Multi-energy coupling complementary light storage and charging management system

Abstract

The invention provides a multi-energy coupling complementary light storage and charging management system. The multi-energy coupling complementation light storage and charging management system comprises a photovoltaic power generation module, an energy storage module, a lithium battery pack, a power grid input end, an energy conversion module, an energy storage management module, an energy scheduling module, an energy storage management module and a power supply management module, electric energy input by a photovoltaic power generation module, an energy storage module and a power grid is allocated through a multi-energy coupling algorithm, a charging management module is used for controlling the stability of the electric energy output outwards, and system control software is used. According to the multi-energy coupling complementary light storage and charge management system, through an innovative multi-energy complementary technology and an energy efficiency optimization algorithm, the problem of energy loss generated in the energy switching process in the prior art is effectively solved, a deep reinforcement learning algorithm and a dynamic energy distribution model are adopted, and the energy efficiency is improved. And electric energy from different sources is intelligently dispatched.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid power supply and energy storage, and in particular to a multi-energy coupling complementary light storage and charging management system. Background Art

[0002] The basic structure of the multi-energy coupling complementary light storage and charging management system includes multiple energy input terminals (such as photovoltaic power generation, energy storage devices, and power grids), energy storage modules (such as battery packs), and charging management modules. The system uses multi-energy coupling technology to complement different energy sources such as photovoltaic power generation and power grids, and uses energy storage devices to store excess electricity when power demand is low, and provide power support when demand is high or light is insufficient. The charging management module is responsible for monitoring and controlling the input and output of energy to ensure the balance between different energy sources and loads.

[0003] The working principle of this management system is to efficiently coordinate photovoltaic power generation, grid power supply and energy storage system. The system uses intelligent algorithms to dynamically allocate energy according to real-time demand to ensure the stability and continuity of load power supply. When there are solar energy resources, the system gives priority to photovoltaic power. If photovoltaic power is insufficient, it will be supplemented by energy storage devices or the grid. When power demand decreases, the system will store excess power in batteries for later use.

[0004] Although this system has efficiency advantages in energy scheduling and optimization, it still has some drawbacks. The system is prone to energy loss during multi-energy switching and management, reducing overall efficiency. The capacity and life of the energy storage module limit the continuous power supply capacity and stability of the system, especially during long periods of no sunlight or high power demand. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a multi-energy coupling complementary light storage and charging management system, which solves the problem that the system is prone to energy loss and reduced overall efficiency during multi-energy switching and management, and solves the problem that the capacity and life of the energy storage module limit the system's continuous power supply capacity and stability.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: photovoltaic power generation module;

[0007] Energy storage modules, including lithium battery packs;

[0008] Grid input terminal;

[0009] An energy conversion module for converting photovoltaic power into storable electrical energy;

[0010] Energy storage management module, used to monitor and control the charging and discharging process of energy storage equipment;

[0011] The energy dispatching module allocates the electricity from photovoltaic power generation, energy storage modules, and grid input through a multi-energy coupling algorithm;

[0012] Charging management module controls the stability of external power output;

[0013] System control software, integrating multi-energy complementary control models and energy efficiency optimization algorithms;

[0014] The data acquisition module is used to collect real-time data on photovoltaic power generation, energy storage status, grid power supply and load demand.

[0015] Preferably, the multi-energy coupling complementary light storage and charging management system uses an energy storage model based on optimization, which aims to maximize energy storage efficiency and adopts the following energy storage efficiency optimization formula:

[0016]

[0017] Among them, P load (t) is the load demand at time t, P gen (t) is the output of photovoltaic power generation at time t, η bat (t) is the charging and discharging efficiency of the energy storage device at time t. The goal of maximizing this formula is to improve the overall efficiency of energy storage.

[0018] Preferably, the energy conversion module adopts bidirectional power flow control technology to support bidirectional energy transfer, realize energy complementarity between systems, and maximize the use of renewable energy resources in the region. The energy storage management module adopts a mathematical model based on battery life optimization, through the following battery life attenuation formula:

[0019] L(t)=L0-k·DOD·N cycles

[0020] Where L0 is the initial battery life, DOD is the depth of discharge of the battery, N cycles According to the number of charge and discharge cycles, the battery's charge and discharge strategy is dynamically adjusted to reduce the impact of the cycle on battery life, and the charge and discharge process is further optimized by monitoring the device temperature and current status.

[0021] Preferably, a hybrid energy storage structure of supercapacitors and lithium battery packs is also included, and the management system dynamically coordinates the energy distribution between the two through the following energy distribution formula:

[0022] P total (t) = P cap (t)+P bat (t)

[0023] Among them, P total (t) is the total output power at time t, P cap(t) is the output power of the supercapacitor, P bat (t) is the output power of the lithium battery pack. This formula optimizes the system dynamic performance by balancing rapid response capability and long-term energy supply.

[0024] Preferably, the energy scheduling module adopts a deep reinforcement learning algorithm, which optimizes the scheduling strategy through the following reward function:

[0025]

[0026] Among them, R is the optimized reward function, P load (t) is the load demand at time t, P gen (t) is the photovoltaic power generation at time t, E switch (t) is the loss caused by energy switching at time t, λ and μ are weight coefficients respectively. By optimizing the reward function, the system adaptively learns the best time and strategy for energy switching to reduce losses and maximize system stability.

[0027] Preferably, the deep reinforcement learning algorithm uses time series data to perform adaptive optimization of energy scheduling, predicts future load demand and power generation through training models, and adjusts energy storage charging and discharging strategies in advance to reduce energy waste.

[0028] Preferably, the multi-energy coupling complementary light storage and charging management system control software integrates a regional energy sharing model based on game theory, which realizes energy sharing among multiple systems through the following formula:

[0029]

[0030] Among them, U i is the utility function of system i, E i (t) is the energy generated by system i at time t, C ij (t) is the cost when system i shares energy with system j. Through the game theory model, the system dynamically adjusts the energy sharing strategy between neighboring areas to improve the overall energy efficiency. The game theory model is based on blockchain smart contract technology to achieve transparent, secure and automated processing of energy transactions, ensuring the real-time and fairness of cross-system energy sharing. The energy storage module includes an intelligent thermal management system, which adopts the following temperature control formula:

[0031]

[0032] Among them, T bat (t) is the temperature of the energy storage battery at time t, T env (t) is the ambient temperature, P bat (t) is the discharge power of the battery, h conv(t) is the heat dissipation coefficient of the battery. The system automatically adjusts the cooling system according to the battery temperature and load requirements to avoid efficiency loss and equipment damage caused by overheating.

[0033] Preferably, the multi-energy coupling complementary optical storage and charging management system has an intelligent fault self-healing function, which monitors the working status of each module in real time through multi-sensor feedback and edge computing technology, automatically detects and handles abnormalities, and identifies fault modes through the following formula:

[0034]

[0035] Among them, F(t) represents the fault state at time t, ΔP(t) is the difference between the actual power and the expected power, and θ is the fault judgment threshold. Through this fault mode recognition algorithm, the system can respond quickly and switch to the backup power supply to ensure the continuity of power supply.

[0036] Preferably, the multi-energy coupling complementary light storage and charging management system can automatically adjust the working mode of the energy storage module in the event of a fault, and give priority to ensuring the power supply of key loads through the following formula:

[0037]

[0038] Among them, P crit (t) is the power demand of the critical load at time t, L crit is the critical load, L total To ensure the priority power supply to the key loads in emergency situations, the multi-energy coupled complementary light storage and charging management system, the data acquisition module can monitor and feedback the light intensity, load demand and energy storage status in real time, and optimize the long-term operation strategy of the system through big data analysis and machine learning models.

[0039] Preferably, the multi-energy coupling complementary light storage and charging management system adopts an adaptive learning control algorithm to dynamically adjust the energy distribution of photovoltaic, energy storage and power grid through the following optimization model:

[0040]

[0041] Among them, P load (t), P gen (t) and P grid (t) represents the power output of load demand, photovoltaic power generation and grid power supply at time t, respectively. α, β, γ are the optimization weights of different energy sources. The weight ratio is dynamically adjusted to adapt to different energy supply and demand conditions.

[0042] The present invention provides a multi-energy coupling complementary light storage and charging management system. It has the following beneficial effects:

[0043] This multi-energy coupling complementary photo-storage and charging management system effectively solves the energy loss problem caused by energy switching in the existing technology through innovative multi-energy complementary technology and energy efficiency optimization algorithm. It adopts a deep reinforcement learning algorithm and a dynamic energy distribution model to achieve efficient complementarity of photovoltaic power generation, grid power supply, and energy storage power through intelligent scheduling of electricity from different sources, reducing energy loss in traditional energy switching and improving overall system efficiency. The system also introduces a regional energy sharing model based on game theory to achieve energy sharing and trading among multiple systems, maximizing the utilization rate of renewable energy.

[0044] In terms of energy storage modules, the battery life optimization model and intelligent thermal management system are used to effectively extend the life of the energy storage equipment and improve the system's continuous power supply capability and stability. The system can dynamically adjust the charging and discharging strategy and heat dissipation solution of the energy storage equipment according to load demand and ambient temperature, thereby avoiding overheating damage and ensuring efficient operation of the energy storage module. Through this innovative design, the present invention achieves a technological breakthrough in efficient coordination of multiple energy sources, energy storage optimization, and stable power supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 It is a schematic diagram of the internal structure of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1 As shown, an embodiment of the present invention provides a multi-energy coupling complementary light storage and charging management system, including a photovoltaic power generation module;

[0048] The energy storage module, including the lithium battery pack, uses an optimized energy storage model that aims to maximize energy storage efficiency and uses the following energy storage efficiency optimization formula:

[0049]

[0050] Among them, P load (t) is the load demand at time t, P gen (t) is the output of photovoltaic power generation at time t, η bat (t) is the charge and discharge efficiency of the energy storage device at time t. The goal of maximizing this formula is to improve the overall efficiency of energy storage;

[0051] Grid input terminal;

[0052] Energy conversion module, used to convert photovoltaic power into storable power. The energy conversion module adopts bidirectional power flow control technology to support bidirectional energy transfer, realize energy complementarity between systems, and maximize the use of renewable energy resources in the region;

[0053] The energy storage management module is used to monitor and control the charging and discharging process of the energy storage equipment. The energy storage management module adopts a mathematical model based on battery life optimization, through the following battery life attenuation formula:

[0054] L(t)=L0-k·DOD·N cycles

[0055] Where L0 is the initial battery life, DOD is the depth of discharge of the battery, N cycles Dynamically adjust the battery's charge and discharge strategy to reduce the impact of the cycle on battery life based on the number of charge and discharge cycles, and further optimize the charge and discharge process by monitoring device temperature and current status;

[0056] The energy dispatch module uses a multi-energy coupling algorithm to dispatch the electricity from photovoltaic power generation, energy storage modules, and grid input. The energy dispatch module uses a deep reinforcement learning algorithm that optimizes the dispatch strategy through the following reward function:

[0057]

[0058] Among them, R is the optimized reward function, P load (t) is the load demand at time t, P gen (t) is the photovoltaic power generation at time t, E switch (t) is the loss caused by energy switching at time t, λ and μ are weight coefficients respectively. By optimizing the reward function, the system adaptively learns the best time and strategy for energy switching, reduces losses and maximizes system stability. The deep reinforcement learning algorithm uses time series data to adaptively optimize energy scheduling, predicts future load demand and power generation through training models, and adjusts energy storage charging and discharging strategies in advance to reduce energy waste;

[0059] Charging management module controls the stability of external power output;

[0060] System control software, integrating multi-energy complementary control models and energy efficiency optimization algorithms;

[0061] The data acquisition module is used to collect real-time data on photovoltaic power generation, energy storage status, grid power supply and load demand. It also includes a hybrid energy storage structure of supercapacitors and lithium battery packs. The management system dynamically coordinates the energy distribution between the two through the following energy distribution formula:

[0062] P total (t) = Pcap (t)+P bat (t)

[0063] Among them, P total (t) is the total output power at time t, P cap (t) is the output power of the supercapacitor, P bat (t) is the output power of the lithium battery pack. The formula optimizes the system dynamic performance by balancing the rapid response capability and long-term energy supply. The system control software integrates a regional energy sharing model based on game theory, which realizes energy sharing among multiple systems through the following formula:

[0064]

[0065] Among them, U i is the utility function of system i, E i (t) is the energy generated by system i at time t, C ij (t) is the cost of system i sharing energy with system j. Through this game theory model, the system dynamically adjusts the energy sharing strategy between neighboring areas to improve the overall energy efficiency. The game theory model is based on blockchain smart contract technology to achieve transparent, secure and automated processing of energy transactions, ensuring the real-time and fairness of cross-system energy sharing. The energy storage module includes an intelligent thermal management system that uses the following temperature control formula:

[0066]

[0067] Among them, T bat (t) is the temperature of the energy storage battery at time t, T env (t) is the ambient temperature, P bat (t) is the discharge power of the battery, h conv (t) is the heat dissipation coefficient of the battery. The system automatically adjusts the cooling system according to the battery temperature and load requirements to avoid efficiency loss and equipment damage caused by overheating. The system has intelligent fault self-healing function. Through multi-sensor feedback and edge computing technology, it monitors the working status of each module in real time, automatically detects and handles abnormalities, and identifies fault modes through the following formula:

[0068]

[0069] Among them, F(t) represents the fault state at time t, ΔP(t) is the difference between the actual power and the expected power, and θ is the fault judgment threshold. Through this fault mode recognition algorithm, the system can respond quickly and switch to the backup power supply to ensure the continuity of power supply. The system can automatically adjust the working mode of the energy storage module in the event of a fault, and prioritize the power supply of key loads through the following formula:

[0070]

[0071] Among them, P crit (t) is the power demand of the critical load at time t, L crit is the critical load, L total To ensure the priority power supply of key loads in emergency situations, the multi-energy coupling complementary photovoltaic storage and charging management system, the data acquisition module can monitor and feedback the light intensity, load demand and energy storage status in real time, and optimize the long-term operation strategy of the system through big data analysis and machine learning models. The system adopts an adaptive learning control algorithm to dynamically adjust the energy distribution of photovoltaic, energy storage and power grid through the following optimization model:

[0072]

[0073] Among them, P load (t), P gen (t) and P grid (t) represents the power output of load demand, photovoltaic power generation and grid power supply at time t, respectively. α, β, γ are the optimization weights of different energy sources. The weight ratio is dynamically adjusted to adapt to different energy supply and demand conditions.

[0074] Table 1: Advantages comparison table

[0075] Technical characteristics Before optimization After optimization Energy storage efficiency 80％ 95％ Battery life extension (multiple) 1x 1.5 times Improvement in overall system efficiency (%) 0％ 15％ Energy loss reduction (%) 0％ 20％

[0076] 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 multi-energy coupling complementary optical storage and charging management system, characterized in that: include: Photovoltaic power generation modules; Energy storage modules, including lithium battery packs; Grid input terminal; An energy conversion module for converting photovoltaic power into storable electrical energy; Energy storage management module, used to monitor and control the charging and discharging process of energy storage equipment; The energy dispatching module allocates the electricity from photovoltaic power generation, energy storage modules, and grid input through a multi-energy coupling algorithm; Charging management module controls the stability of external power output; System control software, integrating multi-energy complementary control models and energy efficiency optimization algorithms; The data acquisition module is used to collect real-time data on photovoltaic power generation, energy storage status, grid power supply and load demand.

2. A multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The multi-energy coupling complementary light storage and charging management system uses an optimization-based energy storage model, which aims to maximize energy storage efficiency and adopts the following energy storage efficiency optimization formula: Among them, P load (t) is the load demand at time t, P gen (t) is the output of photovoltaic power generation at time t, η bat (t) is the charging and discharging efficiency of the energy storage device at time t. The goal of maximizing this formula is to improve the overall efficiency of energy storage.

3. A multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The energy conversion module adopts bidirectional power flow control technology to support bidirectional energy transfer, realize energy complementarity between systems, and maximize the use of renewable energy resources in the region. The energy storage management module adopts a mathematical model based on battery life optimization, through the following battery life attenuation formula: L(t)=L0-k·DOD·N cycles Where L0 is the initial battery life, DOD is the depth of discharge of the battery, N cycles According to the number of charge and discharge cycles, the battery's charge and discharge strategy is dynamically adjusted to reduce the impact of the cycle on battery life, and the charge and discharge process is further optimized by monitoring the device temperature and current status.

4. A multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: It also includes a hybrid energy storage structure of supercapacitors and lithium battery packs. The management system dynamically coordinates the energy distribution between the two through the following energy distribution formula: P total (t)=P cap (t)+P bat (t) Among them, P total (t) is the total output power at time t, P cap (t) is the output power of the supercapacitor, P bat (t) is the output power of the lithium battery pack. This formula optimizes the system dynamic performance by balancing rapid response capability and long-term energy supply.

5. The multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The energy scheduling module adopts a deep reinforcement learning algorithm, which optimizes the scheduling strategy through the following reward function: Among them, R is the optimized reward function, P load (t) is the load demand at time t, P gen (t) is the photovoltaic power generation at time t, E switch (t) is the loss caused by energy switching at time t, λ and μ are weight coefficients respectively. By optimizing the reward function, the system adaptively learns the best time and strategy for energy switching to reduce losses and maximize system stability.

6. A multi-energy coupling complementary light storage and charging management system according to claim 5, characterized in that: The deep reinforcement learning algorithm uses time series data to perform adaptive optimization of energy scheduling, predicts future load demand and power generation through training models, and adjusts energy storage charging and discharging strategies in advance to reduce energy waste.

7. The multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The control software of the multi-energy coupling complementary light storage and charging management system integrates a regional energy sharing model based on game theory, which realizes energy sharing among multiple systems through the following formula: Among them, U i is the utility function of system i, E i (t) is the energy generated by system i at time t, C ij (t) is the cost of system i sharing energy with system j. Through the game theory model, the system dynamically adjusts the energy sharing strategy between neighboring areas to improve the overall energy efficiency. The game theory model is based on blockchain smart contract technology to achieve transparent, secure and automated processing of energy transactions, ensuring the real-time and fairness of cross-system energy sharing. The energy storage module includes an intelligent thermal management system that uses the following temperature control formula: Among them, T bat (t) is the temperature of the energy storage battery at time t, T env (t) is the ambient temperature, P bat (t) is the discharge power of the battery, h conv (t) is the heat dissipation coefficient of the battery. The system automatically adjusts the cooling system according to the battery temperature and load requirements to avoid efficiency loss and equipment damage caused by overheating.

8. The multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The multi-energy coupling complementary optical storage and charging management system has an intelligent fault self-healing function. Through multi-sensor feedback and edge computing technology, it monitors the working status of each module in real time, automatically detects and handles abnormalities, and identifies fault modes through the following formula: Among them, F(t) represents the fault state at time t, ΔP(t) is the difference between the actual power and the expected power, and θ is the fault judgment threshold. Through the fault mode recognition algorithm, the system can respond quickly and switch to the backup power supply to ensure the continuity of power supply.

9. The multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The multi-energy coupling complementary light storage and charging management system can automatically adjust the working mode of the energy storage module in the event of a fault, and prioritize the power supply of key loads through the following formula: Among them, P crit (t) is the power demand of the critical load at time t, L crit is the critical load, L total To ensure the priority power supply to the key loads in emergency situations, the multi-energy coupled complementary light storage and charging management system, the data acquisition module can monitor and feedback the light intensity, load demand and energy storage status in real time, and optimize the long-term operation strategy of the system through big data analysis and machine learning models.

10. The multi-energy coupling complementary light storage and charging management system according to claim 1, characterized in that: The multi-energy coupling complementary light storage and charging management system adopts an adaptive learning control algorithm to dynamically adjust the energy distribution of photovoltaic, energy storage and power grid through the following optimization model: Among them, P load (t), P gen (t) and P grid (t) represent the power output of load demand, photovoltaic power generation and grid power supply at time t respectively. α, β and γ are the optimization weights of different energy sources. The weight ratio is dynamically adjusted to adapt to different energy supply and demand conditions.

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