Comprehensive energy power supply system based on wind and light energy power supply

Abstract

The invention discloses an integrated energy power supply system based on wind and light energy power supply, and relates to the technical field of integrated energy power supply systems. The system comprises a wind-solar power generation unit, a double-energy-storage cooperation unit, a heat management unit and an intelligent control unit. The wind-solar power generation unit is used for outputting wind-solar power generation power; the double-energy-storage cooperation unit comprises a super capacitor module and a lithium battery module and is used for realizing multi-time-scale power fluctuation absorption; the heat management unit is used for controlling the temperature of the dual-energy-storage cooperation unit; and the intelligent control unit is used for realizing self-adaptive control of the system. According to the invention, through multi-time scale energy decoupling and cooperation of the supercapacitor and the lithium battery, high-frequency and low-frequency power fluctuations are processed respectively, the energy storage efficiency is improved, the service life of equipment is prolonged, and the intelligent control unit combines wavelet transform, a genetic algorithm and model prediction control, so that adaptive power distribution is realized, and the stability of a power grid is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy power supply systems, and in particular to an integrated energy power supply system based on wind and solar energy. Background Art

[0002] As an important component of clean energy, wind and solar power pose challenges to grid stability due to their intermittent and volatile nature. In existing technologies, the capacity allocation of supercapacitors and lithium batteries lacks a quantitative basis, resulting in insufficient absorption of high-frequency fluctuations and reduced lithium battery life. For example, existing solutions do not establish an energy integration model for ultra-short-term power fluctuation characteristics, making it impossible to accurately determine the capacity of supercapacitors. At the same time, they do not construct a dual-energy storage collaborative control architecture, causing lithium batteries to frequently withstand high-frequency, high-current shocks, accelerating aging. This imbalance in the ratio not only reduces system stability but also significantly increases operation and maintenance costs, becoming a key bottleneck restricting the efficient use of wind and solar power. Summary of the Invention

[0003] The purpose of the present invention is to provide an integrated energy supply system based on wind and solar energy to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, the present invention provides the following technical solutions: an integrated energy supply system based on wind and solar power supply, comprising a wind and solar power generation unit, a dual energy storage coordination unit, a thermal management unit and an intelligent control unit;

[0005] The wind-solar power generation unit is used to output wind-solar power generation;

[0006] The dual energy storage collaborative unit includes a supercapacitor module and a lithium battery module, which is used to absorb power fluctuations on multiple time scales;

[0007] The thermal management unit is used to control the temperature of the dual energy storage collaborative unit;

[0008] The intelligent control unit is used to implement adaptive control of the system;

[0009] The intelligent control unit controls the dual energy storage collaborative unit through the following steps:

[0010] The wind and solar power generation power is decomposed into high-frequency components P by wavelet transform. h (t) and low-frequency component P l (t), the decomposition formula is:

[0011]

[0012] Where, P ws (t) is the wind and solar power generation power, n is the number of wavelet decomposition layers;

[0013] Based on the high frequency component Ph (t) Control the supercapacitor module to bear the high-frequency power fluctuations, based on the low-frequency component P l (t) Control the lithium battery module to undertake low-frequency power regulation;

[0014] Based on the model predictive control algorithm, the charging and discharging power of the lithium battery module is optimized. The objective function is:

[0015]

[0016] Where N p is the prediction time domain, γ1, γ2 and γ3 are weight coefficients, ΔP l is the low-frequency power deviation, is the rate of change of the state of charge of the lithium battery, ESR SC is the equivalent series resistance of the supercapacitor.

[0017] Preferably, the capacity of the supercapacitor module is calculated and determined by the following formula:

[0018]

[0019] Where, E h is the high-frequency fluctuation energy integral of wind and solar power generation, and the calculation formula is:

[0020]

[0021] Δt is the time interval, which is 10 seconds, k 裕度 is the margin coefficient, which is 1.2, η SC is the supercapacitor charging and discharging efficiency, which is 95%, ΔU SC is the operating voltage range of the supercapacitor, which is the rated voltage V SC-nom 80% of.

[0022] Preferably, the thermal management unit uses a phase change material to wrap the dual energy storage cooperative unit, and the melting point of the phase change material is 28°C;

[0023] When the temperature T of the dual energy storage collaborative unit is greater than 35°C, the thermal management unit starts forced air cooling, and the intelligent control unit reduces the power consumption of the supercapacitor module by 15%;

[0024] When T<5°C, the thermal management unit activates the heating film of the lithium battery module, and at the same time the intelligent control unit reduces the upper limit of the discharge current of the lithium battery module to the rated current of 0.8C.

[0025] Preferably, the intelligent control unit adaptively optimizes the decomposition layer number n and the threshold TH of the wavelet transform through a genetic algorithm, and the optimization objective function is:

[0026] minJ=ω1·high-frequency energy leakage rate+ω2·low-frequency mixing rate

[0027] Wherein, ω1 and ω2 are weight coefficients, the high-frequency energy omission rate is the ratio of the high-frequency energy not absorbed by the supercapacitor module to the total high-frequency energy, and the low-frequency mixing rate is the ratio of the high-frequency component energy remaining in the low-frequency component to the total low-frequency energy.

[0028] Preferably, the constraints of the model predictive control algorithm include:

[0029] When the health status SOH of the lithium battery module is ≥ 0.8, the upper limit of the charge and discharge power of the lithium battery module is 1C rated current;

[0030] When SOH < 0.8, the upper limit of the charge and discharge power of the lithium battery module is 0.8C rated current;

[0031] The state of charge (SOC) of the lithium battery module satisfies 0.2≤SOC≤0.9.

[0032] Preferably, the supercapacitor module is connected to the DC bus through a bidirectional DC / DC converter with a response time of less than 10ms, and is used to absorb power fluctuations in the frequency range of 0.1 to 10 Hz; the lithium battery module is connected to the DC bus through a conventional energy storage converter, and is used to regulate power fluctuations with a frequency of less than 0.1 Hz.

[0033] Preferably, the intelligent control unit includes an FPGA core and an ARM core, the FPGA core is used to realize real-time processing of wavelet transform at a sampling frequency of 1000 Hz; the ARM core is used to run a model predictive control algorithm.

[0034] Preferably, the system further comprises a data acquisition unit, which is used to collect wind and solar power generation power, equivalent series resistance of supercapacitor modules, state of charge and temperature data of lithium battery modules, with an acquisition frequency of not less than 100 Hz.

[0035] Preferably, the system realizes clock synchronization of each unit through synchronous Ethernet to ensure phase consistency of control signals.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] The present invention achieves multi-timescale energy decoupling, utilizes supercapacitors and lithium batteries in collaboration to process high-frequency and low-frequency power fluctuations respectively, thereby improving energy storage efficiency and equipment life; integrates full-temperature thermal management to ensure stable operation under extreme temperatures; the intelligent control unit combines wavelet transform, genetic algorithm and model predictive control to achieve adaptive power distribution and enhance grid stability; hardware modularization and algorithm portability design reduce operation and maintenance costs, improve system applicability, and provide a reliable solution for high-proportion renewable energy access. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic structural diagram of a comprehensive energy power supply system based on wind and solar power provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0040] See also Figure 1 , the present invention provides a comprehensive energy supply system based on wind and solar power supply, including a wind and solar power generation unit, a dual energy storage coordination unit, a thermal management unit and an intelligent control unit;

[0041] The wind-solar power generation unit is used to integrate solar photovoltaic and wind power generation equipment to output wind-solar power generation power P with fluctuating characteristics. ws (t), whose fluctuation characteristics are determined by meteorological conditions (such as sudden changes in light intensity and step changes in wind speed);

[0042] The dual energy storage collaborative unit includes a supercapacitor module and a lithium battery module, which is used to absorb power fluctuations on multiple time scales;

[0043] Supercapacitor modules utilize their high power density (>10kW / kg) and fast response characteristics (<10ms) to handle high-frequency, short-cycle power fluctuations (such as second-level power changes caused by cloud cover);

[0044] Lithium battery modules, with their high energy density (>200Wh / kg), can handle low-frequency, long-cycle power regulation (such as minute-to-hour fluctuations caused by day-night cycles and gradual changes in wind speed).

[0045] The thermal management unit is used to control the temperature of the dual energy storage collaborative unit. By combining phase change material (PCM) with active temperature control, the energy storage unit temperature is controlled at 25±5°C, solving the problem of battery performance degradation at extreme temperatures (such as increased internal resistance of lithium batteries at low temperatures and electrolyte decomposition at high temperatures).

[0046] The intelligent control unit is used to realize adaptive control of the system and realize power distribution and coordinated control at multiple time scales.

[0047] It should be noted that during the operation of the wind and solar power supply system, the wind and solar power generation power is easily affected by natural environmental factors and exhibits complex fluctuation characteristics. To effectively address this problem, the intelligent control unit controls the dual energy storage collaborative unit through the following steps:

[0048] The wind and solar power generation power is decomposed into high-frequency components P by wavelet transform. h (t) and low-frequency component P l (t);

[0049] High frequency component P h (t) (0.1-10 Hz) corresponds to instantaneous power fluctuations caused by rapid cloud movement and gusty winds, which are absorbed by the supercapacitor module;

[0050] Low frequency component P l (t)(<0.1Hz): corresponds to slowly changing factors such as day and night changes and seasonal changes, which are smoothed by the lithium battery module;

[0051] The decomposition formula is:

[0052]

[0053] Where, P ws (t) is the wind and solar power generation power, and its value will change in real time with factors such as light intensity, wind speed and direction. n is the number of wavelet decomposition layers. This parameter needs to be optimized based on system operation requirements and historical data to ensure that the decomposition result can accurately reflect the power characteristics without excessively increasing the computational burden.

[0054] Based on the high frequency component P h (t) Control the supercapacitor module to bear the high-frequency power fluctuations, based on the low-frequency component P l (t) Control the lithium battery module to undertake low-frequency power regulation.

[0055] To further improve the operating efficiency and service life of the lithium battery module, the intelligent control unit uses the model predictive control (MPC) algorithm to optimize the distribution of the charging and discharging power of the lithium battery module. The MPC algorithm uses a rolling optimization method to predict the future time (prediction time domain N) based on the current state of the system and the prediction model at each sampling moment. p) is used to optimize the operation status of the lithium battery in the system, and its objective function is:

[0056]

[0057] Where N p To predict the time domain, the value needs to take into account the system dynamic characteristics and computing resource limitations. γ1, γ2 and γ3 are weight coefficients, ΔP l is the low-frequency power deviation, is the rate of change of the state of charge of the lithium battery, ESR SC is the equivalent series resistance of the supercapacitor. By adjusting the coefficients γ1, γ2, and γ3, the low-frequency power deviation ΔP can be flexibly balanced. l , lithium battery state of charge change rate and supercapacitor equivalent series resistance ESR SC The relationship between them can extend the service life of energy storage equipment and reduce operating costs while ensuring stable operation of the system.

[0058] It is understandable that after completing the power decomposition, the intelligent control unit is based on the high frequency component P h (t) and low-frequency component P l (t) characteristics, and targeted allocation of energy storage unit work tasks. Supercapacitor modules, with their high power density and fast charging and discharging advantages, are used to bear high-frequency power fluctuations. Such fluctuations are usually caused by factors such as sudden changes in wind speed in a short period of time and clouds quickly blocking sunlight. Supercapacitors can respond quickly, absorbing or releasing electrical energy to maintain system power balance. Lithium battery modules, on the other hand, have the characteristics of high energy density and are suitable for low-frequency power regulation tasks. Low-frequency power changes are often related to factors such as the alternation of day and night and weather trends. Lithium batteries can perform charging and discharging operations over a longer time scale, meeting the continuous power requirements of the system.

[0059] In an optional embodiment, the capacity of the supercapacitor module is determined by calculating the following formula:

[0060]

[0061] Among them, E h It is the high-frequency fluctuation energy integral of wind and solar power generation, and is used to quantify the total amount of energy that the wind and solar power generation power deviates from the average level in a short time scale. The calculation formula is:

[0062]

[0063] Where τ is the integral variable, representing the continuous time series starting from time t, Δt is the time interval, which is set to 10 seconds. This time length can capture the high-frequency fluctuation characteristics of wind and solar power generation and avoid statistical errors caused by too short a time window. ws(τ) represents the real-time power of wind and solar power generation at time τ, P ws_avg (t) is the average wind and solar power generation power at time t, which is obtained by the sliding average algorithm.

[0064] Furthermore, k 裕度 is the margin coefficient, which is 1.2. The setting of this coefficient is the optimal solution after a deep game between system reliability and economy. In the wind and solar power supply system, due to the random fluctuations of natural conditions such as solar irradiance and wind speed, the power generation has significant intermittent and uncertainty. By introducing a margin coefficient of 1.2, it is equivalent to reserving 20% ​​of energy buffer space for the system, which can effectively cope with the power gap in extreme weather. At the same time, the Monte Carlo simulation method is used to construct a full life cycle cost model including equipment investment cost, operation and maintenance cost, and loss cost based on 100,000 random working condition simulations. The results show that compared with other value schemes, k 裕度 =1.2, while ensuring the system's annual reliability reaches 99.5%, the full life cycle cost of the energy storage equipment can be reduced by 15%, significantly improving the economic feasibility of the system.

[0065] Furthermore, η SC The charge and discharge efficiency of supercapacitors is 95%. Under standard laboratory conditions (temperature 25°C ± 1°C, humidity 45% ± 5%), a constant current charge and discharge test method was used to conduct 500 charge and discharge cycle experiments on 10 groups of the same supercapacitor model, and its typical value was 95%. This parameter directly reflects the degree of energy loss in the supercapacitor during the conversion of electrical energy into chemical energy. Compared with the 85% to 90% charge and discharge efficiency of lithium batteries, supercapacitors, with their higher energy conversion efficiency, can quickly respond to power changes within milliseconds, making them ideal energy storage components for smoothing high-frequency power fluctuations in wind and solar power generation.

[0066] Furthermore, ΔU SC is the operating voltage range of the supercapacitor, which is the rated voltage V SC-nom During the design phase, a 2,000-hour aging test was conducted on the supercapacitor. By comparing the capacity decay curves under different voltage ranges, it was found that when the operating voltage range is controlled at 80% of the rated voltage, the cycle life of the supercapacitor is increased by more than 30% compared to operation within the full voltage range. This setting not only meets the supercapacitor's limit on charge and discharge depth, but also effectively delays the aging of the electrode material, achieving a balanced optimization of equipment life and performance.

[0067] In an optional embodiment, the thermal management unit uses a phase change material to wrap the dual energy storage cooperative unit. The melting point of the phase change material is 28°C. This temperature value has been optimized through multiple rounds of thermal simulation experiments. It can effectively absorb excess heat generated by the operation of the energy storage unit and release the stored heat energy when the ambient temperature is low.

[0068] When the temperature T of the dual energy storage collaborative unit is greater than 35°C, the thermal management unit starts forced air cooling, and the intelligent control unit reduces the power consumption of the supercapacitor module by 15%;

[0069] As you can see, when the temperature T of the dual energy storage collaborative unit exceeds 35°C, a dual protection mechanism is triggered: on the one hand, the thermal management unit immediately activates the forced air cooling system, which can reduce the temperature by 5-8°C within 5 minutes through high-speed cooling fans and optimized air duct design. On the other hand, the intelligent control unit synchronously adjusts the energy distribution strategy, reducing the power share of the supercapacitor module by 15%, suppressing additional heat generation by reducing its charge and discharge intensity.

[0070] When T<5°C, the thermal management unit activates the heating film of the lithium battery module, and the intelligent control unit reduces the upper limit of the discharge current of the lithium battery module to the rated current of 0.8C;

[0071] Understandably, when T<5℃, the thermal management unit activates the built-in heating film of the lithium battery module. The heating film is made of flexible graphene material and has a heating rate of up to 3℃ / min, which can raise the battery temperature to a suitable working range within 10 minutes; at the same time, the intelligent control unit strictly limits the upper limit of the discharge current of the lithium battery module, reducing it to 0.8C rated current to prevent large current discharge in low temperature environment from causing battery capacity attenuation and safety risks.

[0072] In an optional embodiment, the intelligent control unit adaptively optimizes the number of decomposition layers n and the threshold TH of the wavelet transform through a genetic algorithm. The genetic algorithm simulates the selection, crossover, and mutation operations in the biological evolution process to perform a global search in the solution space to find the optimal parameter combination. The optimization objective function is:

[0073] minJ=ω1·high-frequency energy leakage rate+ω2·low-frequency mixing rate

[0074] In the formula, ω1 and ω2 are weight coefficients, the sum of which is 1 and can be dynamically adjusted according to the system's operating requirements. The high-frequency energy omission rate is the ratio of high-frequency energy not absorbed by the supercapacitor module to the total high-frequency energy, which reflects the supercapacitor's efficiency in capturing high-frequency fluctuating energy. The low-frequency mixing rate is the ratio of the high-frequency component energy remaining in the low-frequency component to the total low-frequency energy, and its value directly affects the operational stability of the low-frequency energy storage device. By minimizing the objective function J, an optimal balance can be achieved between high-frequency energy utilization efficiency and low-frequency energy storage purity, thereby improving the overall performance of the integrated energy supply system.

[0075] In an optional embodiment, the constraints of the model predictive control algorithm include:

[0076] When the health status (SOH) of the lithium battery module is ≥ 0.8, the upper limit of the charge and discharge power of the lithium battery module is set to 1C rated current. To ensure efficient operation within the good performance range and avoid the impact of excessive charge and discharge on battery life, the upper limit of the charge and discharge power of the lithium battery module is set to 1C rated current. This setting can effectively extend the battery life while fully utilizing the battery performance.

[0077] When SOH is less than 0.8, the upper limit of the charge and discharge power of the lithium battery module is 0.8C rated current. Considering that the battery performance has declined to a certain extent, to prevent accelerated battery aging and ensure the safety of system operation, the upper limit of the charge and discharge power of the lithium battery module is reduced to 0.8C rated current. By reducing the charge and discharge intensity, the decline in battery health status is slowed down;

[0078] The state of charge (SOC) of the lithium battery module satisfies 0.2≤SOC≤0.9. The lower limit SOC = 0.2 can avoid irreversible damage caused by deep discharge of the battery and prevent rapid decay of the battery capacity; the upper limit SOC = 0.9 prevents the battery from being in a fully charged state for a long time, reduces the risk of overcharging, reduces battery heating and safety hazards, and maintains the battery's cycle stability.

[0079] In an optional embodiment, the supercapacitor module is connected to the DC bus through a bidirectional DC / DC converter with a response time of less than 10ms, and is used to absorb power fluctuations in the frequency range of 0.1 to 10 Hz; the lithium battery module is connected to the DC bus through a conventional energy storage converter, and is used to regulate power fluctuations with a frequency of less than 0.1 Hz.

[0080] Specifically, the supercapacitor module is connected to the DC bus through a bidirectional DC / DC converter and has a millisecond-level fast response characteristic, with a response time of less than 10ms. It can accurately capture and absorb high-frequency power fluctuations in the frequency range of 0.1 to 10 Hz. Power fluctuations in this frequency band are usually caused by factors such as short-term output changes of wind and solar power generation systems and instantaneous load shocks. The supercapacitor module, with its high power density and fast charging and discharging advantages, can effectively smooth out such fluctuations and maintain the stability of the DC bus voltage.

[0081] The lithium battery module is connected to the DC bus through a conventional energy storage inverter, and is mainly responsible for regulating low-frequency power fluctuations with a frequency of less than 0.1Hz. Such low-frequency fluctuations are mostly caused by factors such as long-term output changes of wind and solar power generation systems and differences in day and night load curves. The lithium battery module, with its large-capacity energy storage characteristics, can smoothly regulate power over a longer time scale, ensuring a continuous and stable supply of system power, and forming a complementary and synergistic relationship with the supercapacitor module in terms of high frequency and low frequency, fast response and large-capacity storage.

[0082] In an optional embodiment, the intelligent control unit includes an FPGA core and an ARM core, the FPGA core is used to realize real-time processing of wavelet transform at a sampling frequency of 1000 Hz, and the processing time does not exceed 32 μs; the ARM core is used to run the model predictive control algorithm, and the single calculation time does not exceed 1.2 ms.

[0083] The intelligent control unit utilizes a heterogeneous multi-core architecture, integrating FPGA and ARM cores to achieve the coordinated operation of high-speed signal processing and complex algorithm control. The FPGA core is based on the Xilinx Kintex UltraScale+ series chipset. Leveraging its parallel processing capabilities, it performs real-time wavelet transform processing on analog signals such as current and voltage at a sampling frequency of 1000Hz. Testing has verified that the module maintains a stable processing time of less than 32μs for the entire decomposition and reconstruction process of a single 1024-point data set, meeting the power system's requirement for rapid transient signal capture. The ARM core utilizes a high-performance Cortex-A72 processor with an embedded real-time operating system, running an energy management algorithm based on Model Predictive Control (MPC). This algorithm builds a dynamic model of the wind, solar, and load-storage system and performs rolling optimization of power flow over the next 30 control cycles. Single prediction calculations are strictly controlled within 1.2ms, ensuring millisecond-level response and optimal power allocation under conditions such as fluctuating sunlight and sudden load changes.

[0084] In an optional embodiment, the system further includes a data acquisition unit, which is used to collect wind and solar power generation power, equivalent series resistance of the supercapacitor module, state of charge and temperature data of the lithium battery module, with an acquisition frequency of not less than 100 Hz.

[0085] Among them, the system is equipped with a high-precision data acquisition unit, which adopts multi-channel synchronous sampling technology and uses current sensors, voltage sensors, temperature sensors and other sensing elements to monitor the wind and solar power generation power, the equivalent series resistance of the supercapacitor module, the state of charge (SOC) and temperature data of the lithium battery module in real time.

[0086] Specifically, Hall current sensors and voltage transformers are used to accurately collect the real-time output power of wind and solar power generation equipment. High-frequency resistance measurement circuits and electrochemical impedance spectroscopy techniques dynamically measure changes in the equivalent series resistance of supercapacitor modules. The ampere-hour integration method, combined with an open-circuit voltage correction algorithm, accurately calculates the state of charge of lithium battery modules, and an NTC thermistor monitors battery temperature in real time. This data acquisition unit boasts high real-time performance, with an acquisition frequency set to no less than 100Hz. This effectively captures transient changes during system operation, providing reliable data support for system optimization, control, and fault diagnosis.

[0087] In an optional embodiment, the system implements clock synchronization of each unit through synchronous Ethernet to ensure phase consistency of control signals.

[0088] The system adopts Synchronous Ethernet (SyncE) technology to build a high-precision clock synchronization network, and realizes the clock synchronization of each distributed unit in the system through the timing information transmission mechanism of the physical layer. This technology uses specific protocol messages in the Ethernet link to periodically calibrate the clock of each node, and can stably control the clock synchronization accuracy within the range of ±1μs. This high-precision clock synchronization capability effectively eliminates the phase difference of the control signal caused by clock offset, ensures the phase consistency of the control signals of key equipment such as photovoltaic inverters and wind turbine converters, and lays the foundation for coordinated operation of the system and power optimization control.

[0089] In this embodiment, the present invention uses multi-time-scale energy decoupling and the collaboration of supercapacitors and lithium batteries to process high-frequency and low-frequency power fluctuations respectively, thereby improving energy storage efficiency and equipment life; integrating full-temperature thermal management to ensure stable operation under extreme temperatures; the intelligent control unit combines wavelet transform, genetic algorithm and model predictive control to achieve adaptive power distribution and enhance grid stability; hardware modularization and algorithm portability design reduce operation and maintenance costs, improve system applicability, and provide a reliable solution for high-proportion renewable energy access.

[0090] In addition, it should be noted that the combination of the various technical features in this case is not limited to the combination described in the claims of this case or the combination described in the specific embodiments. All technical features recorded in this case can be freely combined or combined in any way unless there is a contradiction between them.

[0091] It should be noted that the above examples are merely specific embodiments of the present invention. Obviously, the present invention is not limited to the above examples, and many similar variations are possible. All variations directly derived from or associating with the present invention by those skilled in the art are intended to fall within the scope of protection of the present invention.

[0092] The above are only preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A comprehensive energy power supply system based on wind and solar power, characterized in that: It includes wind and solar power generation unit, dual energy storage coordination unit, thermal management unit and intelligent control unit; The wind-solar power generation unit is used to output wind-solar power generation; The dual energy storage collaborative unit includes a supercapacitor module and a lithium battery module, which is used to absorb power fluctuations on multiple time scales; The thermal management unit is used to control the temperature of the dual energy storage collaborative unit; The intelligent control unit is used to implement adaptive control of the system; The intelligent control unit controls the dual energy storage collaborative unit through the following steps: The wind and solar power generation power is decomposed into high-frequency components P by wavelet transform. h (t) and low-frequency component P l (t), the decomposition formula is: Where, P ws (t) is the wind and solar power generation power, n is the number of wavelet decomposition layers; Based on the high frequency component P h (t) Control the supercapacitor module to bear the high-frequency power fluctuations, based on the low-frequency component P l (t) Control the lithium battery module to undertake low-frequency power regulation; Based on the model predictive control algorithm, the charging and discharging power of the lithium battery module is optimized. The objective function is: Where N p is the prediction time domain, γ1, γ2 and γ3 are weight coefficients, ΔP l is the low-frequency power deviation, is the rate of change of the state of charge of the lithium battery, ESR SC is the equivalent series resistance of the supercapacitor.

2. The integrated energy power supply system based on wind and solar power according to claim 1 is characterized in that: The capacity of the supercapacitor module is calculated and determined by the following formula: Where, E h is the high-frequency fluctuation energy integral of wind and solar power generation, and the calculation formula is: Δt is the time interval, which is 10 seconds, k 裕度 is the margin coefficient, which is 1.2, η SC is the supercapacitor charging and discharging efficiency, which is 95%, ΔU SC is the operating voltage range of the supercapacitor, which is the rated voltage V SC-nom 80% of.

3. The integrated energy power supply system based on wind and solar power according to claim 1 is characterized in that: The thermal management unit uses phase change material to wrap the dual energy storage cooperative unit, and the melting point of the phase change material is 28°C; When the temperature T of the dual energy storage collaborative unit is greater than 35°C, the thermal management unit starts forced air cooling, and the intelligent control unit reduces the power consumption of the supercapacitor module by 15%; When T<5°C, the thermal management unit activates the heating film of the lithium battery module, and at the same time the intelligent control unit reduces the upper limit of the discharge current of the lithium battery module to the rated current of 0.8C.

4. The integrated energy power supply system based on wind and solar power according to claim 1 is characterized in that: The intelligent control unit adaptively optimizes the decomposition layer number n and the threshold TH of the wavelet transform through a genetic algorithm, and the optimization objective function is: minJ=ω1·high-frequency energy leakage rate+ω2·low-frequency mixing rate Wherein, ω1 and ω2 are weight coefficients, the high-frequency energy omission rate is the ratio of the high-frequency energy not absorbed by the supercapacitor module to the total high-frequency energy, and the low-frequency mixing rate is the ratio of the high-frequency component energy remaining in the low-frequency component to the total low-frequency energy.

5. The integrated energy supply system based on wind and solar power according to claim 1 is characterized in that: The constraints of the model predictive control algorithm include: When the health status SOH of the lithium battery module is ≥ 0.8, the upper limit of the charge and discharge power of the lithium battery module is 1C rated current; When SOH < 0.8, the upper limit of the charge and discharge power of the lithium battery module is 0.8C rated current; The state of charge (SOC) of the lithium battery module satisfies 0.2≤SOC≤0.

9.

6. The integrated energy supply system based on wind and solar power according to claim 1 is characterized in that: The supercapacitor module is connected to the DC bus through a bidirectional DC / DC converter with a response time of less than 10ms, and is used to absorb power fluctuations in the frequency range of 0.1 to 10 Hz; the lithium battery module is connected to the DC bus through a conventional energy storage converter, and is used to regulate power fluctuations with a frequency of less than 0.1 Hz.

7. The integrated energy power supply system based on wind and solar power according to claim 1 is characterized in that: The intelligent control unit includes an FPGA core and an ARM core. The FPGA core is used to realize real-time processing of wavelet transform at a sampling frequency of 1000 Hz; the ARM core is used to run a model predictive control algorithm.

8. The integrated energy supply system based on wind and solar power according to claim 1 is characterized in that: The system also includes a data acquisition unit, which is used to collect wind and solar power generation power, equivalent series resistance of supercapacitor modules, and charge state and temperature data of lithium battery modules, with an acquisition frequency of not less than 100 Hz.

9. The integrated energy power supply system based on wind and solar power according to claim 1 is characterized in that: The system achieves clock synchronization of each unit through synchronous Ethernet to ensure phase consistency of control signals.

Images