Photovoltaic storage and charging energy management method and system

Abstract

The present invention relates to a photovoltaic storage and charging energy management method, comprising the following steps: S1, a fuzzy inference system collects status data of each electric vehicle and calculates the available energy value of each electric vehicle for vehicle-to-vehicle (V2G) or vehicle-to-vehicle (G2V) based on the status data; S2, the available energy value of each electric vehicle for V2G or vehicle-to-vehicle (G2V) is transmitted to a smart energy platform; S3, the smart energy platform calculates the difference between the photovoltaic system power generation and the load power of the microgrid, and sets the charging and discharging power point of each electric vehicle based on the difference and the available energy value of each electric vehicle; S4, the smart energy platform controls the operating mode of the microgrid based on the charging and discharging power point of each electric vehicle. The present invention greatly improves the economy and flexibility of the photovoltaic storage and charging microgrid, and can effectively reduce the number of interactions between the microgrid and the main grid, thereby improving the stability of the grid.

Description

Technical Field

[0001] The present invention relates to the field of power grid energy distribution, and in particular to a method and system for managing photovoltaic storage and charging energy. Background Art

[0002] As an emerging microgrid, the integrated photovoltaic, energy storage, and charging system, primarily composed of photovoltaics, energy storage, and charging stations, holds enormous research and development potential. Whether operating in a self-sufficient, isolated island or connected to a larger power grid, the integrated photovoltaic, energy storage, and charging system can promote supply-demand balance through demand response, ensuring the reliability and economic efficiency of system operation. However, if system operation optimization is not considered, not only will the system's economic efficiency be poor, but power supply reliability will also be difficult to guarantee, and the load may even be left without power for long periods, severely impacting system stability. Therefore, in-depth research on operational optimization technologies for integrated photovoltaic, energy storage, and charging systems is essential. Only by designing a rationally optimized operation and scheduling plan can we minimize investment and operating costs while ensuring power supply reliability, maximizing benefits and efficiency.

[0003] At present, many solar-storage-charging strategies lack real-time monitoring of available energy of electric vehicles to formulate reasonable management strategies. Therefore, the management results are not very reasonable and there are many unreasonable applications. Summary of the Invention

[0004] The present invention fully considers the energy status of each electric vehicle and the parking time set by the owner, and reasonably formulates a corresponding energy management strategy.

[0005] The present invention provides a method for managing solar energy storage and charging, comprising the following steps:

[0006] S1. The fuzzy inference system collects status data of each electric vehicle and calculates the available energy value of each electric vehicle that can be used for V2G or G2V based on the status data;

[0007] S2. Transmitting the value of available energy of each electric vehicle that can be used for V2G or G2V to the smart energy platform;

[0008] S3. The smart energy platform calculates the difference between the photovoltaic system power generation of the microgrid and the load power, and sets the charging and discharging power point of each electric vehicle based on the difference and the available energy value of each electric vehicle;

[0009] S4. The smart energy platform controls the operation mode of the microgrid according to the charging and discharging power points of each electric vehicle.

[0010] Preferably, the smart energy platform in S3 calculates the difference between the load power and the photovoltaic power generation of the microgrid, and sets the power point of each electric vehicle according to the difference: wherein the difference calculation formula is:

[0011] ΔP=P load -P PV

[0012] Where ΔP is the power difference, P load is the load power, P PV Producing power for photovoltaics;

[0013] The power point is set according to the following formula:

[0014]

[0015] Among them, P s,m is the power point of the mth electric vehicle, C v2gm 、C g2vm are the available power for V2G and G2V of the mth electric vehicle, and M represents the total number of electric vehicles.

[0016] Preferably, the operation mode of the microgrid in S4 is carried out according to the following conditions:

[0017] If ΔP≤0, the microgrid operates in mode 1, which includes controlling the photovoltaic system in the microgrid to charge each electric vehicle and the energy storage system;

[0018] If ΔP>0, and ΔP≤P s ,Then the second microgrid operation mode includes controlling at least a part of electric vehicles to participate in V2G services and coordinate the output of the photovoltaic system;

[0019] If ΔP>0, and ΔP>P s , then the microgrid operation mode three includes controlling at least a part of the energy storage system and at least a part of the electric vehicles to provide energy to the load;

[0020] Among them, the charging station power P s Expressed as:

[0021] Preferably, in the first mode, the photovoltaic system is in a maximum power point tracking mode. If the load cannot be absorbed at this time, the photovoltaic system switches to a constant power mode.

[0022] Preferably, the photovoltaic systems in Mode 2 and Mode 3 are both in Maximum Power Point Tracking mode.

[0023] Preferably, setting the power point further includes the following constraints:

[0024]

[0025] If the power point calculated by the smart energy platform exceeds the set power limit If the power point calculated by the smart energy platform is lower than the set power lower limit, Then take the lower power limit.

[0026] Preferably, the S1 further comprises: the fuzzy controller outputs two indexes V2GN and G2VN according to the state data of the electric vehicle and the rules of the rule base, and multiplies the values ​​of the two indexes by the capacity of the electric vehicle to obtain the available energy C of the electric vehicle that can be used for V2G or G2V. v2g and C g2v ;

[0027] The status data at least includes the state of charge and parking time of each electric vehicle.

[0028] Preferably, the values ​​of the two indexes are allocated according to the rules in the rule base; and the value of V2GN is positively correlated with the state of charge and the parking time; the value of G2VN is negatively correlated with the state of charge and the parking time.

[0029] The present invention also proposes a solar energy storage and charging management system, comprising:

[0030] A fuzzy controller is provided at each charging station and is used to obtain status data of each electric vehicle charging at each charging station;

[0031] A rule base, connected to the fuzzy controller, is provided with rules for determining the capability of an electric vehicle to participate in V2G or G2V;

[0032] The fuzzy controller calculates the available energy of each electric vehicle for V2G or G2V based on the rules and the status data of each electric vehicle;

[0033] The smart energy platform is connected to the microgrid and the fuzzy controller. The fuzzy controller obtains the available energy of each electric vehicle for V2G or G2V, as well as the photovoltaic power generation and load power of the microgrid.

[0034] The smart energy platform allocates the power points of each electric vehicle for V2G or G2V based on the difference between load power and photovoltaic power generation.

[0035] Preferably, the fuzzy controller comprises two fuzzy inference systems for respectively determining the readiness state of the electric vehicle for participating in V2G and the readiness state of G2V.

[0036] The present invention can effectively and targetedly manage energy according to the real-time status of the electric vehicle and the parking time set by the owner, effectively absorb new energy (photovoltaic power generation energy), greatly improve the economy and flexibility of the photovoltaic storage and charging microgrid, and effectively reduce the number of interactions between the microgrid and the main power grid, thereby improving the stability of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of the energy management method of the present invention;

[0038] Figure 2 is a control block diagram of the fuzzy controller of the present invention;

[0039] Figure 3 This is the topology diagram of the solar-storage-charging microgrid energy management system of the present invention;

[0040] Figure 4 A rule table of the rule base of the present invention;

[0041] Figure 5 This is the model coefficient table of the present invention. DETAILED DESCRIPTION

[0042] The following is a further detailed description of the light storage and charging energy management method and system proposed in the present invention in conjunction with the accompanying drawings and specific embodiments. According to the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are in a very simplified form and are not in precise proportions, which are only used to conveniently and clearly assist in explaining the purpose of the embodiments of the present invention. In order to make the purposes, features and advantages of the present invention more obvious and easy to understand, please refer to the drawings. It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the implementation conditions of the present invention, so they have no technical significance. Any modification of the structure, change in the proportional relationship or adjustment of the size should still fall within the scope of the technical content disclosed in the present invention without affecting the efficacy and purpose that can be achieved by the present invention.

[0043] Current PV-storage-charging microgrids lack effective optimization methods and fail to fully utilize the energy storage properties of electric vehicles. This results in long-term power shortages, reducing the economic viability and compromising the safety of the microgrid. In grid-connected mode, the main grid is expected to absorb or generate balancing power for the microgrid. The designed energy management system minimizes this reliance on the grid by utilizing the reserve capacity of electric vehicle batteries. Furthermore, excess energy generated from photovoltaics will be used to charge the batteries rather than being exported to the main grid. This will alleviate the expected significant charging load from future electric vehicles, which could overload distribution equipment.

[0044] The present invention proposes a method for managing solar energy storage to solve this problem. Figure 1 As shown, the method comprises the following steps:

[0045] S1. The fuzzy controller collects the status data of each electric vehicle and calculates the available energy value of each electric vehicle that can be used for V2G (Vehicle to Grid) or G2V (Grid to Vehicle) based on the status data.

[0046] S2. Transmitting the value of available energy of each electric vehicle that can be used for V2G or G2V to the smart energy platform;

[0047] S3. The smart energy platform calculates the difference between the photovoltaic system power generation of the microgrid and the load power, and sets the charging and discharging power points of each charging station based on the difference and the available energy value of each electric vehicle;

[0048] S4. The smart energy platform controls the operation mode of the microgrid according to the power points of each charging and discharging.

[0049] like Figure 2 The fuzzy controller shown has two fuzzy inference systems (FISs): one for determining the EV's readiness for V2G and the other for G2V. This determination is based on each EV's status data, including its instantaneous state of charge (SOC) and time-to-return (TRD). The EV's SOC is typically estimated by the EV's battery management unit and used for EV propulsion control. EV owners can input their time-to-return (TRD) into the charging station.

[0050] In S1, the fuzzy controller outputs two indexes V2GN and G2VN according to the state data of the electric vehicle and the rules of the rule base. The values ​​of the two indexes are multiplied by the capacity of the electric vehicle to obtain the available energy C of the electric vehicle that can be used for V2G or G2V. v2g and C g2v The two indices are V2GN, representing V2G service capability, and G2VN, representing G2V service capability. These indices represent each EV's ability to generate or consume balancing power in the microgrid, respectively. Two fuzzy inference systems multiply these indices by the corresponding EV's capacity to determine the available energy for each EV to provide V2G or G2V services. The values ​​of these two indices are assigned based on rules in a rule base. Furthermore, the value of V2GN is positively correlated with the state of charge and parking time, while the value of G2VN is negatively correlated with the state of charge and parking time.

[0051] The smart energy platform in S3 calculates the difference between the load power and the photovoltaic power generation of the microgrid, and sets the power point of each electric vehicle based on the difference. The difference calculation formula is as follows:

[0052] ΔP=P load -P PV

[0053] ΔP is the power difference, P load is the load power, P PV Producing power for photovoltaics;

[0054] The power point is set according to the following formula:

[0055]

[0056] Among them, P s,m is the power point of the mth electric vehicle, C v2gm 、C g2vm are the available power for V2G and G2V of the mth electric vehicle, and M represents the total number of electric vehicles.

[0057] In this example, setting the power point in S3 also includes the following constraints:

[0058]

[0059] If the power point calculated by the smart energy platform exceeds the set power limit If the power point calculated by the smart energy platform is lower than the set power lower limit, Then take the lower power limit.

[0060] Furthermore, the operation mode of the microgrid in S4 is carried out according to the following conditions:

[0061] If ΔP≤0, the microgrid operates in mode 1, which includes controlling the photovoltaic system in the microgrid to charge each electric vehicle and the energy storage system;

[0062] If ΔP>0, and ΔP≤P s ,Then the second microgrid operation mode includes controlling at least a part of electric vehicles to participate in V2G services and coordinate the output of the photovoltaic system;

[0063] If ΔP>0, and ΔP>P s , then the microgrid operation mode three includes controlling at least a part of the energy storage system and at least a part of the electric vehicles to provide energy to the load;

[0064] Among them, the charging station power P s Expressed as:

[0065] Furthermore, in Mode 1, the load power is relatively low, and the PV system alone can meet the load power. At this point, the energy storage system and electric vehicles are charging, and the PV system is in maximum power point tracking mode. If the load cannot be absorbed at this point, the PV system switches to constant power mode.

[0066] In both Modes 2 and 3, the PV system is in maximum power point tracking (MPPT) mode. In Mode 2, the load is high, and the PV system alone cannot meet the load's power needs. In this case, the electric vehicle provides V2G service, collaborating with the PV system. In Mode 3, the load is heavy, and the energy storage system also participates, collaborating with the PV system and the electric vehicle.

[0067] The present invention also proposes a solar energy storage and charging management system for implementing the above management method, such as Figure 3 As shown, the system includes:

[0068] A fuzzy controller is provided at each charging station and is used to obtain status data of each electric vehicle charging at each charging station;

[0069] A rule base, connected to the fuzzy controller, is provided with rules for determining the capability of an electric vehicle to participate in V2G or G2V;

[0070] The fuzzy controller calculates the available energy of each electric vehicle for V2G or G2V based on the rules and the status data of each electric vehicle;

[0071] The smart energy platform is connected to the microgrid and the fuzzy controller. The fuzzy controller obtains the available energy of each electric vehicle for V2G or G2V, as well as the photovoltaic power generation and load power of the microgrid.

[0072] The smart energy platform allocates the power points of each electric vehicle for V2G or G2V based on the difference between the load power and the photovoltaic power generation.

[0073] The fuzzy controller includes two fuzzy inference systems for determining the readiness state of the electric vehicle for V2G and G2V respectively, and outputs the value of the available energy for V2G or G2V according to the rules of the rule base and the state data of the electric vehicle. The rules of the rule base can be found in Figure 4 、 5 ,like Figure 4 As shown in the figure, the value of V2GN is positively correlated with the state of charge and parking time; the value of G2VN is negatively correlated with the state of charge and parking time. That is, the ability of an electric vehicle to participate in V2G services is positively correlated with its current state of charge and parking time, while the ability of an electric vehicle to participate in G2V services is negatively correlated with its current state of charge and parking time. Figure 4 As shown, the index values ​​in this example are divided into four gears, each gear corresponds to a different value, and the value can be freely set by the user under the above-mentioned conditions. In this example, when the state of charge of the electric vehicle meets 30%<SOC≤50% and the parking time meets TRD≥10h, the V2GN and G2VN indexes are respectively IV and II. Figure 5 As shown, the values ​​indicating its application to V2G and G2V capabilities are 0.8~1 and 0.1~0.6 respectively.

[0074] In summary, the present invention relates to a fuzzy controller-based energy management method and system. This method monitors load power and categorizes the operating modes into three modes based on the load power demand. The fuzzy controller evaluates the state of the electric vehicle to determine the available energy for vehicle-to-vehicle (V2G) or vehicle-to-vehicle (G2V) operation. The fuzzy controller then transmits this data to a smart energy platform, which uses this data to determine the power point of each charging station and ultimately calculate the power flow.

[0075] The present invention solves the problem of low economic efficiency caused by poor optimization scheduling strategy of photovoltaic storage and charging microgrid, and improves the system stability of the microgrid.

[0076] The present invention can effectively and targetedly manage energy according to the real-time status of electric vehicles and the parking time set by the owner, effectively absorb new energy, greatly improve the economy and flexibility of the photovoltaic storage and charging microgrid, and effectively reduce the number of interactions between the microgrid and the main power grid, thereby improving the stability of the power grid.

[0077] Although the present invention has been described in detail through the above preferred embodiments, it should be understood that the above description is not intended to limit the present invention. After reading the above description, various modifications and substitutions of the present invention will become apparent to those skilled in the art. Therefore, the scope of protection of the present invention should be defined by the appended claims.

Claims

1. A method for managing solar energy storage and charging, characterized in that: The following steps are involved: S1. The fuzzy inference system collects status data of each electric vehicle and calculates the available energy value of each electric vehicle that can be used for V2G or G2V based on the status data; S2. Transmitting the value of available energy of each electric vehicle that can be used for V2G or G2V to the smart energy platform; S3. The smart energy platform calculates the difference between the photovoltaic system power generation of the microgrid and the load power, and sets the charging and discharging power point of each electric vehicle based on the difference and the available energy value of each electric vehicle; S4. The smart energy platform controls the operation mode of the microgrid according to the charging and discharging power points of each electric vehicle; The smart energy platform in S3 calculates the difference between the load power and the photovoltaic power generation of the microgrid, and sets the power point of each electric vehicle according to the difference: wherein the difference calculation formula is: ΔP=P load -P PV Where ΔP is the power difference, P load is the load power, P PV Producing power for photovoltaics; The power point is set according to the following formula: Among them, P s,m is the power point of the mth electric vehicle, C v2gm 、C g2vm are the available power for V2G and G2V of the mth electric vehicle, respectively, and M represents the total number of electric vehicles; The operation mode of the microgrid in S4 is based on the following conditions: If ΔP≤0, the microgrid operates in mode 1, which includes controlling the photovoltaic system in the microgrid to charge each electric vehicle and the energy storage system; If ΔP>0, and ΔP≤P s ,Then the second microgrid operation mode includes controlling at least a part of electric vehicles to participate in V2G services and coordinate the output of the photovoltaic system; If ΔP>0, and ΔP>P s , then the microgrid operation mode three includes controlling at least a part of the energy storage system and at least a part of the electric vehicles to provide energy to the load; Among them, the charging station power P s Expressed as:

2. The method for managing solar energy storage and charging according to claim 1, wherein: In the first mode, the photovoltaic system is in the maximum power point tracking mode. If the load cannot be absorbed at this time, the photovoltaic system switches to the constant power mode.

3. The method for managing solar energy storage and charging according to claim 1, wherein: In both Mode 2 and Mode 3, the photovoltaic system is in the maximum power point tracking mode.

4. The method for managing solar energy storage and charging according to claim 1, wherein: Setting the power point also includes the following constraints: If the power point calculated by the smart energy platform exceeds the set power limit If the power point calculated by the smart energy platform is lower than the set power lower limit, Then take the lower power limit.

5. The method for managing solar energy storage and charging according to claim 1, wherein: Said S1 further comprises: the fuzzy controller outputs two indexes V2GN and G2VN according to the state data of the electric vehicle and the rules of the rule base, and multiplies the values ​​of the two indexes by the capacity of the electric vehicle to obtain the available energy C of the electric vehicle that can be used for V2G v2g , or the available energy C that can be used for G2V g2v ; The status data at least includes the state of charge and parking time of each electric vehicle.

6. The method for managing solar energy storage and charging according to claim 5, wherein: The values ​​of the two indexes are assigned according to the rules in the rule base; Moreover, the value of V2GN is positively correlated with the state of charge and parking time; The value of G2VN is negatively correlated with the state of charge and parking time.

7. A solar energy storage and charging management system, characterized in that: The method for managing solar energy storage and charging according to any one of claims 1 to 6 comprises: A fuzzy controller is provided at each charging station and is used to obtain status data of each electric vehicle charging at each charging station; A rule base, connected to the fuzzy controller, is provided with rules for determining the capability of an electric vehicle to participate in V2G or G2V; The fuzzy controller calculates the available energy of each electric vehicle for V2G or G2V based on the rules and the status data of each electric vehicle; The smart energy platform is connected to the microgrid and the fuzzy controller. The fuzzy controller obtains the available energy of each electric vehicle for V2G or G2V, as well as the photovoltaic power generation and load power of the microgrid. The smart energy platform allocates the power points of each electric vehicle for V2G or G2V based on the difference between the load power and the photovoltaic power generation.

8. The solar energy storage and charging management system according to claim 7, characterized in that: The fuzzy controller includes two fuzzy inference systems for determining the readiness state of the electric vehicle for participating in V2G and the readiness state of G2V respectively.

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