Intraday Optimal Scheduling Method for Offshore Wind Farms Considering the Operating Characteristics of Power Collection Networks

Abstract

This invention discloses a method for intraday optimal scheduling of offshore wind farms considering the operating characteristics of the power collection network. The method includes: Step 1: The offshore wind farm control center obtains the ultra-short-term wind speed forecast curve and scheduling plan curve for the next several hours and performs intraday rolling optimal scheduling; Step 2: The offshore wind farm control center calculates the optimal scheduling result for the next time period at the current time segment and issues the scheduling instruction for the first time segment in the result to the wind turbine; Step 3: When the next time segment arrives, the wind turbine executes the issued scheduling instruction; Step 4: Repeat steps 2 and 3. This invention can realize closed-loop operation of active power control of offshore wind farm clusters, better track the scheduling plan curve issued by the superior authority, improve the accuracy of wind farm tracking the scheduling plan curve issued by the power grid, and ensure the safety of wind farm operation corresponding to the optimal decision result through network security constraints.

Description

Technical Field

[0001] This invention relates to the field of power technology, and specifically to an intraday optimized scheduling method for offshore wind farms that takes into account the operating characteristics of the power collection network. Background Technology

[0002] Currently, existing offshore wind power optimization scheduling methods can be divided into day-ahead optimization scheduling methods and intraday / real-time optimization scheduling methods. Day-ahead optimization scheduling methods do not focus on the specific control of the wind turbines, often using the active power output of the turbines as the decision variable. In contrast, real-time optimization scheduling methods on a short timescale use precise internal control variables of the wind turbines, such as pitch angle and rotational speed, as decision variables. Both existing technologies neglect the impact of the operating characteristics of the power collection network in offshore wind farms on the total power output and safe operation of the wind farm.

[0003] Most existing intraday / real-time optimization scheduling methods aim to maximize the active power output of wind farms, optimizing the optimal pitch angle and speed of each wind turbine under the current predicted wind speed, while ignoring the mechanical loss cost of adjusting the wind turbine pitch angle.

[0004] Existing offshore wind farm optimization and dispatch technologies do not take into account the structure and power flow distribution of the collection network within the offshore wind farm, lack sufficient consideration for the economic efficiency and safety of the operation within the offshore wind farm, and have insufficient tracking capability for the dispatch plans issued by the power grid company. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for intraday optimized scheduling of offshore wind farms that considers the operating characteristics of the power collection network, so as to ensure the safety of wind farm operation.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows:

[0007] A method for intraday optimal scheduling of offshore wind farms considering the operating characteristics of the power collection network, the method comprising:

[0008] Step 1: The offshore wind farm control center obtains the ultra-short-term wind speed forecast curve for the next few hours and the active power dispatch plan curve issued by the power grid dispatching agency;

[0009] Step 2: Based on the ultra-short-term wind speed forecast curve and active power dispatch plan curve for the next few hours, calculate the optimized dispatch result for the next time period at the current time segment, and issue the dispatch instruction for the first time segment in the result to each wind turbine.

[0010] Step 3: When the next time segment arrives, the wind turbine executes the issued dispatch instructions;

[0011] Step 4: Repeat steps 1, 2 and 3.

[0012] Furthermore, the intraday rolling optimization scheduling is obtained based on the intraday rolling optimization scheduling model of offshore wind farms. The intraday rolling optimization scheduling model of offshore wind farms is based on the active power output characteristics of wind turbine units, takes into account the wake effect of offshore wind speed propagation, and is constructed using the pitch angle and speed reference values ​​of each wind turbine unit as decision variables.

[0013] Furthermore, the active power output characteristics of the wind turbine include:

[0014] The thrust exerted by the offshore wind speed on the wind turbine rotor g is shown in equation (1), and the axial induction factor is defined as shown in equation (2):

[0015]

[0016]

[0017] In the formula, ρ is the air density; R is the blade radius of the fan; v g1 The speed of the wind after it passes through the wind turbine rotor; v g Let be the actual wind speed flowing into the wind turbine; since the wind turbine rotor can only absorb part of the energy, substituting equation (2) into (1) yields:

[0018]

[0019] According to the energy equation, the energy absorbed by a wind turbine is equal to the difference in kinetic energy between the airflow before and after the wind turbine rotor, as shown in the formula:

[0020]

[0021] In the formula, This represents the average wind speed before and after the wind turbine.

[0022] Substituting equation (2) into equation (4), we get the actual active power absorbed by the wind turbine:

[0023]

[0024] Furthermore, the active power output characteristics of the wind turbine also include:

[0025] Define the wind energy utilization factor C P For equation (6), then when C P,g For a g The maximum wind energy utilization factor is obtained when the derivative equals 0. As in equation (7):

[0026]

[0027]

[0028] In actual control, the wind energy utilization coefficient C of the wind turbine unit P Depending on the turbine's pitch angle β and tip speed ratio λ, their approximate relationship is as follows:

[0029]

[0030] The maximum achievable active power output of a single wind turbine g is expressed as follows:

[0031]

[0032] In the formula, C P (β g ,λ g Let β be the wind energy utilization coefficient of wind turbine g, and its value depends on the pitch angle β of wind turbine g. g and tip speed ratio λ g Among them, the tip speed ratio λ g =ωR / v g ω is the angular velocity of the generator rotor; P rated The rated power of the fan (g) is given by ρ; air density is given by R. g v is the rotor radius of the fan; g v is the inflow velocity of the fan g; ci ,v rated and v co These represent the cut-in, rated, and cut-out wind velocities of the fan g, respectively.

[0033] Furthermore, assuming that wind turbines g and w are aligned with the wind speed direction, the wake effect of the sea wind propagation is described as follows:

[0034] R wg =R+αX wg (10)

[0035]

[0036] In the formula, R wg X is the radius of the wake generated by the fan w at point g along the wind speed direction; wg Let w and g be the distances between the wind turbines in the wind speed direction; α be the wake attenuation factor; C Tw The thrust coefficient of the wind turbine w; v0 represents the natural wind speed; v wg S represents the wake velocity generated by fan w at fan g; ov,wg Let be the area where the wake region intersects with the rotor region. Assuming that turbine w is upstream of the wind turbine and turbine g is downstream, the intersection area is as follows:

[0037]

[0038] In the formula, d represents the distance between the center of the wake region and the center of the wind turbine region; when there are G wind turbines upstream of wind turbine g, the formula for calculating the wind speed at wind turbine g is as follows:

[0039]

[0040] Furthermore, the intraday rolling optimization scheduling model for offshore wind farms takes minimizing the penalty cost for power generation plan deviations and the mechanical cost related to the pitch angle adjustment of the wind turbines as its objective function, as follows:

[0041]

[0042] In the formula, C1 is the generation deviation penalty cost coefficient, which is taken as the nodal price of the grid-connected electricity; P ∑,t P represents the net active power output of the wind power cluster during time period t. d,t The active power dispatch plan value is issued by the power grid dispatch center to the wind power cluster; ΔT is the time interval between each period, △ g,t δ represents the pitch angle adjustment of the wind turbine g during time period t; δ is the mechanical cost coefficient per unit adjustment of the wind turbine pitch angle.

[0043] Transforming the absolute value in the objective function into a linear form reduces the difficulty of solving the optimization problem. The transformed form is as follows:

[0044]

[0045]

[0046] Furthermore, the constraints of the intraday rolling optimization scheduling model for offshore wind farms include the pitch angle change rate.

[0047]

[0048] In the formula, β g,t and β g,t-1 These are the pitch angles of the wind turbine g during time periods t and t-1, respectively.

[0049] Furthermore, the constraints of the intraday rolling optimization scheduling model for offshore wind farms also include constraints on the operation of the power collection network:

[0050]

[0051] In the formula, δ(j) / π(j) represents the set of nodes whose parent / child node is j; x ij b is the reactance value of line ij; j P is the susceptance to ground of node j; jk,t and Q jk,t P represents the active and reactive power at the beginning of line jk during the time period. ij,tand Q ij,t Definition and P jk,t and Q jk,t similar; P is the square of the voltage amplitude at node j during time period t; gj,t and Q gj,t P represents the active and reactive power output of the wind turbine g during time period t; ij,min and P ij,max These are the minimum and maximum values ​​of the line power ij, respectively; and This represents the minimum and maximum values ​​of the square of the voltage magnitude at node j.

[0052] Furthermore, the constraints of the intraday rolling optimization scheduling model for offshore wind farms also include constraints on wind turbine output characteristics:

[0053] With the pitch angle and rotational speed of the wind turbine as decision variables, the active power output characteristics of the wind turbine are described by equations (19) and (20):

[0054]

[0055]

[0056] In the formula, and These are the minimum and maximum power factor angles of the fan g, respectively; ρ is the air density at the blade; R g Let g be the blade radius of the fan; v g,t P is the inflow velocity of the fan; rated C is the rated power of the fan; P,g,t , λ g,t ω g,t These represent the wind energy utilization coefficient, tip speed ratio, and rotational speed of the wind turbine g at time period t; γ g,t ω is an intermediate variable; min,g,t and ω max,g,t The lower and upper limits of the permissible speed for the safe operation of the wind turbine.

[0057] Furthermore, the constraints of the intraday rolling optimization scheduling model for offshore wind farms also include wake effect constraints:

[0058]

[0059] In the formula, v 0,t The predicted wind speed at the offshore wind measurement tower; a g,t R is the axial induction factor. wg S is the wake radius of the wind speed at the downstream unit g after passing the upstream unit w; ov,wg v is the area of ​​intersection between the wake area and the area swept by the fan blades g; wg,tThis represents the wind speed loss between fan w and fan g.

[0060] Compared with the prior art, the advantages of this invention are as follows:

[0061] The method of this invention can realize the closed-loop operation of active power regulation of offshore wind farm clusters, better track the dispatch plan curve issued by the superior authority, improve the accuracy of wind farm tracking the dispatch plan curve issued by the power grid, and also ensure the safety of wind farm operation corresponding to the optimization decision results through network security constraints. Attached Figure Description

[0062] Figure 1 Data exchange relationship diagram for an intraday optimized scheduling method for offshore wind farms that considers the operating characteristics of the power collection network, provided in an embodiment of the present invention;

[0063] Figure 2 The active power output characteristic curve of wind power;

[0064] Figure 3 This is a schematic diagram of the wake effect;

[0065] Figure 4 Electrical connection diagram for the Caishitan offshore wind farm;

[0066] Figure 5 The coordinates of each wind turbine and meteorological tower are Cartesian rectangular coordinates.

[0067] Figure 6 Historical nodal electricity price data for the grid connection point over the past 14 days;

[0068] Figure 7-9 This shows the tracking of the total active power output of wind farms against the power generation plan curve issued by the power grid dispatch center, based on rolling optimized scheduling and manual control.

[0069] Figure 10-11 This is a comparison chart of results under different assessment deviation standards. Detailed Implementation

[0070] Example:

[0071] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0072] During grid operation, the dispatching agency performs real-time optimized dispatching based on the ultra-short-term power forecast curves for the next 4 hours of renewable energy power plants and loads, and issues the dispatching plan curves for the next 4 hours to the offshore wind farm control center for power tracking. Wind farms must strictly adhere to the dispatching plan curves (including real-time dispatching plan curves) issued by the power dispatching agency. During periods of restricted output, any deviation of the wind farm's active power output (including the actual power generated by the power plant's energy storage devices) from the dispatching plan curve exceeding 5% (excluding deviations caused by wind speeds exceeding the cut-off wind speed or sudden wind speed drops) will be assessed at twice the amount of integrated power. If the output power of an offshore wind farm deviates significantly from the dispatched value, it will result in substantial deviation penalty costs for the power plant operator.

[0073] Currently, some scholars have conducted theoretical research on optimized scheduling methods for offshore wind farms. However, after field investigations in multiple locations, it has been found that most existing offshore wind farms control the active power output of the cluster by having technicians manually issue scheduling commands to the wind turbines. The accuracy of this control method relies on the historical experience of the technicians, and the cluster power tracking effect is not ideal, resulting in high generation deviation penalty costs. Therefore, in order to achieve closed-loop operation of active power control for offshore wind farm clusters and better track the scheduling plan curves issued by higher authorities, refer to... Figure 1 As shown in the figure, this embodiment provides a method for intraday optimized scheduling of offshore wind farms that considers the operating characteristics of the power collection network, which specifically includes the following steps;

[0074] Step 1: The offshore wind farm control center obtains the ultra-short-term wind speed forecast curve for the next 4 hours and the active power dispatch plan curve issued by the power grid dispatching agency;

[0075] Step 2: Based on the future wind speed forecast curve and the scheduling plan curve, calculate the optimized scheduling result for the next time period at the current time segment, and issue the scheduling instruction for the first time segment in the result to each wind turbine.

[0076] Step 3: When the next time segment arrives, the wind turbine executes the dispatch instructions issued.

[0077] Step 4: Repeat steps 1, 2, and 3 to repeat the calculation for the next time period. For example, perform the optimized scheduling calculation for the time period from 00:15 to 04:15 at 00:00 to obtain the calculation result for the 00:15 time segment. When 00:15 arrives, execute the result and calculate the optimized scheduling result for 00:30 to 04:30, and repeat the above steps.

[0078] In this way, the above methods and steps can realize the closed-loop operation of active power regulation of offshore wind farm clusters, better track the dispatch plan curve issued by the superior authority, improve the accuracy of wind farm tracking the dispatch plan curve issued by the grid, and also ensure the safety of wind farm operation corresponding to the optimization decision results through network security constraints.

[0079] In one specific embodiment, the intraday rolling optimization scheduling is obtained based on the intraday rolling optimization scheduling model of offshore wind farms. The intraday rolling optimization scheduling model of offshore wind farms is based on the active power output characteristics of wind turbine units, takes into account the wake effect of offshore wind speed propagation, and is constructed using the pitch angle and speed reference values ​​of each wind turbine unit as decision variables.

[0080] Specifically, the active power output characteristics of this wind turbine describe the conversion process and numerical relationship from input wind energy to output active power, as well as the relationship between the output power and specific wind turbine control quantities. This is one of the most critical elements in the active power optimization control process of offshore wind farms, and specifically includes:

[0081] According to the aerodynamic momentum theory, the thrust of the offshore wind speed on the wind turbine rotor g is shown in equation (1), and the axial induction factor is defined as shown in equation (2).

[0082]

[0083]

[0084] In the formula, ρ is the air density; R is the blade radius of the fan; v g1 The speed of the wind after it passes through the wind turbine rotor; v g This represents the actual wind speed flowing into the wind turbine. If the wind turbine rotor can absorb all the energy of the wind, i.e., v... g1 When a = 0, g =0.5. However, in reality, only a portion of the energy can be absorbed; in this case, a... g If <0.5, then substituting equation (2) into (1) yields:

[0085]

[0086] According to the energy equation, the energy absorbed by a wind turbine is equal to the difference in kinetic energy between the airflow before and after the wind turbine rotor, as shown in the formula:

[0087]

[0088] In the formula, This represents the average wind speed before and after the wind turbine.

[0089] Substituting equation (2) into equation (4), we obtain the actual active power absorbed by the wind turbine:

[0090]

[0091] The wind energy utilization factor C can be defined. P For equation (6), then when C P,g For a g The maximum wind energy utilization factor is obtained when the derivative equals 0. As in equation (7):

[0092]

[0093]

[0094] Because of a g <0.5, so when a g When C = 1 / 3, P The maximum value is approximately 0.593, which is the Betz limit. However, due to physical limitations, the actual maximum wind energy utilization coefficient will be much lower than 0.593. The wind energy utilization coefficient C of the wind turbine in actual control... P Depending on the turbine's pitch angle β and tip speed ratio λ, the approximate relationship between them is as follows:

[0095]

[0096] From the above formula, it can be seen that when λ=6.325, the wind energy utilization coefficient C P It has a maximum value

[0097] In summary, the maximum active power output of a single wind turbine g can be expressed as:

[0098]

[0099] In the formula, C P (β g ,λ g Let β be the wind energy utilization coefficient of wind turbine g, and its value depends on the pitch angle β of wind turbine g. g Tip speed ratio λ g Among them, the tip speed ratio λ g =ωR / v g ω is the angular velocity of the generator rotor; P rated The rated power of the fan (g) is given by ρ; air density is given by R. g v is the rotor radius of the fan; g v is the inflow velocity of the fan g; ci ,v rated and v co These represent the cut-in, rated, and cut-out wind velocities of the wind turbine g, respectively. The active power output characteristic curve of the wind power system is shown below. Figure 2 As shown.

[0100] When natural wind flows over a wind turbine, it loses some energy, creating a wind speed attenuation zone downstream. In this zone, the wind speed at the wind turbine location decreases; this phenomenon is called the wake effect. A Park wake model, suitable for flat terrain and using the axial induction factor as the control variable, is chosen to describe the wake effect of offshore wind farms. A schematic diagram is shown below. Figure 3 As shown. Assuming that the fan g and fan w are on the same straight line as the wind speed direction, it can be described as:

[0101] R wg =R+αX wg (10)

[0102] v wg =v0[1-2a w (R / R wg ) 2 S ov,wg / πR 2 (11)

[0103] In the formula, R wg X is the radius of the wake generated by the fan w at point g along the direction of wind speed; wg Let w and g be the distances between the wind turbines in the wind speed direction; α is the wake attenuation factor, which is taken as 0.04 in offshore wind farms; C Tw The thrust coefficient of the wind turbine w; v0 represents the natural wind speed; v wg S represents the wake velocity generated by fan w at fan g; ov,wg Let be the area where the wake region intersects with the rotor region. Assuming that turbine w is upstream of the wind turbine and turbine g is downstream, the intersection area is as follows:

[0104]

[0105] In the formula, d represents the distance between the center of the wake region and the center of the wind turbine region. When there are G wind turbines upstream of wind turbine g, the formula for calculating the wind speed at turbine g is:

[0106]

[0107] The output of each wind turbine in an offshore wind farm affects each other. When the upstream wind energy utilization coefficient changes, the axial induction factor also changes, thereby altering the wake's attenuation effect on wind speed, as shown in the equation. When a w When the wind speed loss is reduced, v wg Increase. The above details the active power output characteristics of wind turbines. Based on this, we will construct an intraday rolling optimization scheduling model for offshore wind farm clusters, using the turbine's rotational speed and pitch angle as decision variables.

[0108] The intraday rolling optimization scheduling model for offshore wind farms takes minimizing the penalty cost for power generation plan deviations and the mechanical cost related to the pitch angle adjustment of wind turbines as its objective function, as follows:

[0109]

[0110] In the formula, C1 is the generation deviation penalty cost coefficient, which is taken as the nodal price of the grid-connected electricity; P ∑,t P represents the net active power output of the wind power cluster during time period t. d,t The active power dispatch plan value is issued by the power grid dispatch center to the wind power cluster; ΔT is the time interval between each time period, taken as 15 minutes; △ g,t Let be the pitch angle adjustment of the wind turbine g during time period t; δ is the mechanical cost coefficient per unit adjustment of the wind turbine pitch angle. By transforming the absolute value in the objective function into a linear form to reduce the difficulty of solving the optimization problem, the transformed form is as follows:

[0111]

[0112]

[0113] The final output active power is adjusted by providing reference values ​​for the turbine's rotational speed and pitch angle. After providing the command reference, the turbine regulates the angular velocity of the turbine blades by controlling the electromagnetic torque of the generator, and adjusts the pitch angle through the pitch angle control system. However, frequent adjustments to the turbine's pitch angle can cause mechanical damage to the turbine blades, affecting their service life. Therefore, the following pitch angle change rate constraint needs to be considered in the model to minimize its adjustment:

[0114]

[0115] In the formula, β g,t and β g,t-1 Let be the pitch angles of the wind turbine g at time intervals t and t-1, respectively. Where β is the pitch angle at t=1. g,0 The measured pitch angle of the wind turbine during the current period reflects the feedback and correction effect of the measured data on the decision-making results in the rolling optimization scheduling.

[0116] In actual operation, offshore wind turbines located close to each other are sequentially connected to 35kV AC feeder lines, transmitting the power output from the turbine terminals from the box-type transformers to the 35kV busbar of the offshore substation. The power output from each wind turbine, collected by multiple AC feeder lines at the offshore substation, is then stepped up and transmitted from the 220kV busbar to the onshore power grid via submarine cables. The network from the wind turbine terminals to the onshore grid connection point is called the offshore wind farm's collection network. Its operating characteristics are described using a branch power flow model, which requires the following operating constraints to be met:

[0117]

[0118] In the formula, δ(j) / π(j) represents the set of nodes whose parent / child node is j; x ij b is the reactance value of line ij; j P is the susceptance to ground of node j; jk,t and Q jk,t P represents the active and reactive power at the beginning of line jk during the time period. ij,t and Q ij,t Definition and P jk,t and Q jk,t similar; P is the square of the voltage amplitude at node j during time period t; gj,t and Q gj,t This represents the active and reactive power output of the wind turbine g during time period t. P ij,min and P ij,max These are the minimum and maximum values ​​of the line power ij, respectively; and This represents the minimum and maximum values ​​of the square of the voltage magnitude at node j.

[0119] With the pitch angle and rotational speed of the wind turbine as decision variables, the active power output characteristics of the wind turbine can be described as shown in equations (19) and (20):

[0120]

[0121]

[0122] In the formula, and These are the minimum and maximum power factor angles of the fan g, respectively; ρ is the air density at the blade; R g Let g be the blade radius of the fan; v g,t P is the inflow velocity of the fan; rated C is the rated power of the fan; P,g,t , λ g,t ω g,t These represent the wind energy utilization coefficient, tip speed ratio, and rotational speed of the wind turbine g at time period t; γ g,t ω is an intermediate variable; min,g,t and ω max,g,t The lower and upper limits of the permissible speed for the safe operation of the wind turbine.

[0123] During wind turbine operation, the operating status of upstream units affects the amount of wind energy absorbed by downstream units. When upstream units operate at reduced load and decrease their output power, their wind energy reduction effect also decreases accordingly, leading to an increase in the inflow wind speed to downstream units. Therefore, the following uses a Park wake model based on the axial induction factor to describe the coupling relationship between units in the wind speed propagation direction, in order to obtain an optimal scheduling result that maximizes overall benefits. The specific formula is as follows:

[0124]

[0125] In the formula, v 0,t The predicted wind speed at the offshore wind measurement tower; a g,t R is the axial induction factor. wg S is the wake radius of the wind speed at the downstream unit g after passing the upstream unit w; ov,wg v is the area of ​​intersection between the wake area and the area swept by the fan blades g; wg,t This represents the magnitude of the wind speed loss between fan w and fan g;

[0126] The above-constructed intraday rolling optimization scheduling model for offshore wind farms, considering the operating characteristics of the power collection network, uses the rotational speed and pitch angle of each wind turbine as decision variables, taking into account the wake effect generated during wind speed propagation and the overall adjustment of the pitch angle. In the actual intraday rolling optimization scheduling process, at the current time segment, the measured wind turbine operating status data, i.e., the current pitch angle, is acquired. Then, the optimized scheduling result for the next scheduling time period is calculated, and the scheduling command for the first time segment in the result is issued to each wind turbine. When the next time segment arrives, the wind turbine executes the issued scheduling command and repeats the calculation for the next scheduling time period.

[0127] The invention will be further illustrated below with an application scenario example:

[0128] Taking the data from the CGN Huizhou Caishitan Offshore Wind Farm as an example, this analysis is conducted. The wind farm comprises 40 offshore wind turbines with a single unit capacity of 6.25MW, for a total installed capacity of 250MW. Its internal electrical connection diagram is shown below. Figure 4As shown in Figure 5, the geographical coordinates of each wind turbine and the meteorological tower located at the substation are as follows. The cut-in wind speed, rated wind speed, and cut-out wind speed of the wind turbines are 3 m / s, 10.5 m / s, and 25 m / s, respectively; the rated speed and speed range are 10.8 rpm and 3.2–12.3 rpm, respectively; and the turbine blade radius is 89 m. The historical data used for the test includes 14 days of historical active power dispatching data for the site from 00:15 on September 17, 2023 to 00:00 on October 1, 2023, historical data of ultra-short-term wind speed forecasts, and historical data of real-time nodal electricity prices at the grid connection point, Puzi Substation. The nodal electricity price is used to calculate the penalty for deviations in the power generation plan, such as... Figure 6 As shown.

[0129] Based on 1344 real-time dispatch plan curves from 00:15 on September 17, 2023 to 00:00 on October 1, 2023 (a total of 14 days), continuous rolling optimization calculations were performed, and the decision results corresponding to the first time period on the curves were executed. The tracking performance of the total active power output of wind farms based on rolling optimization dispatch and based on manual control was compared with that based on manual control, showing the following: Figure 7-9 As shown, Figure 7 September 17, 2023

[0130] The tracking results for a total of 14 days, from 00:15 to 100:00 on October 10, 2023. Figure 8 The results are from 2023.9.1700:15 to 2023.9.1800:00. Figure 9 The results are from 00:15 on September 26, 2023 to 00:00 on September 27, 2023. It can be seen that wind farms under manual control tend to generate more electricity, with their active power output almost always exceeding the power generation plan value issued by the dispatch center. In contrast, wind farms under rolling optimization dispatch have better tracking performance of active power output, and the overall power generation deviation is significantly reduced.

[0131] Table 1 compares the dispatch results under different decision-making methods. The deviation assessment fee references Article 14 of the "Implementation Rules for Wind Power Grid Connection Operation and Ancillary Service Management in Southern Region," which stipulates that for wind farm active power output (including actual power generated by the on-site energy storage device) deviating from the dispatch plan curve issued by the grid dispatch center by more than 5% (excluding deviations caused by wind speed exceeding the cut-off wind speed or a sudden drop in wind speed), the portion is assessed at twice the integral electricity price, i.e., the assessment fee is calculated based on double the nodal price. Combined with... Figure 10-11 ( Figure 10 The total deviation assessment cost, Figure 11As shown in Table 1 (total net power generation revenue), manual control often tends to encourage wind farms to generate more electricity, resulting in higher total power generation revenue. However, relying on the experience of technicians, this leads to significant power generation deviations and high deviation assessment costs, ultimately resulting in lower total net power generation revenue. In contrast, the rolling optimization scheduling based on a refined mathematical model more closely matches the planned power output of the wind farm, with a smaller absolute value of the average relative deviation and lower total deviation assessment costs. Compared to manual control, the deviation assessment costs are reduced by 75.5%, effectively increasing the total net power generation revenue of the wind farm by RMB 1,659,465.85, which is 27.53% higher than that of manual control.

[0132] Table 1 Comparison of scheduling results under different decision-making methods

[0133]

[0134] As the proportion of wind power and other new energy power generation in the future power grid continues to increase, in order to maintain real-time power balance and system stability as much as possible, the evaluation standards for the deviation between the active power output of wind farms and the dispatch plan will inevitably become more and more stringent by the power grid dispatching agency. In other words, the closed-loop operation of power optimization dispatching by the wind farm control center will become a development trend. Table 2 and Figure 8 This paper illustrates the changes in total deviation assessment costs and total net power generation revenue over the previous 14 days under different control methods when the critical deviation percentage at the start of the assessment is changed. It can be seen that when assessment begins at a deviation exceeding 30%, the total net power generation revenue based on rolling optimal scheduling decisions is 616,258.4040 yuan higher than that based on manual control decisions; when assessment begins at a deviation exceeding 3%, the difference in total net power generation revenue is 1,684,422.8774 yuan. This means that as the assessment standards become increasingly stringent, the difference in net revenue between rolling optimal scheduling and manual control decisions becomes more pronounced, highlighting the significant advantages of the constructed fine-grained rolling optimal scheduling model for offshore wind farms.

[0135] Table 2 Comparison of results under different deviation assessment criteria

[0136]

[0137] In summary, this invention establishes an intraday optimal scheduling model for offshore wind farms that considers the operating characteristics of the power collection network. This model takes into account active power losses in the power collection network, improving the accuracy of power tracking for offshore wind farms and thus enhancing the economic efficiency of power dispatch decisions. Furthermore, the operating characteristics include network security constraints, ensuring the safe operation of the power collection network during the optimization decision-making process. By minimizing the power generation deviation penalty cost and mechanical loss cost of the offshore wind farm in the objective function, the established intraday optimal scheduling model can track the dispatch plan curve issued by the power grid and minimize the adjustment of the wind turbine blade pitch angle, thereby reducing wind turbine mechanical losses and extending service life. Compared to manual control methods, the proposed model's decision results can significantly reduce the power generation plan deviation of offshore wind farms and increase their net power generation revenue.

[0138] The above embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent changes or modifications made based on the essence of the content of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for intraday optimal scheduling of offshore wind farms considering the operating characteristics of the power collection network, characterized in that, The method includes: Step 1: The offshore wind farm control center obtains the ultra-short-term wind speed forecast curve for the next few hours and the active power dispatch plan curve issued by the power grid dispatching agency; Step 2: Based on the ultra-short-term wind speed forecast curve and active power dispatch plan curve for the next few hours, calculate the optimized dispatch result for the next time period at the current time segment, and issue the dispatch instruction for the first time segment in the result to each wind turbine. Step 3: When the next time segment arrives, the wind turbine executes the issued dispatch instructions; Step 4: Repeat steps 1, 2, and 3; The intraday optimized scheduling is obtained based on the intraday rolling optimized scheduling model of offshore wind farms. The intraday rolling optimized scheduling model of offshore wind farms is based on the active power output characteristics of wind turbine units, takes into account the wake effect of offshore wind speed propagation, and is constructed using the pitch angle and speed reference values ​​of each wind turbine unit as decision variables. The active power output characteristics of the wind turbine generator include: The thrust exerted by the offshore wind speed on the wind turbine rotor g is shown in equation (1), and the axial induction factor is defined as shown in equation (2): In the formula, ρ is the air density; R is the blade radius of the fan; v g1 The speed of the wind after it passes through the wind turbine rotor; v g Let be the actual wind speed flowing into the wind turbine; since the wind turbine rotor can only absorb part of the energy, substituting equation (2) into (1) yields: According to the energy equation, the energy absorbed by a wind turbine is equal to the difference in kinetic energy between the airflow before and after the wind turbine rotor, as shown in the formula: In the formula, This represents the average wind speed before and after the wind turbine. Substituting equation (2) into equation (4), we get the actual active power absorbed by the wind turbine: The active power output characteristics of the wind turbine also include: Define the wind energy utilization factor C P For equation (6), then when C P,g For a g The maximum wind energy utilization factor is obtained when the derivative equals 0. As in equation (7): In actual control, the wind energy utilization coefficient C of the wind turbine unit P Depending on the turbine's pitch angle β and tip speed ratio λ, their approximate relationship is as follows: The maximum achievable active power output of a single wind turbine g is expressed as follows: In the formula, C P (β g ,λ g Let β be the wind energy utilization coefficient of wind turbine g, and its value depends on the pitch angle β of wind turbine g. g and tip speed ratio λ g Among them, the tip speed ratio λ g =ωR / v g ω is the angular velocity of the generator rotor; P rated The rated power of the fan (g) is given by ρ; air density is given by R. g v is the rotor radius of the fan; g v is the inflow velocity of the fan g; ci ,v rated and v co These represent the cut-in, rated, and cut-out velocities of the fan g, respectively. The intraday rolling optimization scheduling model for offshore wind farms uses the minimization of the power generation plan deviation penalty cost and the mechanical cost related to the pitch angle adjustment of the wind turbines as its objective function, as follows: In the formula, C1 is the generation deviation penalty cost coefficient, which is taken as the nodal price of the grid-connected electricity; P ∑,t P represents the net active power output of the wind power cluster during time period t. d,t The active power dispatch plan value is issued by the power grid dispatch center to the wind power cluster; ΔT is the time interval between each period, △ g,t δ represents the pitch angle adjustment of the wind turbine g during time period t; δ is the mechanical cost coefficient per unit adjustment of the wind turbine pitch angle. Transforming the absolute value in the objective function into a linear form reduces the difficulty of solving the optimization problem. The transformed form is as follows:

2. The intraday optimal scheduling method for offshore wind farms considering the operating characteristics of the power collection network as described in claim 1, characterized in that, Assuming that wind turbines g and w are aligned with the wind speed direction, the wake effect of the wind speed propagation at sea is described as follows: R wg =R+αX wg (10) v wg =v0[1-2a w (R / R wg ) 2 S ov,wg / πR 2 ] (11) In the formula, R wg X is the radius of the wake generated by the fan w at point g along the wind speed direction; wg Let w and g be the distances between the wind turbines in the wind speed direction; α be the wake attenuation factor; C Tw The thrust coefficient of the wind turbine w; v0 represents the natural wind speed; v wg S represents the wake velocity generated by fan w at fan g; ov,wg Let be the area where the wake region intersects with the rotor region. Assuming that turbine w is upstream of the wind turbine and turbine g is downstream, the intersection area is as follows: In the formula, d represents the distance between the center of the wake region and the center of the wind turbine region; when there are G wind turbine units upstream of the wind turbine g, the formula for calculating the wind speed at wind turbine g is as follows:

3. The intraday optimal scheduling method for offshore wind farms considering the operating characteristics of the power collection network as described in claim 2, characterized in that, The constraints of the intraday rolling optimization scheduling model for offshore wind farms include the pitch angle change rate constraint: In the formula, β g,t and β g,t-1 These are the pitch angles of the wind turbine g during time periods t and t-1, respectively.

4. The intraday optimal scheduling method for offshore wind farms considering the operating characteristics of the power collection network as described in claim 3, characterized in that, The constraints of the intraday rolling optimization scheduling model for offshore wind farms also include constraints on the operation of the power collection network: In the formula, δ(j) / π(j) represents the set of nodes whose parent / child node is j; x ij b is the reactance value of line ij; j P is the susceptance to ground of node j; jk,t and Q jk,t P represents the active and reactive power at the beginning of line jk during time period t. ij,t and Q ij,t Definition and P jk,t and Q jk,t similar; P is the square of the voltage amplitude at node j during time period t; gj,t and Q gj,t P represents the active and reactive power output of the wind turbine g during time period t; ij,min and P ij,max These are the minimum and maximum values ​​of the line power ij, respectively; and This represents the minimum and maximum values ​​of the square of the voltage magnitude at node j.

5. The intraday optimal scheduling method for offshore wind farms considering the operating characteristics of the power collection network as described in claim 3, characterized in that, The constraints of the intraday rolling optimization scheduling model for offshore wind farms also include constraints on wind turbine output characteristics: With the pitch angle and rotational speed of the wind turbine as decision variables, the active power output characteristics of the wind turbine are described by equations (19) and (20): In the formula, and These are the minimum and maximum power factor angles of the fan g, respectively; R g Let g be the blade radius of the fan; v g,t C is the inflow velocity of the fan; P,g,t , λ g,t ω g,t These represent the wind energy utilization coefficient, tip speed ratio, and rotational speed of the wind turbine g at time period t; γ g,t ω is an intermediate variable; min,g,t and ω max,g,t The lower and upper limits of the permissible speed for the safe operation of the wind turbine.

6. The intraday optimal scheduling method for offshore wind farms considering the operating characteristics of the power collection network as described in claim 3, characterized in that, The constraints of the intraday rolling optimization scheduling model for offshore wind farms also include wake effect constraints: In the formula, v 0,t The predicted wind speed at the offshore wind measurement tower; a g,t v is the axial induction factor at time period t; wg,t This represents the wind speed loss between fan w and fan g.

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