A station-grid collaborative optimization method for regional distributed energy systems considering load point access sequence

Abstract

The application discloses a district distributed energy system (DDES) station-network collaborative optimization technology considering load point access order. According to regional planning data of the research object, the energy distance method is combined with the K-means clustering method to solve the site selection and scale of the energy station. A pipe planning algorithm based on the shortest path method (DA) and the genetic algorithm (GA) is adopted to solve the simultaneous optimization problem of pipe layout and pipe diameter. On this basis, the coupling of the energy station and the pipe network is analyzed, and an energy station equipment capacity configuration optimization model is established, taking the annualized construction cost and operation cost of the equipment as the target, and establishing mathematical models of the internal combustion engine, the auxiliary boiler, the electric refrigerator unit and the absorption refrigerator unit. The equipment capacity configuration of the energy station is obtained by solving the mixed integer linear programming problem, realizing the DDES station-network collaborative optimization design, and improving the economy and technology of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed energy system station-network collaborative optimization, in particular to a regional distributed energy system station-network collaborative optimization method considering load point access sequence. BACKGROUND

[0002] In the face of the current global energy crisis and climate warming, renewable energy is considered an important solution to alleviate environmental problems due to its inexhaustible characteristics. Due to the volatility and randomness of renewable energy generation, its large-scale integration into the public power grid poses a challenge to the stability of the power grid. DDES can make full use of local resources and be customized according to the actual characteristics of users, thereby achieving better economic and environmental benefits. Therefore, the development of DDES is one of the important ways to promote energy structure transformation, achieve energy efficient use, and promote renewable energy consumption.

[0003] The optimization design of DDES mainly includes the determination of the location and number of energy stations, the optimization of pipe network layout and pipe diameter, and the optimization of equipment capacity configuration. However, due to its high complexity and the necessity of integrating various factors including energy stations, pipe networks, and user-side loads, the design of DDES faces many major challenges. Energy stations provide energy to users according to their load demand. A reasonable energy station scheme considering location and layout can reduce initial investment costs and improve equipment utilization efficiency. In addition, the energy station scheme will also affect the topology and diameter of the pipe network. Therefore, the energy station and the pipe network in DDES are mutually coupled and influence each other, making collaborative optimization design difficult. As a result, in actual engineering, DDES does not achieve the expected economic efficiency, and problems such as poor efficiency or forced shutdown occur.

[0004] Currently, most research on pipe topology optimization layout uses the shortest path method, but this method has certain shortcomings. DA is a point-to-point method, and the results will be affected by the sequence of energy-consuming buildings in the calculation sequence, and it is also difficult to consider the characteristics of pipe sharing, which will make it difficult to obtain the optimal solution. The integration of regional distributed energy system stations, networks, and loads mainly from two main aspects: energy stations and pipe network systems. Energy stations include: the number, location, and equipment capacity configuration of energy stations; pipe network systems include pipe network layout and pipe diameter selection. SUMMARY

[0005] The present application provides a regional distributed energy system station-network collaborative optimization method considering load point access sequence, which aims to solve the problems of not considering load point access sequence and difficult station-network collaborative optimization design in the prior art, and to realize DDES collaborative optimization design considering load point access sequence. The present application considers the load point access sequence, analyzes the coupling mechanism of DDES energy stations and pipe networks, and establishes a DDES station-network collaborative optimization model.

[0006] In order to achieve the above object, the present application adopts the following technical scheme, and the steps are as follows:

[0007] Step 1: Collecting the geographic information data of the planning area, the cold and heat and power load, and counting various parameter information in the model building;

[0008] Step 2: Preprocessing the collected geographic information data and cold and heat and power load;

[0009] Step 3: Establishing an energy station, pipe network, and station-network collaborative planning target model, and analyzing the coupling mechanism of the energy station and the pipe network;

[0010] Step 4: Determining the initial station site of the energy station based on K-means clustering analysis, and optimizing the energy station site selection by using the energy relative distance method;

[0011] Step 5: Encoding and optimizing the access sequence of the load point by using GA;

[0012] Step 6: Determining the corresponding energy station of each load point by using the graph theory algorithm, and simultaneously determining the corresponding path and various pipe section flow, flow velocity, and pipe diameter selection information based on the greedy strategy;

[0013] Step 7: Traversing all the load points and calculating the annualized cost of the pipe network system based on the pipe network planning model;

[0014] Step 8: After the pipe network layout is determined, the energy supply range of the corresponding energy station is determined, the cold and heat and power load demand is solved according to the energy supply range of each energy station, the equipment capacity of the corresponding energy station is calculated, and the annualized cost of the energy station is obtained;

[0015] Step 9: Determining whether the number of energy stations reaches the upper limit, selecting the most economical number of energy stations, and outputting the corresponding energy station site, energy station capacity configuration, pipe network layout, and each pipe section pipe diameter.

[0016] In step 1, the collected regional geographic information data includes load point position, road node, road intermediate node, etc. Various parameter information is mainly related to the establishment of energy station and pipe network planning model, including unit investment, energy conversion efficiency, and other related parameters of internal combustion engine, auxiliary boiler, electric refrigeration, absorption refrigeration, etc.

[0017] In step 2, the data preprocessing work includes the following steps:

[0018] Step 2.1: Establishing a planning area schematic diagram according to the load points and road nodes of the region, which needs to consider the pipe network along the road and the connection between the pipe network and the load points.

[0019] Step 2.2: Analyze the distribution and size of the cold and heat power load in the planning area to determine the pipe flow and pipe diameter.

[0020] In step 3, the energy station, pipe network, and station-network collaborative planning model are established to analyze the influencing factors of each unit planning. The planning target model of the energy station includes the establishment of the energy station site selection and energy station equipment capacity configuration model; the planning target model of the pipe network includes the construction cost of the pipe network system, the annual operation cost of the circulating water pump, the energy loss cost of the pipe network system, and the depreciation and maintenance cost of the pipe network system.

[0021] Specifically, the following steps are included:

[0022] Step 3.1: The economy of the pipe network layout determines the site selection of the corresponding energy station, so the target model of the energy station site selection is unified with the target function of the pipe network system, and the target model of the pipe network system needs to be established first.

[0023] Step 3.2: The capacity configuration target model of the energy station equipment mainly includes investment and operation cost. The equipment of the energy station mainly includes: internal combustion engine, auxiliary boiler, absorption refrigeration unit and electric refrigeration unit. Based on the parameter statistics in step 1, the capacity configuration model of different equipment is established.

[0024] Step 3.3: Based on the energy station and pipe network planning target model of step 3.1 and step 3.2, the station-network collaborative optimization target model is established.

[0025] In step 4, the optimization and selection of the energy station site includes the following steps:

[0026] Step 4.1: First, based on K-means, the clustering analysis of each load point is carried out and the initial site of the energy station is determined.

[0027] Step 4.2: Based on the initial site and the energy supply range, the peak load of each load point in the energy supply range and its relative load distance to the corresponding energy station are calculated.

[0028] Step 4.3: The site is re-determined according to the principle of minimum relative load distance.

[0029] Step 4.4: Steps 4.1 to 4.3 are iterated until the energy station site converges.

[0030] The optimization layout of the pipe network system and the pipe diameter optimization include:

[0031] First, the pipe network optimization target model is established, and the selection of the energy station site is an important factor affecting the economy of the pipe network system.

[0032] Secondly, based on the site of the energy station and the energy supply range, the target energy station of each load point is determined. Each load point is coded based on the GA technology, and the calculation order of each load point in the DA is optimized.

[0033] Thirdly, the weight adjacency matrix of each pipe section is initialized, and the weight adjacency matrix of each pipe section is defined as the increase of pipe network cost caused by the new access load point, and is updated constantly in the process of access of the load point.

[0034] Fourthly, based on the DA calculation, the pipeline and pipe diameter of each coded load point to the energy station are optimized, and the flow, flow rate and pipe diameter of each corresponding path are recorded until all the load points are calculated.

[0035] Finally, the final layout of the pipe network system, the pipe diameter of each pipe section and the corresponding annual cost are saved.

[0036] In step 8, after the pipe network layout is determined, the energy supply range is determined, and the annual cost of the energy station is calculated based on the target model of the energy station according to the cold and heat and power load demand of each energy station.

[0037] In step 9, it is judged whether the number of energy stations reaches the upper limit, if not, step 4 is executed, if yes, the total cost of DDES station-network collaborative optimization under each number of energy stations is output, and the optimal number of energy stations, the site of the energy station, the equipment capacity of the energy station, the layout of the pipe network and the pipe diameter of each pipe section under the economic optimization are selected.

[0038] Compared with the prior art, the above at least one technical scheme adopted by the embodiments of the present application can achieve at least the following beneficial effects:

[0039] 1. The improved DA based on GA is proposed, and the optimal access order of the user node is considered when the topology optimization is performed, so that the economy of the pipe network system is improved. The value of the weight adjacency matrix in the pipe network layout is set to the cost difference, instead of the straight-line distance, and the cost difference before and after the pipe section supplies power to the user node is calculated. The improved GA can consider the calculation order of the load point and the influence of the pipe sharing factor at the same time.

[0040] 2. The collaborative iterative optimization framework of the site selection and scale of the energy station and the layout and diameter of the pipe network is proposed, which covers the site selection and number determination of the energy station, the equipment capacity configuration of the energy station, the pipe network layout and the pipe diameter optimization and other aspects in the planning process. DETAILED DESCRIPTION

[0041] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0042] Figure 1 is a specific implementation area schematic diagram of the regional distributed energy system station-network collaborative optimization method considering the load point access sequence provided by the embodiments of the present application.

[0043] Figure 2 is a specific implementation flowchart of the regional distributed energy system station-network collaborative optimization method considering the load point access sequence provided by the embodiments of the present application.

[0044] Figure 3 is the load point cold, heat and electricity comparison data used in the embodiments of the present application.

[0045] Figure 4 is a system structure diagram used in the embodiments of the present application.

[0046] Figure 5 is a specific implementation flowchart of the step S4 energy station provided by the embodiments of the present application.

[0047] Figure 6 is a specific implementation flowchart of the step S7 pipe network provided by the embodiments of the present application.

[0048] Figure 7 is a specific implementation flowchart of the step S9 station-network collaboration provided by the embodiments of the present application.

[0049] Figure 8 (a) is the influence of the system using the traditional serial planning process provided by the embodiments of the present application, Figure 8 (b) is the influence of the system using the station-network collaborative planning process provided by the embodiments of the present application. DETAILED DESCRIPTION

[0050] The embodiments of the present application will be described in detail below with reference to the drawings.

[0051] The following detailed description is presented in order to describe the embodiments of the application and it is not intended that the application be limited thereto. It will be appreciated that modifications to embodiments of the application can occur to persons skilled in the art within the scope of the application. It is the intention, therefore, to be limited only as indicated by the scope of the claims.

[0052] It is to be understood that the embodiments described herein are merely examples of implementations of the application and are not intended to limit the scope of the application in any way. The application can be implemented in any of the ways set forth herein, as well as in other ways known to those skilled in the art. Furthermore, the terminology used herein is for the purpose of describing only the embodiments and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the aspects of the application, as generally described herein, and illustrated in the figures, can be used in combination with any and all other aspects of the application, and that the embodiments of the application not necessarily comprise every combination of these aspects.

[0053] It is also to be understood that the herein-described embodiments are only examples of implementations of the application and that person skilled in the art will be able to derive other embodiments from the description without departing from the scope of the application.

[0054] Furthermore, in the following description, numerous specific details are set forth in order to provide a thorough understanding of the examples. However, it will be recognized by one skilled in the art that embodiments of the application can be practiced without these specific details.

[0055] The technical solutions provided by the embodiments of the application are described below with reference to the drawings.

[0056] The data used in the following embodiments is located in a planning area in Ningbo, Zhejiang Province, China, and the total area of the planning area is 27.61 square kilometers. The relevant latitude and longitude coordinates and node distances of the area are obtained from the open source map system OpenStreet Map, and are edited by JOSM software. The area is abstracted as a road network node graph, as shown in FIG. 1. Figure 1The data in the embodiment is based on the road information and other data to establish the regional distributed energy system station-network collaborative optimization method considering the load point access order proposed in the present application, and the specific implementation process is as shown in Figure 2 .

[0057] The road information data used in the embodiment needs to have a sufficiently complex road network to fully explore the influence of energy station site selection on pipe network layout, and all load point data in the research area is clear, and the positional relationship between load points is clear. In actual planning, since the pipe network needs to be laid along the road and the pipe network needs to be connected with energy-consuming buildings, the intermediate nodes in the road also need to be introduced into the road network node graph. In addition, the region used in the embodiment has a total of 50 load points, which are composed of three types of buildings. In the pipe network layout study, the peak load value is used for target model calculation, therefore, it is necessary to determine the size of the cold, heat and electricity load to determine which pipe diameter is determined by the pipe flow. The comparative data of the load point cold, heat and electricity used in the embodiment is as shown in Figure 3 .

[0058] In addition to the road and load information of the planning area, the data used in the embodiment also needs to set the energy station equipment capacity configuration and the pipe network layout related parameters. The energy station equipment capacity configuration mainly includes: internal combustion engine, auxiliary boiler, electric refrigeration and absorption refrigeration. The equipment parameters include unit capacity investment, energy conversion efficiency, and full life cycle of the equipment. The fixed value economic parameters include: gas price, local time-of-use electricity price, interest rate and low heat value of gas. In the pipe network layout, two parts of pipe line direction and pipe diameter selection are mainly considered, and the parameter setting of the pipe network includes: the pipe price corresponding to each pipe diameter and the case simulation parameters. The case simulation parameters include: the density of the fluid in the pipe, the working time of the circulating water pump, the local resistance coefficient, the equipment loss rate and the performance coefficient. Based on the parameter setting, the DDES energy station and the pipe network and the collaborative optimization planning target model are established. The specific system structure is as shown in Figure 4 .

[0059] According to the planning area information used in the embodiment, the number of energy stations is determined to be in the range of 1-16. Based on the energy station planning target model, the energy station planning is solved, and the specific implementation process is as shown in Figure 5 .

[0060] Firstly, the K-means clustering is used to determine the initial station address of the energy station. The K-means algorithm is the most widely used clustering method at present, each category has a clustering center, which is the average value of all objects in the region, and the clustering center is used as the initial station address of the energy station. Among them, the K-means clustering analysis is based on the distance between the load points as the feature.

[0061] However, directly using distance as a feature cannot consider the load characteristics of different energy consumption points. The site selection of energy stations is the basis for subsequent pipe network layout optimization while ensuring a reasonable energy supply range. The pipe network layout is related to the load characteristics of energy consumption points. Therefore, the site selection of energy stations needs to consider the influence of load characteristics in addition to the distance of each load point.

[0062] When performing site selection of energy stations, a more suitable energy station position can be obtained by changing the objective function of K-means clustering. The relative load distance is the product of the peak load of the load point and the distance. The K-means clustering is combined with the relative energy distance method, and iterated to a more optimal energy station position. This method can well consider the load distribution in the planning area, but cannot consider the influence of pipe network economy on energy station site selection. Therefore, after the energy station site is determined, the energy supply range of the energy station is re-determined based on the pipe network economy, and the new energy station site is re-determined based on the relative load distance method, and the iteration is repeatedly repeated until the energy supply range of the energy station and the energy station position converge.

[0063] On the basis of the energy station site and the energy supply range, the pipe network layout planning is solved based on the pipe network planning target model, and the specific implementation process is as shown in Figure 6

[0064] First, GA is used to optimize the calculation order of the load point. GA algorithm is a point-to-point algorithm, which can only calculate the path of each load point to the energy station one by one in the pipe network layout optimization, so the influence of the calculation order of each load point on the pipe network layout needs to be considered. GA is one of the most classic problems in graph theory research, and the purpose is to find the shortest path between two nodes in a graph. In this embodiment, the shortest path between each load point and the energy station is found. The energy station position information and the GA control parameters are used to encode the calculation order of the load point, and the annual cost of the pipe network system is used as the fitness function to optimize the calculation order of the load point. Finally, the optimized calculation order of the load point is obtained.

[0065] Secondly, GA is a point-to-point calculation, which is performed independently each time and is not affected by the previous and subsequent calculations. Therefore, there is a problem of pipe sharing. The conventional GA usually takes distance as the weight of the adjacency matrix. In this embodiment, the value of the adjacency matrix of GA is set to the difference in cost before and after the pipe segment is used to supply energy to the load point. The energy station with the lowest cost difference is selected as the target energy station of the load point. This method can determine the pipe diameter size while avoiding the generation of pipe sharing.

[0066] ​Finally, all load points are traversed until all load points are input into the pipe network system, and the corresponding pipe network layout and pipe diameter selection are obtained. The pipe network planning objective model in the embodiment is used to obtain the annualized cost of the pipe network system. According to the pipe network layout, the energy supply range of each energy station is obtained, and the K-means and relative load distance methods are used to determine the new energy station site according to the energy supply range of each energy station, and the iteration is continuously performed until convergence to output the optimized energy station position. According to the energy supply range of each energy station, the cold, heat and electricity supply equipment capacity of each energy station is obtained based on the mixed integer linear programming method, and the energy station planning model in the embodiment is used to obtain the annualized cost of the energy station. Finally, the DDES economic efficiency is obtained based on the DDES station-network collaborative planning objective model.

[0067] After the number and position of the energy stations are determined, the pipe network layout is determined, the energy supply range of the energy station is determined, and the equipment capacity of the energy station is determined. However, most of the calculation steps between the energy station and the pipe network are coupled, and the implementation process of the DDES station-network collaborative planning is as shown in Figure 7

[0068] After the annualized cost of the energy station and the pipe network is determined, it is judged whether the number of the energy stations reaches the upper limit. After all the set number of the energy stations is traversed, the number, position, energy supply range, pipe network layout and pipe diameter information of each pipe section of the energy station under the economic efficiency optimal condition are obtained based on the DDES station-network collaborative optimization objective model. To prove the effectiveness of the method, Figure 8 The influence of the collaborative optimization method and the traditional serial method on the DDES is verified. Figure 8 (a) represents that the energy station position and the pipe network system are sequentially planned and designed by using the traditional serial planning process, and the influence of the load point access order on the system is not considered. Figure 8 (b) represents the influence of the DDES station-network collaborative optimization method considering the load point access order in the present application on the system. The results show that the total investment cost of the system planning is reduced by 3.3% compared with the traditional serial technology, and the cost of the pipe network system is reduced by 17%. Figure 8 As can be seen from the above table, more large-diameter pipes are used in the serial technology scheme, which is due to two reasons. One is that the influence of the subsequent pipe network layout is not considered when the energy station site is selected, so that the position of the energy station is not in the center of the load in the energy supply range. The other is that the influence of the load point access order and the pipe sharing characteristic is not considered in the pipe network layout, which causes the unreasonable result. The above results show that the regional distributed energy system station-network collaborative optimization method considering the load point access order provided in the present application has superior effect.

[0069] ​The present application divides the DDES planning and design into three parts, which are energy station site planning, energy station equipment capacity planning and pipe network system planning. Among them, the energy station site planning includes the determination of the number and location of the energy station, and the number of the energy station will affect the cost of the energy station and the subsequent pipe network layout. And the location of the energy station will also cause the influence of the pipe network layout. The pipe network layout is the transmission channel of the energy system, and the pipe diameter will directly affect the cost of the pipe network system. Therefore, the energy station and the pipe network system are input and output parameters of each other, and have strong coupling. Based on the consideration of the collaborative optimization of the energy station and the pipe network, the present application proposes a DA method based on GA, which considers the influence of the access order of the load point on the economy of the pipe network system. And by changing the representation method of the pipe segment right adjacency matrix, the barriers of pipe sharing and pipe diameter optimization are overcome. Thus, the optimal number, location, equipment capacity, pipe network layout and pipe diameter information of the energy station are obtained. The above examples prove that the regional distributed energy system station-network system optimization technology considering the load point calculation order proposed by the present application has high economy. The present application improves the economy of the pipe network system by considering the load point calculation order, and improves the overall economy of the system by considering the coupling of the energy station and the pipe network. Therefore, the regional distributed energy system station-network system optimization technology considering the load point calculation order proposed by the present application has high practical value for the optimization of the energy station and the pipe network of the regional distributed energy system, and provides a feasible scheme for the optimization design of the regional distributed energy system.

[0070] The regional distributed energy system station-network system optimization technology considering the load point calculation order provided by the present application is described in detail above, and the principles and implementation modes of the present application are described by applying specific examples. The above examples are only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed, and the above description should not be understood as a limitation of the present application.

[0071] Each embodiment in the specification is described in a progressive manner, and the same or similar parts of each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, for the product embodiment described later, since it corresponds to the method, the description is relatively simple, and the related parts can be referred to the part of the system embodiment.

[0072] The above is only a specific implementation mode of the present application, but the protection scope of the present application is not limited to this. Any skilled person in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1.A station-grid collaborative optimization method for a regional distributed energy system considering the access order of load points, characterized in that, The collaborative optimization technique specifically comprises the following steps: S1: Collecting geographic information data of a planning area and various parameter information of an energy system, and preprocessing the data; S2: Determining an upper limit of the number of energy stations using the geographic information data of step S1; S3: Establishing an energy station planning objective model using the various parameter information of step S1; S4: Determining initial station sites of the energy stations using a K-means clustering analysis technique, and optimizing the positions of the energy stations in combination with an energy distance technique; S5: Establishing a pipe network planning objective model using the various parameter information of step S1; S6: Encoding and optimizing the calculation sequence of energy-consuming buildings using a GA technique, calculating the path and cost increment of each load point to an energy station using the station sites of step S4 and the pipe network system planning objective model of step S5, obtaining the pipe network layout, pipe diameter of each pipe section, and annualized cost of the pipe network system, and iteratively updating the station sites and energy supply range according to the energy supply range; S7: Determining the energy supply range and cold, heat and electricity supply of each energy station using the pipe network layout of step S6, solving the capacity configuration of the energy station using a mixed integer linear programming technique, and calculating the annualized cost of the energy station using the energy station objective model of step S3; S8: Establishing a station-network collaborative optimization objective model using the various parameter information of step S1; S9: Obtaining the station-network collaborative optimization result of the distributed energy system using the station-network collaborative optimization model of step S8, determining whether the number of energy stations reaches the upper limit, returning to step S2 if not, and outputting the optimal number of energy stations, site selection, capacity configuration, pipe network layout, and pipe diameter of each pipe section if yes; In step S4, the positions of the energy stations are optimized based on K-means and relative load distance and energy supply range, specifically comprising the following steps: S41: Determining the initial station sites of the energy stations by K-means clustering based on the relative load distance as a feature; the relative load distance is the product of the peak value of the building load and the distance between the load point and the energy station; since the pipe network is laid along the road, the distance is calculated using Manhattan distance; the optimization objective function of the energy station site is shown in formulas 6 and 7: In the formula, M represents a set of load points; N represents a set of energy stations; m and n respectively represent the load points and the energy station points; represents the annual maximum load value of the mth energy-consuming point, kW; represents the distance between the mth energy-consuming building and the energy station N, m; is a judgment of whether the energy station n serves the user point m, and is 1 if yes, and is 0 if no; and represents the spatial position attribute of the load point m; and represents the spatial position attribute of the energy station point; S42: Based on the initial station sites, the pipe network layout is determined based on step S6; in the process of traversing all energy-consuming buildings, one of the energy stations is selected as the energy supply station of the energy-consuming building to determine the energy supply range of the energy station; S43: After the pipe network layout and the energy supply range of the energy station are determined, the positions of the energy stations are re-determined based on the principle that the sum of the relative distances from each load point in the energy supply range to the energy station is the lowest; S44: The above process is repeatedly performed until the energy supply range and the positions of the energy stations converge; S45: The positions of the energy stations and the energy supply range of each energy station are obtained; In step S8, the station-network collaborative optimization objective model is established using the various information of step S1, specifically comprising the following steps: S81: The DDES collaborative optimization objective function includes the annualized cost of the energy station and the pipe network; the calculation formula of the objective function is shown in formula 23: wherein represents the annualized cost of the energy station and the pipe network system, in yuan; represents the annualized cost of the energy station and the pipe network system, in yuan; represents the annualized cost of the pipe network system, in yuan; S82: The total annualized cost of the energy station can be shown in formula 24: In the formula, M represents a set of energy stations; m represents an mth energy station; represents an annualized cost of power supply of the mth energy station; represents an annualized cost of cooling supply of the mth energy station; represents an annualized cost of heating supply of the mth energy station; S83: The investment cost of the energy station is calculated according to the installed capacity of the equipment, the land and civil cost of the energy station is estimated according to the electricity load of the energy station, and the annual power supply cost of the energy station is shown in formula 25: In the formula, represents the power supply capacity of the energy station, kW; S84: The annual cooling supply cost of the energy station is shown in formula 26: In the formula, represents the cooling capacity of the energy station, kW; S85: The annual heating supply cost of the energy station is shown in formula 27: In the formula, represents the cooling capacity of the energy station, kW. 2.The method of claim 1, wherein, In step S1, the regional geographic information data includes load point position, energy consumption building node, and road intermediate node; The parameter information mainly includes: equipment related parameters in the distributed energy system, and pipe network layout parameters; The equipment related parameters include: S11: Unit capacity investment, energy conversion efficiency and service life of internal combustion engine, auxiliary boiler, electric refrigeration and absorption refrigeration; gas price, interest rate, low heat value of gas and fixed economic parameters; local time-of-use electricity price; S12: The pipe network layout parameters include: pipe price parameters and case simulation parameters. 3.The method of claim 1, wherein, In step S2, the upper limit of the number of energy stations is determined, and the number of energy stations is determined by circulating 1-16 energy stations. 4.The method of claim 1, wherein, In step S3, an energy station planning target model is established, including an energy station site selection target model and an energy station equipment capacity configuration target model, which specifically includes the following steps: S31: Energy station site selection planning target model; the result evaluation index of energy station site selection is the economy of subsequent pipe network layout, therefore, the objective function of energy station site selection should be consistent with the economic cost and objective function of the pipe network; the cost of the energy station itself includes the construction cost of the energy station and the equipment cost in the energy station; the specific target model of energy station site selection is shown in formula 1: wherein represents the total annualized cost of the piping system, yuan; r represents the annual interest rate, %; represents the service life of the piping system, years; represents the construction cost of the piping system, yuan; represents the annual operating cost of the circulating water pump, yuan; represents the annualized energy loss conversion cost of the piping system, yuan; represents the annualized depreciation and maintenance conversion cost of the piping system, yuan; S32: Energy station equipment capacity configuration target model; the objective function of energy station configuration is the annual cost of energy station equipment, which mainly includes annual investment cost and annual operation cost; the annual investment cost is shown in formula 2: In the formula, represents the annualized cost of the energy station, yuan; represents the equipment construction cost of the energy station, yuan; represents the service life of the equipment in the energy station, years; represents the operating cost of the energy station, years; represents the investment cost of installing equipment in the energy system; S33: The investment cost of the energy station is shown in formula 3: In the formula, i represents the i-th device of the energy system; I represents the device set of the energy system; represents the capacity of the i-th device of the energy system, kW; represents the linear investment cost per unit capacity of the i-th device in the energy system, yuan / kW; S34: The operation cost of the energy station includes the sum of the electricity price and the gas price used by the system in each time compensation, which is shown in formula 4: where t represents the tth time step; T represents the time series, 8760h in total; represents the grid electricity buying amount at the tth time step, kWh; represents the grid electricity selling price at the tth time step, yuan / kWh; represents the gas consumption of the internal combustion engine at the tth time step, m 3 ; represents the gas price of the internal combustion engine, yuan / m 3 ; represents the gas consumption of the auxiliary boiler at the tth time step, m 3 ; represents the gas price of the auxiliary boiler, yuan / m 3 ; S35: Energy station system equipment modeling; the distributed energy system includes four energy supply equipment: internal combustion engine, auxiliary boiler, electric refrigeration unit and absorption refrigeration unit; the internal combustion engine unit generates heat and electricity at the same time, and its output depends on the energy conversion efficiency of the internal combustion engine and the supply amount of natural gas; the model of the internal combustion engine is shown in formula 5: where LCV represents the natural gas heat value, kWh / m 3 ; represents the internal combustion gas power generation amount at the t-th time step, kWh; represents the internal combustion gas waste heat amount at the t-th time step, kWh; represents the internal combustion engine power generation efficiency; represents the internal combustion engine waste heat recovery efficiency. 5.The method of claim 1, wherein, In step S5, the target model of pipe network layout optimization is established, based on step S4, the economy of the pipe network system is taken as the index for evaluating the optimization result of the energy station site, and the planning objective function is consistent; The four components of the pipe network cost include the construction cost of the pipe network system, the annual operation cost of the circulating water pump, the energy loss cost of the pipe network system, and the depreciation and maintenance cost of the pipe network system, which specifically includes the following steps: S51: Construction cost of pipe network system; the construction cost of the pipe network system is mainly related to the pipe diameter and length of the pipe network system, and the cost is shown in formula 8: where i and j are two vertices of the edge e(i, j) in the infinite graph G(V, E, W), i.e. the load point i and the energy station j; denotes the pipe segment diameter of the edge e(i, j), mm; denotes the pipe segment length of the edge e(i, j), mm; S52: Solve the running cost of the circulating water pump; the annual running cost of the circulating water pump is related to the design flow rate, electromechanical efficiency, working pressure, and electricity price of the pump, and the running cost of the circulating water pump is shown in formula 9: In the formula, represents the mass flow of the circulating water pump, kg / h; represents the working pressure of the circulating water pump, Pa; represents the medium density in the circulating water pump, kg / m 3 ; represents the working efficiency of the circulating water pump, %; represents the working time of the circulating water pump, h; represents the electricity price, yuan / kWh; S53: The working pressure of the circulating water pump in step S52; considering a single pipe section, the power provided by the pump is entirely used to overcome the fluid flow resistance of the pipe section, so the working pressure of the circulating water pump is equivalent to the flow resistance of the pipe section, which is composed of the frictional resistance and the local resistance, and the working pressure of the circulating water pump is shown in formula 10: wherein represents the pipe segment flow resistance of edge e(i,j) in the infinite graph G(V, E, W), Pa; represents the on-path resistance of edge e(i,j), Pa; represents the local resistance of edge e(i,j), Pa; S54: The frictional resistance loss in step S53 is calculated by the Darcy formula, as shown in formula 11: wherein represents the frictional resistance coefficient along the inner wall of the pipeline, which is related to the roughness of the pipeline; e (i,j) represents the average flow velocity of the fluid in the pipe section, m / s; The frictional resistance coefficient of the pipe wall in step S54; according to engineering experience, the pipe diameter is not less than 80 mm, so the medium flow in the pipe network is mostly in the resistance square area, and the frictional resistance coefficient is determined by the Schifrin's formula shown in formula 12: In the formula, K represents the equivalent roughness inside the pipe, mm; S55: The local resistance loss in step S53; the local resistance loss of the pipe is related to the geometric shape of the pipe, which is equivalent to the local resistance loss length in engineering calculation, and the local resistance loss of the pipe is shown in formula 13: wherein represents the local drag coefficient, which is generally in the range of 0.15-0.2, S56: Based on steps S52 to S55, the running cost of the circulating water pump can be in a similar form to the construction cost of the pipe network system, as shown in formula 14: S57: Energy loss cost of the pipe network system; the energy loss cost of the pipe network is shown in formula 15: wherein represents the energy loss of the pipe network system, W; represents the system energy efficiency ratio; The energy loss of the pipe network system is related to the pipe diameter, length, and insulation thickness of the pipe, The energy loss of the pipe network is shown in formula 16: wherein, represents the thermal conductivity of the pipe insulation material, W / (m o C); represents the pipe section thickness of edge e(i, j), m; represents the pipe insulation layer thickness of edge e(i, j), m; represents the average temperature of the soil outside the pipe network, o C; represents the supply water temperature of the pipe network, o C; represents the return water temperature of the pipe network, o C; S58: Depreciation and maintenance cost of the pipe network system; the depreciation cost and maintenance cost of the pipe network system are uncertain, and the actual calculation is performed according to a fixed ratio, as shown in formula 17: In the formula, represents the equipment depreciation rate; represents the maintenance cost proportionality coefficient; S59: Pipe network system cost; the pipe network cost is shown in formula 18: wherein represents the annualized cost of pipe segment e (i, j); Based on steps S51 to S58, the annualized cost of the pipe section is shown in formula 19: 。 6.The method of claim 1, wherein, In step S6, GA is used to encode and optimize the calculation sequence of energy-consuming buildings, DA technology is used to obtain the pipe network layout, pipe diameter of each pipe section, and annualized cost of the pipe network system, and the energy station site is iteratively updated to convergence according to the energy supply range, which specifically includes the following steps: S61: Input the GA control parameters based on the energy station site in step S3; S62: Encode the energy-consuming building to facilitate recording the calculation sequence; S63: Randomly generate an initial population, let Gen=1, obtain the parent population sample of the Gen generation under the layout, and calculate the fitness of the parent population of the Gen generation; S64: According to the fitness, select, cross, and mutate to generate a new generation, as shown in formula 20: S65: Repeat steps S63 to S64 until convergence is completed, and save the optimized calculation sequence of the energy-consuming building; S66: Initialize the pipe network cost full adjacency matrix W in network G, which is calculated from the input parameters, as shown in formula 21: In the formula, W is the cost-weight adjacency matrix; S67: When calculating the path of the ith energy-consuming building, the Dijkstra algorithm is used to calculate the path and the increased cost of the energy-consuming building to all energy stations, and the energy station with the lowest cost is selected according to the greedy strategy, and i = i + 1; S68: Update the cost-weighted adjacency matrix of the pipe network, and the weight is the incremental cost of the pipe network caused by the newly connected load point. According to the corresponding economic optimal pipe diameter of different pipe section flow, the calculation formula is shown in formula 22: In the formula, is the pipe section after n load points access to the pipe network The corresponding annual fee, yuan; S69: Repeat steps S67 to S68 until all load points are calculated, and save the final layout of the pipe network and the pipe diameter of each pipe section; according to the new energy supply range step S3, repeat steps S3 to S6 using the relative energy distance method until the energy station site and the energy supply range converge, and use the pipe network planning target model of step S5 to obtain the annualized cost of the pipe network system. 7.The method of claim 1, wherein, In step S7, the energy station equipment capacity is calculated according to the cold, heat and power load demand of the energy station, and the annualized cost of the energy station is calculated, which includes the following steps: S71: The pipe network layout determines the energy supply range of each energy station, and the cold, heat and power supply capacity of each energy station is calculated according to the cold, heat and power demand of each load point in the energy supply range of the energy station; S72: Based on the cold, heat and power demand of the energy station, a mixed integer linear programming method is used to solve the equipment capacity configuration of each energy station; S73: The annualized cost of the energy station equipment and site is calculated using the energy station target model of step S3. 8.The method of claim 1, wherein, In step S9, the DDES economy under different energy station quantities is obtained using the station network collaborative optimization model of S9, and it is judged whether the number of energy stations reaches the upper limit. If not, return to step S2, if yes, output the optimal number of energy stations, site, capacity configuration, pipe network layout and pipe section pipe diameter information.

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