Microgrid system and fuzzy control method

Abstract

The invention discloses a micro-grid system and a fuzzy control method, and belongs to the technical field of micro-grid systems, the micro-grid system comprises a traditional power grid, a distributed power supply and an energy management system, the distributed power grid comprises a solar photovoltaic panel and a wind driven generator, and the distributed power grid is connected with an energy storage device through a power transformation and distribution facility. The energy storage device adopts a high-performance storage battery pack and is used for storing and releasing electric energy, the traditional power grid and the energy storage device are electrically connected with the energy management system, and the energy management system comprises fuzzy control. And a fuzzy control strategy is adopted, so that the adaptive capacity and robustness of the system are improved, various uncertainty and variability factors are dealt with, and the stability of the system is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid systems, and in particular relates to a microgrid system and a fuzzy control method. Background Art

[0002] With the rapid development of renewable energy and the widespread application of distributed generation technology, microgrids have attracted widespread attention as an important part of achieving efficient energy utilization and smart grid construction. However, there are many distributed power sources, energy storage devices and loads in microgrids, and their operating characteristics are complex and changeable, which makes it difficult for traditional control methods to effectively cope with them. Therefore, an intelligent control strategy that can adapt to complex environments and respond quickly to changes is needed. Summary of the invention

[0003] The object of the present invention is to provide a microgrid system and a fuzzy control method to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: including a traditional power grid, a distributed power source, and an energy management system, wherein the distributed power grid includes solar photovoltaic panels and wind turbines, and the distributed power grid is connected to an energy storage device through a power transformation and distribution facility, and the energy storage device uses a high-performance battery pack for storing and releasing electric energy, and the traditional power grid is electrically connected to the energy storage device and the energy management system, and the energy management system includes fuzzy control;

[0005] As a further preferred embodiment of the present technical solution: the fuzzy control includes S1: fuzzification, S2: fuzzy rule base, S3: fuzzy reasoning, S4: defuzzification;

[0006] As a further preferred embodiment of the present technical solution: the process of the fuzzy control is:

[0007] 101: converting the collected real-time data into a fuzzy set, including an input fuzzy quantity and an output fuzzy quantity;

[0008] 102: Establish a fuzzy rule base covering various energy supply and demand situations;

[0009] 103: According to the input fuzzy quantity and the fuzzy rule base, the output fuzzy quantity is calculated through the fuzzy reasoning algorithm;

[0010] 104: Convert the output fuzzy quantity into a clear control signal and send it to the corresponding actuator;

[0011] As a further preferred embodiment of the present technical solution: the strategy of the energy management system is:

[0012] S1: Real-time monitoring and prediction;

[0013] S2: Intelligent scheduling and optimization;

[0014] S3: Switching between grid connection and off-grid connection;

[0015] S4: Fault detection and recovery;

[0016] As a further preferred embodiment of the present technical solution: the operation steps of the energy management system measurement strategy are:

[0017] 201: Use sensors and data analysis technology to monitor the operating status of each device in the microgrid and the energy supply and demand in real time, and make short-term forecasts;

[0018] 202: Based on real-time monitoring and prediction results, combined with fuzzy control strategies, intelligent scheduling and optimization decisions are made to ensure efficient use of energy and balance between supply and demand;

[0019] 203: In grid-connected mode, realize coordinated operation with the large power grid; in off-grid mode, ensure the independent and stable operation of the microgrid;

[0020] 204: Use fault detection algorithms to timely detect and isolate faulty equipment;

[0021] As a further preferred embodiment of the present technical solution: the energy storage device further includes a gas turbine and a diesel generator as backup power sources;

[0022] As a further preferred embodiment of the present technical solution: the real-time data of the fuzzy module includes power supply output power, energy storage device status, and load demand. The calculation formula of the power supply output power is: P out =P solar +P wind +P other ,

[0023] Where P solar is the output power of the solar photovoltaic panel, P wind is the wind turbine output power, P other Output power to other distributed power sources;

[0024] The calculation formula of the energy storage device state is: storage =E initial +ΔE charge -ΔE discharge ,

[0025] Where E storage is the total energy of the current energy storage device, E initial is the initial energy, ΔE charge The energy added by charging, ΔE discharge The energy reduced by the discharge;

[0026] The calculation formula for the load demand is:

[0027] Among them, P demand is the total load demand, is the power demand of the i-th load point;

[0028] As a further preferred embodiment of the present technical solution: the fuzzy rule base of the fuzzification module covers optimal or suboptimal control strategies under different operating scenarios;

[0029] As a further preferred embodiment of the present technical solution: the fuzzy control method realizes the control modes of constant current, constant voltage, constant power, etc. of the battery, so as to extend the service life of the battery and improve the efficiency of the system;

[0030] As a further preferred embodiment of the present technical solution: during the fault detection and recovery, the backup power supply is started or the scheduling strategy is adjusted simultaneously to ensure continuous power supply to the system.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] 1. The present invention realizes efficient utilization of energy and balance of supply and demand through intelligent scheduling and optimized decision-making, improves energy utilization efficiency, adopts fuzzy control strategy, improves the system's adaptability and robustness, copes with various uncertainties and volatile factors, and enhances the stability of the system.

[0033] 2. Through real-time monitoring and prediction technology, deploy a high-sensitivity, low-latency sensor network to achieve real-time monitoring of all aspects of the energy system, including power generation, power consumption, equipment status, etc., to ensure the accuracy and timeliness of the data.

[0034] 3. Optimize energy distribution and scheduling strategies, formulate optimal energy scheduling plans based on energy demand and cost differences in different periods and regions, reduce unnecessary energy waste, conduct cost-benefit analysis regularly, evaluate the impact of different scheduling strategies on operating costs, continuously optimize scheduling strategies, and ensure long-term economic benefits are maximized. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A schematic diagram of the structure of a microgrid system and a fuzzy control method of the present invention;

[0036] Figure 2 A schematic diagram of the structure of fuzzy control of a microgrid system and a fuzzy control method of the present invention;

[0037] Figure 3 A flow chart of fuzzy control of a microgrid system and a fuzzy control method of the present invention;

[0038] Figure 4A schematic diagram of the structure of a microgrid system and a fuzzy control method energy management system strategy of the present invention;

[0039] Figure 5 The present invention is a flow chart of a microgrid system and a fuzzy control method energy management system strategy. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] Example

[0042] See also Figure 1-Figure 5 As shown, the present invention provides a technical solution: including a traditional power grid, a distributed power source, and an energy management system. The distributed power grid includes solar photovoltaic panels and wind turbines. The distributed power grid is connected to an energy storage device through a power transformation and distribution facility. The energy storage device uses a high-performance battery pack for storing and releasing electric energy. The traditional power grid is electrically connected to the energy storage device and the energy management system. The energy management system includes fuzzy control, which realizes efficient use of energy and balance of supply and demand through intelligent scheduling and optimized decision-making, improves energy utilization efficiency, adopts fuzzy control strategy, improves the system's adaptability and robustness, copes with various uncertainties and variability factors, and enhances the stability of the system.

[0043] In this embodiment, specifically: fuzzy control includes S1: fuzzification, S2: fuzzy rule base, S3: fuzzy reasoning, S4: defuzzification;

[0044] In this embodiment, the specific process of fuzzy control is:

[0045] 101: converting the collected real-time data into a fuzzy set, including an input fuzzy quantity and an output fuzzy quantity;

[0046] 102: Establish a fuzzy rule base covering various energy supply and demand situations;

[0047] 103: According to the input fuzzy quantity and the fuzzy rule base, the output fuzzy quantity is calculated through the fuzzy reasoning algorithm;

[0048] 104: Convert the output fuzzy quantity into a clear control signal and send it to the corresponding actuator. Through real-time monitoring and prediction technology, quickly respond to load changes and command changes to ensure the stable operation of the system and adjust the response speed;

[0049] In this embodiment, specifically, the strategy of the energy management system is:

[0050] S1: Real-time monitoring and prediction;

[0051] S2: Intelligent scheduling and optimization;

[0052] S3: Switching between grid connection and off-grid connection;

[0053] S4: Fault detection and recovery;

[0054] In this embodiment, specifically, the operation steps of the energy management system measurement strategy are:

[0055] 201: Use sensors and data analysis technology to monitor the operating status of each device in the microgrid and the energy supply and demand in real time, and make short-term forecasts;

[0056] 202: Based on real-time monitoring and prediction results, combined with fuzzy control strategies, intelligent scheduling and optimization decisions are made to ensure efficient use of energy and balance between supply and demand;

[0057] 203: In grid-connected mode, realize coordinated operation with the large power grid; in off-grid mode, ensure the independent and stable operation of the microgrid;

[0058] 204: Through the fault detection algorithm, faulty equipment can be discovered and isolated in time, energy distribution and scheduling strategies can be optimized, energy waste and operating costs can be reduced, and the operating costs can be effectively reduced;

[0059] In this embodiment, specifically: the energy storage device also includes a gas turbine and a diesel generator as a backup power source;

[0060] In this embodiment, specifically: the real-time data of the fuzzy module includes power supply output power, energy storage device status, and load demand. The calculation formula of the power supply output power is: out =P solar +P wind +P other ,

[0061] Where P solar is the output power of the solar photovoltaic panel, P wind is the wind turbine output power, P other Output power to other distributed power sources;

[0062] The calculation formula of the energy storage device state is: storage =E initial +ΔE charge -ΔE discharge ,

[0063] Where E storage is the total energy of the current energy storage device, Einitial is the initial energy, ΔE charge The energy added by charging, ΔE discharge The energy reduced by the discharge;

[0064] The calculation formula for the load demand is:

[0065] Among them, P demand is the total load demand, is the power demand of the i-th load point;

[0066] In this embodiment, specifically: the fuzzy rule base of the fuzzification module covers the optimal or suboptimal control strategies under different operating scenarios;

[0067] In this embodiment, specifically: the fuzzy control method realizes the constant current, constant voltage, constant power and other control modes of the battery to extend the service life of the battery and improve the efficiency of the system;

[0068] In this embodiment, specifically: during fault detection and recovery, the backup power supply is started or the scheduling strategy is adjusted simultaneously to ensure continuous power supply to the system.

[0069] Working principle or structural principle: The collected real-time data is converted into a fuzzy set, including input fuzzy quantity and output fuzzy quantity, and then a fuzzy rule base containing various energy supply and demand conditions is established. Then, according to the input fuzzy quantity and the fuzzy rule base, the output fuzzy quantity is calculated through the fuzzy reasoning algorithm. Finally, the output fuzzy quantity is converted into a clear control signal and sent to the corresponding actuator. Through real-time monitoring and prediction technology, it can quickly respond to load changes and command changes to ensure the stable operation of the system and adjust the response speed. At the same time, sensors and data analysis technology are used to monitor the operating status and energy supply and demand of each device in the microgrid in real time, and short-term predictions are made. According to the real-time monitoring and prediction results, combined with fuzzy control strategies, intelligent scheduling and optimization decisions are made to ensure efficient utilization of energy and balance between supply and demand. In the grid-connected mode, coordinated operation with the large power grid is achieved; in the off-grid mode, the independent and stable operation of the microgrid is ensured. Finally, through the fault detection algorithm, the faulty equipment is discovered and isolated in time, the energy distribution and scheduling strategies are optimized, energy waste and operating costs are reduced, and the operating costs are effectively reduced.

[0070] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A microgrid system and a fuzzy control method, characterized in that: It includes a traditional power grid, a distributed power source, and an energy management system. The distributed power grid includes solar photovoltaic panels and wind turbines. The distributed power grid is connected to an energy storage device through a power transformation and distribution facility. The energy storage device uses a high-performance battery pack for storing and releasing electrical energy. The traditional power grid is electrically connected to the energy storage device and the energy management system. The energy management system includes fuzzy control.

2. A microgrid system and fuzzy control method according to claim 1, characterized in that: The fuzzy control includes S1: fuzzification, S2: fuzzy rule base, S3: fuzzy reasoning, and S4: defuzzification.

3. A microgrid system and fuzzy control method according to claim 2, characterized in that: The process of the fuzzy control is: 101: converting the collected real-time data into a fuzzy set, including an input fuzzy quantity and an output fuzzy quantity; 102: Establish a fuzzy rule base covering various energy supply and demand situations; 103: According to the input fuzzy quantity and the fuzzy rule base, the output fuzzy quantity is calculated through the fuzzy reasoning algorithm; 104: Convert the output fuzzy quantity into a clear control signal and send it to the corresponding actuator.

4. A microgrid system and fuzzy control method according to claim 3, characterized in that: The strategy of the energy management system is: S1: Real-time monitoring and prediction; S2: Intelligent scheduling and optimization; S3: Switching between grid connection and off-grid connection; S4: Fault detection and recovery.

5. A microgrid system and fuzzy control method according to claim 4, characterized in that: The operation steps of the energy management system measurement strategy are: 201: Use sensors and data analysis technology to monitor the operating status of each device in the microgrid and the energy supply and demand in real time, and make short-term forecasts; 202: Based on real-time monitoring and prediction results, combined with fuzzy control strategies, intelligent scheduling and optimization decisions are made to ensure efficient use of energy and balance between supply and demand; 203: In grid-connected mode, realize coordinated operation with the large power grid; in off-grid mode, ensure the independent and stable operation of the microgrid; 204: Through the fault detection algorithm, the faulty equipment is discovered and isolated in a timely manner.

6. A microgrid system and fuzzy control method according to claim 5, characterized in that: The energy storage device also includes a gas turbine and a diesel generator as backup power sources.

7. A microgrid system and fuzzy control method according to claim 6, characterized in that: The real-time data of the fuzzy module includes power supply output power, energy storage device status, and load demand. The calculation formula of the power supply output power is: out =P solar +P wind +P other , Where P solar is the output power of the solar photovoltaic panel, P wind is the wind turbine output power, P other Output power to other distributed power sources; The calculation formula of the energy storage device state is: storage =E initial +ΔE charge -ΔE discharge , Where E storage is the total energy of the current energy storage device, E initial is the initial energy, ΔE charge The energy added by charging, ΔE discharge The energy reduced by the discharge; The calculation formula for the load demand is: Among them, P demand is the total load demand, is the power demand of the ith load point.

8. A microgrid system and fuzzy control method according to claim 7, characterized in that: The fuzzy rule base of the fuzzification module covers optimal or suboptimal control strategies under different operating scenarios.

9. A microgrid system and fuzzy control method according to claim 8, characterized in that: The fuzzy control method realizes constant current, constant voltage, constant power and other control modes of the battery, so as to extend the service life of the battery and improve the efficiency of the system.

10. A microgrid system and fuzzy control method according to claim 9, characterized in that: During the fault detection and recovery, the backup power supply is started or the scheduling strategy is adjusted simultaneously to ensure continuous power supply to the system.

Images