Partition configuration method for energy storage device of local autonomous power grid

Abstract

The invention belongs to the technical field of autonomous power grids, and particularly relates to a local area autonomous power grid energy storage device partition configuration method. Comprising the following steps: S1, modeling the energy storage device according to the operation characteristics of the energy storage device; s2, based on the division result of the autonomous unit, establishing an energy storage device locating and sizing model taking the optimal comprehensive cost as a target; s3, on the basis of the step S2, a second-order cone relaxation technology and a linear technology are utilized to convert the model into a mixed integer second-order cone programming model for solving; according to the method, the regulation and control range of the local autonomous power grid is obviously reduced, meanwhile, the interaction power between autonomous units is optimized, frequent transmission of a large amount of information is avoided, the power distribution network has better autonomy, renewable energy sources and distributed energy resources can be utilized to the maximum extent in different regions and under different conditions, and the method is suitable for popularization and application. Sustainability of energy is improved, and dependence on traditional fossil energy is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous power grids, and in particular relates to a method for zoning configuration of energy storage devices in a local autonomous power grid. Background Art

[0002] With the development of modern power systems, distribution networks are gradually evolving into a new type of complex and large-scale distribution system. This system not only encompasses the traditional physical power grid but also integrates advanced information technology, achieving a high degree of convergence between physical and cyber systems. In the future, with the high penetration of distributed generation (DG), distribution networks will exhibit the characteristics of bidirectional energy and information flows.

[0003] The transition from traditional distribution networks to active distribution networks (ADNs) is an inevitable trend in power system modernization. During this transformation, the large-scale integration of flexible resources, such as distributed energy resources (DERs), energy storage systems (ESSs), electric vehicles (EVs), and demand response (DR), is gradually increasing the penetration rate of distribution systems. The integration of these flexible resources not only changes the operating model of distribution networks but also brings new challenges to grid regulation and management.

[0004] To address these challenges, improve energy sustainability, and reduce dependence on traditional fossil fuels, it is necessary to maximize the use of renewable energy and distributed energy resources in different regions and under different conditions. To this end, it is necessary to determine an appropriate local autonomous grid energy storage configuration model. Summary of the Invention

[0005] The purpose of the present invention is to overcome the shortcomings of the existing technology and provide a method for zoning configuration of energy storage devices in a local autonomous power grid, which improves the economy and autonomy of the distribution network by optimizing the site selection and capacity configuration of the energy storage devices.

[0006] The object of the present invention is achieved as follows: A method for configuring a partition of an energy storage device in a local autonomous power grid comprises the following steps:

[0007] S1. Model the energy storage device based on its operating characteristics;

[0008] S2. Based on the results of the autonomous unit division, a model for the site selection and sizing of energy storage devices is established with the goal of optimizing the overall cost.

[0009] S3. Based on step S2, use the second-order cone relaxation technique and the linear technique to transform it into a mixed integer second-order cone programming model for solution.

[0010] Furthermore, the step S1 of modeling the energy storage device based on the operating characteristics of the energy storage device includes:

[0011] Taking battery energy storage as the modeling object, its mathematical model is shown in the following formula; Formula (1) to Formula (6) are the constraints of the energy storage device, where Formula (1) is the relationship between the electric energy stored by the energy storage device at time t and its charging and discharging power; Formula (2) is the capacity constraint of the energy storage device; Formula (3) and (4) are the charging power constraint and the discharging power constraint respectively; Formula (5) represents the working state constraint of the energy storage device, the three states are idle, charging and discharging, and it can only be in one working state at the same time; Formula (6) is the full scheduling cycle constraint of the energy storage device, the energy storage device should be at the same power at the beginning and end of the cycle, that is, the charging and discharging power should be consistent;

[0012]

[0013]

[0014] Where, E ess and P ess are the rated capacity and rated power of ESS respectively, S t ESS ,P t ESS,ch , P t ESS,dis They represent the amount of electricity, charging power, and discharging power of ESS at time t respectively; η ch is the charging efficiency of ESS; η dis is the discharge efficiency of ESS; M is an infinite number; is the charging state of ESS at time t, is the discharge state of ESS at time t, and when charging When 1, discharge is 1, and 0 when idle; and Indicates the maximum state of charge of the ESS at time t, which are 0.2 and 0.8 respectively; and Represents the power of ESS at 0 o'clock and 24 o'clock respectively.

[0015] Furthermore, step S2 establishes a model for selecting and sizing an energy storage device based on the division result of the autonomous units with the goal of optimizing the overall cost, including:

[0016] (1) Establishing the objective function

[0017] The planning objective of the energy storage device site selection and sizing planning model is to minimize the comprehensive cost of the distribution network, including the investment cost of the energy storage device, the operation and maintenance cost of the energy storage device, the main grid power purchase cost, and the loss cost of the distribution network, as shown below.

[0018] minC=C Ess +C f +C buy +C loss (7)

[0019]

[0020] Where C Ess is the investment cost of the energy storage device, n is the number of energy storage devices, ξ is the discount rate, y is the service life of ESS, is the investment cost per unit capacity of the energy storage device, The rated capacity of the i-th energy storage device, is the investment cost per unit power of the energy storage device, P i ESS The rated power of the i-th energy storage device; C f is the operation and maintenance cost of the energy storage device, T is 8760h per year, is the operation and maintenance cost of the energy storage device per unit charge and discharge capacity, is the charge and discharge power of ESS at time t, a positive value indicates ESS discharging, and a negative value indicates ESS charging; C buy The electricity purchase cost for the main grid, n cp The number of main network communication branches; is the real-time electricity price of the main network at that moment, P grid,t is the power on the main network connection branch at that moment; C loss is the network loss cost of the distribution network, C l Represents the unit network loss cost, N z is the total number of branches, I i is the current value flowing through branch i during period t, r i is the resistance value of branch i;

[0021] (2) Determine the constraints

[0022] 1) Energy storage device constraints

[0023]

[0024] Where, represents the total power of the i-th energy storage device at time t; Respectively represent the charging and discharging efficiency of the energy storage device; P i,c (t) represents the charging power of the i-th energy storage device at time t; P i,d(t) represents the discharge power of the i-th energy storage device at time t; μ i,c (t) is the charging state of ESS at time t, μ i,d (t) is the discharge state of ESS at time t, and μ i,c (t) is 1, when discharging μ i,d (t) is 1 and 0 when idle; represents the rated capacity of the i-th energy storage device; represents the charging and discharging power rating of the i-th energy storage device; and They represent the maximum and minimum state of charge of the i-th energy storage device, and are 0.2 and 0.8 respectively;

[0025] 2) Power balance constraints

[0026]

[0027] P PV,i,t +P WT,i,t +P ESS,i,t +P liпе,i,t =P loss,i,t +P load,i,t (19)

[0028] Where, P grid,t It indicates the power on the branch line connected to the main grid at time t. It stipulates that power can only flow from the main grid to the distribution network and cannot be sent back; P ESS,i,t Represents the charging and discharging power of the energy stored in the autonomous unit i, which is positive when discharging and negative when charging; P PV,i,t represents the total photovoltaic output within autonomous unit i at time t; P WT,i,t represents the total fan output in autonomous unit i at time t; P load,i,t represents the total load in autonomous unit i at time t; P loss,i,t represents the network loss value of autonomous unit i at time t, P liпе,i,t is the interaction power between autonomous unit i and its adjacent autonomous units. It is positive when other autonomous units input power to autonomous unit i, and negative otherwise.

[0029] 3) Interaction power constraints between autonomous units

[0030] P l min ≤|P line,i,t |≤P l max (20)

[0031] Where: P l max 、P l minThey represent the upper and lower limits of the power allowed to be transmitted on the tie line l between autonomous units, and can be set according to autonomous requirements;

[0032] 4) Power constraints of the main network interconnection branch

[0033] P grid,t ≥0 (21)

[0034] Where, P grid,t Indicates the power on the branch line connecting to the main grid at time t;

[0035] 5) Distribution network flow constraints

[0036]

[0037] Where δ(j) is the set of branch headend nodes with j as the terminal node in the distribution system; ξ(j) is the set of branch terminal nodes with j as the headend node; P ij (t) and Q ij (t) are the active power and reactive power flowing from node i to node j at time t; P j (t) and Q j (t) are the net active power and reactive power injected into node j at time t; U j (t) is the voltage amplitude of node j at time t; rij and xij are the resistance and reactance of branch ij respectively; I ij (t) is the current amplitude of branch ij at time t;

[0038] 6) Voltage deviation constraints

[0039] (1-ε)U n ≤U i (t)≤(1+ε)U n (twenty three)

[0040] Where U i (t) represents the operating voltage of node i at time t; U n Indicates the voltage rating; ε indicates the voltage deviation operating range, taking ε=0.05;

[0041] 7) Thermal stability constraints

[0042] 0≤I ij (t)≤I ij,max (twenty four)

[0043] Where, I ij (t) represents the current flowing through line ij at time t; I ij,max Indicates the maximum current-carrying capacity of line ij.

[0044] Furthermore, step S3, based on step S2, converts it into a mixed integer second-order cone programming model using second-order cone relaxation technology and linear technology for solution, including:

[0045] The standard form of second-order cone programming is shown in formula (25);

[0046]

[0047] In the formula, the variable x l ∈R n , constant d∈R m , constant c l ∈R n , constant D l ∈R m×n , K l is a second-order cone or a rotated second-order cone as shown in formula (26) or formula (27);

[0048] Second-order cone:

[0049]

[0050] Rotate a second-order cone:

[0051]

[0052] The original mixed-integer nonconvex nonlinear model is transformed into a mixed-integer second-order cone programming model by using the second-order cone relaxation technique and linear technique;

[0053] make Use it to replace the relevant terms in formula (22), and perform second-order cone relaxation at the same time to transform the nonlinear constraint into a second-order cone constraint, as shown in formula (28):

[0054]

[0055] Convert Equations (23) and (24) in the distribution network operation constraints into Equations (29) and (30):

[0056]

[0057] The second-order cone relaxation deviation quantification index and distribution network cutting plane constraint are shown in Equations (31) and (32), respectively:

[0058]

[0059] Where i t,ij,k and u t,i,k They represent the square of the current amplitude and the square of the voltage amplitude of node i at time t in the kth iteration respectively; P t,ij,k and Q respectively t,ij,krepresents the active power flow and reactive power flow of branch ij at time t in the kth iteration.

[0060] Beneficial effects of the present invention: A method for zoning and configuring energy storage devices in a local autonomous power grid of the present invention comprises step S1, modeling the energy storage device according to the operating characteristics of the energy storage device; S2, establishing a siting and sizing model for the energy storage device with the goal of optimizing the comprehensive cost based on the partition results of the autonomous units; S3, on the basis of step S2, converting it into a mixed integer second-order cone programming model for solution using the second-order cone relaxation technique and the linear technique; the present invention effectively improves the voltage quality of the distribution network while significantly reducing the network loss and the comprehensive total cost compared with the pre-planning and traditional energy storage device siting and sizing methods, and has good economic efficiency; it significantly reduces the control range of the local autonomous power grid, and at the same time optimizes the interaction power between autonomous units, avoids the frequent transmission of large amounts of information, and makes the distribution network have better autonomy, which helps to maximize the use of renewable energy and distributed energy resources in different regions and conditions, improve energy sustainability, and reduce dependence on traditional fossil energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the improved Portuguese 54-node distribution network example.

[0062] Figure 2 Schematic diagram of the 24-hour load curve for the 54-node case.

[0063] Figure 3 This is a schematic diagram of the 24-hour total photovoltaic output curve for the 54-node example.

[0064] Figure 4 This is a schematic diagram of the 24-hour total wind power output curve for the 54-node example.

[0065] Figure 5 Schematic diagram of 24-hour node voltage without planned energy storage.

[0066] Figure 6 Schematic diagram of 24-hour node voltage for Scheme 1.

[0067] Figure 7 This is a schematic diagram of the 24-hour node voltage for Scheme 2.

[0068] Figure 8 Schematic diagram comparing network losses of different schemes. DETAILED DESCRIPTION

[0069] The present invention will be further described below with reference to the accompanying drawings.

[0070] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0072] The present invention provides a partition configuration method for a local autonomous power grid energy storage device, such as Figure 1 As shown, it includes the following steps:

[0073] S1. Model the energy storage device based on its operating characteristics;

[0074] S2. Based on the results of the autonomous unit division, a model for the site selection and sizing of energy storage devices is established with the goal of optimizing the overall cost.

[0075] S3. Based on step S2, use the second-order cone relaxation technique and the linear technique to transform it into a mixed integer second-order cone programming model for solution.

[0076] Integrating energy storage devices into distribution networks plays a crucial role in enhancing the autonomy of local autonomous power grids. Energy storage devices not only provide power supply support and flexible charge and discharge power regulation, but also enable energy transfer over time. When there is excess energy in the system, the excess energy can be stored as a backup through charging. In response to energy shortages, the stored energy can be released to balance supply and demand, effectively alleviating the intermittent and random nature of wind and solar power output.

[0077] Furthermore, the step S1 of modeling the energy storage device based on the operating characteristics of the energy storage device includes:

[0078] Taking battery energy storage as the modeling object, its mathematical model is shown in the following formula; Formula (1) to Formula (6) are the constraints of the energy storage device, where Formula (1) is the relationship between the electric energy stored by the energy storage device at time t and its charging and discharging power; Formula (2) is the capacity constraint of the energy storage device; Formula (3) and (4) are the charging power constraint and the discharging power constraint respectively; Formula (5) represents the working state constraint of the energy storage device, the three states are idle, charging and discharging, and it can only be in one working state at the same time; Formula (6) is the full scheduling cycle constraint of the energy storage device, the energy storage device should be at the same power at the beginning and end of the cycle, that is, the charging and discharging power should be consistent;

[0079]

[0080] Where, E ess and P essare the rated capacity and rated power of ESS respectively, P t ESS,ch , P t ESS,dis They represent the amount of electricity, charging power, and discharging power of ESS at time t respectively; η ch is the charging efficiency of ESS; η dis is the discharge efficiency of ESS; M is an infinite number; is the charging state of ESS at time t, is the discharge state of ESS at time t, and when charging When 1, discharge is 1, and 0 when idle; and represents the maximum state of charge of the ESS at time t, and the present invention takes values ​​of 0.2 and 0.8 respectively; and Represents the power of ESS at 0 o'clock and 24 o'clock respectively.

[0081] Energy storage devices can provide bidirectional power support for autonomous units. Because nodes within autonomous units are closely connected and inter-unit node correlation is low, the capacity and power of energy storage devices within each autonomous unit are primarily determined by the load and distributed generation output within that unit. Therefore, rationally planning energy storage devices based on the division of autonomous units, and embodying the autonomous characteristics of distribution networks from both structural and functional perspectives, is key to achieving zoning control of local autonomous power grids and improving the regional autonomy of active distribution networks.

[0082] Furthermore, step S2 establishes a model for selecting and sizing an energy storage device based on the division result of the autonomous units with the goal of optimizing the overall cost, including:

[0083] (1) Establishing the objective function

[0084] The planning objective of the energy storage device site selection and sizing planning model is to minimize the comprehensive cost of the distribution network, including the investment cost of the energy storage device, the operation and maintenance cost of the energy storage device, the main grid power purchase cost, and the loss cost of the distribution network, as shown below.

[0085] minC=C Ess +C f +C buy +C loss (7)

[0086]

[0087] Where C Ess is the investment cost of the energy storage device, n is the number of energy storage devices, ξ is the discount rate, y is the service life of ESS, is the investment cost per unit capacity of the energy storage device, The rated capacity of the i-th energy storage device, is the investment cost per unit power of the energy storage device, P i ESS The rated power of the i-th energy storage device; C f is the operation and maintenance cost of the energy storage device, T is 8760h per year, is the operation and maintenance cost of the energy storage device per unit charge and discharge capacity, is the charge and discharge power of ESS at time t, a positive value indicates ESS discharging, and a negative value indicates ESS charging; C buy The electricity purchase cost for the main grid, n cp The number of main network communication branches; is the real-time electricity price of the main network at that moment, P grid,t is the power on the main network connection branch at that moment; C loss is the network loss cost of the distribution network, C l Represents the unit network loss cost, N z is the total number of branches, I i is the current value flowing through branch i during period t, r i is the resistance value of branch i;

[0088] (2) Determine the constraints

[0089] 1) Energy storage device constraints

[0090]

[0091] Where, represents the total power of the i-th energy storage device at time t; Respectively represent the charging and discharging efficiency of the energy storage device; P i,c (t) represents the charging power of the i-th energy storage device at time t; P i,d (t) represents the discharge power of the i-th energy storage device at time t; μ i,c (t) is the charging state of ESS at time t, μ i,d (t) is the discharge state of ESS at time t, and μ i,c (t) is 1, when discharging μ i,d (t) is 1 and 0 when idle; represents the rated capacity of the i-th energy storage device; represents the charging and discharging power rating of the i-th energy storage device; and They represent the maximum and minimum state of charge of the i-th energy storage device, and are 0.2 and 0.8 respectively;

[0092] 2) Power balance constraints

[0093]

[0094] P PV,i,t +P WT,i,t +P ESS,i,t +P liпе,i,t =P loss,i,t +P load,i,t (19)

[0095] Where, P grid,t It indicates the power on the branch line connected to the main grid at time t. It stipulates that power can only flow from the main grid to the distribution network and cannot be sent back; P ESS,i,t Represents the charging and discharging power of the energy stored in the autonomous unit i, which is positive when discharging and negative when charging; P PV,i,t represents the total photovoltaic output within autonomous unit i at time t; P WT,i,t represents the total fan output in autonomous unit i at time t; P load,i,t represents the total load in autonomous unit i at time t; P loss,i,t represents the network loss value of autonomous unit i at time t, P liпе,i,t is the interaction power between autonomous unit i and its adjacent autonomous units. It is positive when other autonomous units input power to autonomous unit i, and negative otherwise.

[0096] 3) Interaction power constraints between autonomous units

[0097] P l min ≤|P line,i,t |≤P l max (20)

[0098] Where: P l max 、P l min They represent the upper and lower limits of the power allowed to be transmitted on the tie line l between autonomous units, and can be set according to autonomous requirements;

[0099] 4) Power constraints of the main network interconnection branch

[0100] P grid,t ≥0 (21)

[0101] Where, P grid,t Indicates the power on the branch line connecting to the main grid at time t;

[0102] 5) Distribution network flow constraints

[0103]

[0104] Where δ(j) is the set of branch headend nodes with j as the terminal node in the distribution system; ξ(j) is the set of branch terminal nodes with j as the headend node; P ij (t) and Q ij (t) are the active power and reactive power flowing from node i to node j at time t; P j (t) and Q j (t) are the net active power and reactive power injected into node j at time t; U j (t) is the voltage amplitude of node j at time t; rij and xij are the resistance and reactance of branch ij respectively; I ij (t) is the current amplitude of branch ij at time t;

[0105] 6) Voltage deviation constraints

[0106] (1-ε)U n ≤U i (t)≤(1+ε)U n (twenty three)

[0107] Where U i (t) represents the operating voltage of node i at time t; U n Indicates the voltage rated value; ε indicates the voltage deviation operating range. According to the "Power Quality Supply Voltage Deviation", the sum of the absolute values ​​of the positive and negative deviations of the 110-35 kV power supply voltage shall not exceed 10% of the nominal voltage; the allowable deviation of the three-phase power supply voltage of 10 kV and below is ±7% of the nominal voltage; the allowable deviation of the 220 V single-phase power supply voltage is +7% and -10% of the nominal voltage.

[0108] The present invention takes ε=0.05;

[0109] 7) Thermal stability constraints

[0110] 0≤I ij (t)≤I ij,max (twenty four)

[0111] Where, I ij (t) represents the current flowing through line ij at time t; I ij,max Indicates the maximum current-carrying capacity of line ij.

[0112] Furthermore, step S3, based on step S2, converts it into a mixed integer second-order cone programming model using second-order cone relaxation technology and linear technology for solution, including:

[0113] Second-order cone programming is a nonlinear optimization method that can be regarded as a generalization of linear programming. It is essentially a convex programming with the characteristics of efficient solution. Its standard form is shown in formula (25);

[0114]

[0115] In the formula, the variable x l ∈R n , constant d∈R m , constant c l ∈R n , constant D l ∈R m×n , K l is a second-order cone or a rotated second-order cone as shown in formula (26) or formula (27);

[0116] Second-order cone:

[0117]

[0118] Rotate a second-order cone:

[0119]

[0120] The original mixed-integer nonconvex nonlinear model is transformed into a mixed-integer second-order cone programming model by using the second-order cone relaxation technique and linear technique;

[0121] make Use it to replace the relevant terms in formula (22), and perform second-order cone relaxation at the same time to transform the nonlinear constraint into a second-order cone constraint, as shown in formula (28):

[0122]

[0123] Convert Equations (23) and (24) in the distribution network operation constraints into Equations (29) and (30):

[0124]

[0125] The accuracy of second-order cone relaxation is closely related to the selected objective function, and the solution may lead to deviations in the second-order cone relaxation. According to the extended second-order cone programming method, effective distribution network cutting plane constraints are continuously added to the mixed integer second-order cone programming model to ensure that the second-order cone relaxation boundary is continuously tightened. The quantitative index of the second-order cone relaxation deviation and the distribution network cutting plane constraint are shown in Equations (31) and (32), respectively:

[0126]

[0127] Where i t,ij,k and u t,i,k They represent the square of the current amplitude and the square of the voltage amplitude of node i at time t in the kth iteration respectively; P t,ij,k and Q respectively t,ij,k represents the active power flow and reactive power flow of branch ij at time t in the kth iteration.

[0128] In order to verify the feasibility and effectiveness of the above-mentioned local autonomous power grid energy storage device site selection and sizing model, a case study was built in the MATLAB environment on a computer with an Intel(R) Core(TM) i5-10210U CPU@1.60GHz, 64-bit, and 16GB RAM for verification, and the optimization software package IBM ILOG CPLEX 12.1 was called through YALMIP for solution.

[0129] The example selected in this paper is the improved Portuguese 54-node distribution network example. The topology of this example is as follows Figure 1 As shown in Figure 1, it includes 4 substations, 50 completed lines and 5 interconnecting lines. The total load of the system is 76.3MW, and the 24-hour load fluctuation curve is as follows: Figure 2 As shown, the system rated voltage is 13.5kV, and the upper and lower limits of the node voltage are 1.05U respectively. n and 0.95U n .

[0130] The calculation example system is configured with wind power and photovoltaic power. The wind power capacity connected to nodes 4, 9, 24, and 39 is 4.2MW, 3.6MW, 4.4MW, and 5MW, and the photovoltaic capacity connected to nodes 10 and 35 is 5MW and 4.2MW respectively. The typical total output curves of photovoltaic and wind power in the distribution network within 24 hours are as follows: Figure 3 、 4 shown.

[0131] Table 1 lists some planning-related parameters, primarily equipment investment and operating costs. The energy storage device to be deployed in this invention is a NaS battery. Parameters such as unit capacity investment cost, unit power generation operating and maintenance costs, and service life are shown in Table 1. The electricity price is 0.5 yuan / kW·h, and the discount rate is 0.08.

[0132] Table 1 NaS battery related parameters

[0133]

[0134] Table 2 shows the improved autonomous unit division of Portugal's 54-node distribution network.

[0135] Table 2 Improved division of autonomous units in Portugal’s 54-node distribution network

[0136]

[0137] In order to highlight the advantages of the energy storage device site selection and capacity determination method proposed in this invention, the following two schemes are constructed for comparative analysis:

[0138] Option 1: Regardless of the division of autonomous units, the energy storage device is sited and capacity determined based on the installation nodes and the number of connected nodes.

[0139] Solution 2: The solution proposed in the present invention considers the division results of autonomous units and selects the location and capacity of the energy storage device in each autonomous unit.

[0140] The energy storage site selection and sizing schemes for Schemes 1 and 2 are shown in Tables 3 and 4, respectively. Scheme 1 integrates 1.84, 3.49, 4.00, 2.13, 3.20, 5.56, 7.58, and 4.42 MW of energy storage at nodes 6, 9, 10, 16, 25, 38, 39, and 50, respectively, for a total storage capacity of 32.23 MW. Scheme 2 integrates 0.08, 1.84, 2.13, 7.69, 4.00, 7.58, 10.71, and 4.42 MW of energy storage within the eight autonomous units, respectively, for a total storage capacity of 38.46 MW. Comparing the results of energy storage device site selection and sizing under the two schemes, it can be seen that although the single capacity of the connected energy storage in Scheme 2 is larger than that in Scheme 1, and the total system-connected energy storage capacity is 6.23MW larger than that in Scheme 1, the energy storage zoning configuration based on the division of autonomous units helps each autonomous unit configure energy storage according to its own situation, better cooperate with the wind and solar power output in the region, coordinate the various resources within the autonomous unit, give full play to the functions of the energy storage device, and enhance the autonomous capability of the active distribution network.

[0141] Table 3 Results of site selection and capacity determination for energy storage device in Scheme 1

[0142]

[0143] Table 4 Results of site selection and capacity determination for energy storage device in Scheme 2

[0144]

[0145] The 24-hour node voltage diagrams before planning and corresponding to Scheme 1 and Scheme 2 are as follows: Figure 5-7 As shown. Before planning, the system node voltage was low, with the minimum node voltage being 0.933U n , and after configuring energy storage, it is increased to the allowable node voltage lower limit of 0.95U n At the same time, the voltage fluctuation indicators at the system nodes in Schemes 1 and 2 were also reduced compared to the pre-planning period, with reductions of 10.49% and 12.76% respectively. Therefore, configuring energy storage in the system has a significant effect on raising the system node voltage and can significantly suppress the system voltage fluctuations.

[0146] In addition, in terms of network loss, the comparison of distribution network loss of each scheme is shown in the figure below: Figure 8As shown in the figure, the average daily grid losses before the plan and for Schemes 1 and 2 were 2.17MW, 1.77MW, and 0.99MW, respectively. Schemes 1 and 2 achieved reductions of 18.43% and 54.38%, respectively, compared to the pre-planning period. This demonstrates that energy storage deployment can effectively reduce active power losses in distribution network operations. A further comparison of Schemes 1 and 2 reveals that the zoning of energy storage based on autonomous unit division significantly improves grid losses compared to traditional energy storage deployment methods, reducing average daily grid losses by 0.78MW, a reduction of 44.07%. This is because the volatility and randomness of wind and solar power generation increase distribution network losses. Energy storage deployment can mitigate power fluctuations. Furthermore, zoning energy storage can leverage the power support provided by energy storage, thereby maximizing the local consumption of surplus power from photovoltaic and wind power within the autonomous unit, effectively reducing losses associated with power redistribution across distribution lines, achieving supply and demand balance within the autonomous unit and thus reducing grid losses.

[0147] The economic comparison of each scheme is shown in Table 5. The data in the table shows that the network loss cost of Scheme 2 is reduced by 1.0314 million yuan and 683,700 yuan compared to the pre-planning and Scheme 1, respectively, which also confirms that the scheme proposed in this article has a significant effect on improving distribution network losses.

[0148] Table 5 Comparison of site selection and capacity determination costs for energy storage devices under different schemes

[0149]

[0150] In addition, because Option 2 has a larger total energy storage capacity, the energy storage investment cost is slightly higher than Option 1, increasing by 779,900 yuan. However, Option 2 takes into account the interactive power between autonomous units when planning energy storage zoning based on the division of autonomous units, fully leveraging the role of energy storage to better cooperate with the wind and solar power output within the autonomous units. As a result, the operation and maintenance costs of energy storage are reduced by 58,100 yuan. Option 2 also achieves a relative balance between source, load and storage within the autonomous units to a certain extent, reducing the distribution network's dependence on the main grid, thereby improving the autonomy of the active distribution network. Therefore, Option 2's main grid electricity purchase cost is lower than Option 1, reducing by 8.2301 million yuan, which is far higher than the increased investment cost of the energy storage device.

[0151] Finally, comparing the total costs of the various options in the table shows that Option 1's total cost increased by 4.8226 million yuan compared to the pre-planning period. This is due to the increased investment and operation and maintenance costs associated with configuring energy storage to improve the voltage quality of the distribution network. Option 2, on the other hand, reduced its total cost by 3.3695 million yuan compared to the pre-planning period and 9.0323 million yuan compared to Option 1. This is because, while the increased storage capacity required for autonomous unit-based energy storage zoning resulted in a slight increase in storage investment costs, it also significantly reduced the main grid's electricity purchase costs and network loss costs. Therefore, overall, Option 2's energy storage planning scheme offers both superior autonomy and excellent economic efficiency.

[0152] In summary, the proposed method for zoning energy storage devices in a local autonomous power grid effectively improves the voltage quality of the distribution network while reducing network losses by 54.38% and 44.07% compared to pre-planning and traditional energy storage device site selection and sizing methods, respectively. It also reduces overall costs by 4.58% and 11.53%, respectively, demonstrating excellent economic efficiency. In summary, the proposed method significantly reduces the control range of the local autonomous power grid, optimizes the interaction power between autonomous units, avoids the frequent transmission of large amounts of information, and enhances the autonomy of the distribution network.

[0153] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0154] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the usual meanings understood by persons of ordinary skill in the field to which this disclosure belongs. The words “include” or “comprise” and the like used in this disclosure mean that the elements or objects preceding the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The words “connect” or “connected” and the like are not limited to physical or mechanical connections, but may also include electrical connections, whether direct or indirect. “Up”, “down”, “left”, “right” and the like are only used to indicate relative positional relationships. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0155] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A method for configuring energy storage devices in a local autonomous power grid, characterized in that: The following steps are involved: S1. Model the energy storage device based on its operating characteristics; S2. Based on the results of the autonomous unit division, a model for the site selection and sizing of energy storage devices is established with the goal of optimizing the overall cost. S3. Based on step S2, use the second-order cone relaxation technique and the linear technique to transform it into a mixed integer second-order cone programming model for solution.

2. A method for configuring a partition of a local autonomous power grid energy storage device according to claim 1, characterized in that: The step S1 of modeling the energy storage device based on the operating characteristics of the energy storage device includes: Taking battery energy storage as the modeling object, its mathematical model is shown in the following formula; Formula (1) to Formula (6) are the constraints of the energy storage device, where Formula (1) is the relationship between the electric energy stored by the energy storage device at time t and its charging and discharging power; Formula (2) is the capacity constraint of the energy storage device; Formula (3) and (4) are the charging power constraint and the discharging power constraint respectively; Formula (5) represents the working state constraint of the energy storage device, the three states are idle, charging and discharging, and it can only be in one working state at the same time; Formula (6) is the full scheduling cycle constraint of the energy storage device, the energy storage device should be at the same power at the beginning and end of the cycle, that is, the charging and discharging power should be consistent; Where, E ess and P ess are the rated capacity and rated power of ESS respectively, P t ESS,ch , P t ESS,dis They represent the amount of electricity, charging power, and discharging power of ESS at time t respectively; η ch is the charging efficiency of ESS; η dis is the discharge efficiency of ESS; M is an infinite number; is the charging state of ESS at time t, is the discharge state of ESS at time t, and when charging When 1, discharge is 1, and 0 when idle; and Indicates the maximum state of charge of the ESS at time t; and Represents the power of ESS at 0 o'clock and 24 o'clock respectively.

3. The method for configuring energy storage devices in a local autonomous power grid according to claim 1, wherein: The step S2, based on the division results of the autonomous units, establishes a model for selecting and sizing the energy storage device with the goal of optimizing the overall cost, including: (1) Establishing the objective function The planning objective of the energy storage device site selection and sizing planning model is to minimize the comprehensive cost of the distribution network, including the investment cost of the energy storage device, the operation and maintenance cost of the energy storage device, the main grid power purchase cost, and the loss cost of the distribution network, as shown below. minC=C Ess +C f +C buy +C loss (7) Where C Ess is the investment cost of the energy storage device, n is the number of energy storage devices, ξ is the discount rate, y is the service life of ESS, is the investment cost per unit capacity of the energy storage device, The rated capacity of the i-th energy storage device, is the investment cost per unit power of the energy storage device, P i ESS The rated power of the i-th energy storage device; C f is the operation and maintenance cost of the energy storage device, T is 8760h per year, is the operation and maintenance cost of the energy storage device per unit charge and discharge capacity, is the charge and discharge power of ESS at time t, a positive value indicates ESS discharging, and a negative value indicates ESS charging; C buy The electricity purchase cost for the main grid, n cp The number of main network liaison branches; is the real-time electricity price of the main network at that moment, P grid,t is the power on the main network connection branch at that moment; C loss is the network loss cost of the distribution network, C l Represents the unit network loss cost, N z is the total number of branches, I i is the current value flowing through branch i during period t, r i is the resistance value of branch i; (2) Determine the constraints 1) Energy storage device constraints Where, represents the total power of the i-th energy storage device at time t; Respectively represent the charging and discharging efficiency of the energy storage device; P i,c (t) represents the charging power of the i-th energy storage device at time t; P i,d (t) represents the discharge power of the i-th energy storage device at time t; μ i,c (t) is the charging state of ESS at time t, μ i,d (t) is the discharge state of ESS at time t, and μ i,c (t) is 1, when discharging μ i,d (t) is 1 and 0 when idle; represents the rated capacity of the i-th energy storage device; represents the charging and discharging power rating of the i-th energy storage device; and represent the maximum and minimum state of charge of the i-th energy storage device respectively; 2) Power balance constraints P PV,i,t +P WT,i,t +P ESS,i,t +P liпе,i,t =P loss,i,t +P load,i,t (19) Where, P grid,t It indicates the power on the branch line connected to the main grid at time t. It stipulates that power can only flow from the main grid to the distribution network and cannot be sent back. ESS,i,t Represents the charging and discharging power of the energy stored in the autonomous unit i, which is positive when discharging and negative when charging; P PV,i,t represents the total photovoltaic output within autonomous unit i at time t; P WT,i,t represents the total fan output in autonomous unit i at time t; P load,i,t represents the total load in autonomous unit i at time t; P loss,i,t represents the network loss value of autonomous unit i at time t, P liпе,i,t is the interaction power between autonomous unit i and its adjacent autonomous units. It is positive when other autonomous units input power to autonomous unit i, and negative otherwise. 3) Interaction power constraints between autonomous units P l min ≤|P line,i,t |≤P l max (20) Where: P l max 、P l min They represent the upper and lower limits of the power allowed to be transmitted on the tie line l between autonomous units; 4) Power constraints of the main network interconnection branch P grid,t ≥0 (21) Where, P grid,t Indicates the power on the branch line connecting to the main grid at time t; 5) Distribution network flow constraints Where δ(j) is the set of branch headend nodes with j as the terminal node in the distribution system; ξ(j) is the set of branch terminal nodes with j as the headend node; P ij (t) and Q ij (t) are the active power and reactive power flowing from node i to node j at time t; P j (t) and Q j (t) are the net active power and reactive power injected into node j at time t; U j (t) is the voltage amplitude of node j at time t; r ij and x ij are the resistance and reactance of branch ij respectively; I ij (t) is the current amplitude of branch ij at time t; 6) Voltage deviation constraints (1-e)U n ≤U i (t)≤(1+ε)U n (23) Where U i (t) represents the operating voltage of node i at time t; U n Indicates the voltage rating; ε indicates the voltage deviation operating range; 7) Thermal stability constraints 0≤I ij (t)≤I ij,max (24) Where, I ij (t) represents the current flowing through line ij at time t; I ij,max Indicates the maximum current-carrying capacity of line ij.

4. A method for configuring energy storage devices in a local autonomous power grid according to claim 1, characterized in that: The step S3, based on step S2, uses the second-order cone relaxation technique and linear technique to convert it into a mixed integer second-order cone programming model for solution, including: The standard form of second-order cone programming is shown in formula (25); In the formula, the variable x l ∈R n , constant d∈R m , constant c l ∈R n , constant D l ∈R m×n , K l is a second-order cone or a rotated second-order cone as shown in formula (26) or formula (27); Second-order cone: Rotate a second-order cone: The original mixed-integer nonconvex nonlinear model is transformed into a mixed-integer second-order cone programming model by using the second-order cone relaxation technique and linear technique; make Use it to replace the relevant terms in formula (22), and perform second-order cone relaxation at the same time to transform the nonlinear constraint into a second-order cone constraint, as shown in formula (28): Convert Equations (23) and (24) in the distribution network operation constraints into Equations (29) and (30): The second-order cone relaxation deviation quantification index and distribution network cutting plane constraint are shown in Equations (31) and (32), respectively: Where i t,ij,k and u t,i,k They represent the square of the current amplitude and the square of the voltage amplitude of node i at time t in the kth iteration respectively; P t,ij,k and Q respectively t,ij,k represents the active power flow and reactive power flow of branch ij at time t in the kth iteration.

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