Charging and discharging real-time optimization control method and system for energy storage system of photovoltaic station

Abstract

The invention discloses a charging and discharging real-time optimization control method and system for an energy storage system of a photovoltaic field station, and the method comprises the steps: dividing time periods of different charging and discharging strategies according to the power utilization conditions through obtaining the first closing parameters of the energy storage system, and executing a real-time optimization charging and discharging strategy when the current time period is judged to belong to the real-time optimization charging and discharging strategy time period, thereby achieving the real-time optimization of the charging and discharging strategies. And if the current energy storage state is within the working range, executing a low-ebb charging strategy, continuously monitoring the current energy storage state, and if the current energy storage state is within the working range, circularly performing a new round of optimization control, otherwise, ending the current cycle. According to different time periods, different control strategies are adopted, the control strategy provided by the invention can be realized only by adding a real-time control software module, the waste of abandoned light and redundant electric quantity is reduced, the requirement of power grid dispatching is met to the maximum extent, the penalty caused by incapability of meeting a dispatching instruction is reduced, and the power grid dispatching efficiency is improved. The optimal economic control of the energy storage system is realized, so that the waste of abandoned light and redundant electric quantity is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power generation, and specifically relates to a real-time optimization control method and system for charging and discharging of a photovoltaic station energy storage system. Background Art

[0002] With the rapid development of renewable energy power generation, the newly added grid-connected capacity and photovoltaic installed capacity have increased significantly. Due to the intermittent nature of photovoltaic power generation, a large number of grid-connected photovoltaics have had a certain impact on the safe and stable operation of the power grid. Therefore, in order to achieve real-time controllable power generation of photovoltaic stations, all large photovoltaic stations are currently required to install an automatic gain control AGC (Automatic Gain Control) system. The grid dispatcher generally sends a dispatch instruction every 5 minutes through the AGC system, requiring the power generation power of the photovoltaic station 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 photovoltaic station exceeds the upper and lower limits of the AGC power, the photovoltaic station will face the risk of being fined. Therefore, it is of great significance to use the flexible adjustment performance of the energy storage system to adjust the total power generated by the photovoltaic station in real time according to the maximum power that the photovoltaic can generate to meet the AGC power value requirements of the power grid dispatching.

[0003] Since photovoltaic power generation is intermittent, in order to reduce the impact on the power grid, the requirement for photovoltaic stations to be equipped with energy storage of 10-20% of the photovoltaic capacity has been promulgated. At present, many large-scale centralized photovoltaic stations are equipped with electrochemical energy storage systems with 10-20% photovoltaic capacity. However, the energy storage systems currently installed in photovoltaic stations are basically set to charge and discharge at fixed times (for example: charging during the low electricity prices from 0-6 o'clock in the middle of the night, and discharging during the peak electricity prices at noon in the day). There is no real-time judgment based on the maximum power that photovoltaics can generate and the dispatching power value issued by the grid dispatcher to see if there is excess electricity to charge the energy storage system, so as to reduce the waste of abandoned light and excess electricity (such as Figure 1 At the same time, when the photovoltaic power generation power cannot meet the power command value issued by the grid dispatch, there is no real-time judgment on whether the energy storage system can discharge to meet the grid dispatch demand, reducing the penalty for failure to meet the dispatch command (such as Figure 1 area a and area c). Summary of the invention

[0004] In order to solve the deficiencies of the prior art and achieve the purpose of optimal economic control of the energy storage system, the present invention adopts the following technical solutions: A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system comprises the following steps: Step 1: Obtain the dispatching power, current energy storage status, real-time photovoltaic power generation and ultra-short-term photovoltaic power forecast; Step 2: Divide the time periods of different charging and discharging strategies according to the power consumption situation, and determine the time period described by the current time; Step 3: If the current time period belongs to the real-time optimization charging and discharging strategy period, execute step 4; if it belongs to the valley charging strategy period, execute step 5; Step 4: Optimize the charging and discharging strategy in real time; when the ultra-short-term predicted power of photovoltaics is greater than the dispatching power, it means that the photovoltaic power generation is greater than the dispatching demand. At this time, the photovoltaic station energy storage system can use the excess photovoltaic power for charging, and then further determine whether the difference between the ultra-short-term predicted power of photovoltaics and the dispatching power is greater than the rated maximum charging power of the energy storage. If so, charge with the rated maximum charging power of the energy storage. If not, charge with the difference between the ultra-short-term predicted power of photovoltaics and the dispatching power as the energy storage charging power; Step 5: Low-peak charging strategy; Step 6: Energy storage status monitoring and cycle preparation: Continuously monitor the current energy storage status. If the current energy storage status is within the working range, the cycle returns to step 1 for a new round of optimization control. Otherwise, the current cycle ends according to the system settings.

[0005] Furthermore, in step 4, when charging the energy storage system, it is necessary to increase the photovoltaic power generation power, and the increased photovoltaic power generation power value is the energy storage charging power value; after being fully charged, the photovoltaic power generation power is reduced to follow the dispatching power.

[0006] Furthermore, in step 4, when the ultra-short-term predicted power of photovoltaic is less than or equal to the dispatching power, in order to meet the power required by the AGC dispatching instruction, the energy storage system needs to discharge to make up for the gap. For the situation where the ultra-short-term predicted power of photovoltaic is greater than the real-time power generation power of photovoltaic, it is further determined whether the difference between the dispatching power and the ultra-short-term predicted power of photovoltaic is greater than the known maximum rated discharge power of energy storage. If so, the rated maximum discharge power of energy storage is used as the energy storage discharge power for discharge. If not, the difference between the dispatching power and the ultra-short-term predicted power of photovoltaic is used as the energy storage discharge power for discharge. In the case where the ultra-short-term predicted power of photovoltaic power is less than or equal to the real-time power generation of photovoltaic power, it is further determined whether the difference between the dispatching power and the real-time power generation of photovoltaic power is greater than the rated maximum discharge power of the energy storage. If so, the rated maximum discharge power of the energy storage is used as the energy storage discharge power for discharge. If not, the difference between the dispatching power and the real-time power generation of photovoltaic power is used as the energy storage discharge power for discharge. Determine whether the current energy storage state is within the working range (5%-100%). If so, loop back to step 1 for a new round of optimization control. If not, end the current loop according to the system settings.

[0007] Furthermore, in the case where the photovoltaic ultra-short-term predicted power is greater than the actual generated power, the photovoltaic generated power is increased during discharge, and the increased photovoltaic generated power is the difference between the photovoltaic ultra-short-term predicted power and the photovoltaic real-time generated power.

[0008] Further, wait for a certain period of time (five minutes) to re-evaluate the current energy storage status, and update the energy storage charging power and energy storage discharging power in real time before performing subsequent operations.

[0009] Furthermore, in step 4, it is determined whether the current energy storage state has reached the minimum value. If so, the current cycle is terminated according to the system setting. Otherwise, the cycle returns to step 1 for a new round of optimization control.

[0010] Furthermore, in step 5, the electricity price is at a low point, and it is most economical to charge the energy storage system at this time. First, it is determined whether the current energy storage state is less than the maximum threshold (100%) to determine whether the energy storage system can still be charged. If so, charging is performed with the rated maximum charging power of the energy storage as the upper limit until the current energy storage state reaches the maximum threshold or the valley charging strategy period ends, and no charging operation is performed.

[0011] Furthermore, in step 6, it is monitored whether the current energy storage state reaches the maximum threshold value. If so, the current cycle is terminated according to the system setting; otherwise, the cycle returns to step 1 for a new round of optimization control.

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

[0013] A photovoltaic energy storage charging and discharging real-time optimization control system includes a photovoltaic station control system and a photovoltaic system, an energy storage system, an automatic gain control system, a photovoltaic power prediction system and a power grid dispatching center respectively connected thereto. The photovoltaic station control system is a photovoltaic station energy storage system charging and discharging real-time optimization control system.

[0014] The advantages and beneficial effects of the present invention are: The present invention provides a real-time optimization control method and system for charging and discharging of a photovoltaic station energy storage system. Different control strategies are adopted according to different time periods. The control strategy proposed by the present invention can be realized by only adding a real-time control software module by utilizing the hardware and data acquisition system of the existing photovoltaic station without increasing the additional hardware cost. The present invention also obtains data in real time, and judges in real time whether there is excess electricity to charge the energy storage system according to the maximum power that the photovoltaic can generate and the dispatching power value issued by the grid dispatch, thereby reducing the waste of abandoned light and excess electricity. If the maximum power that the photovoltaic can generate 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, so as to maximize the satisfaction of the grid dispatch requirements and reduce the penalty for failing to meet the AGC dispatching command. According to the maximum power that the photovoltaic can generate 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 achieve optimal economic control of the energy storage system, thereby reducing the waste of abandoned light and excess electricity. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the existing AGC dispatching power, photovoltaic predicted power and actual power generation power.

[0016] Figure 2 It is a schematic diagram of the structure of the system in an embodiment of the present invention.

[0017] Figure 3a It is a flow chart of the method and real-time optimization charging strategy in an embodiment of the present invention.

[0018] Figure 3b It is a flow chart of the real-time optimized discharge strategy of the method in the embodiment of the present invention.

[0019] Figure 3c It is a flow chart of the valley charging strategy of the method in an embodiment of the present invention.

[0020] Figure 4 It is a schematic diagram of the charging and discharging power of the energy storage system and the real-time photovoltaic power generation power (6-14 o'clock) in an embodiment of the present invention.

[0021] Figure 5 It is a schematic diagram of the charging state Soc of the energy storage system in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] 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.

[0023] The present invention proposes a real-time energy storage system charging and discharging optimization control strategy for photovoltaic stations equipped with energy storage, which obtains the maximum power that can be generated by photovoltaic power and the dispatching power value issued by the grid dispatch in real time, and determines in real time whether there is excess power to charge the energy storage system, thereby reducing the waste of abandoned light and excess power. Figure 1 As shown in area b, when the predicted photovoltaic power is greater than the AGC dispatch power, in order to meet the requirements of the grid dispatch AGC, the photovoltaic station reduces the photovoltaic power generation, so that the actual power generation follows the AGC dispatch instruction value and fluctuates around the AGC power value. Figure 1 As shown in the figure, at 13:00, AGC 80MW, PV can generate 100MW), so the excess power (20MW) can be used to charge the energy storage system to reduce the waste of abandoned light and excess electricity. If the maximum power that PV can generate cannot meet the power command value issued by the grid dispatcher (such as Figure 1 As shown in the figure, at 10 o'clock, AGC 100MW, PV power generation capacity 88MW), the energy storage system will set the discharge power according to the difference between these two values ​​(set to the maximum discharge power of the energy storage system 10MW) to maximize the satisfaction of the grid dispatch requirements, thereby reducing the penalty for failure to meet the grid dispatch instructions.

[0024] Specifically, the present invention proposes a real-time optimization control system for photovoltaic energy storage charging and discharging, and its system architecture and data flow are as follows: Figure 2 As shown, it includes a photovoltaic station control system and photovoltaic systems, energy storage systems, automatic gain control systems, photovoltaic power prediction systems and power grid dispatching centers connected thereto respectively. The photovoltaic station control system includes an energy storage charging and discharging control module. By real-time monitoring of the status of the energy storage equipment and the dispatching needs of the power grid, the energy storage charging and discharging control module can dynamically adjust the charging and discharging power values ​​of the energy storage equipment in real time, reduce the waste of abandoned light and excess electricity, and maximize the satisfaction of the dispatching needs of the power grid.

[0025] On the other hand, the present invention also proposes a real-time optimization control method for charging and discharging of a photovoltaic station energy storage system, such as Figure 3a As shown, the following steps are included: Step 1: Parameter collection and initialization: Key parameters are obtained from the photovoltaic station control system, including the AGC dispatch power value (Sa) issued by the grid dispatch center, the current charging state percentage of the energy storage system (Soc: State of Charge, 100% means fully charged), the real-time photovoltaic power generation power (St), and the ultra-short-term photovoltaic predicted power (Sy).

[0026] Step 2: Determine the time period based on the current time T: According to the current time T, the photovoltaic charging and discharging strategy is divided into two periods: period a (6:00 to 24:00) and period b (0:00 to 6:00).

[0027] Step 3: Select different charging and discharging strategies according to the current period: If T belongs to time period a, the system automatically switches to the real-time optimization charging and discharging strategy mode and executes step 4.

[0028] If T belongs to time period b, the valley charging strategy mode is adopted, focusing on charging the energy storage system at a low electricity price, and executing step 5.

[0029] Step 4: Real-time optimization of charging and discharging strategies: The first case, such as Figure 3a As shown in the figure, when the ultra-short-term predicted photovoltaic power Sy is greater than the AGC dispatching power Sa, the photovoltaic power generation is greater than the dispatching demand. At this time, the energy storage system can use the excess photovoltaic power for charging, and then further determine whether the difference between the predicted power Sy and the dispatching power Sa is greater than the rated maximum charging power Sn of the energy storage system. If so, the energy storage system is charged with the rated maximum power Sn. If not, the difference between the predicted power Sy and the AGC dispatching power Sa is used as the energy storage charging power Pc = Sy - Sa for charging.

[0030] Since charging the energy storage system requires increasing the photovoltaic power generation, the photovoltaic power generation value that needs to be increased is as follows: Figure 3a As shown: △Pg = Pc When the energy storage system is fully charged, the photovoltaic power generation power needs to be reduced so that it follows the AGC dispatching power Sa.

[0031] The second case, such as Figure 3b As shown, when the photovoltaic ultra-short-term predicted power Sy is less than or equal to the AGC dispatching power Sa, in order to meet the power required by the AGC dispatching instruction, the energy storage system needs to discharge to make up for the gap. When the photovoltaic ultra-short-term predicted power Sy is greater than the photovoltaic real-time power generation power St, it is further determined whether the difference between the AGC dispatching power Sa and the photovoltaic ultra-short-term predicted power Sy is greater than the known maximum rated discharge power Sn of the energy storage system. If so, the energy storage system discharges with the rated maximum discharge power Sn as the energy storage discharge power Pf. If not, the energy storage system directly discharges with the difference between the AGC dispatching power Sa and the photovoltaic predicted power Sy as the energy storage discharge power Pf = Sa - Sy.

[0032] If the ultra-short-term predicted photovoltaic power Sy is greater than the actual power generation St, the photovoltaic power generation can be increased. The photovoltaic power generation to be increased is as follows: Figure 3b As shown: △Pg = ( Sy – St ) Among them, △Pg represents the increased power generation of the photovoltaic power generation system.

[0033] When the PV ultra-short-term predicted power Sy is less than or equal to the PV real-time power generation power St, it is further determined whether the difference between the AGC dispatch power Sa and the PV real-time power generation power St is greater than the rated maximum discharge power Sn of the energy storage system. If so, the energy storage system discharges with the rated maximum discharge power Sn as the energy storage discharge power Pf. If not, the energy storage system directly discharges with the difference between the AGC dispatch power Sa and the PV actual power St as the energy storage discharge power Pf = Sa - St, as Figure 3b shown.

[0034] Finally, determine whether the current state of charge Soc of the energy storage system is lower than the lower limit of the working range (5%). If not, the system waits for five minutes to re-evaluate the current conditions and loops back to step 1 for a new round of parameter collection and strategy execution. If so, the current cycle ends according to the system settings.

[0035] Step 5: Low-peak charging strategy: like Figure 3c As shown, in time period b, the electricity price is at a low point, and it is most economical to charge the energy storage system at this time. First, determine whether the current charging state Soc of the energy storage system is less than 100% to determine whether the energy storage system can still be charged. If so, it is preferentially charged with the energy storage system's rated maximum charging power Sn as the upper limit until the current charging state Soc of the energy storage system reaches 100% or the end of time period b, and wait for five minutes to re-evaluate the current conditions. If not, that is, the current charging state Soc of the energy storage system has reached 100%, then no charging operation is performed.

[0036] Step 6: Energy storage status monitoring and cycle preparation: Continuously monitor the current state of charge Soc of the energy storage system to ensure that it is within the working range (5%-100%). This strategy cycles every five minutes and updates the charge and discharge power (Pc, Pf) in real time. When the energy storage system is full (Soc=100%), this strategy immediately stops charging. When the energy storage system's state of charge is equal to 5%, this strategy immediately stops discharging and keeps the energy storage system's minimum state of charge at 5%.

[0037] like Figure 4 , Figure 5As shown, in the embodiment of the present invention, the grid dispatching AGC power Sa, the photovoltaic ultra-short-term predicted power Sy, and the real-time photovoltaic power generation power St are first simulated, and then the charging and discharging power Pc and Pf of the energy storage system, as well as the charging state Soc of the energy storage system and the increased or decreased power generation power △Pg of the photovoltaic power generation system are calculated based on the simulated data using the real-time optimization control strategy of the present invention.

[0038] Data is recorded every five minutes, as shown in Table 1. These key parameters are time, Sa (AGC dispatch power), Sy (ultra-short-term PV forecast power), St (PV real-time power generation), Soc% (energy storage system charging state), and charge and discharge power △Pg: the added power generation of the PV power generation system, where the energy storage system is rated at 10MW and has a maximum capacity of 20MW.h.

[0039] Table 1 Changes of key parameters at different time points time Sa(MW) Sy(MW) St(MW) Soc(%) Charge and discharge power (MW, +charge, -discharge) △Pg (MW) Photovoltaic power generation target value (MW) Total power delivered to the grid (MW) 0:00 40 0 0 0 10 0 40 -10 0:05 40 0 0 4.167 10 0 40 -10 … … … … … … 0 … … 2:00 40 0 0 100 0 0 40 -10 2:05 40 0 0 100 0 0 40 0 … … … … … … 0 … … 5:55 40 0 0 100 0 0 40 0 6:00 70 0 0 100 -10 0 70 10 6:05 70 4.622 2.684 95.833 -10 1.938 70 12.684 6:10 70 9.067 8.252 91.667 -10 0.815 70 18.252 6:15 70 13.34 11.651 87.5 -10 1.689 70 21.651 6:20 70 17.444 18.213 83.333 -10 0 70 28.213 6:25 70 21.384 20.631 79.167 -10 0.753 70 30.631 6:30 70 25.163 23.409 75 -10 1.754 70 33.409 6:35 70 28.784 26.214 70.833 -10 2.57 70 36.214 6:40 70 32.253 31.323 66.667 -10 0.93 70 41.323 6:45 70 35.571 33.943 62.5 -10 1.628 70 43.943 6:50 70 38.744 36.947 58.333 -10 1.797 70 46.947 6:55 70 41.775 40.917 54.167 -10 0.858 70 50.917 7:00 70 44.668 43.999 50 -10 0.669 70 53.999 7:05 70 47.427 46.234 45.833 -10 1.193 70 56.234 7:10 70 50.055 50.251 41.667 -10 0 70 60.251 7:15 70 52.557 51.153 37.5 -10 1.404 70 61.153 7:20 70 54.935 54.18 33.333 -10 0.755 70 64.18 7:25 70 57.195 55.725 29.167 -10 1.47 70 65.725 7:30 70 59.34 57.432 25 -10 1.908 70 67.432 7:35 70 61.373 60.287 20.833 -8.627 1.086 70 68.914 7:40 70 63.298 62.218 17.239 -6.702 1.08 70 68.92 7:45 70 65.12 64.498 14.446 -4.88 0.622 70 69.378 7:50 70 66.841 66.699 12.413 -3.159 0.142 70 69.858 7:55 70 68.467 69.103 11.097 -0.897 0 70 70 8:00 100 70 69.814 10.72325 -10 0.186 100 79.814 8:05 100 71.445 70.518 6.556583 -10 0.927 100 80.518 8:10 100 72.805 72.786 5 0 0 100 72.786 8:15 100 74.083 72.253 5 0 0 100 72.253 8:20 100 75.285 74 5 0 0 100 74 8:25 100 76.414 76.411 5 0 0 100 76.411 8:30 100 77.473 77.412 5 0 0 100 77.412 8:35 100 78.466 78.049 5 0 0 100 78.049 8:40 100 79.398 78.488 5 0 0 100 78.488 8:45 100 80.271 80.016 5 0 0 100 80.016 8:50 100 81.091 81.074 5 0 0 100 81.074 8:55 100 81.86 80.406 5 0 0 100 80.406 9:00 100 82.582 81.874 5 0 0 100 81.874 9:05 100 83.261 81.7 5 0 0 100 81.7 9:10 100 83.902 83.029 5 0 0 100 83.029 9:15 100 84.507 83.634 5 0 0 100 83.634 9:20 100 85.081 84.983 5 0 0 100 84.983 9:25 100 85.627 85.528 5 0 0 100 85.528 9:30 100 86.15 85.968 5 0 0 100 85.968 9:35 100 86.652 85.464 5 0 0 100 85.464 9:40 100 87.139 86.657 5 0 0 100 86.657 9:45 100 87.613 85.93 5 0 0 100 85.93 9:50 100 88.079 86.365 5 0 0 100 86.365 9:55 100 88.54 86.613 5 0 0 100 86.613 10:00 100 89 88.022 5 0 0 100 88.022 10:05 100 89.462 89.021 5 0 0 100 89.021 10:10 100 89.924 89.472 5 0 0 100 89.472 10:15 100 90.385 89.311 5 0 0 100 89.311 10:20 100 90.84 89.316 5 0 0 100 89.316 10:25 100 91.288 90.593 5 0 0 100 90.593 10:30 100 91.726 90.804 5 0 0 100 90.804 10:35 100 92.151 90.872 5 0 0 100 90.872 10:40 100 92.562 90.727 5 0 0 100 90.727 10:45 100 92.954 92.631 5 0 0 100 92.631 10:50 100 93.327 91.896 5 0 0 100 91.896 10:55 100 93.676 92.521 5 0 0 100 92.521 11:00 100 94 93.133 5 0 0 100 93.133 11:05 100 94.297 92.529 5 0 0 100 92.529 11:10 100 94.571 93.785 5 0 0 100 93.785 11:15 100 94.825 94.467 5 0 0 100 94.467 11:20 100 95.065 93.798 5 0 0 100 93.798 11:25 100 95.295 94.047 5 0 0 100 94.047 11:30 100 95.52 94.864 5 0 0 100 94.864 11:35 100 95.743 94.137 5 0 0 100 94.137 11:40 100 95.969 93.97 5 0 0 100 93.97 11:45 100 96.204 94.242 5 0 0 100 94.242 11:50 100 96.451 96.197 5 0 0 100 96.197 11:55 100 96.715 96.251 5 0 0 100 96.251 12:00 80 97 90.801 5 10 10 80 80.801 12:05 80 97.309 89.245 9.167 10 0 80 79.245 12:10 80 97.635 89.405 13.333 10 0 80 79.405 12:15 80 97.971 90.456 17.5 10 0 80 80.456 12:20 80 98.306 90.997 21.667 10 0 80 80.997 12:25 80 98.634 90.661 25.833 10 0 80 80.661 12:30 80 98.946 90.629 30 10 0 80 80.629 12:35 80 99.233 90.675 34.167 10 0 80 80.675 12:40 80 99.487 89.567 38.333 10 0 80 79.567 12:45 80 99.699 90.65 42.5 10 0 80 80.65 12:50 80 99.86 90.429 46.667 10 0 80 80.429 12:55 80 99.964 89.157 50.833 10 0 80 79.157 13:00 80 100 89.463 55 10 0 80 79.463 13:05 80 99.964 90.244 59.167 10 0 80 80.244 13:10 80 99.86 89.393 63.333 10 0 80 79.393 13:15 80 99.699 90.149 67.5 10 0 80 80.149 13:20 80 99.487 89.871 71.667 10 0 80 79.871 13:25 80 99.233 90.442 75.833 10 0 80 80.442 13:30 80 98.946 90.853 80 10 0 80 80.853 13:35 80 98.634 89.991 84.167 10 0 80 79.991 13:40 80 98.306 90.155 88.333 10 0 80 80.155 13:45 80 97.971 90.574 92.5 10 0 80 80.574 13:50 80 97.635 90.064 96.667 10 0 80 80.064 13:55 80 97.309 79.792 100 0 0 80 79.792 14:00 100 97 96.307 100 -3 0.693 100 99.307 14:05 100 96.645 94.874 98.75 -3.355 1.771 100 98.229 … … … … … … … … … It can be seen from Table 1 above that after adopting the optimization control strategy of this patent, the abandoned light is reduced and the waste of 19MW.h of electricity is reduced.

[0040] 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 charging and discharging of a photovoltaic station energy storage system, characterized in that The steps include: Step 1: Obtain the dispatching power, current energy storage status, real-time photovoltaic power generation and ultra-short-term photovoltaic power forecast; Step 2: Divide the time periods into different charging and discharging strategies according to the power consumption situation, and determine the time period to which the current time belongs; Step 3: If the current time period belongs to the real-time optimization charging and discharging strategy period, execute step 4; if it belongs to the valley charging strategy period, execute step 5; Step 4: Real-time optimization of charging and discharging strategies; when the ultra-short-term predicted power of photovoltaic power is greater than the dispatched power, the photovoltaic station energy storage system is charged, and then further judged whether the difference between the ultra-short-term predicted power of photovoltaic power and the dispatched power is greater than the rated maximum charging power of energy storage. If so, it is charged with the rated maximum charging power of energy storage. If not, it is charged with the difference between the ultra-short-term predicted power of photovoltaic power and the dispatched power as the energy storage charging power; Step 5: Low-peak charging strategy; Step 6: Energy storage status monitoring and cycle preparation; The current energy storage state is continuously monitored. If the current energy storage state is within the working range, the cycle returns to step 1 for a new round of optimization control. Otherwise, the current cycle ends.

2. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 1, characterized in that: In the step 4, the photovoltaic power generation is increased during charging, and the increased photovoltaic power generation value is the energy storage charging power value; after being fully charged, the photovoltaic power generation is reduced to follow the dispatching power.

3. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 1, characterized in that: In step 4, when the ultra-short-term predicted power of photovoltaic is less than or equal to the dispatching power, if the ultra-short-term predicted power of photovoltaic is greater than the real-time power generation power of photovoltaic, it is further determined whether the difference between the dispatching power and the ultra-short-term predicted power of photovoltaic is greater than the rated maximum discharge power of energy storage. If so, the rated maximum discharge power of energy storage is used as the energy storage discharge power for discharge. If not, the difference between the dispatching power and the ultra-short-term predicted power of photovoltaic is used as the energy storage discharge power for discharge. In the case where the ultra-short-term predicted power of photovoltaic power is less than or equal to the real-time power generation of photovoltaic power, it is further determined whether the difference between the dispatching power and the real-time power generation of photovoltaic power is greater than the rated maximum discharge power of the energy storage. If so, the rated maximum discharge power of the energy storage is used as the energy storage discharge power for discharge. If not, the difference between the dispatching power and the real-time power generation of photovoltaic power is used as the energy storage discharge power for discharge. Determine whether the current energy storage state is within the working range of 5%-100%. If so, loop back to step 1 for a new round of optimization control. If not, end the current loop.

4. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 4, characterized in that: When the ultra-short-term predicted photovoltaic power is greater than the actual generated power, the photovoltaic generated power is increased during discharge, and the increased photovoltaic generated power is the difference between the ultra-short-term predicted photovoltaic power and the actual photovoltaic generated power.

5. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 4, characterized in that: Wait for a certain period of time to re-evaluate the current energy storage status, and update the energy storage charging power and energy storage discharging power in real time before performing subsequent operations.

6. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 1, characterized in that: In step 4, it is determined whether the current energy storage state reaches the minimum value of 5%. If so, the current cycle is terminated; otherwise, the cycle returns to step 1 for a new round of optimization control.

7. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 1, characterized in that: In step 5, it is determined whether the current energy storage state is less than the maximum threshold. If so, charging is performed with the rated maximum charging power of the energy storage as the upper limit until the current energy storage state reaches the maximum threshold or the valley charging strategy period ends, and no charging operation is performed.

8. A real-time optimization control method for charging and discharging of a photovoltaic station energy storage system according to claim 1, characterized in that: In step 6, it is monitored whether the current energy storage state reaches the maximum threshold value. If so, the current cycle is terminated; otherwise, the cycle returns to step 1 for a new round of optimization control.

9. A real-time optimization control system for charging and discharging of a photovoltaic station energy storage system, comprising an energy storage charging and discharging control module, characterized in that: The energy storage charge and discharge control module performs optimization control according to a real-time optimization control method for charge and discharge of a photovoltaic station energy storage system according to any one of claims 1 to 8.

10. A photovoltaic energy storage charging and discharging real-time optimization control system, comprising a photovoltaic station control system and a photovoltaic system, an energy storage system, an automatic gain control system, a photovoltaic power prediction system and a power grid dispatching center respectively connected thereto, characterized in that: The photovoltaic station control system is a photovoltaic station energy storage system charging and discharging real-time optimization control system as described in claim 9.

Images