An optimization model for distribution network planning and design under a comprehensive energy consumption system

Abstract

The present invention provides a distribution network planning and design optimization model under a comprehensive energy consumption system, including an acquisition module for acquiring planning information; an establishment module for establishing a source-grid-load-storage collaborative planning model according to the planning information; an access module for applying random access schemes of various types of energy and random access schemes of various loads to the target distribution network, and recording access data; an evaluation module for evaluating the comprehensive energy consumption index of the target distribution network under fault conditions according to energy access data, load access data, and planning information; wherein the comprehensive energy consumption index is determined by energy complementarity, energy utilization efficiency, transmission quantity matching, and load power supply reliability index; an optimization module for optimizing the model according to the comprehensive energy consumption index to obtain a target planning scheme. By calculating the comprehensive energy consumption index, the planning of the target distribution network is further optimized on the basis of minimizing the cost, ensuring that the distribution network planning can achieve the collaborative optimization of various energy sources.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid planning, and in particular relates to a distribution network planning and design optimization model under a comprehensive energy consumption system. Background Art

[0002] With the continuous growth of social economy, the demand for electricity in various industries continues to rise. In order to meet this demand, the construction of a modern power system with ultra-high voltage transmission, long-distance power transmission and large-capacity generators as the core has become an inevitable choice for development. Such a power system can transmit electricity more efficiently and meet the growing energy demand.

[0003] The comprehensive energy system is an integrated energy system that integrates multiple energy resources such as coal, oil, natural gas, electricity, and thermal energy within a certain area. This system achieves effective coordination between multiple heterogeneous energy subsystems through coordinated planning and optimized operation, thereby improving energy utilization efficiency and promoting sustainable energy development while meeting the diversified energy needs within the system.

[0004] Under the comprehensive energy system, distribution network planning is required to achieve the coordinated optimization of multiple energy sources and improve energy utilization efficiency. Therefore, the traditional distribution network planning method cannot meet the requirements of the distribution network under the comprehensive energy system. Summary of the invention

[0005] In view of this, the present invention provides a distribution network planning and design optimization model under an integrated energy consumption system, aiming to solve the problem that the existing traditional distribution network planning method cannot meet the requirements of the distribution network under the integrated energy consumption system.

[0006] A first aspect of an embodiment of the present invention provides a distribution network planning and design optimization model under a comprehensive energy consumption system, including:

[0007] An acquisition module is used to obtain planning information of the target distribution network;

[0008] Establish a module for establishing a source-grid-load-storage collaborative planning model based on planning information;

[0009] An access module, used to apply a random access scheme of multiple types of energy to the target distribution network, and at the same time apply a random access scheme of multiple loads to the target distribution network, and record energy access data and load access data;

[0010] An evaluation module is used to evaluate the comprehensive energy consumption index of the target distribution network under fault conditions based on energy access data, load access data, and planning information of the target distribution network; wherein the comprehensive energy consumption index is determined by energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability index;

[0011] The optimization module is used to optimize the source-grid-load-storage collaborative planning model according to the comprehensive energy consumption indicators to obtain the target planning scheme for the target distribution network.

[0012] In a possible implementation, a module is established for:

[0013] A source-grid-load-storage collaborative planning model is established with the goal of minimizing power generation costs, investment costs, operation and maintenance costs, and carbon emission costs.

[0014] In a possible implementation, the objective function of the source-grid-load-storage collaborative planning model is:

[0015] minF=f1+f2+f3+f4+f5

[0016] Among them, F is the total cost, f1 is the power generation cost, f2 is the investment cost, f3 is the operation and maintenance cost, f4 is the carbon emission cost, which is determined by the operation of the carbon capture equipment and the impact of changes in comprehensive energy consumption indicators on carbon emissions, and f5 is the additional energy cost, which is determined by the impact of comprehensive energy consumption indicators on the operation of the power grid.

[0017] In a possible implementation, the access module is further configured to:

[0018] Obtain the expected access information of the target distribution network;

[0019] Formulate multiple random adjustment plans, wherein each random adjustment plan is used for energy access adjustment and load access adjustment; energy access adjustment is used to add / reduce any number and type of energy; load access adjustment is used to add / reduce any number and type of load;

[0020] According to the random adjustment scheme, the expected access information is adjusted to obtain a variety of random access schemes for energy sources and a variety of random access schemes for loads.

[0021] In a possible implementation, the evaluation module is used to:

[0022] Calculate energy complementarity based on energy access data;

[0023] Calculate energy utilization efficiency and transmission matching degree based on energy access data and load access data;

[0024] Calculate the load power supply reliability index based on the load access data and the planning information of the target distribution network;

[0025] The comprehensive energy consumption index is determined based on energy complementarity, energy utilization efficiency, transmission volume matching, and load power supply reliability indicators.

[0026] In a possible implementation, the evaluation module is specifically configured to:

[0027] Obtain the output time series of each type of connected energy;

[0028] The Pearson correlation coefficient of the output time series of each type of energy is calculated, and the energy complementarity is calculated based on the Pearson correlation coefficient.

[0029] In a possible implementation, the evaluation module is specifically configured to:

[0030] Determine the total energy generation and generation change curve based on the energy access data, and determine the total load power consumption and power consumption change curve based on the load access data;

[0031] The difference between total energy generation and total load power consumption is divided by total energy generation to obtain energy utilization efficiency;

[0032] The root mean square error between the power generation change curve and the power consumption change curve is calculated and used as the transmission capacity matching degree.

[0033] In a possible implementation, the evaluation module is specifically configured to:

[0034] According to the load access data, determine the first-class load, the second-class load and the third-class load;

[0035] Determine the power supply structure of various loads based on planning information;

[0036] Determine the reliability score of each type of load according to its power supply structure;

[0037] The reliability score of each type of load is multiplied by the load amount of each type of load, then the sum is divided by the total load amount to obtain the power supply reliability index.

[0038] In a possible implementation, the evaluation module is specifically configured to:

[0039] After normalizing the energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability indicators, they are weighted and summed to obtain the comprehensive energy consumption indicators of the source-grid-load-storage collaborative planning model under fault conditions.

[0040] In a possible implementation, the optimization module is used to:

[0041] With the goal of minimizing power generation cost, investment cost, operation and maintenance cost and maximizing comprehensive energy consumption indicators, a multi-objective particle swarm algorithm is used to optimize the source-grid-load-storage collaborative planning model to obtain the target planning scheme for the target distribution network.

[0042] The distribution network planning and design optimization model under the comprehensive energy consumption system provided by the embodiment of the present invention includes an acquisition module for acquiring the planning information of the target distribution network; an establishment module for establishing a source-grid-load-storage collaborative planning model according to the planning information; an access module for applying random access schemes of various types of energy to the target distribution network, and applying random access schemes of various loads to the target distribution network, and recording energy access data and load access data; an evaluation module for evaluating the comprehensive energy consumption index of the target distribution network under fault conditions according to energy access data, load access data, and planning information of the target distribution network; wherein the comprehensive energy consumption index is determined by energy complementarity, energy utilization efficiency, transmission matching, and load power supply reliability index; an optimization module for optimizing the source-grid-load-storage collaborative planning model according to the comprehensive energy consumption index to obtain the target planning scheme of the target distribution network. The comprehensive energy consumption index is comprehensively calculated through energy complementarity, energy utilization efficiency, transmission matching, and load power supply reliability index. On the basis of minimizing costs, the planning of the target distribution network is further optimized based on the comprehensive energy consumption index to ensure that the distribution network planning can achieve the coordinated optimization of multiple energy sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 It is a structural diagram of a distribution network planning and design optimization model under a comprehensive energy consumption system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0046] Figure 1 Schematic diagram of the structure of the distribution network planning and design optimization model under the comprehensive energy consumption system provided by the embodiment of the present invention. Figure 1 As shown, in some embodiments, the distribution network planning and design optimization model under the comprehensive energy consumption system includes:

[0047] An acquisition module 110 is used to acquire planning information of a target distribution network;

[0048] Establishing module 120, for establishing a source-grid-load-storage collaborative planning model according to the planning information;

[0049] An access module 130, used to apply a random access scheme of multiple types of energy to the target distribution network, and apply a random access scheme of multiple loads to the target distribution network, and record energy access data and load access data;

[0050] An evaluation module 140 is used to evaluate the comprehensive energy consumption index of the target distribution network under fault conditions according to the energy access data, the load access data, and the planning information of the target distribution network; wherein the comprehensive energy consumption index is determined by the energy complementarity, the energy utilization efficiency, the transmission quantity matching degree, and the power supply reliability index of the load;

[0051] The optimization module 150 is used to optimize the source-grid-load-storage collaborative planning model according to the comprehensive energy consumption index to obtain a target planning scheme for the target distribution network.

[0052] In an embodiment of the present invention, the planning information of the target distribution network includes cost information and node information; wherein the cost information includes power generation cost, investment cost, and operation and maintenance cost. The power generation cost can be obtained by multiplying the output data of each generator set by the cost coefficient corresponding to each type of generator set. The investment cost can be obtained by adding the unit construction cost, unit transformation cost, energy storage construction cost, and line construction cost. The sum of the unit start-up and shutdown cost and the energy storage maintenance cost is taken as the operation and maintenance cost. The above costs can be simply calculated based on the planning of the distribution network such as the information of the generator set, and will not be described here.

[0053] Establishing module 120, for establishing a source-grid-load-storage collaborative planning model according to the planning information;

[0054] In some embodiments, a module 120 is established to establish a source-grid-load-storage collaborative planning model with the goal of minimizing power generation costs, investment costs, and operation and maintenance costs.

[0055] In some embodiments, the objective function of the source-grid-load-storage collaborative planning model is:

[0056] minF=f1+f2+f3+f4

[0057] Among them, F is the total cost, f1 is the power generation cost, f2 is the investment cost, f3 is the operation and maintenance cost, f4 is the carbon emission cost, which is determined by the operation of the carbon capture equipment and the impact of changes in comprehensive energy consumption indicators on carbon emissions, and f5 is the additional energy cost, which is determined by the impact of comprehensive energy consumption indicators on the operation of the power grid.

[0058] Comprehensive energy use can optimize the energy structure, improve the reliability and flexibility of energy supply, and fill the valleys in some periods by integrating multiple energy resources, including renewable energy and traditional energy. This helps reduce the grid's dependence on a single energy source, reduces the risk of energy price fluctuations, and thus reduces the overall grid cost. In addition, some distributed energy sources are often set on the load side, which helps reduce the power loss caused by long-distance transmission.

[0059] Therefore, the additional energy cost of the present invention refers to the amount of savings in the cost of the power grid caused by the comprehensive energy consumption, which is specifically a negative value. The better the effect of the comprehensive energy consumption, the higher the amount of cost reduction for the power grid, and the smaller the negative number. The specific impact coefficient of the comprehensive energy cost on the power grid can be determined by simulation experiments. After calculating the comprehensive energy consumption index, the comprehensive energy consumption index is multiplied by the impact coefficient to obtain the additional energy cost.

[0060] In addition, in order to reduce greenhouse gas emissions, some carbon capture equipment is usually introduced during the grid planning process. For each unit of carbon captured by the carbon capture equipment, a fixed amount of electricity will be consumed, which can be regarded as an additional operating cost. The carbon capture equipment installed in the grid is usually fixed, so f4 is also a fixed value. However, after the introduction of the comprehensive energy system into the grid, due to the optimization of the energy structure and the application of renewable energy, the number of carbon capture equipment required by the grid is reduced, and f4 will also be reduced accordingly. The specific reduction coefficient of carbon emissions by the comprehensive energy cost can be determined by simulation experiments. After calculating the comprehensive energy index, the comprehensive energy index is multiplied by the reduction coefficient, which is the carbon emission reduction of the comprehensive energy system, and then divided by the carbon emission reduction of each carbon capture equipment, which is the number of carbon capture equipment that can be reduced, multiplied by the sum of the equipment unit price and the equipment operation and maintenance cost, which is the cost reduction of f4.

[0061] In the embodiment of the present invention, on the basis of the traditional cost minimization model, the consideration of comprehensive energy consumption index is introduced to calculate the minimum cost under the access of multiple energy sources.

[0062] The access module 130 is used to apply a random access scheme to the target distribution network and evaluate the comprehensive energy consumption index of the target distribution network under fault conditions; wherein the comprehensive energy consumption index is determined by energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability index.

[0063] In the embodiment of the present invention, the operation of the distribution network has certain timing characteristics, so the same access scheme can be formulated as the expected access information based on some existing data, or the access data of the load and energy of the distribution network of the same type and scale. However, there are certain differences between the actual operation and the theory, so the access scheme is randomly added / reduced to the access scheme with different quantities and types of energy / load access as a random adjustment scheme, and the expected access information is adjusted to obtain multiple random access schemes, and the comprehensive energy consumption index of the target distribution network under fault is evaluated.

[0064] In some embodiments, a random access scheme is applied to the target distribution network to evaluate the comprehensive energy consumption index of the target distribution network under fault conditions, including: applying multiple types of energy access to the target distribution network and applying multiple load access to the target distribution network at the same time; and evaluating the comprehensive energy consumption index of the target distribution network under fault conditions based on energy access data, load access data, and planning information of the target distribution network.

[0065] In some embodiments, based on energy access data, load access data, and planning information of the target distribution network, the comprehensive energy consumption index of the target distribution network under fault conditions is evaluated, including: calculating energy complementarity based on the energy access data; calculating energy utilization efficiency and transmission volume matching based on the energy access data and load access data; calculating load power supply reliability index based on load access data and planning information of the target distribution network; determining the comprehensive energy consumption index based on energy complementarity, energy utilization efficiency, transmission volume matching, and load power supply reliability index.

[0066] In some embodiments, energy complementarity is calculated based on energy access data, including: obtaining output time series of each type of access energy; calculating the Pearson correlation coefficient of the output time series of each type of energy, and calculating the energy complementarity based on the Pearson correlation coefficient.

[0067] In the embodiment of the present invention, energy complementarity refers to the degree to which different types of energy complement each other and develop in a coordinated manner. In an energy system, various energy sources have different characteristics and limitations. Through complementarity, each energy source can take advantage of its own strengths, make up for its deficiencies, and improve the overall efficiency and reliability of the energy system.

[0068] In the embodiment of the present invention, the output of the connected energy is made into an output sequence. If there is no output within a certain time, the value of the time point in the sequence is 0. Then, the Pearson correlation coefficient of the output time series of each type of energy is calculated. The Pearson correlation coefficient can evaluate the complementarity between the output time series.

[0069] If the value calculated by the Pearson correlation coefficient of the output time series is between [0, 1], the output time series of the two types of energy are completely non-complementary, and the energy complementarity is 0. If the value calculated by the Pearson correlation coefficient of the output time series is between [-1, 0], the output time series of the two types of energy are complementary, and the energy complementarity is the opposite of the Pearson correlation coefficient. When the Pearson correlation coefficient is -1, the output time series of the two types of energy are completely complementary, and the energy complementarity is 1.

[0070] In some embodiments, energy utilization efficiency and transmission power matching are calculated based on energy access data and load access data, including: determining the total energy power generation and the power generation change curve based on the energy access data, and determining the total load power consumption and the power consumption change curve based on the load access data; dividing the difference between the total energy power generation and the total load power consumption by the total energy power generation to obtain the energy utilization efficiency; calculating the root mean square error of the power generation change curve and the power consumption change curve, and using it as the transmission power matching.

[0071] In the embodiment of the present invention, the energy utilization efficiency of the distribution network will be different due to differences in energy conversion, transmission mode and distance, wind and solar power abandonment, etc. Therefore, the present invention extracts the information of the connected energy, determines the power generation during the connection process, and subtracts it from the total load power consumption in this process, which is the energy consumption due to non-load reasons, and then divides it by the total energy power generation to get the energy utilization efficiency.

[0072] In the embodiment of the present invention, the transmission capacity matching degree refers to the matching degree between the power generation and the load power consumption. The root mean square error between the two curves is calculated and used as the transmission capacity matching degree.

[0073] In some embodiments, the power supply reliability index of the load is calculated based on the load access data and the planning information of the target distribution network, including: determining Class I load, Class II load and Class III load based on the load access data; determining the power supply structure of each type of load based on the planning information; determining the reliability score value of each type of load based on the power supply structure of each type of load; multiplying the reliability score value of each type of load by the load amount of each type of load, adding the results, and then dividing by the total load amount to obtain the power supply reliability index.

[0074] In an embodiment of the present invention, different types of loads have different requirements for power supply reliability. For example, Class I loads require at least two independent power supplies, Class II loads require at least dual-circuit power supply, and Class III loads can be powered in a common manner. According to the power supply structure of each type of load in the planning information, determine whether it meets the power supply demand. If it meets the demand, the reliability score is 1, and if it does not meet the demand, it is defined as 0. The reliability score of each type of load is multiplied by the load of each type of load, added, and then divided by the total load to obtain the power supply reliability index.

[0075] In some embodiments, a comprehensive energy consumption index is determined based on energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability index, including: normalizing the energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability index, and performing weighted summation to obtain a comprehensive energy consumption index of the source-grid-load-storage collaborative planning model under fault conditions.

[0076] In the embodiment of the present invention, the weights of various comprehensive energy consumption indicators are preset values ​​and are not limited here.

[0077] The evaluation module 140 is used to optimize the source-grid-load-storage collaborative planning model according to the comprehensive energy consumption index to obtain a target planning scheme for the target distribution network.

[0078] In some embodiments, the evaluation module 140 is used to: optimize the source-grid-load-storage collaborative planning model using a multi-objective particle swarm algorithm with the goal of minimizing power generation costs, investment costs, operation and maintenance costs and maximizing comprehensive energy consumption indicators, and obtain a target planning scheme for the target distribution network.

[0079] In the embodiment of the present invention, the multi-objective particle swarm algorithm is an optimization algorithm based on swarm intelligence, which is used to solve problems with multiple optimization objectives. The steps are as follows:

[0080] Initialize particle swarm: Randomly initialize the positions and velocities of a group of particles. Each particle represents a potential solution to the problem.

[0081] Individual best and global best: Assign an initial individual best position (pbest) and a global best position (gbest) to each particle.

[0082] External Archive: Initialize an external archive to store non-dominated solutions found during the execution of the algorithm.

[0083] Iterative optimization: Update particle speed and position: Update the particle speed and position according to the particle's current speed, individual best position and global best position.

[0084] Update individual best: Compare the new position of each particle with its individual best position, and update the individual best if the new position is better.

[0085] Update the external archive: Add newly discovered non-dominated solutions to the external archive while maintaining the size of the archive according to some mechanism (such as crowding distance).

[0086] Select Global Best: Select a global best position for each particle from an external archive. This can be done in a variety of ways, such as random selection, selection based on crowding distance, etc.

[0087] Termination condition check: Check whether the termination condition is met, such as reaching the maximum number of iterations, the quality of the solution meets the requirements, etc. If the condition is met, the algorithm ends; otherwise, it returns to the iterative optimization step.

[0088] Output results: After the algorithm is completed, a set of approximate non-dominated solutions to the multi-objective optimization problem is saved in the external archive. These solutions can be provided to decision makers for further analysis and decision-making.

[0089] In the iterative optimization process of the multi-objective particle swarm of the present invention, the cost and the comprehensive energy consumption system will affect each other. First, the source-grid-load-storage collaborative planning model with the minimum cost without applying faults is calculated, and then a random access scheme is applied to evaluate the comprehensive energy consumption index of the distribution network under the model when energy and load are randomly accessed. The particle swarm algorithm selects the optimization result with the highest security to obtain a new distribution network planning scheme. On the basis of this scheme, the cost minimization calculation is performed again to obtain a new optimization result. The above process is continuously iterated through the particle swarm algorithm, and finally the obtained result takes into account both low cost and high matching degree of the comprehensive energy consumption system.

[0090] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0091] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0092] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0093] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0096] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A distribution network planning and design optimization model under a comprehensive energy consumption system, characterized in that: include: An acquisition module is used to obtain planning information of the target distribution network; Establishing a module for establishing a source-grid-load-storage collaborative planning model according to the planning information; An access module, used to apply a random access scheme of multiple types of energy to the target distribution network, and at the same time apply a random access scheme of multiple loads to the target distribution network, and record energy access data and load access data; An evaluation module is used to evaluate the comprehensive energy consumption index of the target distribution network under fault conditions according to energy access data, load access data, and planning information of the target distribution network; wherein the comprehensive energy consumption index is determined by energy complementarity, energy utilization efficiency, transmission capacity matching, and load power supply reliability index; An optimization module, used to optimize the source-grid-load-storage collaborative planning model according to the comprehensive energy consumption index to obtain a target planning scheme for the target distribution network; Create modules for: With the goal of minimizing power generation costs, investment costs, operation and maintenance costs, and carbon emission costs, a source-grid-load-storage collaborative planning model is established; The objective function of the source-grid-load-storage collaborative planning model is: minF=f1+f2+f3+f4+f5 Among them, F is the total cost, f1 is the power generation cost, f2 is the investment cost, f3 is the operation and maintenance cost, f4 is the carbon emission cost, which is determined by the operation of the carbon capture equipment and the impact of the change of the comprehensive energy consumption index on carbon emissions, and f5 is the additional energy cost, which is determined by the impact of the comprehensive energy consumption index on the operation of the power grid; The access module is also used for: Obtain the expected access information of the target distribution network; Formulate multiple random adjustment schemes, wherein each random adjustment scheme is used for energy access adjustment and load access adjustment; the energy access adjustment is used to add / reduce any number and type of energy; the load access adjustment is used to add / reduce any number and type of load; According to the random adjustment scheme, the expected access information is adjusted to obtain a variety of random access schemes for energy sources and a variety of random access schemes for loads; Evaluation modules for: Calculating the energy complementarity according to the energy access data; Calculating energy utilization efficiency and power transmission matching degree according to the energy access data and the load access data; Calculate the load power supply reliability index based on the load access data and the planning information of the target distribution network; A comprehensive energy consumption index is determined based on the energy complementarity, the energy utilization efficiency, the transmission volume matching degree, and the power supply reliability index of the load.

2. The distribution network planning and design optimization model under the comprehensive energy consumption system according to claim 1 is characterized in that: Evaluation modules specifically for: Obtain the output time series of each type of connected energy; The Pearson correlation coefficients of the output time series of various energy sources are calculated, and the energy complementarity is calculated based on the Pearson correlation coefficients.

3. The distribution network planning and design optimization model under the comprehensive energy consumption system according to claim 1 is characterized in that: Evaluation modules specifically for: Determine the total energy power generation and power generation change curve according to the energy access data, and determine the total load power consumption and power consumption change curve according to the load access data; The difference between the total energy generation and the total load power consumption is divided by the total energy generation to obtain the energy utilization efficiency; The root mean square error between the power generation variation curve and the power consumption variation curve is calculated and used as the power transmission matching degree.

4. The distribution network planning and design optimization model under the comprehensive energy consumption system according to claim 1 is characterized in that: Evaluation modules specifically for: Determine a first-class load, a second-class load, and a third-class load according to the load access data; Determine the power supply structure of various loads according to the planning information; Determine the reliability score of each type of load according to its power supply structure; The reliability score of each type of load is multiplied by the load amount of each type of load, then the sum is divided by the total load amount to obtain the power supply reliability index.

5. The distribution network planning and design optimization model under the comprehensive energy consumption system according to claim 2 is characterized in that: Evaluation modules specifically for: The energy complementarity, the energy utilization efficiency, the transmission capacity matching degree, and the power supply reliability index of the load are normalized and weighted summed to obtain a comprehensive energy consumption index of the source-grid-load-storage collaborative planning model under fault conditions.

6. The distribution network planning and design optimization model under the comprehensive energy consumption system according to claim 1 is characterized in that: Optimization modules for: With the goal of minimizing power generation cost, investment cost, operation and maintenance cost and maximizing comprehensive energy consumption index, a multi-objective particle swarm algorithm is used to optimize the source-grid-load-storage collaborative planning model to obtain the target planning scheme of the target distribution network.

Images