claim 1, characterized in that: The distributed photovoltaic carrying capacity assessment model is solved to obtain the distributed photovoltaic installed capacity that can be connected to the distribution network, including: The column and constraint generation algorithm is used in combination with the duality theorem to solve the distributed photovoltaic carrying capacity assessment model, and the distributed photovoltaic installed capacity that can be connected to the distribution network is obtained.

8. A distributed photovoltaic carrying capacity assessment system for a distribution network, characterized in that: include: A fuzzy set construction module, used to construct a source-load fuzzy set based on the historical source-load data of the distribution network according to the credibility theory, wherein the historical source-load data of the distribution network includes historical distributed photovoltaic output data and historical load data, and the source-load fuzzy set includes a distributed photovoltaic fuzzy set and a load fuzzy set; An evaluation model construction module, used to construct a distributed photovoltaic carrying capacity evaluation model based on the source-load fuzzy set and distributed blue-rod optimization, wherein the distributed photovoltaic carrying capacity evaluation model includes a first optimization objective function and a second optimization objective function, the first optimization objective function is constructed based on the fuzzy expectation of maximizing the distributed photovoltaic installed capacity in the distribution network, and the second optimization objective function is constructed based on the fuzzy expectation of minimizing the operating cost of the distributed units in the distribution network; The photovoltaic capacity assessment module is used to solve the distributed photovoltaic carrying capacity assessment model to obtain the distributed photovoltaic installed capacity that can be connected to the distribution network.

9. A terminal device, characterized in that: The method comprises a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the method for evaluating the distributed photovoltaic carrying capacity of a distribution network as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is run, it controls the device where the computer-readable storage medium is located to execute the method for evaluating the distributed photovoltaic carrying capacity of a distribution network as described in any one of claims 1 to 7.

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