Layout optimization method for improved honeycomb-shaped power distribution network

Abstract

The invention discloses a layout optimization method for an improved honeycomb power distribution network, and the method comprises the steps: building an HDN planning model considering the civil engineering cost of an intelligent base station and the whole life cycle cost of an energy storage power distribution network system, taking the position and internal energy storage capacity of the intelligent base station as variables, and taking the planning cost as a target function on the basis of a honeycomb structure; according to an HDN plane structure, a non-rectangular coordinate system is designed, locating and sizing coupling constraints of intelligent base stations and energy storage are set, a topology structure of a power distribution network system is described, the positions of the intelligent base stations are reasonably selected according to an original structure of the power distribution network system, and the intelligent base stations do not need to be established at vertexes of all boundaries; the internal energy storage capacity of the intelligent base station is optimized according to the internal load characteristics of the micro-grid, so that the flexibility advantage of micro-grid resources is exerted, and the energy storage resources are efficiently utilized; and a planning result with optimal investment and operation economical efficiency of the power distribution network system is considered, so that the method has good economical efficiency and expandability.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution network system planning, and in particular to a layout optimization method for an improved honeycomb distribution network. Background Art

[0002] Under the strategic background of "carbon peak and carbon neutrality", the access of emerging technologies such as distributed power sources, composite energy storage distribution network systems and electric vehicles has triggered a profound change in the topological structure of the distribution network system and prompted further optimization of its internal resource allocation; this technology iteration and resource reorganization has spawned new demands such as deep coupling of user resource management and information distribution network systems, and promoted the distribution network from a traditional single power distribution network to a comprehensive platform integrating multi-terminal energy transmission, distribution, storage and trading; at the same time, with the widespread application of bidirectional power flow, source-load uncertainty, power electronics technology and flexible power devices, the complexity and scale of the distribution network are constantly increasing, and control and safety issues have greatly increased the difficulty and challenges of distribution network planning and operation. Against this background, microgrids came into being. With their unique advantages of realizing local consumption of various types and large-scale new energy and plug-and-play, they have gradually become an important part of the distribution network system. With the advancement of energy storage technology, the types of power sources in microgrids have become more diverse, and they have certain energy management capabilities and independent operation characteristics. It is foreseeable that a large number of microgrids will be interconnected in groups in the distribution network, which provides new opportunities and paths for the development of the distribution network in the future.

[0003] In the coordinated development of microgrids and distribution networks, there are certain similarities in the inherent elements of distribution networks and microgrids, and there are certain commonalities in structure; in the process of intelligentization, multi-level autonomous operation areas may appear in the distribution network, and the application of multi-terminal soft open points (SOP) blurs the boundary between distribution networks and microgrids, allowing the two to adapt to each other in the development of power distribution network systems.

[0004] The coordinated development of distribution and microgrids has brought about the evolution of the distribution network system. Among them, the most promising distribution network structure is the honeycomb distribution network (HDN). It also demonstrates the advantages of HDN, explores the key issues in building HDN and its research ideas in operation. Existing research involves many aspects of HDN, but no research has considered the planning and layout of HDN itself. In practice, microgrid sources and loads are complex and have significant regional characteristics. The unified configuration of smart base stations at all locations is likely to cause redundancy and waste of smart base station resources. A large number of smart base stations will undoubtedly increase the complexity of information and increase management difficulty. In HDN, the reasonable planning of smart base stations is an issue worthy of attention. Summary of the invention

[0005] The purpose of the present invention is to provide an improved layout optimization method for a honeycomb distribution network, which can effectively solve the problems existing in the above-mentioned prior art.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solution: a layout optimization method of an improved honeycomb distribution network, comprising the following steps: S1, taking the location and internal energy storage capacity of the smart base station as variables and the planning cost as the first objective function: ; S2. Design a non-rectangular coordinate system to establish the site selection and capacity coupling constraints of smart base stations and energy storage; in, The civil construction cost of the smart base station; The life cycle cost of the energy storage device; S3. Under the constraints of site selection and fixed capacity coupling, the construction location and energy storage capacity of the smart base station are optimized based on the existing distribution network structure based on the first objective function; and Taking the economic goal as the second objective function, the power flow distribution that optimizes the operating cost of the distribution network system is obtained.

[0007] Optimized construction cost of smart base station for: ; in, A status bit indicating whether an intelligent base station is established at the node; The status bit indicating whether to convert this energy storage node into a smart base station. and Take 1 for yes and 0 for no; , and They are the construction cost converted from labor, materials, and equipment during the construction of smart base stations, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; and They are respectively a set of nodes that can be established and rebuilt into intelligent base stations; Life cycle cost of energy storage devices for: ; in, is a collection of intelligent base station nodes; The installation cost of the energy storage device; For replacement costs; To recover costs, Indicates whether the node has an intelligent base station.

[0008] Preferably, the installation cost of the energy storage device for: ; in, is the installation cost per unit capacity, is the installed capacity of the battery in the i-th intelligent base station; is the installation cost per unit power, is the rated installed power of the battery; is the discount rate, Y is the useful life of the smart base station; Replacement cost for: ; in, is the operating life of the energy storage device, To find the floor function of the number of energy storage replacements; is the replacement cost per unit capacity, is the replacement cost per unit power; Cost recovery for: ; in, To find the upward rounding function of the number of energy storage recovery times; is the unit capacity recovery cost, is the unit power recovery cost.

[0009] Preferably, in step S2, the site selection and capacity determination coupling constraints are: With the microgrid as the core, it is only guaranteed that any microgrid can be connected to at least one intelligent base station, and the energy storage capacity in the intelligent base station can meet the power demand of any surrounding microgrid, satisfying: ; ; in, is the reserve coefficient, is the set of candidate intelligent base stations around microgrid i; is the node set in microgrid i, is the power demand of node i in the microgrid at time t, is the output value of DG in the microgrid at time t, is the peak net power demand of microgrid i in one day.

[0010] Preferably, in step S2, the site selection and capacity determination coupling constraints are: With intelligent base stations as the core, ensure that the combination of intelligent base stations within the preset range can meet the power requirements of all surrounding microgrids: ; in, is the reserve coefficient, is the peak net power demand of microgrid i in one day.

[0011] Preferably, in step S2, the site selection and capacity determination coupling constraints are: The smart base station on the shared boundary of any two microgrids should meet the power requirements of the two microgrids; a single smart base station can support multiple microgrids at the same time, and any microgrid can also receive support from multiple smart base stations at the same time, and with the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met: ; in, is the reserve coefficient, is the peak net power demand of microgrid i in one day; The capacity of energy storage is within the constraints: ; in, and are the upper and lower limits of the battery capacity respectively.

[0012] Preferably, in step S3, the optimized layout is scheduled on the day-ahead, and preset indicators are set to evaluate the operating status of the distribution network system; the economic objectives include: demand response cost, energy storage loss cost, network loss cost and wind and solar power abandonment penalty cost.

[0013] Preferably, in step S3, the second objective function is: ; in, is the unit power cost of purchasing electricity from the upper power grid through the smart base station, is the active power purchased from the upper grid at time t; is the unit power loss cost of battery charging and discharging, and are the charging and discharging power at time t respectively; and are the current prices for compensating for the transferred load and the removed load, and are the power transferred and removed during demand response, respectively; is the unit network loss cost, is the current of branch ij at time t, is the resistance of branch ij; The penalty cost for unit wind and solar curtailment, and are the predicted power values ​​of distributed photovoltaic and wind power at node i at time t, and are the actual distributed photovoltaic and wind power values ​​adopted respectively; , and They are respectively a demand response node set, a branch set and a renewable energy access point set.

[0014] Preferably, in step S3, the indicators include: The power transmitted by the intelligent base station meets the following requirements: ; in, and They are the upper and lower limits of the active power transmitted at port i of the intelligent base station respectively; and are the upper and lower limits of the reactive power transmitted at port i of the intelligent base station respectively; is the reactive power compensated at the intelligent base station i at time t; Based on the upper and lower limit coefficients of charge and To control the depth of charge and discharge, introduce binary variables and Indicates the battery charging and discharging status. The internal energy storage of the smart base station meets the following requirements during operation: ; in, is the charge of the battery at time t, is the self-loss coefficient, and are the charging and discharging efficiency of the battery respectively; and They are the upper limits of battery charging and discharging power respectively; The total amount of load transferred during the demand response process of the microgrid within a dispatch cycle is 0, that is: ; ; ; in, and are the proportional coefficients for controlling the removable load and the transferable load respectively.

[0015] Preferably, the indicators also include: The output of renewable energy meets: ; The honeycomb distribution network meets the node voltage constraints and branch current constraints during operation: ; in, is the voltage of node i at time t, and are the upper and lower limits of the node voltage respectively; is the upper limit of branch current; After the second-order cone relaxation, the power flow constraints of the honeycomb distribution network are as follows: ; in, and are the active and reactive power injected into node j at time t, respectively; is the set of head-end nodes of the branch with j as the terminal node in HDN; Then it is the set of end nodes of the branch with j as the head node in HDN; and are the active and reactive powers flowing through branch ij, is the reactance of branch ij; Generate power balance constraints: ; in, is the reactive power demand of node j at time t; Ohm's law constraints: ; in, is the square of the voltage at node j at time t.

[0016] Beneficial effects: The present invention models the unique HDN structure through a non-rectangular coordinate system; starting from the energy storage of the intelligent base station, the site selection and capacity determination are carried out for it, the energy storage utilization rate is improved, and the intelligent base station is arranged according to local conditions. The hexagonal dense planar structure of the HDN and the difference in internal microgrid power requirements are taken into consideration. By reducing the number of intelligent base stations, the power supply stability is not affected, and the improved HDN that meets the load requirements is guaranteed; and according to the original structure of the distribution network system, the location of the intelligent base station is reasonably selected, and there is no need to establish an intelligent base station at each boundary vertex; according to the internal load characteristics of the microgrid, the internal energy storage capacity of the intelligent base station is optimized, so that the flexibility advantage of the microgrid resources is brought into play and the energy storage resources are efficiently utilized; The planning results taking into account the optimal investment and operation economy of the distribution network system have good economy and scalability, and provide theoretical support and practical reference for the flexible planning and low-carbon operation of the smart power distribution network system, which has certain engineering use value. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0018] In the attached picture: Figure 1 It is a schematic diagram of the representation of the HDN of the present invention in a non-rectangular coordinate system; Figure 2 It is a schematic diagram of a 33-node distribution network system improved by the present invention; Figure 3 This is a schematic diagram of the predicted output of a distributed power source of the present invention; Figure 4 It is a schematic diagram of the HDN structure adopted in the example of the present invention; Figure 5 It is a schematic diagram of the site selection and capacity determination results of each scheme of the present invention; Figure 6 is an active load curve diagram of the present invention; Figure 7 It is a curve diagram of energy storage charge variation in the intelligent base station of the present invention. DETAILED DESCRIPTION

[0019] The embodiments of the present invention are described below in conjunction with the drawings in the embodiments of the present invention. The terms used in the implementation mode of the present invention are only used to explain the specific embodiments of the present invention, and are not intended to limit the present invention. The embodiments of the present application are described below in conjunction with the drawings.

[0020] Embodiment: An improved honeycomb distribution network layout optimization method is proposed. By constructing an HDN planning model that considers the civil construction cost of smart base stations and the full life cycle cost of energy storage distribution network systems, based on the honeycomb structure, the location of the smart base station and the internal energy storage capacity are used as variables, and the planning cost is used as the objective function. According to the HDN plane structure, a non-rectangular coordinate system is designed to establish the site selection and capacity coupling constraints of the smart base station and energy storage to describe the topological structure of the distribution network system. Specifically, it includes the following: S1, taking the location and internal energy storage capacity of the smart base station as variables and the planning cost as the first objective function: ; Civil construction cost of smart base station for: ; in, A status bit indicating whether an intelligent base station is established at the node; The status bit indicating whether to convert this energy storage node into a smart base station. and Take 1 for yes and 0 for no; , and They are the construction cost converted from labor, materials, and equipment during the construction of smart base stations, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; and They are respectively a set of nodes that can be established and rebuilt into intelligent base stations; Doing so ensures that the honeycomb distribution network is planned based on the original energy storage power station, making full use of existing resources; Life cycle cost of energy storage devices for: ; in, is a collection of intelligent base station nodes; The installation cost of the energy storage device; For replacement costs; To recover costs, Indicates whether the node has an intelligent base station.

[0021] During the construction period, the construction cost of the energy storage configuration capacity and power in the smart base station is the installation cost of the energy storage device. for: ;in, is the installation cost per unit capacity, is the installed capacity of the battery in the i-th intelligent base station; is the installation cost per unit power, is the rated installed power of the battery; is the discount rate, Y is the useful life of the smart base station; During the operation cycle of the smart base station, the energy storage device needs to be replaced, which incurs corresponding replacement costs. for: ; in, is the operating life of the energy storage device, To find the floor function of the number of energy storage replacements; is the replacement cost per unit capacity, is the replacement cost per unit power; When the energy storage in the smart base station reaches its service life or the smart base station is no longer in use, each device will be safely recycled and the corresponding recycling cost will be for: ; in, To find the upward rounding function of the number of energy storage recovery times; is the unit capacity recovery cost, is the unit power recovery cost.

[0022] S2. Design a non-rectangular coordinate system to establish the site selection and capacity coupling constraints of smart base stations and energy storage; the "many-to-many" connection relationship between smart base stations and microgrids; from the perspective of the self-distribution grid system, smart base stations show the characteristics of mutual cooperation; as shown in the attached Figure 1 As shown, the microgrid and intelligent base station in the HDN distribution network system can be located; Depend on Figure 1 It can be seen that any microgrid and smart base station have specific coordinates, ensuring that the special structure of HDN can be described using mathematical relationships. For any microgrid i with coordinates (i, j), the coordinates of the smart base stations around it constitute a set Di=[(i-1, j), (i+1, j), (i-1, j+1), (i, j+1), (i, j-1), (i+1, j-1)]; therefore, the smart base station energy storage site selection and capacity coupling constraints that are compatible with the structure can be established, including the following three types: Solution 1: With microgrid as the core, it only ensures that any microgrid can be connected to at least one smart base station, and the energy storage capacity in the smart base station can meet the power demand of any surrounding microgrid. Therefore, it must meet the following requirements: ; ; in, is the reserve coefficient, is the set of candidate intelligent base stations around microgrid i; is the node set in microgrid i, is the power demand of node i in the microgrid at time t, is the output value of DG in the microgrid at time t, is the peak net power demand of microgrid i in one day.

[0023] Solution 2: With intelligent base stations as the core, ensure that the combination of intelligent base stations within the preset range can meet the power requirements of all surrounding microgrids, refer to Figure 2 It can be manifested as: ; in, is the reserve coefficient, is the peak net power demand of microgrid i in one day.

[0024] Solution 3: The smart base station on the shared boundary of any two microgrids should meet the power requirements of the two microgrids; a single smart base station can support multiple microgrids at the same time, and any microgrid can also receive support from multiple smart base stations at the same time; with the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met; refer to Figure 2 There are three situations where smart base stations share the same border: ; in, is the reserve coefficient, is the peak net power demand of microgrid i in one day; The capacity of energy storage is within the constraints: ; in, and are the upper and lower limits of the battery capacity respectively.

[0025] S3. Under the constraints of site selection and fixed capacity coupling, the construction location and energy storage capacity of the smart base station are optimized based on the existing distribution network structure based on the first objective function; and Taking the economic goal as the second objective function, the power flow distribution that optimizes the operation cost of the distribution network system is obtained; The optimized layout is dispatched on the day-ahead to test the effectiveness of the proposed method. Given the structural parameters and load conditions of the distribution network system, the available resources in the distribution network system are dispatched to find the power flow distribution that can meet all operating constraints and optimize the operating cost of the distribution network system. At the same time, relevant indicators are established to judge the operating status of the distribution network system. For the optimal dispatch of HDN, the power of each microgrid is dispatched while ensuring the normal operation of each microgrid. With economy as the goal, the second objective function is constructed by considering the power purchase cost, demand response cost, energy storage loss cost, network loss cost and wind and solar abandonment penalty cost: ; in, is the unit power cost of purchasing electricity from the upper power grid through the smart base station, is the active power purchased from the upper grid at time t; is the unit power loss cost of battery charging and discharging, and are the charging and discharging power at time t respectively; and are the current prices for compensating for the transferred load and the removed load, and are the power transferred and removed during demand response, respectively; is the unit network loss cost, is the current of branch ij at time t, is the resistance of branch ij; The penalty cost for unit wind and solar curtailment, and are the predicted power values ​​of distributed photovoltaic and wind power at node i at time t, and are the actual distributed photovoltaic and wind power values ​​adopted respectively; , and They are respectively a demand response node set, a branch set and a renewable energy access point set.

[0026] Indicators include: (1) In order to suppress the impact of power fluctuations on the upper power grid and ensure operational stability, the power transmitted by the intelligent base station needs to be controlled within a certain range, that is, the power transmitted by the intelligent base station meets the following requirements: ; in, and They are the upper and lower limits of the active power transmitted at port i of the intelligent base station respectively; and are the upper and lower limits of the reactive power transmitted at port i of the intelligent base station respectively; is the reactive power compensated at the intelligent base station i at time t; (2) Based on the upper and lower limit coefficients of charge and To control the depth of charge and discharge, introduce binary variables and Indicates the battery charging and discharging status. The internal energy storage of the smart base station meets the following requirements during operation: ; in, is the charge of the battery at time t, is the self-loss coefficient, and are the charging and discharging efficiency of the battery respectively; and They are the upper limits of battery charging and discharging power respectively; they stipulate that the energy storage in all smart base stations is conserved from beginning to end and that smart base stations are not allowed to charge and discharge externally at the same time; (3) The total amount of load transferred during the demand response process of the microgrid within a dispatch cycle is 0, that is: ; ; ; in, and are the proportional coefficients for controlling the removable load and the transferable load respectively.

[0027] The output of renewable energy meets: ; The honeycomb distribution network meets the node voltage constraints and branch current constraints during operation: ; in, is the voltage of node i at time t, and are the upper and lower limits of the node voltage respectively; is the upper limit of branch current; After the second-order cone relaxation, the power flow constraints of the honeycomb distribution network are as follows: ; in, and are the active and reactive power injected into node j at time t, respectively; is the set of head-end nodes of the branch with j as the terminal node in HDN; Then it is the set of end nodes of the branch with j as the head node in HDN; and are the active and reactive powers flowing through branch ij, is the reactance of branch ij; Generate power balance constraints: ; in, is the reactive power demand of node j at time t; Ohm's law constraints: ; in, is the square of the voltage at node j at time t.

[0028] In a specific embodiment: Modifications are made based on the IEEE 33 node distribution network system, and the nodes and lines are reconstructed to form the following Figure 2 The 33-node distribution network system with distributed power sources is shown in the figure. Considering that the microgrid is small in scale and still has the concept of "grid" inside, several microgrids can be divided on the basis of this distribution network system to build a honeycomb active distribution network model with distributed power sources and multiple energy storage distribution network systems. The rated voltage of the busbar of this distribution network system is 10kV, and distributed photovoltaic power sources are connected at node 24, and wind turbines are connected at nodes 16 and 27. Take the predicted output of distributed power sources in the distribution network at different time periods on a typical day in the local area, as shown in the attached figure. Figure 3 As shown in the figure. Due to the large amount of distributed wind power access in this area and its strong volatility, the site selection and capacity reserve coefficient of the intelligent base station is set to 1.2; the proposed distribution network system is scheduled with a period of T=24 and a step length of 1h.

[0029] In reality, due to geographical, economic and other factors, HDN cannot form a standard regular hexagonal dense paving shape, but it can still be divided into reasonable areas to form a microgrid.

[0030] When dividing the microgrid, the distribution network system nodes that connect multiple microgrids are selected as the vertices of the hexagonal power supply boundary of the microgrid, that is, the candidate nodes for establishing the intelligent base station; at the same time, the nodes belonging to the same microgrid are determined according to the connection relationship between the nodes; As attached Figure 4 As shown, nodes 1, 3, 8, 12, 13, 18, 23, and 26 can be considered as candidate nodes. At the same time, the load requirements of different microgrids are obtained, as shown in Table 1. Among them, node 18 is the original energy storage node of the distribution network system. If an intelligent base station is built here, it will enjoy lower civil engineering costs; if an additional intelligent base station is built at other nodes, it will face higher civil engineering costs including materials, land, construction and other costs; Table 1 Microgrid division results and load demand

[0031]

[0032] In the context of a one-year trial operation, using Schemes 1, 2, 3 and the conventional honeycomb scheme, the optimization results of intelligent base station site selection and capacity determination are obtained as shown in the attached figure. Figure 5 As shown in the figure, the planning cost under this scheme is 215,150 yuan. Because intelligent base stations with energy storage capacities of 510kW, 600kW, 540kW, 150kW and 210kW are selected at nodes 3, 8, 12, 23 and 26 respectively; 18 nodes are transformed into intelligent base stations and equipped with energy storage capacity of 150kW, the civil engineering cost is 44,500 yuan.

[0033] For Solution 1, only smart base stations need to be built at nodes 3 and 8; the planning cost is 68,440 yuan, of which the civil construction cost is 14,500 yuan. This solution greatly reduces the number of smart base stations and the capacity of energy storage, thereby achieving low costs.

[0034] For scheme 2, there is no need to specifically select nodes that connect multiple microgrids as candidate smart base stations, that is, all nodes on the microgrid boundary can establish smart base stations, such as Figure 5 As shown in (c), the planning cost of this scheme is 216,720 yuan, of which the civil construction cost is the same as that of scheme 3; however, this scheme has a relatively free choice of the location of the smart base station, so the smart base station only serves a single microgrid; The planned cost for the conventional honeycomb structure is 446,720 yuan. The establishment of more intelligent base stations will undoubtedly greatly increase the civil engineering and equipment costs; the unchanged energy storage capacity cannot well meet the geographically uneven load demand.

[0035] Given the comprehensive load active power of the distribution network under day-ahead dispatch, based on the HDN planned by Scheme 3, the day-ahead operation of the HDN is tested and the optimization results are compared with the improved 33-node distribution network system. Figure 6 The charge status of the energy storage devices in each intelligent base station in the previous day is shown in the attached figure. Figure 7 As shown. Set an evaluation index: the fluctuation composite index F, which is used to measure the load fluctuation of each node in the distribution network system. F is composed of the load fluctuation amplitude and fluctuation rate: ; in, is the load value of the power grid at time t, is the load average value. ω1 and ω2 are weight coefficients. Since the load fluctuation rate has a great influence on the stability of the distribution network, ω1 is set to 0.25 and ω2 is set to 0.75.

[0036] Table 2 compares the indicators of the distribution network system before and after the intelligent base station is put into use. Figure 6 It can be seen that the investment in intelligent base stations brings better peak-shaving and valley-filling capabilities. The real-time charging and discharging of multiple energy storages can smooth the load curve, reducing both the load fluctuation amplitude and the load fluctuation rate. The use of HDN can obtain a more ideal load curve and improve power supply stability.

[0037] Geographically, smart base stations are distributed throughout the distribution network system, shortening the electrical distance between source and storage, and load and storage. The abundant renewable energy generation can be absorbed by the nearest energy storage, and the power demand of the microgrid is met by the nearest energy storage, greatly reducing the network loss cost of the distribution network system.

[0038] In addition, compared with conventional distribution networks, HDN is less dependent on the upper grid, and accordingly, the carbon emission costs it needs to bear are also lower; Table 2 Scenario comparison

[0039] The above simulation results verify the effectiveness and practicality of the model constructed by the present invention. It shows that by using the layout optimization method of the present invention applicable to the improved HDN and solving the intelligent base station site selection and capacity determination model, an optimized HDN layout can be obtained. It can significantly improve the operating efficiency and stability of the power distribution network system in terms of peak shaving and valley filling, reducing the network loss of the distribution network system, and suppressing load fluctuations, and has certain engineering practical value.

[0040] The embodiments of the present invention are described in detail above in conjunction with the accompanying drawings, but the present invention is not limited to the above embodiments. For ordinary technicians in this technical field, after knowing the contents recorded in the present invention, they can make several equivalent changes and substitutions without departing from the principle of the present invention. These equivalent changes and substitutions should also be regarded as belonging to the protection scope of the present invention.

Claims

1. A layout optimization method for an improved honeycomb distribution network, characterized in that: The steps include: S1, taking the location and internal energy storage capacity of the smart base station as variables and the planning cost as the first objective function: ； S2. Design a non-rectangular coordinate system to establish the site selection and capacity coupling constraints of smart base stations and energy storage; in, The civil construction cost of the smart base station; The life cycle cost of the energy storage device; S3. Under the constraints of site selection and fixed capacity coupling, the construction location and energy storage capacity of the smart base station are optimized based on the existing distribution network structure based on the first objective function; and Taking the economic goal as the second objective function, the power flow distribution that optimizes the operating cost of the distribution network system is obtained.

2. The layout optimization method of an improved honeycomb distribution network according to claim 1, characterized in that: Civil construction cost of smart base station for: ； in, A status bit indicating whether an intelligent base station is established at the node; The status bit indicating whether to convert this energy storage node into a smart base station. and Take 1 for yes and 0 for no; , and They are the construction cost converted from labor, materials, and equipment during the construction of smart base stations, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; and They are respectively a set of nodes that can be established and rebuilt into intelligent base stations; Life cycle cost of energy storage devices for: ； in, is a collection of intelligent base station nodes; The installation cost of the energy storage device; For replacement costs; To recover costs, Indicates whether the node has an intelligent base station.

3. The layout optimization method of an improved honeycomb distribution network according to claim 2, characterized in that: Energy storage installation costs for: ； in, is the installation cost per unit capacity, is the installed capacity of the battery in the i-th intelligent base station; is the installation cost per unit power, is the rated installed power of the battery; is the discount rate, Y is the useful life of the smart base station; Replacement costs for: ； in, is the operating life of the energy storage device, To find the floor function of the number of energy storage replacements; is the replacement cost per unit capacity, is the replacement cost per unit power; Cost recovery for: ； in, To find the upward rounding function of the number of energy storage recovery times; is the unit capacity recovery cost, is the unit power recovery cost.

4. The layout optimization method of an improved honeycomb distribution network according to claim 3 is characterized in that: In step S2, the site selection and capacity determination coupling constraints are: With the microgrid as the core, it is only guaranteed that any microgrid can be connected to at least one intelligent base station, and the energy storage capacity in the intelligent base station can meet the power demand of any surrounding microgrid, satisfying: ； ； in, is the reserve coefficient, is the set of candidate intelligent base stations around microgrid i; is the node set in microgrid i, is the power demand of node i in the microgrid at time t, is the output value of DG in the microgrid at time t, is the peak net power demand of microgrid i in one day.

5. The layout optimization method of an improved honeycomb distribution network according to claim 3, characterized in that: In step S2, the site selection and capacity determination coupling constraints are: With intelligent base stations as the core, ensure that the combination of intelligent base stations within the preset range can meet the power requirements of all surrounding microgrids: ； in, is the reserve coefficient, is the peak net power demand of microgrid i in one day.

6. The improved honeycomb distribution network layout optimization method according to claim 3, characterized in that: In step S2, the site selection and capacity determination coupling constraints are: The smart base station on the shared boundary of any two microgrids should meet the power requirements of the two microgrids; a single smart base station can support multiple microgrids at the same time, and any microgrid can also receive support from multiple smart base stations at the same time, and with the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met: ； in, is the reserve coefficient, is the peak net power demand of microgrid i in one day; The capacity of energy storage is within the constraints: ； in, and are the upper and lower limits of the battery capacity respectively.

7. The improved honeycomb distribution network layout optimization method according to claim 1, characterized in that: In step S3, the optimized layout is scheduled on the day-ahead, and preset indicators are set to evaluate the operating status of the distribution network system; the economic objectives include: demand response cost, energy storage loss cost, network loss cost and wind and solar power abandonment penalty cost.

8. The improved honeycomb distribution network layout optimization method according to claim 7, characterized in that: In step S3, the second objective function is: ； in, is the unit power cost of purchasing electricity from the upper grid through the smart base station, is the active power purchased from the upper grid at time t; is the unit power loss cost of battery charging and discharging, and are the charging and discharging power at time t respectively; and are the current prices for compensating for the transferred load and the removed load, and are the power transferred and removed during demand response, respectively; is the unit network loss cost, is the current of branch ij at time t, is the resistance of branch ij; The penalty cost for unit wind and solar curtailment, and are the predicted power values ​​of distributed photovoltaic and wind power at node i at time t, and are the actual distributed photovoltaic and wind power values ​​respectively; , and They are respectively a demand response node set, a branch set and a renewable energy access point set.

9. The improved honeycomb distribution network layout optimization method according to claim 7, characterized in that: In step S3, the indicators include: The power transmitted by the intelligent base station meets the following requirements: ； in, and are the upper and lower limits of the active power transmitted at port i of the intelligent base station respectively; and are the upper and lower limits of the reactive power transmitted at port i of the intelligent base station respectively; is the reactive power compensated at the intelligent base station i at time t; Based on the upper and lower limit coefficients of charge and To control the depth of charge and discharge, introduce binary variables and Indicates the battery charging and discharging status. The internal energy storage of the smart base station meets the following requirements during operation: ； in, is the charge of the battery at time t, is the self-loss coefficient, and are the charging and discharging efficiency of the battery respectively; and They are the upper limits of battery charging and discharging power respectively; The total amount of load transferred during the demand response process of the microgrid within a dispatch cycle is 0, that is: ； ； ； in, and are the proportional coefficients for controlling the removable load and the transferable load respectively.

10. The improved honeycomb distribution network layout optimization method according to claim 9, characterized in that: The indicators also include: The output of renewable energy meets: ； The honeycomb distribution network meets the node voltage constraints and branch current constraints during operation: ； in, is the voltage of node i at time t, and are the upper and lower limits of the node voltage respectively; is the upper limit of branch current; After the second-order cone relaxation, the power flow constraints of the honeycomb distribution network are as follows: ； in, and are the active and reactive power injected into node j at time t, respectively; is the set of head-end nodes of the branch with j as the terminal node in HDN; Then it is the set of end nodes of the branch with j as the head node in HDN; and are the active and reactive powers flowing through branch ij, is the reactance of branch ij; Generate power balance constraints: ； in, is the reactive power demand of node j at time t; Ohm's law constraints: ； in, is the square of the voltage at node j at time t.

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