Photovoltaic energy storage and cooling coupling system and method based on copper-based composite phase change material

Abstract

The invention discloses a photovoltaic energy storage and cooling coupled system and method based on a copper-based composite phase change material. A power generation module, a cooling energy storage module, an energy conversion module and the like are included. In the daytime, the solar photovoltaic panel emits heat, and the copper-based composite phase change material absorbs heat, stores energy and cools the photovoltaic panel; at night, the phase change material releases heat, and the thermoelectric material converts heat energy into electric energy. The system uses a DDPG algorithm of deep reinforcement learning to optimize operation. The state space comprises parameters such as the temperature of the solar photovoltaic panel and the temperature of the phase-change material, and the action space relates to operations such as control of cooling energy storage and energy conversion. The reward function is designed in multiple aspects of comprehensive efficiency, energy storage, electric energy output and stability. By constructing a strategy network and a value network, the system can make decisions according to environmental changes. And extracting data from the experience playback buffer area to update network parameters so as to enable the system to perform self-adaptive adjustment. The system not only solves the problems of high temperature of the photovoltaic panel and power generation at night, but also improves the energy utilization efficiency by using an algorithm, and the performance is far better than that of a traditional photovoltaic system.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic energy storage and cooling, and in particular to a photovoltaic energy storage and cooling coupling system and method based on copper-based composite phase change materials. Background Art

[0002] Against the backdrop of global warming, photovoltaic power generation is increasingly being used. However, the photoelectric conversion efficiency of photovoltaic panels in converting solar energy into electrical energy is not fixed. When photovoltaic panels are exposed to light radiation, part of the solar energy will be converted into electrical energy, while the other part will inevitably be converted into heat energy, which will cause the temperature of the photovoltaic panels to rise. Relevant studies have shown that under normal circumstances, the photoelectric conversion efficiency of photovoltaic panels will decrease by about 4-5% for every 10°C increase in temperature. In addition, photovoltaic power generation has obvious intermittent characteristics, and its power generation efficiency is greatly affected by weather conditions. On rainy days and at night, the efficiency of photovoltaic power generation is extremely low, and it is almost impossible to achieve effective power generation. Therefore, it needs to be coordinated with an efficient energy storage system to achieve a balance between energy supply and demand, thereby improving the reliability and stability of energy supply.

[0003] Phase change materials have such a property: they can undergo phase change within a specific temperature range, and absorb or release a large amount of latent heat during the phase change process. At the same time, their own temperature will remain in a relatively stable state. This unique property makes phase change materials show potential application prospects in the cooling and energy storage fields of photovoltaic panels. In the early research process, traditional phase change materials such as paraffin and fatty acids have been tried in photovoltaic systems. However, these traditional phase change materials have many problems, such as their poor thermal conductivity and unsatisfactory thermal stability.

[0004] With the continuous development of materials science, composite phase change materials have become a hot topic in the current research field. Compared with the traditional aluminum-based phase change materials with a thermal conductivity of only about 200W / (m·K), copper-based materials have a thermal conductivity of up to 400W / (m·K). Therefore, copper-based materials can conduct the heat generated by photovoltaic panels at a faster rate, thereby effectively reducing the temperature of photovoltaic panels, thereby reducing the efficiency loss caused by high temperature. At the same time, the metal bracket adopts a three-periodic minimal surface structure. The uniqueness of this structure is that the three-periodic minimal surface will show a periodic change law in three independent dimensions. Its unit cell structure is interconnected, forming a continuous channel inside. It has ultra-high specific strength and specific stiffness, good energy absorption capacity and anisotropic mechanical properties, giving the material better thermal conductivity and thermal stability, so that it can maintain good performance in the repeated heat absorption and release process, and is more suitable for photovoltaic energy storage and cooling coupling systems.

[0005] Compared with the prior art, the differences are as follows:

[0006] Technical comparison with patent CN116120899A "A porous copper-based composite phase change heat storage material and its preparation method"

[0007] Material structure and composition: The porous copper-based composite phase-change heat storage material prepared by patent CN116120899A is composed of a porous copper thermal conductive frame and a phase change material (such as paraffin, sodium acetate trihydrate, etc.), wherein the porous copper frame is prepared by copper powder, salt crystals and chloride salt, etc., and salt is used as a pore-forming agent to form a pore structure. The copper-based composite phase change material of this patent is composed of a phase change material and a metal bracket (aluminum alloy) with a three-period minimal surface structure. The unique three-period minimal surface structure enables the material to have ultra-high specific strength, specific stiffness and good energy absorption capacity, and there are obvious differences in material structure and composition from the former.

[0008] Preparation method: The preparation method of CN116120899A includes preparing a copper salt mixed tablet, a porous copper heat-conducting frame, and then immersing the porous copper frame into a liquid phase change material. It involves multiple steps such as dispersing copper powder in an organic solvent and mixing it with salt crystals and chloride salt. The preparation method of the copper-based composite phase change material of this patent is to first prepare copper-based powder by mechanical crushing, then use electroplating to improve compatibility with the phase change material, and then mix, cast, solidify, heat treat and process, etc. The preparation process and specific operations are different from the former.

[0009] Application scenarios and functional realization: CN116120899A is mainly used in the field of heat storage and heat conduction, aiming to solve the problems of traditional materials in the process of heat storage and utilization, and realize efficient heat storage and heat conduction functions. This patent's photovoltaic energy storage system coupled with photovoltaic panel cooling based on copper-based composite phase change materials is applied in the photovoltaic field, focusing on solving the problem of low power due to excessive temperature of solar photovoltaic panels and the inability to generate electricity at night or in rainy weather. Through the phase change of materials, photovoltaic panel cooling, daytime waste heat storage and nighttime heat and electricity conversion are realized. The application scenarios and realized functions are essentially different from the former.

[0010] Performance improvement focus: CN116120899A focuses on improving the heat storage and thermal conductivity of materials by increasing the porosity of materials, using the rough surface of copper sheets to limit the leakage of phase change materials, and improving the efficiency of light-to-heat conversion. In addition to using the high thermal conductivity of copper-based composite phase change materials to improve the heat dissipation effect, this patent also uses a three-period minimal surface structure to improve the thermal stability of the material, and focuses more on improving the power generation efficiency of the entire photovoltaic system, achieving efficient use of energy, and enhancing the adaptability of the system in different environments. The two have different focuses on performance improvement.

[0011] Algorithm application: Patent CN116120899A does not mention algorithm-related content. This patent innovatively introduces the DDPG algorithm in deep reinforcement learning, defines the state space of each parameter, and the action space including the control of the phase change material cooling and energy storage module, the thermoelectric material energy conversion module and the electric energy conduction module, and designs a reward function that comprehensively considers efficiency, energy storage, electric energy output and stability, so as to realize the system dynamically adjusting the control strategy according to the environment, improve the system flexibility and adaptability, and optimize the system performance. This is the unique technical advantage of this patent that distinguishes it from CN116120899A. Summary of the invention

[0012] In view of the above problems, the present invention proposes a photovoltaic energy storage and cooling coupling system and method based on copper-based composite phase change materials. The purpose is to provide a system that is different from the traditional photovoltaic system. The photovoltaic energy storage and photovoltaic panel cooling system are coupled internally in the system, thereby reducing the loss of power generation efficiency of the solar photovoltaic panel caused by high temperature. At the same time, the addition of thermoelectric materials and micro-electric energy collection and inverter systems enables the excess heat wasted by the traditional photovoltaic water cooling system to continue to generate electricity at night, thereby playing a role in converting heat energy into electrical energy, improving the power generation efficiency of solar photovoltaic panels, slowing down the aging of solar photovoltaic panels, and playing a role in shaving the peak of the overall power generation of the system. A photovoltaic energy storage and photovoltaic panel cooling coupling system based on copper-based composite phase change materials and a preparation method of some materials.

[0013] To achieve the above object, the technical solution adopted by the present invention is:

[0014] A photovoltaic energy storage and cooling coupling system based on copper-based composite phase change materials includes a cooling and energy storage module of copper-based composite phase change materials and aluminum alloy brackets; a power generation module of a solar photovoltaic panel installed at the top; an energy conversion module of a thermoelectric material installed below, and a support module composed of a stainless steel bracket, an energy collection and inversion system module, and an electric energy conduction module composed of external wires at the bottom. The solar panel, the copper-based composite phase change material, the aluminum alloy bracket, and the thermoelectric material are placed close to each other from top to bottom. The energy collection and inversion system consists of an electric energy collection module, a control unit, and an inverter circuit.

[0015] As a further improvement of the system of the present invention, the thermoelectric material used for the conversion of thermal energy and electrical energy adopts silicon-germanium alloy.

[0016] As a further improvement of the system of the present invention, the method for preparing the copper-based composite phase change material comprises the following steps:

[0017] Step 1, preparing copper-based powder by mechanical pulverization;

[0018] Step 2: surface treating the copper-based powder by electroplating to improve its compatibility with the phase change material;

[0019] Step 3, the surface treated copper-based powder is mixed evenly with the phase change material, etc., and the mass fraction of the copper-based powder is preferably 10%-30%;

[0020] Step 4: pour the mixed material into the mold and solidify it at a certain temperature and pressure. For paraffin with a melting point of 50-60°C, liquid paraffin has better fluidity. The pouring temperature can be controlled at 70-80°C; the pouring pressure is controlled at 0.1-0.5MPa;

[0021] Step 5: Heat treat the solidified material to improve its phase change performance and stability. The temperature and time of heat treatment are determined according to the composition and performance requirements of the material. In the temperature range of 100-150°C, the paraffin wax is fully melted and better integrated with the copper-based powder. The heat treatment time is about 2-6 hours.

[0022] Step 6: Process the heat-treated material, such as cutting, grinding, polishing, etc., to obtain the desired shape and size.

[0023] The present invention provides a control method for a system coupled with photovoltaic energy storage and cooling based on a copper-based composite phase change material, and the specific steps are as follows:

[0024] First, define the state space, action space, and reward function to facilitate the implementation of the algorithm:

[0025] State Space:

[0026] The state space covers multiple parameters that have an important impact on system performance, including but not limited to the solar panel temperature T solar , copper-based composite phase change material temperature T pcm , the phase change degree of phase change material α pcm The temperature difference ΔT between the upper and lower surfaces of the thermoelectric material is estimated by using the temperature sensor and the material property formula. th ermo 、Current energy storage capacity E storage , real-time power grid price P grid And the light intensity I ligh t , these parameters together constitute the state vector,

[0027] s=[T solar ,T pcm ,α pcm ,ΔT th ermo ,E storage ,P grid ,I ligh t ], (1)

[0029] It fully reflects the performance of the system's operating status at a certain moment;

[0030] Action Space:

[0031] The action space defines the set of operations that the system can take, including the operation of controlling the phase change material cooling and energy storage modules, such as adjusting the working intensity u of the heat dissipation device cool , whose range is Control the operation of the thermoelectric material energy conversion module, such as adjusting the thermal conduction efficiency η between the thermoelectric material and the phase change material and the heat dissipation environment th ermo , the range is And the operation of the power conduction module determines the power output power P cool , the range is and output timing, so the action vector can be expressed as

[0032] [u cool ,η th ermo ,P cool ], (2)

[0034] Reward function:

[0035] The total reward includes four sub-rewards: efficiency, energy storage, power output, and stability;

[0036] The efficiency bonus is:

[0037] R efficiency =k1×(Δη solar ),(3)

[0038] where Δη solar Represents the change in photoelectric conversion efficiency. When the phase change material works effectively, the photoelectric conversion efficiency increases. solar >0, k1 is the corresponding weight coefficient.

[0039] Energy storage rewards are:

[0040] R storage =k2×(E storage (t)-E storage (t-1))-k3×E loss ,(4)

[0041] Where E storage (t) and E storage (t-1) represents the energy storage capacity at the current and previous moment, E loss Represents the loss during energy storage, k2 and k3 are weight coefficients. When the energy storage effect is good, R storage >0;

[0042] The power output rewards are;

[0043] R output =k4×P out×P grid -k5×|ΔP out |,(5)

[0044] Among them, k4 and k5 are weight coefficients. This function comprehensively considers the economic benefits of output power and the stability of output power. When the electricity price is high, outputting power with stable power will get higher rewards;

[0045] The stability rewards are;

[0046]

[0047] where x i (t) represents the key parameters of the system, n represents the number of parameters, the reward is intended to keep the system parameters stable, and k6 is the weight coefficient.

[0048] So the total reward function can be calculated as

[0049] R stability =R efficiency +R storage +R output +R stability ,(7)

[0050] Next, the specific implementation algorithm includes the following steps:

[0051] Step 1: Initialize the algorithm:

[0052] First build the policy network π(s|θ π ) and the value network Q(s,a|θ Q ), the task of the policy network is to generate actions based on the input state information, while the value network is to evaluate the value of the current state-action pair. These networks include an input layer, multiple hidden layers, and an output layer. The input layer receives the state vector, and the output layer outputs the corresponding action or value evaluation result;

[0053] Then set the parameters θ of the initialization network π and θ Q , these parameters are randomly generated, but it is necessary to ensure that the output range of the network meets the range requirements of the action space and value assessment;

[0054] At the same time, other parameters are initialized and the experience replay buffer D is created with a capacity of N, which is used to store the experience tuples during the interaction between the agent and the environment:

[0055] (s,a,R,s′),(8)

[0056] This helps break the correlation between samples and improves the stability of learning;

[0057] Step 2: Interact the algorithm with the environment and perform actions:

[0058] The sensor transmits the collected system status information to the agent to form a state vector s t The agent will s t Input the policy network and get the action vector:

[0059] a t =π(s t |θ π ),(9)

[0060] The action vector contains control instructions for different modules of the system and the working intensity u of the heat sink. cool Adjustment of the thermal conductivity of thermoelectric materials η th ermo The regulation of the electric energy output power P out These actions will be converted into specific control signals and sent to the corresponding actuators;

[0061] After executing the action, the system state will change, and the new state s t+1 It will be collected by the sensor, and the system will calculate the corresponding reward based on the new state. If the system's photoelectric conversion efficiency is improved, the positive efficiency reward R will be calculated according to the corresponding formula t ; If the energy storage capacity increases and the energy loss is small, you will get a positive energy storage reward;

[0062] Step 3: Use the algorithm to learn and update:

[0063] First, a batch of experience tuples of size m are randomly extracted from the experience replay buffer D, denoted as (s i ,a i ,R i ,s i+1 ).

[0064] Calculate the target value:

[0065] For each experience tuple, the target value is calculated according to the Bellman equation:

[0066] y i =R i +γQ(s i+1 ,π(s i+1 |θ π )|θ Q ),(10)

[0067] Estimate the target value of the current state-action pair based on the current reward and the expected value of the next state;

[0068] Then update the value network by minimizing the temporal difference error, i.e.

[0069]

[0070] To update the parameters of the value network, this enables the value network to more accurately evaluate the value of state-action pairs.

[0071] Finally, update the policy network and update the parameters of the policy network according to the deterministic policy gradient algorithm, that is,

[0072]

[0073] Encourage the policy network to generate actions that can achieve higher cumulative rewards in the long run.

[0074] Beneficial effects: Compared with the prior art, the present invention adopts the above technical solution and has the following advantages:

[0075] Solve the problem of solar photovoltaic panels being too cold or too hot;

[0076] With the high thermal conductivity of copper-based composite phase change materials, the heat generated by photovoltaic panels can be quickly conducted, thereby effectively reducing the temperature of photovoltaic panels and reducing the loss of photoelectric conversion efficiency caused by high temperature. In northern my country, frost and ice will appear at night in winter, and copper-based composite phase change materials can release a large amount of heat accumulated during the day to prevent solar photovoltaic panels from freezing.

[0077] Realize waste heat utilization and nighttime power generation;

[0078] Traditional solar cooling systems cannot fully utilize waste heat, but in this patent, the copper-based composite phase change material solidifies and releases heat in the evening and night, which is transferred to the silicon-germanium alloy thermoelectric material. The temperature difference between the upper and lower parts of the thermoelectric material is used to move electrons to generate a potential difference, converting thermal energy into electrical energy. At the same time, the energy collection and inversion system can achieve efficient capture of weak electrical energy and accurately invert the collected electrical energy into alternating current. It realizes the storage of excess heat during the day and power generation at night, improves the power generation efficiency of the system, and plays a role in peak shaving and valley filling.

[0079] Improve material performance;

[0080] The metal bracket in the copper-based composite phase change material adopts a three-period minimal surface structure, which has ultra-high specific strength and specific stiffness, good energy absorption capacity and anisotropic mechanical properties, giving the material better thermal conductivity and thermal stability, so that it can maintain good performance during repeated heat absorption and release. Compared with traditional phase change materials (such as pure paraffin and fatty acids with poor thermal conductivity and insufficient thermal stability), it is more suitable for photovoltaic energy storage and cooling coupling systems. At the same time, the copper-based phase change material has a higher thermal conductivity than the traditional aluminum-based phase change material, about 380-400W / (m·K), which can conduct the heat generated by the photovoltaic panel more quickly, thereby effectively reducing the temperature of the photovoltaic panel and reducing the efficiency loss caused by high temperature.

[0081] Reasonable structural design;

[0082] In terms of the overall system design, the solar photovoltaic panels, copper-based composite phase change materials, aluminum alloy brackets and thermoelectric materials are closely connected from top to bottom, and the layout of each module is reasonable, which is conducive to the efficient conduct of heat transfer and energy conversion. At the same time, the aluminum alloy bracket supports the copper-based composite phase change material to form the cooling and energy storage module, and the stainless steel bracket forms the support module. The structure is stable and can meet different functional requirements.

[0083] Algorithm innovation;

[0084] Traditional systems mostly use fixed rule control, which has poor adaptability, processing power and optimizability. The DDPG algorithm introduced in this patent can dynamically adjust the control strategy according to key parameters such as solar photovoltaic panel temperature, phase change degree, electricity price, etc., such as adjusting the heat dissipation intensity and power output power, adapting to complex environments, improving system flexibility and adaptability, and avoiding the limitations of traditional control. Through a carefully designed reward function, the system performance is comprehensively optimized from multiple dimensions such as improving energy conversion efficiency, optimizing the power storage process, achieving power output with maximum economic benefits, and ensuring system stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 A system diagram of the present invention coupling photovoltaic energy storage with photovoltaic panel cooling;

[0086] Figure 2 It is a flow chart of the energy collection and inverter system;

[0087] Figure 3 It is a copper-based three-periodic minimal surface structure phase change material;

[0088] Figure 4 Flowchart for algorithm implementation;

[0089] Attachment tag;

[0090] 1. Solar photovoltaic panels; 2. Copper-based composite phase change materials; 3. Aluminum alloy brackets; 4. Thermoelectric materials; 5. Wires; 6. Brackets; 7. Energy collection and inverter systems. DETAILED DESCRIPTION

[0091] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The following embodiments are used to illustrate the present invention but are not used to limit the scope of the present invention.

[0092] Structural aspects, such as Figure 1 First, install the solar photovoltaic panel 1 (using single silicon crystal material) at the top as a power generation module to receive solar energy and convert it into electrical energy. Then, the four corners of the solar photovoltaic panel 1 are fixed to the top of the aluminum alloy bracket 3 by detachable screws, so that the solar photovoltaic panel can be disassembled and maintained conveniently. At the same time, a gap of about 10 cm is left between the solar photovoltaic panel 1 and the bottom plate of the aluminum alloy bracket 3. The cooling and energy storage module composed of the copper-based composite phase change material 2 supported by the aluminum alloy bracket 3 is installed closely below the solar photovoltaic panel 1, such as Figure 2 As shown in the microstructure, the phase change material used in the copper-based composite phase change material 2 is paraffin or fatty acid or inorganic compound, etc. The copper-based composite phase change material 2 is made by a specific preparation method (such as preparing copper-based powder by atomization method, mixing it with the phase change material after electroplating, and then casting, heat treatment, processing and other steps). The aluminum alloy bracket 3 plays a role of stable support to ensure that the copper-based composite phase change material 2 is in close contact with the solar photovoltaic panel so as to effectively absorb the heat generated by the solar photovoltaic panel.

[0093] Then, a thermoelectric material 4 (using a tellurium-bismuth alloy) is installed under the copper-based composite phase change material 2 as an energy conversion module for converting heat into electrical energy. The upper part of the thermoelectric material 4 is closely attached to the aluminum alloy bracket 3 by brazing and maintains a good heat conduction relationship, so that an effective temperature difference is formed between the upper and lower parts of the thermoelectric material 4. Wires 5 are installed on both sides of the thermoelectric material 4, and an energy collection and inverter system 7 is applied to both ends of the wire 5. Figure 3The power collection module includes sensors such as high-sensitivity current transformers and voltage transformers. The current transformer can convert tiny AC current signals into voltage signals that can be processed by the collection circuit in proportion; the voltage transformer collects and converts tiny voltages. The control unit includes multiple microcontrollers or digital signal processors, which receive digital signals from the collection module, analyze the data according to the set program algorithm, and generate control pulse signals based on these data to control the working state of the inverter circuit to ensure the stable and efficient operation of the entire system. The inverter circuit is composed of a power switch tube, a drive circuit, a filter circuit, etc. Driven by the pulse signal of the control unit, the power switch tube works in a high-frequency switching state to convert DC power into AC power, which can capture weak power and invert it into 220V / 50Hz AC power and directly integrate it into the power grid. Then a support module composed of a stainless steel bracket 6 is set at the bottom to provide a stable support structure for the entire system to ensure that the system can be placed firmly under various terrain conditions.

[0094] The solar photovoltaic panel 1, the copper-based composite phase change material 2, the aluminum alloy bracket 3 and the thermoelectric material 4 are placed close to each other from top to bottom. This layout is conducive to the efficient transfer of heat and the effective conversion of energy. The close cooperation between the modules ensures the stability and efficiency of the system operation. For example, heat can be quickly transferred from the solar photovoltaic panel 1 to the copper-based composite phase change material 2, and can be smoothly transferred from the phase change material to the thermoelectric material 4 at night.

[0095] Working principle:

[0096] During the day, when the daytime light becomes stronger, the solar photovoltaic panel 1 starts to work, converting solar energy into electrical energy, while itself generates heat due to absorbing solar energy, and the temperature rises. The heat generated by the solar photovoltaic panel 1 is quickly transferred to the copper-based composite phase change material 2 cooling and energy storage module below. Since phase change materials such as paraffin and fatty acids are encapsulated in the pores of the copper base, copper has good thermal conductivity and can quickly transfer external heat to phase change materials such as paraffin and fatty acids, so that phase change materials such as paraffin and fatty acids can undergo phase change faster and absorb heat for energy storage. At the same time, after the paraffin melts, the copper-based pore structure can also prevent the leakage of paraffin and ensure the stability of the composite phase change material. Since the copper-based composite phase change material 2 has high thermal conductivity, heat can be quickly conducted in the material. As heat is absorbed, the phase change materials such as paraffin or fatty acids in the copper-based composite phase change material 2 gradually melt, absorbing a large amount of heat in this process, thereby cooling the solar photovoltaic panel 1, reducing its temperature, and reducing the loss of photoelectric conversion efficiency caused by high temperature. At the same time, liquid phase change materials such as paraffin or fatty acids can carry a large amount of heat, and their metal brackets ensure the high thermal conductivity and stability of the cooling and energy storage module during the heat absorption process. They can not only quickly transfer heat to the entire phase change material such as paraffin or fatty acids, but also prevent the material from becoming unstable due to temperature changes.

[0097] From dusk to night, the light intensity decreases to zero, the solar photovoltaic panel 1 no longer generates new heat, and the temperature of the entire system gradually decreases. The copper-based composite phase change material 2 begins to solidify, releasing a large amount of heat during the solidification process. The released heat is transferred to the thermoelectric material 4 below, and the thermoelectric material uses the temperature difference between the upper and lower parts to move a large number of electrons up and down in the material. By connecting the wires between the upper and lower parts of the thermoelectric material 4, the directional movement of the electrons generates a potential difference, which then converts the thermal energy into electrical energy. By using the potential difference and weak current, the energy collection and inversion system 7 can be used to capture weak electrical energy and accurately invert the collected electrical energy into alternating current. The storage of excess heat during the day and the conversion of thermal energy into electrical energy at night are realized, which increases the overall power generation of the system, plays a role in peak shaving and valley filling, and enables the system to effectively utilize energy at different times and improve energy efficiency.

[0098] Specific implementation of the algorithm:

[0099] Connect the solar photovoltaic panel to the maximum power point tracking controller through wires to ensure that the solar photovoltaic panel can output maximum power under different lighting conditions. The maximum power point tracking controller is responsible for adjusting the operating voltage and current of the solar photovoltaic panel to match the load and improve the photoelectric conversion efficiency.

[0100] The phase change material container is arranged on the back side or near the solar photovoltaic panel to effectively absorb the excess heat generated by the solar photovoltaic panel. A heat dissipation device, such as a heat pipe or a heat sink, can be installed around the phase change material container to assist in the transfer and regulation of heat.

[0101] Various types of sensors are installed in each system of this device, including temperature sensors: multiple temperature sensors, such as thermocouples or thermistors, are arranged on the surface of the solar photovoltaic panel, inside and outside the phase change material container, at both ends of the thermoelectric material and in the environment to monitor temperature changes. These sensors transmit temperature data to the controller through an analog-to-digital converter (ADC). Light sensor: A photodiode or phototransistor is selected as a light sensor and placed in a suitable position to measure the light intensity. Similarly, the data of the light sensor is transmitted to the controller through the ADC. Electricity sensor: A power sensor, such as a coulomb meter, is installed in the energy storage module to monitor the stored electricity, and its data will also be transmitted to the controller.

[0102] Deployment and operation of algorithms in the system

[0103] Algorithm deployment process:

[0104] Processor selection: Depending on the system's computing needs and real-time requirements, you can choose a low-cost microcontroller (such as the ARM Cortex-M series) or an embedded processor (such as the STM32 series).

[0105] Software environment: Build a corresponding operating system (such as Linux) or real-time operating system (such as FreeRTOS) on the embedded processor, and install a lightweight version of the deep learning framework (such as TensorFlow Lite or PyTorch Mobile) to provide a platform for algorithm operation. Use programming languages ​​(such as Python or C / C++) to write algorithms for continuous action space (DDPG) in deep reinforcement learning.

[0106] Code.

[0107] The strategy network and the value network are combined and implemented as a deep neural network. The deep learning framework can provide an application programming interface (API). For the network structure, a multi-layer perceptron (MLP) can be used. The input layer is determined according to the dimension of the state space, the hidden layer sets the appropriate number of neurons according to the computing power and performance requirements, and the output layer is designed according to the dimension of the action space. The initial parameters of the network are stored in the storage device of the embedded processor (such as an SD card or flash memory) to facilitate subsequent reading and updating.

[0108] Use the corresponding hardware interface (such as I2C, SPI, or GPIO) to read the sensor data from the analog-to-digital converter. Write a driver to convert the sensor data into the state vector required by the algorithm. For example, combine the temperature sensor data, light intensity data, and battery data into a state vector.

[0109] The actuator is then controlled by GPIO or PWM (pulse width modulation) signals. For heat dissipation devices, PWM signals can be used to adjust their working intensity to achieve continuous control of heat dissipation power; for power output, GPIO is used to control relays or power switches to adjust the power output power according to the actions generated by the algorithm.

[0110] The operation process of the algorithm:

[0111] like Figure 4 The flowchart shown runs the algorithm. First, the algorithm is initialized to complete the network construction and initialization work required for deep learning, preparing for the decision-making and evaluation of the intelligent agent; then the environment interaction and action execution are carried out, and the intelligent agent generates and executes actions based on the current state, thereby changing the system state, and at the same time calculates rewards based on the changes in the system state to reflect the quality of the action; then the experience replay buffer stores the information of each interaction, providing experience data for subsequent learning, which helps to improve the stability and robustness of the algorithm. In order to avoid overfitting and enhance the learning effect, a part of the stored experience is randomly extracted for learning to prevent the algorithm from falling into a local optimum;

[0112] The Bellman equation is then used to calculate the target value, which is the key basis for updating the value network. Based on the calculated target value, the value network is updated by minimizing the temporal difference error to improve the accuracy of its value assessment of state-action pairs. The policy network is then updated based on the calculation results to guide the policy network to generate better actions in order to obtain higher long-term cumulative rewards. Finally, the system performance is evaluated to determine whether it has met expectations or stabilized, and to decide whether to continue optimizing or end the algorithm.

[0113] The above description is only a preferred embodiment of the present invention and does not constitute any other form of limitation to the present invention. Any modification or equivalent change made based on the technical essence of the present invention still falls within the scope of protection required by the present invention.

Claims

1. A photovoltaic energy storage and cooling coupling system based on copper-based composite phase change materials, characterized in that: The invention comprises a cooling and energy storage module of a copper-based composite phase change material (2) and an aluminum alloy bracket (3); a power generation module of a solar photovoltaic panel (1) installed at the top; an energy conversion module of a thermoelectric material (4) installed at the bottom; and a support module composed of a stainless steel bracket (6), an energy collection and inversion system (7) module and an electric energy conduction module composed of an external wire (5) at the bottom, wherein the solar panel (1) and the copper-based composite phase change material (2), the aluminum alloy bracket (3) and the thermoelectric material (4) are closely connected to each other from top to bottom, and the energy collection and inversion system (7) is composed of an electric energy collection module, a control unit and an inversion circuit.

2. The photovoltaic energy storage and cooling coupled system based on copper-based composite phase change materials according to claim 1 is characterized in that: The thermoelectric material (4) used for converting thermal energy into electrical energy adopts silicon-germanium alloy.

3. The photovoltaic energy storage and cooling coupled system based on copper-based composite phase change materials according to claim 1 is characterized in that: The method for preparing the copper-based composite phase change material (2) comprises the following steps: Step 1, preparing copper-based powder by mechanical pulverization; Step 2: surface treating the copper-based powder by electroplating to improve its compatibility with the phase change material; Step 3, the surface treated copper-based powder is mixed evenly with the phase change material, etc., and the mass fraction of the copper-based powder is preferably 10%-30%; Step 4: pour the mixed material into the mold and solidify it at a certain temperature and pressure. For paraffin with a melting point of 50-60°C, liquid paraffin has better fluidity. The pouring temperature can be controlled at 70-80°C; the pouring pressure is controlled at 0.1-0.5MPa; Step 5: Heat treat the solidified material to improve its phase change performance and stability. The temperature and time of heat treatment are determined according to the composition and performance requirements of the material. In the temperature range of 100-150°C, the paraffin wax is fully melted and better integrated with the copper-based powder. The heat treatment time is about 2-6 hours. Step 6: Process the heat-treated material, such as cutting, grinding, polishing, etc., to obtain the desired shape and size.

4. The control method of the photovoltaic energy storage and cooling coupled system based on copper-based composite phase change materials according to any one of claims 1 to 3, characterized in that: The specific steps are as follows: First, define the state space, action space, and reward function to facilitate the implementation of the algorithm: State Space: The state space covers multiple parameters that have an important impact on system performance, including but not limited to the solar panel temperature T solar , copper-based composite phase change material temperature T pcm , the phase change degree of phase change material α pcm The temperature difference ΔT between the upper and lower surfaces of the thermoelectric material is estimated by using the temperature sensor and the material property formula. thermo 、Current energy storage capacity E storage , real-time power grid price P grid And the light intensity I light , these parameters together constitute the state vector, s=[T solar ,T pcm ,α pcm ,ΔT thermo ,E storage ,P grid ,I light ], (1) It fully reflects the performance of the system's operating status at a certain moment; Action Space: The action space defines the set of operations that the system can take, including the operation of controlling the phase change material cooling and energy storage modules, such as adjusting the working intensity u of the heat sink cool , whose range is Control the operation of the thermoelectric material energy conversion module, such as adjusting the thermal conduction efficiency η between the thermoelectric material and the phase change material and the heat dissipation environment thermo , the range is And the operation of the power conduction module determines the power output power P cool , the range is and output timing, so the action vector can be expressed as [u cool ,or thermo ,P cool ], (2) Reward function: The total reward includes four sub-rewards: efficiency, energy storage, power output, and stability; The efficiency bonus is: R efficiency =k1×(Dη solar ),(3) where Δη solar Represents the change in photoelectric conversion efficiency. When the phase change material works effectively, the photoelectric conversion efficiency increases. solar >0, k1 is the corresponding weight coefficient. Energy storage rewards are: R storage =k2×(E storage (t)-E storage (t-1))-k3×E loss ,(4) Where E storage (t) and E storage (t-1) represents the energy storage capacity at the current and previous moment, E loss Represents the loss during energy storage, k2 and k3 are weight coefficients. When the energy storage effect is good, R storage >0; The power output rewards are; R output =k4×P out ×P grid -k5×|ΔP out |,(5) Among them, k4 and k5 are weight coefficients. This function comprehensively considers the economic benefits of output power and the stability of output power. When the electricity price is high, outputting power with stable power will get higher rewards; The stability rewards are; where x i (t) represents the key parameters of the system, n represents the number of parameters, the reward is intended to keep the system parameters stable, and k6 is the weight coefficient. So the total reward function can be calculated as R stability =R efficiency +R storage +R output +R stability ,(7) Next, the specific implementation algorithm includes the following steps: Step 1: Initialize the algorithm: First build the policy network π(s|θ π ) and the value network Q(s,a|θ Q ), the task of the policy network is to generate actions based on the input state information, while the value network is to evaluate the value of the current state-action pair. These networks include an input layer, multiple hidden layers, and an output layer. The input layer receives the state vector, and the output layer outputs the corresponding action or value evaluation result; Then set the parameters θ of the initialization network π and θ Q , these parameters are randomly generated, but it is necessary to ensure that the output range of the network meets the range requirements of the action space and value assessment; At the same time, other parameters are initialized and the experience replay buffer D is created with a capacity of N, which is used to store the experience tuples during the interaction between the agent and the environment: (s,a,R,s′),(8) This helps break the correlation between samples and improves the stability of learning; Step 2: Interact the algorithm with the environment and perform actions: The sensor transmits the collected system status information to the agent to form a state vector s t The agent will s t Input the policy network and get the action vector: a t =π(s t |θ π ),(9) The action vector contains control instructions for different modules of the system and the working intensity u of the heat sink. cool Adjustment of the thermal conductivity of thermoelectric materials η thermo The regulation of the electrical output power P out These actions will be converted into specific control signals and sent to the corresponding actuators; After executing the action, the system state will change, and the new state s t+1 It will be collected by the sensor, and the system will calculate the corresponding reward based on the new state. If the system's photoelectric conversion efficiency is improved, the positive efficiency reward R will be calculated according to the corresponding formula t ; If the energy storage capacity increases and the energy loss is small, you will get a positive energy storage reward; Step 3: Use the algorithm to learn and update: First, a batch of experience tuples of size m are randomly extracted from the experience replay buffer D, denoted as (s i ,a i ,R i ,s i+1 ). Calculate the target value: For each experience tuple, the target value is calculated according to the Bellman equation: y i =R i +γQ(s i+1 ,π(s i+1 |θ π )|θ Q ),(10) Estimate the target value of the current state-action pair based on the current reward and the expected value of the next state; Then update the value network by minimizing the temporal difference error, i.e. To update the parameters of the value network, this enables the value network to more accurately evaluate the value of state-action pairs. Finally, update the policy network and update the parameters of the policy network according to the deterministic policy gradient algorithm, that is, Encourage the policy network to generate actions that can achieve higher cumulative rewards in the long run.

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