An improved layout optimization method for honeycomb distribution network

Abstract

The present invention discloses an improved honeycomb distribution network layout optimization method. By constructing an HDN planning model that considers the civil construction cost of smart base stations and the full life cycle cost of the energy storage distribution network system, based on the honeycomb structure, the location and internal energy storage capacity of the smart base stations 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 determination coupling constraints of the smart base stations and energy storage to describe the topological structure of the distribution network system. According to the original structure of the distribution network system, the location of the smart base station is reasonably selected, and there is no need to establish a smart base station at each boundary vertex. According to the internal load characteristics of the microgrid, the internal energy storage capacity of the smart 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 result is optimized considering the investment and operation economic efficiency of the distribution network system, and has good economy and scalability.

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] Against the backdrop of the "carbon peak and carbon neutrality" strategy, the integration of emerging technologies such as distributed power generation (DGs), hybrid energy storage distribution network systems, and electric vehicles has triggered profound changes in the topology of distribution networks and prompted further optimization of their internal resource allocation. This technological iteration and resource reorganization has given rise to new demands, such as the deep coupling of user resource management and information distribution network systems. This has driven the gradual transformation of distribution networks from traditional single-source power distribution networks to integrated platforms integrating multi-terminal energy transmission, distribution, storage, and trading. At the same time, with the increasing complexity and scale of distribution networks, the rise of bidirectional power flows, source-load uncertainty, and the widespread application of power electronics and flexible power devices has significantly increased the difficulty and challenges of distribution network planning and operation. Against this backdrop, microgrids have emerged. With their unique advantages of enabling local consumption of diverse and large-scale renewable energy resources and plug-and-play integration, they have gradually become a vital component of distribution network systems. With advances in energy storage technology, microgrids have become increasingly diverse in terms of power source types, possessing 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 future development of the distribution network.

[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 their structures. In the process of intelligentization, distribution networks may have multi-level autonomous operation areas. Combined with the application of multi-terminal soft open points (SOPs), the boundaries between distribution networks and microgrids are blurred, 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 led to an evolution in the morphology of distribution network systems. Among them, the honeycomb distribution network (HDN) has shown great potential and demonstrated the advantages of HDN. The key issues in building HDN and its operational research ideas were discussed. Existing research covers many aspects of HDN, but no research has yet considered the planning and layout of HDN itself. In practice, microgrids have complex power sources and loads and 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] To solve the above technical problems, the present invention adopts the following technical solution: a layout optimization method for an improved honeycomb distribution network, comprising the following steps:

[0007] 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:

[0008] ;

[0009] S2. Design a non-rectangular coordinate system to establish the site selection and capacity coupling constraints of smart base stations and energy storage;

[0010] in, The civil construction cost of the smart base station; The full life cycle cost of the energy storage device;

[0011] S3. Under the coupled constraints of site selection and fixed capacity, optimize the construction location and energy storage capacity of the smart base station based on the existing distribution network structure based on the first objective function; and

[0012] 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.

[0013] Optimum construction cost of smart base station for:

[0014] ;

[0015] 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 an intelligent 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 the smart base station, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; and They are respectively a collection of nodes that can be established and rebuilt into intelligent base stations;

[0016] Life cycle cost of energy storage devices for:

[0017] ;

[0018] 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.

[0019] Preferably, the installation cost of the energy storage device for:

[0020] ;

[0021] 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;

[0022] Replacement cost for:

[0023] ;

[0024] 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;

[0025] Recovery costs for:

[0026] ;

[0027] in, To find the upward rounding function of the energy storage recovery times; is the unit capacity recovery cost, is the unit power recovery cost.

[0028] Preferably, in step S2, the site selection and capacity determination coupling constraints are:

[0029] With the microgrid as the core, it is only guaranteed that any microgrid can be connected to at least one smart base station, and the energy storage capacity of the smart base station can meet the power demand of any surrounding microgrid, meeting the following requirements:

[0030] ;

[0031] ;

[0032] in, is the reserve coefficient, is the set of candidate smart base stations around microgrid i; is the set of nodes 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.

[0033] Preferably, in step S2, the site selection and capacity determination coupling constraints are:

[0034] With smart base stations as the core, ensure that the combination of smart base stations within the preset range can meet the power requirements of all surrounding microgrids:

[0035] ;

[0036] in, is the reserve coefficient, is the peak net power demand of microgrid i in one day.

[0037] Preferably, in step S2, the site selection and capacity determination coupling constraints are:

[0038] Any smart base station on the shared boundary of any two microgrids should meet the power requirements of both microgrids. A single smart base station can support multiple microgrids simultaneously, and any microgrid can also receive support from multiple smart base stations simultaneously. With the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met:

[0039] ;

[0040] in, is the reserve coefficient, is the peak net power demand of microgrid i in one day;

[0041] The capacity of energy storage is within the constraints:

[0042] ;

[0043] in, and are the upper and lower limits of the battery capacity respectively.

[0044] Preferably, in step S3, the optimized layout is scheduled on a day-ahead basis, 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 curtailment penalty cost.

[0045] Preferably, in step S3, the second objective function is:

[0046] ;

[0047] 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 charge and discharge power at time t respectively; and are the current prices for compensating for transferred loads and shedding loads, and are the power transferred and removed when performing 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.

[0048] Preferably, in step S3, the indicators include:

[0049] The power transmitted through the intelligent base station meets the following requirements:

[0050] ;

[0051] 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;

[0052] 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:

[0053] ;

[0054] 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 are the upper limits of battery charging and discharging power respectively;

[0055] The total amount of load transferred during the demand response process of the microgrid within a dispatch cycle is 0, that is:

[0056] ;

[0057] ;

[0058] ;

[0059] in, and are the proportional coefficients for controlling the removable load and the transferable load respectively.

[0060] Preferably, the indicators also include:

[0061] The output of renewable energy meets:

[0062] ;

[0063] The honeycomb distribution network satisfies the node voltage constraints and branch current constraints during operation:

[0064] ;

[0065] 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;

[0066] After the second-order cone relaxation, the power flow constraints of the honeycomb distribution network are as follows:

[0067] ;

[0068] in, and are the active and reactive powers 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;

[0069] Generate power balance constraints:

[0070] ;

[0071] in, is the reactive power demand of node j at time t;

[0072] Ohm's law constraints:

[0073] ;

[0074] in, is the square of the voltage at node j at time t.

[0075] Beneficial Effects: This invention uses a non-rectangular coordinate system to model the unique HDN structure. Starting with the energy storage of smart base stations, the invention selects their locations and capacities, improves energy storage utilization, and implements the tailored deployment of smart base stations. This takes into account the HDN's hexagonal, densely packed planar structure and the differences in internal microgrid power requirements. By reducing the number of smart base stations while maintaining power supply stability, an improved HDN that meets load requirements is achieved. Furthermore, the locations of smart base stations are rationally selected based on the existing structure of the distribution network system, eliminating the need to establish smart base stations at every boundary vertex. The internal energy storage capacity of smart base stations is optimized based on the internal load characteristics of the microgrid, thereby leveraging the flexibility advantages of microgrid resources and efficiently utilizing energy storage resources.

[0076] The planning results, which take 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, and have certain engineering use value. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] 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.

[0078] In the attached figure:

[0079] Figure 1 It is a schematic diagram of the representation of the HDN of the present invention in a non-rectangular coordinate system;

[0080] Figure 2 It is a schematic diagram of the improved 33-node distribution network system of the present invention;

[0081] Figure 3 This is a schematic diagram of the predicted output of a distributed power supply according to the present invention;

[0082] Figure 4 This is a schematic diagram of the HDN structure used in the example of the present invention;

[0083] Figure 5 This is a schematic diagram of the site selection and volume determination results of each scheme of the present invention;

[0084] Figure 6 It is the active load curve diagram of the present invention;

[0085] Figure 7 It is a curve diagram of energy storage charge variation in the intelligent base station of the present invention. DETAILED DESCRIPTION

[0086] The following describes the embodiments of the present invention in conjunction with the accompanying drawings. The terms used in the embodiments 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 following describes the embodiments of the present application in conjunction with the accompanying drawings.

[0087] Example: An improved honeycomb distribution network layout optimization method is proposed. This method constructs an HDN planning model that considers the construction costs of smart base stations and the lifecycle costs of energy storage distribution network systems. Based on the honeycomb structure, the model uses the location and internal energy storage capacity of smart base stations as variables, and the planning cost as the objective function. Based on the HDN planar structure, a non-rectangular coordinate system is designed to establish coupled constraints for the location and capacity of smart base stations and energy storage to describe the topology of the distribution network system. Specifically, the model includes the following:

[0088] 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:

[0089] ;

[0090] Civil construction costs of smart base stations for:

[0091] ;

[0092] 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 an intelligent 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 the smart base station, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; and They are respectively a collection of nodes that can be established and rebuilt into intelligent base stations;

[0093] This ensures that the honeycomb distribution network is planned based on the existing energy storage power station, making full use of existing resources;

[0094] Life cycle cost of energy storage devices for:

[0095] ;

[0096] 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.

[0097] During the construction period, the construction cost of the energy storage configuration capacity and power in the smart base station is equal to the installation cost of the energy storage device. for:

[0098] ;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;

[0099] During the operation cycle of the smart base station, the energy storage device needs to be replaced, which incurs corresponding replacement costs. for:

[0100] ;

[0101] 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;

[0102] When the energy storage in the smart base station reaches its service life or the smart base station is no longer in use, the equipment will be safely recycled and the corresponding recycling cost will be for:

[0103] ;

[0104] in, To find the upward rounding function of the energy storage recovery times; is the unit capacity recovery cost, is the unit power recovery cost.

[0105] 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 smart base station in the HDN distribution network system can be located;

[0106] Depend on Figure 1 As can be seen, any microgrid and smart base station have specific coordinates, ensuring that the unique structure of the HDN can be described using mathematical relationships. For any microgrid i with coordinates (i, j), the coordinates of the surrounding smart base stations form a set Di = [(i-1, j), (i+1, j), (i-1, j+1), (i, j+1), (i, j-1), (i+1, j-1)]. Therefore, coupled constraints for the location and capacity of smart base station energy storage that are compatible with the structure can be established, including the following three:

[0107] Solution 1, with the microgrid as the core, only ensures that any microgrid can be connected to at least one smart base station, and the energy storage capacity of the smart base station can meet the power requirements of any surrounding microgrid. Therefore, the following conditions must be met:

[0108] ;

[0109] ;

[0110] in, is the reserve coefficient, is the set of candidate smart base stations around microgrid i; is the set of nodes 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.

[0111] Solution 2: With smart base stations as the core, ensure that the combination of smart base stations within the preset range can meet the power requirements of all surrounding microgrids, refer to Figure 2 It can be manifested as:

[0112] ;

[0113] in, is the reserve coefficient, is the peak net power demand of microgrid i in one day.

[0114] Solution 3: The smart base station on the shared boundary of any two microgrids should meet the power requirements of these 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 scenarios where smart base stations share common boundaries:

[0115] ;

[0116] in, is the reserve coefficient, is the peak net power demand of microgrid i in one day;

[0117] The capacity of energy storage is within the constraints:

[0118] ;

[0119] in, and are the upper and lower limits of the battery capacity respectively.

[0120] S3. Under the coupled constraints of site selection and fixed capacity, optimize the construction location and energy storage capacity of the smart base station based on the existing distribution network structure based on the first objective function; and

[0121] 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;

[0122] The optimized layout is dispatched on a day-ahead basis 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 satisfies all operating constraints and optimizes the operating cost of the distribution network system. At the same time, relevant indicators are established to evaluate 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 curtailment penalty cost:

[0123] ;

[0124] 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 charge and discharge power at time t respectively; and are the current prices for compensating for transferred loads and shedding loads, and are the power transferred and removed when performing 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.

[0125] Indicators include:

[0126] (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:

[0127] ;

[0128] 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;

[0129] (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:

[0130] ;

[0131] 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 state of all smart base stations must be conserved from the beginning to the end and that smart base stations are not allowed to charge or discharge externally at the same time;

[0132] (3) The total amount of load transferred during the demand response process of the microgrid within a dispatch cycle is 0, that is:

[0133] ;

[0134] ;

[0135] ;

[0136] in, and are the proportional coefficients for controlling the removable load and the transferable load respectively.

[0137] The output of renewable energy meets:

[0138] ;

[0139] The honeycomb distribution network satisfies the node voltage constraints and branch current constraints during operation:

[0140] ;

[0141] 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;

[0142] After the second-order cone relaxation, the power flow constraints of the honeycomb distribution network are as follows:

[0143] ;

[0144] in, and are the active and reactive powers 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;

[0145] Generate power balance constraints:

[0146] ;

[0147] in, is the reactive power demand of node j at time t;

[0148] Ohm's law constraints:

[0149] ;

[0150] in, is the square of the voltage at node j at time t.

[0151] In a specific embodiment:

[0152] 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 generation 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 construct a honeycomb active distribution network model with distributed power generation and multiple energy storage distribution network systems. The busbar rated voltage of this distribution network system is 10kV, distributed photovoltaic power generation is connected at node 24, and wind turbines are connected at nodes 16 and 27. Take the predicted output of distributed power generation in the distribution network at each time period 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 smart base station is set to 1.2; the proposed distribution network system is scheduled with a period of T = 24 and a step size of 1 hour.

[0153] 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.

[0154] When dividing the microgrid, the distribution network system nodes that connect multiple microgrids are selected as the vertices of the microgrid's hexagonal power supply boundary, that is, the candidate nodes for establishing smart base stations; at the same time, the nodes belonging to the same microgrid are determined based on the connection relationship between the nodes;

[0155] As attached Figure 4 As shown, nodes 1, 3, 8, 12, 13, 18, 23, and 26 can be considered as candidate nodes. The load requirements of different microgrids are also obtained, as shown in Table 1. Node 18 is the original energy storage node of the distribution network system. Building an intelligent base station here will result in lower civil engineering costs. Building additional intelligent base stations at other nodes will result in higher civil engineering costs, including materials, land, and construction costs.

[0156] Table 1 Microgrid division results and load demand

[0157]

[0158] 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, the planned cost under this plan is 215,150 yuan. This is because smart base stations with energy storage capacities of 510kW, 600kW, 540kW, 150kW, and 210kW are built at nodes 3, 8, 12, 23, and 26, respectively. Furthermore, 18 nodes are converted into smart base stations and equipped with 150kW of energy storage, resulting in a total construction cost of 44,500 yuan.

[0159] For Option 1, only smart base stations need to be built at nodes 3 and 8. The planned cost is 68,440 RMB, of which 14,500 RMB is for civil engineering. This solution significantly reduces the number of smart base stations and the energy storage capacity, resulting in lower costs.

[0160] For solution 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 solution is 216,720 yuan, of which the civil construction cost is the same as that of Solution 3. However, this solution allows for more freedom in the choice of smart base station locations, resulting in the situation where the smart base station only serves a single microgrid.

[0161] The planned cost for a conventional honeycomb structure is 446,720 yuan. Building a large number of intelligent base stations will undoubtedly significantly increase construction and equipment costs; and a fixed energy storage capacity cannot effectively meet the unevenly distributed load demand.

[0162] Given the comprehensive load active power of the distribution network under day-ahead dispatch, based on the HDN planned in Scheme 3, the day-ahead operation status of the HDN is tested and the optimization results are compared with the improved 33-node distribution network system. Figure 6 The state of charge of the energy storage devices in each smart base station in the past few days 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:

[0163] ;

[0164] in, is the load value of the power grid at time t, is the average load value. ω1 and ω2 are weight coefficients. Since load fluctuation has a significant impact on distribution network stability, ω1 is set to 0.25 and ω2 is set to 0.75.

[0165] Table 2 compares the indicators of the distribution network system before and after the intelligent base station is put into use. Figure 6 As can be seen, the deployment of intelligent base stations has improved peak-shaving and valley-filling capabilities. The real-time charging and discharging of multiple energy storage systems has smoothed the load curve, reducing both the amplitude and rate of load fluctuations. Using HDN can achieve a more ideal load curve and improve power supply stability.

[0166] Geographically, smart base stations are distributed throughout the distribution network system, shortening the electrical distance between source and storage, and load and storage. 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.

[0167] In addition, compared with conventional distribution networks, HDNs are less dependent on upstream power grids, and accordingly, the carbon emission costs they need to bear are also lower;

[0168] Table 2 Scenario comparison

[0169]

[0170] The above simulation results verify the effectiveness and practicality of the model constructed in this paper. They demonstrate that using the layout optimization method for improved HDNs and solving the intelligent base station site selection and sizing model can yield an optimized HDN layout. This significantly improves the operating efficiency and stability of the power distribution network system by reducing peak loads, reducing network losses, and suppressing load fluctuations, demonstrating its engineering value.

[0171] The above describes the embodiments of the present invention in detail with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. After knowing the contents described in the present invention, ordinary technicians in this technical field can make several equivalent changes and substitutions without departing from the principles of the present invention. These equivalent changes and substitutions should also be regarded as falling within the scope of protection 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: min{f1}=C b +C ES ; Among them, C b is the construction cost of the smart base station; C ES The full life cycle cost of the energy storage device; S2. Design a non-rectangular coordinate system to establish the site selection and capacity coupling constraints of smart base stations and energy storage; With the microgrid as the core, it is only guaranteed 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; 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; The smart base station on the shared boundary of any two microgrids should meet the power requirements of both microgrids. A single smart base station can support multiple microgrids simultaneously, and any microgrid can also receive support from multiple smart base stations simultaneously. With the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met. S3. Under the coupled constraints of site selection and fixed capacity, optimize the construction location and energy storage capacity of the smart base station 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 improved honeycomb distribution network layout optimization method according to claim 1, characterized in that: Civil construction cost of smart base station C b 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 an intelligent base station. and Take 1 for yes and 0 for no; π b ,π r and π de They are the construction cost converted from labor, materials, and equipment during the construction of the smart base station, the reconstruction cost, and the construction cost when dismantling the original energy storage equipment; Ω pre and Ω ren They are respectively a collection of nodes that can be established and rebuilt into intelligent base stations; The life cycle cost of energy storage device C ES for: Among them, Ω bs 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 improved honeycomb distribution network layout optimization method according to claim 2, characterized in that: Installation costs of energy storage devices for: Among them, π Ein is the installation cost per unit capacity, is the installed capacity of the battery in the i-th intelligent base station; π Pin is the installation cost per unit power, P i ES is the rated installed power of the battery; σ is the discount rate, and Y is the service life of the smart base station; Replacement cost for: Among them, Y ES is the operating life of the energy storage device, To find the floor function of the number of energy storage replacements; π Ere is the unit capacity replacement cost, π Pre is the replacement cost per unit power; Recovery costs for: in, To find the upward rounding function of the energy storage recovery times; π Ede is the unit capacity recovery cost, π Pde is the unit power recovery cost.

4. 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: With the microgrid as the core, it is only guaranteed that any microgrid can be connected to at least one smart base station, and the energy storage capacity of the smart base station can meet the power demand of any surrounding microgrid, meeting the following requirements: Among them, μ>1 is the reserve coefficient, is the set of candidate smart base stations around microgrid i; is the set of nodes 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, L i,max is the peak net power demand of microgrid i in one day.

5. 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: With smart base stations as the core, ensure that the combination of smart base stations within the preset range can meet the power requirements of all surrounding microgrids: Among them, μ>1 is the reserve coefficient, L i,max 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: Any smart base station on the shared boundary of any two microgrids should meet the power requirements of both microgrids. A single smart base station can support multiple microgrids simultaneously, and any microgrid can also receive support from multiple smart base stations simultaneously. With the cooperation of all smart base stations, the power requirements of all microgrids in the area can be met: Among them, μ>1 is the reserve coefficient, L i,max 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 a day-ahead basis, 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 curtailment 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 charge and discharge power at time t respectively; and are the current prices for compensating for transferred loads and shedding loads, and are the power transferred and removed during demand response, respectively; π loss is the unit network loss cost, I ij,t is the current of branch ij at time t, r ij is the resistance of branch ij; π aba 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; Ω DR ,Ω L and Ω RES 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 through 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 coefficient δ of the charge max and δ min 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, η loss is the self-loss coefficient, η ch and η dis are the charging and discharging efficiency of the battery respectively; and 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: Among them, δ cut and δ tran 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 satisfies the node voltage constraints and branch current constraints during operation: Among them, U i,t is the voltage of node i at time t, U i,max and U i,min are the upper and lower limits of the node voltage respectively; I ij,max 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: Among them, P j,t and Q j,t are the active and reactive power injected into node j at time t; Ω ij is the set of head-end nodes of the branch with j as the terminal node in HDN; Ω jk is the set of end nodes of the branch with j as the head node in HDN; P ij,t and Q ij,t are the active and reactive powers flowing through branch ij, respectively, x 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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