Optimized regulation and control absorption method for distributed photovoltaic in power distribution area

Abstract

The invention relates to a power distribution area distributed photovoltaic optimization regulation and control consumption method, which comprises the following steps of: firstly, acquiring sunshine data of the day by using an environment acquisition module through position information of a photovoltaic power station, so as to establish a power generation prediction model based on power data and sunshine data of the photovoltaic power station; calculating and obtaining an optimal photovoltaic absorption path according to the position of the photovoltaic access point and the position of the load node, and then determining a node voltage maximum value and a distributed photovoltaic installation capacity corresponding to the node voltage maximum value according to the active power of the distributed photovoltaic, the active power of the load node of the power distribution network and the reactive power of the load node of the power distribution network; and finally, outputting a photovoltaic regulation and control instruction and an energy storage regulation and control instruction in the power distribution network through a photovoltaic energy storage linkage regulation and control model in combination with the photovoltaic power generation and load change curve, thereby outputting photovoltaic electric quantity to a regulation and control object along the optimal photovoltaic absorption path according to the regulation and control instruction. The invention has the advantages of stable and reliable use, safety and high efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power grid consumption, and in particular relates to a method for optimizing the control and consumption of distributed photovoltaic power in a distribution station area. Background Art

[0002] With the country building a new power system with new energy as the main body, and the start of the work of promoting rooftop distributed photovoltaics in the whole county, the development of distributed photovoltaics has ushered in new opportunities, and the installed capacity has maintained a rapid growth trend; however, photovoltaic power generation has the characteristics of volatility, randomness, intermittency and time mismatch. Compared with thermal power and hydropower, it brings greater challenges to the safe and stable operation of the power grid. When the adjustable resources of the power grid are insufficient, it is inevitable that there will be a phenomenon of abandonment of light and wind, resulting in the inability to fully absorb photovoltaic power. Traditional solutions may simply limit the amount of photovoltaic grid-connected power generation or use energy storage equipment to adjust the power, but these methods have problems such as low efficiency and high cost, and cannot timely predict and peak the power generation of dispersed photovoltaic power stations, which makes it easy for power grid fluctuations to occur in the process of absorbing fluctuating photovoltaic power generation, resulting in high cost of distributed photovoltaic absorption in distribution networks, and low efficiency and reliability; therefore, in order to solve the above problems, it is necessary to develop an optimization regulation and absorption method for distributed photovoltaics in distribution stations that uses stable, reliable, safe and efficient distribution stations. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art and to provide a stable, reliable, safe and efficient method for optimizing the control and consumption of distributed photovoltaic power in a power distribution area.

[0004] The object of the present invention is achieved as follows: a method for optimizing the control and consumption of distributed photovoltaic power in a distribution station area, step 1: obtaining the location information, external environment information and power data information of each photovoltaic power station in the distribution station area;

[0005] Step 2: The sunshine data of the day corresponding to the location information of the photovoltaic power station is collected through the environment collection module, and then the sunshine data is transmitted to the power generation prediction module. The power generation prediction module predicts the photovoltaic power generation according to the power data and sunshine data of the photovoltaic power station, thereby obtaining multiple influencing factor data sets and multiple output sets of the same period with the multiple influencing factor data sets. Finally, the multiple influencing factor data sets are respectively input into the basic model to obtain the power generation prediction model;

[0006] Step 3: Collect the locations of photovoltaic access points and load nodes in the distribution network, construct a network diagram of photovoltaic access points and load nodes, and thus obtain the optimal photovoltaic consumption path;

[0007] Step 4: Based on the line parameters extracted from the distribution network, the power output of the power source point and the node load, the active power on each branch is obtained through power flow calculation. The maximum node voltage and the distributed photovoltaic installation capacity corresponding to the maximum node voltage are determined based on the active power of distributed photovoltaics, the active power of the distribution network load node and the reactive power of the distribution network load node;

[0008] Step 5: Combine the forecast data of distributed photovoltaic power generation and load at the dual scales of week before and day before, and draw a change curve;

[0009] Step 6: Construct a photovoltaic energy storage linkage control model, and input the load data of each node and the control instruction data of photovoltaic energy storage, so as to output the photovoltaic control instructions and energy storage control instructions in the distribution network;

[0010] Step 7: Output photovoltaic power to the control object along the optimal photovoltaic consumption path according to the control instructions;

[0011] Furthermore, the external environment information in step 1 includes ambient temperature and wind speed information.

[0012] Furthermore, in step 4, the active power of the distribution network load node can be determined according to the active power of the distribution network feeder root node, the initial active power of the distributed photovoltaic, the proportion of the distribution network active power loss to the load active power and the number of distribution network load nodes.

[0013] Furthermore, in step 4, the reactive power of the load nodes of the distribution network can be determined according to the reactive power of the root node of the distribution network feeder, the proportion of the reactive power loss of the distribution network to the reactive power of the load, and the number of load nodes in the distribution network.

[0014] Furthermore, in step 3, both the photovoltaic access point and the load node are equipped with intelligent metering devices, and the intelligent metering devices, as edge computing units, have a two-way power measurement function and can read the power purchase and sales data of the nodes.

[0015] Beneficial effects of the present invention: The present invention first collects the sunshine data of the day through the location information of the photovoltaic power station and the environment collection module, thereby establishing a power generation prediction model based on the power data and sunshine data of the photovoltaic power station, and then calculates and obtains the optimal photovoltaic consumption path according to the location of the photovoltaic access point of the distribution network and the location of the load node of the distribution network, and then determines the reactive power of the load node of the distribution network according to the reactive power of the root node of the distribution network feeder, the proportion of the reactive power loss of the distribution network to the reactive power of the load and the number of load nodes of the distribution network, and calculates the reactive power of the load node of the distribution network according to the active power of the root node of the distribution network feeder, the initial active power of the distributed photovoltaic, and the active power of the distribution network. The active power of the distribution network load node is determined by the ratio of power loss to load active power and the number of distribution network load nodes, and finally the maximum node voltage and the distributed photovoltaic installation capacity corresponding to the maximum node voltage are determined according to the active power of distributed photovoltaic, the active power of the distribution network load node and the reactive power of the distribution network load node; finally, combined with the change curve of photovoltaic power generation and load, the photovoltaic control instructions and energy storage control instructions in the distribution network are output through the photovoltaic energy storage linkage control model, so as to output the photovoltaic power to the control object according to the control instructions and along the optimal photovoltaic consumption path; in general, the present invention has the advantages of stable and reliable use, safety and high efficiency. DETAILED DESCRIPTION

[0016] The present invention is further described below.

[0017] Embodiment: A method for optimizing, regulating and absorbing distributed photovoltaic power in a distribution area, step 1: obtaining location information, external environment information and power data information of each photovoltaic power station in the distribution area;

[0018] Step 2: The sunshine data of the day corresponding to the location information of the photovoltaic power station is collected through the environment collection module, and then the sunshine data is transmitted to the power generation prediction module. The power generation prediction module predicts the photovoltaic power generation according to the power data and sunshine data of the photovoltaic power station, thereby obtaining multiple influencing factor data sets and multiple output sets of the same period as the multiple influencing factor data sets. Finally, the multiple influencing factor data sets are respectively input into the basic model to obtain the power generation prediction model;

[0019] Step 3: Collect the locations of photovoltaic access points and load nodes in the distribution network, construct a network diagram of photovoltaic access points and load nodes, and thus obtain the optimal photovoltaic consumption path;

[0020] Step 4: Based on the line parameters extracted from the distribution network, the power output of the power source point and the node load, the active power on each branch is obtained through power flow calculation. The maximum node voltage and the distributed photovoltaic installation capacity corresponding to the maximum node voltage are determined based on the active power of distributed photovoltaics, the active power of the distribution network load node and the reactive power of the distribution network load node;

[0021] Step 5: Combine the forecast data of distributed photovoltaic power generation and load at the dual scales of week before and day before, and draw a change curve;

[0022] Step 6: Construct a photovoltaic energy storage linkage control model, and input the load data of each node and the control instruction data of photovoltaic energy storage, so as to output the photovoltaic control instructions and energy storage control instructions in the distribution network;

[0023] Step 7: Output photovoltaic power to the control object along the optimal photovoltaic consumption path according to the control instructions;

[0024] Furthermore, the external environment information in step 1 includes ambient temperature and wind speed information.

[0025] Furthermore, in step 4, the active power of the distribution network load node can be determined according to the active power of the distribution network feeder root node, the initial active power of the distributed photovoltaic, the proportion of the distribution network active power loss to the load active power and the number of distribution network load nodes.

[0026] Furthermore, in step 4, the reactive power of the load nodes of the distribution network can be determined according to the reactive power of the root node of the distribution network feeder, the proportion of the reactive power loss of the distribution network to the reactive power of the load, and the number of load nodes in the distribution network.

[0027] Furthermore, in step 3, both the photovoltaic access point and the load node are equipped with intelligent metering devices, and the intelligent metering devices, as edge computing units, have a two-way power measurement function and can read the power purchase and sales data of the nodes.

[0028] When the present invention is used, first, the location information, external environment information and power data information of each photovoltaic power station in the distribution station area are obtained, and the sunshine data of the day is collected by using the environment collection module through the location information of the photovoltaic power station, so as to establish a power generation prediction model based on the power data and sunshine data of the photovoltaic power station, and then, according to the location of the photovoltaic access point of the distribution network and the location of the load node of the distribution network, the optimal photovoltaic absorption path is calculated and obtained, and then, according to the line parameters extracted from the distribution network, the power output of the power supply point and the node load, the active power on each branch is obtained through the flow calculation, and the reactive power of the load node of the distribution network is determined according to the reactive power of the root node of the distribution network feeder, the proportion of the reactive power loss of the distribution network to the reactive power of the load and the number of load nodes of the distribution network. The active power of the root node, the initial active power of distributed photovoltaics, the proportion of active power loss of the distribution network to the active power of the load and the number of load nodes in the distribution network are used to determine the active power of the load node of the distribution network. Finally, the maximum node voltage and the distributed photovoltaic installation capacity corresponding to the maximum node voltage are determined according to the active power of distributed photovoltaics, the active power of the load node of the distribution network and the reactive power of the load node of the distribution network. Finally, the predicted data of distributed photovoltaic power generation and load at the dual scales of the week before and the day before are combined to draw a change curve, and the photovoltaic control instructions and energy storage control instructions in the distribution network are output through the photovoltaic energy storage linkage control model, so as to output the photovoltaic power to the control object according to the control instructions and along the optimal photovoltaic consumption path. In general, the present invention has the advantages of stable and reliable use, safety and high efficiency.

[0029] The above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, a person skilled in the art should understand that the specific implementation modes of the present invention can still be modified or replaced by equivalents, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing, regulating and accommodating distributed photovoltaic power in a distribution station area, characterized in that: Step 1: Obtain the location information, external environment information and power data information of each photovoltaic power station in the distribution station area; Step 2: The sunshine data of the day corresponding to the location information of the photovoltaic power station is collected through the environment collection module, and then the sunshine data is transmitted to the power generation prediction module. The power generation prediction module predicts the photovoltaic power generation according to the power data and sunshine data of the photovoltaic power station, thereby obtaining multiple influencing factor data sets and multiple output sets of the same period with the multiple influencing factor data sets. Finally, the multiple influencing factor data sets are respectively input into the basic model to obtain the power generation prediction model; Step 3: Collect the locations of photovoltaic access points and load nodes in the distribution network, construct a network diagram of photovoltaic access points and load nodes, and thus obtain the optimal photovoltaic consumption path; Step 4: Based on the line parameters extracted from the distribution network, the power output of the power source point and the node load, the active power on each branch is obtained through power flow calculation. The maximum node voltage and the distributed photovoltaic installation capacity corresponding to the maximum node voltage are determined based on the active power of distributed photovoltaics, the active power of the distribution network load node and the reactive power of the distribution network load node; Step 5: Combine the forecast data of distributed photovoltaic power generation and load at the dual scales of week before and day before, and draw a change curve; Step 6: Construct a photovoltaic energy storage linkage control model, and input the load data of each node and the control instruction data of photovoltaic energy storage, so as to output the photovoltaic control instructions and energy storage control instructions in the distribution network; Step 7: Output photovoltaic power to the control object along the optimal photovoltaic consumption path according to the control instructions.

2. The method for optimizing, controlling and accommodating distributed photovoltaic power generation in a power distribution area according to claim 1, characterized in that: The external environment information in step 1 includes ambient temperature and wind speed information.

3. The method for optimizing, controlling and accommodating distributed photovoltaic power generation in a power distribution area according to claim 1, characterized in that: In step 4, the active power of the distribution network load node can be determined according to the active power of the distribution network feeder root node, the initial active power of the distributed photovoltaic, the proportion of the distribution network active power loss to the load active power and the number of distribution network load nodes.

4. The method for optimizing, regulating and accommodating distributed photovoltaic power generation in a power distribution area according to claim 1, characterized in that: In step 4, the reactive power of the load nodes of the distribution network can be determined according to the reactive power of the root node of the distribution network feeder, the proportion of the reactive power loss of the distribution network to the reactive power of the load, and the number of load nodes in the distribution network.

5. The method for optimizing, controlling and accommodating distributed photovoltaic power generation in a power distribution area according to claim 1, characterized in that: In step 3, both the photovoltaic access point and the load node are equipped with intelligent metering devices, and the intelligent metering devices, as edge computing units, have a two-way power measurement function and can read the power purchase and sales data of the nodes.