Distributed photovoltaic cluster-oriented power distribution network collaborative optimization control method

Abstract

The invention discloses a distributed photovoltaic cluster-oriented power distribution network collaborative optimization control method, relates to the technical field of power distribution network optimization, and solves the technical problem that resources are unreasonably used in an optimization process due to the fact that comprehensive optimization processing is not performed in combination with actual conditions and power distribution network data. According to the method, power distribution network parameters are compared, abnormal conditions are analyzed, power parameters and voltage parameters are analyzed during analysis, photovoltaic clusters are divided into different levels according to the power parameters, and analysis is carried out based on regional power supply power of different levels; according to the method, comprehensive processing is performed in combination with corresponding load periods, and different power supply processing is performed for different load periods, so that on one hand, reasonable utilization of resources can be realized, energy consumption can be saved, and on the other hand, healthy and sustainable development of distributed photovoltaic power generation can be promoted through reasonable adjustment and optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of distribution network optimization, and in particular to a distribution network collaborative optimization control method for distributed photovoltaic clusters. Background Art

[0002] At present, more and more distributed photovoltaic power generation systems are connected to the grid and generate electricity, and the penetration rate of distributed photovoltaics in active distribution networks is gradually increasing. Since the output of distributed photovoltaic power generation systems is very susceptible to weather conditions and sunshine intensity, there will be large power fluctuations. The power fluctuations and intermittent characteristics of distributed photovoltaics will make the voltage limit problem of the distribution network more and more serious.

[0003] According to the application number and Chinese publication number CN117767291A, a coordinated optimization control method for a distributed photovoltaic distribution network is disclosed, including obtaining data information of a target distributed photovoltaic distribution network; constructing a coordinated optimization control model of the target distributed photovoltaic distribution network; constructing a decision network; and realizing coordinated optimization control of the target distributed photovoltaic distribution network based on the constructed coordinated optimization control model and decision network.

[0004] When some existing distribution network collaborative optimization control systems are used, they do not perform comprehensive optimization processing based on actual conditions and corresponding distribution network data for distribution network power supply processing in different situations, which will further lead to unreasonable overall optimization and waste of resources. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a distribution network collaborative optimization control method for distributed photovoltaic clusters, which solves the problem of unreasonable use of resources during the optimization process without combining actual conditions and distribution network data for comprehensive optimization processing.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a distribution network collaborative optimization control method for distributed photovoltaic clusters, the method specifically comprising the following steps:

[0007] Step 1: Acquire the distribution network parameters, monitor the distribution network parameters and generate monitoring information, and the monitoring information includes normal monitoring signals and abnormal management signals;

[0008] Step 2: Analyze the power parameters in the generated abnormal management signal, obtain the power parameters in the distribution network parameters, and perform corresponding load regulation control according to the specific values ​​of the power parameters, and generate regulation control information at the same time;

[0009] Step 3: Analyze the obtained regulation control information, and adjust different load periods in combination with the power grid big data, and generate period regulation information by combining different methods of regulation according to the corresponding load power;

[0010] Step 4: Analyze the voltage parameters in the generated abnormal management signal, analyze the reactive output of the distributed power source, and adjust based on the standard parameters to generate corresponding adjustment control information.

[0011] As a further solution of the present invention: the specific method of generating monitoring information in step 1 is:

[0012] The distribution network parameters are acquired, including power parameters and voltage parameters, and then the acquired distribution network parameters are compared with the standard parameters. When the distribution network parameters are different from the standard parameters, an abnormal management signal is generated. Conversely, when the distribution network parameters are the same as the standard parameters, a normal monitoring signal is generated.

[0013] As a further solution of the present invention: the specific method of analyzing the power parameters in the abnormal management signal in step 2 is:

[0014] Screen the abnormal parameters in the distribution network parameters, calculate the difference between the screened abnormal parameters and the standard parameters, and generate difference information. Then, the photovoltaic clusters of the distribution network are layered to obtain the top layer, upper load and bottom load;

[0015] Then the upper and lower loads are analyzed, and whether they can be adjusted is determined based on the corresponding load values. The layer area that meets the adjustment requirements is selected and recorded as the target layer area. Then the power supply area in the target layer area is obtained and labeled as a, and a=1, 2, ..., b, where b represents the number of power supply areas, and the power supply area a is analyzed at the same time.

[0016] As a further solution of the present invention: the specific method of analyzing the power supply area in step 2 is:

[0017] Obtain all power supply areas a, and obtain the real-time load power corresponding to the power supply area a, record it as Ga, and judge the real-time load power Ga, then obtain the maximum load power corresponding to the power supply area a, record it as Gmax, and calculate the difference between the real-time load power Ga and the maximum load power Gmax, record it as the load difference to be adjusted, and sort them from large to small according to the load difference to be adjusted, then obtain the difference information, divide the difference evenly to obtain the average difference, and adjust the two power supply areas with the largest load difference values ​​​​based on the average difference, and generate adjustment control information at the same time.

[0018] As a further solution of the present invention: the specific method of analyzing the adjustment control information in step 3 is:

[0019] Obtaining power grid big data, and dividing the load period according to the power grid big data to obtain load period information, and the load period information includes: load valley period and load peak period, and then analyzing the load valley period and load peak period respectively;

[0020] Analyze the current time period and determine the corresponding load time period. If the current time period is a low load period, distributed power generation is used preferentially for power generation. At the same time, the distributed power generation is analyzed to obtain the load change during the low load period, and the load power corresponding to the stable load change is selected as the stable load power. Then, the distributed power source is adjusted based on the stable load power, and the time period control information is generated.

[0021] As a further solution of the present invention: the specific method of analyzing the load valley period and the load peak period in step 3 is:

[0022] If the current period is a peak load period, power generation is performed by combining distributed power sources and traditional power sources to obtain the maximum load power corresponding to the peak load period, and the load power corresponding to the distributed power sources is analyzed to obtain the stable load power corresponding to the distributed power sources, which is recorded as the pre-adjustment power. The difference between the pre-adjustment power and the maximum load power is then calculated and recorded as the adjustment difference. The load power of the traditional power source is adjusted based on the adjustment difference, and time period control information is generated.

[0023] As a further solution of the present invention: the specific method of analyzing the voltage parameters in the abnormal management signal in step 4 is:

[0024] The real-time voltage of the current distributed power source is obtained, and the power factor corresponding to the distributed power source is obtained and recorded as the current power factor. Then the standard power factor is obtained, and the current power factor is adjusted with the standard power factor to generate power adjustment information. Then the real-time voltage of the current distributed power source is adjusted with the power adjustment information, and adjustment control information is generated.

[0025] The present invention provides a distribution network coordinated optimization control method for distributed photovoltaic clusters. Compared with the prior art, it has the following beneficial effects:

[0026] The present invention compares the distribution network parameters and analyzes the abnormal situations. It analyzes the power parameters and voltage parameters during the analysis, divides the photovoltaic clusters into different levels according to the power parameters, and analyzes based on the regional power supply power of different levels. It performs comprehensive processing in combination with the corresponding load time periods, and performs different power supply processing for different load time periods. On the one hand, it can realize the rational use of resources and save energy consumption. On the other hand, reasonable adjustment and optimization can promote the healthy and sustainable development of distributed photovoltaic power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 is a flow chart of the method of the present invention;

[0028] Figure 2 This is a three-level load hierarchy structure diagram of the present invention. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0030] See also Figure 1 and Figure 2 The present application provides a distribution network coordinated optimization control method for distributed photovoltaic clusters, which specifically includes the following steps:

[0031] Step 1: Acquire the distribution network parameters, monitor the distribution network parameters and generate monitoring information, and the monitoring information includes normal monitoring signals and abnormal management signals. The specific method of generating the monitoring information is:

[0032] The distribution network parameters are obtained, wherein the distribution network parameters include power parameters and voltage parameters, and then the obtained distribution network parameters are compared with standard parameters, and the standard parameters refer to the power parameters and voltage parameters corresponding to the normal operation of the distribution network. At the same time, the specific values ​​of the standard parameters are set by the operator. When the distribution network parameters are different from the standard parameters, it means that there is an abnormality in the distribution network parameters, and the distribution network needs to be adjusted, and an abnormal management signal is generated. On the contrary, when the distribution network parameters are the same as the standard parameters, it means that there is no abnormality in the distribution network parameters, and the distribution network does not need to be adjusted, and a normal monitoring signal is generated;

[0033] Assume that in a distribution network, operators set the standard power parameters to active power fluctuations within a specific range, such as between 1000 kW and 1200 kW, and the power factor is kept above 0.9 based on experience and relevant standards; the standard voltage parameters are set to phase voltage of 220 volts, with an allowable deviation of ±10%. After the parameters are acquired, if the active power is detected to be 1300 kW, which exceeds the standard range, or the power factor is lower than 0.9, or the phase voltage is 200 volts, which is lower than the minimum value of the standard, then it is determined that the distribution network parameters are abnormal and an abnormal management signal needs to be generated. For example, if the active power is 1100 kW, the power factor is 0.95, and the phase voltage is 215 volts, then it means that the distribution network parameters are normal, a normal monitoring signal is generated, and the distribution network continues to be routinely monitored.

[0034] Step 2: Analyze the power parameters in the generated abnormal management signal, obtain the power parameters in the distribution network parameters, and perform corresponding load regulation control according to the specific values ​​of the power parameters, and generate regulation control information at the same time. The specific method of generating the regulation control information is:

[0035] The abnormal parameters in the distribution network parameters are screened, and the power parameter abnormalities in the distribution network parameters are analyzed as the target here, and the screened abnormal parameters are calculated with the standard parameters, and the difference information is generated at the same time. The difference information here indicates whether the power parameter is increased or decreased. Then the distribution network photovoltaic cluster is layered to obtain the top layer, upper load and bottom load. The specific layering method is as shown in the attached figure. Figure 2 As shown in the figure, L2 has a total of h nodes, and the number of child nodes under the jth node is m j The bottom nodes are numbered in sequence. Specifically, the child nodes of M1 are power supply areas 1 to m1, and the child nodes of M2 are power supply areas m1+1 to m1+m. 2， Similarly, n is defined as the total number of bottom-level L1 power supply areas, that is, At the same time, it can be seen that the load is accumulated layer by layer, the upper layer load includes the lower layer load associated with it, and the load of the parent node and the load of the child node have aggregation consistency. Based on the above analysis, n load forecasting models are built, corresponding to n underlying power supply areas respectively. First, the prediction accuracy of the underlying area is considered, and the overall load forecasting objective function is defined as:

[0036]

[0037] Where: f i (·) represents the load forecast result of L1 power supply area i; θ i and x i Represent model parameters and model input features respectively; Indicates the actual load value of L1 power supply area i.

[0038] Then, the upper layer load and the bottom layer load are analyzed, and whether they can be adjusted is determined according to the corresponding load values. The determination here is based on the corresponding difference information. If the difference information is an increase, the layer area that can be adjusted for increase is screened for analysis. On the contrary, if the difference information is a decrease, the layer area that can be adjusted for decrease is screened for analysis, and the layer area that meets the adjustment requirements is selected and recorded as the target layer area. Then, the power supply area in the target layer area is obtained and labeled as a, and a=1, 2, ..., b, where b represents the number of power supply areas;

[0039] Obtain all power supply areas a, and obtain the real-time load power corresponding to the power supply area a, record it as Ga, and judge the real-time load power Ga, then obtain the maximum load power corresponding to the power supply area a, record it as Gmax, and calculate the difference between the real-time load power Ga and the maximum load power Gmax, record it as the load difference to be adjusted, and sort them from large to small according to the load difference to be adjusted, then obtain the difference information, divide the difference evenly to obtain the average difference, and adjust the two power supply areas with the largest load difference values ​​​​based on the average difference, and generate adjustment control information at the same time.

[0040] Assume that there are three power supply areas A1, A2 and A3. Through monitoring, it is obtained that the real-time load power of A1 is 800 kilowatts, the real-time load power of A2 is 1000 kilowatts, and the real-time load power of A3 is 1200 kilowatts. The corresponding maximum load powers are Gmax1 = 1000 kilowatts, Gmax2 = 1200 kilowatts, and Gmax3 = 1500 kilowatts. Then the calculation shows that the load difference to be adjusted for A1 is 1000-800 = 200 kilowatts; the load difference to be adjusted for A2 is 1200-1000 = 200 kilowatts; and the load difference to be adjusted for A3 is 1500-1200 = 300 kilowatts. These load differences to be adjusted are sorted from large to small as A3, A1 and A2. Assuming that the average difference is 150 kilowatts, A3 with the largest load difference to be adjusted and A1 (or A2) with the second largest value can be adjusted.

[0041] Step 3: Analyze the obtained regulation control information, and adjust different load periods in combination with the power grid big data. Generate period regulation information by combining different methods of regulation according to the corresponding load power. The specific method of generating period regulation information is as follows:

[0042] Obtain power grid big data, and divide the load time periods according to the power grid big data to obtain load time period information, and the load time period information includes: load valley period and load peak period, and the specific division standard is to collect a large amount of power grid big data through smart meters, sensors and other equipment, including power consumption, power factor, voltage and other data in different time periods, and observe the change trend of power consumption over time. If it remains at a low level for a period of time, then this time period can be divided into a load valley period. If the power consumption rises sharply and is much higher than other time periods, then these time periods can be divided into load peak periods, and then the load valley period and load peak period are analyzed separately;

[0043] For example, for a residential area, the load trough period may be from 11pm to 6am the next day, because most residents are resting at this time and use less electrical equipment. On the other hand, the load peak period may be from 7am to 9am and from 6pm to 9pm, because residents get up to go to work, go home after get off work to cook, etc., and use electrical equipment frequently.

[0044] Analyze the current time period and determine the corresponding load time period. If the current time period is a low load period, distributed power generation is used preferentially for power generation. At the same time, the distributed power generation is analyzed to obtain the load change during the low load period, and the load power corresponding to the stable load change is selected as the stable load power. Then, the distributed power source is adjusted based on the stable load power, and the time period control information is generated.

[0045] If the current period is a peak load period, power generation is performed by combining distributed power sources and traditional power sources to obtain the maximum load power corresponding to the peak load period, and the load power corresponding to the distributed power sources is analyzed to obtain the stable load power corresponding to the distributed power sources, which is recorded as the pre-adjustment power. The difference between the pre-adjustment power and the maximum load power is then calculated and recorded as the adjustment difference. The load power of the traditional power source is adjusted based on the adjustment difference, and time period control information is generated.

[0046] For example: suppose there is a regional power grid system. After analyzing the current time period, it is determined that 3 a.m. is a low load period. At this time, the power generation of the distributed power source is monitored and analyzed, and it is found that the load change is relatively stable over a period of time, and the corresponding load power is 500 kilowatts, which is recorded as the stable load power. Subsequently, the distributed power source is adjusted according to this stable load power, such as adjusting its output power to make it more efficient to meet the current load demand, and the corresponding time period regulation information is generated to record the situation and effect of this regulation.

[0047] For example, at 6 pm, it is determined to be the peak load period. Through monitoring, it is known that the maximum load power during this period is 5,000 kilowatts. After analyzing the load power of the distributed power source, it is determined that its stable load power is 2,000 kilowatts, which is recorded as the pre-adjustment power. The difference between the pre-adjustment power and the maximum load power is calculated to be 3,000 kilowatts (adjustment difference). Then, based on this difference, the load power of the traditional power source is adjusted, such as increasing the output power of the traditional power plant to ensure that it can meet the power demand during peak hours.

[0048] Step 4: Analyze the voltage parameters in the generated abnormal management signal, analyze the reactive output of the distributed power source, and generate corresponding regulation control information based on the standard parameters. The specific method of generating the regulation control information is as follows:

[0049] The real-time voltage of the current distributed power source is obtained, and the power factor corresponding to the distributed power source is obtained and recorded as the current power factor. Then the standard power factor is obtained, and the standard power factor here represents the power factor corresponding to the normal operation of the distributed power source. At the same time, the specific value is set by the operator, and the current power factor is adjusted with the standard power factor to generate power adjustment information. Then, the real-time voltage of the current distributed power source is adjusted with the power adjustment information, and the adjustment control information is generated.

[0050] Assume that there is a solar distributed power system. The real-time voltage of the current distributed power source is 218 volts obtained through a high-precision voltage sensor. At the same time, the current power factor is measured to be 0.85 using an intelligent power factor meter. The operator sets the standard power factor to 0.92 based on the rated capacity of the solar distributed power source, the requirements of the grid to which it is connected, and previous operating experience. With the standard power factor of 0.92 as the target, the current power factor of 0.85 is adjusted. The power factor can be improved by controlling the output reactive power of the solar inverter. Power regulation information is generated, such as the need to increase a certain amount of capacitive reactive output. Based on this information, the real-time voltage of the current distributed power source is adjusted. Since increasing the capacitive reactive output may slightly increase the voltage, the output parameters of the inverter are adjusted through the intelligent control system at this time to stabilize the real-time voltage within a reasonable range of about 220 volts. At the same time, the regulation control information is generated to record that the power factor is increased from 0.85 to 0.91 and the voltage is adjusted from 218 volts to 220 volts in this regulation.

[0051] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0052] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A distribution network collaborative optimization control method for distributed photovoltaic clusters, characterized in that: The method specifically comprises the following steps: Step 1: Acquire the distribution network parameters, monitor the distribution network parameters and generate monitoring information, and the monitoring information includes normal monitoring signals and abnormal management signals; Step 2: Analyze the power parameters in the generated abnormal management signal, obtain the power parameters in the distribution network parameters, and perform corresponding load regulation control according to the specific values ​​of the power parameters, and generate regulation control information at the same time; Step 3: Analyze the obtained regulation control information, and adjust different load periods in combination with the power grid big data, and generate period regulation information by combining different methods of regulation according to the corresponding load power; Step 4: Analyze the voltage parameters in the generated abnormal management signal, analyze the reactive output of the distributed power source, and adjust based on the standard parameters to generate corresponding adjustment control information.

2. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 1 is characterized in that: The specific method of generating monitoring information in step 1 is: The distribution network parameters are acquired, including power parameters and voltage parameters, and then the acquired distribution network parameters are compared with the standard parameters. When the distribution network parameters are different from the standard parameters, an abnormal management signal is generated. Conversely, when the distribution network parameters are the same as the standard parameters, a normal monitoring signal is generated.

3. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 1 is characterized in that: The specific method of analyzing the power parameters in the abnormal management signal in step 2 is: Screen the abnormal parameters in the distribution network parameters, calculate the difference between the screened abnormal parameters and the standard parameters, and generate difference information. Then, the photovoltaic clusters of the distribution network are layered to obtain the top layer, upper load and bottom load; Then the upper and lower loads are analyzed, and whether they can be adjusted is determined based on the corresponding load values. The layer area that meets the adjustment requirements is selected and recorded as the target layer area. Then the power supply area in the target layer area is obtained and labeled as a, and a=1, 2, ..., b, where b represents the number of power supply areas, and the power supply area a is analyzed at the same time.

4. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 3 is characterized in that: The specific method of analyzing the power supply area in step 2 is: Obtain all power supply areas a, and obtain the real-time load power corresponding to the power supply area a, record it as Ga, and judge the real-time load power Ga, then obtain the maximum load power corresponding to the power supply area a, record it as Gmax, and calculate the difference between the real-time load power Ga and the maximum load power Gmax, record it as the load difference to be adjusted, and sort them from large to small according to the load difference to be adjusted, then obtain the difference information, divide the difference evenly to obtain the average difference, and adjust the two power supply areas with the largest load difference values ​​​​based on the average difference, and generate adjustment control information at the same time.

5. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 1 is characterized in that: The specific method of analyzing the adjustment control information in step 3 is: Obtaining power grid big data, and dividing the load period according to the power grid big data to obtain load period information, and the load period information includes: load valley period and load peak period, and then analyzing the load valley period and load peak period respectively; Analyze the current time period and determine the corresponding load time period. If the current time period is a low load period, distributed power generation is used preferentially for power generation. At the same time, the distributed power generation is analyzed to obtain the load change during the low load period, and the load power corresponding to the stable load change is selected as the stable load power. Then, the distributed power source is adjusted based on the stable load power, and the time period control information is generated.

6. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 5 is characterized in that: The specific method for analyzing the load valley period and the load peak period in step 3 is as follows: If the current period is a peak load period, power generation is performed by combining distributed power sources and traditional power sources to obtain the maximum load power corresponding to the peak load period, and the load power corresponding to the distributed power sources is analyzed to obtain the stable load power corresponding to the distributed power sources, which is recorded as the pre-adjustment power. The difference between the pre-adjustment power and the maximum load power is then calculated and recorded as the adjustment difference. The load power of the traditional power source is adjusted based on the adjustment difference, and time period control information is generated.

7. The distribution network coordinated optimization control method for distributed photovoltaic clusters according to claim 1 is characterized in that: The specific method of analyzing the voltage parameters in the abnormal management signal in step 4 is: The real-time voltage of the current distributed power source is obtained, and the power factor corresponding to the distributed power source is obtained and recorded as the current power factor. Then the standard power factor is obtained, and the current power factor is adjusted with the standard power factor to generate power adjustment information. Then the real-time voltage of the current distributed power source is adjusted with the power adjustment information, and adjustment control information is generated.

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