Active power real-time optimization control method and system of wind power station

Abstract

The invention discloses an active power real-time optimization control method and system for a wind power station, and the method comprises the steps: circularly carrying out the following operations in an active power real-time optimization control process: when wind power ultra-short-term prediction power is greater than dispatching power, judging whether an energy storage charging state is not fully charged, if yes, enabling an energy storage system to carry out the charging through the excess electric quantity of wind power, and if not, carrying out the energy storage; further judging whether the difference value between the wind power ultra-short-term predicted power and the dispatching power is greater than the energy storage rated power or not, if so, taking the energy storage rated power as the energy storage charging power for charging by the energy storage system, and if not, directly taking the difference value between the wind power ultra-short-term predicted power and the dispatching power as the energy storage charging power for charging; when the wind power ultra-short-term prediction power is smaller than the dispatching power, whether the energy storage charging state is larger than the minimum threshold value or not is judged, if yes, whether the difference value between the dispatching power and the wind power ultra-short-term prediction power is larger than the energy storage rated power or not is further judged, if yes, the energy storage system takes the energy storage rated power as the energy storage discharging power to conduct discharging, and if not, the energy storage system conducts discharging. And the energy storage system directly takes the difference value between the dispatching power and the wind power ultra-short-term prediction power as the energy storage discharge power for discharging.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power generation, and in particular relates to a real-time optimization control method and system for active power of a wind farm. Background Art

[0002] With the rapid development of wind power generation, the power grid has strict requirements on the power output of wind farms. Wind farms must meet the Automatic Generation Control (AGC) dispatch instructions to ensure the stable operation of the power grid and the balance of supply and demand. However, due to the significant intermittent characteristics of wind farm power generation, the unpredictability of wind resources leads to large fluctuations in power generation.

[0003] On the one hand, when wind power generation is insufficient to meet AGC dispatch instructions, the current energy storage system on wind farms lacks a real-time supplement mechanism. Since the output power cannot be adjusted immediately to match the dispatch requirements, wind farms may be fined, further increasing operating costs.

[0004] On the other hand, when wind power generation is at its peak, that is, when wind resources are abundant and power generation exceeds the AGC dispatching instructions, the energy storage system is often unable to effectively handle this excess power, resulting in frequent "wind abandonment" and a large amount of clean energy being wasted. This is not only a waste of natural resources, but also affects the economic benefits of wind farms. Summary of the invention

[0005] In order to solve the shortcomings of the existing technology, so that it can maximize the satisfaction of grid dispatching needs, reduce fines, and at the same time store excess electricity, reduce wind abandonment, and improve the utilization rate of electricity. The present invention adopts the following technical solutions: A method for real-time optimization control of active power of a wind farm station comprises the following steps: Step 1: Obtain dispatch power Sa, energy storage charging state Soc, and wind power ultra-short-term predicted power Sy; Step 2: Real-time optimization control of active power; When the ultra-short-term predicted power Sy of wind power is greater than the dispatching power Sa, the available wind power generation is greater than the dispatching demand, and it is determined whether the energy storage charging state Soc is not fully charged. If so, the energy storage system uses the excess wind power to charge, and further determines whether the difference between the ultra-short-term predicted power Sy of wind power and the dispatching power Sa is greater than the known energy storage rated power Sn. If it is greater than or equal to, the energy storage system uses the energy storage rated power Sn as the energy storage charging power Pc to charge. If it is less than, the difference Sy-Sa between the ultra-short-term predicted power Sy of wind power and the dispatching power Sa is directly used as the energy storage charging power Pc for charging; By acquiring data in real time, based on the ultra-short-term predicted maximum power of wind power and the dispatched power value issued by the grid dispatcher, it is determined in real time whether there is excess electricity to charge the energy storage system. By adjusting the energy storage charging, the wind power abandonment and electricity waste can be reduced. Step 3: After a certain period of time, return to step 1 and execute it in a loop to ensure that the wind farm station meets the AGC dispatching power to the maximum extent, while reducing wind abandonment and electricity waste.

[0006] Furthermore, in step 2, if the energy storage charging state Soc is full, it means that the energy storage system is full and no longer needs to be charged.

[0007] Furthermore, in step 2, since the energy storage system is charged, the wind power generation power Pz needs to be adjusted, and the sum of the energy storage charging power Pc and the dispatching power Sa is used as the wind power generation power target value Pz=Sa+Pc.

[0008] Furthermore, in step 2, when the ultra-short-term predicted power Sy of wind power is less than the dispatching power Sa, the energy storage system needs to discharge to make up for the shortfall, and then determine whether the energy storage charging state Soc is greater than the minimum threshold. If so, further determine whether the difference between the dispatching power Sa and the ultra-short-term predicted power Sy of wind power is greater than the known energy storage rated power Sn. If so, the energy storage system discharges with the energy storage rated power Sn as the energy storage discharge power Pf. If not, the energy storage system directly discharges with the difference Sa-Sy between the dispatching power Sa and the ultra-short-term predicted power Sy of wind power as the energy storage discharge power Pf. If the maximum power that can be generated by wind power cannot meet the power command value issued by the grid dispatch, the energy storage system will set the discharge power according to the difference between the two values, and reduce the difference between the real-time power and the AGC dispatching power by adjusting the energy storage discharge, thereby reducing the fine.

[0009] Furthermore, in step 2, if the energy storage charging state Soc is below the minimum threshold (including being equal to the minimum threshold), it means that the energy storage system has been exhausted and the energy storage system is no longer discharging.

[0010] Furthermore, the minimum threshold in step 2 is 5%.

[0011] Furthermore, the certain time in step 3 is executed every 5 minutes, and a cyclic judgment is performed. According to the maximum power that can be generated by the ultra-short-term forecast of wind power and the dispatching power value issued by the grid dispatch, the charging and discharging power value of the energy storage system is dynamically adjusted in real time (updated every 5 minutes) to maximize the energy storage and meet the AGC dispatching instructions.

[0012] A real-time optimization control system for active power of a wind farm station comprises an energy storage charging and discharging control module, wherein the energy storage charging and discharging control module performs optimization control according to a real-time optimization control method for active power of a wind farm station.

[0013] A control system for a wind farm comprises a wind farm station control system and a wind power system, an energy storage system, a power grid dispatching center and a wind power prediction system respectively connected thereto. The wind farm station control system is a real-time optimization control system for active power of a wind farm station.

[0014] The advantages and beneficial effects of the present invention are: The present invention can dynamically adjust the output active power of the wind farm by real-time monitoring of the AGC dispatching instructions and the maximum power that the wind farm can currently generate, so that it can not only maximize the satisfaction of the grid dispatching requirements, but also store the excess electricity. In addition, the control strategy can be implemented by only adding a real-time control software module using the existing wind farm hardware and data acquisition system without increasing the additional hardware cost. When the power generation is insufficient, the energy storage system releases the stored electric energy according to actual needs and replenishes it to the grid in real time, maximizing the satisfaction of the AGC dispatching instructions and effectively reducing the fines caused by insufficient output active power. When the wind power generation exceeds the AGC dispatching instructions, the energy storage system is used to absorb the excess electricity for charging, thereby greatly reducing the phenomenon of wind abandonment and achieving efficient use of electricity. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of AGC dispatching power, wind power ultra-short-term predicted power and real-time power generation power in the prior art.

[0016] Figure 2 Schematic diagram of the structure of a wind farm control system in an embodiment of the present invention.

[0017] Figure 3 It is a flow chart of the active power real-time optimization control method in an embodiment of the present invention.

[0018] Figure 4 It is a schematic diagram of the power transmitted by the wind farm to the power grid and the charging and discharging power of the energy storage system (0-14 hours) in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] The specific implementation of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the present invention, and is not used to limit the present invention.

[0020] Due to the intermittent nature of wind power generation, a large amount of grid-connected wind power has had a certain impact on the safe and stable operation of the power grid. Therefore, in order to achieve real-time control of the power generation of wind farms, all large wind farms are currently required to install AGC (Automatic Generation Control) systems. The power grid dispatcher generally sends a dispatch instruction every 5 minutes through the AGC system, requiring the power generation of the wind farm to follow the power value of the AGC, which cannot exceed the upper limit of the AGC, and cannot be lower than the lower limit of the AGC power value (such as Figure 1 As shown in the figure, the AGC power value is 70MW at 6-8. If the power generated by the wind farm exceeds the upper and lower limits of the AGC power, the wind farm will face the risk of being fined. When the wind power generation power cannot meet the power command value issued by the grid dispatcher, there is currently no real-time judgment on whether the energy storage system can discharge to meet the grid dispatch demand, thereby reducing the fine for failing to meet the dispatch command (such as Figure 1 When the wind power generation power is greater than the AGC dispatch instruction (as shown in areas b and d). Figure 1 As shown in the middle area a, c, and e), since there is no real-time regulation of energy storage, the real-time wind power generation power is equal to the power delivered to the grid, and the energy storage system is not used for charging to reduce wind abandonment and waste of excess electricity. Therefore, how to use the flexible regulation performance of the energy storage system to adjust the total power generated by the wind farm station in real time according to the maximum power that wind power can generate to meet the needs of the grid dispatch AGC power value and reduce wind abandonment is of great significance.

[0021] The present invention proposes a control system for a wind farm equipped with energy storage, such as Figure 2 As shown, it includes a wind farm control system and the wind power system, energy storage system, power grid dispatch center and wind power prediction system connected thereto. Through the wind farm control system, the maximum power that can be generated by wind power and the dispatch power value issued by the power grid dispatch are obtained in real time, and it is judged in real time whether the energy storage system needs to be discharged so that the power transmitted from the wind farm to the power grid meets the AGC dispatch instruction. In addition, if there is excess electricity, the energy storage system can also be charged to reduce the waste of wind abandonment and excess electricity.

[0022] If the maximum power that wind power can generate cannot meet the power command value issued by the grid dispatcher (such as Figure 1 At 10 o'clock as shown, AGC: 100MW, wind power generation capacity: 77MW), the energy storage system will set the discharge power to 10MW (set to the maximum discharge power of the energy storage system of 10MW) according to the difference between these two values ​​(23MW) to maximize the satisfaction of the grid dispatch requirements, thereby reducing the fines for failure to meet the grid dispatch instructions.

[0023] like Figure 1As shown in area a, when the predicted wind power generation capacity is greater than the AGC dispatch power, in order to meet the AGC dispatch requirements, the wind farm reduces the wind power generation capacity, so that the actual power generation capacity follows the AGC dispatch instruction value and fluctuates around the AGC power value. Figure 1 At 0:00 as shown, AGC: 40MW, maximum wind power generation power: 95MW), so the excess power (55MW) can be used to charge the energy storage system (set to the maximum charging power of the energy storage system 10MW) to reduce wind abandonment and waste of excess electricity.

[0024] The present invention dynamically adjusts the charging and discharging power value of the energy storage system in real time (updated every 5 minutes) according to the maximum power that can be generated by wind power and the dispatching power value issued by the grid dispatch, so as to maximize the active power output of the wind farm and meet the AGC dispatching instructions.

[0025] The present invention also proposes a real-time optimization control method for active power of a wind farm station, such as Figure 3 As shown, the following steps are included: Step 1: Parameter collection and initialization Obtain key parameters from the wind farm control system, including AGC dispatching power (Sa), current charging state of the energy storage system (Soc), and ultra-short-term predicted power of wind power (Sy).

[0026] Step 2: Real-time optimization control method of active power of wind farm In the first case, when the ultra-short-term predicted power Sy of wind power is greater than the AGC dispatch power Sa, the available wind power generation is greater than the dispatch demand. It is determined whether the current charging state Soc of the energy storage system is less than 100%. If so, the energy storage system can use the excess wind power for charging. It is further determined whether the difference between the predicted power Sy and the dispatch power Sa is greater than the rated maximum charging power Sn of the energy storage system. If so, the energy storage system is charged at the rated maximum power Sn. If not, it is directly charged with the difference (Sy-Sa) between the predicted power Sy and the AGC dispatch power Sa, such as Figure 3 As shown. To charge the energy storage system, the wind power generation power needs to be adjusted to Pz=Sa+Pc. If the current charging state Soc of the energy storage system is equal to 100%, it means that the energy storage system is fully charged and the energy storage system is no longer charged. Figure 3 shown.

[0027] In the second case, when the ultra-short-term predicted wind power Sy is less than the AGC dispatch power Sa, the energy storage system needs to discharge to make up for the shortfall. Then determine whether the current charging state Soc of the energy storage system is greater than 5%. If so, further determine whether the difference between Sa and Sy is greater than the rated maximum discharge power Sn of the energy storage system. If so, the energy storage system discharges at the rated maximum discharge power Sn. If not, the energy storage system directly discharges at the difference (Sa-Sy) between the AGC dispatch power Sa and the wind power predicted power Sy. Figure 3 If the current charging state Soc of the energy storage system is equal to 5%, it means that the energy storage system has been exhausted and the energy storage system will no longer discharge.

[0028] Step 3: Loop execution After the wind farm's generating power and the energy storage system's charging and discharging power are run for 5 minutes according to the settings in step 2, the system will read key parameters from the wind farm control system again and repeat steps 1 and 2. This process is repeated to ensure that the wind farm can meet the AGC dispatching power to the maximum extent possible, while reducing wind abandonment and electricity waste.

[0029] In order to illustrate the application example of the present invention, the grid dispatching AGC power Sa, the wind power ultra-short-term forecast power Sy, and the real-time wind power generation power St are simulated first, and then the charging and discharging power Pc, Pf of the energy storage system and the charging state Soc of the energy storage system are calculated based on the simulated data using the real-time optimization control strategy of the present invention.

[0030] Data is recorded every five minutes, as shown in Table 1. These parameters are time, Sa (AGC dispatch power), Sy (ultra-short-term wind power forecast power), Pz (wind power target value), St (wind power real-time power generation), Sd (power delivered to the grid by the wind farm), and energy storage charging and discharging power and Soc (energy storage system charging state), where the energy storage system has a rated power of 10MW and a maximum capacity of 20MW.h.

[0031] Table 1 Real-time optimization control parameters time Sa (MW) Sy (MW) Pz (MW) St (MW) Sd (MW) Energy storage charging and discharging power (MW) Soc (MW) 0:00 40 95 40 38.897 38.897 10 0 0:05 40 94.917 50 50.354 40.354 10 4.167 0:10 40 94.835 50 50.347 40.347 10 8.333 0:15 40 94.752 50 49.987 39.987 10 12.5 0:20 40 94.669 50 50.178 40.178 10 16.667 0:25 40 94.585 50 49.874 39.874 10 20.833 0:30 40 94.502 50 49.898 39.898 10 25 0:35 40 94.419 50 50.373 40.373 10 29.167 0:40 40 94.335 50 49.701 39.701 10 33.333 0:45 40 94.251 50 49.653 39.653 10 37.5 0:50 40 94.168 50 50.494 40.494 10 41.667 0:55 40 94.084 50 50.194 40.194 10 45.833 1:00 40 94 50 50.04 40.04 10 50 1:05 40 93.916 50 50.313 40.313 10 54.167 1:10 40 93.832 50 50.413 40.413 10 58.333 1:15 40 93.749 50 49.71 39.71 10 62.5 1:20 40 93.665 50 50.378 40.378 10 66.667 1:25 40 93.581 50 50.309 40.309 10 70.833 1:30 40 93.498 50 50.071 40.071 10 75 1:35 40 93.415 50 49.506 39.506 10 79.167 1:40 40 93.331 50 50.281 40.281 10 83.333 1:45 40 93.248 50 49.774 39.774 10 87.5 1:50 40 93.165 50 50.323 40.323 10 91.667 1:55 40 93.083 50 49.788 39.788 10 95.833 2:00 40 93 40 40.934 40.934 0 100 2:05 40 92.918 40 40.958 40.958 0 100 2:10 40 92.836 40 39.857 39.857 0 100 2:15 40 92.753 40 40.64 40.64 0 100 2:20 40 92.671 40 39.81 39.81 0 100 2:25 40 92.589 40 40.479 40.479 0 100 2:30 40 92.506 40 40.938 40.938 0 100 2:35 40 92.423 40 39.677 39.677 0 100 2:40 40 92.34 40 39.348 39.348 0 100 2:45 40 92.256 40 39.303 39.303 0 100 2:50 40 92.171 40 39.472 39.472 0 100 2:55 40 92.086 40 39.508 39.508 0 100 3:00 40 92 40 39.984 39.984 0 100 3:05 40 91.913 40 39.961 39.961 0 100 3:10 40 91.826 40 40.634 40.634 0 100 3:15 40 91.738 40 39.656 39.656 0 100 3:20 40 91.65 40 39.461 39.461 0 100 3:25 40 91.563 40 40.199 40.199 0 100 3:30 40 91.477 40 40.902 40.902 0 100 3:35 40 91.392 40 39.417 39.417 0 100 3:40 40 91.309 40 39.063 39.063 0 100 3:45 40 91.228 40 39.778 39.778 0 100 3:50 40 91.149 40 39.913 39.913 0 100 3:55 40 91.073 40 40.839 40.839 0 100 4:00 40 91 40 40.211 40.211 0 100 4:05 40 90.93 40 39.516 39.516 0 100 4:10 40 90.862 40 39.364 39.364 0 100 4:15 40 90.795 40 39.923 39.923 0 100 4:20 40 90.728 40 40.916 40.916 0 100 4:25 40 90.658 40 40.217 40.217 0 100 4:30 40 90.585 40 39.704 39.704 0 100 4:35 40 90.507 40 40.283 40.283 0 100 4:40 40 90.423 40 40.734 40.734 0 100 4:45 40 90.332 40 39.141 39.141 0 100 4:50 40 90.232 40 40.038 40.038 0 100 4:55 40 90.122 40 39.654 39.654 0 100 5:00 40 90 40 39.535 39.535 0 100 5:05 40 89.866 40 39.683 39.683 0 100 5:10 40 89.721 40 39.473 39.473 0 100 5:15 40 89.565 40 39.966 39.966 0 100 5:20 40 89.402 40 40.133 40.133 0 100 5:25 40 89.233 40 39.312 39.312 0 100 5:30 40 89.058 40 39.264 39.264 0 100 5:35 40 88.881 40 40.241 40.241 0 100 5:40 40 88.701 40 39.835 39.835 0 100 5:45 40 88.522 40 40.225 40.225 0 100 5:50 40 88.344 40 39.327 39.327 0 100 5:55 40 88.17 40 39.955 39.955 0 100 6:00 70 88 70 69.853 69.853 0 100 6:05 70 87.836 70 69.693 69.693 0 100 6:10 70 87.677 70 69.704 69.704 0 100 6:15 70 87.521 70 69.535 69.535 0 100 6:20 70 87.367 70 70.699 70.699 0 100 6:25 70 87.212 70 70.538 70.538 0 100 6:30 70 87.057 70 70.58 70.58 0 100 6:35 70 86.898 70 69.588 69.588 0 100 6:40 70 86.734 70 70.812 70.812 0 100 6:45 70 86.564 70 70.526 70.526 0 100 6:50 70 86.386 70 70.784 70.784 0 100 6:55 70 86.199 70 69.141 69.141 0 100 7:00 70 86 70 70.826 70.826 0 100 7:05 70 85.789 70 70.628 70.628 0 100 7:10 70 85.567 70 70.64 70.64 0 100 7:15 70 85.335 70 69.844 69.844 0 100 7:20 70 85.094 70 69.504 69.504 0 100 7:25 70 84.845 70 70.896 70.896 0 100 7:30 70 84.59 70 70.922 70.922 0 100 7:35 70 84.33 70 69.651 69.651 0 100 7:40 70 84.067 70 70.523 70.523 0 100 7:45 70 83.801 70 70.929 70.929 0 100 7:50 70 83.533 70 70.684 70.684 0 100 7:55 70 83.266 70 69.881 69.881 0 100 8:00 100 83 100 81.632 91.632 -10 100 8:05 100 82.736 100 81.081 91.081 -10 95.833 8:10 100 82.476 100 80.895 90.895 -10 91.667 8:15 100 82.217 100 84.874 94.874 -10 87.5 8:20 100 81.961 100 81.302 91.302 -10 83.333 8:25 100 81.708 100 79.317 89.317 -10 79.167 8:30 100 81.457 100 81.36 91.36 -10 75 8:35 100 81.208 100 81.171 91.171 -10 70.833 8:40 100 80.962 100 82.428 92.428 -10 66.667 8:45 100 80.718 100 77.888 87.888 -10 62.5 8:50 100 80.477 100 79.99 89.99 -10 58.333 8:55 100 80.237 100 77.427 87.427 -10 54.167 9:00 100 80 100 82.751 92.751 -10 50 9:05 100 79.765 100 81.633 91.633 -10 45.833 9:10 100 79.531 100 79.412 89.412 -10 41.667 9:15 100 79.296 100 78.451 88.451 -10 37.5 9:20 100 79.061 100 80.017 90.017 -10 33.333 9:25 100 78.823 100 77.933 87.933 -10 29.167 9:30 100 78.582 100 79.449 89.449 -10 25 9:35 100 78.336 100 76.233 86.233 -10 20.833 9:40 100 78.085 100 77.828 87.828 -10 16.667 9:45 100 77.826 100 76.788 86.788 -10 12.5 9:50 100 77.56 100 79.021 89.021 -10 8.333 9:55 100 77.285 100 79.557 79.557 0 5 10:00 100 77 100 79.756 79.756 0 5 10:05 100 76.705 100 74.377 74.377 0 5 10:10 100 76.402 100 74.883 74.883 0 5 10:15 100 76.098 100 74.286 74.286 0 5 10:20 100 75.796 100 76.148 76.148 0 5 10:25 100 75.5 100 78.049 78.049 0 5 10:30 100 75.216 100 77.291 77.291 0 5 10:35 100 74.948 100 76.465 76.465 0 5 10:40 100 74.7 100 77.225 77.225 0 5 10:45 100 74.476 100 72.357 72.357 0 5 10:50 100 74.282 100 76.339 76.339 0 5 10:55 100 74.122 100 73.422 73.422 0 5 11:00 100 74 100 73.829 73.829 0 5 11:05 100 73.918 100 75.771 75.771 0 5 11:10 100 73.87 100 72.311 72.311 0 5 11:15 100 73.844 100 73.154 73.154 0 5 11:20 100 73.831 100 75.919 75.919 0 5 11:25 100 73.821 100 74.093 74.093 0 5 11:30 100 73.804 100 72.711 72.711 0 5 11:35 100 73.77 100 75.082 75.082 0 5 11:40 100 73.709 100 72.96 72.96 0 5 11:45 100 73.612 100 70.962 70.962 0 5 11:50 100 73.468 100 73.067 73.067 0 5 11:55 100 73.267 100 72.108 72.108 0 5 12:00 60 73 70 69.385 59.385 10 5 12:05 60 72.661 70 69.162 59.162 10 9.167 12:10 60 72.268 70 69.379 59.379 10 13.333 12:15 60 71.84 70 69.662 59.662 10 17.5 12:20 60 71.4 70 70.096 60.096 10 21.667 12:25 60 70.97 70 70.266 60.266 10 25.833 12:30 60 70.569 70 70.283 60.283 10 30 12:35 60 70.22 70 70.557 60.557 10 34.167 12:40 60 69.945 70 70.839 60.839 10 38.333 12:45 60 69.763 70 70.272 60.272 10 42.5 12:50 60 69.698 70 70.117 60.117 10 46.667 12:55 60 69.77 70 70.551 60.551 10 50.833 13:00 60 70 70 69.114 59.114 10 55 13:05 60 70.402 70 70.829 60.829 10 59.167 13:10 60 70.953 70 69.363 59.363 10 63.333 13:15 60 71.623 70 70.312 60.312 10 67.5 13:20 60 72.382 70 70.933 60.933 10 71.667 13:25 60 73.2 70 70.217 60.217 10 75.833 13:30 60 74.045 70 70.459 60.459 10 80 13:35 60 74.888 70 69.043 59.043 10 84.167 13:40 60 75.698 70 69.245 59.245 10 88.333 13:45 60 76.444 70 70.566 60.566 10 92.5 13:50 60 77.097 70 69.275 59.275 10 96.667 13:55 60 77.626 60 60.793 60.793 0 100 14:00 100 78 100 79.662 89.662 -10 100 14:05 100 78.199 100 77.701 87.701 -10 95.833 14:10 100 78.245 100 77.218 87.218 -10 91.667 From the above table and Figure 4 It can be seen that after adopting the optimization control method of the present invention, the power transmitted by the wind farm to the power grid can maximize the needs of the AGC dispatching instructions, while reducing wind abandonment and reducing the waste of 19MW.h of electricity.

[0032] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A real-time optimization control method for active power of a wind farm, characterized in that The steps include: Step 1: Obtain dispatch power, energy storage charging status, and ultra-short-term predicted wind power; Step 2: Real-time optimization control of active power; When the ultra-short-term predicted power of wind power is greater than the dispatching power, it is determined whether the energy storage charging state is not fully charged. If so, the energy storage system uses the excess wind power for charging, and further determines whether the difference between the ultra-short-term predicted power of wind power and the dispatching power is greater than the rated power of the energy storage. If it is greater than or equal to, the energy storage system uses the rated power of the energy storage as the energy storage charging power for charging. If it is less than, the difference between the ultra-short-term predicted power of wind power and the dispatching power is directly used as the energy storage charging power for charging; Step 3: After executing for a certain period of time, return to step 1 and execute it in a loop.

2. The method for real-time optimization control of active power of a wind farm according to claim 1, characterized in that: In step 2, if the energy storage charging state is fully charged, no further charging is performed.

3. The method for real-time optimization control of active power of a wind farm according to claim 1, characterized in that: In the step 2, the wind power generation power is adjusted, and the sum of the obtained energy storage charging power and the dispatching power is used as the wind power generation power target value.

4. The method for real-time optimization control of active power of a wind farm according to claim 1, characterized in that: In step 2, when the ultra-short-term predicted power of wind power is less than the dispatching power, it is determined whether the energy storage charging state is greater than the minimum threshold value. If so, it is further determined whether the difference between the dispatching power and the ultra-short-term predicted power of wind power is greater than the rated power of the energy storage. If so, the energy storage system discharges with the rated power of the energy storage as the energy storage discharge power. If not, the energy storage system directly discharges with the difference between the dispatching power and the ultra-short-term predicted power of wind power as the energy storage discharge power.

5. The method for real-time optimization control of active power of a wind farm according to claim 4, characterized in that: In step 2, if the energy storage charging state is below the minimum threshold, the energy storage system will no longer discharge.

6. The method for real-time optimization control of active power of a wind farm according to claim 4, characterized in that: The minimum threshold in step 2 is 5%.

7. The method for real-time optimization control of active power of a wind farm according to claim 1, characterized in that: The certain time in step 3 is executed every 5 minutes, and a cycle judgment is performed.

8. A real-time optimization control system for active power of a wind farm station, including an energy storage charging and discharging control module, characterized in that: The energy storage charging and discharging control module performs optimization control according to the real-time optimization control method for active power of a wind farm station according to any one of claims 1 to 7.

9. A control system for a wind farm, comprising a wind farm station control system and a wind power system, an energy storage system, a power grid dispatching center and a wind power prediction system respectively connected thereto, characterized in that: The wind farm station control system is a wind farm station active power real-time optimization control system as described in claim 8.

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