Energy storage micro-grid system cooperatively controlled by dual-redundancy lithium battery pack

Abstract

An energy storage micro-grid system cooperatively controlled by dual-redundancy lithium battery packs relates to the technical field of power systems and energy storage, and comprises a micro-grid main controller module used for carrying out centralized control and management on the micro-grid system, receiving and processing various signals and sending out control instructions; the dual-redundancy lithium battery pack module monitors the lithium battery pack state parameters in real time, and performs fault diagnosis and switching on the dual-redundancy lithium battery pack according to the lithium battery pack state parameters to obtain the reliability of the dual-redundancy lithium battery pack; the energy balance distribution module is used for evaluating the state of the battery based on a dual-redundancy lithium battery pack cooperative control strategy of fuzzy logic and model prediction control and formulating an optimal energy distribution scheme; and the charging and discharging power optimization module is used for carrying out optimization scheduling on the charging and discharging power of the lithium battery pack based on an improved particle swarm optimization algorithm. The problem that the service life of the battery pack is shortened due to uneven energy distribution is solved, and the operation and maintenance cost of the system is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power systems and energy storage technology, and more particularly, to a dual-redundant lithium battery pack cooperative control energy storage micro-grid system. BACKGROUND

[0002] Dual redundancy refers to the configuration of two sets of functionally identical lithium battery packs in the system, which realizes fault tolerance through redundant design. When one set of battery pack fails, the other set can immediately take over the work, ensuring the continuous operation of the system and significantly improving the power supply reliability. Lithium battery pack is composed of multiple lithium batteries in series or parallel, which realizes energy storage and release through the movement of lithium ions between the positive and negative electrodes. The energy storage micro-grid system is a relatively independent small power system, which can contain distributed power sources (such as solar energy, wind energy), loads (electric equipment), energy storage devices and control devices, and can operate in grid-connected or off-grid mode. The energy storage micro-grid balances the energy supply and demand in the micro-grid through energy storage devices (herein lithium battery packs), solves the intermittency problem of renewable energy generation (such as solar energy not generating at night), and improves the power supply stability.

[0003] Problems existing in the prior art:

[0004] During operation, due to the differences in manufacturing process of each battery, different use environments, and the limitations of existing control strategies, the state of charge (SOC) of each battery in the lithium battery pack is inconsistent. For example, in some micro-grid projects, some batteries are in a high SOC state for a long time, while some batteries have a low SOC. This imbalance causes the high-load batteries to age faster. According to industry statistics, uneven energy distribution can reduce the overall service life of the battery pack by 30%-50%, greatly increasing the operation and maintenance cost of the system.

[0005] Traditional energy storage micro-grid systems mostly use single lithium battery packs or simple backup structures. Once the lithium battery pack fails, such as short circuit or open circuit, the entire energy storage system cannot work normally. In fields with extremely high requirements for power supply reliability, such as medical treatment and industrial production, instantaneous power interruption can cause serious consequences and huge economic losses. For example, in some precision electronic manufacturing workshops, temporary power outage can cause production lines to stop and products to be scrapped, resulting in losses of hundreds of thousands of yuan or even more. The existing energy management strategy cannot realize efficient energy distribution and utilization according to the real-time operation status of the micro-grid, such as the output fluctuation of distributed power sources, the rapid change of load, and the dynamic performance of lithium battery packs. During the peak load period of the micro-grid, the output of lithium battery packs and distributed power sources cannot be coordinated in time, resulting in increased cost of purchasing electricity from the large power grid; during the trough period of the load, the excess energy cannot be fully utilized to reasonably charge the lithium battery pack, causing energy waste.

[0006] To solve the above problems, the present application provides a solution. SUMMARY

[0007] In order to overcome the above-mentioned defects of the prior art, embodiments of the present application provide a dual-redundant lithium battery pack cooperative control energy storage micro-grid system, which comprises a micro-grid main controller module, a dual-redundant lithium battery pack module, an energy balance distribution module, a charging and discharging power optimization module, and connections between the modules.

[0008] The micro-grid main controller module is used for centralized control and management of the micro-grid system, receives and processes various signals, and issues control instructions.

[0009] The dual-redundant lithium battery pack module monitors lithium battery pack state parameters in real time, performs fault diagnosis and switching on the dual-redundant lithium battery pack according to the lithium battery pack state parameters, and obtains the reliability of the dual-redundant lithium battery pack.

[0010] The energy balance distribution module evaluates the state of the battery based on a fuzzy logic and model predictive control dual-redundant lithium battery pack cooperative control strategy, and formulates an optimal energy distribution scheme.

[0011] The charging and discharging power optimization module optimizes the charging and discharging power of the lithium battery pack based on an improved particle swarm optimization algorithm with the lowest system operating cost and the highest energy utilization efficiency as the target, so as to solve the problems raised in the above background art.

[0012] To achieve the above-mentioned purposes, the present application provides the following technical solutions:

[0013] A dual-redundant lithium battery pack cooperative control energy storage micro-grid system comprises a micro-grid main controller module, a dual-redundant lithium battery pack module, an energy balance distribution module, a charging and discharging power optimization module, and connections between the modules.

[0014] The micro-grid main controller module is used for centralized control and management of the micro-grid system, receives and processes various signals, and issues control instructions.

[0015] The dual-redundant lithium battery pack module monitors lithium battery pack state parameters in real time, performs fault diagnosis and switching on the dual-redundant lithium battery pack according to the lithium battery pack state parameters, and obtains the reliability of the dual-redundant lithium battery pack.

[0016] The energy balance distribution module evaluates the state of the battery based on a fuzzy logic and model predictive control dual-redundant lithium battery pack cooperative control strategy, and formulates an optimal energy distribution scheme.

[0017] The charging and discharging power optimization module optimizes the charging and discharging power of the lithium battery pack based on an improved particle swarm optimization algorithm with the lowest system operating cost and the highest energy utilization efficiency as the target.

[0018] In a preferred embodiment, the micro-grid master controller adopts a multi-core microprocessor with high-speed data processing capability, and communicates with the battery management system in the dual-redundant lithium battery pack module in real time through the CAN bus;

[0019] Meanwhile, the micro-grid master controller is connected to the energy management unit, distributed power supply and load through Ethernet for data interaction, centralized control and management of the micro-grid system.

[0020] In a preferred embodiment, the dual-redundant lithium battery pack is composed of two lithium battery packs with the same structure connected in parallel through a bidirectional DC-DC converter, and each lithium battery pack is equipped with an independent battery management system. The two lithium battery packs are backup for each other and are provided with a fault diagnosis and switching module.

[0021] The state parameters of the lithium battery pack include the voltage, current, temperature and SOC parameters of the battery.

[0022] In a preferred embodiment, the bidirectional DC-DC converter adopts a soft-switching topology, and adjusts the charging and discharging current and voltage of the lithium battery pack according to the instructions of the micro-grid master controller.

[0023] The battery management system collects the key parameters of the battery in real time and transmits them to the micro-grid master controller module.

[0024] In a preferred embodiment, the fault diagnosis module detects and locates the fault by real-time monitoring and analyzing the parameters of the lithium battery pack using wavelet transform and neural network algorithm.

[0025] When a fault is detected in a lithium battery pack, the fault switching module isolates the faulty lithium battery pack and transfers its work to another normally working lithium battery pack.

[0026] In a preferred embodiment, the reliability of the dual-redundant lithium battery pack is obtained as follows:

[0027] The average failure interval time and the average repair time are obtained through the fault diagnosis and switching process, and the reliability of the dual-redundant lithium battery pack is calculated based on the reliability of a single lithium battery pack. The calculation formula is as follows:

[0028]

[0029] Wherein, R is the reliability of the dual-redundant lithium battery pack, R1 is the reliability of a single lithium battery pack, MTTR is the average repair time, and MTBF is the average failure interval time.

[0030] In a preferred embodiment, the process of formulating the optimal energy allocation scheme is as follows:

[0031] The double-redundant lithium battery pack cooperative control strategy based on fuzzy logic and model predictive control evaluates the state of the battery, and then based on the model predictive control algorithm, predicts the load demand of the micro-grid and the output power of the distributed power source in the future period of time, formulates the optimal energy distribution scheme, and realizes the energy equalization distribution between the two lithium battery packs.

[0032] The input of the fuzzy logic is the SOC deviation of the two lithium battery packs and the change rate of the SOC deviation, and the output is the energy distribution coefficients k1 and k2, wherein k1+k2=1.

[0033] In a preferred embodiment, the energy distribution coefficients k1 and k2 are obtained as follows:

[0034] In the model predictive control, the SOC equalization degree of the battery pack after energy distribution and the minimum system energy loss and the maximum reliability of the double-redundant lithium battery pack are taken as the objective function.

[0035] By solving the objective function, the optimal energy distribution coefficients k1 and k2 are obtained.

[0036] By solving the objective function J, the optimal energy distribution coefficients k1 and k2 are obtained, and the formula is as follows:

[0037] J=w1×∑(ΔSOC(t+i))+w2×∑Ploss(t+i)+w3×R 2

[0038] Wherein w1, w2, w3 are weight coefficients, R is the reliability of the double-redundant lithium battery pack, ΔSOC(t+i) is the SOC deviation at future i time, and Ploss(t+i) is the system energy loss at future i time.

[0039] In a preferred embodiment, the process of optimizing and scheduling the charging and discharging power of the lithium battery pack with the lowest system operation cost and the highest energy utilization efficiency as the target is as follows:

[0040] The charging and discharging power of the lithium battery pack is optimized and scheduled by comprehensively considering the system operation cost, the maintenance cost of the lithium battery pack, the income of selling electric energy, and the constraint condition model, and the formula is as follows:

[0041] minF=C operation +C maintenance -E sale ×P sale

[0042] In the formula, minF represents the objective function; C operation represents the system operation cost; C maintenance represents the maintenance cost of the lithium battery pack; E​sale represents the revenue from selling electric energy; P sale is the price of selling electric energy.

[0043] In a preferred embodiment, the constraint model is as follows:

[0044]

[0045] where SOC is the state of charge, SOC min and SOC max are the minimum state of charge and the maximum state of charge, respectively; P charge is the charging power of the lithium battery pack, P chargemin and P chargemax are the minimum charging power and the maximum charging power allowed for the lithium battery pack, respectively; P discharge is the discharging power of the lithium battery pack, P dischargemin and P dischargemax are the minimum discharging power and the maximum discharging power allowed for the lithium battery pack, respectively; P grid is the interaction power between the power grid and the microgrid, P dg is the output power of the distributed power source, and P load is the load power of the microgrid.

[0046] The technical effects and advantages of the energy storage microgrid system with dual-redundant lithium battery pack collaborative control are as follows:

[0047] 1. The energy storage microgrid system with dual-redundant lithium battery pack collaborative control strategy based on fuzzy logic and model predictive control is used to monitor the SOC, voltage, current, and temperature of the two lithium battery packs in real time, evaluate the battery state using a fuzzy logic controller, and predict the microgrid load demand and distributed power output power using a model predictive control algorithm. The optimal energy distribution coefficient is solved with the battery pack SOC balance degree and the minimum system energy loss as the objective function, and the energy balance distribution between the two lithium battery packs is realized. According to experimental verification, the SOC deviation of the two lithium battery packs can be stably controlled within 5% after using the strategy, and compared with the traditional system, the service life of the battery pack is prolonged by 40% to 60%, effectively solving the problem of shortened battery pack life caused by uneven energy distribution in the existing system, and greatly reducing the operation and maintenance cost of the system. The dual-redundant lithium battery pack structure is used, and the two lithium battery packs are backup for each other and are equipped with a perfect fault diagnosis and switching mechanism. The fault diagnosis module uses wavelet transform and neural network algorithm to monitor and analyze the parameters of the lithium battery pack in real time, which can quickly and accurately detect and locate faults; when a fault is detected in a lithium battery pack, the fault switching module can isolate the faulty lithium battery pack in a very short time (less than 50 ms) and transfer its work to another normal working lithium battery pack, ensuring the normal power supply of the microgrid.

[0048] 2.The application optimizes the charging and discharging power of the lithium battery pack by introducing an intelligent energy management algorithm based on an improved particle swarm optimization (IPSO) algorithm, aiming at the lowest system operation cost and the highest energy utilization efficiency, comprehensively considering the system operation cost (including the charging and discharging energy cost of the lithium battery pack and the operation cost of other equipment), the maintenance cost of the lithium battery pack (related to the charging and discharging depth and the cycle number), and the selling electricity revenue, while meeting the lithium battery pack SOC constraint, the charging and discharging power constraint, and the micro-grid power balance constraint. Experimental results show that, compared with the traditional energy management strategy, the algorithm can reduce the system operation cost by 15% to 25%, and increase the energy utilization efficiency by 20% to 30%, realizing the efficient utilization and economic operation of the micro-grid system energy, and greatly improving the overall economic benefit of the system. Using a multi-core microprocessor with high-speed data processing capability, various signals can be quickly received and processed, and control instructions can be sent in time to ensure efficient and reliable centralized control and management of the micro-grid system. In the dual-redundant lithium battery pack module, the bidirectional DC-DC converter adopts a high-efficiency soft-switching topology structure with a response time less than 10 ms, which can accurately adjust the lithium battery pack charging and discharging current and voltage according to the main controller instructions, ensuring the stability and accuracy of the charging and discharging process; the BMS collects the key parameters of the battery in real time through high-precision sensors (Hall current sensor with an accuracy of ±0.1%, linear optocoupler isolation type voltage sensor with an accuracy of ±0.5%, and thermistor type temperature sensor with an accuracy of ±1℃), and transmits them to the main controller, providing reliable data support for the accurate control of the system and ensuring the stable operation of the entire energy storage micro-grid system. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The application is a dual-redundant lithium battery pack collaborative control energy storage micro-grid system structure diagram. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the application.

[0051] Embodiment 1, Figure 1 The application is a dual-redundant lithium battery pack collaborative control energy storage micro-grid system.

[0052] The micro-grid main controller module is used for centralized control and management of the micro-grid system, receiving and processing various signals, and sending control instructions.

[0053] The micro-grid master controller adopts a multi-core microprocessor with high-speed data processing capability, communicates with the independent battery management system (BMS) of each lithium battery pack in the dual-redundancy lithium battery pack module through a CAN bus, receives the voltage, current, temperature and SOC parameters of the battery, and communicates with the energy management unit, distributed power and load through an Ethernet to realize data interaction and centralized control and management of the micro-grid system.

[0054] Meanwhile, the micro-grid master controller communicates with the energy management unit, distributed power and load through an Ethernet to realize data interaction and centralized control and management of the micro-grid system.

[0055] The multi-core microprocessor with high-speed data processing capability can quickly receive and process the battery state parameters (such as voltage, current, temperature, SOC, etc.) from the dual-redundancy lithium battery pack module and the operation data of the distributed power, load and other devices in the micro-grid, ensure real-time monitoring and analysis of the system operation state, provide strong computing power support for timely and accurate control instructions, avoid control lag caused by data processing delay, and ensure stable and reliable operation of the micro-grid system.

[0056] The multi-communication interface realizes collaborative control of the system: real-time communication with the BMS of the dual-redundancy lithium battery pack module through a CAN bus to realize accurate monitoring of the battery state; communication connection with the energy management unit, distributed power and load through an Ethernet to realize data interaction and information sharing of the entire micro-grid system, thereby realizing centralized control and management of the micro-grid system, forming an organic whole between the modules, and improving the overall operation efficiency and stability of the system.

[0057] The dual-redundancy lithium battery pack module can monitor the state parameters of the lithium battery pack in real time, diagnose and switch the dual-redundancy lithium battery pack according to the state parameters of the lithium battery pack, and obtain the reliability of the dual-redundancy lithium battery pack.

[0058] The dual-redundancy lithium battery pack module is composed of two lithium battery packs with the same structure connected in parallel through a bidirectional DC-DC converter, and each lithium battery pack is equipped with an independent battery management system (BMS).

[0059] The state parameters of the lithium battery pack include the voltage, current, temperature and SOC parameters of the battery.

[0060] The bidirectional DC-DC converter adopts a high-efficiency soft-switching topology structure, with a response time less than 10 ms, and can accurately adjust the charging and discharging current and voltage of the lithium battery pack according to the instructions of the micro-grid master controller.

[0061] The BMS measures current by a Hall current sensor with an accuracy of ±0.1%, measures voltage by a linear optical coupling isolation voltage sensor with an accuracy of ±0.5%, and measures temperature by a thermistor temperature sensor with an accuracy of ±1℃, collects key parameters of the battery in real time and transmits them to the micro-grid main controller module; the dual-redundancy lithium battery module adopts a dual-redundancy lithium battery structure, two lithium battery groups are backup for each other, and a set of fault diagnosis and switching mechanism is provided;

[0062] The fault diagnosis module detects and locates faults by real-time monitoring and analyzing various parameters of the lithium battery group by using wavelet transform and neural network algorithm.

[0063] When a fault is detected in a lithium battery group, the fault switching module isolates the faulty lithium battery group in a very short time and transfers the work task to another normally working lithium battery group.

[0064] The mean time between failures and the mean time to repair are obtained through the fault diagnosis and switching process, and the reliability of the dual-redundancy lithium battery group is calculated in combination with the reliability of a single lithium battery group, and the calculation formula is as follows:

[0065]

[0066] Wherein, R is the reliability of the dual-redundancy lithium battery group, R1 is the reliability of a single lithium battery group, MTTR is the mean time to repair, and MTBF is the mean time between failures.

[0067] It should be noted that the dual-redundancy structure improves system reliability: two lithium battery groups with the same structure are connected in parallel through a bidirectional DC-DC converter, and the two lithium battery groups are backup for each other, when one of the lithium battery groups fails, the other lithium battery group can immediately undertake all the charging and discharging tasks, ensuring the normal power supply of the micro-grid. In combination with the perfect fault diagnosis and switching mechanism, the fault diagnosis module uses wavelet transform and neural network algorithm to monitor and analyze various parameters of the lithium battery group in real time, which can quickly and accurately detect and locate faults, and the fault switching module can isolate the faulty lithium battery group in a very short time (less than 50ms) and complete task transfer. Compared with the traditional single lithium battery group system, the reliability of the dual-redundancy system is improved by 80% to 90%, which greatly improves the reliability of the system, and has important application value in scenes with high power supply reliability requirements.

[0068] High-precision sensors and high-efficiency converters guarantee battery performance: Each lithium battery pack is equipped with an independent BMS that collects key parameters of the battery in real time through high-precision sensors (Hall current sensors with an accuracy of ±0.1%, linear optocoupler isolation voltage sensors with an accuracy of ±0.5%, and thermistor temperature sensors with an accuracy of ±1°C) to provide accurate and reliable battery state data for the microgrid master controller module, facilitating precise management of the battery. The bidirectional DC-DC converter uses a high-efficiency soft-switching topology with a response time of less than 10 ms, which can accurately adjust the charging and discharging current and voltage of the lithium battery pack according to the instructions of the microgrid master controller, ensuring the stability and accuracy of the charging and discharging process, achieving efficient energy conversion, and avoiding battery performance degradation caused by improper charging and discharging parameter control, thereby extending the service life of the battery pack.

[0069] Energy equalization distribution prolongs the service life of the battery pack: Through the dual-redundancy lithium battery pack collaborative control strategy based on fuzzy logic and model predictive control, the energy equalization distribution between the two lithium battery packs is realized, and the SOC deviation of the two lithium battery packs is stably controlled within 5%, avoiding the acceleration of aging caused by the long-term high-load state of part of the battery. Compared with traditional systems, the service life of the battery pack is extended by 40% to 60%, and the operation and maintenance cost of the system is reduced.

[0070] Energy equalization distribution module: The dual-redundancy lithium battery pack collaborative control strategy based on fuzzy logic and model predictive control evaluates the state of the battery and develops the optimal energy distribution scheme;

[0071] Dual-redundancy lithium battery pack collaborative control strategy based on fuzzy logic and model predictive control: This strategy monitors the SOC, voltage, current, and temperature parameters of the two lithium battery packs in real time, evaluates the state of the battery using a fuzzy logic controller, and then predicts the load demand of the microgrid and the output power of the distributed power source in the future period of time based on the model predictive control algorithm, thereby developing the optimal energy distribution scheme and achieving energy equalization distribution between the two lithium battery packs.

[0072] The inputs of the fuzzy logic controller are the SOC deviation ΔSOC = SOC1-SOC2 and the change rate dΔSOC / dt of the two lithium battery packs, and the outputs are the energy distribution coefficients k1 and k2, where k1+k2=1;

[0073] In the model predictive control, the SOC equalization degree of the battery pack after energy distribution, the minimum system energy loss, and the maximum reliability of the dual-redundancy lithium battery pack are taken as the objective function. By solving the objective function, the optimal energy distribution coefficients k1 and k2 are obtained;

[0074] By solving the objective function J, the optimal energy distribution coefficients k1 and k2 are obtained, and the formula is as follows:

[0075] J = w1 x ∑(ΔSOC(t+i)) 2 +w2 x ∑Ploss(t+i) + w3 x R

[0076] where w1, w2, w3 are weight coefficients, R is the reliability of the dual-redundant lithium battery pack, ΔSOC(t+i) is the SOC deviation in the future i time, Ploss(t+i) is the system energy loss in the future i time.

[0077] The charge-discharge power optimization module is an intelligent energy management algorithm based on an improved particle swarm optimization (IPSO) algorithm. The algorithm combines the operating state of the microgrid, parameters of the lithium battery pack, and electricity price information, etc., to optimize and schedule the charge-discharge power of the lithium battery pack with the lowest system operating cost and the highest energy utilization efficiency as the target.

[0078] The formula for optimizing and scheduling the charge-discharge power of the lithium battery pack with the lowest system operating cost and the highest energy utilization efficiency as the target is as follows:

[0079] min F = C operation +C maintenance -E sale x P sale

[0080] In the formula, min F represents that the target is to minimize the value of the function F, and F comprehensively reflects the cost and benefit of the system operation; C operation represents the system operating cost; C maintenance represents the maintenance cost of the lithium battery pack; E sale represents the revenue from selling electricity; and P sale is the price of the sold electricity.

[0081] The system operating cost (including the charge-discharge energy cost of the lithium battery pack and the operating cost of other equipment), the maintenance cost of the lithium battery pack (related to the charge-discharge depth and cycle number of the battery), and the revenue from selling electricity are comprehensively considered, and the charge-discharge power of the lithium battery pack is optimized and scheduled according to the constraint condition model.

[0082] The constraint condition model is as follows:

[0083]

[0084] In the formula, SOC is the state of charge, SOC min and SOC max are the minimum state of charge and the maximum state of charge, respectively; P charge is the charge power of the lithium battery pack, P chargemin and P chargemax are the minimum charge power and the maximum charge power allowed for the lithium battery pack, respectively.discharge is the discharge power of lithium battery pack, P dischargemin and P dischargemax are the minimum and maximum discharge power allowed for lithium battery pack, respectively; P grid is the interaction power between grid and microgrid, P dg is the output power of distributed power sources (such as photovoltaic, wind power, etc.), P load is the load power of microgrid.

[0085] The constraint condition model provides clear operating boundaries for the microgrid energy management system (EMS), facilitating the development of optimal scheduling strategies. For example, during low-price periods, charge the battery pack according to the charge power constraint; during peak price periods, release electrical energy according to the discharge power constraint, reducing electricity costs and achieving economic operation.

[0086] Combined with the output characteristics of distributed power sources (such as the "abandoned light" period of photovoltaic), the use of renewable energy can be maximized through the charge and discharge constraints of the battery pack, reducing the phenomenon of abandoned light and wind, and improving energy utilization. Avoid premature aging or damage of the battery, reduce the frequency of replacing the battery pack, and reduce maintenance and investment costs. At the same time, by reasonably constraining power interaction, the transaction cost (such as electricity purchase cost, grid service fee, etc.) between microgrid and large grid can be reduced.

[0087] The constraints of charge and discharge power and SOC conform to the safe operation specifications of lithium batteries, ensuring that the microgrid meets the safety requirements of the power system in both grid-connected and off-grid modes, avoiding system-level accidents caused by battery failures.

[0088] The constraint conditions can be flexibly adjusted according to the actual needs of the microgrid (such as capacity, load characteristics, proportion of distributed power sources, etc.), improving the adaptability of the system to different scenarios. For example, in remote off-grid microgrids, strict SOC constraints can be used to ensure continuous power supply for critical loads; in high-proportion renewable energy grid-connected microgrids, power constraints can be used to optimize grid interaction and improve system compatibility.

[0089] The constraint condition model provides a basis for the collaborative optimization of microgrids and other energy systems (such as heat and gas systems). For example, in integrated energy systems, the power and SOC constraints of the battery pack can be used to coordinate with combined heat and power units, heat storage devices, etc., to achieve multi-energy complementation and improve the efficiency and stability of the overall system.

[0090] The lithium battery pack constraint model plays a key role in device protection, system stability, economic operation, safety compatibility and other aspects by reasonable limiting of SOC, charging and discharging power and grid interaction power, and is an important guarantee for efficient and reliable operation of the micro-grid. The model not only prolongs the battery life, but also optimizes the utilization of renewable energy and the grid interaction strategy, laying a foundation for the large-scale application and sustainable development of the micro-grid.

[0091] Compared with the traditional energy management strategy, the algorithm can reduce the system operation cost by 15% to 25%, and improve the energy utilization efficiency by 20% to 30%, realizing efficient utilization and economic operation of the micro-grid system energy.

[0092] The micro-grid master controller is in communication connection with the double-redundancy lithium battery pack module, the energy management unit and other devices in the micro-grid, forming an organic whole and realizing efficient collaborative control of the energy storage micro-grid system.

[0093] Based on the improved particle swarm optimization (IPSO) algorithm, the charging and discharging power of the lithium battery pack is optimized and scheduled with the lowest system operation cost and the highest energy utilization efficiency as the target, in combination with the operation state of the micro-grid, the parameters of the lithium battery pack and the electricity price information. The algorithm comprehensively considers the system operation cost (including the charging and discharging energy cost of the lithium battery pack and the operation cost of other devices), the maintenance cost of the lithium battery pack (related to the charging and discharging depth and the cycle number), and the income from selling electric energy, while meeting the SOC constraint, the charging and discharging power constraint and the micro-grid power balance constraint of the lithium battery pack, realizing scientific and reasonable management of the micro-grid energy. Compared with the traditional energy management strategy, the algorithm can reduce the system operation cost by 15% to 25%, and improve the energy utilization efficiency by 20% to 30%, greatly improving the overall economic benefit of the system.

[0094] Through comprehensive analysis of the real-time operation data of the micro-grid, the state parameters of the lithium battery pack and the market electricity price information, the algorithm uses advanced algorithms and models to develop scientific and reasonable energy management strategies, and sends the strategy instructions to the micro-grid master controller module through a high-speed communication interface, providing scientific and reasonable strategy support for the control of the system by the micro-grid master controller module, so that the micro-grid system can realize optimal configuration and utilization of energy under different operating conditions, and improve the operation efficiency and economy of the system.

[0095] The above formulas are dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation. The preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0096] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product.

[0097] Those skilled in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0098] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0099] The above is merely specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0100] Finally, the above is merely preferred embodiments of the present application, and is not intended to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A dual-redundant lithium battery pack cooperative control energy storage microgrid system, characterized in that, It comprises a micro-grid main controller module, a double-redundancy lithium battery pack module, an energy balance distribution module, a charging and discharging power optimization module, and connections between the modules. The micro-grid main controller module is used for centralized control and management of the micro-grid system, receives and processes various signals, and issues control instructions. The double-redundancy lithium battery pack module monitors the state parameters of the lithium battery pack in real time, diagnoses and switches the double-redundancy lithium battery pack according to the state parameters of the lithium battery pack, and obtains the reliability of the double-redundancy lithium battery pack. The energy balance distribution module evaluates the state of the battery based on the double-redundancy lithium battery pack collaborative control strategy of fuzzy logic and model predictive control, and formulates an optimal energy distribution scheme. The charging and discharging power optimization module optimizes the charging and discharging power of the lithium battery pack based on an improved particle swarm optimization algorithm, with the lowest system operating cost and the highest energy utilization efficiency as the targets. 2.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 1, wherein, The micro-grid main controller adopts a multi-core microprocessor with high-speed data processing capability, and communicates with the battery management system in the double-redundancy lithium battery pack module in real time through a CAN bus. Meanwhile, the micro-grid main controller communicates with the energy management unit, distributed power source and load through Ethernet to realize data interaction, and performs centralized control and management of the micro-grid system. 3.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 2, wherein, The double-redundancy lithium battery pack is composed of two lithium battery packs with the same structure connected in parallel through a bidirectional DC-DC converter, and each lithium battery pack is equipped with an independent battery management system. 4.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 3, wherein, The two lithium battery packs back up each other and are provided with a fault diagnosis and switching module. The state parameters of the lithium battery pack include the voltage, current, temperature and SOC parameters of the battery.

5. The dual-redundant lithium battery pack coordinated control energy storage microgrid system of claim 4, wherein, The bidirectional DC-DC converter adopts a soft-switching topology structure, and adjusts the charging and discharging current and voltage of the lithium battery pack according to the instructions of the micro-grid main controller. The battery management system collects the key parameters of the battery in real time and transmits them to the micro-grid main controller module. 6.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 5, wherein, The fault diagnosis module detects and locates faults by monitoring and analyzing various parameters of the lithium battery pack in real time using wavelet transform and neural network algorithms. When a fault is detected in a lithium battery pack, the fault switching module isolates the faulty lithium battery pack and transfers its work tasks to another normally operating lithium battery pack. The reliability of the double-redundancy lithium battery pack is obtained as follows: 7.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 6, wherein, The average failure interval time and the average repair time are obtained through the fault diagnosis and switching process, and the reliability of the double-redundancy lithium battery pack is calculated based on the reliability of a single lithium battery pack, with the formula as follows: Wherein, R is the reliability of the double-redundancy lithium battery pack, R1 is the reliability of a single lithium battery pack, MTTR is the average repair time, and MTBF is the average failure interval time. The process of formulating an optimal energy distribution scheme is as follows: The double-redundancy lithium battery pack collaborative control strategy based on fuzzy logic and model predictive control evaluates the state of the battery, then predicts the load demand of the micro-grid and the output power of the distributed power source in a future period of time based on the model predictive control algorithm, formulates an optimal energy distribution scheme, and realizes energy balance distribution between the two lithium battery packs. The input of the fuzzy logic is two lithium battery SOC deviations and the change rate of the SOC deviation, and the output is energy distribution coefficients k1 and k2, wherein k1+k2=1. 8.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 7, wherein, The energy distribution coefficients k1 and k2 are obtained as follows: In the model predictive control, the SOC uniformity of the battery after energy distribution, the minimum system energy loss and the maximum reliability of the dual-redundancy lithium battery are taken as the objective function; By solving the objective function, the optimal energy distribution coefficients k1 and k2 are obtained; By solving the objective function J, the optimal energy distribution coefficients k1 and k2 are obtained, and the formula is as follows: J = wl x∑(ASOC(t+i)) + w2x∑Ploss(t+i) + w3xR 2 + w2x∑Ploss(t+i) + w3xR Wherein w1, w2, w3 are weight coefficients, R is the reliability of the dual-redundancy lithium battery, ΔSOC(t+i) is the SOC deviation in the future i time, and Ploss(t+i) is the system energy loss in the future i time. 9.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 8, wherein, The process of optimizing and scheduling the charging and discharging power of the lithium battery group with the lowest system operation cost and the highest energy utilization efficiency as the target is as follows: The charging and discharging power of the lithium battery group is optimized and scheduled by comprehensively considering the system operation cost, the maintenance cost of the lithium battery group, the income of selling electric energy, and the constraint condition model, and the formula is as follows: minF = C operation +C maintenance -E sale x P sale where minF represents the objective function; C operation represents the system operating cost; C maintenance represents the maintenance cost of the lithium battery pack; E sale represents the revenue from selling electricity; P sale is the price of selling electricity. 10.The dual-redundant lithium battery pack coordinated control energy storage micro-grid system of claim 9, wherein, The constraint condition model is as follows: where SOC is the state of charge, SOC min and SOC max are the minimum and maximum state of charge, respectively; P charge is the charging power of the lithium battery pack, P chargemin and P chargemax are the minimum and maximum charging power allowed for the lithium battery pack, respectively; P discharge is the discharging power of the lithium battery pack, P dischargemin and P dischargemax are the minimum and maximum discharging power allowed for the lithium battery pack, respectively; P grid is the power exchanged between the grid and the microgrid, P dg is the output power of the distributed power source, and P load is the load power of the microgrid.

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