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

Abstract

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

Description

Technical Field

[0001] The present invention relates to the technical field of microgrids, and in particular to a microgrid energy storage optimization configuration method and a microgrid system. Background Art

[0002] A microgrid refers to a small power generation and distribution system consisting of distributed power sources, energy storage devices, energy conversion devices, loads, and monitoring and protection devices. Energy storage, as a key means of voltage regulation in the system, effectively mitigates the impacts of renewable energy grid integration and electric vehicle charging and discharging, improving the static and transient stability of the power system. A properly configured distributed energy storage system can improve power flow distribution in the distribution network, reduce economic losses caused by line losses, and thus enhance the economic efficiency of the power system. However, distributed energy storage systems also present challenges such as high equipment investment costs. Achieving a reasonable configuration of distributed energy storage systems is crucial for addressing these issues.

[0003] Currently, the large-scale integration of distributed photovoltaic power generation and the disorderly charging of electric vehicles have brought difficult-to-solve economic and stability challenges to the power system. Deploying energy storage systems for microgrids is an effective way to enhance peak load shifting, reduce network losses, and improve energy efficiency. During periods of high photovoltaic power generation, energy storage systems can store excess energy and release it during peak load periods. By regulating the storage and release of energy, energy storage systems enable local consumption of electricity, thereby reducing current fluctuations, transmission demand, and energy losses.

[0004] Optimizing energy storage configuration in microgrids typically focuses on the economics, stability, and reliability of grid operation after the energy storage system is connected. This research includes metrics 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 objective factors such as the random characteristics of renewable energy power generation systems, battery life degradation, and electric vehicle charging and discharging, existing microgrid energy storage optimization methods often yield inaccurate results. Summary of the Invention

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

[0006] In one aspect, the present invention provides a method for optimizing energy storage configuration in a microgrid, comprising:

[0007] 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, where 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 according to the active power loss of the microgrid system, and the loss sensitivity fitting goodness value of each node is calculated according to the loss sensitivity of each node;

[0010] The deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity fitting goodness of fit value of each node.

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

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

[0013] The calculation expression of the output power of the photovoltaic power generation system is:

[0014]

[0015] P V (t) = M·P;

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

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

[0018] In an embodiment of the present invention, calculating the active power of an electric vehicle charging station at each node in a microgrid system includes:

[0019] Calculating the charging power of an electric vehicle based on the battery state of charge of an electric vehicle connected to an 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 grid is calculated based on the output power of the charging piles, and the grid output power required for charging all electric vehicles connected to the electric vehicle charging station is used as the active power of the electric vehicle charging station.

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

[0023] The power of each energy storage device is calculated based on the energy storage charge, charging efficiency, discharging efficiency and charging and discharging time interval of the energy storage device at each node.

[0024] In the embodiment of the present invention, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system of 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 of n nodes n , the active power P of the net load of n+1 nodes F(n+1) And the line loss P between node n and node n+1 L(n+1) ;

[0026] According to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn , and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node 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 the n+1 node n+1 Subtract the net active power P of n nodes n Get the active power loss of the line between node n and node n+1;

[0029] The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss of the microgrid system.

[0030] In the embodiment of the present invention, the line loss P between node n and node n+1 is 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 represents the net active power of n nodes, Q n Represents the reactive power of n nodes, U n Represents the voltage at node n.

[0033] In an embodiment of the present 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 an embodiment of the present invention, the deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity goodness of fit value of each node, including: sorting the nodes from large to small according to the loss sensitivity goodness of fit value of each node, and preferentially deploying the energy storage device at the corresponding position of the node with the highest ranking.

[0035] In the embodiment of the present invention, the calculation expression of the loss sensitivity fitting goodness of each node is:

[0036]

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

[0038] represents the sum of the loss sensitivities of node n within a 24-hour period;

[0039] It represents the mean loss sensitivity of node n within a 24-hour period.

[0040] Another aspect of the present invention provides a microgrid system, comprising a photovoltaic power generation system, an electric vehicle charging station, and an energy storage device distributedly deployed at each node, wherein the deployment location of the energy storage device is optimized and adjusted based on the loss sensitivity goodness of fit value of each node in the microgrid system;

[0041] The method for optimizing and adjusting the deployment position of the energy storage device comprises:

[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 according to the active power loss of the microgrid system, and the loss sensitivity fitting goodness value of each node is calculated according to the loss sensitivity of each node;

[0045] The deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity fitting goodness of fit value of each node.

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

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

[0048] The calculation expression of the output power of the photovoltaic power generation system is:

[0049]

[0050] P V (t) = M·P;

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

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

[0053] In an embodiment of the present invention, calculating the active power of an electric vehicle charging station at each node in a microgrid system includes:

[0054] Calculating the charging power of an electric vehicle based on the battery state of charge of an electric vehicle connected to an 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 grid is calculated based on the output power of the charging piles, and the grid output power required for charging all electric vehicles connected to the electric vehicle charging station is used as the active power of the electric vehicle charging station.

[0057] In the embodiment of the present invention, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system of 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 of n nodes n , the active power P of the net load of n+1 nodes F(n+1) And the line loss P between node n and node n+1 L(n+1) ;

[0059] According to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn , and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node 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 the n+1 node n+1 Subtract the net active power P of n nodes n Get the active power loss of the line between node n and node n+1;

[0062] The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss of the microgrid system.

[0063] In an embodiment of the present 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 an embodiment of the present invention, the deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity goodness of fit value of each node, including: sorting the nodes from large to small according to the loss sensitivity goodness of fit value of each node, and preferentially deploying the energy storage device at the corresponding position of the node with the highest ranking.

[0065] The present invention calculates the loss sensitivity of each node in the microgrid system based on 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. The access location of the energy storage device is optimized based on the node loss sensitivity fitting goodness of fit value, thereby improving the accuracy of the energy storage optimization configuration results and providing 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 specific implementation section below. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0068] Figure 1 This is a flow chart of a microgrid energy storage optimization configuration method provided by an embodiment of the present invention;

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

[0070] Figure 3 4 is a block diagram of a microgrid system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] To make the technical solutions and advantages of the embodiments of the present invention more clearly understood, exemplary embodiments of the present invention are further described in detail below with reference to the accompanying drawings. It should be noted that the embodiments described are only a portion of the embodiments of the present invention, and are not an exhaustive list of all embodiments. It should be noted that the embodiments of the present invention and the features thereof may be combined with each other unless they conflict.

[0072] An embodiment of the present invention provides a microgrid energy storage optimization configuration method that takes into account electric vehicle charging and discharging, distributed energy access and consumption, and energy storage charging and discharging. By constructing active power models of 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 fit goodness of fit value of each node in the microgrid system. The energy storage access location is optimized based on the node loss sensitivity fit goodness of fit value.

[0073] Figure 1 This is a flow chart of the microgrid energy storage optimization configuration method provided by the 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, respectively calculating the output power of the photovoltaic power generation system of each node in the microgrid system, the active power of the electric vehicle charging station, and the power of the energy storage device, wherein the energy storage device is distributedly deployed at a corresponding position of each node;

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

[0076] S300, calculating the loss sensitivity of each node in the microgrid system according to the active power loss of the microgrid system, and calculating the loss sensitivity fitting goodness value of each node according to the loss sensitivity of each node;

[0077] S400: Optimize and adjust the deployment position of the energy storage device at each node according to the loss sensitivity fitting goodness of fit value of each node.

[0078] In the above step S100, a photovoltaic power generation model in the microgrid is constructed, and the output power of the photovoltaic power generation system of each node in the microgrid system is calculated by the photovoltaic power generation model in the microgrid. The factors affecting the power generation capacity of a photovoltaic power station mainly include light radiation intensity, ambient temperature and weather. The changes in light radiation intensity and temperature have different impacts on photovoltaic power generation, and the impact of light radiation intensity on photovoltaic power generation is dominant. Taking light radiation intensity as the primary influencing factor of the power generation capacity of a photovoltaic power generation system, the output power of a single photovoltaic panel is mainly determined by the solar radiation heat value and the change in the operating temperature of the battery array caused by light. The calculation expression of photovoltaic output power P is:

[0079]

[0080] P V (t) = M·P;

[0081] Among them, E is the light intensity, E B The standard light radiation intensity is 1000W / m 2 , k is the power temperature coefficient (-0.28% / K to -0.32% / K), T is the operating temperature of the solar panel, T B The standard test temperature is 25℃, P B.max Indicates that the photovoltaic module is tested under standard test conditions (light radiation intensity of 1000W / m 2 , rated power at a temperature of 25°C; P V (t) is the output power of the photovoltaic power generation system within the selected calculation range, and M is the number of photovoltaic panels.

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

[0083] The electric vehicle power model is a mathematical model that can be used to analyze the impact of electric vehicle charging behavior on grid load. In the embodiment of the present 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 the electric vehicle 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 used as the active power of the electric vehicle charging station.

[0084] State of charge (SOC) indicates the ratio of the remaining battery power to the total capacity, expressed as a percentage, with a value between 0 and 1. Assume that the current state of charge is SOC i (t), the battery state of charge of electric vehicle i is:

[0085]

[0086] Among them, P i is the charging power of the electric vehicle, η1 is the charging efficiency, T i is the charging time, B i is the battery capacity.

[0087] Output power P of the charging pile j Expressed as:

[0088]

[0089] Among them, P i is the charging power of the electric vehicle, and η2 is the charging efficiency of the charging pile.

[0090] The output power of the power grid P l Expressed as:

[0091]

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

[0093] Assume that electric vehicle i is connected 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] Among them, M is the total number of electric vehicles, P l (t) is the grid output power required to charge the electric vehicle at time t.

[0096] In the above step S100, the energy storage system power model is constructed, and the power of the energy storage device of each node in the microgrid system is predicted by the energy storage system power model. The energy storage device can quickly absorb most of the power load. In order to avoid excessive charging power or deep amplification that may shorten the life of the energy storage system, the energy storage charge E when the battery is fully charged is set.B =1, the energy storage charge E when fully discharged B =0.2, the power of the energy storage device P B The dynamic calculation expression of (t) is:

[0097]

[0098] Among them, P B (t) is the power of the energy storage device, E B (t) is the energy storage charge at time t, η c is the charging efficiency, η d is the discharge efficiency, and Δt is the charge and discharge time interval.

[0099] In the above step S200, the active power loss of the microgrid system is calculated based on the output power of the photovoltaic power generation system of each node, the active power of the electric vehicle charging station and the power of the energy storage device. Figure 2 In a specific embodiment, according to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn , and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node n+1 (i.e., the power supply power 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 values and voltage values involved in the above calculation formula can be measured in actual production, can be seen in the power flow diagram of the power grid, or can be calculated using known parameters. Among them, the net active power P of the n node is n And the active power P of the net load of n+1 nodes F(n+1) (i.e. the power required for the n+1 node to absorb the load) can be measured, and the line loss P between the n node and the n+1 node L(n+1) It can be obtained by calculation.

[0101] The line loss P 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 represents the net active power of n nodes, Q n Represents the reactive power of n nodes, U n Represents the voltage at node n.

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

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

[0106] The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss E of the microgrid system. Loss :

[0107]

[0108] To reduce network losses when energy storage devices are connected to microgrid systems, node loss sensitivity is used as a criterion for energy storage site selection. Node loss sensitivity reflects changes in distribution network line losses caused by changes in node load power. The greater the node loss sensitivity, the more sensitive the node load is to changes in network loss. Charging when node loss sensitivity is low minimizes the increase in distribution network nodes, while discharging when node loss sensitivity is high minimizes distribution network losses.

[0109] In the above step S300, the active power loss E of the microgrid system is Loss The net active power P of each node n Calculate the partial derivative and obtain the loss sensitivity A of each node n :

[0110]

[0111] In microgrids, power forecasting is an important basis for energy scheduling strategies. The energy management system predicts photovoltaic power generation, wind power generation, and load power, and uses 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] Find the total active power loss.

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

[0115] Due to the variability of energy storage device charging and discharging conditions and fluctuations in load curves, optimizing distributed energy storage placement based solely on peak loads is inaccurate. To minimize grid losses, this paper considers the 24-hour variation in loss sensitivity at each grid node. This embodiment of the present invention proposes a method for energy storage access location based on the goodness of fit of node loss sensitivity. By comprehensively considering the daily variation in node loss sensitivity, this method evaluates energy storage access to microgrids, reduces network losses, and improves power quality.

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

[0117]

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

[0119] represents the sum of the loss sensitivities of node n within a period (24 hours);

[0120] It represents the mean loss sensitivity of node n within 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, and the more beneficial it is to deploy an energy storage system at this node to reduce grid losses. When installing distributed energy storage, nodes can be ranked by their sensitivity goodness-of-fit values, with nodes with sensitivity goodness-of-fit values closest to 1 selected for installation. Energy storage devices are prioritized for nodes with the highest rankings, allowing active power optimization calculations to prioritize compensating for the nodes with the best loss reduction effects, reducing calculation time and improving model solution efficiency.

[0122] In step S400, the energy storage devices installed and deployed in the microgrid system can be dynamically sorted from largest to smallest based on their loss sensitivity goodness-of-fit values. Within a certain time period, the deployment locations of the energy storage devices at the nodes can be optimized and adjusted based on the dynamic sorting. For example, energy storage devices can be added or removed from certain nodes, or the energy storage device at a certain node can be replaced with another node, or the installation location of the energy storage device at a certain node can be adjusted.

[0123] The above-mentioned microgrid energy storage optimization configuration method calculates the loss sensitivity of each node in the microgrid system based on 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. The access location of the energy storage device is optimized based on the node loss sensitivity fitting goodness of fit value, which improves the accuracy of the energy storage optimization configuration results and provides effective data support for microgrid planning and design.

[0124] The embodiment of the present invention also provides a microgrid system. Figure 3 As shown, the microgrid system includes a photovoltaic power generation system, an electric vehicle charging station, and an energy storage device distributed and deployed at each node. The deployment location of the energy storage device is optimized and adjusted based on the loss sensitivity goodness of fit value of each node in the microgrid system. The method for optimizing and adjusting the deployment location of the energy storage device includes: calculating 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; calculating the active power loss of the microgrid system based on 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; calculating the loss sensitivity of each node based on the active power loss of the microgrid system, and calculating the loss sensitivity goodness of fit value of each node based on the loss sensitivity of each node; and optimizing and adjusting the deployment location of the energy storage device at each node based on the loss sensitivity goodness of fit value of each node.

[0125] In a specific embodiment, the output power of the photovoltaic power generation system of each node in the microgrid system is calculated by the photovoltaic power generation power model in the microgrid. The factors affecting the power generation capacity of the photovoltaic power station mainly include light radiation intensity, ambient temperature and weather. The changes in light radiation intensity and temperature have different impacts on photovoltaic power generation, and the impact of light radiation intensity on photovoltaic power generation is dominant. Taking light radiation intensity as the primary influencing factor of the power generation capacity of the photovoltaic power generation system, the output power of a single photovoltaic panel is mainly determined by the solar radiation heat value and the operating temperature change of the battery array caused by light. The calculation expression of photovoltaic output power P is:

[0126]

[0127] P V (t) = M·P;

[0128] Among them, E is the light intensity, E B The standard light radiation intensity is 1000W / m 2 , k is the power temperature coefficient (-0.28% / K to -0.32% / K), T is the operating temperature of the solar panel, T B The standard test temperature is 25℃, P B.max Indicates that the photovoltaic module is tested under standard test conditions (light radiation intensity of 1000W / m 2 , rated power at a temperature of 25°C; P V (t) is the output power of the photovoltaic power generation system within the selected calculation range, and M is 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 the electric vehicle 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 used as the active power of the electric vehicle charging station.

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

[0131]

[0132] Among them, P i is the charging power of the electric vehicle, η1 is the charging efficiency, T i is the charging time, B i is the battery capacity.

[0133] Output power P of the charging pile j Expressed as:

[0134]

[0135] Among them, P i is the charging power of the electric vehicle, and η2 is the charging efficiency of the charging pile.

[0136] The output power of the power grid P l Expressed as:

[0137]

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

[0139] Assume that electric vehicle i is connected 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] Among them, M is the total number of electric vehicles, P l (t) is 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 of each node is predicted based on the energy storage charge, charging efficiency, discharging efficiency and charging and discharging time interval of the energy storage device of each node. B =1, the energy storage charge E when fully discharged B =0.2, the power of the energy storage device P B The dynamic calculation expression of (t) is:

[0143]

[0144] Among them, P B (t) is the power of the energy storage device, E B (t) is the energy storage charge at time t, η c is the charging efficiency, η d is the discharge efficiency, and Δt is the charge and discharge time interval.

[0145] In a specific embodiment, according to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn, and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node n+1 (i.e., the power supply power 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 values and voltage values involved in the above calculation formula can be measured in actual production, can be seen in the power flow diagram of the power grid, or can be calculated using known parameters. Among them, the net active power P of the n node is n And the active power P of the net load of n+1 nodes F(n+1) (i.e. the power required for the n+1 node to absorb the load) can be measured, and the line loss P between the n node and the n+1 node L(n+1) It can be obtained by calculation.

[0147] The line loss P 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 represents the net active power of n nodes, Q n Represents the reactive power of n nodes, U n Represents the voltage at node n.

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

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

[0152] The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss E of the microgrid system. Loss :

[0153]

[0154] The active power loss E of the microgrid system Loss The net active power P of each node n Calculate the partial derivative and obtain the loss sensitivity A of each node n :

[0155]

[0156] In microgrids, power forecasting is an important basis for energy scheduling strategies. The energy management system predicts photovoltaic power generation, wind power generation, and load power, and uses 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] Find the total active power loss.

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

[0160] Due to the variability of energy storage device charging and discharging conditions and fluctuations in load curves, optimizing distributed energy storage placement based solely on peak loads is inaccurate. To minimize grid losses, this paper considers the 24-hour variation in loss sensitivity at each grid node. This embodiment of the present invention proposes a method for energy storage access location based on the goodness of fit of node loss sensitivity. By comprehensively considering the daily variation in node loss sensitivity, this method evaluates energy storage access to microgrids, reduces network losses, and improves power quality.

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

[0162]

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

[0164] represents the sum of the loss sensitivities of node n within a period (24 hours);

[0165] It represents the mean loss sensitivity of node n within 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, and the more beneficial it is to deploy an energy storage system at this node to reduce grid losses. When installing distributed energy storage, nodes can be ranked by their sensitivity goodness-of-fit values, with nodes with sensitivity goodness-of-fit values closest to 1 selected for installation. Energy storage devices are prioritized for nodes with the highest rankings, allowing active power optimization calculations to prioritize compensating for the nodes with the best loss reduction effects, reducing calculation time and improving model solution efficiency.

[0167] The embodiment of the present invention constructs an active power model of 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 at each node in the microgrid system, the active power of the electric vehicle charging station, and the power of the energy storage device can be obtained, thereby calculating the loss sensitivity of each node. The access location of the energy storage device is optimized based on the node loss sensitivity goodness of fit value, thereby improving the accuracy of the energy storage optimization configuration results and providing effective data support for microgrid planning and design.

[0168] An embodiment of the present invention further provides a computer device, comprising: a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to implement the above-mentioned microgrid energy storage optimization configuration method.

[0169] It will be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may 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 may be implemented in various computer languages, for example, the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0170] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0173] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A microgrid energy storage optimization configuration method, characterized in that: include: 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, where 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 according to the active power loss of the microgrid system, and the loss sensitivity fitting goodness value of each node is calculated according to the loss sensitivity of each node; The deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity fitting goodness of fit value of each node.

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 the light radiation intensity as the primary factor affecting the power generation capacity of the photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated; The calculation expression of the output power of the photovoltaic power generation system is: P V (t)=M·P; Among them, E is the light intensity, E B is the standard light radiation intensity, k is the power temperature coefficient, T is the operating temperature of the solar panel, T B is the standard test temperature, P B.max Indicates the rated power of the photovoltaic module; P V (t) is the output power of the photovoltaic power generation system within the selected calculation range, and M is 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: Calculating the charging power of an electric vehicle based on the battery state of charge of an electric vehicle connected to an 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 grid is calculated based on the output power of the charging piles, and the grid output power required for charging all electric vehicles connected to the electric vehicle charging station is used 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 device at each node in the microgrid system, including: The power of each energy storage device is calculated based on the energy storage charge, charging efficiency, discharging efficiency and charging and discharging time interval of the energy storage device at each node.

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 of n nodes n , the active power P of the net load of n+1 nodes F(n+1) And the line loss P between node n and node n+1 L(n+1) ; According to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn , and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node n+1 , the calculation expression is: P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z ; The net active power P of the n+1 node n+1 Subtract the net active power P of n nodes n Get the active power loss of the line between node n and node n+1; The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss of the microgrid system.

6. The microgrid energy storage optimization configuration method according to claim 5, characterized in that: The line loss P 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 Represents the net active power of n nodes, Q n Represents the reactive power of n nodes, U n 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 the microgrid system is calculated based on the active power loss of the microgrid system, including: The active power loss of the microgrid system is partially derived from the net active power of each node to obtain the loss sensitivity of each node.

8. The microgrid energy storage optimization configuration method according to claim 1, characterized in that: The deployment location of the energy storage device at each node is optimized and adjusted based on the loss sensitivity fitting goodness of fit value of each node, including: The nodes are sorted from large to small according to their loss sensitivity goodness of fit values, and the energy storage devices are preferentially deployed at the corresponding positions of the nodes with the highest sorting.

9. The microgrid energy storage optimization configuration method according to claim 1, characterized in that: The calculation expression of the loss sensitivity fitting goodness of each node is: Where An(t) represents the loss sensitivity of node n at time t; represents the sum of the loss sensitivities of node n within a 24-hour period; It represents the mean loss sensitivity of node n within a 24-hour period.

10. A microgrid system comprising a photovoltaic power generation system, an electric vehicle charging station and an energy storage device distributed at each node, characterized in that: The deployment location of the energy storage device is optimized and adjusted according to the loss sensitivity goodness of fit value of each node in the microgrid system; The method for optimizing and adjusting the deployment position of the energy storage device comprises: 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 according to the active power loss of the microgrid system, and the loss sensitivity fitting goodness value of each node is calculated according to the loss sensitivity of each node; The deployment position of the energy storage device at each node is optimized and adjusted according to the loss sensitivity fitting goodness of fit value of each node.

11. The microgrid system according to claim 10, characterized in that: Calculate the output power of the photovoltaic power generation system at each node, including: Taking the light radiation intensity as the primary factor affecting the power generation capacity of the photovoltaic power generation system, the output power of the photovoltaic power generation system at each node is calculated; The calculation expression of the output power of the photovoltaic power generation system is: P V (t)=M·P; Among them, E is the light intensity, E B is the standard light radiation intensity, k is the power temperature coefficient, T is the operating temperature of the solar panel, T B is the standard test temperature, P B.max Indicates the rated power of the photovoltaic module; P V (t) is the output power of the photovoltaic power generation system within the selected calculation range, and M is the number of photovoltaic panels.

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

13. The microgrid system according to claim 10, 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 of n nodes n , the active power P of the net load of n+1 nodes F(n+1) And the line loss P between node n and node n+1 L(n+1) ; According to the output power P of the photovoltaic power generation system of n nodes Vn , the active power P of the electric vehicle charging station En , the power P of the energy storage device Bn , and the net active power P of n nodes n , the active power P of the net load of n+1 nodes F(n+1) , the line loss P between node n and node n+1 L(n+1) And the loss of other equipment P z , calculate the net active power P of n+1 node n+1 , the calculation expression is: P n+1 =P n -P Bn -P Vn -P En -P F(n+1) -P L(n+1) -P z ; The net active power P of the n+1 node n+1 Subtract the net active power P of n nodes n Get the active power loss of the line between node n and node n+1; The active power loss of the lines between adjacent nodes in the microgrid system is summed to obtain the active power loss of the microgrid system.

14. The microgrid system according to claim 10, characterized in that: The loss sensitivity of each node is calculated based on the active power loss of the microgrid system, including: The active power loss of the microgrid system is partially derived from the net active power of each node to obtain the loss sensitivity of each node.

15. The microgrid system according to claim 10, characterized in that: The deployment location of the energy storage device at each node is optimized and adjusted based on the loss sensitivity fitting goodness of fit value of each node, including: The nodes are sorted from large to small according to their loss sensitivity goodness of fit values, and the energy storage devices are preferentially deployed at the corresponding positions of the nodes with the highest sorting.

16. A computer device, characterized in that: include: a memory storing a computer program; A processor, configured to execute the computer program to implement the microgrid energy storage optimization configuration method according to any one of claims 1 to 9.

Images