Method for reinforcing integration of wind power based on reactive power battery energy storage system and dynamic line rating

Abstract

This invention relates to a method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings, belonging to the field of wind power integration technology. Specifically, it provides a method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings. The invention includes the following steps: Step 1): Establishing a power system analysis model, which includes a battery energy storage system model, a dynamic line rating model, and an optimal power flow model; Step 2): Constructing a simulation node system and integrating the developed model into the IEEE 24-node reliability test system. This invention can effectively analyze the impact of battery energy storage systems and dynamic line ratings on wind power grid connection, improve wind power utilization, reduce system costs, and enhance system reliability.

Description

Technical Field

[0001] This invention belongs to the field of wind power generation integration technology, and in particular relates to a method for enhancing wind power integration based on reactive power battery energy storage system and dynamic line rating. Background Technology

[0002] With the increasing proportion of renewable energy sources such as wind power, the power system faces severe challenges in terms of power fluctuations, transmission constraints, and operational reliability. Strengthening the effective integration of wind power has become a key issue in grid development. Battery energy storage systems can enhance wind power absorption capacity through charge and discharge regulation and provide reactive power support, making them valuable in alleviating network congestion and maintaining system stability. Dynamic line rating technology can dynamically adjust line transmission capacity based on real-time environmental conditions, helping to increase wind power penetration.

[0003] However, existing technologies still have limitations. On the one hand, there is a lack of systematic research on the combined application of battery energy storage systems and dynamic line ratings to improve wind power integration, making it difficult to accurately reflect the synergistic effect of the two under different operating conditions. On the other hand, existing research mainly focuses on the active power regulation of battery energy storage systems, failing to consider their reactive power support role, thus limiting their application in power system optimization. Therefore, it is necessary to propose a modeling and evaluation method that can simultaneously consider the active and reactive power characteristics of battery energy storage systems and adapt to the operating conditions of dynamic line ratings, in order to further enhance the efficient integration of wind power. Summary of the Invention

[0004] This invention addresses the aforementioned problems by providing a method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings. By integrating wind farms, battery energy storage systems, and dynamic line rating models into the IEEE 24-bus reliability test system, it is possible to effectively analyze the system's operating characteristics under wind power grid-connected conditions, improve wind power absorption and utilization efficiency, reduce wind curtailment levels, and evaluate the comprehensive improvement effect of battery energy storage and dynamic line ratings on the economic efficiency and reliability of the power system. This solves the technical problems in existing technologies that make it difficult to simultaneously consider the fluctuation characteristics of wind power, the operating characteristics of battery energy storage, and the real-time transmission capacity of lines, resulting in low wind power utilization, high system dispatching costs, and insufficient reliability.

[0005] The first aspect of this invention provides a method for enhancing wind power integration based on a reactive power battery energy storage system and dynamic line ratings, the technical solution of which includes:

[0006] 1) Establish a power system analysis model, which includes a battery energy storage system model, a dynamic line rating model, and an optimal power flow model, to analyze the impact of the battery energy storage system and dynamic line rating on the wind power generation system;

[0007] 2) Construct a test case node system and integrate the developed model into the IEEE 24-node reliability testing system;

[0008] 3) Conduct test and calculation of the example node system, and evaluate the economy and reliability of the improved IEEE 24-node reliability test system to verify the grid connection effect of wind power.

[0009] Preferably, the step of establishing the power system analysis model includes:

[0010] Step 1): Establish a battery energy storage system model

[0011] Battery energy storage systems can store energy during periods of low demand and discharge during periods of high demand, enabling energy arbitrage and load transfer, thereby reducing overall electricity costs and improving system performance. To fully utilize the role of battery energy storage systems, precise modeling and optimized scheduling are required, while power limiting and regulation are performed by the charge / discharge control unit during actual operation. The battery energy storage system begins daily operation at its initial energy storage level, as shown in equation (1):

[0012] （1）

[0013] In equation (1), B represents the battery set, indexed by b; T represents time, indexed by t. Energy stored in a battery energy storage system (unit: kWh); The initial energy stored in the battery energy storage system (unit: kWh).

[0014] During any given time period, the energy stored in the battery energy storage system is a function of the energy storage level, charging power, and discharging power of the previous time period, as shown in Equation (2). Furthermore, due to internal consumption in the charging and discharging control units of the battery energy storage system, a portion of the charging power extracted from the grid and a portion of the power released during discharging modes will be lost.

[0015] （2）

[0016] In equation (2), and These represent the active charging and active discharging power of the battery energy storage system during time period t (unit: kW). and These represent the charging and discharging efficiencies of the battery energy storage system, respectively.

[0017] To ensure normal operation, the inequality in equation (3) guarantees that the energy stored in the battery energy storage system remains positive and is limited to its rated energy capacity.

[0018] （3）

[0019] In equation (3), Rated energy capacity of the battery energy storage system (unit: kWh).

[0020] Furthermore, at the end of the operating period, the energy stored in the battery energy storage system should be equal to its initial value, as shown in (4):

[0021] （4）

[0022] The battery energy storage system operates under predetermined constraints to ensure safe, efficient, and reliable energy management. It can only be in one of three mutually exclusive modes at any given time: charging, discharging, or idle. Mode switching is achieved through binary variables, whose values ​​alternate between 0 and 1. The exchange of active and reactive power is shown in equations (5) and (6):

[0023] （5） （6）

[0024] In equations (5) and (6), and These are binary variables representing the active charging and active discharging power states of the battery energy storage system within time period t, respectively. and These are binary variables representing the reactive charging and reactive discharging power states of the battery energy storage system within time period t, respectively.

[0025] Battery energy storage systems must comply with apparent power limits during charging and discharging. When the corresponding binary value is set to 1, the active charging and discharging power must not exceed the apparent power of the battery energy storage system, as shown in equations (7) and (8):

[0026] （7） （8）

[0027] In equations (7) and (8), The rated apparent power of the battery energy storage system (unit: kVA).

[0028] Similarly, constraints (9) and (10) impose limits on reactive power during charging and discharging to ensure that it does not exceed the apparent power of the battery storage system.

[0029] （9） （10）

[0030] In equations (9) and (10), and These represent the reactive charging and reactive discharging power of the battery energy storage system during time period t (unit: kVA).

[0031] Furthermore, the battery energy storage system is subject to discharge power constraints, which ensure that the power injected into the grid by the system at any given time cannot exceed the product of its stored energy and discharge efficiency, as shown in equation (11):

[0032] （11）

[0033] The apparent power flow of a battery energy storage system is a nonlinear function of its active and reactive power, as shown in equation (12):

[0034] （12）

[0035] In equation (12), P, Q and S represent active power, reactive power and apparent power, respectively.

[0036] This constraint raises two key issues.

[0037] First, although one variable is defined for active power and reactive power, the battery energy storage system uses two separate variables for each variable: one for charging and the other for discharging. Assuming that at any given time only one charging or discharging power is non-zero, the net active and reactive power exchange can be expressed as the sum of charging and discharging power. This relationship has already been explicitly stated in equations (5) and (6). By substituting these terms into equation (12), the power flow constraint can be redefined as shown in equation (13).

[0038] （13）

[0039] The second problem stems from the nonlinearity of the constraints. This problem is solved by replacing the nonlinear second-order terms with approximate linear terms. As defined in equations (14) and (15), the quadratic terms of active power and reactive power are replaced with their respective linear approximations.

[0040] （14） （15）

[0041] In equations (14) and (15), and Let represent the linearized variables representing the exchange squares of net active power and reactive power of the battery energy storage system during time period t.

[0042] By substituting the above linear function into equation (13), we obtain equation (16):

[0043] （16）

[0044] Furthermore, if the rated apparent power of the battery energy storage system is constant, the square root can be eliminated, as shown in equation (17):

[0045] （17）

[0046] Next, the linear terms of the squares of active power and reactive power are defined by piecewise linearization as shown in equations (18) and (19):

[0047] （18） （19）

[0048] In equations (18) and (19), Y represents the expected number of linearized blocks, indexed by y; and Each linearized block y represents the active and reactive power of the battery energy storage system within time period t.

[0049] By substituting equations (18) and (19) into equation (17), the final linear constraint of the apparent power flow of the battery energy storage system is derived and expressed in equation (20), replacing the original nonlinear formula in equation (12).

[0050] （20）

[0051] To further support the piecewise linearized approximation formula for the active and reactive power operation of battery energy storage systems, a set of auxiliary constraints is introduced to define the piecewise limitations.

[0052] Equations (21) and (22) ensure power consistency by defining the total active and reactive power of the battery energy storage system within time period t as the sum of all segmented active and reactive components, respectively:

[0053] （21） （22）

[0054] Equations (23) and (24) are nonnegative and limit the active and reactive power of each segment to their respective allocated shares of the rated apparent power:

[0055] （23） （24）

[0056] Equations (25) and (26) introduce binary variables to activate the power of each segment:

[0057] （25） （26）

[0058] In equations (25) and (26), A set of auxiliary binary variables, and These represent the widths of each linearization block for active and reactive power, respectively. These constraints are based on... and Corresponding adjacent binary variables , , and This is defined to ensure the consistency of active and reactive power in each segment.

[0059] Step 2): Establish a dynamic line rating model

[0060] The methods for determining dynamic line ratings include meteorological monitoring, temperature sensing, and conductor tension and sag measurement. The current-carrying capacity of conductors is limited by the maximum allowable temperature; exceeding this limit will lead to annealing or excessive sag. Wind speed, wind direction, ambient temperature, and solar radiation are key inputs for calculating current-carrying capacity. This invention establishes a conductor heating and heat dissipation model based on the principle of thermal balance according to the IEEE 738 standard, ensuring energy conservation. Under steady-state conditions, the nonlinear derivative term is often ignored to simplify the calculation, as shown in equation (27):

[0061] （27）

[0062] In equation (27), For convection cooling, For radiative heat dissipation, It absorbs heat from solar radiation. It is Joule heat.

[0063] The dynamic line rating formula (27) for overhead lines can be summarized as follows:

[0064] （28）

[0065] In equation (28), This refers to the rated current of the dynamic circuit.

[0066] It is worth noting that the impact of solar radiation on line ratings is negligible, and IEEE standard values ​​can be used in calculations. Considering the relatively small regional differences in ambient temperature, the line ratings are usually estimated using the central atmospheric temperature. Therefore, dynamic line ratings are mainly determined by wind speed and direction. Since transmission lines cross different meteorological zones, hotspots may form locally; therefore, the rating at the point of highest conductor temperature is often used as the dynamic line rating for the entire line.

[0067] Step 3): Establish the optimal power flow model

[0068] To integrate the above model into the power grid assessment framework, this model aims to quantify the impact of battery energy storage systems and dynamic line ratings on the dispatch of wind power grid-connected systems. The optimization objective is to minimize the total system cost, which includes three parts: unit dispatch cost, wind curtailment cost, and load reduction cost, as shown in equation (29):

[0069] （29）

[0070] In equation (29), G represents the set of generator sets, indexed by g; W represents the set of wind power plants, indexed by w; and L represents the set of loads, indexed by i. , , and These are the operating, startup, shutdown, and no-load costs of the generator set, respectively. For the cost of wind curtailment, To reduce costs by reducing workload. Let g be the active power of generator set g at time t. and These are the startup and shutdown parameters of generator set g at time t, respectively; This represents the on / off state of generator set g at time t; Wind power generation parameter mismatch; This is a parameter indicating a mismatch between load supply and demand.

[0071] In power flow modeling, the charging of the battery energy storage system is regarded as a load, and the discharging is regarded as a power generation. Its influence is reflected in both the active and reactive power balance equations. In the constraint formulas (30) and (31), the consumption side includes the charging power of the battery energy storage system, the bus load and the power flow of the output line, while the power generation side includes the discharging power of the battery energy storage system, the unit output, the upstream power source and the load reduction.

[0072] （30） （31）

[0073] In equations (30) and (31), N represents the set of bus lines, indexed by n / m. Let g be the reactive power of generator set g at time t. and These represent the total active power and reactive power demand of bus n during time period t, respectively. and These represent the active power flow and reactive power flow between bus n and m within time period t, respectively. and These represent the reduction in active and reactive loads of bus n during time period t.

[0074] The constraints on the system's generator sets and wind farm turbines are as follows:

[0075] （32） （33） （34） （35）

[0076] In equation (32), and Let be the minimum and maximum active power of generator set g at time t, respectively. In equation (33), and Let be the minimum and maximum reactive power of generator set g at time t, respectively. In equations (34) and (35), and These represent the active power and reactive power of the wind farm w during time period t, respectively. and These represent the maximum active and reactive power of the wind farm w within time period t.

[0077] The system load requirements are constrained as follows:

[0078] （36） （37）

[0079] In equations (36) and (37), and These represent the maximum values ​​of the total active power and reactive power demand of bus n during time period t, respectively.

[0080] The load reduction constraints are shown in equations (38) and (39), where equation (38) ensures that the reduction amount is non-negative and does not exceed the bus demand, and equation (39) stipulates that when the active load is reduced, the reactive load is reduced by the same proportion.

[0081] （38） （39）

[0082] To ensure the feasibility of unit scheduling, a ramp rate constraint R is introduced, as shown in equation (40), which limits the variation in output power of the units between adjacent time periods t and t+1. Therefore, throughout the entire period T, each unit must satisfy this constraint to track the predicted load curve.

[0083] （40）

[0084] In equation (40), and These are the upper and lower limits of the generator set's gradeability, respectively. This represents the change in the output power of the generator set between adjacent time periods t and t+1.

[0085] Equation (41) gives a more specific form of the constraint:

[0086] （41）

[0087] In equation (41), The gradeability limit for generator set g, Let t be the time difference between any two adjacent time periods t and t+1.

[0088] Preferably, the step of constructing the computing node system includes:

[0089] The simulation node system is built based on the IEEE 24-node reliability test system, which includes 24 nodes and 34 transmission lines. Based on the daily load curve of Sunday in week 7, it is assumed that the day-ahead load demand is known, and modeling is performed according to the ratio of hourly peak load to daily peak load. To construct a grid congestion scenario, the system load and generation level are uniformly scaled up by a factor of 6.

[0090] Regarding wind power integration, 200MW wind farms will be connected at nodes 3 and 5 respectively, and new wind farms will replace existing conventional turbines at nodes 7, 16, 21, and 23, with the wind power capacity matching the capacity of the replaced turbines. When the battery energy storage system is deployed, it will be located at the same node as the wind farm to achieve joint operation.

[0091] Preferably, the steps of conducting test and calculation of the example node system include:

[0092] During the simulation of the example node system, hourly wind speed, wind direction, and ambient temperature data were acquired, and the dynamic ratings of the transmission line were calculated based on the IEEE-738 standard. Wind power was calculated by combining wind speed data with the power output curve of a standard wind turbine. The cut-in wind speed, rated wind speed, and cut-out wind speed of the wind farm were set to 3 m / s, 11 m / s, and 25 m / s, respectively. The cost of wind curtailment was set at $58.8 / MWh, and the cost of load shedding was set at $800 / MWh. Operating parameters of conventional units were comprehensively considered, including minimum start-up and shutdown time, ramp constraints, start-up and shutdown costs, and no-load costs.

[0093] Based on the IEEE 24-node reliability testing system, load curves, conventional units, wind farms, meteorological and energy storage parameters were updated to establish a testing system that includes wind power and energy storage.

[0094] Among them, the capacity of the battery energy storage system is set at 40% of the capacity of the wind farm at the same node, and the rated power is set at 80% of the capacity, in order to meet the necessary SOC maintenance requirements.

[0095] Perform 24-hour hourly AC optimal power flow calculations, comprehensively consider the time-varying interaction between wind power output, battery energy storage system operation and system load, and capture the dynamic characteristics of the power system under different operating conditions;

[0096] The hourly simulation results are summarized to obtain system scheduling costs, load reduction and wind curtailment, and to evaluate the effect of dynamic line rating and battery energy storage system synergy on improving system economy and reliability.

[0097] Compared to existing technologies, the method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings provided in the first aspect of this invention establishes a comprehensive operation model that considers wind speed fluctuations, grid load changes, and line transmission capacity. This model yields the reactive power regulation characteristics of battery energy storage and the transmission capacity of dynamic lines under different meteorological conditions. Furthermore, by constructing a joint optimization model of wind farms and the power grid, the output and power flow characteristics under different operating scenarios are compared and verified. This explains the mechanism by which reactive power support from energy storage and dynamic line ratings enhance wind power grid connection capabilities under grid-constrained conditions, effectively improving wind power absorption and reducing system operation risks. Attached Figure Description

[0098] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent upon reading the following detailed description with reference to the accompanying drawings. In the drawings, several embodiments of the invention are illustrated by way of example and not limitation, with the same or corresponding reference numerals denoteing the same or corresponding parts, wherein:

[0099] Figure 1 The example node system of the present invention

[0100] Figure 2 This is a flowchart of the overall simulation process of the present invention.

[0101] Figure 3 The percentage savings in generator dispatching cost, load reduction cost, and wind curtailment cost in Examples 2 and 3 compared to Example 1 are the percentages of savings in these inventions.

[0102] Figure 4 The hourly distribution values ​​of generator dispatching cost, load reduction cost, and wind curtailment cost for Examples 1-3 of this invention are shown below.

[0103] Figure 5 The percentage reduction in total cost, active power loss, reactive power loss, and voltage deviation in Example 5 compared to Example 4 is the percentage reduction of the present invention.

[0104] Figure 6 The hourly distribution values ​​of active power loss and reactive power loss in Examples 4 and 5 of this invention.

[0105] Figure 7 The peak-hour bus voltage amplitudes of Examples 4 and 5 of this invention

[0106] Figure 8 The reliability impact results of the battery energy storage system of the present invention Detailed Implementation

[0107] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey its scope to those skilled in the art. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art.

[0108] It should be noted that, unless otherwise stated, the technical or scientific terms used in this invention should be understood in their ordinary sense by those skilled in the art. In this document, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising…” does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0109] This invention provides a method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings, comprising the following steps:

[0110] 1) Establish a power system analysis model, which includes a battery energy storage system model, a dynamic line rating model, and an optimal power flow model, to analyze the impact of the battery energy storage system and dynamic line rating on the wind power generation system;

[0111] 2) Construct a test case node system and integrate the developed model into the IEEE 24-node reliability testing system;

[0112] 3) Conduct test and calculation of the example node system, and evaluate the economy and reliability of the improved IEEE 24-node reliability test system to verify the grid connection effect of wind power.

[0113] This invention also includes a part on establishing a power system analysis model, comprising the following steps:

[0114] Step 1): Establish a battery energy storage system model

[0115] Battery energy storage systems can store energy during periods of low demand and discharge during periods of high demand, enabling energy arbitrage and load transfer, thereby reducing overall electricity costs and improving system performance. To fully utilize the role of battery energy storage systems, precise modeling and optimized scheduling are required, while power limiting and regulation are performed by the charge / discharge control unit during actual operation. The battery energy storage system begins daily operation at its initial energy storage level, as shown in equation (1):

[0116] （1）

[0117] In equation (1), B represents the battery set, indexed by b; T represents time, indexed by t. Energy stored in a battery energy storage system (unit: kWh); The initial energy stored in the battery energy storage system (unit: kWh).

[0118] During any given time period, the energy stored in the battery energy storage system is a function of the energy storage level, charging power, and discharging power of the previous time period, as shown in Equation (2). Furthermore, due to internal consumption in the charging and discharging control units of the battery energy storage system, a portion of the charging power extracted from the grid and a portion of the power released during discharging modes will be lost.

[0119] （2）

[0120] In equation (2), and These represent the active charging and active discharging power of the battery energy storage system during time period t (unit: kW). and These represent the charging and discharging efficiencies of the battery energy storage system, respectively.

[0121] To ensure normal operation, the inequality in equation (3) guarantees that the energy stored in the battery energy storage system remains positive and is limited to its rated energy capacity.

[0122] （3）

[0123] In equation (3), Rated energy capacity of the battery energy storage system (unit: kWh).

[0124] Furthermore, at the end of the operating period, the energy stored in the battery energy storage system should be equal to its initial value, as shown in (4):

[0125] （4）

[0126] The battery energy storage system operates under predetermined constraints to ensure safe, efficient, and reliable energy management. It can only be in one of three mutually exclusive modes at any given time: charging, discharging, or idle. Mode switching is achieved through binary variables, whose values ​​alternate between 0 and 1. The exchange of active and reactive power is shown in equations (5) and (6):

[0127] （5） （6）

[0128] In equations (5) and (6), and These are binary variables representing the active charging and active discharging power states of the battery energy storage system within time period t, respectively. and These are binary variables representing the reactive charging and reactive discharging power states of the battery energy storage system within time period t, respectively.

[0129] Battery energy storage systems must comply with apparent power limits during charging and discharging. When the corresponding binary value is set to 1, the active charging and discharging power must not exceed the apparent power of the battery energy storage system, as shown in equations (7) and (8):

[0130] （7） （8）

[0131] In equations (7) and (8), The rated apparent power of the battery energy storage system (unit: kVA).

[0132] Similarly, constraints (9) and (10) impose limits on reactive power during charging and discharging to ensure that it does not exceed the apparent power of the battery storage system.

[0133] （9） （10）

[0134] In equations (9) and (10), and These represent the reactive charging and reactive discharging power of the battery energy storage system during time period t (unit: kVA).

[0135] Furthermore, the battery energy storage system is subject to discharge power constraints, which ensure that the power injected into the grid by the system at any given time cannot exceed the product of its stored energy and discharge efficiency, as shown in equation (11):

[0136] （11）

[0137] The apparent power flow of a battery energy storage system is a nonlinear function of its active and reactive power, as shown in equation (12):

[0138] （12）

[0139] In equation (12), P, Q and S represent active power, reactive power and apparent power, respectively.

[0140] This constraint raises two key issues.

[0141] First, although one variable is defined for active power and reactive power, the battery energy storage system uses two separate variables for each variable: one for charging and the other for discharging. Assuming that at any given time only one charging or discharging power is non-zero, the net active and reactive power exchange can be expressed as the sum of charging and discharging power. This relationship has already been explicitly stated in equations (5) and (6). By substituting these terms into equation (12), the power flow constraint can be redefined as shown in equation (13).

[0142] （13）

[0143] The second problem stems from the nonlinearity of the constraints. This problem is solved by replacing the nonlinear second-order terms with approximate linear terms. As defined in equations (14) and (15), the quadratic terms of active power and reactive power are replaced with their respective linear approximations.

[0144] （14） （15）

[0145] In equations (14) and (15), and Let represent the linearized variables representing the exchange squares of net active power and reactive power of the battery energy storage system during time period t.

[0146] By substituting the above linear function into equation (13), we obtain equation (16):

[0147] （16）

[0148] Furthermore, if the rated apparent power of the battery energy storage system is constant, the square root can be eliminated, as shown in equation (17):

[0149] （17）

[0150] Next, the linear terms of the squares of active power and reactive power are defined by piecewise linearization as shown in equations (18) and (19):

[0151] （18） （19）

[0152] In equations (18) and (19), Y represents the expected number of linearized blocks, indexed by y; and Each linearized block y represents the active and reactive power of the battery energy storage system within time period t.

[0153] By substituting equations (18) and (19) into equation (17), the final linear constraint of the apparent power flow of the battery energy storage system is derived and expressed in equation (20), replacing the original nonlinear formula in equation (12).

[0154] （20）

[0155] To further support the piecewise linearized approximation formula for the active and reactive power operation of battery energy storage systems, a set of auxiliary constraints is introduced to define the piecewise limitations.

[0156] Equations (21) and (22) ensure power consistency by defining the total active and reactive power of the battery energy storage system within time period t as the sum of all segmented active and reactive components, respectively:

[0157] （21） （22）

[0158] Equations (23) and (24) are nonnegative and limit the active and reactive power of each segment to their respective allocated shares of the rated apparent power:

[0159] （23） （24）

[0160] Equations (25) and (26) introduce binary variables to activate the power of each segment:

[0161] （25） （26）

[0162] In equations (25) and (26), A set of auxiliary binary variables, and These represent the widths of each linearization block for active and reactive power, respectively. These constraints are based on... and Corresponding adjacent binary variables , , and This is defined to ensure the consistency of active and reactive power in each segment.

[0163] Step 2): Establish a dynamic line rating model

[0164] The methods for determining dynamic line ratings include meteorological monitoring, temperature sensing, and conductor tension and sag measurement. The current-carrying capacity of conductors is limited by the maximum allowable temperature; exceeding this limit will lead to annealing or excessive sag. Wind speed, wind direction, ambient temperature, and solar radiation are key inputs for calculating current-carrying capacity. This invention establishes a conductor heating and heat dissipation model based on the principle of thermal balance according to the IEEE 738 standard, ensuring energy conservation. Under steady-state conditions, the nonlinear derivative term is often ignored to simplify the calculation, as shown in equation (27):

[0165] （27）

[0166] In equation (27), For convection cooling, For radiative heat dissipation, It absorbs heat from solar radiation. It is Joule heat.

[0167] The dynamic line rating formula (27) for overhead lines can be summarized as follows:

[0168] （28）

[0169] In equation (28), This refers to the rated current of the dynamic circuit.

[0170] It is worth noting that the impact of solar radiation on line ratings is negligible, and IEEE standard values ​​can be used in calculations. Considering the relatively small regional differences in ambient temperature, the line ratings are usually estimated using the central atmospheric temperature. Therefore, dynamic line ratings are mainly determined by wind speed and direction. Since transmission lines cross different meteorological zones, hotspots may form locally; therefore, the rating at the point of highest conductor temperature is often used as the dynamic line rating for the entire line.

[0171] Step 3): Establish the optimal power flow model

[0172] To integrate the above model into the power grid assessment framework, this model aims to quantify the impact of battery energy storage systems and dynamic line ratings on the dispatch of wind power grid-connected systems. The optimization objective is to minimize the total system cost, which includes three parts: unit dispatch cost, wind curtailment cost, and load reduction cost, as shown in equation (29):

[0173] （29）

[0174] In equation (29), G represents the set of generator sets, indexed by g; W represents the set of wind power plants, indexed by w; and L represents the set of loads, indexed by i. , , and These are the operating, startup, shutdown, and no-load costs of the generator set, respectively. For the cost of wind curtailment, To reduce costs by reducing workload. Let g be the active power of generator set g at time t. and These are the startup and shutdown parameters of generator set g at time t, respectively; This represents the on / off state of generator set g at time t; Wind power generation parameter mismatch; This is a parameter indicating a mismatch between load supply and demand.

[0175] In power flow modeling, the charging of the battery energy storage system is regarded as a load, and the discharging is regarded as a power generation. Its influence is reflected in both the active and reactive power balance equations. In the constraint formulas (30) and (31), the consumption side includes the charging power of the battery energy storage system, the bus load and the power flow of the output line, while the power generation side includes the discharging power of the battery energy storage system, the unit output, the upstream power source and the load reduction.

[0176] （30） （31）

[0177] In equations (30) and (31), N represents the set of bus lines, indexed by n / m. Let g be the reactive power of generator set g at time t. and These represent the total active power and reactive power demand of bus n during time period t, respectively. and These represent the active power flow and reactive power flow between bus n and m within time period t, respectively. and These represent the reduction in active and reactive loads of bus n during time period t.

[0178] The constraints on the system's generator sets and wind farm turbines are as follows:

[0179] （32） （33） （34） （35）

[0180] In equation (32), and Let be the minimum and maximum active power of generator set g at time t, respectively. In equation (33), and Let be the minimum and maximum reactive power of generator set g at time t, respectively. In equations (34) and (35), and These represent the active power and reactive power of the wind farm w during time period t, respectively. and These represent the maximum active and reactive power of the wind farm w within time period t.

[0181] The system load requirements are constrained as follows:

[0182] （36） （37）

[0183] In equations (36) and (37), and These represent the maximum values ​​of the total active power and reactive power demand of bus n during time period t, respectively.

[0184] The load reduction constraints are shown in equations (38) and (39), where equation (38) ensures that the reduction amount is non-negative and does not exceed the bus demand, and equation (39) stipulates that when the active load is reduced, the reactive load is reduced by the same proportion.

[0185] （38） （39）

[0186] To ensure the feasibility of unit scheduling, a ramp rate constraint R is introduced, as shown in equation (40), which limits the variation in output power of the units between adjacent time periods t and t+1. Therefore, throughout the entire period T, each unit must satisfy this constraint to track the predicted load curve.

[0187] （40）

[0188] In equation (40), and These are the upper and lower limits of the generator set's gradeability, respectively. This represents the change in the output power of the generator set between adjacent time periods t and t+1.

[0189] Equation (41) gives a more specific form of the constraint:

[0190] （41）

[0191] In equation (41), The gradeability limit for generator set g, Let t be the time difference between any two adjacent time periods t and t+1.

[0192] This invention also includes a part for constructing a computational node system, comprising the following steps:

[0193] The example node system is as follows Figure 1 As shown, the system is built based on the IEEE 24-node reliability test system, which includes 24 nodes and 34 transmission lines. Based on the daily load curve of Sunday in week 7, as shown in Table 1, it is assumed that the day-ahead load demand is known, and the model is constructed according to the ratio of hourly peak load to daily peak load. To construct a grid congestion scenario, the system load and generation level are uniformly scaled up by a factor of 6.

[0194] Table 1. Percentage of Hourly Peak Load to Daily Peak Load

[0195] Hour 1 2 3 4 5 6 Peak load percentage (%) 78 72 68 66 64 65 Hour 7 8 9 10 11 12 Peak load percentage (%) 66 70 80 88 90 91 Hour 13 14 15 16 17 18 Peak load percentage (%) 90 88 87 8 91 100 Hour 19 20 21 22 23 24 Peak load percentage (%) 99 97 94 92 87 81

[0196] Regarding wind power integration, 200MW wind farms will be connected at nodes 3 and 5 respectively, and new wind farms will replace existing conventional turbines at nodes 7, 16, 21, and 23, with the wind power capacity matching the capacity of the replaced turbines. When the battery energy storage system is deployed, it will be located at the same node as the wind farm to achieve joint operation.

[0197] This invention also includes a part for conducting test and calculation of the example node system, including the following steps:

[0198] The simulation process of the example node system is as follows: Figure 2 As shown, hourly wind speed, wind direction, and ambient temperature data were acquired, and the dynamic ratings of the transmission line were calculated based on the IEEE-738 standard. Wind power was calculated by combining wind speed data with standard wind turbine power output curves. The cut-in wind speed, rated wind speed, and cut-out wind speed of the wind farm were set to 3 m / s, 11 m / s, and 25 m / s, respectively. The cost of wind curtailment was set at $58.8 / MWh, and the load shedding cost was set at $800 / MWh. Operating parameters of conventional units were comprehensively considered, including minimum start-up and shutdown time, ramp constraints, start-up and shutdown costs, and no-load costs.

[0199] Based on the IEEE 24-bus reliability testing system, load curves, conventional units, wind farms, meteorological and energy storage parameters were updated to establish a testing system incorporating wind power and energy storage. To evaluate the effectiveness of the proposed model, five simulation examples were designed, as shown in Table 2.

[0200] Table 2 Summary of calculation examples for integrating different battery energy storage systems and dynamic line ratings

[0201] Calculation example describe Calculation example 1 Only basic power systems with integrated wind power are included; battery energy storage systems and dynamic line ratings are not included. Calculation example 2 Example 1 + Battery Energy Storage System; Reactive power support of the battery energy storage system is not considered. Calculation example 3 Example 1 + Dynamic line rating. Calculation example 4 Example 2 + Example 3; both battery energy storage system and dynamic line ratings are integrated; reactive power support of battery energy storage system is not considered. Calculation example 5 Extension of Example 4: Consider reactive power support of battery energy storage system.

[0202] Among them, the capacity of the battery energy storage system is set at 40% of the capacity of the wind farm at the same node, and the rated power is set at 80% of the capacity, in order to meet the necessary SOC maintenance requirements.

[0203] Perform 24-hour hourly AC optimal power flow calculations, comprehensively consider the time-varying interaction between wind power output, battery energy storage system operation and system load, and capture the dynamic characteristics of the power system under different operating conditions;

[0204] The hourly simulation results are summarized to obtain system scheduling costs, load reduction, and wind curtailment. The synergistic effect of dynamic line ratings and battery energy storage systems on improving system economy and reliability is evaluated, including the following steps:

[0205] 1) Economic analysis of the example node system

[0206] Table 3 summarizes the system operating cost results of simulation examples 1-3. After introducing dynamic line ratings and a battery energy storage system into the baseline system, the generator dispatching cost, load shedding cost, and wind power curtailment cost all decreased significantly.

[0207] Table 3 Cost simulation results of Examples 1-3

[0208] Calculation example Generator dispatch cost (M$) Load reduction cost ($) Cost of wind curtailment ($) Calculation example 1 2.06 892.22 65.71 Calculation example 2 1.85 805.85 32.35 Calculation example 3 1.91 583.24 50.79

[0209] Percentage of cost savings as follows Figure 3 As shown. Compared to Example 1, Example 2 achieved a decrease in all types of costs, with wind curtailment costs decreasing by 50.77%. Compared to Example 3, the standalone integration of the battery energy storage system in Example 2 can further reduce wind curtailment costs by 28.06%, demonstrating the advantage of the battery energy storage system in storing excess wind energy through energy time-shifting and releasing it when supply is insufficient. In Example 3, all costs decreased, with load shedding costs showing the largest decrease at 34.63%. Compared to Example 2, deploying dynamic line ratings further reduced load shedding costs by 24.95%, proving its effectiveness in mitigating load losses by increasing real-time transmission capacity. For generator dispatch costs, Example 2 and Example 3 decreased by 10.19% and 7.28% respectively compared to the baseline. The battery energy storage system showed a more significant improvement due to its energy storage capacity, reducing reliance on high-cost units during low power generation, thereby reducing the overall system cost.

[0210] The hourly variations of generator dispatching costs, load reduction costs, and wind curtailment costs in Examples 1, 2, and 3 are as follows: Figure 4 As shown, generator dispatch costs and load shedding costs peak between 9 and 21 hours, corresponding to the period of highest electricity demand. During this time, increased generator output leads to higher costs and the risk of grid congestion. Failures in transmission lines, cables, transformers, or generators will further exacerbate the supply-demand imbalance and increase shedding costs. In contrast, as more wind power is used to meet growing load demand, wind curtailment costs during peak hours show a downward trend.

[0211] 2) Reactive power support effect of the example node system

[0212] Table 4 compares the total cost, active power loss, reactive power loss, and voltage deviation of Examples 4 and 5. It is evident that Example 5, which incorporates reactive power support, significantly reduces the system's total cost, power loss, and voltage deviation. Voltage deviation represents the absolute deviation of all bus voltages from the nominal value of 1 over the entire time period; it is a measure of voltage distribution flatness and is calculated to demonstrate the impact of the battery energy storage system's reactive power contribution on the grid voltage distribution.

[0213] Table 4 Simulation results for Examples 4 and 5

[0214] Calculation example Total cost (M$) Active power loss (MW) Reactive power loss (MVAr) Voltage deviation Calculation example 4 2.21 1725.87 15290.83 33.93 Calculation example 5 2.15 1329.95 11137.84 25.92

[0215] Based on the calculations in Table 4, compared to Example 4, Example 5 shows the percentage reduction in total cost, active power loss, reactive power loss, and voltage deviation as follows: Figure 5 As shown in the figure. Example 5 considers the reactive power contribution of the battery energy storage system. Compared with Example 4, the total operating cost is reduced by approximately US$60,000, a reduction of 2.71%. In addition to economic benefits, the reactive power support of the battery energy storage system also brings significant technical contributions, especially the reduction in power loss. Specifically, the active power loss and reactive power loss in Example 5 are reduced by 22.94% and 27.16% respectively compared with Example 4, and the voltage deviation is also reduced by approximately 25%, indicating that the system voltage distribution is more stable.

[0216] The active power loss and reactive power loss over 24 hours in Examples 4 and 5 are as follows: Figure 6 As shown, the power loss in Example 5 is significantly reduced during peak load periods, with daily active and reactive power losses reduced by 395.92 MW and 4152.99 MVAr, respectively.

[0217] The bus voltage amplitude during peak hours (hours 18 and 19) is as follows: Figure 7 As shown in the figure, the results demonstrate that the battery energy storage system effectively supports voltage during periods of most significant voltage drop, significantly improving voltage distribution and enhancing grid stability. Since the grid primarily faces voltage drop issues, the battery energy storage system continuously injects reactive power to offset the voltage drop caused by long radial feeders. As a reactive power compensator, the battery energy storage system not only reduces reactive power demand and line losses but also lowers the reactive power output of substations, thereby improving substation efficiency, releasing capacity, and increasing active power transmission capacity without requiring infrastructure expansion.

[0218] 3) Reliability analysis of the example node system

[0219] Under average dynamic line rating conditions, a reliability impact analysis was conducted on battery energy storage systems of different capacities based on the system in Example 5. The results are as follows: Figure 8As shown in the figure, the expected power shortage, a widely accepted probabilistic indicator, quantifies the average energy shortage during emergencies. A higher value indicates lower reliability, and it is used as a primary reliability indicator to evaluate the performance of battery energy storage systems under various configurations. When the reliability of the battery energy storage system decreases from 100% to 80%, the expected power shortage increases significantly, and this effect becomes more pronounced with the increase in battery energy storage system capacity. Under low-capacity conditions, the impact of reliability degradation on the expected power shortage is relatively small, while under high-capacity conditions, the increase in the expected power shortage is greater. This indicates that the larger the capacity of the battery energy storage system, the greater its contribution to the power grid, and the more severe the negative impact of its failure on the network. Therefore, by modularizing a large-capacity battery energy storage system into multiple small-capacity components, it is possible to effectively mitigate sudden supply interruptions and improve system reliability in emergency situations.

[0220] In summary, this invention, by introducing a battery energy storage system and dynamic line ratings into the power system, effectively reduces operating costs and improves system performance. The battery energy storage system leverages energy time-shifting characteristics to reduce wind curtailment and decreases reliance on high-cost generators during periods of low power generation; dynamic line ratings significantly reduce load shedding by enhancing real-time transmission capabilities. Furthermore, combining the reactive power support of the battery energy storage system can simultaneously reduce active and reactive power losses, improve voltage distribution, and enhance voltage stability. Regarding reliability, while large-capacity battery energy storage systems can significantly reduce the expected amount of power shortages, their reliability degradation is more sensitive to system impacts; modular design can effectively improve system resilience. Therefore, this invention demonstrates comprehensive advantages in terms of economy, safety, and stability, and possesses strong engineering application value.

[0221] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for enhancing wind power integration based on reactive power battery energy storage systems and dynamic line ratings, characterized in that... Includes the following steps: Step 1): Establish a power system analysis model, which includes a battery energy storage system model, a dynamic line rating model, and an optimal power flow model, to analyze the impact of the battery energy storage system and dynamic line rating on the wind power generation system; Step 2): Construct a test case node system and integrate the developed model into the IEEE 24-node reliability test system.

2. The method for enhancing wind power integration based on reactive power battery energy storage system and dynamic line rating as described in claim 1, characterized in that, The establishment of the battery energy storage system model includes: Battery energy storage systems can store energy during periods of low demand and discharge during periods of high demand, enabling energy arbitrage and load transfer, thereby reducing overall electricity costs and improving system performance. In order to fully utilize the role of battery energy storage systems, they need to be accurately modeled and optimized for scheduling. In actual operation, the power is limited and regulated by the charge and discharge control unit. The battery energy storage system starts daily operation with the initial energy storage level, as shown in equation (1): （1） In equation (1), B represents the battery set, indexed by b; T represents time, indexed by t; Energy stored in a battery energy storage system (unit: kWh); The initial energy stored in the battery energy storage system (unit: kWh); In any given time period, the energy stored in the battery energy storage system is a function of the energy storage level, charging power and discharging power of the previous time period, as shown in Equation (2); in addition, due to the internal consumption of the charging and discharging control unit of the battery energy storage system, a portion of the charging power extracted from the grid and a portion of the power released during the discharging mode will be lost. （2） In equation (2), and These represent the active charging and active discharging power of the battery energy storage system during time period t (unit: kW). and These represent the charging and discharging efficiencies of the battery energy storage system, respectively. To ensure normal operation, the inequality in equation (3) guarantees that the energy stored in the battery energy storage system remains positive and is limited to its rated energy capacity. （3） In equation (3), Rated energy capacity of the battery energy storage system (unit: kWh); Furthermore, at the end of the operating period, the energy stored in the battery energy storage system should be equal to its initial value, as shown in (4): （4） The battery energy storage system operates under predetermined constraints to ensure the safety, efficiency, and reliability of energy management; it can only be in one of three mutually exclusive modes at any given time: charging, discharging, or idle; mode switching is achieved through binary variables, whose values ​​alternate between 0 and 1; the exchange of active and reactive power is shown in equations (5) and (6): （5） （6） In equations (5) and (6), and These are binary variables representing the active charging and active discharging power states of the battery energy storage system within time period t, respectively. and These are binary variables representing the reactive charging and reactive discharging power states of the battery energy storage system within time period t, respectively. The battery energy storage system must comply with the apparent power limit in charging and discharging modes; when the corresponding binary value is set to 1, the active charging and discharging power must not exceed the apparent power of the battery energy storage system, as shown in equations (7) and (8): （7） （8） In equations (7) and (8), The rated apparent power of the battery energy storage system (unit: kVA); Similarly, constraints (9) and (10) impose limits on reactive power during charging and discharging to ensure that it does not exceed the apparent power of the battery storage system. （9） （10） In equations (9) and (10), and These represent the reactive charging and reactive discharging power of the battery energy storage system during time period t (unit: kVA). Furthermore, the battery energy storage system is subject to discharge power constraints, which ensure that the power injected into the grid by the system at any given time cannot exceed the product of its stored energy and discharge efficiency, as shown in equation (11): （11） The apparent power flow of a battery energy storage system is a nonlinear function of its active and reactive power, as shown in equation (12): （12） In equation (12), P, Q and S represent active power, reactive power and apparent power, respectively; This constraint raises two key issues; First, although one variable is defined for active power and reactive power, the battery energy storage system uses two separate variables for each variable: one for charging and the other for discharging; assuming that at any given time only one charging or discharging power is non-zero, the net active power and reactive power exchange can be expressed as the sum of charging and discharging power; this relationship has been explicitly stated in equations (5) and (6); by substituting these terms into equation (12), the power flow limit can be redefined as shown in equation (13); （13） The second problem stems from the nonlinearity of the constraints; this problem is solved by replacing the nonlinear second-order terms with approximate linear terms; as defined in equations (14) and (15), the quadratic terms of active power and reactive power are replaced with their respective linear approximations. （14） （15） In equations (14) and (15), and Let represent the linearized variables representing the exchange squares of net active power and reactive power of the battery energy storage system during time period t, respectively. By substituting the above linear function into equation (13), we obtain equation (16): （16） Furthermore, if the rated apparent power of the battery energy storage system is constant, the square root can be eliminated, as shown in equation (17): （17） Next, the linear terms of the squares of active power and reactive power are defined by piecewise linearization as shown in equations (18) and (19): （18） （19） In equations (18) and (19), Y represents the expected number of linearized blocks, indexed by y; and Each linearized block y represents the active and reactive power of the battery energy storage system within time period t; By substituting equations (18) and (19) into equation (17), the final linear constraint of the apparent power flow of the battery energy storage system is derived and expressed in equation (20), replacing the original nonlinear formula in equation (12). （20） To further support the piecewise linearized approximation formula for the active and reactive power operation of battery energy storage systems, a set of auxiliary constraints is introduced to define the piecewise limitations. Equations (21) and (22) ensure power consistency by defining the total active and reactive power of the battery energy storage system within time period t as the sum of all segmented active and reactive components, respectively: （21） （22） Equations (23) and (24) are nonnegative and limit the active and reactive power of each segment to their respective allocated shares of the rated apparent power: （23） （24） Equations (25) and (26) introduce binary variables to activate the power of each segment: （25） （26） In equations (25) and (26), A set of auxiliary binary variables, and Let represent the width of each linearization block for active power and reactive power, respectively; these constraints are based on... and Corresponding adjacent binary variables , , and This is defined to ensure the consistency of active and reactive power in each segment.

3. The method for enhancing wind power integration based on reactive power battery energy storage system and dynamic line rating as described in claim 1, characterized in that, The establishment of the dynamic line rating model includes: The methods for determining the dynamic line rating include meteorological monitoring, temperature sensing, and conductor tension and sag measurement; the conductor current carrying capacity is limited by the maximum allowable temperature, exceeding which will lead to annealing or excessive sag; wind speed, wind direction, ambient temperature, and solar radiation are key inputs for calculating the current carrying capacity; this invention establishes a conductor heating and heat dissipation model based on the principle of thermal balance according to the IEEE 738 standard to ensure energy conservation; under steady-state conditions, the nonlinear derivative term is often ignored to simplify the calculation, as shown in equation (27): （27） In equation (27), For convection cooling, For radiative heat dissipation, It absorbs heat from solar radiation. It is Joule fever; The dynamic line rating formula (27) for overhead lines can be summarized as follows: （28） In equation (28), This refers to the rated current of the dynamic line. It is worth noting that the influence of solar radiation on the line rating can be ignored, and the IEEE standard value can be used in the calculation. Considering that the regional differences in ambient temperature are small, the line rating is usually estimated by the central atmospheric temperature. Therefore, the dynamic line rating is mainly determined by wind speed and wind direction. Since the transmission line crosses different meteorological zones, hot spots may form in some areas. Therefore, the rating at the point with the highest conductor temperature is often used as the dynamic line rating of the entire line.

4. The method for enhancing wind power integration based on reactive power battery energy storage system and dynamic line rating as described in claim 1, characterized in that, The establishment of the optimal power flow model includes: To integrate the above model into the power grid assessment framework, this model aims to quantify the impact of battery energy storage systems and dynamic line ratings on the dispatch of wind power grid-connected systems; the optimization objective is to minimize the total system cost, which includes three parts: unit dispatch cost, wind curtailment cost, and load reduction cost, as shown in equation (29): （29） In equation (29), G represents the set of generator sets, indexed by g; W represents the set of wind power plants, indexed by w; and L represents the set of loads, indexed by i. , , and These are the operating, startup, shutdown, and no-load costs of the generator set, respectively. For the cost of wind curtailment, To reduce costs for the load; Let g be the active power of generator set g at time t; and These are the startup and shutdown parameters of generator set g at time t, respectively; This represents the on / off state of generator set g at time t; Wind power generation parameter mismatch; For load supply and demand mismatch parameters; In power flow modeling, the charging of the battery energy storage system is regarded as a load and the discharging is regarded as power generation. Its influence is reflected in the active and reactive power balance equations. In the constraint formulas (30) and (31), the consumption side includes the charging power of the battery energy storage system, the bus load and the power flow of the output line, while the power generation side includes the discharging power of the battery energy storage system, the unit output, the upstream power supply and the load reduction. （30） （31） In equations (30) and (31), N represents the set of busbars, indexed by n / m; Let g be the reactive power of generator set g at time t; and These represent the total active power and reactive power demand of bus n during time period t, respectively. and These represent the active power flow and reactive power flow between bus n and m within time period t, respectively. and These represent the reduction in active and reactive loads of bus n during time period t, respectively. The constraints on the system's generator sets and wind farm turbines are as follows: （32） （33） （34） （35） In equation (32), and Let be the minimum and maximum active power of generator set g at time t, respectively; in equation (33), and Let be the minimum and maximum reactive power of generator set g at time t, respectively; in equations (34) and (35), and These represent the active power and reactive power of the wind farm w during time period t, respectively. and These represent the maximum active and reactive power of the wind farm w within time period t, respectively. The system load requirements are constrained as follows: （36） （37） In equations (36) and (37), and These represent the maximum values ​​of the total active power and reactive power demand of bus n during time period t, respectively. The load reduction constraints are shown in equations (38) and (39), where equation (38) ensures that the reduction amount is non-negative and does not exceed the bus demand, and equation (39) stipulates that when the active load is reduced, the reactive load is reduced by the same proportion. （38） （39） To ensure the feasibility of unit scheduling, a ramp rate constraint R is introduced, as shown in equation (40), which limits the variation of the unit's output power between adjacent time periods t and t+1. Therefore, throughout the entire period T, each unit must satisfy this constraint to track the predicted load curve. （40） In equation (40), and These are the upper and lower limits of the generator set's gradeability, respectively. This represents the change in the output power of the generator set between adjacent time periods t and t+1. Equation (41) gives a more specific form of the constraint: （41） In equation (41), The gradeability limit for generator set g, Let t be the time difference between any two adjacent time periods t and t+1.

5. The method for enhancing wind power integration based on reactive power battery energy storage system and dynamic line rating as described in claim 1, characterized in that, The system for constructing computational nodes includes: The simulation node system is built based on the IEEE 24-node reliability test system, which includes 24 nodes and 34 transmission lines. Based on the daily load curve of Sunday of the 7th week, it is assumed that the day-ahead load demand is known and modeled according to the ratio of hourly load peak to daily peak. A grid congestion scenario is constructed, and the system load and generation level are uniformly scaled up by 6 times. Regarding wind power integration, 200MW wind farms will be connected at nodes 3 and 5 respectively, and new wind farms will replace existing conventional units at nodes 7, 16, 21 and 23, with the wind power capacity matching the capacity of the replaced units; when deploying the battery energy storage system, it will be located at the same node as the wind farm to achieve joint operation.

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