Distributed photovoltaic consumption method and system, electronic device and readable storage medium

Abstract

This invention discloses a distributed photovoltaic (PV) consumption method, system, electronic device, and readable storage medium. The method includes: collecting load data from each user in a distribution area to perform load forecasting and obtain first load forecast data; using the first load forecast data and meteorological data to perform power generation forecasting on distributed power sources to obtain first power generation forecast data; performing autonomous optimization by using the first load forecast data and the first power generation forecast data as the initial operating state, wherein the autonomous optimization is used to adjust a first control variable based on a first constraint condition to minimize the total cost of adjustable resources in the distribution area; determining that the autonomous optimization is complete, sending the first distribution area tie-line power to the distribution cloud master station, wherein the first distribution area tie-line power is the tie-line power between the distribution area and the medium-voltage feeder; receiving the second distribution area tie-line power from the distribution cloud master station, adding the second distribution area tie-line power to the first constraint condition, and performing autonomous optimization again.

Description

Technical Field

[0001] This invention relates to the field of distributed photovoltaic (PV) grid integration, and in particular to a distributed PV grid integration method, system, electronic device, and readable storage medium. Background Technology

[0002] In recent years, a large number of users have connected photovoltaic (PV) systems to distribution areas in low-voltage power distribution networks, leading to changes in power flow distribution, difficulty in local consumption of distributed PV power generation, backfeeding of power at various levels, increased system network losses, and voltage exceeding limits at nodes. These issues have impacted the safe and economical operation of the power distribution system, making it urgent to ensure the consumption of distributed PV and resolve the relationship between distributed PV and the safe operation of the power distribution system. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method, system, electronic device, and readable storage medium for distributed photovoltaic power absorption, so as to ensure the absorption of distributed photovoltaic power.

[0004] One aspect of the present invention provides a distributed photovoltaic (PV) consumption method applied to a transformer substation integration terminal. The method includes: collecting load data from each user in the transformer substation to perform load forecasting and obtain first load forecast data; using the first load forecast data and meteorological data to perform power generation forecasting on distributed power sources to obtain first power generation forecast data; performing autonomous optimization by using the first load forecast data and the first power generation forecast data as an initial operating state, wherein the autonomous optimization is used to adjust a first control variable based on a first constraint condition to minimize the total cost of adjustable resources in the transformer substation; determining that the autonomous optimization is complete, sending the first transformer substation tie-line power to the distribution cloud master station, wherein the first transformer substation tie-line power is the tie-line power between the transformer substation and the medium-voltage feeder; receiving the second transformer substation tie-line power from the distribution cloud master station, adding the second transformer substation tie-line power to the first constraint condition, and performing autonomous optimization again, wherein the second transformer substation tie-line power is generated by the distribution cloud master station after optimization control.

[0005] According to some embodiments of the present invention, the self-regulating optimization includes the following steps: obtaining the total cost of adjustable resources under the operating state of the first load forecast data and the first power generation forecast data, wherein the cost of adjustable resources includes the cost of load shedding, wind curtailment, and solar curtailment; adjusting the first control variable under the premise of meeting the first constraint condition, so that the total cost of the adjustable resources is the lowest price; and obtaining the high-voltage side power of the first transformer area when the total cost of the adjustable resources is the lowest.

[0006] According to some embodiments of the present invention, the formula for obtaining the total cost of the adjustable resource is: minC R =∑ t∈T [C ens ΔP ens +Cw ΔP w +C pv ΔP pv +cP loss ]Δt+C soc , where C ens C w C pv The unit costs of load shedding, wind curtailment, and solar curtailment are ΔP, respectively. ens ΔP w ΔP pv Let t represent the total amount of load shedding, wind curtailment, and solar curtailment at time t, c represent the local unit electricity price, and P represent the total amount of load shedding, wind curtailment, and solar curtailment at time t. loss For the line loss of the transformer area, C soc Operating costs for charging and discharging the charging device.

[0007] Another aspect of the present invention provides a distributed photovoltaic (PV) consumption method applied to a distribution cloud master station. The method includes: receiving the power of a first distribution area tie line from a distribution area fusion terminal; collecting medium-voltage power grid load data and combining it with the power of the first distribution area tie line to perform load prediction and obtain second load prediction data; performing power generation prediction on distributed power sources using the second load prediction data and meteorological data to obtain second power generation prediction data; performing power flow calculation on a per-distribution-line basis according to the second load prediction data and the second power generation prediction data to obtain a calculation result; determining whether the medium-voltage distribution network has an operational anomaly based on the calculation result; if the operational anomaly is determined, obtaining the second distribution area tie line power through medium-voltage distribution network optimization control, wherein the medium-voltage distribution network optimization control is used to adjust a second control variable based on a second constraint condition to minimize the total operating cost of the distribution network; the operational anomaly includes: overload, power backfeeding to the upper level, and voltage exceeding the limit; and sending the second distribution area tie line power to the distribution area fusion terminal.

[0008] According to some embodiments of the present invention, the medium-voltage distribution network optimization control includes the following steps: obtaining the total operating cost of the medium-voltage distribution network, wherein the operating cost of the medium-voltage distribution network includes the power cost of the transformer area tie line; and adjusting a second control variable to minimize the total operating cost of the medium-voltage distribution network, wherein the second control variable includes the power of the second transformer area tie line.

[0009] According to some embodiments of the present invention, after obtaining the total operating cost of the medium-voltage distribution network, the method further includes: removing the transformer interconnection power cost from the total operating cost of the medium-voltage distribution network, and performing medium-voltage distribution network optimization control to confirm whether the safe and economical operation of the medium-voltage distribution network is satisfied, wherein satisfying the safe and economical operation of the medium-voltage distribution network means that the optimization objective solution can be obtained under the second constraint condition; if not satisfied, the transformer interconnection power cost is included in the total operating cost of the medium-voltage distribution network for medium-voltage distribution network optimization control.

[0010] Another aspect of the present invention provides a distributed photovoltaic (PV) consumption method, comprising: a distribution area fusion terminal collecting load data from each user in the distribution area to perform load forecasting and obtain first load forecast data; the distribution area fusion terminal using the first load forecast data and meteorological data to perform power generation forecasting for distributed power sources to obtain first power generation forecast data; the distribution area fusion terminal performing self-regulatory optimization by using the first load forecast data and the first power generation forecast data as an initial operating state, wherein the self-regulatory optimization is used to adjust a first control variable based on a first constraint condition to minimize the total cost of adjustable resources in the distribution area; the distribution area fusion terminal determining that the self-regulatory optimization is complete, sending a first distribution area tie-line power to the distribution cloud master station, wherein the first distribution area tie-line power is the tie-line power between the distribution area and the medium-voltage feeder; the distribution cloud master station receiving the first distribution area tie-line power from the distribution area fusion terminal; the distribution cloud master station collecting medium-voltage grid load data and combining it with the first distribution area tie-line power to perform load forecasting and obtain a second load forecast data. The distribution cloud master station obtains second power generation forecast data by performing power generation forecast on distributed power sources using the second load forecast data and meteorological data. Based on the second load forecast data and the second power generation forecast data, the distribution cloud master station performs power flow calculations on a per-distribution-line basis to obtain the calculation results. The distribution cloud master station determines whether the medium-voltage distribution network is experiencing operational anomalies based on the calculation results. If an operational anomaly is detected, the distribution cloud master station obtains the second transformer area tie-line power through medium-voltage distribution network optimization control. The medium-voltage distribution network optimization control is used to adjust the second control variable based on a second constraint condition to minimize the total operating cost of the distribution network. The operational anomalies include: overload, power backfeeding to the upper level, and voltage exceeding limits. The distribution cloud master station sends the second transformer area tie-line power to the transformer area fusion terminal. The transformer area fusion terminal receives the second transformer area tie-line power from the distribution cloud master station, adds the second transformer area tie-line power to the first constraint condition, and performs self-regulatory optimization again.

[0011] Another aspect of the present invention provides a distributed photovoltaic (PV) grid integration system, comprising: a distribution area fusion terminal, configured to collect load data from each user in the distribution area to perform load forecasting and obtain first load forecast data; configured to perform power generation forecasting on distributed power sources using the first load forecast data and meteorological data to obtain first power generation forecast data; configured to perform self-regulatory optimization by using the first load forecast data and the first power generation forecast data as an initial operating state, wherein the self-regulatory optimization is configured to adjust a first control variable based on a first constraint condition to minimize the adjustable resources of the distribution area; configured to determine that the self-regulatory optimization is completed and send the first distribution area tie-line power to the distribution cloud master station, wherein the first distribution area tie-line power is the tie-line power between the distribution area and the medium-voltage feeder; configured to receive the second distribution area tie-line power from the distribution cloud master station, add the second distribution area tie-line power to the first constraint condition, and perform self-regulatory optimization again, wherein the second distribution area tie-line power is optimized by the distribution cloud master station. After optimization control, the following steps are performed: A distribution cloud master station is used to receive the first distribution area tie-line power from the distribution area fusion terminal; collect medium-voltage power grid load data and combine it with the first distribution area tie-line power to perform load prediction and obtain second load prediction data; use the second load prediction data and meteorological data to perform power generation prediction for distributed power sources and obtain second power generation prediction data; use the second load prediction data and the second power generation prediction data to perform power flow calculations on a per-distribution-line basis to obtain the calculation results; use the calculation results to determine whether the medium-voltage distribution network has an operational anomaly, and if the operational anomaly is determined, then the second distribution area tie-line power is obtained through medium-voltage distribution network optimization control, wherein the medium-voltage distribution network optimization control is used to adjust the second control variable based on the second constraint condition to minimize the total distribution network operating cost, the operational anomaly including: overload, power backfeeding to the upper level, and voltage exceeding the limit; and send the second distribution area tie-line power to the distribution area fusion terminal.

[0012] Another aspect of the present invention provides an electronic device, including a processor and a memory; the memory is used to store a program; the processor executes the program to implement the distributed photovoltaic power consumption method as described above.

[0013] The electronic device according to embodiments of the present invention has at least the same beneficial effects as the distributed photovoltaic consumption method described above.

[0014] Another aspect of the present invention provides a computer-readable storage medium storing a program that is executed by a processor to implement the distributed photovoltaic power consumption method as described above.

[0015] The computer-readable storage medium according to embodiments of the present invention has at least the same beneficial effects as the distributed photovoltaic power consumption method described above.

[0016] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to perform the aforementioned method.

[0017] In embodiments of the present invention, the first control variable is adjusted through self-regulation of the distribution area fusion terminal to achieve maximum absorption of distributed photovoltaic power within the distribution area; the second control variable is adjusted through the medium-voltage distribution network optimization control of the distribution cloud master station to ensure the economical and safe operation of the medium-voltage distribution network and further improve the absorption of distributed photovoltaic power within the distribution area; when the self-regulation of the distribution area fails to absorb the distributed photovoltaic power within the distribution area, resulting in power backfeeding to the medium-voltage distribution network, or when the high penetration of distributed photovoltaic power in the distribution area causes the distribution cloud master station to fail to meet the safe operation of the medium-voltage distribution network through optimization and adjustment control, an operational anomaly occurs. The distribution area fusion terminal adds the power of the second distribution area tie line sent by the distribution cloud master station to the first constraint condition, performs self-regulation optimization again, optimizes the first control variable, and ensures the safe operation of the medium and low voltage distribution network while maximizing the absorption of distributed photovoltaic power. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a first flowchart of a distributed photovoltaic power consumption method provided in an embodiment of this application;

[0020] Figure 2 This is a second flowchart of the distributed photovoltaic power consumption method provided in the embodiments of this application;

[0021] Figure 3 This is a third flowchart of the distributed photovoltaic power consumption method provided in the embodiments of this application. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0023] With the widespread integration of large-scale distributed photovoltaic (PV) systems, a significant amount of surplus PV power will create reverse current flow in feeders, causing voltage exceedances. The randomness and dispersion of residential PV integration exacerbate low-voltage three-phase imbalance. Furthermore, a large amount of surplus PV power in low-voltage lines will be fed into the medium-voltage distribution network through the point of common coupling, exhibiting significant imbalance and fluctuation characteristics. Based on the security of distributed PV systems, this solution proposes a method, system, electronic equipment, and readable storage medium for distributed PV power absorption, which can be widely applied in the power industry and in the field of power distribution technology absorption.

[0024] Reference Figure 1 , Figure 1 This is a first flowchart of the distributed photovoltaic (PV) grid integration method provided in this application embodiment. In this embodiment, the method is described using a transformer substation fusion terminal 100 as the executing entity, and includes steps S110-150.

[0025] Step S110: Collect load data of each user in the distribution area to perform load prediction and obtain the first load prediction data.

[0026] Specifically, the integrated terminal 100 manages and controls distributed photovoltaic systems within the jurisdiction of a transformer substation, taking the substation as the unit. The integrated terminal 100 collects load data from each user in the substation in real time and uses existing load forecasting models to perform load forecasting to obtain the first load forecast data.

[0027] Step S120: First power generation forecast data is obtained by performing power generation forecast on distributed power sources using first load forecast data and meteorological data.

[0028] Specifically, by combining the first load forecast data and meteorological data, the existing power generation forecast model is applied to obtain the first power generation forecast data.

[0029] Step S130: Perform autonomous optimization based on the first load forecast data and the first power generation forecast data. The autonomous optimization is used to adjust the first control variable based on the first constraint condition so that the total cost of the adjustable resources of the transformer area is minimized.

[0030] It should be understood that the self-regulatory optimization of the distribution area integration terminal 100 includes: obtaining the total cost of adjustable resources under the operating state of the first load forecast data and the first power generation forecast data, wherein the adjustable resources include load shedding, wind curtailment, and solar curtailment; adjusting the first control variable under the premise of meeting the first constraint condition so that the total cost of the adjustable resources is the lowest price; and obtaining the high-voltage side power of the first distribution area when the total cost of the adjustable resources is determined to be the lowest.

[0031] Specifically, the integrated terminal 100 in the distribution area performs autonomous optimization, using the currently obtained first load forecast data and first power generation forecast data as the initial operating state, and obtaining the total cost of the currently adjustable resources as the objective function. The adjustable resources include: curtailed solar power, curtailed wind power, and load shedding. The formula for calculating the total cost of the adjustable resources is:

[0032]

[0033] Where: C ens C w C pv The unit costs of load shedding, wind curtailment, and solar curtailment are ΔP, respectively. ens ΔP w ΔP pv Let t represent the total amount of load shedding, wind curtailment, and solar curtailment at time t, c represent the local unit electricity price, and P represent the total amount of load shedding, wind curtailment, and solar curtailment at time t. loss For the line loss of the transformer area, C soc Operating costs for charging and discharging the charging device.

[0034] By continuously calculating the total cost of adjustable resources by adjusting the first control variable, the data corresponding to the first control variable when the total cost is lowest is obtained, thus obtaining the optimal solution of the objective function. The first control variable includes: adjustable distributed photovoltaic grid-connected power, low-voltage switch position, and energy storage device charging / shortage power. When adjusting the first control variable, the calculation of the total cost of adjustable resources must satisfy the first constraints, which include: power balance constraints within the distribution area, node voltage constraints, line branch transmission power constraints, energy storage charging / discharging power constraints and capacity constraints, adjustable distributed photovoltaic / wind power capacity constraints, interruptible load interruption capacity and time constraints, transformer capacity constraints, and distribution area tie line power constraints. It should be noted that when the 100-level self-regulatory optimization of the distribution area integration terminal is initially started, i.e., in the first iteration, the power limitations of the distribution area and tie lines (i.e., the power constraints of the distribution area tie lines) are not considered. When the total cost of adjustable resources is lowest, the current first control variable is obtained, and the corresponding strategy is derived based on the first control variable, namely the low-voltage switch position, energy storage device charging / discharging strategy, and active power and active power output of distributed photovoltaic, thereby achieving maximum absorption of distributed photovoltaic power.

[0035] Step S140: Confirm that the autonomous optimization is complete, and send the power of the first transformer area tie line to the distribution cloud master station 200. The power of the first transformer area tie line is the power of the tie line between the transformer area and the medium voltage feeder.

[0036] Specifically, after the integrated distribution terminal 100 completes its initial self-regulatory optimization, it uploads the power of the connection between the distribution area and the medium-voltage feeder to the distribution cloud master station 200. This power is the power of the first distribution area connection. It should be noted that after self-regulatory optimization and adjustment of the first control variable, the most economical operation of adjustable resources is achieved. At this point, the integrated distribution terminal 100 has achieved maximum absorption of distributed photovoltaic power within the distribution area. Then, the power of the first distribution area connection is sent to the distribution cloud master station 200 to ensure safe and economical operation while further enhancing the absorption of distributed photovoltaic power within the distribution area.

[0037] Step S150: Receive the power of the second transformer area tie line from the distribution cloud master station 200, add the power of the second transformer area tie line to the first constraint condition, and perform self-regulatory optimization again.

[0038] Specifically, when the integrated distribution terminal 100 receives the power from the second distribution area tie line from the distribution cloud master station 200, it means that even with the medium-voltage distribution network optimization control of the distribution cloud master station 200, the medium-voltage distribution network still cannot operate safely. Therefore, the integrated distribution terminal 100 receives the power from the second distribution area tie line from the distribution cloud master station 200 and performs self-regulatory optimization again. At this time, the power from the second distribution area tie line is added to the first constraint condition. Under the condition of satisfying the tie line power constraint, the distributed photovoltaic, energy storage device, and switch position of the distribution area are optimized again by adjusting various first control variables, so as to ensure the safe operation of the medium and low voltage distribution network while maximizing the absorption of distributed photovoltaic.

[0039] Reference Figure 2 , Figure 2 This is a second flowchart of the distributed photovoltaic (PV) grid integration method provided in this application embodiment. In this embodiment, the distribution cloud master station 200 is used as the execution subject for illustration. The distributed PV grid integration method includes steps S210-S260.

[0040] Step S210: Receive the first area connection line power from the area convergence terminal 100.

[0041] Step S220: Collect medium-voltage power grid load data to perform load forecasting and obtain second load forecast data.

[0042] Step S230: Obtain second power generation forecast data by performing power generation forecast on distributed power sources using second load forecast data and meteorological data.

[0043] Specifically, the distribution cloud master station 200 collects medium-voltage power grid load data in real time, combines it with the power of the first distribution area tie line received from the distribution area fusion terminal 100, applies the existing load forecasting model to carry out load forecasting, and obtains the second load forecasting data. In addition, it combines meteorological data and applies the existing power generation forecasting model to carry out power generation forecasting for medium-voltage distributed power sources to obtain the second power generation forecasting model. It should be noted that the forecasting is carried out at intervals within a certain time period, and the load and power generation values ​​are calculated by applying the forecasting algorithm. For example, in this application, the load and power generation are predicted every 15 minutes within 2 hours.

[0044] Step S240: Based on the second load forecast data and the second power generation forecast data, power flow calculation is performed on the distribution line as a unit to obtain the calculation result. The power flow calculation is used to calculate the distribution of active power, reactive power and voltage in the power grid. When performing the power flow calculation, the distribution area is equivalent to a node with known injected active power and reactive power.

[0045] Specifically, the Distribution Cloud Master Station 200 uses the second load forecast data and the second generation forecast data, combined with the medium-voltage distribution network model library data, to perform power flow calculations on a per-distribution-line basis to obtain the results. Power flow calculation refers to calculating the distribution of active power, reactive power, and voltage in the power grid under given power system network topology, component parameters, and generation and load parameters. Power flow calculation determines the steady-state operating parameters of each part of the power system based on the given grid structure, parameters, and operating conditions of components such as generators and loads. The medium-voltage distribution network model library data provides topology, resistance, reactance, and other parameters for calculation. During power flow calculation, the distribution area is equivalent to a PQ node participating in the calculation. PQ nodes are nodes where active power P and reactive power Q are injected by known nodes.

[0046] Step S250: Determine whether there is an operational abnormality in the medium-voltage distribution network based on the calculation results. If an operational abnormality is determined, the power of the second transformer area tie line is obtained through medium-voltage distribution network optimization control. The medium-voltage distribution network optimization control is used to adjust the second control variable based on the second constraint condition to minimize the total operating cost of the distribution network. Operational abnormalities include: overload, power backfeeding to the upper level, and voltage exceeding the limit.

[0047] Specifically, power flow calculations determine the operating parameters of various parts of the power system. Based on these parameters, an assessment is conducted to determine the safe operation of the medium-voltage distribution network. If operational anomalies occur, such as overload, backfeeding of power to higher levels, or voltage exceeding limits, which violate the safe operation requirements of the distribution network, the power of the second transformer interconnection line is obtained through medium-voltage distribution network optimization control. Overload refers to exceeding the rated load, meaning the electrical load in the power system exceeds the actual power of the generator or the rated power of the transformer, causing equipment overload. Voltage exceeding limits refers to voltage that is too high or too low, exceeding the prescribed limits. It can be understood that by comparing the operating results obtained from the power flow calculations, it can be determined whether operational anomalies have occurred. This step ensures the safe operation of the medium-voltage distribution network.

[0048] Once an operational anomaly is identified, a medium-voltage distribution network collaborative optimization service aimed at minimizing distribution network operating costs is initiated through medium-voltage distribution network optimization control. The specific operation method is as follows: obtain the total operating costs of the medium-voltage distribution network, which includes the generation costs of medium-voltage distributed power sources, the operating costs of medium-voltage energy storage devices, the line loss costs of the medium-voltage distribution network, the power costs of the transformer interconnection lines, the operating costs of medium-voltage switches, and the operating costs of capacitor SVC / SVG. The objective function for the operating cost of the medium-voltage distribution network is set. The objective function is then optimized while satisfying the second constraint condition. The optimization aims to minimize the operating cost of the medium-voltage distribution network, and the result is the strategy for the second control variable. The second constraint condition includes: power flow constraints, node voltage constraints, branch transmission power constraints, output constraints of medium-voltage adjustable distributed power sources, output constraints of medium-voltage adjustable distributed energy storage, state of charge (SOC) constraints, capacitor SVG / SVG regulation and capacity constraints, medium-voltage feeder backfeed power constraints, radial distribution network constraints, and medium-voltage switch operation frequency constraints. The second control variable includes: grid-connected power of medium-voltage adjustable distributed power sources, charging and outage power of medium-voltage adjustable distributed energy storage, power of the second transformer area tie line, and the opening and closing status of the medium-voltage switch. Among them, the particle algorithm can be selected for the optimization of the objective function, and the strategy corresponding to the second control variable is adjusted. The grid-connected power of medium-voltage adjustable distributed power source, the charging and outage power of medium-voltage adjustable distributed energy storage, the power of the second transformer area tie line, and the opening and closing status of medium-voltage switch are optimized, thereby further improving the absorption of distributed photovoltaic power in the transformer area and ensuring the economical and safe operation of the medium-voltage distribution network.

[0049] In another embodiment, after obtaining the total operating cost of the medium-voltage distribution network, the method further includes the following steps: removing the transformer interconnection power cost from the total operating cost of the medium-voltage distribution network, and performing medium-voltage distribution network optimization control to confirm whether the safe and economical operation of the medium-voltage power grid is satisfied. Satisfying the safe and economical operation of the medium-voltage power grid means that the optimization objective solution can be obtained under the condition of satisfying the second constraint. If it is not satisfied, the transformer interconnection power cost is included in the total operating cost of the medium-voltage distribution network for medium-voltage distribution network optimization control.

[0050] Specifically, during the optimization control of the distribution cloud master station 200, the optimization calculation is first carried out with the power cost of the transformer area tie line as 0 to confirm whether the safe and economical operation of the medium-voltage power grid is met, that is, to determine whether the optimization can meet the second constraint condition of the power grid. If it is not met, the power cost of the transformer area tie line is included in the objective function to participate in the optimization.

[0051] Step S260: Send the power of the second area communication line to the area convergence terminal 100.

[0052] Specifically, the distribution cloud master station 200, through SVG / SVG optimization control of distributed power sources, energy storage, switches, and capacitors on the medium-voltage side, ensures the economical and safe operation of the medium-voltage distribution network while improving the absorption of distributed photovoltaic power in the distribution area. However, when the self-regulatory optimization of the distribution area fusion terminal 100 fails to absorb the distributed photovoltaic power in its own area, resulting in power being fed back to the medium-voltage distribution network, or when the high penetration of distributed photovoltaic power in the distribution area means that even with medium-voltage distribution network optimization control, the distribution cloud master station 200 still cannot meet the safe operation requirements of the medium-voltage distribution network, the power of the distribution area tie line needs to be further optimized. Therefore, the power of the second distribution area tie line is sent to the distribution area fusion terminal 100. The power of the second distribution area tie line is added to the first constraint condition, and self-regulatory optimization is performed again in the distribution area fusion terminal 100.

[0053] Figure 3 This is a third flowchart of a distributed photovoltaic (PV) grid integration method provided in this application embodiment. In this embodiment, a system including a distribution substation fusion terminal 100 and a distribution cloud master station 200 is used as the execution subject for explanation. (Refer to...) Figure 3 The distributed absorption method includes the following steps:

[0054] In step S310, the integrated terminal 100 of the distribution area collects the load data of each user in the distribution area to perform load prediction and obtain the first load prediction data.

[0055] In step S320, the distribution area fusion terminal 100 obtains the first power generation forecast data by performing power generation forecast on the distributed power source through the first load forecast data and meteorological data.

[0056] In step S330, the distribution area fusion terminal 100 performs autonomous optimization by using the first load forecast data and the first power generation forecast data as the initial operating state. The autonomous optimization is used to adjust the first control variable based on the first constraint condition so that the total cost of the adjustable resources of the distribution area is minimized.

[0057] In step S340, the transformer substation fusion terminal 100 confirms that the autonomous optimization is complete and sends the power of the first transformer substation tie line to the distribution cloud master station 200. The power of the first transformer substation tie line is the power of the tie line between the transformer substation and the medium-voltage feeder.

[0058] In step S350, the power distribution cloud master station 200 receives the first area tie line power from the area convergence terminal 100.

[0059] In step S360, the distribution cloud master station 200 collects medium-voltage power grid load data and combines it with the power of the first transformer area tie line to perform load prediction and obtain the second load prediction data.

[0060] In step S370, the distribution cloud master station 200 obtains the second power generation forecast data by performing power generation forecast on the distributed power source through the second load forecast data and meteorological data.

[0061] In step S380, the power distribution cloud master station 200 performs power flow calculations based on the second load forecast data and the second power generation forecast data, taking the power distribution line as the unit, to obtain the calculation results.

[0062] In step S390, the distribution cloud master station 200 determines whether there is an operational abnormality in the medium-voltage distribution network based on the calculation results. If an operational abnormality is determined, the power of the second transformer area tie line is obtained through medium-voltage distribution network optimization control. The medium-voltage distribution network optimization control is used to adjust the second control variable based on the second constraint condition to minimize the total operating cost of the distribution network. Operational abnormalities include: overload, power backfeeding to the upper level, and voltage exceeding the limit.

[0063] In step S3100, the power distribution cloud master station 200 sends the power of the second transformer area interconnection line to the transformer area fusion terminal 100.

[0064] In step S3110, the distribution area fusion terminal 100 receives the power of the second distribution area tie line from the distribution cloud master station 200, adds the power of the second distribution area tie line to the first constraint condition, and performs self-regulatory optimization again.

[0065] It should be noted that when the distribution area fusion terminal 100 performs its first self-regulatory optimization, it adjusts the first control variable by satisfying the first constraint condition, thereby realizing the absorption of distributed photovoltaic power within the distribution area and sending the power of the first distribution area tie line to the distribution cloud master station 200. After the distribution cloud master station 200 determines that an operational anomaly has occurred, it first removes the distribution area tie line power cost from the total operating cost of the medium-voltage distribution network. Then, it adjusts the second control variable to minimize the operating cost of the medium-voltage distribution network by satisfying the second constraint condition. If the optimal solution can be obtained under the second constraint condition, it means that the optimization control of the distribution master station 200 can meet the safe operation of the medium-voltage distribution network. At this time, the distribution area fusion terminal 100 does not need to perform further self-regulatory optimization. If the optimal solution cannot be obtained under the second constraint, it means that the variable to be optimized cannot be obtained, or that the distribution cloud master station 200 has failed to meet the safe operation of the medium-voltage distribution network through optimization control. At this time, the power cost of the transformer area tie line needs to be included in the total operating cost of the medium-voltage distribution network. That is to say, the power of the second transformer area tie line is used as the second control variable to realize the optimization control of the distribution cloud master station 200. When the minimum value of the total operating cost of the medium-voltage distribution network is obtained, the corresponding power of the second transformer area tie line is sent to the transformer area fusion terminal 100. This is equivalent to telling the transformer area fusion terminal 100 how much power of the second transformer area tie line is needed to maintain the safe operation of the medium and low voltage distribution network. The current power of the transformer area tie line is too high or too low, which leads to unsafe problems. Therefore, the transformer substation fusion terminal 100 adds the received power of the second transformer substation tie line as a constraint to the original first constraint to further perform autonomous optimization. By adjusting the first control variable, the power of the transformer substation tie line is adjusted to achieve the safe operation of the medium and low voltage distribution network. It is understandable that although optimization can also adjust the power of the transformer substation tie line while satisfying the original first constraint, only by adding the power of the second transformer substation tie line to the original first constraint can the optimization know how to adjust it to ensure the safe operation of the medium and low voltage distribution network.

[0066] Furthermore, for ease of calculation, removing the transformer tie line power cost from the total operating cost of the medium-voltage distribution network can be expressed as setting the transformer tie line power cost to 0. In this case, the corresponding power of the second transformer tie line in the second control variable is also 0.

[0067] Reference Figure 3 This application also discloses a distributed photovoltaic (PV) grid connection system, which includes:

[0068] The distribution area fusion terminal 100 is used to collect load data from each user in the distribution area to perform load forecasting and obtain first load forecast data; it is used to perform power generation forecasting for distributed power sources using the first load forecast data and meteorological data to obtain first power generation forecast data; it is used to perform autonomous optimization based on the first load forecast data and the first power generation forecast data, wherein the autonomous optimization is used to adjust the first control variable based on the first constraint condition to minimize the adjustable resources of the distribution area; it is used to determine that the autonomous optimization is completed and send the first distribution area tie line power to the distribution cloud master station 200, wherein the first distribution area tie line power is the tie line power between the distribution area and the medium voltage feeder; it is used to receive the second distribution area tie line power from the distribution cloud master station 200, add the second distribution area tie line power to the first constraint condition, and perform autonomous optimization again.

[0069] The distribution cloud master station 200 is used to receive the high-voltage side power of the first distribution area from the distribution area fusion terminal 100; collect medium-voltage power grid load data to perform load forecasting to obtain second load forecast data; use the second load forecast data and meteorological data to perform generation forecasting for distributed power sources to obtain second generation forecast data; use the second load forecast data and second generation forecast data to perform power flow calculations on a per-distribution-line basis to obtain calculation results, wherein the power flow calculation is used to calculate the distribution of active power, reactive power and voltage in the power grid, and the distribution area is equivalent to a node with known injected active power and reactive power during the power flow calculation; use the calculation results to determine whether there is an operational anomaly in the medium-voltage distribution network, and if an operational anomaly is determined, the power of the second distribution area tie line is obtained through medium-voltage distribution network optimization control, wherein the medium-voltage distribution network optimization control is used to adjust the second control variable based on the second constraint condition to minimize the total operating cost of the distribution network, and operational anomalies include: overload, backfeeding power to the upper level, and voltage exceeding the limit; and use the second distribution area tie line power to send to the distribution area fusion terminal 100.

[0070] This invention also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, causing the computer device to perform... Figure 1 , Figure 2 , Figure 3 The method shown.

[0071] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0072] In addition, embodiments of this application also disclose an electronic device, which includes:

[0073] Includes the processor and memory;

[0074] Memory is used to store programs;

[0075] When at least one program is executed by at least one processor, it implements the distributed photovoltaic consumption method as described in any of the preceding embodiments with the first terminal 100 or the first server 200 as the execution subject.

[0076] This application also discloses a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the distributed photovoltaic consumption method as described in any of the preceding embodiments.

[0077] This application also discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the distributed photovoltaic power consumption method described in any of the preceding embodiments.

[0078] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this invention are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is altered and sub-operations described as part of a larger operation are executed independently.

[0079] Furthermore, although the invention has been described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the described functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the invention. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of conventional skill of an engineer. Therefore, those skilled in the art can implement the invention as set forth in the claims using ordinary techniques without excessive experimentation. It is also understood that the specific concepts disclosed are merely illustrative and not intended to limit the scope of the invention, which is determined by the full scope of the appended claims and their equivalents.

[0080] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0081] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0082] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0083] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0084] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0085] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0086] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for distributed photovoltaic power consumption, characterized in that, Applied to a converged terminal in a distribution area, the method includes: The first load forecast data is obtained by collecting load data from each user in the distribution area and performing load forecasting. The first power generation forecast data is obtained by performing power generation forecast on distributed power sources using the first load forecast data and meteorological data. The system performs autonomous optimization by using the first load forecast data and the first power generation forecast data as the initial operating state. The autonomous optimization is used to adjust the first control variables based on the first constraints to minimize the total cost of the adjustable resources in the distribution area. The first constraints include power balance constraints within the distribution area, node voltage constraints, line branch transmission power constraints, energy storage charging and discharging power constraints and capacity constraints, adjustable distributed photovoltaic / wind power capacity constraints, interruptible load interruption capacity and time constraints, transformer capacity constraints, and distribution area tie line power constraints. The first control variables include adjustable distributed photovoltaic grid-connected power, low-voltage switch position, and energy storage device charging and power outage power. Once the self-regulatory optimization is confirmed to be complete, the power of the first transformer area tie line is sent to the distribution cloud master station, wherein the power of the first transformer area tie line is the power of the tie line between the transformer area and the medium voltage feeder. The second transformer area tie line power is received from the power distribution cloud master station, and the second transformer area tie line power is added to the first constraint condition for further self-regulatory optimization. The second transformer area tie line power is generated by the power distribution cloud master station after optimization control.

2. The distributed photovoltaic power consumption method according to claim 1, wherein the self-regulating optimization includes the following steps: The total cost of adjustable resources is obtained under the operating conditions of the first load forecast data and the first power generation forecast data, wherein the adjustable resources include load shedding, wind curtailment, and solar curtailment; Under the premise of meeting the first constraint, the first control variable is adjusted so that the total cost of the adjustable resource is the lowest price; When the total cost of the adjustable resources is minimized, the high-voltage side power of the first transformer area is obtained.

3. The formula for obtaining the total cost of the adjustable resources in the distributed photovoltaic power consumption method according to claim 2 is as follows: in, , , These are the unit costs for load shedding, wind curtailment, and solar curtailment, respectively. , , They are respectively The total amount of load shedding, wind curtailment, and solar curtailment at all times. Local unit electricity price, For the line loss in the transformer area, Operating costs for charging and discharging the charging device.

4. A method for distributed photovoltaic power consumption, characterized in that, Applied to a power distribution cloud master station, the method includes: Receives the power from the first interconnecting line of the integrated distribution terminal; Collect medium-voltage power grid load data and combine it with the power of the first transformer area's tie line to perform load forecasting and obtain second load forecast data; The second power generation forecast data is obtained by performing power generation forecasting on distributed power sources using the second load forecast data and meteorological data. Based on the second load forecast data and the second power generation forecast data, power flow calculations are performed on a per-distribution-line basis to obtain the calculation results. Based on the calculation results, it is determined whether an operational anomaly has occurred in the medium-voltage distribution network. If the operational anomaly is confirmed, the power of the second transformer area tie line is obtained through medium-voltage distribution network optimization control. The medium-voltage distribution network optimization control is used to adjust the second control variables based on the second constraint conditions to minimize the total operating cost of the distribution network. The operational anomalies include: overload, power backfeeding to the upper level, and voltage exceeding limits. The second constraint conditions include power flow constraints, node voltage constraints, branch transmission power constraints, medium-voltage adjustable distributed power generation output constraints, medium-voltage adjustable distributed energy storage output constraints, nuclear state of charge (SOC) constraints, capacitor SVG / SVG regulation and capacity constraints, medium-voltage feeder backfeeding power constraints, medium-voltage distribution network radial constraints, and medium-voltage switch operation frequency constraints. The second control variables include: medium-voltage adjustable distributed power generation grid-connected power, medium-voltage adjustable distributed energy storage charging and outage power, second transformer area tie line power, and medium-voltage switch opening and closing status. Send the power of the second area communication line to the area convergence terminal.

5. The distributed photovoltaic power consumption method according to claim 4, wherein the medium-voltage distribution network optimization control includes the following steps: Obtain the total operating cost of the medium-voltage distribution network, wherein the operating cost of the medium-voltage distribution network includes the power cost of the transformer area tie lines; Under the premise of meeting the second constraint, the second control variable is adjusted to minimize the total operating cost of the medium-voltage distribution network, wherein the second control variable includes the power of the second transformer area tie line.

6. The distributed photovoltaic power consumption method according to claim 5, further comprising, after obtaining the total operating costs of the medium-voltage distribution network: Remove the transformer interconnection power cost from the total operating cost of the medium-voltage distribution network, and perform medium-voltage distribution network optimization control to confirm whether the safe and economical operation of the medium-voltage power grid is satisfied. Satisfying the safe and economical operation of the medium-voltage power grid means that the optimization objective solution can be obtained under the second constraint condition of the distribution network. If the conditions are not met, the power cost of the aforementioned transformer area connection will be included in the total operating cost of the medium-voltage distribution network for medium-voltage distribution network optimization control.

7. A method for distributed photovoltaic power consumption, characterized in that, include: The integrated terminal in the distribution area collects load data from each user in the distribution area to perform load prediction and obtain the first load prediction data. The integrated terminal of the distribution area obtains the first power generation prediction data by performing power generation prediction on the distributed power source using the first load prediction data and meteorological data. The integrated distribution terminal performs autonomous optimization by using the first load forecast data and the first power generation forecast data as the initial operating state. The autonomous optimization is used to adjust the first control variables based on the first constraints to minimize the total cost of the adjustable resources in the distribution area. The first constraints include power balance constraints within the distribution area, node voltage constraints, line branch transmission power constraints, energy storage charging and discharging power constraints and capacity constraints, adjustable distributed photovoltaic / wind power capacity constraints, interruptible load interruption capacity and time constraints, transformer capacity constraints, and distribution area tie line power constraints. The first control variables include adjustable distributed photovoltaic grid-connected power, low-voltage switch position, and energy storage device charging and power outage power. The integrated terminal of the distribution area determines that the self-regulatory optimization is completed and sends the power of the first distribution area tie line to the distribution cloud master station, wherein the power of the first distribution area tie line is the power of the tie line between the distribution area and the medium voltage feeder. The power distribution cloud master station receives the power from the first transformer area interconnection line from the transformer area integration terminal; The power distribution cloud master station collects medium-voltage power grid load data and combines it with the power of the first transformer area tie line to perform load prediction and obtain second load prediction data. The power distribution cloud master station obtains the second power generation forecast data by performing power generation forecast on distributed power sources using the second load forecast data and meteorological data. The power distribution cloud master station performs power flow calculations on a per-distribution-line basis based on the second load forecast data and the second power generation forecast data. The distribution cloud master station determines whether the medium-voltage distribution network is experiencing operational anomalies based on the calculation results. If an operational anomaly is confirmed, the power of the second transformer area tie line is obtained through medium-voltage distribution network optimization control. The medium-voltage distribution network optimization control is used to minimize the total operating cost of the distribution network by adjusting the second control variables based on second constraints. The operational anomalies include: overload, power backfeeding to the upper level, and voltage exceeding limits. The second constraints include power flow constraints, node voltage constraints, branch transmission power constraints, medium-voltage adjustable distributed power generation output constraints, medium-voltage adjustable distributed energy storage output constraints, nuclear state of charge (SOC) constraints, capacitor SVG / SVG adjustment and capacity constraints, medium-voltage feeder backfeeding power constraints, medium-voltage distribution network radial constraints, and medium-voltage switch operation frequency constraints. The second control variables include: medium-voltage adjustable distributed power generation grid-connected power, medium-voltage adjustable distributed energy storage charging / outage power, second transformer area tie line power, and medium-voltage switch on / off status. The power distribution cloud master station sends the power of the second transformer area interconnection line to the transformer area fusion terminal; The integrated distribution terminal receives the power of the second distribution area tie line from the distribution cloud master station, adds the power of the second distribution area tie line to the first constraint condition, and performs self-regulatory optimization again.

8. A distributed photovoltaic (PV) grid connection system, characterized in that, include: The transformer area convergence terminal is used to collect load data from each user in the transformer area to perform load forecasting and obtain the first load forecast data. This is used to obtain the first power generation forecast data by performing power generation forecasting on distributed power sources using the first load forecasting data and meteorological data. This system is used to perform autonomous optimization by using first load forecast data and first power generation forecast data as the initial operating state. The autonomous optimization is used to adjust a first control variable based on a first constraint condition to minimize the adjustable resources of the distribution area. It is also used to determine when the autonomous optimization is complete and send the first distribution area tie-line power to the distribution cloud master station, where the first distribution area tie-line power is the tie-line power between the distribution area and the medium-voltage feeder. Furthermore, it is used to receive the second distribution area tie-line power from the distribution cloud master station, add the second distribution area tie-line power to the first constraint condition, and perform autonomous optimization again, where the second distribution area tie-line power is generated by the distribution cloud master station after optimization control. The first constraint conditions include power balance constraints within the distribution area, node voltage constraints, line branch transmission power constraints, energy storage charging and discharging power constraints and capacity constraints, adjustable distributed photovoltaic / wind power capacity constraints, interruptible load interruption capacity and time constraints, transformer capacity constraints and distribution area tie line power constraints. The first control variables include adjustable distributed photovoltaic grid-connected power, low-voltage switch position and energy storage device charging and power outage power. The distribution cloud master station is used to receive the first distribution area tie-line power from the distribution area fusion terminal; collect medium-voltage power grid load data and combine it with the first distribution area tie-line power to perform load forecasting to obtain second load forecast data; perform distributed generation forecasting based on the second load forecast data and meteorological data to obtain second generation forecast data; perform power flow calculations on a per-distribution-line basis according to the second load forecast data and the second generation forecast data to obtain calculation results; determine whether the medium-voltage distribution network has an operational anomaly based on the calculation results; if the operational anomaly is determined, obtain the second distribution area tie-line power through medium-voltage distribution network optimization control, wherein the medium-voltage distribution network optimization control is used to adjust the second control variable based on the second constraint condition to minimize the total distribution network operating cost; the operational anomaly includes: overload, power backfeeding to the upper level, and voltage exceeding the limit; and send the second distribution area tie-line power to the distribution area fusion terminal. The second constraint includes power flow constraints, node voltage constraints, branch transmission power constraints, medium-voltage adjustable distributed power generation output constraints, medium-voltage adjustable distributed energy storage output constraints, nuclear state of charge (SOC) constraints, capacitor SVG / SVG regulation and capacity constraints, medium-voltage feeder backfeed power constraints, medium-voltage distribution network radial constraints, and medium-voltage switch operation frequency constraints. The second control variables include: medium-voltage adjustable distributed power generation grid-connected power, medium-voltage adjustable distributed energy storage charging and shutdown power, second transformer area tie line power, and medium-voltage switch opening and closing status.

9. An electronic device, characterized in that, Including the processor and memory; The memory is used to store programs; The processor executes the program to implement the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a program that is executed by a processor to implement the method as described in any one of claims 1 to 7.

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