A method and system for intelligent control of photovoltaic energy storage in distribution station area

Abstract

The present invention discloses a method and system for intelligent control of photovoltaic energy storage in a distribution station area. At present, when the voltage exceeds the limit at the grid-connected node of the photovoltaic inverter in the distribution station area, the photovoltaic inverter itself starts the off-grid protection to ensure that the voltage operates at a normal level, but this method will cause power generation loss and reduce the amount of power generated online. The intelligent control method for photovoltaic energy storage in the distribution station area adopted by the present invention includes: collecting data from photovoltaic inverters, energy storage devices and power grid equipment, and through real-time monitoring of the operating status, current, voltage, active power, reactive power and power factor of the aforementioned equipment, constructing a voltage over-limit control strategy for the grid-connected node of the photovoltaic inverter in the distribution station area based on data analysis to control the photovoltaic inverter. The present invention solves the problem of voltage over-limit at the node where the photovoltaic inverter in the distribution station area is connected to the grid, and increases the amount of power generated online.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and specifically relates to a method and system for intelligent control of photovoltaic energy storage in a distribution station area. Background Art

[0002] With the development of renewable energy generation, the current distribution area is experiencing an overload of distributed photovoltaic installations. During the operation of the distribution area, the voltage of the photovoltaic inverter grid-connected node is prone to exceed the limit, which directly threatens the safe and stable operation of the power grid. When the voltage of the photovoltaic inverter grid-connected node exceeds the limit, the photovoltaic inverter itself starts the off-grid protection to ensure that the voltage operates at a normal level, but this method will cause power generation loss and reduce the amount of power generated on the grid.

[0003] In addition, during the operation of the distribution substation, the phenomenon of reverse overload of photovoltaic power generation is prone to occur, which directly threatens the safe and stable operation of the power grid. At present, the mainstream method uses energy storage coordinated scheduling to consume photovoltaic power generation and eliminate the reverse overload phenomenon in the distribution substation, but the energy storage device is not accurately and economically scheduled, and the true value of the energy storage device cannot be brought into play. Summary of the invention

[0004] In view of the problems existing in the above-mentioned prior art, the present invention proposes a method and system for intelligent control of photovoltaic energy storage in distribution station area, which is based on real-time operation monitoring of photovoltaic, energy storage and power distribution in the substation area, and combines historical data, external data, etc. to perform prediction and analysis calculation, and forms a voltage over-limit control strategy for the grid-connected nodes of photovoltaic inverters to control the photovoltaic inverters, so as to solve the problem of voltage over-limit at the nodes at the photovoltaic grid-connected location and increase the online power generation; in addition, the present invention also constructs a reverse heavy-load peak-shaving scheduling strategy for the substation area, and performs scheduling of energy storage charging and discharging, so as to accurately and economically schedule the energy storage device.

[0005] To this end, the present invention adopts the following technical solution.

[0006] In a first aspect, the present invention provides a method for intelligent control of photovoltaic energy storage in a distribution station area, comprising: Collect data from photovoltaic inverters, energy storage devices and power grid equipment, and build a voltage over-limit control strategy for the photovoltaic inverter grid-connected nodes in the distribution station area based on data analysis through real-time monitoring of the operating status, current, voltage, active power, reactive power and power factor of the aforementioned equipment to control the photovoltaic inverter; The voltage over-limit control strategy of the photovoltaic inverter grid-connected node in the distribution station area is constructed based on data analysis to perform voltage over-limit control, including: Step 1), setting a voltage over-limit action threshold value for the AC side voltage of the photovoltaic inverter grid-connected node; Step 2), collect voltage, current, active power and reactive power data on the AC side of the photovoltaic inverter grid-connected node; Step 3), based on the data obtained in step 2), determine whether the voltage exceeds the limit. If the voltage exceeds the limit, execute step 4). If the voltage does not exceed the limit, execute step 8); Step 4), analyze and calculate to obtain the voltage regulation control strategy for the over-limit node; Step 5), issuing control instructions for the voltage regulation control strategy of the over-limit node to adjust the active power and reactive power of the corresponding photovoltaic inverter; Step 6), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the photovoltaic inverter acts according to the issued adjustment instruction, if so, execute step 7); if not, return to step 5); Step 7), record the current control strategy issuance time and control instructions, and then return to step 2); Step 8), determine whether the voltage regulation control strategy of the over-limit node has been issued and the control instruction has not been restored, if so, execute step 9), if not, return to step 2); Step 9), restore the control instruction of the voltage regulation control strategy of the over-limit node issued last time, that is, according to the recorded control strategy, restore the active power value and reactive power value of the photovoltaic inverter to the state before the regulation; Step 10), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the control instruction is issued successfully. If successful, return to step 2), if unsuccessful, return to step 9).

[0007] Furthermore, the intelligent control method of photovoltaic energy storage in the distribution station area of ​​the present invention further includes: constructing a reverse heavy load peak-shaving scheduling strategy for the station area to schedule energy storage charging and discharging, specifically including: Step A), predict the 24h load of the next day, and form the next day's overall load prediction curve of the area; Step B), determine the predicted load margin coefficient, use the predicted load margin coefficient to correct the next day's overall load forecast curve of the substation to obtain a corrected overall load forecast curve; Step C), judging whether a reverse overload situation will occur according to the corrected overall load prediction curve; Step D), when reverse overload is detected in the substation, the energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is started; otherwise, the energy storage peak-shaving and valley-filling dispatching mode based on time-of-use electricity price is started; Step E), after completing the charge and discharge control strategy scheduling of the mode set in step D), the energy storage device ends the scheduling for the day; The present invention adopts a solution combining load forecasting and energy storage scheduling to schedule energy storage charging and discharging, accurately and economically schedule energy storage devices, and maximize the value of energy storage scheduling.

[0008] Furthermore, the specific steps of predicting the 24h load of the next day and forming the next day's overall load prediction curve of the area (i.e., step A) are as follows: a) According to the photovoltaic installed capacity of the substation, the photovoltaic power generation power of 96 points in the substation every 15 minutes for 24 hours the next day is predicted by using the method based on LSTM long short-term memory neural network to obtain the photovoltaic power generation prediction curve; b) Extract the historical meter load data and photovoltaic power generation data of the substation area where the photovoltaic power generation in the substation area did not experience reverse overload due to special weather conditions, analyze and process the above data to form a historical load forward consumption database in the substation area, and use the LSTM long short-term memory neural network method to predict the forward consumption power of the substation area load at 96 points every 15 minutes for 24 hours the next day to obtain the forward consumption curve of the substation area load; c) Fitting the photovoltaic power generation prediction curve with the forward load consumption curve of the substation to form the overall load prediction curve of the substation for the next day.

[0009] Furthermore, the step of determining the predicted load margin coefficient is as follows: the initial value is to amplify the predicted value of the overall load of the substation on the next day by 1.05 times to determine the overload capacity, and when the data accumulates a preset amount, the average positive deviation of the last three predicted state days with the same parameter conditions is taken.

[0010] Furthermore, the judgment basis for judging whether there is a reverse overload situation is I 反向 -kI N , if it is greater than 0, there is a reverse overload condition, where k is the transformer allowable overcurrent coefficient, I 反向 I is the reverse current value flowing through the transformer when photovoltaic power is connected to the grid. N is the rated current of the transformer.

[0011] Furthermore, the energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is as follows: the energy storage charging capacity is reserved according to the reverse overload capacity and time period in the day before, the photovoltaic consumption capacity is provided, and then the energy storage charging and discharging control strategy is determined as a whole according to the peak and valley time-of-use electricity price mechanism; during the predicted overload period, the current of the substation is monitored in real time at the minute level, and the subsequent energy storage charging and discharging control strategy is corrected at the hourly level in combination with the peak and valley time-of-use electricity price mechanism; The energy storage peak shaving and valley filling scheduling mode based on time-of-use electricity price is as follows: the energy storage charging and discharging strategy is determined according to the local peak-valley time-of-use electricity price mechanism, and scheduling is performed.

[0012] Furthermore, in step 3), the basis for determining whether voltage over-limit occurs is that the photovoltaic AC side voltage in step 2) is greater than the voltage over-limit action threshold value set in step 1) in two adjacent continuous sampling periods.

[0013] Furthermore, in step 4), the calculation process of the voltage regulation control strategy of the out-of-limit node is as follows: a) According to the DistFlow power flow equation, the relationship between the voltage at node N and the voltage at node N-1 in the distribution network under the substation is expressed as: (V N-1 ) 2 =(V N ) 2 +2(P N R N +Q N X N )+((R N ) 2 +(X N ) 2 )((P N ) 2 +(Q N ) 2 ) / (V N ) 2 Formula 1 Where V N-1 Represents the voltage of node N-1, V N represents the voltage at node N, P N Represents the active power of node N; R N represents the resistance of node N; Q N represents the reactive power of node N; X N represents the reactance of node N; The above formula is simplified to: (V N ) 2 =(V0) 2 -2 Formula 2 Where V0 represents the voltage at the starting point of the busbar in the distribution network; P n Represents the active power of the node; R n represents the resistance of the node; Q n Represents the reactive power of the node; X n represents the reactance of the node; b) If the active and reactive output power of the PV system upstream of node N is kept unchanged and the PV output reactive power of node N or a node j downstream thereof is changed, the reactive power change is assumed to be , then the power flowing through branches 1 to N will change accordingly, and the amount of change is , the voltage at node N will be increased from the initial value V N0 Reduce to V N1 , and satisfy: (V N1 ) 2 =(V N0) 2 -2 Formula 3 c) When the reactive power changes of a node upstream and a node downstream of node N are equal When the power is high, the downstream photovoltaic power generation is selected for reactive power regulation; d) When the grid-connected photovoltaic power generation of node N exceeds the limit, V N1 The value is set to 1.05U N -1.10U N , U N =220V, calculate the reactive power change according to formula 3 , X in Formula 3 N =(Q N-1 -Q N ) / (I N ) 2 , I N represents the current of node N; e) Arrange the reactive power adjustable capacity of node N and downstream grid-connected photovoltaic inverters in descending order, and calculate the reactive power change according to the calculated value. , select the least number of photovoltaic inverters for reactive power regulation, and the calculation formula for the adjustable reactive power capacity of the photovoltaic inverter is: , In the formula, P represents the active power of the current photovoltaic inverter, Q represents the reactive power of the current photovoltaic inverter; S N Indicates the rated power of the photovoltaic inverter at node N; f) When the reactive power of node N and the downstream inverter cannot meet the regulation requirements, the active power control strategy of adjusting the photovoltaic inverter is adopted. Only the active power and reactive power of the photovoltaic inverter at the node that exceeds the limit are adjusted. The regulation capacity is calculated by equations 7 and 8: (V N1 ) 2 =(V N0 ) 2 - Formula 4 X N =(Q N-1 -Q N ) / (I N ) 2 Formula 5 R N =(P N-1 -P N ) / (I N ) 2 Formula 6 =P-P1 Formula 7 Formula 8 Where, P1 is the active power of the PV inverter after adjustment. Substitute Equation 5, Equation 6, Equation 7, and Equation 8 into Equation 4 to obtain P1.

[0014] In a second aspect, the present invention provides a photovoltaic energy storage intelligent control system for a distribution station area, which is used to implement the above-mentioned photovoltaic energy storage intelligent control method for the distribution station area, and includes an energy storage charging and discharging scheduling and inverter control module, a prediction and calculation module and a monitoring control module, wherein the monitoring control module includes a photovoltaic operation monitoring module, an energy storage operation monitoring module, an area power distribution monitoring module and other data monitoring modules; The energy storage charging and discharging scheduling and inverter control module: performs charging, discharging and stopping scheduling operations on the energy storage device, and adjusts the active power, reactive power and power factor of the photovoltaic inverter; The prediction and calculation module and the monitoring and control module: predict the overall load prediction curve of the next day 0-24h in the area when the energy storage does not participate in the scheduling; calculate the reverse overload condition of the area on the next day based on the overall load prediction curve and the predicted load margin coefficient; calculate the preliminary strategy for the charging and discharging state of the energy storage on the next day in combination with the peak-valley time-of-use electricity price mechanism implemented by the area; according to the real-time data of reverse overload and the real-time data of energy storage charging and discharging, make appropriate corrections to the energy storage charging and discharging strategy at important time nodes; calculate the power adjustment parameters of each photovoltaic inverter based on the collected photovoltaic inverter node voltage over-limit data and the active power, reactive power and power factor of the connected photovoltaic inverter; calculate the adjustment amount of the photovoltaic inverter based on the real-time photovoltaic inverter monitoring data, and judge whether the photovoltaic inverter can be restored to the state before adjustment based on the adjustment amount; The photovoltaic operation monitoring module: monitors and counts the current, voltage, active power, reactive power and power factor of the photovoltaic inverter, and controls and adjusts the active power, reactive power and power factor; The energy storage operation monitoring module: monitors the energy storage system charging and discharging status, SOC, SOH, charging capacity, discharging capacity, total battery voltage and total battery current, and controls the energy storage charging, discharging and stopping status; The power distribution monitoring module of the substation: monitors the current, voltage, active power and reactive power of important nodes on the low-voltage side of the substation; The other data monitoring module is used to monitor and record meteorological data and obtain meteorological and operation inspection data for the next 24 hours.

[0015] The beneficial effects of the present invention are as follows: the present invention effectively solves the problems of reverse overload, abandoned light, and over-limit voltage of photovoltaic inverter grid-connected nodes caused by the large-scale access of distributed photovoltaics in the current low-voltage distribution station area; by jointly adjusting the active power and reactive power of multiple inverters for node voltage over-limit faults, the power generation loss caused by the fault can be effectively reduced, and the online power generation and the benefits of photovoltaic power generation users can be increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a flow chart of voltage over-limit control of the photovoltaic inverter grid-connected node in the distribution station area of ​​the present invention; Figure 2 A simplified diagram of the power distribution network circuit under the power distribution station area of ​​the present invention; Figure 3 This is a flow chart of the reverse heavy load peak-shaving scheduling of the substation area of ​​the present invention; Figure 4 This is the architecture diagram of the photovoltaic energy storage intelligent control system for the distribution station area of ​​the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1 This embodiment provides a method for intelligent control of photovoltaic energy storage in a distribution station area, which collects data from photovoltaic inverters, energy storage devices and power grid equipment, and through real-time monitoring of the operating status, current, voltage, active power, reactive power and power factor of the aforementioned equipment, constructs a voltage over-limit control strategy for the grid-connected nodes of photovoltaic inverters in the distribution station area based on data analysis to control the photovoltaic inverter; constructs a reverse heavy load peak-shaving scheduling strategy for the station area to schedule energy storage charging and discharging.

[0020] Specifically, the voltage over-limit control strategy of the photovoltaic inverter grid-connected node in the distribution station area is constructed based on data analysis to perform voltage over-limit control, such as Figure 1 As shown, the specific steps are as follows: Step 1), setting a voltage over-limit action threshold value for the AC side voltage of the photovoltaic inverter grid-connected node; Step 2), collect voltage, current, active power and reactive power data on the AC side of the photovoltaic inverter grid-connected node; Step 3), based on the data obtained in step 2), determine whether the voltage exceeds the limit. If the voltage exceeds the limit, execute step 4). If the voltage does not exceed the limit, execute step 8); Step 4), analyze and calculate to obtain the voltage regulation control strategy for the over-limit node; Step 5), issuing control instructions for the voltage regulation control strategy of the over-limit node to adjust the active power and reactive power of the corresponding photovoltaic inverter; Step 6), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the photovoltaic inverter acts according to the issued control instruction, if so, execute step 7); if not, return to step 5); Determine whether the PV inverter is operating according to the adjustment command issued: set an error (5%) when comparing data. For example, if the issued command is 100kW, if the read active power and reactive power data are within the range of 95-105kW, it is considered to be operating according to the control command issued; Step 7), record the current control strategy issuance time and control instructions, and then return to step 2); Step 8), determine whether the voltage regulation control strategy of the over-limit node has been issued and the control instruction has not been restored, if so, execute step 9), if not, return to step 2); Step 9), restore the control instruction of the voltage regulation control strategy of the over-limit node issued last time, that is, according to the recorded control strategy, restore the active power value and reactive power value of the photovoltaic inverter to the state before the regulation; Step 10), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the control instruction is issued successfully. If successful, return to step 2); if unsuccessful, return to step 9); Determine whether the control command is successfully issued: set an error (5%) when comparing data. For example, if the control command issued is 100kW, if the read active power and reactive power data are within the range of 95-105kW, it is considered that the control command is successfully issued.

[0021] In the step 1), the voltage over-limit action threshold is 1.05U N -1.10U N , threshold value configurable, U N =220V.

[0022] In the step 2), the voltage, current, active power and reactive power data are collected, including: simplifying the distribution network circuit under the substation area as follows: Figure 2 As shown, data such as active power, reactive power and current at each node are collected; data such as active power, reactive power, AC side voltage and rated power of each photovoltaic inverter are used. Figure 2 In, P LN represents the active power of the load at node N, Q LN Represents the reactive power of the load at node N; P GN represents the active power of the photovoltaic inverter at node N, Q GN Represents the reactive power of the photovoltaic inverter at node N; R N represents the resistance of node N; Q N represents the reactive power of node N; In the step 3), the basis for judging whether the voltage exceeds the limit is that the photovoltaic AC side voltage in step 2) is greater than the voltage exceeding the limit action threshold value set in step 1) in two consecutive adjacent sampling periods.

[0023] In the step 4), the calculation process of the voltage regulation control strategy of the off-limit node is as follows: a) According to the DistFlow power flow equation, the relationship between the voltage at node N and the voltage at node N-1 in the distribution network under the substation is expressed as: (V N-1 ) 2 =(V N ) 2 +2(P N R N +Q N X N )+((R N ) 2 +(X N ) 2 )((P N ) 2 +(Q N ) 2 ) / (V N ) 2 Formula 1 Where V N-1 Represents the voltage of node N-1, V N represents the voltage at node N, P N Represents the active power of node N; R N represents the resistance of node N; Q N represents the reactive power of node N; X N represents the reactance of node N; The above formula is simplified to: (V N )2 =(V0) 2 -2 Formula 2 Where V0 represents the voltage at the starting point of the busbar in the distribution network; P n Represents the active power of the node; R n represents the resistance of the node; Q n Represents the reactive power of the node; X n represents the reactance of the node; b) If the active and reactive output power of the PV system upstream of node N is kept unchanged and the PV output reactive power of node N or a node j downstream thereof is changed, the reactive power change is assumed to be , then the power flowing through branches 1 to N will change accordingly, and the amount of change is , the voltage at node N will be increased from the initial value V N0 Reduce to V N1 , and satisfy: (V N1 ) 2 =(V N0 ) 2 -2 Formula 3 c) When the reactive power changes of a node upstream and a node downstream of node N are equal When the power is high, the downstream photovoltaic power generation is selected for reactive power regulation; d) When the grid-connected photovoltaic power generation of node N exceeds the limit, V N1 The value is set to 1.05U N -1.10U N , U N =220V, calculate the reactive power change according to formula 3 , X in Formula 3 N =(Q N-1 -Q N ) / (I N ) 2 , I N represents the current of node N; e) Arrange the reactive power adjustable capacity of node N and downstream grid-connected photovoltaic inverters in descending order, and calculate the reactive power change according to the calculated value. , select the least number of photovoltaic inverters for reactive power regulation, and the calculation formula for the adjustable reactive power capacity of the photovoltaic inverter is: , In the formula, P represents the active power of the current photovoltaic inverter, Q represents the reactive power of the current photovoltaic inverter; S N Indicates the rated power of the photovoltaic inverter at node N; f) When the reactive power of node N and the downstream inverter cannot meet the regulation requirements, the active power control strategy of adjusting the photovoltaic inverter is adopted. Only the active power and reactive power of the photovoltaic inverter at the node that exceeds the limit are adjusted. The regulation capacity is calculated by equations 7 and 8: (V N1 ) 2 =(V N0 ) 2 - Formula 4 X N =(Q N-1 -Q N ) / (I N ) 2 Formula 5 R N =(P N-1 -P N ) / (I N ) 2 Formula 6 =P-P1 Formula 7 Formula 8 Where, P1 is the active power of the PV inverter after adjustment. Substitute Equation 5, Equation 6, Equation 7, and Equation 8 into Equation 4 to obtain P1.

[0024] In the step 5), reactive power is preferentially used for regulation. When the reactive power does not meet the regulation requirement, the active power of the photovoltaic inverter at the over-limit node is selected for regulation.

[0025] In order to reduce the implementation cost, the inverter whose cable length from the grid-connected photovoltaic inverter to the low-voltage side of the substation exceeds the preset threshold and whose photovoltaic installed capacity is greater than the preset value (initial 5kW for single-phase grid connection and 15kW for three-phase grid connection) is selected for access control. The preset threshold calculation formula for the cable length is as follows: 5%U N (A·S) / P 额 , A is the material coefficient of the cable, S is the cross-sectional area of ​​the cable, P 额 It is the rated power of the low voltage side of the substation.

[0026] Specifically, the reverse heavy load peak-shaving scheduling strategy is constructed to schedule energy storage charging and discharging, such as Figure 3 As shown, the specific steps are as follows: Step A), predict the 24h load of the next day, and form the next day's overall load prediction curve of the area; Step B), determine the predicted load margin coefficient, use the predicted load margin coefficient to correct the next day's overall load forecast curve of the substation to obtain a corrected overall load forecast curve; Step C), judging whether a reverse overload situation will occur according to the corrected overall load prediction curve; Step D), when reverse overload is detected in the substation, the energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is started; otherwise, the energy storage peak-shaving and valley-filling dispatching mode based on time-of-use electricity price is started; Step E), after completing the charge and discharge control strategy scheduling of the mode set in step D), the energy storage device ends the scheduling for the day; The specific steps of predicting the 24h load of the next day and forming the next day's overall load prediction curve of the area (i.e., step A) are as follows: a) According to the photovoltaic installed capacity of the substation, the photovoltaic power generation power of 96 points in the substation every 15 minutes for 24 hours the next day is predicted by using the method based on LSTM long short-term memory neural network to obtain the photovoltaic power generation prediction curve; b) Extract the historical meter load data and photovoltaic power generation data of the substation area where the photovoltaic power generation in the substation area did not experience reverse overload due to special weather conditions, analyze and process the above data to form a historical load forward consumption database in the substation area, and use the LSTM long short-term memory neural network method to predict the forward consumption power of the substation area load at 96 points every 15 minutes for 24 hours the next day to obtain the forward consumption curve of the substation area load; c) Fitting the photovoltaic power generation prediction curve with the forward load consumption curve of the substation to form the overall load prediction curve of the substation for the next day.

[0027] The steps for determining the predicted load margin coefficient are as follows: the initial value is to amplify the predicted value of the overall load of the substation on the next day by 1.05 times to determine the overload capacity, and when the data accumulates a preset amount, the average positive deviation of the three predicted state days with the same parameter conditions is taken.

[0028] The judgment basis for judging whether there is a reverse overload situation is I 反向 -kI N , if it is greater than 0, there is a reverse overload condition, where k is the transformer allowable overcurrent coefficient, the default value is 1.05, and k is configurable; I 反向 I is the reverse current value flowing through the transformer when photovoltaic power is connected to the grid (the current from the grid to the load is positive), N is the rated current of the transformer.

[0029] The energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is as follows: reserve energy storage charging capacity according to the reverse overload capacity and time period in the day ahead to provide photovoltaic consumption capacity, and then determine the overall energy storage charging and discharging control strategy according to the peak and valley time-of-use electricity price mechanism; conduct real-time monitoring of the current in the substation area at the minute level during the predicted overload period during the day, and correct the subsequent energy storage charging and discharging control strategy at the hourly level in combination with the peak and valley time-of-use electricity price mechanism; The energy storage peak shaving and valley filling scheduling mode based on time-of-use electricity price is as follows: the energy storage charging and discharging strategy is determined according to the local peak-valley time-of-use electricity price mechanism, and scheduling is performed.

[0030] The present invention effectively solves the problems of reverse overload, abandoned light, and over-limit voltage of photovoltaic inverter grid-connected nodes caused by the large-scale access of distributed photovoltaics in the current low-voltage distribution station area. By combining load forecasting with energy storage scheduling, the value of energy storage scheduling is maximized; by selecting an access control inverter solution, the communication access construction cost of the overall system can be effectively reduced; by jointly adjusting the active power and reactive power of multiple inverters for node voltage over-limit faults, the power generation loss caused by the fault can be effectively reduced, and the online power generation and the benefits of photovoltaic power generation users can be increased.

[0031] Application Examples The photovoltaic energy storage intelligent control method for distribution station area described in Example 1 is used as follows.

[0032] A rural power distribution area is a radial distribution network, including a 10kV bus (V=10.5kV) and 5 low-voltage nodes (U N =220V). Nodes 3 and 5 are connected to the photovoltaic system with capacities of 80kW and 105kW respectively, and the rated capacity of the inverter = 100kVA. The cable parameters on the low-voltage side of the substation are as follows: Cable length: node 1→2 (200m), node 2→3 (300m), node 3→4 (500m), node 4→5 (600m); unit impedance: R=0.05Ω / km, X=0.08Ω / km.

[0033] Screening conditions for controllable inverters: cable length > 500m and installed capacity ≥ 50kW (only node 5 meets the requirements).

[0034] Step 1) Set the voltage over-limit action threshold value for the AC side voltage of the photovoltaic inverter grid-connected node, 1.05×220=231V (upper limit); Step 2) collect the voltage, current, active power and reactive power data of the AC side of the photovoltaic inverter grid-connected node, with a data sampling period of 1 second; Step 3), the full load output of the PV inverter at node 5 is P=105kW (over-capacity operation), Q=0kVar (unity power factor), and the AC side voltage V is measured 50 =240V (exceeding the limit for 2 consecutive cycles); Step 4), V 50 =240V, I5=167A (inverter output capacity: active power is 105kW, reactive power is 0kVar); Branch impedance: ∑R n=0.05×(0.2+0.3+0.5+0.6)=0.08Ω,∑X n =0.08×(0.2+0.3+0.5+0.6)=0.128Ω (node ​​5 to bus path).

[0035] Target voltage setting: V 50 =240V down to V 51 =231V (less than or equal to 231V); Reactive power regulation calculation (V N1 ) 2 =(V N0 ) 2 -2 : 231 2 =240 2 -2 ×0.128, =(240 2 -231 2 ) / (2×0.128)=16558Var≈16.6kVar.

[0036] Adjustable capacity verification: Node 5 inverter adjustable reactive power (S5=100kVA): = -Q, due to 100 2 -105 2 <0.

[0037] Conclusion: The reactive power of node 5 cannot be adjusted (P is overloaded), and active power regulation needs to be started.

[0038] Adjustment strategy: Reduce P and inject Q at the same time: According to equation 4, equation 7 and equation 8, we can get: (231) 2 =(240) 2 -

[0039] Calculated P1≈99.16kW, then =12.93kVar.

[0040] To set the total adjustment required: =105-99=6kW, =13kVar; Step 5), according to the above calculation and analysis results, issue an instruction: the photovoltaic inverter of node 5 is limited to P=99kW, Q=13kVar; Step 6), data verification: read P=98.8kW (error <5%), Q=12.5kVar (error within 5%), judgment is successful; Step 7), record the time when the current control strategy is issued and the control instructions; Step 8), data monitoring, the voltage of node 5 drops to 230.8V (lower than 231V), and the over-limit is lifted; Step 9), restore the control instruction of the voltage regulation control strategy of the over-limit node sent last time; Step 10), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the control instruction is issued successfully. If successful, return to step 2), if unsuccessful, return to step 9).

[0041] Results and Conclusions 1) Regulation effect: The voltage at node 5 drops to 230.8V (lower than 231V), and the over-limit is lifted; PV utilization loss: 105→99kW (acceptable in the short term to avoid the risk of grid disconnection).

[0042] 2) Economical: Only one PV inverter (node ​​5) is regulated, which complies with the principle of “minimum equipment intervention”; When the priority utilization of reactive resources fails, it quickly switches to active power regulation with a response time of <5 seconds.

[0043] 3) Strategic advantages: Accurately locate the adjustment amount through the DistFlow equation to reduce the cost of trial and error; According to the dynamic recovery mechanism of step 9) and step 10), the maximum output of photovoltaic power is ensured during the non-limit-exceeding period.

[0044] Example 2 This embodiment provides a photovoltaic energy storage intelligent control system for a distribution station area, such as Figure 4 As shown, it is used to implement the method described in Example 1, which includes an energy storage charging and discharging scheduling and inverter control module, a prediction and calculation module and a monitoring control module; the monitoring control module includes a photovoltaic operation monitoring module, an energy storage operation monitoring module, an area power distribution monitoring module and other data monitoring modules.

[0045] The energy storage charging and discharging scheduling and inverter control module performs charging, discharging and stopping scheduling operations on the energy storage device, and adjusts the active power, reactive power and power factor of the photovoltaic inverter.

[0046] The prediction and calculation module and the monitoring and control module: predict the overall load prediction curve of the substation for 0-24 hours the next day when energy storage does not participate in scheduling, accurate to 15min granularity; calculate the reverse overload condition of the substation the next day based on the overall load prediction curve and the predicted load margin coefficient; calculate the preliminary strategy for the charging and discharging state of the energy storage the next day in combination with the peak-valley time-of-use electricity price mechanism implemented by the substation; make appropriate corrections to the energy storage charging and discharging strategy at important time nodes based on the real-time data of reverse overload and real-time data of energy storage charging and discharging; calculate the power adjustment parameters of each photovoltaic inverter based on the collected photovoltaic inverter node voltage over-limit data and the active power, reactive power and power factor of the connected photovoltaic inverter; calculate the adjustment amount of the photovoltaic inverter based on the real-time photovoltaic inverter monitoring data, and judge whether the photovoltaic inverter can be restored to the state before adjustment based on the adjustment amount.

[0047] The photovoltaic operation monitoring module monitors and counts the current, voltage, active power, reactive power and power factor of the photovoltaic inverter, and controls and adjusts the active power, reactive power and power factor.

[0048] The energy storage operation monitoring module monitors the energy storage system's charge and discharge status, SOC, SOH, charge capacity, discharge capacity, total battery voltage and total battery current, and controls the energy storage charging, discharging and stopping status.

[0049] The substation power distribution monitoring module monitors the current, voltage, active power and reactive power of important nodes on the low-voltage side of the substation.

[0050] The other data monitoring module is used to monitor and record meteorological data and obtain meteorological and operation inspection data for the next 24 hours.

[0051] It should be noted that each module in the above-mentioned intelligent control system for photovoltaic energy storage in a distribution station area can be fully or partially implemented by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules. For the specific definition of an intelligent control system for photovoltaic energy storage in a distribution station area, please refer to the definition of an intelligent control method for photovoltaic energy storage in a distribution station area above. The two have the same functions and effects, which will not be repeated here.

[0052] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for intelligent control of photovoltaic energy storage in a distribution station area, characterized in that: include: Collect data from photovoltaic inverters, energy storage devices and power grid equipment, and build a voltage over-limit control strategy for the photovoltaic inverter grid-connected nodes in the distribution station area based on data analysis through real-time monitoring of the operating status, current, voltage, active power, reactive power and power factor of the aforementioned equipment to control the photovoltaic inverter; The voltage over-limit control strategy of the photovoltaic inverter grid-connected node in the distribution station area is constructed based on data analysis to perform voltage over-limit control, including: Step 1), setting a voltage over-limit action threshold value for the AC side voltage of the photovoltaic inverter grid-connected node; Step 2), collect voltage, current, active power and reactive power data on the AC side of the photovoltaic inverter grid-connected node; Step 3), based on the data obtained in step 2), determine whether the voltage exceeds the limit. If the voltage exceeds the limit, execute step 4). If the voltage does not exceed the limit, execute step 8); Step 4), analyze and calculate to obtain the voltage regulation control strategy for the over-limit node; Step 5), issuing control instructions for the voltage regulation control strategy of the over-limit node to adjust the active power and reactive power of the corresponding photovoltaic inverter; Step 6), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the photovoltaic inverter acts according to the issued control instruction, if so, execute step 7); if not, return to step 5); Step 7), record the current control strategy issuance time and control instructions, and then return to step 2); Step 8), determine whether the voltage regulation control strategy of the over-limit node has been issued and the control instruction has not been restored, if so, execute step 9), if not, return to step 2); Step 9), restore the control instruction of the voltage regulation control strategy of the over-limit node sent last time; Step 10), after the control instruction is issued, read the active power and reactive power data of the corresponding photovoltaic inverter to determine whether the control instruction is issued successfully. If successful, return to step 2), if unsuccessful, return to step 9).

2. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 1 is characterized in that: Also includes: Construct a reverse heavy load peak-shaving dispatching strategy for the substation area to dispatch energy storage charging and discharging, including: Step A), predict the 24h load of the next day, and form the next day's overall load prediction curve of the area; Step B), determine the predicted load margin coefficient, and use the predicted load margin coefficient to correct the overall load forecast curve of the next day's substation to obtain a corrected overall load forecast curve; Step C), judging whether a reverse overload situation will occur according to the corrected overall load prediction curve; Step D), when reverse overload is detected in the substation, the energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is started; otherwise, the energy storage peak-shaving and valley-filling dispatching mode based on time-of-use electricity price is started; Step E), after completing the charging and discharging control strategy scheduling of the mode set in step D), the energy storage device ends the scheduling for the day.

3. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 2 is characterized in that: The specific steps of predicting the 24h load of the next day and forming the next day's overall load prediction curve of the area are as follows: a) According to the photovoltaic installed capacity of the substation, the photovoltaic power generation power of 96 points in the substation every 15 minutes for 24 hours the next day is predicted by using the method based on LSTM long short-term memory neural network to obtain the photovoltaic power generation prediction curve; b) Extract the historical meter load data and photovoltaic power generation data of the substation area where the photovoltaic power generation in the substation area did not experience reverse overload due to special weather conditions, analyze and process the above data to form a historical load forward consumption database in the substation area, and use the LSTM long short-term memory neural network method to predict the forward consumption power of the substation area load at 96 points every 15 minutes for 24 hours the next day to obtain the forward consumption curve of the substation area load; c) Fitting the photovoltaic power generation prediction curve with the forward load consumption curve of the substation to form the overall load prediction curve of the substation for the next day.

4. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 2 is characterized in that: The steps for determining the predicted load margin coefficient are as follows: the initial value is to amplify the predicted value of the overall load of the substation on the next day by 1.05 times to determine the overload capacity, and when the data accumulates a preset amount, the average positive deviation of the three predicted state days with the same parameter conditions is taken.

5. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 2 is characterized in that: The judgment basis for judging whether there is a reverse overload situation is I 反向 -kI N , if it is greater than 0, there is a reverse overload condition, where k is the transformer allowable overcurrent coefficient, I 反向 I is the reverse current value flowing through the transformer when photovoltaic power is connected to the grid. N is the rated current of the transformer.

6. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 2 is characterized in that: The energy storage dispatching mode based on the combination of photovoltaic consumption and time-of-use electricity price is as follows: reserve energy storage charging capacity according to the reverse overload capacity and time period in the day ahead to provide photovoltaic consumption capacity, and then determine the overall energy storage charging and discharging control strategy according to the peak and valley time-of-use electricity price mechanism; conduct real-time monitoring of the current in the substation area at the minute level during the predicted overload period during the day, and correct the subsequent energy storage charging and discharging control strategy at the hourly level in combination with the peak and valley time-of-use electricity price mechanism; The energy storage peak shaving and valley filling scheduling mode based on time-of-use electricity price is as follows: the energy storage charging and discharging strategy is determined according to the local peak-valley time-of-use electricity price mechanism, and scheduling is performed.

7. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 1 or 2, characterized in that: In step 3), the basis for judging whether the voltage exceeds the limit is: in step 2), the voltage on the PV AC side is greater than the voltage exceeding the limit action threshold value set in step 1) in two consecutive sampling periods; the voltage exceeding the limit action threshold value set in step 1) is 1.05U N -1.10U N , U N =220V.

8. The method for intelligent control of photovoltaic energy storage in a distribution station area according to claim 1 or 2, characterized in that: In step 4), the calculation process of the voltage regulation control strategy of the off-limit node is as follows: a) According to the DistFlow power flow equation, the relationship between the voltage of node N and the voltage of node N-1 in the distribution network under the substation is expressed as: (V N-1 ) 2 =(V N ) 2 +2(P N R N +Q N X N )+((R N ) 2 +(X N ) 2 )((P N ) 2 +(Q N ) 2 ) / (V N ) 2 Formula 1 Where V N-1 Represents the voltage of node N-1, V N represents the voltage at node N, P N Represents the active power of node N; R N represents the resistance of node N; Q N represents the reactive power of node N; X N represents the reactance of node N; The above formula is simplified to: (V N ) 2 =(V0) 2 -2 Formula 2 Where V0 represents the voltage at the starting point of the busbar in the distribution network; P n Represents the active power of the node; R n represents the resistance of the node; Q n Represents the reactive power of the node; X n represents the reactance of the node; b) If the active and reactive output power of the PV system upstream of node N is kept unchanged and the PV output reactive power of node N or a node j downstream thereof is changed, the reactive power change is assumed to be , then the power flowing through branches 1 to N will change accordingly, and the amount of change is , the voltage at node N will be increased from the initial value V N0 Reduce to V N1 , and satisfy: (V N1 ) 2 =(V N0 ) 2 -2 Formula 3 c) When the reactive power changes of a node upstream and a node downstream of node N are equal When the power is high, the downstream photovoltaic power generation is selected for reactive power regulation; d) When the grid-connected photovoltaic power generation of node N exceeds the limit, V N1 The value is set to 1.05U N -1.10U N , U N =220V, calculate the reactive power change according to formula 3 , X in Formula 3 N =(Q N-1 -Q N ) / (I N ) 2 , I N represents the current of node N; e) Arrange the reactive power adjustable capacity of node N and downstream grid-connected photovoltaic inverters in descending order, and calculate the reactive power change according to the calculated value. , select the least number of photovoltaic inverters for reactive power regulation, and the calculation formula for the adjustable reactive power capacity of the photovoltaic inverter is: , In the formula, P represents the active power of the current photovoltaic inverter, Q represents the reactive power of the current photovoltaic inverter; S N Indicates the rated power of the photovoltaic inverter at node N; f) When the reactive power of node N and the downstream inverter cannot meet the regulation requirements, the active power control strategy of adjusting the photovoltaic inverter is adopted. Only the active power and reactive power of the photovoltaic inverter at the node that exceeds the limit are adjusted. The regulation capacity is calculated by equations 7 and 8: (V N1 ) 2 =(V N0 ) 2 - Formula 4 X N =(Q N-1 -Q N ) / (I N ) 2 formula 5 R N =(P N-1 -P N ) / (I N ) 2 Formula 6 =P-P1 formula 7 Formula 8 Where P1 is the active power of the PV inverter after adjustment.

9. The intelligent control method for photovoltaic energy storage in a distribution station area according to claim 1 or 2, characterized in that: Step 2) specifically includes: a) Simplify the distribution network circuit under the substation and collect the active power P at each node N , reactive power Q N and current I N ; b) Collect active power P, reactive power Q, AC side voltage U and rated power S of each PV inverter N .

10. A photovoltaic energy storage intelligent control system for a distribution substation, used to implement the photovoltaic energy storage intelligent control method for a distribution substation according to any one of claims 1 to 9, characterized in that: It includes energy storage charging and discharging scheduling and inverter control module, prediction and calculation module and monitoring control module. The monitoring control module includes photovoltaic operation monitoring module, energy storage operation monitoring module, substation power distribution monitoring module and other data monitoring modules. The energy storage charging and discharging scheduling and inverter control module: performs charging, discharging and stopping scheduling operations on the energy storage device, and adjusts the active power, reactive power and power factor of the photovoltaic inverter; The prediction and calculation module and the monitoring and control module: predict the overall load prediction curve of the next day 0-24h in the area when the energy storage does not participate in the scheduling; calculate the reverse overload condition of the area on the next day based on the overall load prediction curve and the predicted load margin coefficient; calculate the preliminary strategy for the charging and discharging state of the energy storage on the next day in combination with the peak-valley time-of-use electricity price mechanism implemented by the area; according to the real-time data of reverse overload and the real-time data of energy storage charging and discharging, make appropriate corrections to the energy storage charging and discharging strategy at important time nodes; calculate the power adjustment parameters of each photovoltaic inverter based on the collected photovoltaic inverter node voltage over-limit data and the active power, reactive power and power factor of the connected photovoltaic inverter; calculate the adjustment amount of the photovoltaic inverter based on the real-time photovoltaic inverter monitoring data, and judge whether the photovoltaic inverter can be restored to the state before adjustment based on the adjustment amount; The photovoltaic operation monitoring module: monitors and counts the current, voltage, active power, reactive power and power factor of the photovoltaic inverter, and controls and adjusts the active power, reactive power and power factor; The energy storage operation monitoring module: monitors the energy storage system charging and discharging status, SOC, SOH, charging capacity, discharging capacity, total battery voltage and total battery current, and controls the energy storage charging, discharging and stopping status; The power distribution monitoring module of the substation: monitors the current, voltage, active power and reactive power of important nodes on the low-voltage side of the substation; The other data monitoring module is used to monitor and record meteorological data and obtain meteorological and operation inspection data for the next 24 hours.

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