SOP locating and sizing planning method considering energy storage system

Abstract

The invention discloses an SOP locating and sizing planning method considering an energy storage system. The method comprises the following steps: step 1, obtaining data of a new energy distributed power supply and a load in a power distribution network through a detection technology; 2, according to the data, obtained in the step 1, of the distributed power supply and the load, fine modeling and SOP operation constraint modeling are carried out on an energy storage system; the refined modeling of the energy storage system comprises operation constraint modeling of the energy storage system and economic cost modeling of the energy storage system; 3, listing the data obtained in the step 1 and the active and reactive power of the energy storage system operation constraint model and the SOP operation constraint model obtained in the step 2 into active and reactive power balance constraint conditions of power distribution network nodes; 4, calculating the planning cost under the condition that the constraint conditions of the step 2 and the step 3 are met; planning by taking the minimum planning cost as an objective function to obtain a locating and sizing scheme of the SOP; and 5, calculating a branch load margin and a supply-demand balance flexibility index, and measuring the operation flexibility of the power distribution network by calculating the difference between the two.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method for site selection and capacity planning of soft open point (SOP) of a distribution network considering an energy storage system. Background Art

[0002] With the continuous increase in the penetration rate of distributed generation (DG), the problems of supply and demand imbalance and voltage over-limit in distribution networks have intensified, and there is an urgent need to introduce flexible resources for regulation. Intelligent soft switch is a flexible power electronic device installed in the distribution network to replace the traditional tie switch, which can achieve fast voltage regulation and accurate flow control, thereby effectively solving the corresponding problems brought by DG; intelligent soft switch technology is an important innovation in the field of distribution network, which represents the development direction of flexible and intelligent power system. The development history of SOP technology can be traced back to the end of the 20th century, but it was not until the beginning of the 21st century that SOP technology gradually matured and began to receive widespread attention with the significant progress of power electronics technology, control strategy and information technology.

[0003] The site selection and capacity planning of SOP is also a key technology in the flexible development of smart distribution networks. Early SOP planning research mainly focused on site selection and capacity planning methods based on traditional optimization algorithms. These methods have low computational complexity when solving problems with small scale and simple constraints, but they mainly focus on single goals such as minimizing network losses and improving power supply reliability. They often ignore the complexity and variability of distribution network operation, resulting in limited practicality and adaptability of planning results. With the rapid development of smart distribution networks and the increasing complexity of optimization problems, researchers have begun to explore more advanced planning methods. In recent years, with the improvement of computing power and the development of optimization algorithms, SOP planning models based on modern heuristic algorithms, mixed integer nonlinear programming (MINLP) and machine learning methods have gradually become a research hotspot. These methods can better handle large-scale, multi-objective, and multi-constrained planning problems, and improve the accuracy and efficiency of planning. In particular, considering the uncertainty brought by renewable energy access and load fluctuations, some studies have begun to integrate probabilistic models and scenario analysis techniques to enhance the robustness of SOP planning models. In addition, deep learning technology has shown great potential in processing large-scale data and identifying complex patterns, and has gradually been applied to the site selection and capacity planning of SOP, in order to achieve more refined and personalized planning schemes.

[0004] It can be seen from the existing research that when most scholars make the site selection and capacity planning of the distribution network SOP, they usually plan it separately, or combine it with the energy storage system for single-objective or single-constraint planning. For example, in the paper "Optimal Configuration of Intelligent Soft Switches in Active Distribution Networks Based on Improved Sensitivity Analysis", an improved sensitivity analysis algorithm is used to consider the high penetration rate of a large number of intermittent distributed power sources to carry out site selection and capacity planning for SOP. This planning method has the following disadvantages: First, when planned separately, it can balance the flow on a spatial scale and improve the flexibility of distribution network operation, but on a time scale, the capacity investment cost of SOP is relatively high, so the flexibility improvement is relatively small; second, when planning the SOP in combination with the energy storage system, fewer constraints are considered. In addition to considering the economic cost, there is less consideration of flexibility indicators. Most of them only consider the problem of voltage exceeding the limit, while there is less consideration of branch adequacy and supply and demand balance indicators. Summary of the invention

[0005] The purpose of the present invention is to solve the technical problem of insufficient flexibility in time scale when planning SOP in the prior art, and to provide a SOP site selection and sizing planning method considering energy storage systems.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A SOP site selection and capacity planning method considering an energy storage system includes the following steps:

[0008] Step 1: Obtain data on new energy distributed power sources and loads in the distribution network through detection technology;

[0009] Step 2: Based on the distributed power source and load data obtained in step 1, the energy storage system is refinedly modeled and the SOP operation constraint modeling is performed; the refined modeling of the energy storage system includes the energy storage system operation constraint modeling and the energy storage system economic cost modeling;

[0010] Step 3: The data obtained in step 1 and the active and reactive power of the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 are included in the active and reactive balance constraint conditions of the distribution network node;

[0011] Step 4: Under the constraints of step 2 and step 3, calculate the planning cost; plan with the minimum planning cost as the objective function to obtain the site selection and capacity determination plan of SOP;

[0012] Step 5: Calculate branch load margin I BF and supply-demand balance flexibility index I FSD , and the operation flexibility of the distribution network is measured by calculating the difference between the two.

[0013] In step 2, the energy storage system is refined and the operating constraints of the SOP are modeled, which specifically includes the following steps:

[0014] Step 2.1, before modeling the operation constraints of the energy storage system, in order to meet the actual operation conditions of the energy storage system and SOP, the upper and lower limit parameters of the charging and discharging power of the energy storage system, the upper and lower limit parameters of the energy storage, and the loss coefficient of the converter in the SOP are preset;

[0015] Step 2.2: Model the operation constraints and economic costs of the energy storage system. At the same time, model the operation constraints of the SOP. The details are as follows:

[0016] The charging and discharging power of the energy storage system cannot exceed the preset upper and lower limits of the charging and discharging power. In addition, the energy storage system cannot charge and discharge at the same time, so there are the following constraints:

[0017]

[0018] 0≤ξ ch +ξ dis ≤1 (2)

[0019] Where: P ch,n,t , P dis,n,t are the real-time charging and discharging power of the energy storage system n at time point t; ch , dis It is a variable of 0 or 1. When it is 1, it indicates that the energy storage system is in the state of charging or discharging. When it is 0, it indicates that the energy storage system is in the state of shutting down. max , P min They are the upper and lower limits of the charging and discharging power of the energy storage system respectively;

[0020] In the energy storage system, there are 4 energy storage devices, which are located at nodes 8, 14, 21 and 25. In order to meet the operation requirements of the energy storage devices themselves, the energy stored in the energy storage devices must be between 0.2 and 0.9 times the rated capacity. The constraints are as follows:

[0021] E socmin ≤E n,t ≤E socmax (3)

[0022]

[0023] Where: E n,t is the real-time energy storage capacity of energy storage system n at time point t; E socmax 、E socmin are the upper and lower limits of the charge and discharge capacity of the energy storage system respectively; S n,B is the rated capacity of the energy storage device n;

[0024] When modeling the economic cost of the energy storage system, in order to measure the economic cost of the energy storage system, the cost of the energy storage system is annualized. At the same time, the cost includes three parts: the annual investment, maintenance and loss cost of the energy storage system. The specific constraints are as follows:

[0025] W ESS =w tz +w wh +w loss (5)

[0026]

[0027] Where: W ESS is the total annual investment cost of the energy storage system; w tz 、w wh and w loss are the annual investment, maintenance and loss costs of the energy storage system respectively; η t is the investment cost per unit capacity of the energy storage system; η w is the maintenance cost per unit capacity of the energy storage system; η loss is the loss cost per unit capacity of the energy storage system; y is the service life of the energy storage system;

[0028] SOP will have certain losses during operation. It can also perform certain charging and discharging when necessary. As a soft switch, its power inflow and outflow should be balanced, with power injection as negative and outflow as positive. It has the following constraints:

[0029]

[0030] Where: and are the active and reactive powers injected by SOP converters a and b at time t, respectively; is the charging / discharging power of the energy storage system in the SOP at time t; is the SOP transmission loss at time t; A SOP,a and A SOP,b are the loss coefficients of converter a and b respectively; and are the access capacities of converter a and converter b respectively. They are usually considered equal and are recorded as

[0031] At the same time, when planning SOP, the upper limit of the number of planned SOP is 5, and the number of planned SOPs can be changed at will. If the number is different, the capacity and position of each SOP will also change accordingly; the unit optimization capacity of SOP is 10kV·A.

[0032] In the above step 3, for the entire distribution network, at each moment, the power purchased from the upper main grid, the distributed power source, the energy storage system in the distribution network, the power provided by the SOP, and the power consumed by the load and line loss in the distribution network are equal. At the same time, when the data obtained in step 1 is introduced, the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 must be satisfied, so there are the following constraints:

[0033]

[0034] Where: P t MG and is the active and reactive power purchased from the upper main grid during period t; and are the active and reactive power generated by the nth DG in period t, respectively; is the network loss of branch n in period t; and are respectively the active and reactive power consumed by the nth load in period t; Ω ESS ,Ω DG and Ω SOP They are the installation node sets of energy storage systems, distributed generation and SOPs; is the active power detection value output by the nth DG at time t; P DG,n,t , Q DG,n,t are the active power and reactive power output by DG at node n during period t; β DG,n,t is the power factor angle of DG at node n during period t; is the net active power generated by the nth ESS at time t; and are the active and reactive power generated by the nth SOP at time t; β SOP,n,t is the power factor angle of the nth SOP at time t.

[0035] In step 4 above, under the constraints of step 2 and step 3, the planning cost is calculated and the SOP planning scheme with the minimum cost is found:

[0036] Step 4.1: The planning cost is modeled with the minimum annual comprehensive operating cost of the distribution network as the objective function. The model is as follows:

[0037] f med =C SI +C OMS +C PC +C NLC (18)

[0038]

[0039] Where: Fmed is the annual comprehensive operating cost; C SI is the annual investment cost of SOP; C OMS is the annual operation and maintenance cost of SOP; C PC The cost of purchasing electricity from the upper grid; C NLC is the network loss cost; λ is the discount rate; and are the annual investment cost and annual operation and maintenance cost of ESS respectively. Since the upper and lower limits of ESS power and capacity have been determined, these two values ​​are fixed constants. SOP is the useful life of the SOP; c s is the unit capacity investment cost of SOP; is the SOP capacity to be installed between nodes i and j; η SOP is the annual operation and maintenance cost of SOP; c g The unit price of electricity purchased from the upper power grid; c loss is the unit power network loss cost; P t MG is the active power injected by the upper main grid into the distribution network at time t; is the network loss power of branch i, j at time t;

[0040] Step 4.2: Use the solver to solve the above model, and obtain the node position and capacity size of the SOP that minimizes the objective function value, which is the planning result.

[0041] In step 5, the flexibility of distribution network operation is indexed from two dimensions. One dimension is the flexibility index of branch load margin in space, and the other dimension is the flexibility index of supply and demand balance in time. The flexibility of the distribution network is obtained by calculating the flexibility of the two dimensions and making a difference. The specific steps are as follows:

[0042] Step 5.1: Calculate the branch load margin flexibility index and supply-demand balance flexibility index respectively:

[0043] The branch load margin flexibility index is used to evaluate the transmission capacity of network flexibility resources. It is mainly represented by the ratio of the current in the branch to the maximum current that the branch can withstand. The smaller the value, the greater the load margin of each branch, and the stronger the ability to cope with the uncertainty changes in flexibility demand:

[0044]

[0045] Where: I BF is the average load rate of the network branch, Ω B is the branch set of the distribution network; L zm,t is the load rate of branch z,m at time t; I zm,t is the current flowing through branch z,m at time t; Izm is the maximum value of the current allowed to flow through branch z,m;

[0046] The supply-demand balance flexibility index is used to measure whether the flexibility supply can meet the flexibility demand. The larger the value, the more the flexibility resources can meet the flexibility demand, and the better the overall flexibility of the distribution network:

[0047]

[0048]

[0049] Where: I FSD It is an indicator of supply and demand balance flexibility; UFSD ,I DFSD The sum of the ratios of the dispatchable margin of flexible resources to the change in net load when the net load increases or decreases within a dispatch cycle; are the total upward and downward flexibility adjustment capabilities of the distribution network at time t; T is the total number of time periods in the dispatching cycle. The dispatching cycle in this paper is 24 hours, and the dispatching time interval is 1 hour; wt is the state variable of the net load power change at time t; P t nl , are the net load power at time t and t+1 respectively;

[0050] Step 5.2: The two distribution network flexibility evaluation indicators calculated in the first two steps are coupled, mutually influential, and interact with each other; in the planning process, node flexibility and branch flexibility are combined to jointly determine the quality of system flexibility:

[0051] F I =I FSD -I BF (19)

[0052] Where: FI represents system flexibility. The larger its value is, the better the system flexibility is.

[0053] Compared with the prior art, the present invention has the following technical effects:

[0054] 1) In the process of fine-tuning the modeling of the energy storage system (ESS), the present invention not only comprehensively considers the constraints of its charging and discharging power, ensuring that the power output and input of the system under different operating conditions can meet the actual needs, but also deeply analyzes the state of charge (SOC) constraints of the ESS capacity, ensuring that the system can operate safely and stably under different SOC levels. In addition, in terms of economy, the present invention also comprehensively considers the investment cost, maintenance cost and possible loss cost of the ESS during operation, and achieves the goal of reducing the overall operating cost while ensuring system performance by accurately calculating and optimizing these cost factors.

[0055] 2) In the operation and management of the distribution network, the present invention pays special attention to the time-series variation characteristics of the output of distributed generation (DG) units, which is crucial to the stability and economy of the power grid. In order to adapt to the dynamic nature of DG output, the present invention establishes a distribution network operation constraint model that can monitor and respond to changes in DG output in real time, ensuring that the power grid can maintain stable operation under various output conditions.

[0056] 3) In terms of planning and optimization of the distribution network, the present invention establishes a site optimization (SOP) site selection and capacity planning model with the annual comprehensive operating cost minimization as the optimization goal. The model comprehensively considers multiple factors such as the geographical layout, load characteristics, and energy supply of the distribution network. Through advanced optimization algorithms, it realizes the optimal site selection and capacity configuration of each site in the distribution network; and calculates the operational flexibility of the distribution network, which not only achieves significant improvement in economy, but also makes important breakthroughs in flexibility, providing an effective solution for the efficient, reliable, and economical operation of modern distribution networks. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0058] Figure 1 The overall framework flow chart of the SOP site selection and capacity planning method considering the energy storage system;

[0059] Figure 2 Improved wiring diagram for 33 nodes of the distribution network;

[0060] Figure 3 To obtain data waveforms of distributed power sources (wind power, photovoltaic power) and loads in the distribution network through detection technology;

[0061] Figure 4 It is the real-time charging and discharging power diagram of energy storage device 1 (positive means charging, negative means discharging);

[0062] Figure 5 It is the real-time charging and discharging power diagram of energy storage device 2 (positive means charging, negative means discharging);

[0063] Figure 6 It is the real-time charging and discharging power diagram of energy storage device 3 (positive means charging, negative means discharging);

[0064] Figure 7 It is the real-time charging and discharging power diagram of the energy storage device 4 (positive means charging, negative means discharging);

[0065] Figure 8 This is the node voltage diagram for planning SOP without considering energy storage;

[0066] Fig. 9Node voltage diagram for planning SOP when considering energy storage;

[0067] Fig.10 This is a diagram of branch margin index (average current ratio). DETAILED DESCRIPTION

[0068] A SOP site selection and capacity planning method considering an energy storage system includes the following steps:

[0069] Step 1: Obtain data on new energy distributed power sources and loads in the distribution network through detection technology;

[0070] Step 2: Based on the distributed power source and load data obtained in step 1, the energy storage system is refinedly modeled and the SOP operation constraint modeling is performed; the refined modeling of the energy storage system includes the energy storage system operation constraint modeling and the energy storage system economic cost modeling;

[0071] Step 3: The data obtained in step 1 and the active and reactive power of the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 are included in the active and reactive balance constraint conditions of the distribution network node;

[0072] Step 4: Under the constraints of step 2 and step 3, calculate the planning cost; plan with the minimum planning cost as the objective function to obtain the site selection and capacity determination plan of SOP;

[0073] Step 5: Calculate branch load margin I BF and supply-demand balance flexibility index I FSD , and the operation flexibility of the distribution network is measured by calculating the difference between the two.

[0074] In step 2, the energy storage system is refined and the operating constraints of the SOP are modeled, which specifically includes the following steps:

[0075] Step 2.1, before modeling the operation constraints of the energy storage system, in order to meet the actual operation conditions of the energy storage system and SOP, the upper and lower limit parameters of the charging and discharging power of the energy storage system, the upper and lower limit parameters of the energy storage, and the loss coefficient of the converter in the SOP are preset;

[0076] Step 2.2: Model the operation constraints and economic costs of the energy storage system. At the same time, model the operation constraints of the SOP. The details are as follows:

[0077] The charging and discharging power of the energy storage system cannot exceed the preset upper and lower limits of the charging and discharging power. In addition, the energy storage system cannot charge and discharge at the same time, so there are the following constraints:

[0078]

[0079] 0≤ξ ch+ξ dis ≤1 (2)

[0080] Where: P ch,n,t , P dis,n,t are the real-time charging and discharging power of the energy storage system n at time point t; ch , dis It is a variable of 0 or 1. When it is 1, it indicates that the energy storage system is in the state of charging or discharging. When it is 0, it indicates that the energy storage system is in the state of shutting down. max , P min They are the upper and lower limits of the charging and discharging power of the energy storage system respectively;

[0081] In the energy storage system, there are 4 energy storage devices, which are located at nodes 8, 14, 21 and 25. In order to meet the operation requirements of the energy storage devices themselves, the energy stored in the energy storage devices must be between 0.2 and 0.9 times the rated capacity. The constraints are as follows:

[0082] E socmin ≤E n,t ≤E socmax (3)

[0083]

[0084] Where: E n,t is the real-time energy storage capacity of energy storage system n at time point t; E socmax 、E socmin are the upper and lower limits of the charge and discharge capacity of the energy storage system respectively; S n,B is the rated capacity of the energy storage device n;

[0085] When modeling the economic cost of the energy storage system, in order to measure the economic cost of the energy storage system, the cost of the energy storage system is annualized. At the same time, the cost includes three parts: the annual investment, maintenance and loss cost of the energy storage system. The specific constraints are as follows:

[0086] W ESS =w tz +w wh +w loss (5)

[0087]

[0088] Where: W ESS is the total annual investment cost of the energy storage system; w tz 、w wh and w loss are the annual investment, maintenance and loss costs of the energy storage system respectively; η t is the investment cost per unit capacity of the energy storage system; η w is the maintenance cost per unit capacity of the energy storage system; ηloss is the loss cost per unit capacity of the energy storage system; y is the service life of the energy storage system;

[0089] SOP will have certain losses during operation. It can also perform certain charging and discharging when necessary. As a soft switch, its power inflow and outflow should be balanced, with power injection as negative and outflow as positive. It has the following constraints:

[0090]

[0091] Where: and are the active and reactive powers injected by SOP converters a and b at time t, respectively; is the charging / discharging power of the energy storage system in the SOP at time t; is the SOP transmission loss at time t; A SOP,a and A SOP,b are the loss coefficients of converter a and b respectively; and are the access capacities of converter a and converter b respectively. They are usually considered equal and are recorded as

[0092] At the same time, when planning SOP, the upper limit of the number of planned SOP is 5, and the number of planned SOPs can be changed at will. If the number is different, the capacity and position of each SOP will also change accordingly; the unit optimization capacity of SOP is 10kV·A.

[0093] In the above step 3, for the entire distribution network, at each moment, the power purchased from the upper main grid, the distributed power source, the energy storage system in the distribution network, the power provided by the SOP, and the power consumed by the load and line loss in the distribution network are equal. At the same time, when the data obtained in step 1 is introduced, the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 must be satisfied, so there are the following constraints:

[0094]

[0095] Where: P t MG and is the active and reactive power purchased from the upper main grid during period t; and are the active and reactive power generated by the nth DG in period t, respectively; is the network loss of branch n in period t; and are respectively the active and reactive power consumed by the nth load in period t; Ω ESS ,Ω DG ,Ω SOP and Ωb They are the energy storage system, distributed generation, SOP installation nodes and all branches of the distribution network; is the active power detection value output by the nth DG at time t; P DG,n,t , Q DG,n,t are the active power and reactive power output by DG at node n during period t; β DG,n,t is the power factor angle of DG at node n during period t; is the net active power generated by the nth ESS at time t; and are the active and reactive power generated by the nth SOP at time t; β SOP,n,t is the power factor angle of the nth SOP at time t.

[0096] In step 4 above, under the constraints of step 2 and step 3, the planning cost is calculated and the SOP planning scheme with the minimum cost is found:

[0097] Step 4.1: The planning cost is modeled with the minimum annual comprehensive operating cost of the distribution network as the objective function. The model is as follows:

[0098] f med =C SI +C OMS +C PC +C NLC (18)

[0099]

[0100] Where: F med is the annual comprehensive operating cost; C SI is the annual investment cost of SOP; C OMS is the annual operation and maintenance cost of SOP; C PC The cost of purchasing electricity from the upper grid; C NLC is the network loss cost; λ is the discount rate; and are the annual investment cost and annual operation and maintenance cost of ESS respectively. Since the upper and lower limits of ESS power and capacity have been determined, these two values ​​are fixed constants. SOP is the useful life of the SOP; c s is the unit capacity investment cost of SOP; is the SOP capacity to be installed between nodes i and j; η SOP is the annual operation and maintenance cost of SOP; c g The unit price of electricity purchased from the upper power grid; c loss is the unit power network loss cost; P t MGis the active power injected by the upper main grid into the distribution network at time t; is the network loss power of branch i, j at time t;

[0101] Step 4.2: Use the solver to solve the above model, and obtain the node position and capacity size of the SOP that minimizes the objective function value, which is the planning result.

[0102] In step 5, the flexibility of distribution network operation is indexed from two dimensions. One dimension is the flexibility index of branch load margin in space, and the other dimension is the flexibility index of supply and demand balance in time. The flexibility of the distribution network is obtained by calculating the flexibility of the two dimensions and making a difference. The specific steps are as follows:

[0103] Step 5.1: Calculate the branch load margin flexibility index and supply-demand balance flexibility index respectively:

[0104] The branch load margin flexibility index is used to evaluate the transmission capacity of network flexibility resources. It is mainly represented by the ratio of the current in the branch to the maximum current that the branch can withstand. The smaller the value, the greater the load margin of each branch, and the stronger the ability to cope with the uncertainty changes in flexibility demand:

[0105]

[0106] Where: I BF is the average load rate of the network branch, Ω B is the branch set of the distribution network; L zm,t is the load rate of branch z,m at time t; I zm,t is the current flowing through branch z,m at time t; I zm is the maximum value of the current allowed to flow through branch z,m;

[0107] The supply-demand balance flexibility index is used to measure whether the flexibility supply can meet the flexibility demand. The larger the value, the more the flexibility resources can meet the flexibility demand, and the better the overall flexibility of the distribution network:

[0108]

[0109] Where: I FSD It is an indicator of supply and demand balance flexibility; UFSD ,I DFSD The sum of the ratios of the dispatchable margin of flexible resources to the change in net load when the net load increases or decreases within a dispatch cycle; are the total upward and downward flexibility adjustment capabilities of the distribution network at time t; T is the total number of time periods in the dispatching cycle. The dispatching cycle in this paper is 24 hours, and the dispatching time interval is 1 hour; wt is the state variable of the net load power change at time t; P tnl , are the net load power at time t and t+1 respectively;

[0110] Step 5.2: The two distribution network flexibility evaluation indicators calculated in the first two steps are coupled, mutually influential, and interact with each other; in the planning process, node flexibility and branch flexibility are combined to jointly determine the quality of system flexibility:

[0111] F I =I FSD -I BF (19)

[0112] Where: F I Indicates system flexibility. The larger the value, the better the system flexibility.

[0113] Example:

[0114] Combined with the data set and attached diagrams of distributed power sources (wind power, photovoltaic power) and loads in the distribution network, a detailed explanation is given. The specific implementation steps are as follows:

[0115] S1: In this embodiment, 4 photovoltaic systems are connected at nodes 7, 15, 22, and 26, with access capacities of 800kVA, 800kVA, 500kVA, and 400kVA respectively; 3 wind turbines are connected at nodes 9, 23, and 30, with access capacities of 800kVA, 500kVA, and 300kVA respectively; it contains the operating data of distributed power sources (wind power, photovoltaic power) and loads for one day, with a measurement interval of 1 hour; the data set is shown in Table 1 below:

[0116]

[0117]

[0118] Table 1 Distributed power supply and load operation data set

[0119] S2: Perform a refined modeling of the energy storage system. The energy storage installation locations are nodes 8, 14, 21, and 25. The modeling parameters are as follows:

[0120]

[0121] Table 2 Energy storage model parameters Step 2.1. Energy storage system operation constraint modeling:

[0122]

[0123] 0≤ξ ch +ξ dis ≤1 (2)

[0124] Esocmin ≤E n,t ≤E socmax (3)

[0125]

[0126] Where: P ch,n,t , P dis,n,t are the real-time charging and discharging power of the energy storage system n at time point t; ch ,

[0127] ξ dis is a variable of 0 or 1, which represents the charging and discharging state of the energy storage system; E n,t is the real-time energy storage capacity of energy storage system n at time point t; P max , P min They are the upper and lower limits of the charging and discharging power of the energy storage system respectively;

[0128] E socmax 、E socmin are the upper and lower limits of the charge and discharge capacity of the energy storage system respectively; S n,B is the rated capacity of the energy storage system n.

[0129] Step 2.2, Energy Storage System Economic Cost Modeling:

[0130] W ESS =w tz +w wh +w loss (5)

[0131]

[0132] Where: W ESS is the total annual investment cost of the energy storage system; w tz 、w wh and w loss are the annual investment, maintenance and loss costs of the energy storage system respectively; η t is the investment cost per unit capacity of the energy storage system; η w is the maintenance cost per unit capacity of the energy storage system; η loss is the loss cost per unit capacity of the energy storage system; y is the service life of the energy storage system.

[0133] S3: define the decision variables of SOP, and include SOP, energy storage system, distributed generation and load data into the active and reactive balance constraints of distribution network nodes;

[0134]

[0135] v i,min ≤v i,t ≤vi,max (8)

[0136]

[0137] Where: Q i,t is the injected reactive power of node i during period t; v i,t and θ ij,t are the voltage amplitude of node j in period t and the phase angle difference between nodes i and j; G ii , B ii and G ij , B ij are the self-conductance, self-susceptance, mutual conductance and mutual susceptance in the node admittance matrix respectively; is the predicted value of active power output by DG; P DG,i,t , Q DG,i,t are node i in period t

[0138] The active power and reactive power output by DG; P L,i,t , Q L,i,t are the active power and reactive power injected by the load at node i in period i; v i,max 、v i,min are the upper and lower limits of the voltage amplitude of node i, respectively; Iij,t and Iij,max are the current amplitude and upper limit of branch ij in period t, respectively; β DG,i,t , S DG,i are the power factor angle and access capacity of DG at node i in period t respectively.

[0139] S4: Construct SOP planning constraints, where the upper limit of the number of SOPs is 5. The number of SOPs can be changed at will. If the number is different, the capacity and location of each SOP will also change accordingly. The planning parameters are shown in Table 3 below:

[0140]

[0141] Table 3 SOP planning parameters

[0142] S5: Construct the calculation formula for the annual comprehensive operation cost of the distribution network, and select the location and capacity of the SOP with the goal of minimizing the cost:

[0143] f med =C SI +C OMS +C PC +C NLC (12)

[0144]

[0145] Where: C SIis the annual investment cost of SOP; C OMS is the annual operation and maintenance cost of SOP; C PC The cost of purchasing electricity from the upper grid; C NLC is the network loss cost; θ SOP Install node collection for SOP;y SOP is the useful life of the SOP; c s is the unit capacity investment cost of SOP; is the SOP capacity to be installed between nodes i and j; η SOP is the annual operation and maintenance cost of SOP; c g The unit price of electricity purchased from the upper power grid; c loss is the unit power network loss cost; P t MG is the active power injected by the upper main grid into the distribution network at time t; is the network loss power of branch i, j at time t. The planning results are shown in Table 4 below:

[0146]

[0147] Table 4 Economic comparison of SOP planning results

[0148] S6: The distribution network operation flexibility is indexed from two dimensions. One dimension is the branch load margin flexibility index in space, and the other dimension is the supply and demand balance flexibility index in time. The flexibility of the two dimensions is calculated and the difference is made to obtain the distribution network flexibility. The specific steps are as follows:

[0149] Step 6.1. The branch load margin flexibility index is used to evaluate the transmission capacity of network flexibility resources. It is mainly represented by the ratio of the current in the branch to the maximum current that the branch can withstand. The smaller the value, the greater the load margin of each branch, and the stronger the ability to cope with the uncertainty changes in flexibility demand:

[0150]

[0151] Where: I BF is the average load rate of the network branch, Ω B is the branch set of the distribution network; NB is the number of branches of the distribution network; L ij,t is the load rate of branch ij at time t; I ij,t is the current flowing through branch ij at time t; I ij is the maximum value of the current allowed to flow through branch ij.

[0152] Step 6.2: The supply-demand balance flexibility index is used to measure whether the flexibility supply can meet the flexibility demand. The larger the value, the more the flexibility resources can meet the flexibility demand, and the better the overall flexibility of the distribution network:

[0153]

[0154]

[0155] Where: I UFSD ,I DFSD The sum of the ratios of the dispatchable margin of flexible resources to the change in net load when the net load increases or decreases within a dispatch cycle; are the total upward and downward flexibility adjustment capabilities of the distribution network at time t; T is the total number of time periods in the dispatching cycle. The dispatching cycle in this paper is 24 hours, and the dispatching time interval is 1 hour; wt is the state variable of the net load power change at time t; P t nl , are the net load power at time t and t+1 respectively; φ ESS The node set is installed for the energy storage system. The flexibility calculation results are shown in Table 5 below:

[0156] index <![CDATA[I FSD ]]> <![CDATA[I BF ]]> <![CDATA[F I ]]> Numerical (traditional planning) 3.2830 <![CDATA[2.1105×10 -2 ]]> 3.2619 Value (considering energy storage system planning) 4.6318 <![CDATA[3.2465×10 -2 ]]> 4.5993

[0157] Step 6.3: The two distribution network flexibility evaluation indicators calculated in the first two steps are mutually coupled, mutually influential, and mutually interactive. In the planning process, node flexibility and branch flexibility are combined to jointly determine the quality of system flexibility:

[0158] F I =I FSD -I BF (19)

[0159] Where: FI represents system flexibility. The larger its value is, the better the system flexibility is.

Claims

1. A SOP site selection and capacity planning method considering energy storage system, characterized in that: The following steps are involved: Step 1: Obtain data on new energy distributed power sources and loads in the distribution network through detection technology; Step 2: Based on the distributed power source and load data obtained in step 1, the energy storage system is refinedly modeled and the SOP operation constraint modeling is performed; the refined modeling of the energy storage system includes the energy storage system operation constraint modeling and the energy storage system economic cost modeling; Step 3: The data obtained in step 1 and the active and reactive power of the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 are included in the active and reactive balance constraint conditions of the distribution network node; Step 4: Under the constraints of step 2 and step 3, calculate the planning cost; plan with the minimum planning cost as the objective function to obtain the site selection and capacity determination plan of SOP; Step 5: Calculate branch load margin I BF and supply-demand balance flexibility index I FSD , and the operation flexibility of the distribution network is measured by calculating the difference between the two.

2. The method according to claim 1 is characterized in that In step 2, the energy storage system is refined and the operating constraints of the SOP are modeled, which specifically includes the following steps: Step 2.1, before modeling the operation constraints of the energy storage system, in order to meet the actual operation conditions of the energy storage system and SOP, the upper and lower limit parameters of the charging and discharging power of the energy storage system, the upper and lower limit parameters of the energy storage and the loss coefficient of the converter in the SOP are preset; Step 2.2: Model the operation constraints and economic costs of the energy storage system. At the same time, model the operation constraints of the SOP. The details are as follows: The charging and discharging power of the energy storage system cannot exceed the preset upper and lower limits of the charging and discharging power. In addition, the energy storage system cannot charge and discharge at the same time, so there are the following constraints: 0≤ξ ch +ξ dis ≤1 (2) Where: P ch,n,t , P dis,n,t are the real-time charging and discharging power of the energy storage system n at time point t; ch , dis It is a variable of 0 or 1. When it is 1, it indicates that the energy storage system is in the state of charging or discharging. When it is 0, it indicates that the energy storage system is in the state of shutting down. max , P min They are the upper and lower limits of the charging and discharging power of the energy storage system respectively; In order to meet the operation requirements of the energy storage device itself, the energy stored in the energy storage device must be between 0.2 and 0.9 times the rated capacity, with the following constraints: AND socmin ≤E n,t ≤E socmax (3) Where: E n,t is the real-time energy storage capacity of energy storage system n at time point t; E socmax 、E socmin are the upper and lower limits of the charge and discharge capacity of the energy storage system respectively; S n,B is the rated capacity of the energy storage device n; When modeling the economic cost of the energy storage system, in order to measure the economic cost of the energy storage system, the cost of the energy storage system is annualized. At the same time, the cost includes three parts: the annual investment, maintenance and loss cost of the energy storage system. The specific constraints are as follows: IN ESS =in tz +in wh +in loss (5) Where: W ESS is the total annual investment cost of the energy storage system; w tz 、w wh and w loss They are the annual investment, maintenance and loss costs of the energy storage system; η t is the investment cost per unit capacity of the energy storage system; η w is the maintenance cost per unit capacity of the energy storage system; η loss is the loss cost per unit capacity of the energy storage system; y is the service life of the energy storage system; SOP will have certain losses during operation. It can also perform certain charging and discharging when necessary. As a soft switch, its power inflow and outflow should be balanced, with power injection as negative and outflow as positive. It has the following constraints: Where: and are the active and reactive powers injected by SOP converters a and b at time t, respectively; is the charging / discharging power of the energy storage system in the SOP at time t; is the SOP transmission loss at time t; A SOP,a and A SOP,b are the loss coefficients of converter a and b respectively; and are the access capacities of converter a and converter b respectively. They are usually considered equal and are recorded as At the same time, when planning SOP, you can change the planned quantity at your own will. If the quantity is different, the capacity and location of each SOP obtained in the plan will also change accordingly.

3. The method according to claim 1, characterized in that In the above step 3, for the entire distribution network, at each moment, the power purchased from the upper main grid, the distributed power source, the energy storage system in the distribution network, the power provided by the SOP, and the power consumed by the load and line loss in the distribution network are equal. At the same time, when the data obtained in step 1 is introduced, the energy storage system operation constraint model and the SOP operation constraint model obtained in step 2 must be satisfied, so there are the following constraints: Where: P t MG and is the active and reactive power purchased from the upper main grid during period t; and are the active and reactive power generated by the nth DG in period t, respectively; and are respectively the active and reactive power consumed by the nth load in period t; Ω ESS ,Ω DG and Ω SOP They are the installation node sets of energy storage systems, distributed generation and SOPs; is the active power detection value output by the nth DG at time t; P DG,n,t , Q DG,n,t are the active power and reactive power output by DG at node n during period t; β DG,n,t is the power factor angle of DG at node n during period t; is the net active power generated by the nth ESS at time t; and are the active and reactive power generated by the nth SOP at time t; β SOP,n,t is the power factor angle of the nth SOP at time t.

4. The method according to claim 1, characterized in that: In step 4 above, under the constraints of step 2 and step 3, the planning cost is calculated and the SOP planning scheme with the minimum cost is found: Step 4.1: The planning cost is modeled with the minimum annual comprehensive operating cost of the distribution network as the objective function. The model is as follows: minF med =C SI +C OMS +C PC +C NLC (18) Where: F med is the annual comprehensive operating cost; C SI is the annual investment cost of SOP; C OMS is the annual operation and maintenance cost of SOP; C PC The cost of purchasing electricity from the upper grid; C NLC is the network loss cost; λ is the discount rate; and are the annual investment cost and annual operation and maintenance cost of ESS respectively. Since the upper and lower limits of ESS power and capacity have been determined, these two values ​​are fixed constants. SOP is the useful life of the SOP; c s is the unit capacity investment cost of SOP; is the SOP capacity to be installed between nodes i and j; η SOP is the annual operation and maintenance cost of SOP; c g The unit price of electricity purchased from the upper power grid; c loss is the unit power network loss cost; P t MG is the active power injected by the upper main grid into the distribution network at time t; is the network loss power of branch i, j at time t; Step 4.2: Use the solver to solve the above model, and obtain the node position and capacity size of the SOP that minimizes the objective function value, which is the planning result.

5. The method according to claim 1, characterized in that In the above step 5, the flexibility of distribution network operation is indexed from two dimensions, one dimension is the spatial branch load margin flexibility index, and the other dimension is the temporal supply and demand balance flexibility index. The flexibility of the distribution network is obtained by calculating the flexibility of the two dimensions and making a difference. The specific steps are as follows: Step 5.1: Calculate the branch load margin flexibility index and supply-demand balance flexibility index respectively: The branch load margin flexibility index is used to evaluate the transmission capacity of network flexibility resources. It is mainly represented by the ratio of the current in the branch to the maximum current that the branch can withstand. The smaller the value, the greater the load margin of each branch, and the stronger the ability to cope with the uncertainty changes in flexibility demand: Where: I BF is the average load rate of the network branch, Ω B is the branch set of the distribution network; L zm,t is the load rate of branch z,m at time t; I zm,t is the current flowing through branch z,m at time t; I zm is the maximum value of the current allowed to flow through branch z,m; The supply-demand balance flexibility index is used to measure whether the flexibility supply can meet the flexibility demand. The larger the value, the more the flexibility resources can meet the flexibility demand, and the better the overall flexibility of the distribution network: Where: I FSD It is an indicator of supply and demand balance flexibility; UFSD ,I DFSD The sum of the ratios of the dispatchable margin of flexible resources to the change in net load when the net load increases or decreases within a dispatch cycle; are the total upward and downward flexibility adjustment capabilities of the distribution network at time t; T is the total number of time periods in the dispatching cycle; w t is the state variable of the net load power change at time t; P t nl , are the net load power at time t and t+1 respectively; Step 5.2: The two distribution network flexibility evaluation indicators calculated in the first two steps are coupled, mutually influential, and interact with each other; in the planning process, node flexibility and branch flexibility are combined to jointly determine the quality of system flexibility: F I =I FSD -I BF (19) Where: F I Indicates system flexibility. The larger the value, the better the system flexibility.

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