Distribution network planning method and device based on mixed integer programming

Abstract

The invention discloses a power distribution network planning method and device based on mixed integer programming. The method comprises the steps of obtaining a topological structure, equipment information and load characteristics of a power distribution network; taking an investment mode as an input parameter, and constructing a hybrid model according to the topological structure, the equipment information and the load characteristics; obtaining a preset problem matrix, and obtaining an optimal investment objective function in combination with the preset problem matrix on the basis of the hybrid model; constructing an uncertainty function according to the uncertainty of power distribution network planning; adjusting variables in the optimal investment objective function according to the uncertainty function to obtain a mixed integer optimization model; and solving the mixed integer optimization model through a greedy algorithm to obtain an optimal investment mode and a corresponding optimal profit. It is ensured that the model can accurately describe the operation and development conditions of the power distribution network, the risk is evaluated more accurately, the investment strategy is adjusted more accurately, the reliability and robustness of investment decision making are improved, and the solving efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system distribution network asset planning, and in particular to a distribution network planning method and device based on mixed integer programming. Background Art

[0002] With the growth of electricity demand and the adjustment of energy structure, traditional distribution networks face many challenges, including the renewal of aging facilities, the management of load volatility, the integration of renewable energy, and the continuous improvement of network reliability and efficiency.

[0003] Traditional distribution network investment decisions are often based on empirical rules or static methods based on simulation optimization, mainly focusing on static optimization, dynamic programming, and probability analysis of distribution networks. Among them, static optimization methods are usually based on fixed load conditions and equipment status, and cannot fully consider the dynamic changes of the network. Although dynamic programming methods take time factors into account, they often face problems of high computational complexity and difficulty in solving. Although probability analysis methods can take uncertainty factors into account, they lack overall optimization of multi-period investment decisions. Therefore, the existing technology cannot comprehensively consider the dynamic characteristics of the distribution network, multi-period investment decisions and uncertainty factors to formulate a comprehensive optimal investment strategy. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a distribution network planning method and device based on mixed integer programming, so as to achieve comprehensive consideration of various factors of the distribution network and formulate a comprehensive optimal investment strategy.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A distribution network planning method based on mixed integer programming, comprising:

[0007] Obtain the topology, equipment information and load characteristics of the power distribution network;

[0008] Taking the investment method as an input parameter and building a hybrid model according to the topology, equipment information and load characteristics;

[0009] Obtaining a preset problem matrix, and obtaining an optimal investment objective function based on the hybrid model and combining the preset problem matrix;

[0010] Construct uncertainty function based on the uncertainty of distribution network planning;

[0011] Adjust the variables in the optimal investment objective function according to the uncertainty function to obtain a mixed integer optimization model;

[0012] The mixed integer optimization model is solved by a greedy algorithm to obtain the optimal investment method and the corresponding optimal profit.

[0013] In order to solve the above technical problems, another technical solution adopted by the present invention is:

[0014] A distribution network planning device based on mixed integer programming includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step in the distribution network planning method based on mixed integer programming as described above is implemented.

[0015] The beneficial effects of the present invention are as follows: by establishing a mixed integer optimization model including variables such as network status, device status and cost function, that is, the model takes into account the topological constraints of the network, the operating limitations of the equipment and the impact of investment decisions, ensuring that the model can accurately describe the operation and development of the distribution network; at the same time, by introducing practical problem factors such as load demand and market changes, and describing the uncertainty of practical problem factors such as load demand and market changes based on uncertainty functions, it is possible to more accurately assess risks and adjust investment strategies, thereby improving the reliability and robustness of investment decisions; finally, the solution is obtained through a greedy algorithm to improve the solution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of the steps of a distribution network planning method based on mixed integer programming in an embodiment of the present invention;

[0017] Figure 2 Schematic diagram of the structure of a distribution network planning device based on mixed integer programming in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to explain the technical content, achieved objectives and effects of the present invention in detail, the following is an explanation in combination with the implementation modes and the accompanying drawings.

[0019] In the operation and management of distribution networks, investment decision-making is a crucial link in order to achieve sustainable development and optimal operation of the network. However, traditional investment decision-making methods often fail to fully consider multi-period, dynamic and uncertain factors, resulting in the decision-making results being unscientific and unreliable. The present invention aims to solve the technical problems existing in distribution network investment decision-making, as follows:

[0020] Multi-period investment decision-making problem: Traditional static optimization methods cannot effectively handle long-term investment decision-making problems, and dynamic programming methods face the problem of high computational complexity. Therefore, how to formulate the best investment strategy within a multi-period range to ensure the sustainability of long-term network development and maximize benefits is a key issue worthy of study.

[0021] Dynamic issues: The dynamic nature of the distribution network operating environment means that investment decisions need to take into account factors such as load changes and the evolution of equipment life. Traditional methods often fail to fully consider the actual operation of the network. Therefore, how to effectively resolve the challenges brought by network dynamics to investment decisions and ensure the flexibility and effectiveness of investment plans is an important issue.

[0022] Uncertainty issues: There are various uncertainties in the operation of distribution networks, such as fluctuations in future load demand and changes in market prices, which bring risks and challenges to investment decisions. Therefore, how to consider uncertainties and make corresponding adjustments in the investment planning process to improve the risk control ability and feasibility of investment has become a difficult problem that needs to be solved.

[0023] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0024] Please refer to Figure 1 , a distribution network planning method based on mixed integer programming, comprising:

[0025] Obtain the topology, equipment information and load characteristics of the power distribution network;

[0026] Taking the investment method as an input parameter and building a hybrid model according to the topology, equipment information and load characteristics;

[0027] Obtaining a preset problem matrix, and obtaining an optimal investment objective function based on the hybrid model and combining the preset problem matrix;

[0028] Construct uncertainty function based on the uncertainty of distribution network planning;

[0029] Adjust the variables in the optimal investment objective function according to the uncertainty function to obtain a mixed integer optimization model;

[0030] The mixed integer optimization model is solved by a greedy algorithm to obtain the optimal investment method and the corresponding optimal profit.

[0031] From the above description, it can be seen that the beneficial effects of the present invention are: by establishing a mixed integer optimization model including variables such as network status, device status and cost function, that is, the model takes into account the topological constraints of the network, the operating limitations of the equipment and the impact of investment decisions, ensuring that the model can accurately describe the operation and development of the distribution network; at the same time, by introducing practical problem factors such as load demand and market changes, and describing the uncertainty of practical problem factors such as load demand and market changes based on uncertainty functions, it is possible to more accurately assess risks and adjust investment strategies, thereby improving the reliability and robustness of investment decisions; finally, the solution is solved through a greedy algorithm to improve the solution efficiency.

[0032] Furthermore, the step of taking the investment mode as an input parameter and constructing a hybrid model according to the topology structure, device information and load characteristics includes:

[0033]

[0034] Among them, Y = {0, 1, ..., N} is a time period set, i∈Y represents the time period index; determine the continuous input In each area The funds invested in maintenance It is a finite set of distribution network areas; It is a binary input, indicating whether the network upgrade must be implemented; represents the investment in the region from the initial period to the i∈Y year Funding for local improvements; binary status Indicates whether the line upgrade has been completed before time period i∈Y; ∨ is a logical OR; Represents a set of investment problems.

[0035] From the above description, we can see that the funds used for maintenance and network upgrade decisions in each region are used as inputs, and combined with the investment problem set to describe the changes in regional funds and lines, thereby ensuring that the model can accurately describe the operation and development of the distribution network.

[0036] Furthermore, it also includes determining the investment problem set:

[0037]

[0038]

[0039] Among them, l ij represents the user minute loss value in region j during time period i, where subscript 0 represents the initial loss; ΔL lj The mass growth of region j is represented by ; f is an affine function; represents the function coefficient; For segmented investment funds, The specific value of , the subscript j represents the region, and the superscripts 1, 2, 3, 4 represent the number of segments of the piecewise function; represents the cost of investment l; U max is the maximum amount that can be invested in each i year.

[0040] From the above description, it can be seen that by using the user minute loss values ​​in the corresponding regions at different time periods and linking the quality improvement with the funds invested in each region through an affine function, the relationship between the investment funds and the improvement of grid quality can be predicted more accurately.

[0041] Furthermore, the obtaining of a preset problem matrix and obtaining an optimal investment objective function based on the hybrid model and combining the preset problem matrix include:

[0042]

[0043] Among them, c, f, A, b, W, h, T represent different preset problem matrices; u is the decision variable, including all z is an auxiliary variable.

[0044] From the above description, it can be seen that based on a variety of different preset problem matrices to describe actual problems such as future load demand and market changes during power grid operation, the model can achieve sustainable development of the network and maximum benefits.

[0045] Further, the variables in the optimal investment objective function are adjusted according to the uncertainty function to obtain a mixed integer optimization model;

[0046]

[0047] Among them, f(E i )、T(E i )、W(E i )、h(E i ) represents the functional form related to uncertainty Ei; Q represents the probability quantity of uncertain variables; z i is the auxiliary variable of the i-th uncertain scenario.

[0048] From the above description, we can see that the idea of ​​stochastic programming is used to deal with the uncertainty problem in investment decisions, and the probability distribution function is introduced to analyze practical problems such as future loads and market prices in an uncertain way, so as to more accurately assess risks and adjust investment strategies, thereby improving the reliability and robustness of investment decisions.

[0049] Furthermore, an uncertainty function is constructed according to the uncertainty of distribution network planning:

[0050] Obtaining a random variable, wherein the random variable is associated with uncertainty of a relationship between an investment method and user minute costs;

[0051] Sampling each of the random variables to obtain the number of possible values ​​corresponding to each of the random variables;

[0052] The probability quantity of the uncertain variable is obtained according to the product of the number of possible values ​​corresponding to each of the random variables.

[0053] From the above description, it can be seen that by sampling each random variable to obtain its corresponding number of possible values, and then based on the number of possible values ​​corresponding to each random variable, the probability number of uncertain variables is obtained, thereby covering all possible uncertain scenarios.

[0054] Furthermore, the method of solving the mixed integer optimization model by a greedy algorithm to obtain the optimal investment method and the corresponding optimal profit includes:

[0055] obtaining an optimality index according to a ratio between profit and investment cost, and generating additional investment according to the optimality index;

[0056] In each iteration of the greedy algorithm, the additional investment is added to the current solution and the cost of the additional investment is subtracted from the remaining budget;

[0057] The mixed integer optimization model is solved based on the investment constraints until the solution is no longer optimal.

[0058] From the above description, it can be seen that by adding additional investment to the current solution in each iteration of the greedy algorithm, it is possible to effectively determine whether the current solution is the best solution.

[0059] Furthermore, the investment constraints include:

[0060]

[0061] in, The region index.

[0062] From the above description, it can be seen that by solving for each region j where investment has not yet been determined under the condition of satisfying the investment constraints, the best solution can be obtained.

[0063] Further, the optimality index obtained according to the ratio between profit and investment cost includes:

[0064]

[0065] Among them, I j (k) represents the optimality index; represents the profit of the best investment that can be completed in the jth region in the kth iteration; r is the discount rate.

[0066] From the above description, it can be seen that the optimality index evaluates the profit of the best investment that can be completed in the jth region and the remaining budget after k-1 iterations, so that in each iteration, the current solution and budget are updated by selecting the investment project with the largest optimality index.

[0067] Another embodiment of the present invention provides a distribution network planning device based on mixed integer programming, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, each step in a distribution network planning method based on mixed integer programming as described above is implemented.

[0068] The distribution network planning method and device based on mixed integer programming provided by the present invention can be applied to power grid investment planning scenarios, which are described below through specific implementation methods:

[0069] Embodiment 1

[0070] Please refer to Figure 1 , a distribution network planning method based on mixed integer programming, comprising:

[0071] S1. Obtain the topology, equipment information and load characteristics of the power distribution network;

[0072] S2. Use the investment method as an input parameter and build a hybrid model based on the topology, equipment information and load characteristics. Use the topology, equipment information, load characteristics and other data of the distribution network to establish a mixed integer optimization model that includes variables such as network status, equipment status and cost function. Consider the topological constraints of the network, the operating limitations of the equipment and the impact of investment decisions in the model to ensure that the model can accurately describe the operation and development of the distribution network.

[0073] Specifically, an optimal control problem is first established to determine the optimal allocation of investments in maintenance and upgrades of the electric energy distribution network on a multi-period time basis. The distribution network is treated as a large-scale discrete-time dynamic system with a sampling time of one year (decisions are made on an annual basis), whose state is defined by the quality of energy supply in each region and whose input is the amount of investment in the region in a given year. The optimal investment decision depends on the forecast of the evolution of the network quality for a certain number N of years in the future; according to the rolling horizon principle, the forecast is repeated every year, moving forward one year on a multi-year horizon.

[0074] In each time period i∈Y, Y={0,1,…,N}, determine the continuous input The input is in each area The funds invested in maintenance; and binary input This input determines whether a network upgrade must be implemented (1 if a network upgrade is required, 0 if no upgrade is required). Therefore, the equation that determines the system dynamics is:

[0075]

[0076] in, It is a finite set of distribution network areas; represents the investment in the region from the initial period to the i∈Y year Funds for local improvements; ∨ is logical OR.

[0077] Further, represents a set of investment problems, where the lth investment is defined by the investment cost and the quality increase of each affected area. Binary state Indicates whether the line upgrade has been completed before time period i∈Y (1 means completed, 0 means not completed). The cost of investment l is The mass growth of region j is represented by ΔL lj Indicates; ij It represents the user minute loss value in region j during time period i. Subscript 0 represents the initial loss, which is described in the following form:

[0078]

[0079]

[0080] Here, the affine function f relates the improvement in quality to the amount of money invested in each region, Represents function coefficients (coefficients are constants and have no specific physical meaning; the specific values ​​of the parameters can be determined by fitting the function parameters based on historical data); For segmented investment funds, The subscript j represents the region, and the superscripts 1, 2, 3, and 4 represent the number of segments of the piecewise function.

[0081] The dynamics of each region j are coupled only through the binary upgrading decision, and the other coupling is due to the constraint on the capital that can be invested in each time period i, namely:

[0082]

[0083] U max is the maximum amount that can be invested in each i year.

[0084] S3. Obtain the preset problem matrix, and obtain the optimal investment objective function based on the hybrid model and the preset problem matrix. That is, based on the mixed integer optimization model established in step S2, use the optimal control theory to determine the optimal investment strategy within a multi-period range; by considering factors such as future load demand and market changes, formulate a continuous multi-period investment planning plan to achieve sustainable development and maximum benefits of the network; the optimal control problem that provides the required investment allocation can be redefined as the following optimization problem, and the specific expression is as follows:

[0085]

[0086] Wherein, c, f, A, b, W, h, T represent different preset problem matrices (depending on the actual problem parameters and system model); the model parameters in this embodiment are the parameters of the system itself, which can be determined according to the parameters of the system itself and the parameters set manually; the constraint condition Au≤b in formula (5) corresponds to formula (4); u is the decision variable, including all A is the coefficient, corresponding to the b is a coefficient, corresponding to U in equation (4) max ; The same applies to the remaining parameters. z is an auxiliary variable, which is a virtual decision variable. It can be regarded as having the same function as u, but u has a specific physical meaning. z is just a virtual auxiliary variable set to introduce the previously constructed piecewise function f. It has no actual physical meaning itself, but by introducing the function f, the function f is taken into account in the optimization problem, and the role of evaluation dynamics and cost functions is realized. For example, the affine function f of this embodiment links the improvement of quality with the funds invested in each region. Therefore, in addition to considering the original decision variable u, the optimization problem also considers the meaning represented by f. That is, the optimization variables u and z are subdivided into two categories: "actual" decision variables which constitutes the investment strategy within the forecast horizon; and the auxiliary variable z, introduced to evaluate the dynamics and cost functions.

[0087] S4. Construct an uncertainty function based on the uncertainty of distribution network planning. In actual distribution network planning, a large number of problems in production planning and scheduling, site selection, transportation, finance, and engineering design require that decisions must be made under uncertainty. In the problems dealt with in this embodiment, uncertainty affects the function between supply quality and funds invested in improvement projects. Therefore, this embodiment innovatively adopts the idea of ​​stochastic programming to deal with the uncertainty problem in investment decisions, introduces probability distribution functions to describe uncertainty factors, so as to more accurately assess risks and adjust investment strategies, and improve the reliability and robustness of investment decisions.

[0088] Since the quality increase brought about by an investment project is difficult to predict, the user minute cost in each region is subject to uncertainty, that is, the model (1)-(2) constructed in step S2 is uncertain. Therefore, all variables (and constraints) defined to evaluate the profit are subject to uncertainty, which depends on the quality level in each region and period. Stochastic programming optimizes the mean value of the cost function while taking into account causal relationships, which means that it is necessary to decide how to invest the available funds before knowing the real effect of the investment project, that is, before knowing the values ​​of the random variables and the actual evolution of the user minute cost in the region.

[0089] The construction of uncertainty function specifically includes the following steps: obtaining random variables, which are associated with the uncertainty of the relationship between investment mode and user minute cost; sampling each random variable to obtain the number of possible values ​​corresponding to each random variable; and obtaining the probability number of uncertain variables according to the product of the number of possible values ​​corresponding to each random variable. For example, variable E = {ξ i ,ξ l Collect all random variables associated with the uncertainty of the relationship between improvement / upgrade investment and user minute cost; for each independent continuous distribution ξ i,l Sampling, ξ j,l The random variable is expressed as a general formula. The specific form is determined according to the actual random variable. If there is only one index value, it is expressed as: ξ i or l , if the index value is two, it is represented as ξ i,l The number of possible values ​​q of each uncertain variable determines the complexity of the random optimal control problem; the number q is determined according to the specific physical meaning of the random variable. For example, for equipment upgrade, the variable includes two cases: upgrade and no upgrade, so the number of possible values ​​of the variable is q = 2. Since each variable is independent, the uncertain variable E can be expressed as p1,…,p Q The value is E1,…,E Q , where Q = q D+I , the number of uncertain variable probabilities Q is the combination of different numbers of variables, and therefore is the product of the number of possible values ​​q corresponding to each variable; D is the total number of regions, I is the number of time periods, and the region set and time period set have been defined in the previous text.

[0090] S5. Adjust the variables in the optimal investment objective function according to the uncertainty function to obtain a mixed integer optimization model. Specifically: for each fixed uncertainty E i is called a scene; all problem parameters f(E i )、T(E i )、W(E i )、h(E i ) are all fixed, and the problem parameters refer to the matrix f in formula (5). T, W, and h represent system parameters, but because they contain uncertainties, they are expressed as functions related to the uncertainty Ei. By enumerating all possible Q scenarios, a large-scale mixed integer linear programming (MILP) problem is proposed. For each scenario, a set of auxiliary variables z is assigned. i , but in fact this problem only optimizes a set of decision variables u, and the equivalent MILP problem is described as:

[0091]

[0092] Q represents the probability quantity of uncertain variables; z i is the auxiliary variable for the ith uncertain scenario. After reducing the number of scenarios in each smaller subproblem associated with a single region, the stochastic equivalent of the optimization problem (5) can be solved using the MILP model (6), so that a feasible suboptimal investment policy can be obtained that takes into account the uncertainty of each investment project.

[0093] S6. Solve the mixed integer optimization model through the greedy algorithm to obtain the optimal investment method and the corresponding optimal profit. In view of the computational challenges of complexity problems, a heuristic solution is introduced to improve the efficiency of solution; through the optimization algorithm design and heuristic search strategy, a fast and feasible solution is provided for large-scale problems, and the solution process of multi-period dynamic investment decision is optimized. The heuristic algorithm used in this embodiment is a greedy algorithm. The algorithm first starts with a simple solution, iteratively changes the relevant variables in turn, and selects the variable that achieves the maximum immediate improvement in the objective function each time. In addition, once the value of the variable is fixed, it will not be changed again.

[0094] In each iteration k of the greedy algorithm, the additional investment U is added to the current solution and the cost of U is subtracted from the remaining budget. At the same time, the additional investment U is selected according to the optimality index, which is the ratio between the corresponding profit obtained and the investment cost U. The investments with the optimality index are selected among the local improvement projects and different network upgrades in each district. For each district j where the investment has not yet been determined, the profit obtained and the corresponding optimal investment are obtained by solving (5) / (6) under the following additional constraints:

[0095]

[0096] in, The region index.

[0097] This problem evaluates the profit of the best investment that can be made in the jth region and the remaining budget after k-1 iterations; then, the optimality index I of a given investment option in region j is j (k) is given by:

[0098]

[0099] Among them, I j (k) represents the optimality index; represents the profit of the best investment that can be completed in the jth region in the kth iteration; r is the discount rate.

[0100] The optimization problem is easy to solve because most of the decision variables (i.e., the inputs to the system) are fixed; since the dynamics of each region are independent, the corresponding problem can be solved by considering only the variables and constraints of that region; in each iteration, the investment project with the largest optimality index is selected, and the current solution and budget are updated as:

[0101]

[0102] If there is I j (k) = I * (k), then:

[0103]

[0104] The iterative algorithm is run until the solution is no longer optimal, i.e. J profit (k) = (k-1); the feasible suboptimal solution is Given.

[0105] Embodiment 2

[0106] Please refer to Figure 2 A distribution network planning device based on mixed integer programming includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, each step of a distribution network planning method based on mixed integer programming as described in Example 1 is implemented.

[0107] In summary, the distribution network planning method and device based on mixed integer programming provided by the present invention has the following beneficial effects compared with the traditional investment decision-making method which can only handle single-period static problems and is difficult to fully consider multi-period, dynamic and uncertain factors:

[0108] 1. It combines mixed integer programming, optimal control theory and stochastic programming ideas, and can comprehensively and dynamically formulate multi-period investment strategies, make adjustments under uncertain situations, and consider investment decisions from a holistic and long-term perspective.

[0109] 2. By introducing optimal control theory and heuristic solutions, we can formulate the optimal investment strategy based on multi-period dynamic information, improve the scientificity and robustness of decision-making; at the same time, by dealing with uncertain factors through stochastic programming ideas, we can further improve the risk control ability of investment decisions and ensure the reliability and effectiveness of decisions.

[0110] 3. The introduction of heuristic solutions and optimization algorithms can effectively improve the efficiency of problem solving, shorten decision-making time, and reduce the cost of decision-making; especially for large-scale and complex problems, this technical solution has higher feasibility and practicality, and helps to achieve fast and effective investment decisions.

[0111] 4. Through scientific and comprehensive investment decision support, distribution network managers can better optimize network operation, improve efficiency, and achieve sustainable development of the network. Based on multi-period dynamic investment planning, the distribution network can better adapt to future development needs, ensure the stable operation of the network and maximize profits.

[0112] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent transformations made using the contents of the present invention's specification and drawings, or directly or indirectly applied in related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A distribution network planning method based on mixed integer programming, characterized in that: include: Obtain the topology, equipment information and load characteristics of the power distribution network; Taking the investment method as an input parameter and building a hybrid model according to the topology, equipment information and load characteristics; Obtaining a preset problem matrix, and obtaining an optimal investment objective function based on the hybrid model and combining the preset problem matrix; Construct uncertainty function based on the uncertainty of distribution network planning; Adjust the variables in the optimal investment objective function according to the uncertainty function to obtain a mixed integer optimization model; The mixed integer optimization model is solved by a greedy algorithm to obtain the optimal investment method and the corresponding optimal profit.

2. A distribution network planning method based on mixed integer programming according to claim 1, characterized in that: The method of using the investment method as an input parameter and constructing a hybrid model according to the topology structure, device information and load characteristics includes: Where Y = {0, 1, ..., N} is a time period set, i∈Y represents the time period index; determine the continuous input In each area The funds invested in maintenance It is a finite set of distribution network areas; It is a binary input, indicating whether the network upgrade must be implemented; represents the investment in the region from the initial period to the i∈Y year Funding for local improvements; binary status Indicates whether the line upgrade has been completed before time period i∈Y; ∨ is a logical OR; Represents a set of investment problems.

3. A distribution network planning method based on mixed integer programming according to claim 2, characterized in that: It also includes determining the investment problem set: Among them, l ij represents the user minute loss value in region j during time period i, where subscript 0 represents the initial loss; ΔL lj The mass growth of region j is represented by ; f is an affine function; represents the function coefficient; For segmented investment funds, The specific value of , the subscript j represents the region, and the superscripts 1, 2, 3, 4 represent the number of segments of the piecewise function; represents the cost of investment l; U max is the maximum amount that can be invested in each i year.

4. A distribution network planning method based on mixed integer programming according to claim 2, characterized in that: The step of obtaining a preset problem matrix and obtaining an optimal investment objective function based on the hybrid model and combining the preset problem matrix comprises: Among them, c, f, A, b, W, h, T represent different preset problem matrices; u is the decision variable, including all z is an auxiliary variable.

5. A distribution network planning method based on mixed integer programming according to claim 4, characterized in that: said adjusting the variables in said optimal investment objective function according to said uncertainty function to obtain a mixed integer optimization model; Among them, f(E i )、T(E i )、W(E i )、h(E i ) represents the uncertainty E i The relevant functional form; Q represents the probability quantity of uncertain variables; z i is the auxiliary variable of the i-th uncertain scenario.

6. A distribution network planning method based on mixed integer programming according to claim 5, characterized in that: Construct uncertainty function based on the uncertainty of distribution network planning: Obtaining a random variable, wherein the random variable is associated with uncertainty of a relationship between an investment method and user minute costs; Sampling each of the random variables to obtain the number of possible values ​​corresponding to each of the random variables; The probability quantity of the uncertain variable is obtained according to the product of the number of possible values ​​corresponding to each of the random variables.

7. A distribution network planning method based on mixed integer programming according to claim 2, characterized in that: Solving the mixed integer optimization model by a greedy algorithm to obtain the optimal investment method and the corresponding optimal profit includes: obtaining an optimality index according to a ratio between profit and investment cost, and generating additional investment according to the optimality index; In each iteration of the greedy algorithm, the additional investment is added to the current solution and the cost of the additional investment is subtracted from the remaining budget; The mixed integer optimization model is solved based on the investment constraints until the solution is no longer optimal.

8. A distribution network planning method based on mixed integer programming according to claim 7, characterized in that: The investment constraints include: in, The region index.

9. A distribution network planning method based on mixed integer programming according to claim 7, characterized in that: The optimality index obtained according to the ratio between profit and investment cost includes: Among them, I j (k) represents the optimality index; represents the profit of the best investment that can be completed in the jth region in the kth iteration; r is the discount rate.

10. A distribution network planning device based on mixed integer programming, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, each step in the distribution network planning method based on mixed integer programming as described in any one of claims 1 to 9 is implemented.

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