Smart energy storage soft-switching expansion planning method considering distribution line reconstruction

Abstract

The present application relates to a kind of smart energy storage soft switch extension planning method considering distribution line reconstruction, comprising the following steps: obtaining flexible interconnection distribution network parameter information according to selected flexible interconnection distribution network;Intelligent energy storage soft switch multi-stage extension planning constraint is constructed, intelligent energy storage soft switch extension planning model considering distribution line reconstruction is established, the obtained intelligent energy storage soft switch extension planning model considering distribution line reconstruction is solved using second order cone programming method, and the solution result is output, including: intelligent energy storage soft switch equipment extension planning scheme and equipment planning capacity, distribution line reconstruction scheme, the investment construction cost of distribution system containing intelligent energy storage soft switch;The present application can improve the operation flexibility of active distribution system containing intelligent energy storage soft switch, reduce equipment investment cost, and realize the maximization of equipment utilization efficiency.

Description

TECHNICAL FIELD

[0001] The application relates to an intelligent soft switch optimization configuration method considering multi-period planning, in particular to an intelligent soft switch expansion planning method considering power distribution line reconstruction. BACKGROUND

[0002] With the rapid development of power electronic technology, intelligent energy storage soft open points (ESOP) as a new type of flexible power distribution equipment have gradually replaced traditional power distribution tie switches and are widely used in active power distribution systems. Meanwhile, considering the large number of new loads represented by electric vehicles, the observability and controllability requirements of power distribution systems are continuously enhanced. Intelligent energy storage soft switch devices can realize system energy integration and balance regional electric vehicle loads on the basis of real-time control of feeder power flow, and can greatly improve the operation flexibility and reliability of active power distribution systems.

[0003] Intelligent energy storage soft switches are based on fully controlled power electronic devices, and the devices can be highly integrated. The number of device converter ports and the capacity of each port can be freely and flexibly designed. Considering the long planning, construction and operation period of such devices, in order to maximize the utilization efficiency of the devices, the construction and expansion planning problem of intelligent energy storage soft switches should be considered in multiple stages on the basis of the current system network architecture to meet the increasing flexible operation requirements of active power distribution systems.

[0004] Researches on the operation optimization problem of power distribution systems based on intelligent energy storage soft switches have been carried out at home and abroad, but the researches mainly focus on the flexible and controllable device characteristics of intelligent energy storage soft switches to improve the operation flexibility of power distribution systems. There are still some deficiencies and gaps in the multi-period expansion planning problem of intelligent energy storage soft switches. On the one hand, not only the connection position of the tie switch on the existing network architecture should be considered, but also the inherent device resources of the system should be utilized to improve the utilization efficiency of the devices and reduce the investment and construction cost of intelligent soft switches. On the other hand, in the process of multi-period evolution and promotion, the proportion of renewable clean energy access to the system is continuously improved, and different flexible operation requirements will have certain influence on the selection of intelligent energy storage soft switch expansion planning scheme. Therefore, an intelligent energy storage soft switch expansion planning method considering power distribution line reconstruction is urgently needed. The method further considers the expansion planning and construction scheme of the devices in different stages according to the characteristics of flexible configuration of the number and capacity of intelligent energy storage soft switch ports, reduces the investment cost of the devices, and realizes the flexible operation of active power distribution networks containing intelligent energy storage soft switches. SUMMARY

[0005] The technical problem to be solved by the present application is that, aiming at the intelligent energy storage soft switch optimization configuration method, an intelligent energy storage soft switch expansion planning model considering distribution line reconstruction is established, and the intelligent energy storage soft switch planning constraint, the intelligent energy storage soft switch operation constraint, the renewable energy penetration rate constraint and the distribution system operation constraint are comprehensively considered, so as to finally determine the intelligent energy storage soft switch multi-stage equipment planning expansion scheme.

[0006] The technical scheme adopted by the present application is:

[0007] A kind of intelligent energy storage soft switch expansion planning method considering distribution line reconstruction, comprising the following steps:

[0008] 1) according to the selected flexible interconnected distribution network, input flexible interconnected distribution network parameter information, including: network topology and line parameter information, including electric vehicle load parameter information and annual growth rate, distributed power parameter information and annual growth rate, system reference voltage and reference power and other basic parameter information;Intelligent energy storage soft switch parameter information is input, including: port converter device parameters, DC converter device parameters, energy storage system device parameters, intelligent energy storage soft switch investment cost, equipment planning stage number and each stage planning year limit;

[0009] 2) according to the flexible interconnected distribution network parameter information and intelligent energy storage soft switch parameter information provided in step 1), the multi-stage expansion planning constraint of intelligent energy storage soft switch is constructed, including intelligent energy storage soft switch configuration scheme correlation constraint, intelligent energy storage soft switch port equipment planning constraint, intelligent energy storage soft switch multi-stage capacity expansion constraint;

[0010] 3) according to the multi-stage expansion planning constraint of intelligent energy storage soft switch provided in step 2), the intelligent energy storage soft switch expansion planning model considering distribution line reconstruction is established, including: setting the sum of intelligent energy storage soft switch equipment investment cost, intelligent energy storage soft switch equipment site cost, intelligent energy storage soft switch equipment maintenance cost, distribution line reconstruction cost and distribution system loss cost as the objective function, considering intelligent energy storage soft switch multi-stage expansion planning constraint, intelligent energy storage soft switch operation constraint, renewable energy penetration rate constraint and distribution system operation constraint respectively;

[0011] 4) the intelligent energy storage soft switch expansion planning model considering distribution line reconstruction obtained in step 3) is solved by using the second order cone programming method, and the solving result is output, including: intelligent energy storage soft switch equipment expansion planning scheme and equipment planning capacity, distribution line reconstruction scheme, distribution system investment construction cost with intelligent energy storage soft switch.

[0012] Further, the intelligent energy storage soft switch configuration scheme correlation constraint in step 2) can be expressed as:

[0013]

[0014] wherein, denotes the set of intelligent energy storage soft-switching planning schemes; N k denotes the total number of intelligent energy storage soft-switching device planning schemes; denotes the set of device planning schemes obtained by expanding the kth intelligent energy storage soft-switching device planning scheme; denotes the set of device planning schemes with τ ports obtained by expanding the kth intelligent energy storage soft-switching device planning scheme; M τ denotes the maximum number of intelligent energy storage soft-switching port planning; α r，h denotes the 0-1 variable indicating whether the hth intelligent energy storage soft-switching device planning scheme is selected in the rth stage; U denotes the set union operation.

[0015] Further, the intelligent energy storage soft-switching port device planning constraint in step 2) can be represented as:

[0016]

[0017] wherein, and denote the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the kth intelligent energy storage soft-switching device planning scheme in the rth stage, respectively; Ω k denotes the set of device port nodes in the kth intelligent energy storage soft-switching device planning scheme; denotes the port converter planning capacity at node i in the kth intelligent energy storage soft-switching device planning scheme in the rth stage; α r，k denotes the 0-1 variable indicating whether the kth intelligent energy storage soft-switching device planning scheme is selected in the rth stage; ε denotes a small positive number; M denotes a large constant.

[0018] Further, the intelligent energy storage soft-switching multi-stage capacity expansion constraint in step 2) can be represented as:

[0019]

[0020] wherein, and denote the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the kth intelligent energy storage soft-switching device planning scheme in the r-1th stage, respectively; and denote the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the hth intelligent energy storage soft-switching device planning scheme in the rth stage, respectively.

[0021] Further, the objective function of minimizing the sum of the investment cost of the intelligent energy storage soft-switching device, the site cost of the intelligent energy storage soft-switching device, the maintenance cost of the intelligent energy storage soft-switching device, the reconstruction cost of the power distribution line and the loss cost of the power distribution system in step 3) can be expressed as:

[0022]

[0023] wherein f represents the objective function; f d represents the investment cost of the intelligent energy storage soft-switching device; f s represents the site cost of the intelligent energy storage soft-switching device; f m represents the maintenance cost of the intelligent energy storage soft-switching device; f b represents the reconstruction cost of the power distribution line; f l represents the loss cost of the power distribution system; N r represents the total number of planning stages; and respectively represent the investment cost of the intelligent energy storage soft-switching device in stage r and stage r-1; and respectively represent the unit capacity investment cost of the port AC / DC converter, the DC converter and the energy storage battery in the intelligent energy storage soft-switching device in stage r; represents the investment cost of the intelligent energy storage soft-switching device in the initial stage of the planning; and respectively represent the planning capacity of the port AC / DC converter, the DC converter and the energy storage battery in the kth intelligent energy storage soft-switching device planning scheme in stage r; represents the site cost of a single intelligent energy storage soft-switching device in stage r; a r，k and a r-1，k respectively represent the 0-1 variable of whether the kth intelligent energy storage soft-switching device planning scheme is selected in stage r and stage r-1; represents the unit capacity maintenance cost of the intelligent energy storage soft-switching device in stage r; T r represents the number of years in each planning stage; represents the unit length reconstruction cost of the power distribution line in stage r; w represents the topographic correction coefficient of the power distribution line; represents the length of the power distribution line to be reconstructed when the kth intelligent energy storage soft-switching device planning scheme is selected; c P represents the system electricity price; N t represents the total number of time sections; represents the power distribution line loss in stage r at time t per day; represents the intelligent energy storage soft-switching device loss in stage r at time t per day; D represents the time length of the time period; l all represents the set of all lines of the system; rij represents the line ij resistance value; l r，t，ij represents the square of the line ij current amplitude; represents the port converter transmission power loss at node i in the kth intelligent energy storage soft switching device planning scheme in the tth time period of the rth stage; represents the DC converter transmission power loss in the kth intelligent energy storage soft switching device planning scheme in the tth time period of the rth stage.

[0024] Further, the intelligent energy storage soft switching operation constraint in step 3) can be represented as:

[0025]

[0026] In the formula, and respectively represent the active power and reactive power injected by the port converter at node i in the kth intelligent energy storage soft switching device planning scheme in the tth time period of the rth stage; represents the power injected by the energy storage system in the kth intelligent energy storage soft switching device planning scheme in the tth time period of the rth stage; A ad represents the intelligent energy storage soft switching port converter loss coefficient; and respectively represent the intelligent energy storage soft switching storage power in the tth time period and the t+1th time period of the rth stage; A dc represents the intelligent energy storage soft switching DC converter loss coefficient; and respectively represent the minimum and maximum state of charge of the intelligent energy storage soft switching; and respectively represent the intelligent energy storage soft switching storage power in the t0th time period and the tth time period of the rth stage. N

[0027] The intelligent energy storage soft switching expansion planning method considering distribution line reconstruction of the application solves the optimization configuration problem of intelligent energy storage soft switching devices, fully considers the accurate power flow regulation and device operation characteristics of energy storage of intelligent energy storage soft switching, establishes an intelligent energy storage soft switching expansion planning model considering distribution line reconstruction according to the device planning idea of overall planning and segmented construction, obtains an intelligent energy storage soft switching multi-stage device planning expansion scheme, improves the operation flexibility of active power distribution systems containing intelligent energy storage soft switching, reduces the equipment investment cost, and realizes the maximization of equipment utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 is a flow chart of the intelligent energy storage soft switching expansion planning method considering distribution line reconstruction of the application;

[0029] Figure 2 ​This is a schematic diagram of an improved power distribution system calculation example;

[0030] Figure 3 These are curves showing the changes in photovoltaic load (including electric vehicle load) and system electricity price during the initial planning phase.

[0031] Figure 4 This is a schematic diagram of the planning and selection scheme for intelligent energy storage soft switching equipment in the four stages of Scenario 1; Detailed Implementation

[0032] The following detailed description of the intelligent energy storage soft switch expansion planning method considering power distribution line renovation proposed in this invention, with reference to embodiments and accompanying drawings, provides a detailed explanation.

[0033] The present invention provides an intelligent energy storage soft switch expansion planning method considering power distribution line renovation, such as... Figure 1 As shown, it includes the following steps:

[0034] 1) Based on the selected flexible interconnected distribution network, input the flexible interconnected distribution network parameter information, including: network topology and line parameter information, electric vehicle load parameter information and annual growth rate, distributed power source parameter information and annual growth rate, system reference voltage and reference power, and other basic parameter information; input the intelligent energy storage soft switch parameter information, including: port converter equipment parameters, DC converter equipment parameters, energy storage system equipment parameters, intelligent energy storage soft switch investment cost, number of equipment planning stages and planning years for each stage;

[0035] For this embodiment, the first step is to input the line parameters, including electric vehicle load parameters, and network topology connections in the improved power distribution system. Detailed parameters are shown in Tables 1-3. The intelligent energy storage soft-switching plan is structured in four phases, each lasting five years. Eight groups of potential access nodes for intelligent energy storage soft-switching port converters are selected, such as... Figure 2 The nodes are marked in red. The loss factor of both the intelligent energy storage soft-switching port converter and the DC converter is set to 0.01, and the maximum and minimum states of charge of the energy storage system are 90% and 20%, respectively. In the initial stage, two sets of interconnecting switches are connected, located between nodes 6-21 and 8-27, respectively. The total active and reactive loads of the system are 7.54MW and 5.23Mvar, respectively. Three photovoltaic systems are connected at nodes 4, 7, and 15, with installed capacities of 1.8MW, 1.8MW, and 1.2MW, respectively. The investment cost of the intelligent energy storage soft-switching equipment, system load growth rate, and renewable energy penetration requirements for each stage are detailed in Tables 4 and 5. Finally, the AC system voltage is set to 11.4kV, and the system base power is 1MVA.

[0036] 2) According to the flexible interconnection power distribution network parameter information and the intelligent energy storage soft switch parameter information provided in step 1), a multi-stage expansion planning constraint of the intelligent energy storage soft switch is constructed, including an intelligent energy storage soft switch configuration scheme correlation constraint, an intelligent energy storage soft switch port device planning constraint, and an intelligent energy storage soft switch multi-stage capacity expansion constraint;

[0037] (1) The intelligent energy storage soft switch configuration scheme correlation constraint can be expressed as:

[0038]

[0039] In the formula, denotes a set of intelligent energy storage soft switch planning schemes; N k denotes the total number of intelligent energy storage soft switch device planning schemes; denotes a set of device planning schemes obtained by expanding the kth intelligent energy storage soft switch device planning scheme; denotes a set of device planning schemes with τ ports obtained by expanding the kth intelligent energy storage soft switch device planning scheme; M τ denotes the maximum number of intelligent energy storage soft switch port planning; α r，h denotes a 0-1 variable indicating whether the hth intelligent energy storage soft switch device planning scheme is selected in stage r; U denotes a set union operation.

[0040] (2) The intelligent energy storage soft switch port device planning constraint can be expressed as:

[0041]

[0042] In the formula, and denote the planning capacity of the AC-DC converter, the DC converter and the energy storage battery in the kth intelligent energy storage soft switch device planning scheme in stage r; Ω k denotes a set of device port nodes in the kth intelligent energy storage soft switch device planning scheme; denotes the planning capacity of the port converter at node i in the kth intelligent energy storage soft switch device planning scheme in stage r; α r，k denotes a 0-1 variable indicating whether the kth intelligent energy storage soft switch device planning scheme is selected in stage r; ε denotes a small positive number; M denotes a large constant.

[0043] (3) The intelligent energy storage soft switch multi-stage capacity expansion constraint can be expressed as:

[0044]

[0045] In the formula, and respectively represent the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the kth intelligent energy storage soft switching device planning scheme in stage r-1; and respectively represent the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the hth intelligent energy storage soft switching device planning scheme in stage r.

[0046] 3) According to the intelligent energy storage soft switching multi-stage expansion planning constraints provided in step 2), an intelligent energy storage soft switching expansion planning model considering distribution line reconstruction is established, including: setting the sum of intelligent energy storage soft switching device investment cost, intelligent energy storage soft switching device site cost, intelligent energy storage soft switching device maintenance cost, distribution line reconstruction cost and distribution system loss cost as the objective function, considering intelligent energy storage soft switching multi-stage expansion planning constraints, intelligent energy storage soft switching operation constraints, renewable energy penetration rate constraints and distribution system operation constraints respectively;

[0047] (1) The setting of the sum of intelligent energy storage soft switching device investment cost, intelligent energy storage soft switching device site cost, intelligent energy storage soft switching device maintenance cost, distribution line reconstruction cost and distribution system loss cost as the objective function can be represented as:

[0048]

[0049] In the formula, f represents the objective function; f d represents the intelligent energy storage soft switching device investment cost; f s represents the intelligent energy storage soft switching device site cost; f m represents the intelligent energy storage soft switching device maintenance cost; f b represents the distribution line reconstruction cost; f l represents the distribution system loss cost; N r represents the total number of planning stages; and respectively represent the intelligent energy storage soft switching device investment cost in stage r and stage r-1; represents the intelligent energy storage soft switching planning scheme set; and respectively represent the unit capacity investment cost of the port AC / DC converter, DC converter and energy storage battery in the intelligent energy storage soft switching device in stage r; and respectively represent the planning capacity of the port AC / DC converter, DC converter and energy storage battery in the kth intelligent energy storage soft switching device planning scheme in stage r; represents the intelligent energy storage soft switching device site cost in stage r; α r，k and αr-1，k is a 0-1 variable representing whether the kth intelligent energy storage soft-switching device planning scheme is selected in stage r and stage r-1, respectively; is the maintenance cost of unit capacity intelligent energy storage soft-switching device in stage r; T r is the number of years in each planning stage; is the reconstruction cost of unit length distribution line in stage r; ω is the terrain correction coefficient of distribution line; is the length of distribution line to be reconstructed when the kth intelligent energy storage soft-switching device planning scheme is selected; c P is the system electricity price; N t is the total number of time sections; is the loss of distribution line in stage r at time period t per day; is the loss of intelligent energy storage soft-switching device in stage r at time period t per day; Δt is the length of time period; all is the set of all lines of the system; r ij is the resistance value of line ij; l r，t，ij is the square of the current amplitude of line ij; Ω k is the set of device port nodes in the kth intelligent energy storage soft-switching device planning scheme; is the loss of port converter transmission power in the kth intelligent energy storage soft-switching device planning scheme at node i in stage r at time period t per day; is the loss of direct current converter transmission power in the kth intelligent energy storage soft-switching device planning scheme in stage r at time period t per day.

[0050] (2) The intelligent energy storage soft-switching operation constraint can be represented as:

[0051]

[0052] In the formula, and are the active power and reactive power injected by the port converter at node i in the kth intelligent energy storage soft-switching device planning scheme in stage r at time period t per day, respectively; is the injected power of the energy storage system in the kth intelligent energy storage soft-switching device planning scheme in stage r at time period t per day; A ad is the loss coefficient of the intelligent energy storage soft-switching port converter; and are the stored electric quantities of the intelligent energy storage soft-switching in stage r at time period t per day and time period t+1 per day, respectively; A dc is the loss coefficient of the intelligent energy storage soft-switching direct current converter; and are the minimum and maximum state of charge of the intelligent energy storage soft-switching, respectively; and P N P

[0053] (3) The renewable energy penetration rate constraint can be expressed as:

[0054]

[0055] where Ω all represents the set of all nodes in the system; P and Q r represent the active power and reactive power injected by the distributed generator at node i in period r and day t, respectively; γ represents the active load at node i in period r and day t; represents the upper limit of the active power output of the distributed generator at node i in period r and day t; represents the minimum power factor at which the distributed generator at node i operates; represents the installed capacity of the distributed generator at node i.

[0056] (4) The distribution system operation constraint can be expressed as:

[0057]

[0058] where P r，t，ij and Q r，t，ij represent the active power and reactive power transmitted on line ij in period r and day t, respectively; r ij and x ij represent the resistance and reactance of line ij, respectively; P r，t，j and Q r，t，j represent the active power and reactive power injected at node j in period r and day t; v r，t，i and v r，t，j represent the square of the voltage amplitude at node i and node j in period r and day t, respectively; and represent the lower and upper voltage limits at node i, respectively; represents the maximum current-carrying capacity of line ij.

[0059] 4) The smart energy storage soft switch expansion planning model considering distribution line reconstruction obtained in step 3) is solved using a second-order cone programming method, and the solving results are output, including: smart energy storage soft switch device expansion planning scheme and device planning capacity, distribution line reconstruction scheme, investment construction cost of distribution system containing smart energy storage soft switch.

[0060] To fully verify the advancement of the method of the present invention, this embodiment adopts two sets of scenarios for comparative analysis:

[0061] Scenario 1: Using the method of this invention, we can obtain an expansion plan for intelligent energy storage soft switching devices and get the corresponding operating costs of the active distribution network in each stage.

[0062] Scenario 2: Without planning for intelligent energy storage soft switching equipment, the operating costs of the active power distribution network at each stage are obtained;

[0063] The results of the expansion plan for intelligent energy storage soft switching equipment in Scenario 1 are shown below. Figure 4 Tables 6 and 7 show the planned capacity of the smart energy storage soft switch port and the total investment cost of the distribution network including the smart energy storage soft switch in Scenario 1. Table 8 shows the system operation cost results for Scenario 1 and Scenario 2.

[0064] The computer hardware environment for performing the optimized calculations was an Intel(R) Core(TM) i7-12700 with a clock speed of 2.10GHz and 16GB of memory; the software environment was a Windows 11 operating system.

[0065] The planning scheme for intelligent energy storage soft switching equipment in Scenario 1 is as follows: Figure 4 As shown in Table 6, the planned capacity of the equipment ports at each stage is as follows. Based on the existing interconnection switch lines between nodes 8-27, a transmission line is added between the switch station and node 21 to construct a three-terminal intelligent energy storage soft switch. By expanding the port capacity of the intelligent energy storage soft switch equipment in stages, the ever-increasing operational requirements of the system are met, and the equipment investment and construction costs are reduced.

[0066] Comparing the system operation results under the two scenarios shows that intelligent energy storage soft switching can effectively improve the absorption level of distributed power sources, alleviate voltage fluctuation problems, reduce system network losses, and comprehensively improve the system's operational flexibility. Therefore, by planning and constructing intelligent energy storage soft switching over multiple cycles, not only can the system's operational level be effectively improved, but also the equipment investment and construction costs can be effectively reduced, maximizing the utilization efficiency of intelligent energy storage soft switching equipment.

[0067] When optimizing the configuration of power distribution system equipment, the system network structure will have a certain impact on the selection of equipment planning schemes. It is necessary to further consider the expansion planning of intelligent energy storage soft switches under different system network architectures in order to cope with the increasingly complex network structure of active power distribution systems and achieve efficient operation of active power distribution networks with intelligent energy storage soft switches.

[0068] Table 1 shows the load connection locations and power in the improved distribution network example.

[0069]

[0070]

[0071] Line parameters in improved distribution network example

[0072]

[0073] Node location parameters in improved distribution network example

[0074]

[0075]

[0076] Investment cost parameters of intelligent energy storage soft switch equipment

[0077] Parameter name Stage 1 Stage 2 Stage 3 Stage 4 Port converter investment cost (RMB / kVA) 1000 800 600 500 Energy storage converter investment cost (RMB / kW) 500 400 350 300 Energy storage battery investment cost (RMB / kWh) 500 400 350 300 Annual maintenance cost of equipment (RMB / kW) 60 80 100 120 Line investment construction cost (10 4 × RMB / km) 10 12 16 20 Device site cost (10 4 × RMB) 500 550 600 700 Distributed power generation cost (RMB / kWh) 0.25 0.22 0.17 0.15

[0078] EV load and distributed power growth in each stage

[0079] Parameter name Initial Stage 1 Stage 2 Stage 3 Stage 4 Annual load growth rate (%) —— 4 3 1.5 1.0 Peak load (MW) 7.54 9.17 10.63 11.46 12.04 Total DG installed capacity (MW) 4.80 6.80 9.80 12.80 16.80 Total electricity load (MWh) 51.88 63.11 73.17 78.82 82.84 DG power generation (MWh) 19.30 27.34 39.40 51.46 67.54 Renewable energy penetration rate (%) 37 42 52 64 80

[0080] Intelligent energy storage soft switch port planning capacity under scenario 1

[0081]

[0082] Intelligent energy storage soft switch equipment investment cost under scenario 1

[0083] Unit: ten thousand yuan

[0084] Investment cost Stage 1 Stage 2 Stage 3 Stage 4 ESOP investment cost 400.40 205.79 694.16 1016.64 ESOP site cost 500.00 0.00 0.00 0.00 Annual maintenance cost of ESOP 3.36 8.25 28.28 71.46 Annual loss cost 48.49 51.89 56.28 77.02 Line transformation cost 78.10 0.00 0.00 0.00 Total cumulative investment cost 1237.76 1744.25 2861.19 4620.24

[0085] System operation cost under scenario 1 and scenario 2

[0086] Unit: ten thousand yuan

[0087]

[0088]

Claims

1. A method for expanding the planning of intelligent energy storage soft switches considering power distribution line renovation, characterized in that, Includes the following steps: 1) Obtain the parameter information of the selected flexible interconnected distribution network; 2) Based on the flexible interconnected distribution network parameter information and smart energy storage soft switch parameter information obtained in step 1), construct multi-stage expansion planning constraints for smart energy storage soft switch, including smart energy storage soft switch configuration scheme association constraints, smart energy storage soft switch port equipment planning constraints, and smart energy storage soft switch multi-stage capacity expansion constraints. 3) Based on the multi-stage expansion planning constraints of the smart energy storage soft switch provided in step 2), establish a smart energy storage soft switch expansion planning model that considers the transformation of power distribution lines. This model includes setting the minimum sum of the investment cost of the smart energy storage soft switch equipment, the site cost of the smart energy storage soft switch equipment, the maintenance cost of the smart energy storage soft switch equipment, the cost of the transformation of power distribution lines, and the loss cost of the power distribution system as the objective function. The model also considers the multi-stage expansion planning constraints of the smart energy storage soft switch, the operation constraints of the smart energy storage soft switch, the renewable energy penetration rate constraints, and the operation constraints of the power distribution system. 4) Solve the intelligent energy storage soft switch expansion planning model obtained in step 3) considering the transformation of power distribution lines using the second-order cone programming method, and output the solution results, including: intelligent energy storage soft switch equipment expansion planning scheme and equipment planning capacity, power distribution line transformation scheme, and investment and construction cost of power distribution system including intelligent energy storage soft switch. The associated constraints of the intelligent energy storage soft-switching configuration scheme described in step 2) can be expressed as follows: In the formula, This represents a set of intelligent energy storage soft-switching planning schemes; N k This indicates the total number of planned schemes for intelligent energy storage soft switching devices; This represents the set of equipment planning schemes derived from the k-th intelligent energy storage soft switching device planning scheme; M represents the set of device planning schemes with τ ports obtained by extending the planning scheme of the k-th intelligent energy storage soft-switching device; τ Indicates the maximum planned number of smart energy storage soft-switching ports; α r,h ∪ represents a 0-1 variable indicating whether the h-th intelligent energy storage soft-switching device planning scheme is selected within stage r; ∪ represents the set union operation; The planning constraints for the intelligent energy storage soft-switching port device mentioned in step 2) can be expressed as: In the formula, and Ω represents the planned capacity of the AC / DC converter, DC converter, and energy storage battery in the k-th intelligent energy storage soft-switching device planning scheme within stage r; k This represents the set of device port nodes in the planning scheme of the k-th type of intelligent energy storage soft switching device; α represents the planned capacity of the port converter at node i in the k-th intelligent energy storage soft-switching device planning scheme within phase r; r,k ε represents a 0-1 variable indicating whether to select the k-th intelligent energy storage soft-switching device planning scheme within stage r; M represents a large constant. The multi-stage capacity expansion constraint of the intelligent energy storage soft switch mentioned in step 2) can be expressed as: In the formula, and These represent the planned capacities of the AC / DC converter, DC converter, and energy storage battery in the k-th intelligent energy storage soft-switching device planning scheme within stage r-1, respectively. and These represent the planned capacities of the port AC / DC converter, DC converter, and energy storage battery in the h-th intelligent energy storage soft-switching device planning scheme within phase r.

2. The intelligent energy storage soft switch expansion planning method considering power distribution line renovation as described in claim 1, characterized in that, Step 3) describes setting the objective function to minimize the sum of the investment cost of the intelligent energy storage soft switching device, the site cost of the intelligent energy storage soft switching device, the maintenance cost of the intelligent energy storage soft switching device, the power distribution line renovation cost, and the power distribution system loss cost. This objective function can be expressed as: In the formula, f represents the objective function; f d Indicates the investment cost of intelligent energy storage soft switching equipment; f s This indicates the site cost of intelligent energy storage soft-switching equipment; f m Indicates the maintenance cost of intelligent energy storage soft switching equipment; f b Indicates the cost of upgrading power distribution lines; f l N represents the cost of power distribution system losses; r Indicates the total number of planning stages; and These represent the investment costs of smart energy storage soft switching equipment in stage r and stage r-1, respectively. and These represent the unit capacity investment costs of the port AC / DC converter, DC converter, and energy storage battery in the intelligent energy storage soft-switching equipment within stage r, respectively. This indicates the investment cost of intelligent energy storage soft-switching equipment in the initial planning stage; and These represent the planned capacities of the AC / DC converter, DC converter, and energy storage battery in the k-th intelligent energy storage soft-switching device planning scheme within phase r, respectively. Indicates the site cost of a single smart energy storage soft-switching device within phase r; α r,k and α r-1,k These represent 0-1 variables indicating whether the k-th intelligent energy storage soft-switching device planning scheme is selected in stage r and stage r-1, respectively. This represents the maintenance cost per unit capacity of intelligent energy storage soft-switching equipment within phase r; Τ r Indicates the number of years within each planning phase; ω represents the cost of upgrading a unit length of power distribution line within stage r; ω represents the terrain correction factor for power distribution lines. c represents the length of the power distribution line that needs to be modified when selecting the k-th intelligent energy storage soft-switching device planning scheme; P Indicates the system electricity price; N t Indicates the total number of time segments; This represents the power distribution line loss during the t-hour period of each day within phase r; Δt represents the loss of the smart energy storage soft-switching equipment during the daily time period t within phase r; Δt represents the duration of the time period. l all Represents the set of all lines in the system; r ij Indicates the resistance value of line ij; l r,t,ij This represents the square of the amplitude of the line current ij; The power loss of the port converter at node i in the planning scheme of the k-th type of smart energy storage soft switching equipment during the t-period of each day within phase r is represented. This represents the power loss of the DC converter in the planning scheme of the k-th intelligent energy storage soft-switching device within the time period t of each day in phase r.

3. The intelligent energy storage soft switch expansion planning method considering power distribution line renovation as described in claim 2, characterized in that, The operational constraints of the intelligent energy storage soft switch mentioned in step 3) can be expressed as: In the formula, and These represent the active power and reactive power injected by the port converter at node i in the planning scheme of the k-th type of smart energy storage soft switching equipment during the t-period of each day within stage r; A represents the power injected into the energy storage system in the planning scheme of the k-th intelligent energy storage soft-switching device within the daily time period t within phase r; ad This represents the loss coefficient of the intelligent energy storage soft-switching port converter; and These represent the stored electricity of the smart energy storage soft switch during time period t and time period t+1 each day within stage r; A dc This represents the loss coefficient of a smart energy storage soft-switching DC converter. and These represent the minimum and maximum states of charge of the intelligent energy storage soft switch, respectively. and They represent the time intervals t0 and t0 within each day of phase r, respectively. N The intelligent energy storage soft switch stores electricity during the specified time period.

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