A method, system and storage medium for improving the risk resistance of distribution network

Abstract

The present invention relates to a method, system and storage medium for improving the anti-risk capability of a distribution network. The improvement method includes establishing a distributed power configuration model, establishing an attack uncertainty set U, establishing a distribution network anti-risk capability improvement model C, converting the model C into a three-layer problem model through an NC&CG algorithm and substituting it into a software solution; the system includes a distributed power configuration model establishment module, an attack uncertainty set establishment module, a distribution network anti-risk capability improvement model establishment module, a model simplification and conversion module, and a model calculation and result output module; a readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned distribution network anti-risk capability improvement method are implemented; the present invention reasonably combines the investment strategy and operation strategy of the distribution network, and effectively increases the anti-risk capability of the distribution network. The present invention builds a system platform and a storage medium, so that the improvement method can be more smoothly applied to different scenarios.

Description

Technical Field

[0001] The present invention specifically relates to a method, system and storage medium for improving the risk resistance of a distribution network, and belongs to the technical field of improving the risk resistance of a distribution network. Background Art

[0002] In recent years, the frequent occurrence of natural disasters has caused huge losses to the power system. The distribution network is directly connected to the user, which has a direct impact on whether the user can use electricity normally. Therefore, increasing the risk resistance of the distribution network in the face of natural disasters has become a solution for the distribution network to provide reliable power supply to users when it is affected by disasters. The strategies for improving the risk capacity of the distribution network are divided into investment strategies and operation strategies. The investment strategy is to improve the power system's disaster defense capability by configuring additional equipment (mainly distributed power sources) before the disaster occurs; the operation strategy is to enhance the elasticity of the distribution network by flexibly regulating existing resources when the disaster occurs. The existing technology mainly focuses on the investment strategy to improve the power system's disaster defense capability by expanding additional configuration equipment, while the operation strategy is to simply flexibly regulate existing resources through historical experience. Because the existing technology attaches too much importance to the investment strategy to improve the risk resistance of the distribution network, and the operation strategy only stays at the level of past experience, it is difficult for the existing technology to combine the advantages of the investment strategy and the operation strategy to obtain the best comprehensive benefit distribution network risk resistance improvement method. Summary of the invention

[0003] The first technical problem to be solved by the present invention is to provide a method for improving the risk resistance of a distribution network, so that the distribution network investment strategy and the distribution network operation strategy are reasonably combined to improve the risk resistance of the distribution network under the condition of optimal comprehensive benefits.

[0004] The second technical problem to be solved by the present invention is: a system and storage medium for improving the risk resistance of a distribution network, which can carry the above-mentioned method for improving the risk resistance of a distribution network.

[0005] In order to solve the above-mentioned first technical problem, the first technical solution proposed by the present invention is: a method for improving the risk resistance of a distribution network, comprising the following steps:

[0006] Step 1: Establish a distributed power configuration model, as shown in the following equation (1):

[0007]

[0008] In formula (1), C DG is the cost of configuring distributed generation; i is the node on the distribution network; N is the set of all nodes on the distribution network; δ iis a 0-1 variable, indicating whether the distribution network node i is equipped with distributed generation, if so, it is 1, if not, it is 0; c M is the maintenance cost of distributed generation; K DG is the unit capacity cost of distributed generation; P i DG,r is the rated capacity of the distributed generation configured at the distribution network node i; B DG is the fixed cost required to install a distributed power source;

[0009] Obtain the constraint conditions of the distributed power configuration model, as shown in the following formula (2):

[0010]

[0011] In formula (2), It is the upper limit of the investment cost of distributed power generation; and P DG,r are the upper and lower limits of the capacity of the distributed generation installed on the distribution network node i, respectively; It is the upper limit of the number of distributed generation resources to be configured;

[0012] Step 2: According to the number limit of historical risk attacks on the distribution network, establish the attack uncertainty set U, as shown in the following formula (3):

[0013]

[0014] In formula (3), (i, j) is the branch of the distribution network; B is the set of all branches of the distribution network; atk ij is a 0-1 variable, indicating the state of the distribution network branch ij under attack. If attacked, it takes 1, and if not attacked, it takes 0; k is the number of risk attacks; Z ij is a 0-1 variable, indicating the final switching state of the distribution network branch ij, if it is put into use, it is 1, if it is disconnected, it is 0; a ij It is a 0-1 variable, indicating the availability of the line, if available, it is 1, if unavailable, it is 0;

[0015] Step 3: According to formula (1), establish the distribution network risk resistance improvement model C, as shown in formula (4):

[0016]

[0017] In formula (4), C cur is the total load shedding loss during the operating cycle; C DR is the demand response compensation cost during the operation cycle; c cur is the unit load shedding loss; is the load shedding amount of distribution network node i at time t; Δt is the optimization time interval; T is the time set; Λ is the set of distribution network nodes participating in demand response; D is the set of demand response blocks; is the unit cost of demand response in block d where the load is located at node i of the distribution network; It is the amount of demand response participation in block d where the load is located at distribution network node i at time t;

[0018] The constraint conditions of the distribution network risk resistance improvement model C are obtained through formula (3), where the distribution network flow constraint is as shown in formula (5):

[0019]

[0020] In formula (5), P ij,t is the active power flowing through the distribution network branch ij at time t; Q ij,t is the reactive power flowing through the distribution network branch ij at time t; π(i) is the set of parent nodes of distribution network node i; θ(i) is the set of child nodes of distribution network node i; is the active output of the DG configured on the node i of the distribution network at time t; is the reactive power output of the distributed generation configured on the node i of the distribution network at time t; is the active load demand at distribution network node i at time t; is the reactive load demand at time t on the distribution network node i; is the active load of distribution network node i participating in demand response at time t; is the reactive load of distribution network node i participating in demand response at time t; is the active load reduction of distribution network node i at time t; is the reactive load reduction of distribution network node i at time t; V i,t is the voltage amplitude at time t on the distribution network node i; V0 is the reference voltage; R ij is the resistance of the distribution network branch ij; X ij is the reactance of the distribution network branch ij; M is a large constant; is the capacity of the distribution network branch ij; V max and V min They are the upper and lower limits of the voltage amplitude of the distribution network respectively;

[0021] The distribution network reconstruction constraint is shown in equation (6):

[0022]

[0023] In formula (6), N bus is the total number of distribution network nodes; Root iis a 0-1 variable, indicating whether the distribution network node i is selected as the root node. If it is selected as the root node, it takes 1, and if it is not selected as the root node, it takes 0; F ij is the virtual power flow on the distribution network branch ij; e i is a 0-1 variable, indicating whether the distribution network node i is the endpoint of the fault branch. If so, it takes 1, otherwise it takes 0;

[0024] The distribution network demand response constraint is shown in equation (7):

[0025]

[0026] In formula (7), τ d,i is the proportion of the total load in block d where the load is located at node i of the distribution network;

[0027] Step 4: Simplify the constraint conditions of the distribution network risk resistance improvement model C as shown in the following formula (8):

[0028]

[0029] In formula (8), z is the variable of the power flow operation h is a set of decision variables about distributed generation configuration (δ i ,P i DG,r ) set; u is the attack variable (atk ij ) set; r is the continuous variable in the reconstruction constraint (F ij ) set; c is the integer variable (Z ij ,Root i ,e i )

[0030] The distribution network risk resistance improvement model C is transformed into a three-layer problem model through the NC&CG algorithm. The upper layer problem model is shown in the following formula (9):

[0031]

[0032] The constraints of the upper-level problem model are as follows (10):

[0033]

[0034] The lower-level main problem model is shown in the following formula (11):

[0035]

[0036] Since the lower-level main problem model is an opposing max-min problem, it cannot be solved directly and must be converted into a same-direction problem for solution. According to the strong duality theory, the upper-level main problem model is converted into a solvable same-direction problem form, as shown in the following formula (12):

[0037]

[0038] In formula (12), π is the set of dual variables introduced; β is the auxiliary variable;

[0039] The constraint conditions of the lower-level main problem model are shown in equation (13):

[0040]

[0041] In formula (13), ξ is the number of iterations of the lower-level main problem model;

[0042] The lower-level sub-problem model is shown in the following formula (14):

[0043]

[0044] The constraint conditions of the lower-level sub-problem model are shown in the following formula (15):

[0045] A1 * +B1z+C1c+D1u * +E1r≥F1(15);

[0046] Step 5: Substitute the three-layer problem model into the software for solution, and output the solution result as a guide for improving the risk resistance of the distribution network.

[0047] Further, the specific solution steps in step 5 are as follows:

[0048] Step 5.1: Load the basic data of the distribution network; set the iterative convergence gap; initialize the number of iterations; set the upper bound initial value UB←+∞ and the lower bound initial value LB←-∞ for the upper problem; set the upper bound initial value LUB←+∞ and the lower bound initial value LLB←-∞ for the lower problem;

[0049] Step 5.2: Set attack strategy u * And solve the upper problem to obtain the distributed power configuration scheme h; update the lower bound of the upper problem so that the lower bound value LB=max{LB,α};

[0050] Step 5.3: Set the distributed generation configuration strategy h * = h and system topology c * And solve the lower-level main problem to obtain the attack strategy u; update the upper bound of the lower-level problem so that the upper bound value LUB = min{LUB,β};

[0051] Step 5.4: Set the distributed generation configuration strategy h * = h and attack strategy u * =u and solve the lower sub-problem to obtain the system topology c; update the upper bound of the lower problem so that the lower bound value LLB = min{LLB,C cur +C DR};

[0052] Step 5.5: Determine whether the lower layer problem converges. The convergence condition is shown in the following formula (16); if converged, proceed to the next step; if not, add the topology c obtained in step 5.4 to the system topology set, and return to step 5.3 to continue iteration;

[0053] LUB-LLB>gap (16);

[0054] Step 5.6: Update the upper bound of the upper problem UB = min{UB, LLB}. The convergence condition for judging whether the upper problem converges is shown in equation (17); if converged, proceed to the next step; if not, add the attack strategy u obtained in step 5.4 to the attack set, and return to step 5.2 to continue iteration.

[0055] UB-LB>gap (17);

[0056] Step 5.7: End the calculation and output the optimal strategy and load shedding amount P of distributed generation configuration cur Serves as a guide for improving the risk resistance of distribution networks.

[0057] In order to solve the above second technical problem, the second technical solution proposed by the present invention is: a system for improving the risk resistance of a distribution network, comprising:

[0058] A distributed power configuration model building module is used to give distributed power configuration constraints and calculate distributed power configuration costs;

[0059] The attack uncertainty set building module is used to determine the state of distribution network branches given the attack quantity constraint;

[0060] A distribution network risk resistance improvement model establishment module is used to establish the distribution network risk resistance improvement model under certain constraints with the system load loss minimization as a technical indicator;

[0061] Model simplification and transformation module, used to transform the proposed model for easy solution;

[0062] The model calculation and result output module is used to efficiently solve the proposed model through software and output the calculation results.

[0063] To solve the above-mentioned second technical problem, the third technical solution proposed in the present invention is: a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the steps of the above-mentioned method for improving the risk resistance of the distribution network.

[0064] Beneficial effects of the present invention: 1. Since the present invention reasonably combines the investment strategy and operation strategy of the distribution network, taking into account situations such as the network reconstruction and demand response of the distribution network, the combination of the investment strategy and operation strategy of the distribution network achieves the optimal effect, which greatly reduces the load shedding of the distribution network and effectively increases the risk resistance of the distribution network. 2. The present invention builds a system platform and storage medium that can carry a method for improving the risk resistance of the distribution network, so that a method for improving the risk resistance of the distribution network can be more smoothly applied to different scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 The present invention is a flow chart of a method for improving the risk resistance of a distribution network.

[0066] Figure 2 The present invention is a flow chart of step 5 of a method for improving the risk resistance of a distribution network.

[0067] Figure 3 It is the structural diagram of the IEEE33-node distribution network test system under implementation.

[0068] Figure 4 This is a comparison diagram of the load loss obtained by using this method and the prior art.

[0069] Figure 5 The present invention is a module diagram of a system for improving the risk resistance of a distribution network. DETAILED DESCRIPTION

[0070] The following is a further description of a method, system and storage medium for improving the risk resistance of a distribution network according to the present invention in conjunction with the accompanying drawings and specific embodiments.

[0071] The present invention provides a method for improving the risk resistance of a distribution network, such as Figure 1 As shown, the following steps are included:

[0072] Step 1: Establish a distributed power configuration model, as shown in the following equation (1):

[0073]

[0074] In formula (1), C DG is the cost of configuring distributed generation; i is the node on the distribution network; N is the set of all nodes on the distribution network; δ iis a 0-1 variable, indicating whether the distribution network node i is equipped with distributed generation, if so, it is 1, if not, it is 0; c M is the maintenance cost of distributed generation; K DG is the unit capacity cost of distributed generation; P i DG,r is the rated capacity of the distributed generation configured at the distribution network node i; B DG is the fixed cost required to install a distributed power source;

[0075] Obtain the constraints of the distributed power configuration model as shown in the following formula (2):

[0076]

[0077] In formula (2), It is the upper limit of the investment cost of distributed power generation; and P DG,r are the upper and lower limits of the capacity of the distributed generation installed on the distribution network node i, respectively; It is the upper limit of the number of distributed generation resources to be configured;

[0078] Step 2: According to the number of risk attacks on the historical distribution network, establish the attack uncertainty set U, as shown in the following formula (3):

[0079]

[0080] In formula (3), (i, j) is the branch of the distribution network; B is the set of all branches of the distribution network; atk ij is a 0-1 variable, indicating the state of the distribution network branch ij under attack. If attacked, it takes 1, and if not attacked, it takes 0; k is the number of risk attacks; Z ij is a 0-1 variable, indicating the final switching state of the distribution network branch ij, if it is put into use, it is 1, if it is disconnected, it is 0; a ij It is a 0-1 variable, indicating the availability of the line, if available, it is 1, if unavailable, it is 0;

[0081] Step 3: According to formula (1), establish the distribution network risk resistance improvement model C, as shown in formula (4):

[0082]

[0083] In formula (4), h is XX; H is XX; z, r, c are XX; C cur is the total load shedding loss during the operating cycle; C DR is the demand response compensation cost during the operation cycle; c cur is the unit load shedding loss; is the load shedding amount of distribution network node i at time t; Δt is the optimization time interval; T is the time set; Λ is the set of distribution network nodes participating in demand response; D is the set of demand response blocks; is the unit cost of demand response in block d where the load is located at node i of the distribution network; It is the amount of demand response participation in block d where the load is located at distribution network node i at time t;

[0084] The constraint conditions of the distribution network risk resistance improvement model C are obtained through formula (3), where the distribution network flow constraint is as shown in formula (5):

[0085]

[0086] In formula (5), P ij,t is the active power flowing through the distribution network branch ij at time t; Q ij,t is the reactive power flowing through the distribution network branch ij at time t; π(i) is the set of parent nodes of distribution network node i; θ(i) is the set of child nodes of distribution network node i; is the active output of the distributed generation configured on the node i of the distribution network at time t; is the reactive power output of the distributed generation configured on the node i of the distribution network at time t; is the active load demand at distribution network node i at time t; is the reactive load demand at time t on the distribution network node i; is the active load of distribution network node i participating in demand response at time t; is the reactive load of distribution network node i participating in demand response at time t; is the active load reduction of distribution network node i at time t; is the reactive load reduction of distribution network node i at time t; V i,t is the voltage amplitude at time t on the distribution network node i; V0 is the reference voltage; R ij is the resistance of the distribution network branch ij; X ij is the reactance of the distribution network branch ij; M is a large constant; is the capacity of the distribution network branch ij; V max and V min They are the upper and lower limits of the voltage amplitude of the distribution network respectively;

[0087] The distribution network reconstruction constraint is shown in equation (6):

[0088]

[0089] In formula (6), N bus is the total number of distribution network nodes; Root iis a 0-1 variable, indicating whether the distribution network node i is selected as the root node. If it is selected as the root node, it takes 1, and if it is not selected as the root node, it takes 0; F ij is the virtual power flow on the distribution network branch ij; e i is a 0-1 variable, indicating whether the distribution network node i is the endpoint of the fault branch. If so, it takes 1, otherwise it takes 0;

[0090] The distribution network demand response constraint is shown in equation (7):

[0091]

[0092] In formula (7), τ d,i is the proportion of the total load in block d where the load is located at node i of the distribution network;

[0093] Step 4: Simplify the constraints of the distribution network risk resistance improvement model as shown in the following formula (8):

[0094]

[0095] In formula (8), z is the variable of the power flow operation h is a set of decision variables about distributed generation configuration (δ i ,P i DG,r ) set; u is the attack variable (atk ij ) set; r is the continuous variable in the reconstruction constraint (F ij ) set; c is the integer variable (Z ij ,Root i ,e i )

[0096] The distribution network risk resistance improvement model is transformed into a three-layer problem model through the NC&CG algorithm. The upper layer problem model is shown in the following formula (9):

[0097]

[0098] The constraints of the upper problem model are shown in (10).

[0099]

[0100] The lower-level main problem model is shown in equation (11):

[0101]

[0102] Since the lower-level main problem model is an opposing max-min problem, it cannot be solved directly and must be converted into a same-direction problem for solution. According to the strong duality theory, the upper-level main problem model is converted into a solvable same-direction problem form, as shown in the following formula (12):

[0103]

[0104] In formula (12), π is the set of dual variables introduced; β is the auxiliary variable;

[0105] The constraints of the lower-level main problem model are shown in equation (13):

[0106]

[0107] In formula (13), ξ is the number of iterations of the lower-level main problem model;

[0108] The lower-level sub-problem model is shown in equation (14):

[0109]

[0110] The constraints of the lower-level sub-problem model are shown in equation (15):

[0111] A1 * +B1z+C1c+D1u * +E1r≥F1 (15);

[0112] Step 5: Substitute the three-layer problem model transformed from the distribution network risk resistance improvement model into the software for solution, and output the solution results as a guide for the distribution network risk resistance improvement method; Figure 2 As shown, the specific solution steps in step 5 are as follows:

[0113] Step 5.1: Load the basic data of the distribution network; set the iterative convergence gap; initialize the number of iterations; set the upper bound initial value UB←+∞ and the lower bound initial value LB←-∞ for the upper problem; set the upper bound initial value LUB←+∞ and the lower bound initial value LLB←-∞ for the lower problem;

[0114] Step 5.2: Set attack strategy u * And solve the upper problem to obtain the distributed power configuration scheme h; update the lower bound of the upper problem so that the lower bound value LB=max{LB,α};

[0115] Step 5.3: Set the distributed generation configuration strategy h * = h and system topology c * And solve the lower-level main problem to obtain the attack strategy u; update the upper bound of the lower-level problem so that the upper bound value LUB = min{LUB,β};

[0116] Step 5.4: Set the distributed generation configuration strategy h * = h and attack strategy u * =u and solve the lower sub-problem to obtain the system topology c; update the upper bound of the lower problem so that the lower bound value LLB = min{LLB,C cur +C DR};

[0117] Step 5.5: Determine whether the lower layer problem converges. The convergence condition is shown in the following formula (16); if converged, proceed to the next step; if not, add the topology c obtained in step 5.4 to the system topology set, and return to step 5.3 to continue iteration;

[0118] LUB-LLB>gap (16);

[0119] Step 5.6: Update the upper bound of the upper problem UB = min{UB, LLB}. The convergence condition for judging whether the upper problem converges is shown in equation (17); if converged, proceed to the next step; if not, add the attack strategy u obtained in step 5.4 to the attack set, and return to step 5.2 to continue iteration.

[0120] UB-LB>gap (17);

[0121] Step 5.7: End the calculation and output the optimal strategy and load shedding amount P of distributed generation configuration cur Serves as a guide for improving the risk resistance of distribution networks.

[0122] The following is a test of the risk resistance of a distribution network test system with IEEE 33 nodes, which is structured as follows: Figure 3 As shown; the parameters of the IEEE 33-node distribution network test system are shown in Table 1. Since this method uses the strong duality theory to transform the distribution network risk resistance improvement model into a solvable three-layer problem model, the three-layer problem model is substituted into the GAMS software for solution. Since the goal of formulas (11), (12), (13), (14) and (15) in the three-layer model is to minimize the load shedding cost generated by the distribution network under the worst attack situation, the distribution network under the worst attack situation can be obtained by solving formulas (11), (12), (13), (14) and (15).

[0123]

[0124]

[0125] Table 1

[0126] The minimum total load shedding capacity P under cur It is 864kWh. Since the goal of formulas (9) and (10) in the three-layer model is to minimize the configuration cost of distributed power sources in the distribution network, the optimal configuration strategy of distributed power sources calculated by solving formulas (9) and (10) is to configure 208KW at node 8, 584KW at node 15, 457KW at node 24, and 573KW at node 29. Since the ultimate goal of the three-layer model is to minimize the total investment and operating cost under the entire method, the minimum total investment under this method is 345709.2 yuan and the minimum operating cost is 28035.37 yuan by solving formulas (9), (10), (11), (12), (13), (14) and (15).

[0127] The comparison chart of various indicators calculated by this method and historical indicators obtained by existing technology is as follows: Figure 4 As shown in the comparison table below Table 2, it can be clearly seen that after adopting this method, the total load shedding is reduced by 386.5KWh, indicating that the risk resistance of the distribution network has been greatly improved; the total investment has been reduced by 43,342.582 yuan, and the operating cost has been reduced by 6,739.772 yuan.

[0128]

[0129]

[0130] Table 2

[0131] A distribution network risk resistance improvement system of this embodiment is specifically described as follows: A distribution network risk resistance improvement system can be used to achieve the most severe situation in which investment measures and operation measures are combined to efficiently improve the risk resistance of the distribution network, specifically, Figure 5 As shown, the distribution network risk resistance improvement system includes a distributed power configuration model building module, an attack uncertainty set building module, a distribution network risk resistance improvement model building module, a model simplification and conversion module, and a model calculation and result output module.

[0132] Among them, the distributed power supply configuration model establishment module is used to give distributed power supply configuration constraints and calculate the distributed power supply configuration cost; the attack uncertainty set establishment module is used to give attack quantity constraints and determine the distribution network branch status; the distribution network risk resistance improvement model establishment module is used to establish the distribution network risk resistance improvement model under certain constraints with the system load loss minimization as the technical indicator; the model simplification and conversion module is used to convert the proposed model for easy solution; the model calculation and result output module is used to efficiently solve the proposed model through software and output the calculation results.

[0133] The distributed power configuration model building module is specifically used for:

[0134] First, according to the fixed cost, capacity cost and operation and maintenance cost required for the actual configuration of distributed power sources, the annual cost required for configuring distributed power sources is determined. Secondly, based on the actual situation in the region, the investment constraints, capacity constraints and quantity constraints of distributed power generation configuration are given.

[0135] The attack uncertainty set establishment module is specifically used for:

[0136] An attack uncertainty set is established to describe the attack uncertainty caused by extreme events on the distribution network. Based on the attack uncertainty set, the state of the distribution network branch is further determined.

[0137] The risk resistance improvement model building module is specifically used for:

[0138] Under the given distributed power configuration constraints, attack uncertainty set, power flow constraints, reconstruction constraints and demand response constraints, the distribution network risk resistance improvement model is established. The model takes reducing the loss of important loads in disasters as a technical indicator, and comprehensively optimizes the investment cost (distributed power configuration cost) and operation cost (loss of load cost and demand response cost).

[0139] The model simplification and transformation module is specifically used for:

[0140] The model expression is simplified, and the proposed model is converted into a three-layer problem model according to the NC&CG algorithm, which is divided into an upper-layer problem, a lower-layer main problem, and a lower-layer sub-problem. The upper-layer problem is used to solve the configuration decision of distributed power sources under a given set of faults, the lower-layer main problem is used to solve the most serious attack decision under a given distributed power configuration decision and system topology, and the lower-layer sub-problem is used to solve the system topology under a given distributed power configuration decision and a set of faults. Among them, the lower-layer main problem is still a max-min two-layer problem and cannot be solved directly. Therefore, a conversion method based on strong duality theory is proposed to convert the two-layer model into a single-layer model.

[0141] The model calculation and result output module is specifically used for:

[0142] According to the proposed steps, the distribution network risk resistance improvement model is substituted into GAMS software for efficient solution and output of calculation results, where the load shedding P cur Serves as a guide for improving the risk resistance of distribution networks.

[0143] All relevant contents of each step involved in the embodiment of the aforementioned method for improving the risk resistance of the distribution network can be referred to the functional description of the functional modules corresponding to the system for improving the risk resistance of the distribution network in the embodiment of the present invention, and will not be repeated here.

[0144] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or software modules.

[0145] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for improving the risk resistance of the distribution network in the above embodiment.

Claims

1. A method for improving the risk resistance of a distribution network, characterized in that: The following steps are involved: Step 1: Establish a distributed power configuration model, as shown in the following equation (1): In formula (1), C DG is the cost of configuring distributed generation; i is a node on the distribution network; N is the set of all nodes on the distribution network; δ i is a 0-1 variable, indicating whether the distribution network node i is equipped with distributed generation, if so, it is 1, if not, it is 0; c M is the maintenance cost of distributed generation; K DG is the unit capacity cost of distributed generation; P i DG,r is the rated capacity of the distributed generation configured at the distribution network node i; B DG is the fixed cost required to install a distributed power source; Obtain the constraint conditions of the distributed power configuration model, as shown in the following formula (2): In formula (2), It is the upper limit of the investment cost of distributed power generation; and P DG ,r are the upper and lower limits of the capacity of the distributed generation installed on the distribution network node i; It is the upper limit of the number of distributed generation resources to be configured; Step 2: According to the number limit of historical risk attacks on the distribution network, establish the attack uncertainty set U, as shown in the following formula (3): In formula (3), (i, j) is the branch of the distribution network; B is the set of all branches of the distribution network; atk ij It is a 0-1 variable, indicating the attack status of the distribution network branch ij. If it is attacked, it takes 1, and if it is not attacked, it takes 0; k is the number of risky attacks; Z ij is a 0-1 variable, indicating the final switching state of the distribution network branch ij, if it is put into use, it is 1, if it is disconnected, it is 0; a ij It is a 0-1 variable, indicating the availability of the line, if available, it is 1, if unavailable, it is 0; Step 3: According to formula (1), establish the distribution network risk resistance improvement model C, as shown in formula (4): In formula (4), C cur is the total load shedding loss during the operating cycle; C DR is the demand response compensation cost during the operation cycle; c cur is the unit load shedding loss; is the load shedding amount of distribution network node i at time t; Δt is the optimization time interval; T is the time set; Λ is the set of distribution network nodes participating in demand response; D is the set of demand response blocks; is the unit cost of demand response in block d where the load is located at node i of the distribution network; It is the amount of the load in block d on the distribution network node i that participates in the demand response at time t; The constraint conditions of the distribution network risk resistance improvement model C are obtained through formula (3), where the distribution network flow constraint is as shown in formula (5): In formula (5), P ij,t is the active power flowing through the distribution network branch ij at time t; Q ij,t is the reactive power flowing through the distribution network branch ij at time t; π(i) is the set of parent nodes of distribution network node i; θ(i) is the set of child nodes of distribution network node i; is the active output of the DG configured on the node i of the distribution network at time t; is the reactive power output of the distributed generation configured on the node i of the distribution network at time t; is the active load demand at distribution network node i at time t; is the reactive load demand at time t on the distribution network node i; is the active load of distribution network node i participating in demand response at time t; is the reactive load of distribution network node i participating in demand response at time t; is the active load reduction of distribution network node i at time t; is the reactive load reduction of distribution network node i at time t; V i,t is the voltage amplitude at time t on the distribution network node i; V0 is the reference voltage; R ij is the resistance of the distribution network branch ij; X ij is the reactance of the distribution network branch ij; M is a large constant; is the capacity of the distribution network branch ij; V max and V min They are the upper and lower limits of the voltage amplitude of the distribution network respectively; The distribution network reconstruction constraint is shown in equation (6): In formula (6), N bus is the total number of distribution network nodes; Root i is a 0-1 variable, indicating whether the distribution network node i is selected as the root node. If it is selected as the root node, it takes 1, and if it is not selected as the root node, it takes 0; F ij is the virtual power flow on the distribution network branch ij; e i is a 0-1 variable, indicating whether the distribution network node i is the endpoint of the fault branch. If so, it takes 1, otherwise it takes 0; The distribution network demand response constraint is shown in equation (7): In formula (7), τ d,i is the proportion of the total load in block d where the load is located at node i of the distribution network; Step 4: Simplify the constraint conditions of the distribution network risk resistance improvement model C as shown in the following formula (8): In formula (8), z is the variable of the power flow operation h is a set of decision variables about distributed generation configuration (δ i ,P i DG,r ) set; u is the attack variable (atk ij ) set; r is the continuous variable in the reconstruction constraint (F ij ) set; c is the integer variable (Z ij ,Root i ,e i ) The distribution network risk resistance improvement model C is transformed into a three-layer problem model through the NC&CG algorithm, which includes an upper-layer problem model, a lower-layer main problem model, and a lower-layer sub-problem model. The upper-layer problem model is shown in the following formula (9): The constraints of the upper-level problem model are as follows (10): The lower-level main problem model is shown in the following formula (11): Since the lower-level main problem model is an opposing max-min problem, it cannot be solved directly and must be converted into a same-direction problem for solution. According to the strong duality theory, the upper-level main problem model is converted into a solvable same-direction problem form, as shown in the following formula (12): In formula (12), π is the set of dual variables introduced; β is the auxiliary variable; The constraint conditions of the lower-level main problem model are shown in equation (13): In formula (13), ξ is the number of iterations of the lower-level main problem model; The lower-level sub-problem model is shown in the following formula (14): The constraint conditions of the lower-level sub-problem model are shown in the following formula (15): A1h * +B1z+C1c+D1u * +E1r≥F1(15); Step 5: Substitute the three-layer problem model into the software for solution, and output the solution result as a guide for improving the risk resistance of the distribution network.

2. According to the method for improving the risk resistance of the distribution network of claim 1: the specific solution steps in step 5 are as follows: Step 5.1: Load the basic data of the distribution network; set the iterative convergence gap; initialize the number of iterations; set the upper bound initial value UB←+∞ and the lower bound initial value LB←-∞ for the upper problem; set the upper bound initial value LUB←+∞ and the lower bound initial value LLB←-∞ for the lower problem; Step 5.2: Set attack strategy u * And solve the upper problem to obtain the distributed power configuration scheme h; update the lower bound of the upper problem so that the lower bound value LB=max{LB,α}; Step 5.3: Set the distributed generation configuration strategy h * = h and system topology c * And solve the lower-level main problem to obtain the attack strategy u; update the upper bound of the lower-level problem so that the upper bound value LUB = min{LUB,β}; Step 5.4: Set the distributed generation configuration strategy h * = h and attack strategy u * =u and solve the lower sub-problem to obtain the system topology c; update the upper bound of the lower problem so that the lower bound value LLB = min{LLB,C cur +C DR}; Step 5.5: Determine whether the lower layer problem converges. The convergence condition is shown in the following formula (16); if converged, proceed to the next step; if not, add the topology c obtained in step 5.4 to the system topology set, and return to step 5.3 to continue iteration; LUB-LLB>gap (16); Step 5.6: Update the upper bound of the upper problem UB = min{UB, LLB}; Determine whether the upper problem converges. The convergence condition is shown in the following formula (17); If converged, proceed to the next step; If not, add the attack strategy u obtained in step 5.4 to the attack set, and return to step 5.2 to continue iteration; UB-LB>gap (17); Step 5.7: End the calculation and output the optimal strategy and load shedding amount P of distributed generation configuration curServes as a guide for improving the risk resistance of distribution networks.

3. A distribution network risk resistance improvement system equipped with the distribution network risk resistance improvement method according to claim 1, characterized in that: Includes the following modules: A distributed power configuration model building module, used to implement step 1 in claim 1 to give distributed power configuration constraints and calculate distributed power configuration costs; An attack uncertainty set establishment module, used to implement step 2 in claim 1 to give attack quantity constraints and determine the state of distribution network branches; A distribution network risk resistance improvement model establishment module, used to implement step 3 in claim 1 to establish the distribution network risk resistance improvement model under certain constraints with the system load shedding minimization as a technical indicator; A model simplification and conversion module, used to implement step 4 in claim 1 to convert the proposed model for easy solution; The model calculation and result output module is used to implement step 5 in claim 1 to efficiently solve the proposed model through software and output the calculation results.

4. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for improving the risk resistance of the distribution network are implemented.

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