Micro-grid energy storage optimal configuration method and micro-grid system

Abstract

The application provides a micro-grid energy storage optimization configuration method and a micro-grid system, and relates to the technical field of micro-grid. The method comprises the following steps: calculating the output power of a photovoltaic power generation system, the active power of an electric vehicle charging station and the power of an energy storage device of each node in a micro-grid system respectively; calculating the active loss of the micro-grid system according to the power of the photovoltaic power generation system, the charging station and the energy storage device of each node; calculating the loss sensitivity and the loss sensitivity goodness-of-fit value of each node in the micro-grid system according to the active loss of the micro-grid system; and optimizing and adjusting the deployment position of the energy storage device at each node according to the loss sensitivity goodness-of-fit value of each node. According to the application, the loss sensitivity goodness-of-fit value of the node is calculated according to the output power of the photovoltaic power generation, the active power of the charging station and the power of the energy storage device, the energy storage access position is optimized based on the loss sensitivity goodness-of-fit value of the node, and the accuracy of the energy storage optimization configuration result is improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid technology, and more specifically to a method for optimizing the configuration of energy storage in a microgrid and a microgrid system. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system composed of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices, etc. Energy storage, as an important means of voltage regulation in the system, effectively suppresses various impacts from the grid connection of new energy sources and the charging and discharging of electric vehicles, improving the static and transient stability of the power system. A well-configured distributed energy storage system can improve the power flow distribution of the distribution network, reduce economic losses caused by line losses, and thus improve the economic efficiency of the power system. However, distributed energy storage systems also have problems such as high equipment investment costs. Achieving a reasonable configuration of distributed energy storage systems is a crucial step in solving these problems.

[0003] Currently, the large-scale integration of distributed photovoltaic (PV) power sources and the unregulated charging of electric vehicles have brought intractable economic problems and stability challenges to the power system. Configuring energy storage systems for microgrids is an effective method to enhance peak shaving and valley filling, reduce grid losses, and improve energy utilization efficiency. During periods of high PV power generation, energy storage systems can store excess electricity; during peak load periods, they can release the stored energy. By regulating the storage and release of electricity, energy storage systems enable local power consumption, thereby reducing current fluctuations and transmission demand, and minimizing energy losses.

[0004] For the problem of optimal configuration of energy storage in microgrids, research typically focuses on the economic efficiency, stability, and reliability of grid operation after the energy storage system is integrated. This includes data indicators such as optimal capacity configuration, initial equipment investment costs, and operating costs. Intelligent algorithms are widely used to solve mathematical models to achieve optimal configuration. However, due to the limitations of application scenarios and the stochastic characteristics of new energy power generation systems, as well as objective factors such as battery lifespan degradation and electric vehicle charging and discharging, the accuracy of results obtained using existing microgrid energy storage optimization methods is not high. Summary of the Invention

[0005] This invention provides a microgrid energy storage optimization configuration method and microgrid system, which can improve the accuracy of energy storage optimization configuration results.

[0006] This invention provides a method for optimizing the configuration of energy storage in a microgrid, comprising:

[0007] The output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device are calculated for each node in the microgrid system, wherein the energy storage device is distributed and deployed at the corresponding location of each node.

[0008] The active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device.

[0009] The loss sensitivity of each node in the microgrid system is calculated based on the active power loss of the microgrid system, and the goodness of fit of the loss sensitivity of each node is calculated based on the loss sensitivity of each node.

[0010] The deployment location of the energy storage device at each node is optimized and adjusted based on the goodness of fit of the loss sensitivity of each node.

[0011] In this embodiment of the invention, calculating the output power of the photovoltaic power generation system at each node in the microgrid system includes:

[0012] Taking solar radiation intensity as the primary factor affecting the power generation capacity of a photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated.

[0013] The formula for calculating the output power of the photovoltaic power generation system is as follows:

[0014]

[0015] P V (t) = M·P;

[0016] Where E is the light intensity, E B Where is the standard solar radiation intensity, k is the power temperature coefficient, and T is the operating temperature of the solar panel. B For the standard test temperature, P B.max Indicates the rated power of the photovoltaic module;

[0017] P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

[0018] In this embodiment of the invention, calculating the active power of electric vehicle charging stations at each node in the microgrid system includes:

[0019] The charging power of electric vehicles is calculated based on the state of charge of the batteries of the electric vehicles connected to the electric vehicle charging station.

[0020] Calculate the output power of the charging pile based on the charging power of the electric vehicle;

[0021] The output power of the power grid is calculated based on the output power of the charging piles, and the power grid output required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

[0022] In this embodiment of the invention, calculating the power of the energy storage device at each node in the microgrid system includes:

[0023] The power of each energy storage device is calculated based on its stored charge, charging efficiency, discharging efficiency, and charging / discharging time interval.

[0024] In this embodiment of the invention, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device, including:

[0025] Calculate the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) And the loss P of the line between node n and node n+1 L(n+1) ;

[0026] Based on the output power P of the photovoltaic power generation system at node n Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bn and the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) The loss P of the line between node n and node n+1 L(n+1) And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 The calculation expression is:

[0027] P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z ;

[0028] The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1;

[0029] The active power loss of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system.

[0030] In this embodiment of the invention, the loss P of the line between node n and node n+1 L(n+1) The calculation formula is:

[0031]

[0032] Among them, R n+1 P represents the resistance of the line between node n and node n+1.n Q represents the net active power at node n. n U represents the reactive power at node n. n This represents the voltage at node n.

[0033] In this embodiment of the invention, the loss sensitivity of each node in the microgrid system is calculated based on the active power loss of the microgrid system, including: taking the partial derivative of the active power loss of the microgrid system with respect to the net active power of each node to obtain the loss sensitivity of each node.

[0034] In this embodiment of the invention, the deployment position of the energy storage device at each node is optimized and adjusted according to the goodness of fit of the loss sensitivity of each node, including: sorting each node from largest to smallest according to the goodness of fit of the loss sensitivity of each node, and preferentially deploying the energy storage device at the corresponding position of the node with the highest ranking.

[0035] In this embodiment of the invention, the expression for calculating the goodness-of-fit value of the loss sensitivity of each node is as follows:

[0036]

[0037] Where An(t) represents the loss sensitivity of node n at time t;

[0038] This represents the total loss sensitivity of node n over a 24-hour period;

[0039] This represents the average loss sensitivity of node n over a 24-hour period.

[0040] Another aspect of the present invention provides a microgrid system, including a photovoltaic power generation system, an electric vehicle charging station, and an energy storage device distributed at each node, wherein the deployment location of the energy storage device is optimized and adjusted according to the goodness-of-fit value of the loss sensitivity of each node in the microgrid system.

[0041] The method for optimizing the deployment location of the energy storage device includes:

[0042] Calculate the output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device at each node.

[0043] The active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device.

[0044] The loss sensitivity of each node is calculated based on the active power loss of the microgrid system, and the goodness-of-fit value of the loss sensitivity of each node is calculated based on the loss sensitivity of each node.

[0045] The deployment location of the energy storage device at each node is optimized and adjusted based on the goodness of fit of the loss sensitivity of each node.

[0046] In this embodiment of the invention, calculating the output power of the photovoltaic power generation system at each node includes:

[0047] Taking solar radiation intensity as the primary factor affecting the power generation capacity of a photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated.

[0048] The formula for calculating the output power of the photovoltaic power generation system is as follows:

[0049]

[0050] P V (t) = M·P;

[0051] Where E is the light intensity, E B Where is the standard solar radiation intensity, k is the power temperature coefficient, and T is the operating temperature of the solar panel. B For the standard test temperature, P B.max Indicates the rated power of the photovoltaic module;

[0052] P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

[0053] In this embodiment of the invention, calculating the active power of electric vehicle charging stations at each node in the microgrid system includes:

[0054] The charging power of electric vehicles is calculated based on the state of charge of the batteries of the electric vehicles connected to the electric vehicle charging station.

[0055] Calculate the output power of the charging pile based on the charging power of the electric vehicle;

[0056] The output power of the power grid is calculated based on the output power of the charging piles, and the power grid output required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

[0057] In this embodiment of the invention, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device, including:

[0058] Calculate the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) And the loss P of the line between node n and node n+1 L(n+1) ;

[0059] Based on the output power P of the photovoltaic power generation system at node n Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bn and the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) The loss P of the line between node n and node n+1 L(n+1) And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 The calculation expression is:

[0060] P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z ;

[0061] The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1;

[0062] The active power loss of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system.

[0063] In this embodiment of the invention, the loss sensitivity of each node is calculated based on the active power loss of the microgrid system, including: taking the partial derivative of the active power loss of the microgrid system with respect to the net active power of each node to obtain the loss sensitivity of each node.

[0064] In this embodiment of the invention, the deployment position of the energy storage device at each node is optimized and adjusted according to the goodness of fit of the loss sensitivity of each node, including: sorting each node from largest to smallest according to the goodness of fit of the loss sensitivity of each node, and preferentially deploying the energy storage device at the corresponding position of the node with the highest ranking.

[0065] This invention calculates the loss sensitivity of each node based on the output power of the photovoltaic power generation system at each node in the microgrid system, the active power of the electric vehicle charging station, and the power of the energy storage device. Based on the goodness of fit of the node loss sensitivity, the connection location of the energy storage device is optimized, which improves the accuracy of the energy storage optimization configuration results and provides effective data support for microgrid planning and design.

[0066] Other features and advantages of the technical solution of the present invention will be described in detail in the following detailed embodiments section. Attached Figure Description

[0067] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0068] Figure 1 This is a flowchart of the microgrid energy storage optimization configuration method provided in the embodiments of the present invention;

[0069] Figure 2 This is a structural diagram of two adjacent nodes in a microgrid system provided in an embodiment of the present invention;

[0070] Figure 3 This is a block diagram of a microgrid system provided in an embodiment of the present invention. Detailed Implementation

[0071] To make the technical solutions and advantages of the embodiments of the present invention clearer, the exemplary embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0072] This invention provides a microgrid energy storage optimization configuration method that considers electric vehicle charging and discharging, distributed energy access and consumption, and energy storage charging and discharging. By constructing active power models for photovoltaic power generation, electric vehicle charging, and energy storage systems in the microgrid, the active power loss of the microgrid system is solved, thereby obtaining the loss sensitivity and loss sensitivity goodness-of-fit value of each node in the microgrid system. Based on the node loss sensitivity goodness-of-fit value, the location of energy storage access is optimized.

[0073] Figure 1 This is a flowchart of the microgrid energy storage optimization configuration method provided in an embodiment of the present invention. Figure 1 As shown, the microgrid energy storage optimization configuration method provided in this embodiment includes the following steps:

[0074] S100, calculate the output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device at each node in the microgrid system, wherein the energy storage device is distributed and deployed at the corresponding location of each node.

[0075] S200 calculates the active power loss of the microgrid system based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device.

[0076] S300 calculates the loss sensitivity of each node in the microgrid system based on the active power loss of the microgrid system, and calculates the goodness-of-fit value of the loss sensitivity of each node based on the loss sensitivity of each node.

[0077] S400 optimizes and adjusts the deployment location of the energy storage device at each node based on the goodness of fit of the loss sensitivity of each node.

[0078] In step S100 above, a photovoltaic (PV) power generation model in the microgrid is constructed, and the output power of the PV power generation system at each node in the microgrid system is calculated using this model. The main factors affecting the power generation capacity of a PV power plant include solar radiation intensity, ambient temperature, and weather. Changes in solar radiation intensity and temperature have different magnitudes of impact on PV power generation, with solar radiation intensity having a dominant effect. Taking solar radiation intensity as the primary influencing factor on the power generation capacity of the PV power generation system, the output power of a single PV panel is mainly determined by the solar radiation heat value and the change in the operating temperature of the battery array caused by sunlight. The expression for calculating the PV output power P is:

[0079]

[0080] P V (t) = M·P;

[0081] Where E is the light intensity, E B Standard light radiation intensity is 1000W / m 2 k is the power temperature coefficient (ranging from -0.28% / K to -0.32% / K), and T is the operating temperature of the solar panel. B The standard test temperature is 25℃, P B.max This indicates that the photovoltaic module is operating under standard test conditions (solar radiation intensity of 1000 W / m²). 2 Rated power at 25℃; P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

[0082] In step S100 above, an electric vehicle charging power model is constructed, and the active power of electric vehicle charging stations at each node in the microgrid system is predicted through the electric vehicle charging power model.

[0083] Electric vehicle power models can be used as mathematical models to analyze the impact of electric vehicle charging behavior on grid load. In this embodiment of the invention, the charging power P of the electric vehicle is calculated based on the battery state of charge (SOC) of the electric vehicle connected to the electric vehicle charging station. i According to the charging power P of electric vehicles i Calculate the output power P of the charging pile j According to the output power P of the charging pilej Calculate the output power P of the power grid l The grid output power required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

[0084] State of charge (SOC) represents the ratio of a battery's remaining charge to its total capacity, expressed as a percentage, and ranges from 0 to 1. Let's assume the current SOC is... i (t), the state of charge of the battery of electric vehicle i is:

[0085]

[0086] Where P i The charging power of the electric vehicle is η1, and the charging efficiency is T. i For charging time, B i This refers to the battery capacity.

[0087] The output power P of the charging pile j Represented as:

[0088]

[0089] Among them, P i η1 represents the charging power of the electric vehicle, and η2 represents the charging efficiency of the charging station.

[0090] Power output P of the power grid l Represented as:

[0091]

[0092] Among them, P j η is the output power of the charging pile, and η3 is the power conversion efficiency of the power grid.

[0093] Assuming that electric vehicle i connects to the electric vehicle charging station at time t, then the total charging power P of all electric vehicles at time t is... E (t) (i.e., the active power of the electric vehicle charging station) is expressed as:

[0094]

[0095] Where M is the total number of electric vehicles, P l (t) represents the grid output power required to charge the electric vehicle at time t.

[0096] In step S100 above, a power model of the energy storage system is constructed to predict the power of the energy storage devices at each node in the microgrid system. Since the energy storage devices can quickly absorb most of the electrical load, to avoid excessive charging power or deep scaling leading to a reduction in the lifespan of the energy storage system, the energy storage charge E when the battery is fully charged is set.B =1, the amount of stored charge E when fully discharged B =0.2, power P of the energy storage device B The dynamic calculation expression for (t) is:

[0097]

[0098] Among them, P B (t) represents the power of the energy storage device, E B (t) represents the stored charge at time t, η c For charging efficiency, η d The discharge efficiency is given by Δt, which is the charge / discharge time interval.

[0099] In step S200 above, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device. (Refer to...) Figure 2 In a specific embodiment, based on the output power P of the photovoltaic power generation system at node n... Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bn and the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) The loss P of the line between node n and node n+1 L(n+1) And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 (i.e., the power supply of node n+1), its calculation expression is:

[0100] P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z The various power and voltage values ​​involved in the above calculations, and other power grid parameters, can be measured and determined in actual production, can be seen in the power flow diagram, or can be calculated from known parameters. Among them, the net active power P at node n... n and the active power P of the net load at node n+1 F(n+1) (That is, the power required for load absorption by node n+1) can be measured, and the line loss P between node n and node n+1 can be measured. L(n+1) It can be obtained through calculation.

[0101] The loss P of the line between node n and node n+1 L(n+1)The calculation formula is:

[0102]

[0103] Among them, R n+1 P represents the resistance of the line between node n and node n+1. n Q represents the net active power at node n. n U represents the reactive power at node n. n This represents the voltage at node n.

[0104] The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1:

[0105] P Loss =P n+1 -P n ;

[0106] The active power loss E of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system. Loss :

[0107]

[0108] To reduce network losses when energy storage devices are integrated into microgrid systems, node loss sensitivity is used as a criterion for energy storage location selection. Node loss sensitivity reflects the changes in distribution network line losses caused by variations in node load power; the higher the node loss sensitivity, the more sensitive the node load is to changes in network losses. Charging when node loss sensitivity is low minimizes the number of distribution network nodes to be added, while discharging when node loss sensitivity is high maximizes the reduction of distribution network losses.

[0109] In step S300 above, the active power loss E of the microgrid system is... Loss For the net active power P of each node n By taking the partial derivative, we can obtain the loss sensitivity A at each node. n :

[0110]

[0111] In microgrids, power prediction is a crucial basis for energy dispatch strategies. The energy management system predicts the power output of photovoltaic (PV) power generation, wind power generation, and load power, based on the following formula:

[0112] P n+1 '=P n '-P Bn '-P Vn '-P En '-PF(n+1) '-P L(n+1) '-P z ';

[0113] Calculate the total active power loss.

[0114] Among them, the predicted output power P of the photovoltaic power generation system of the system Vn’ Predicted active power P of electric vehicle charging stations En’ Predicted power P of energy storage device Bn’ And the predicted net active power P at node n n’ The active power P of the predicted net load at node n+1 F(n+1)’ The loss P of the line between node n and node n+1 L(n+1)’ And the losses Pz' of other equipment, calculate the predicted net active power P of node n+1. n+1’ Then An' represents the loss sensitivity of node n at time t, calculated based on the predicted value.

[0115] Due to variations in the charging and discharging operation of energy storage devices and fluctuations in load curves, optimizing the layout of distributed energy storage based solely on load peaks is inaccurate. To minimize grid losses, this invention proposes an energy storage access location method based on the goodness of fit of node loss sensitivity, taking into account the 24-hour loss sensitivity variations of each grid node. By comprehensively considering the daily changes in node loss sensitivity, this method evaluates the access of microgrid energy storage, reducing grid losses and improving power quality.

[0116] The expression for calculating the goodness-of-fit value R² for the node's 24-hour loss sensitivity is as follows:

[0117]

[0118] Where An(t) represents the loss sensitivity of node n at time t;

[0119] This represents the total loss sensitivity of node n within a period (24 hours);

[0120] This represents the average loss sensitivity of node n over a period (24 hours).

[0121] For each node in a microgrid, the closer the node sensitivity goodness-of-fit value is to 1, the greater the daily fluctuation range of the node sensitivity. Therefore, configuring an energy storage system at that node is more beneficial for reducing grid losses. When installing distributed energy storage, nodes can be ranked according to their sensitivity goodness-of-fit values. The node with the closest goodness-of-fit value to 1 is selected for installation. Prioritizing the deployment of energy storage devices at the corresponding locations of these higher-ranked nodes ensures that active power optimization calculations prioritize compensating the node with the best loss reduction effect, reducing computation time and improving model solution efficiency.

[0122] In step S400 above, for energy storage devices that have been installed and deployed in the microgrid system, the nodes can be dynamically sorted from largest to smallest according to the goodness of fit of the loss sensitivity of each node. Within a certain period of time, the deployment position of the energy storage devices at each node can be optimized and adjusted according to the dynamic sorting. For example, energy storage devices can be added or removed at some nodes, or the energy storage devices at a certain node can be replaced to other nodes, or the installation position of the energy storage devices at a certain node can be adjusted.

[0123] The aforementioned microgrid energy storage optimization configuration method calculates the loss sensitivity of each node based on the output power of the photovoltaic power generation system at each node in the microgrid system, the active power of the electric vehicle charging station, and the power of the energy storage device. Based on the goodness of fit of the node loss sensitivity, the access location of the energy storage device is optimized, which improves the accuracy of the energy storage optimization configuration results and provides effective data support for microgrid planning and design.

[0124] This invention also provides a microgrid system. For example... Figure 3 As shown, the microgrid system includes distributed photovoltaic (PV) power generation systems, electric vehicle (EV) charging stations, and energy storage devices deployed at various nodes. The deployment locations of the energy storage devices are optimized based on the goodness-of-fit values ​​of the loss sensitivity of each node in the microgrid system. The optimization method for the deployment locations of the energy storage devices includes: calculating the output power of the PV power generation system at each node, the active power of the EV charging station, and the power of the energy storage device; calculating the active power loss of the microgrid system based on the output power of the PV power generation system at each node, the active power of the EV charging station, and the power of the energy storage device; calculating the loss sensitivity of each node based on the active power loss of the microgrid system; calculating the goodness-of-fit value of the loss sensitivity of each node based on the loss sensitivity of each node; and optimizing the deployment locations of the energy storage devices at each node based on the goodness-of-fit values ​​of the loss sensitivity of each node.

[0125] In a specific embodiment, the output power of the photovoltaic (PV) power generation system at each node in the microgrid is calculated using a PV power generation model within the microgrid. The main factors influencing the power generation capacity of a PV power plant include solar radiation intensity, ambient temperature, and weather. Changes in solar radiation intensity and temperature have different magnitudes of impact on PV power generation, with solar radiation intensity having a dominant effect. Taking solar radiation intensity as the primary influencing factor on the power generation capacity of the PV power generation system, the output power of a single PV panel is mainly determined by the solar radiation heat value and the change in the operating temperature of the battery array caused by sunlight. The expression for calculating the PV output power P is:

[0126]

[0127] P V (t) = M·P;

[0128] Where E is the light intensity, E B Standard light radiation intensity is 1000W / m 2 k is the power temperature coefficient (ranging from -0.28% / K to -0.32% / K), and T is the operating temperature of the solar panel. B The standard test temperature is 25℃, P B.max This indicates that the photovoltaic module is operating under standard test conditions (solar radiation intensity of 1000 W / m²). 2 Rated power at 25℃; P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

[0129] In a specific embodiment, the charging power P of the electric vehicle is calculated based on the battery state of charge (SOC) of the electric vehicle connected to the electric vehicle charging station. i According to the charging power P of electric vehicles i Calculate the output power P of the charging pile j According to the output power P of the charging pile j Calculate the output power P of the power grid l The grid output power required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

[0130] Assume the current state of charge is SOC. i (t), the state of charge of the battery of electric vehicle i is:

[0131]

[0132] Where P i The charging power of the electric vehicle is η1, and the charging efficiency is T. i For charging time, B i This refers to the battery capacity.

[0133] The output power P of the charging pile j Represented as:

[0134]

[0135] Among them, P i η1 represents the charging power of the electric vehicle, and η2 represents the charging efficiency of the charging station.

[0136] Power output P of the power grid l Represented as:

[0137]

[0138] Among them, P j η is the output power of the charging pile, and η3 is the power conversion efficiency of the power grid.

[0139] Assuming that electric vehicle i connects to the electric vehicle charging station at time t, then the total charging power P of all electric vehicles at time t is... E (t) (i.e., the active power of the electric vehicle charging station) is expressed as:

[0140]

[0141] Where M is the total number of electric vehicles, P l (t) represents the grid output power required to charge the electric vehicle at time t.

[0142] In a specific embodiment, the power of the energy storage device at each node is predicted based on the stored charge, charging efficiency, discharging efficiency, and charge / discharge time interval of the energy storage device at each node. The stored charge E when the battery is fully charged is set. B =1, the amount of stored charge E when fully discharged B =0.2, power P of the energy storage device B The dynamic calculation expression for (t) is:

[0143]

[0144] Among them, P B (t) represents the power of the energy storage device, E B (t) represents the stored charge at time t, η c For charging efficiency, η d The discharge efficiency is given by Δt, which is the charge / discharge time interval.

[0145] In a specific embodiment, based on the output power P of the photovoltaic power generation system at node n... Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bnand the net active power P at node n. n The active power P of the net load at node n+1 F(n+1) The loss P of the line between node n and node n+1 L(n+1) And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 (i.e., the power supply of node n+1), its calculation expression is:

[0146] P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z The various power and voltage values ​​involved in the above calculations, and other power grid parameters, can be measured and determined in actual production, can be seen in the power flow diagram, or can be calculated from known parameters. Among them, the net active power P at node n... n and the active power P of the net load at node n+1 F(n+1) (That is, the power required for load absorption by node n+1) can be measured, and the line loss P between node n and node n+1 can be measured. L(n+1) It can be obtained through calculation.

[0147] The loss P of the line between node n and node n+1 L(n+1) The calculation formula is:

[0148]

[0149] Among them, R n+1 P represents the resistance of the line between node n and node n+1. n Q represents the net active power at node n. n U represents the reactive power at node n. n This represents the voltage at node n.

[0150] The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1:

[0151] P Loss =P n+1 -P n ;

[0152] The active power loss E of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system. Loss :

[0153]

[0154] The active power loss E of the microgrid system Loss For the net active power P of each node n By taking the partial derivative, we can obtain the loss sensitivity A at each node. n :

[0155]

[0156] In microgrids, power prediction is a crucial basis for energy dispatch strategies. The energy management system predicts the power output of photovoltaic (PV) power generation, wind power generation, and load power, based on the following formula:

[0157] P n+1 '=P n '-P Bn '-P Vn '-P En '-P F(n+1) '-P L(n+1) '-P z ';

[0158] Calculate the total active power loss.

[0159] Among them, the predicted output power P of the photovoltaic power generation system of the system Vn’ Predicted active power P of electric vehicle charging stations En’ Predicted power P of energy storage device Bn’ And the predicted net active power P at node n n’ The active power P of the predicted net load at node n+1 F(n+1)’ The loss P of the line between node n and node n+1 L(n+1)’ And the losses Pz' of other equipment, calculate the predicted net active power P of node n+1. n+1’ Then An' represents the loss sensitivity of node n at time t, calculated based on the predicted value.

[0160] Due to variations in the charging and discharging operation of energy storage devices and fluctuations in load curves, optimizing the layout of distributed energy storage based solely on load peaks is inaccurate. To minimize grid losses, this invention proposes an energy storage access location method based on the goodness of fit of node loss sensitivity, taking into account the 24-hour loss sensitivity variations of each grid node. By comprehensively considering the daily changes in node loss sensitivity, this method evaluates the access of microgrid energy storage, reducing grid losses and improving power quality.

[0161] The expression for calculating the goodness-of-fit value R² for the node's 24-hour loss sensitivity is as follows:

[0162]

[0163] Where An(t) represents the loss sensitivity of node n at time t;

[0164] This represents the total loss sensitivity of node n within a period (24 hours);

[0165] This represents the average loss sensitivity of node n over a period (24 hours).

[0166] For each node in a microgrid, the closer the node sensitivity goodness-of-fit value is to 1, the greater the daily fluctuation range of the node sensitivity. Therefore, configuring an energy storage system at that node is more beneficial for reducing grid losses. When installing distributed energy storage, nodes can be ranked according to their sensitivity goodness-of-fit values. The node with the closest goodness-of-fit value to 1 is selected for installation. Prioritizing the deployment of energy storage devices at the corresponding locations of these higher-ranked nodes ensures that active power optimization calculations prioritize compensating the node with the best loss reduction effect, reducing computation time and improving model solution efficiency.

[0167] This invention constructs an active power model for photovoltaic power generation, electric vehicle charging, and energy storage systems in a microgrid. By solving the model, the output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device at each node in the microgrid system can be obtained. This allows for the calculation of the loss sensitivity of each node. Based on the goodness-of-fit value of the node loss sensitivity, the access location of the energy storage device is optimized, improving the accuracy of the energy storage optimization configuration results and providing effective data support for microgrid planning and design.

[0168] The present invention also provides a computer device, including: a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the above-described microgrid energy storage optimization configuration method.

[0169] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention can be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0170] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0171] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0173] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for optimizing the configuration of energy storage in a microgrid, characterized in that, include: The output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device are calculated for each node in the microgrid system, wherein the energy storage device is distributed and deployed at the corresponding location of each node. The active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device. The loss sensitivity of each node in the microgrid system is calculated based on the active power loss of the microgrid system, and the goodness of fit of the loss sensitivity of each node is calculated based on the loss sensitivity of each node. The deployment location of the energy storage device at each node is optimized and adjusted based on the goodness of fit of the loss sensitivity of each node, including: sorting each node from largest to smallest according to the goodness of fit of the loss sensitivity of each node, and prioritizing the deployment of the energy storage device at the corresponding location of the node with the highest ranking. The expression for calculating the goodness-of-fit value of the loss sensitivity of each node is as follows: ； Where R² represents the goodness-of-fit value of the loss sensitivity at node n, and A n (t) represents the loss sensitivity of node n at time t, A n (t)' represents the loss sensitivity of node n at time t, calculated based on the power prediction value; This represents the total loss sensitivity of node n over a 24-hour period; This represents the average loss sensitivity of node n over a 24-hour period.

2. The microgrid energy storage optimization configuration method according to claim 1, characterized in that, Calculate the output power of the photovoltaic power generation system at each node in the microgrid system, including: Taking solar radiation intensity as the primary factor affecting the power generation capacity of a photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated. The formula for calculating the output power of the photovoltaic power generation system is as follows: ； ； in, E Light intensity, E B Where is the standard solar radiation intensity, k is the power temperature coefficient, and T is the solar panel operating temperature. T B The standard test temperature, P B.max Indicates the rated power of the photovoltaic module; P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

3. The microgrid energy storage optimization configuration method according to claim 1, characterized in that, Calculate the active power of electric vehicle charging stations at each node in the microgrid system, including: The charging power of electric vehicles is calculated based on the state of charge of the batteries of the electric vehicles connected to the electric vehicle charging station. Calculate the output power of the charging pile based on the charging power of the electric vehicle; The output power of the power grid is calculated based on the output power of the charging piles, and the power grid output required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

4. The microgrid energy storage optimization configuration method according to claim 1, characterized in that, Calculate the power of the energy storage devices at each node in the microgrid system, including: The power of each energy storage device is calculated based on its stored charge, charging efficiency, discharging efficiency, and charging / discharging time interval.

5. The microgrid energy storage optimization configuration method according to claim 1, characterized in that, Based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device, the active power loss of the microgrid system is calculated, including: Calculate the net active power P at node n. n The active power P of the net load at node n+1 F（n+1） And the loss P of the line between node n and node n+1 L（n+1） ; Based on the output power P of the photovoltaic power generation system at node n Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bn and the net active power P at node n. n The active power P of the net load at node n+1 F（n+1） The loss P of the line between node n and node n+1 L（n+1） And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 The calculation expression is: ； The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1; The active power loss of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system.

6. The microgrid energy storage optimization configuration method according to claim 5, characterized in that, The loss P of the line between node n and node n+1 L（n+1） The calculation formula is: ； Among them, R n+1 P represents the resistance of the line between node n and node n+1. n Q represents the net active power at node n. n U represents the reactive power at node n. n This represents the voltage at node n.

7. The microgrid energy storage optimization configuration method according to claim 1, characterized in that, The loss sensitivity of each node in a microgrid system is calculated based on the active power loss of the microgrid system, including: The loss sensitivity of each node is obtained by taking the partial derivative of the active power loss of the microgrid system with respect to the net active power of each node.

8. A microgrid system, comprising a distributed photovoltaic power generation system, an electric vehicle charging station, and an energy storage device deployed at various nodes, characterized in that, The deployment location of the energy storage device is optimized and adjusted based on the goodness of fit of the loss sensitivity of each node in the microgrid system. The method for optimizing the deployment location of the energy storage device includes: Calculate the output power of the photovoltaic power generation system, the active power of the electric vehicle charging station, and the power of the energy storage device at each node. The active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device. The loss sensitivity of each node is calculated based on the active power loss of the microgrid system, and the goodness-of-fit value of the loss sensitivity of each node is calculated based on the loss sensitivity of each node. The deployment location of the energy storage device at each node is optimized and adjusted based on the goodness of fit of the loss sensitivity of each node, including: sorting each node from largest to smallest according to the goodness of fit of the loss sensitivity of each node, and prioritizing the deployment of the energy storage device at the corresponding location of the node with the highest ranking. The expression for calculating the goodness-of-fit value of the loss sensitivity of each node is as follows: ； Where R² represents the goodness-of-fit value of the loss sensitivity at node n, and A n (t) represents the loss sensitivity of node n at time t, A n (t)' represents the loss sensitivity of node n at time t, calculated based on the power prediction value; This represents the total loss sensitivity of node n over a 24-hour period; This represents the average loss sensitivity of node n over a 24-hour period.

9. The microgrid system according to claim 8, characterized in that, Calculate the output power of the photovoltaic power generation system at each node, including: Taking solar radiation intensity as the primary factor affecting the power generation capacity of a photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated. The formula for calculating the output power of the photovoltaic power generation system is as follows: ； ； in, E Light intensity, E B Where is the standard solar radiation intensity, k is the power temperature coefficient, and T is the solar panel operating temperature. T B The standard test temperature, P B.max Indicates the rated power of the photovoltaic module; P V (t) represents the output power of the photovoltaic power generation system within the selected calculation range, and M represents the number of photovoltaic panels.

10. The microgrid system according to claim 8, characterized in that, Calculate the active power of electric vehicle charging stations at each node in the microgrid system, including: The charging power of electric vehicles is calculated based on the state of charge of the batteries of the electric vehicles connected to the electric vehicle charging station. Calculate the output power of the charging pile based on the charging power of the electric vehicle; The output power of the power grid is calculated based on the output power of the charging piles, and the power grid output required for charging all electric vehicles connected to the electric vehicle charging station is taken as the active power of the electric vehicle charging station.

11. The microgrid system according to claim 8, characterized in that, Based on the output power of the photovoltaic power generation system at each node, the active power of the electric vehicle charging station, and the power of the energy storage device, the active power loss of the microgrid system is calculated, including: Calculate the net active power P at node n. n The active power P of the net load at node n+1 F（n+1） And the loss P of the line between node n and node n+1 L（n+1） ; Based on the output power P of the photovoltaic power generation system at node n Vn The active power P of electric vehicle charging stations En Power P of the energy storage device Bn and the net active power P at node n. n The active power P of the net load at node n+1 F（n+1） The loss P of the line between node n and node n+1 L（n+1） And the losses of other equipment P z Calculate the net active power P at node n+1. n+1 The calculation expression is: ； The net active power P of node n+1 n+1 Subtract the net active power P at node n n Obtain the active power loss of the line between node n and node n+1; The active power loss of the microgrid system is obtained by summing the active power losses of the lines between adjacent nodes in the microgrid system.

12. The microgrid system according to claim 8, characterized in that, The loss sensitivity of each node is calculated based on the active power loss of the microgrid system, including: The loss sensitivity of each node is obtained by taking the partial derivative of the active power loss of the microgrid system with respect to the net active power of each node.

13. A computer device, characterized in that, include: Memory, which stores computer programs; A processor for executing the computer program to implement the microgrid energy storage optimization configuration method according to any one of claims 1-7.

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