210 results about "Green power" patented technology

The invention relates to the technical field of power grids, and discloses a port green power virtual energy storage aggregation regulation and control method, which comprises the following steps: acquiring an aggregation scene and resource data; generating a power hard constraint mapping and pile division execution reference by calculating a multi-band energy index and identifying a system power transfer characteristic; a local rapid control configuration set is generated by solving a virtual impedance feasible region and combining power hard constraint allocation compensation budget; and fusing a sub-pile execution reference (slow channel) generated based on the power hard constraint with a fast channel execution track obtained after local online execution compensation, performing priority arbitration under the constraint of the power hard constraint, and outputting a final execution power track. According to the invention, a double-layer regulation and control framework of slow channel planning and fast channel compensation is constructed, and through a core link of power hard constraint mapping, coordinated operation of fast and slow channels and safety and stability of the whole system are ensured, and fast and reliable dynamic support capability can be provided for a power grid.

The invention discloses a wind, light and green power coupled pumping unit well group operation collaborative optimization method. The method comprises the following steps: acquiring field data of a multi-energy micro-grid pumping unit well group; establishing a wind and light output uncertainty model, an energy storage battery model, a pumping unit power model and bidirectional AC / DC converter equipment; based on each model, establishing a multi-objective optimization scheduling model for the intermittent pumping operation of the multi-energy micro-grid pumping unit well group; an NSGA-II method is adopted to solve the established multi-objective optimization scheduling model, and a Pareto optimal scheduling scheme is obtained; and carrying out comprehensive evaluation and decision analysis based on an entropy weight TOPSIS method, and selecting an optimal scheme from a Pareto optimal scheduling scheme set obtained by NSGA-II. According to the multi-energy micro-grid rod-pumped well group off-peak interval pumping operation multi-objective optimization method based on NSGA-II and entropy weight TOPSIS, the power utilization requirement of the rod-pumped well group can be met, the working system of rod-pumped well group interval pumping operation is optimized, the economical efficiency and stability of the operation of a rod-pumped well interval pumping system under a wind-solar storage micro-grid are ensured, and the green power consumption capability of the system is enhanced.

The invention relates to the technical field of charging scheduling, in particular to an expressway unmanned heavy truck intelligent charging system which comprises a data acquisition and preprocessing system and a vehicle pile network cloud integrated cooperative operation and green electricity source tracing carbon emission reduction module. Driving and charging strategies are optimized in combination with algorithms such as reinforcement learning, the energy consumption of the unmanned heavy truck can be effectively reduced, the transportation resource utilization rate is improved, and it is guaranteed that transportation tasks are efficiently completed in a time window; green power full-link traceability is realized based on the block chain technology, and the charging time is dynamically adjusted in combination with peak and valley electricity prices, so that the authenticity of use of green power is ensured, low-cost green power can be preferentially selected, and the charging and overall operation cost is remarkably reduced; the carbon emission in the charging and operation process is accurately quantified, the emission reduction benefit of a traditional heavy truck is scientifically evaluated, meanwhile, the method is in butt joint with a green logistics authentication system, support is provided for obtaining authoritative green authentication for transportation services, and low-carbon transformation of the industry is assisted.

The invention provides a green electricity consumption accounting optimization method and device based on a carbon emission factor and a medium, and belongs to the technical field of carbon emission optimization based on green electricity accounting. The method comprises the following steps: establishing a corresponding relation table between a power purchasing form and a tracing tool according to multi-source data to execute power-proof collaborative tracing; performing inter-region green attribute replacement through a calculation engine, constructing a region green power consumption accounting model, and calculating the total region green power consumption; and calculating a residual power carbon emission factor based on the accounting result, and generating a cross-regional green power consumption report so as to realize carbon emission optimization. According to the method, a green electricity consumption source is accurately positioned through an evidence-electricity collaborative traceability mechanism, the problem of cross-regional repeated calculation is reduced, the calculation reliability is remarkably improved based on a carbon emission factor generated by an accurate accounting result, and thus the path and the cost for achieving a carbon reduction target are optimized.

The invention discloses a charging and discharging cooperative control method for a green power direct connection energy storage system, and aims to solve the problems of low efficiency, short service life and weak power grid support caused by renewable energy source volatility and energy storage response lag. The method comprises the following steps: collecting green power output, energy storage state and load demand data in real time; based on a three-layer collaborative architecture, an upper-layer energy scheduling layer determines target power according to an operation mode (such as green power consumption, peak clipping and valley filling), a middle-layer power distribution layer dynamically distributes power to each sub-cluster according to a health state and temperature and voltage consistency, and a lower-layer equipment execution layer drives a converter to perform precise execution through double-closed-loop control; and a closed-loop feedback correction and hierarchical protection mechanism is introduced. The green power consumption rate is effectively improved, the energy storage life is prolonged, and the system safety and the power grid supporting capacity are enhanced.

The invention relates to a breakwater structure for wave power generation and a construction method of the breakwater structure. The breakwater structure comprises a lower barrel body, an upper caisson and a power generation device. A compartment is arranged in the upper caisson, and the power generation device is installed in the compartment. A wave inlet hole is formed in the wave facing side of the upper caisson, a cast-in-place adjusting node is arranged on the top of the upper caisson, and a wave wall, a cable pipe trench, a detachable road plate and an overhauling device are sequentially arranged above the node. The breakwater structure and the wave power generation system are integrated by adopting standard barrel type foundation factory prefabrication and floating transportation installation and combining cast-in-place joint leveling and the detachable road plates. The structure has the advantages of being convenient to construct, controllable in cost, stable in structure, convenient to maintain and the like, is suitable for wave power generation under medium and small wave conditions, and is particularly suitable for green power supply and coast protection in areas such as ports and islands.

The invention relates to a power grid hour-level green electricity source tracing method and system based on space-time matching. The method comprises the following steps: firstly, collecting power generation data of a power generation end containing a power generation equipment identifier, power generation quantity and a power generation timestamp and power utilization data of a power utilization end containing a user identifier, power utilization quantity and a power utilization timestamp, and preprocessing and integrating into a power generation record and a power utilization record; for each to-be-traced power utilization record, dynamically delimiting a time window according to the power utilization timestamp; all power generation records in the window are inquired and aggregated to calculate the total power generation amount; then, according to the proportion of the generating capacity of each power generation device in the total generating capacity, green electric quantity distributed to the electricity utilization record is calculated, and a distribution relation is recorded to generate a traceability result containing a user identifier, a power generation device identifier, the distributed electric quantity, a time window and matching confidence; and finally, generating an ESG report and an electronic certificate based on the traceability result. Compared with the prior art, the method has the advantages of high traceability precision, high automation degree, high reliability and the like.

The invention discloses a balance unit quotation decision-making method considering uncertainty, and relates to the technical field of power systems, and the method comprises the steps: describing the output of renewable energy sources based on a scene method, carrying out the new energy modeling of the uncertainty of the output of the renewable energy sources, outputting a green power distribution proportion, and collecting market data and the data of a balance unit; building a balance unit quotation decision model based on new energy modeling and input balance unit data; a combined clearing model of the spot market and the green electricity market is constructed through new energy modeling and input market data and balance unit data; and optimizing the balance unit quotation decision model and the joint clearing model, and outputting an optimal quotation strategy of the balance unit. The method has the beneficial effects that the uncertainty of renewable energy sources can be effectively dealt with, the operation risk of the balance unit is reduced, and the operation stability of the system is improved.

The invention discloses an electric power and computing power coordinated regulation and control method and system, and relates to the field of load regulation and control, and the method comprises the steps: obtaining the electric power data of a power generation side and the electric power data of a computing power center, inputting the electric power data into a preset multi-target coordinated optimization algorithm, and obtaining the power generation and consumption data of a target time period meeting the requirements of the minimum power consumption cost and the maximum green power consumption, load distribution parameters are generated in combination with load demand data of the computing power center, and power generation and utilization loads of a computing task, computing power equipment, refrigeration equipment, a power generation side and the like of the computing power center are accurately controlled in a target time period. Through collaborative optimization of the power generation side and the computing power center, the green power intermittency and the computing power load fluctuation are effectively balanced, the green power consumption rate is improved, the power consumption cost of the computing power center is reduced, and the power supply and computing power operation stability is guaranteed.

The present application relates to photovoltaic lithium electricity energy storage and air conditioning system intelligent control technical field, specifically, the present application relates to photovoltaic lithium electricity air conditioner terminal machine room AI optimization method based on digital twinning, the present application is through the deployment of distributed optical fiber temperature sensor array, combined with k-epsilon turbulence model generates three-dimensional temperature field positioning hotspot area and calculates the maximum refrigerating capacity increment of air conditioner, fusion this increment, photovoltaic forecast power and lithium charge state, superimposed inverter efficiency compensation factor gets air conditioner callable green electricity quota, bind hot spot and quota generates thermal-electric coupling factor, combined with photovoltaic fluctuation intensity activates linkage mode, triggers deep deterministic policy gradient model to execute air valve opening, water temperature setting, lithium charge and discharge collaborative decision, through joint reward function binding evaluation three control quantity, output joint control vector, the method improves green power consumption rate and temperature control precision, guarantees equipment safety, applicable to machine room efficient operation.

The invention relates to the technical field of electricity market transaction and new energy scheduling, and discloses a high altitude area green electricity transaction volume decomposition method, system, device and medium, and the method comprises the steps: constructing a probability distribution model of historical electricity prices, generating an initial electricity price path reflecting spot electricity price fluctuation characteristics through a stratified sampling method, k representative scenes and corresponding probabilities are screened through a scene similarity calculation method to form a representative scene set; based on a multi-dimensional power transmission constraint condition, constructing a dynamic constraint system for sensing the power transmission capability in real time; the multi-dimensional power transmission constraint conditions comprise wind and light output prediction, power transmission line icing risk, spot electricity price fluctuation, green power total amount and time period performance constraint; and based on the representative scene set and the dynamic constraint system, a green power distribution scheme per hour is solved through a random optimization method, and green power transaction volume decomposition is realized. According to the invention, safe performance and economic benefit maximization of the green electricity contract can be realized.

The invention relates to a user distribution and storage and contract electric quantity collaborative optimization method oriented to green power performance, belongs to the technical field of power system optimization configuration, and solves the problems of low long-term contract performance rate, insufficient time sequence matching degree and unreasonable contract electric quantity configuration in the existing green power. Comprising the steps of predicting a new energy output curve and a user load curve in a planning period; constructing a first objective function and a first constraint condition for minimizing the power utilization cost of the user as an upper layer model; constructing a second objective function and a second constraint condition for minimizing the operating cost of the power system as a lower-layer model; and performing iterative solution on the upper layer model and the lower layer model based on the predicted new energy output curve and the user load curve in the planning period, and finally obtaining the optimal energy storage configuration capacity and the green electricity contract signing electric quantity of each user in the planning period. The green power performance capability and the resource configuration efficiency are improved.

The invention discloses a park-level energy and carbon accounting collaborative scheduling method and system, and the method and system achieve the efficient integration and collaborative utilization of data through breaking the barrier between energy data and IT data, improve the green power consumption rate, effectively reduce the carbon emission, and remarkably improve the overall operation efficiency and economical efficiency of a park energy system. According to the method, energy and IT data are collected and processed in real time, a basis is provided for accurate prediction and optimal scheduling, an LSTM model and an XGBoost model are adopted for prediction, an NSGA-II multi-target optimization algorithm is combined, and two-channel optimal control over green power consumption and carbon emission is achieved under the condition that energy storage and server operation constraints are met; meanwhile, scheduling instructions are continuously optimized through a feedback mechanism, and continuous stability and high efficiency of operation of the whole system are ensured.

The invention relates to the technical field of power system operation regulation and control, in particular to a green electricity source tracing method and system based on parameter and power flow coupling, electronic equipment and a medium. The method comprises the following steps: acquiring power system operation data and green electricity transaction information; constructing a power network topology model; constructing a load flow calculation model according to the determined network topology constraint and the electrical parameter constraint; configuring the load flow calculation model to execute iterative solution according to the node injection power and the real-time voltage to obtain whole network load flow distribution, and embedding a dynamic weight factor during iterative solution; and in response to a green electricity source tracing request of a user side, calculating a node load green electricity proportion and a branch transmission green electricity proportion so as to generate a green electricity source tracing result. Through the mode, the technical problem that the traceability precision is insufficient due to the fact that an existing green power traceability technology cannot adapt to the dynamic operation state of a power grid is solved, and the accuracy and the real-time performance of a traceability result are improved.

The invention discloses a deep correlation meteorological computing power center load prediction method and system based on an artificial intelligence distillation algorithm, and belongs to the technical field of computing power center energy efficiency management. Comprising the following steps: processing multi-source data such as weather and business through a multi-modal spatial-temporal feature fusion network, and generating a unified spatial-temporal fusion feature tensor; a space-time Transform is used as a teacher model, lightweight ConvLSTM is used as a student model, training is carried out by introducing a distillation loss function of attention weight, and a high-precision and low-delay load prediction model is obtained; on the basis of a prediction result, combining clean energy and a cooling system model, constructing and solving a multi-objective optimization problem taking the minimization of the total operation cost and the maximization of the green power consumption as objectives, and generating a collaborative scheduling curve; and after deployment, the model is dynamically updated through real-time feedback and self-adaptive online distillation to form intelligent operation and maintenance. According to the method, the balance between prediction precision and speed is realized, the energy efficiency and the green power consumption rate are improved, and the method has long-term self-adaptive capability.

The invention provides a power resource dynamic scheduling and security tracing system and method based on edge computing and block chain fusion, and the system employs a hierarchical collaborative architecture, and comprises an edge computing layer, a block chain layer and a cross-layer security module. The edge layer collects photovoltaic output and load demand data, and predicts short-term output and load through LSTM. The ACEGA algorithm is combined with the micro-service priority and the edge node resource state to generate a scheduling instruction; and the scheduling instruction hash value is subjected to uplink evidence storage, the energy storage device is triggered to charge and discharge after verification of the smart contract, and meanwhile, a green power consumption voucher is recorded. According to the invention, power resource dynamic scheduling optimization, data credibility evidence storage and security protection are realized, the problems of distributed new energy consumption, energy storage resource configuration and power transaction tracing are solved, and the defects of low edge computing scheduling efficiency, high data security risk and insufficient cross-technology collaboration in the prior art are overcome.

The invention discloses a low-voltage distributed multi-energy complementary and coordinated green power consumption and conversion system and optimization method, and the method comprises the steps: data collection and state monitoring: collecting the output data of a distributed energy unit in real time through a communication module of an intelligent control unit, and improving the power supply reliability to 99.99%; a plug-and-play interface supports flexible access of newly-added distributed energy or loads, system architecture does not need to be reconstructed, and the system adapts to multiple scenes such as villages, industrial parks and micro-grids. The intelligent control unit is compatible with multi-protocol communication of DL698, Modbus and the like and can be seamlessly connected with an existing power grid dispatching system, landing application of the technology is accelerated, the consumption capacity, the operation efficiency and the reliability of low-voltage distributed green power are comprehensively improved through innovative integration of multi-energy complementary configuration, multi-form energy storage fusion, intelligent cooperative regulation and control and a gradient utilization technology, and the system is suitable for large-scale popularization and application. And a key technical support is provided for low-carbon transformation of a novel power system.

This invention discloses a scheduling method for direct green power connection to cement industrial parks, including acquiring data information of the target power system; generating green power scenarios for the target power system; establishing an optimized scheduling model for green power-cement industrial parks; and solving the constructed optimized scheduling model to complete the scheduling of direct green power connection to cement industrial parks. This invention also discloses a system for implementing the aforementioned scheduling method for direct green power connection to cement industrial parks. This invention not only achieves optimized scheduling of direct green power connection to cement industrial parks but also offers higher reliability and better accuracy.

The invention discloses a multi-source cooperative green electric power energy optimization scheduling system, and relates to the technical field of electric power system computing power scheduling, and the system comprises an interaction construction module which constructs a direct connection signal channel of a supercomputing CPU and an intelligent computing GPU; the impedance matching module monitors a signal transmission reflection coefficient in real time and obtains signal transmission delay to realize impedance compensation; the supercomputing power supply layer configuration module regulates and controls output voltage in real time through a voltage monitoring unit; the intelligent calculation power supply layer configuration module adapts to the instantaneous peak current demand of the intelligent calculation module; the supercomputing water-cooling heat dissipation execution module is used for adjusting the flow of a water-cooling pump after receiving the temperature signal; the intelligent calculation air cooling linkage control module is used for collecting the real-time temperature of the intelligent calculation module and controlling the rotating speed of a speed-adjustable fan according to a fan rotating speed adjusting formula so as to realize pulse type heating adaptive heat dissipation; and the comprehensive evaluation module constructs a comprehensive evaluation model and evaluates the system performance. The problems of high transmission delay, mismatched heat dissipation and unstable power supply are solved.

The invention relates to the technical field of new energy station design and control, in particular to an energy station model, a system, a control method and a medium in a green power grid-connected scene, and provides the energy station model in the green power grid-connected scene, which is characterized in that a power stabilizing module is added on the basis of an existing power generation part and an existing power storage part; therefore, four dimensions of generation, storage, use and stability are formed in the dimension, the four dimensions are coordinated and complementary with renewable energy sources such as photovoltaic power and wind power, the inherent defects of instability, uncontrollability and the like of new energy sources are overcome, the problems of intermittency and volatility of the renewable energy sources are solved to the maximum extent, and the energy efficiency is improved. The method has the capabilities of dynamically regulating current and voltage, resisting power grid fluctuation, resisting superposition impact and flexibly participating in distributed grid connection, improves the adaptability of an energy station and the reliability of power supply, and generates a control decision by adopting two dimensions of power generation evaluation information of a power generation module and energy storage evaluation information of a power storage module in the aspect of new energy station control. And valley-time charging and peak-time discharging can be intelligently controlled.

The invention discloses high-toughness and high-ductility degradable insulation paper and a preparation method thereof, and belongs to the technical field of electrical insulation materials. The insulation paper is composed of 35-65 wt% of plant fibers, 15-45 wt% of polylactic acid, 3-15 wt% of a bio-based flexibilizer and 0.5-5 wt% of nanocellulose, a three-dimensional network structure is formed through wet molding and hot pressing process compounding, the elongation at break of the insulation paper is larger than or equal to 15%, and the tensile index is larger than or equal to 100 N.m / g. The preparation method comprises the following steps: defibering and pulping the fibers, mixing with the PLA emulsion and other components, carrying out papermaking molding, and carrying out hot-pressing curing. Through multi-component cooperation and process innovation, the problems that traditional insulation paper is large in brittleness and poor in ductility and pure PLA is insufficient in toughness are solved, and the product has excellent mechanical property, insulativity and degradability and is suitable for flexible electronic and green power equipment.

The application belongs to the technical field of energy storage optimization and energy-saving control, and specifically discloses a data center standby power energy storage optimization configuration energy-saving control method, which comprises the following steps: determining the grid disturbance intensity according to the grid-connected point voltage and frequency, and determining the minimum safety boundary of the discharge termination threshold in combination with the IT load standby power duration requirement; collecting photovoltaic power, identifying the change trend through linear regression, and determining the reference discharge termination threshold in combination with the minimum safety boundary; detecting the IT load power surge event, and if the event is detected, generating a pulse compensation amount according to the surge amplitude and duration, taking the sum of the reference threshold and the compensation amount as the final discharge termination threshold, or taking the reference threshold as the final threshold; when the energy storage SOC decreases to the final threshold, cutting off the non-IT load and retaining the IT load power supply. The application can improve the green power consumption capacity under the premise of guaranteeing the standby power reliability by dynamically adapting the grid disturbance, photovoltaic fluctuation and load pulse, and can avoid the power failure of the IT load.

A green power integrated hub for ocean cruise ships, which hub comprises a relay mother ship hull (1) and a green energy complex. The relay mother ship hull (1) cruises on a fixed schedule along a designated route between fixed locations. The green energy complex is arranged at the starting point of the route of the relay mother ship hull (1) and is used for re-fueling the relay mother ship hull (1). Ten connection assemblies are respectively arranged on two sides of the relay mother ship hull (1) and are used for connecting cruise ships (16) located on both sides. Two personnel access galleries (17) are respectively connected to the two sides of the relay mother ship hull (1) and are used for communicating the cruise ships (16) on both sides with the relay mother ship hull (1). Each connection assembly comprises a structural reinforcing member (14), a connecting arm support (13), a connecting arm device (12) and a hull connecting member (11). The structural reinforcing member (14) is arranged on a side wall of the relay mother ship hull (1).

The invention relates to a block chain-based green power consumption and carbon asset collaborative management system, provides a carbon emission transaction, and belongs to the field of purchasing, selling or leasing transactions. An intelligent joint prediction model of a set geographic area is adopted to intelligently predict green power supply data of a future time segment and carbon emission data of each electricity consumption enterprise; the upper chain evidence storage device writes the intelligent prediction data into the block chain network; and the contract generation device is used for automatically matching and forming a carbon transaction contract of each electricity consumption enterprise based on the intelligent prediction data. According to the invention, for the technical problems that the overall level of green power consumption and carbon asset collaborative management is insufficient and the performance is low, the same intelligent joint prediction model can be adopted to complete synchronous intelligent prediction of green power generation data and carbon emission data, and then automatic matching is carried out to form each carbon transaction contract. And meanwhile, the uplink evidence storage of the key data is executed, so that the technical problem is solved.

The invention provides a multi-data center green power optimization scheduling method and device based on deep reinforcement learning, and belongs to the technical field of intelligent scheduling. The method provided by the invention comprises the following steps: carrying out resource modeling on multiple data center nodes, and determining resource parameters of each node and output characteristics of renewable energy sources changing along with time; modeling a calculation task with a hierarchical structure, and decomposing the calculation task into hierarchical task units migrating across a data center; based on the resource modeling result and the calculation task modeling result, constructing a multi-data center simulation environment, and generating an input state vector of an intelligent agent; designing an action space of the intelligent agent; designing a reward function; based on a near-end strategy optimization algorithm, in a simulation environment, taking an input state vector as an input, and combining an action space and a reward function to carry out multiple rounds of training, and iteratively updating a scheduling strategy until the scheduling strategy reaches the standard in comprehensive performance indexes; and the standard scheduling strategy is applied to a real multi-data center environment, and the scheduling task is optimized.

The utility model discloses computing equipment. The computing equipment comprises a cabinet body, a green power module arranged in the cabinet body, and a deployment module, an energy storage module and a load which are arranged in the cabinet body, the allocation module comprises a green power adjustment module, a mains supply adjustment module and a power distribution module, and is used for outputting power adjusted by the green power adjustment module and the mains supply adjustment module from the power distribution module; the green power adjusting module is electrically connected with the green power module, and the commercial power adjusting module is electrically connected with a power grid. The energy storage module is electrically connected with the power distribution module. The power distribution module is used for preferentially using the electric power adjusted by the green power adjustment module to supply power to the energy storage module. The load is electrically connected with the power distribution module and the energy storage module. The computing equipment is used for ensuring that businesses can be orderly and continuously carried out while the increasing computing power requirement is met, and the power utilization cost is effectively reduced.

The invention discloses a full-link collaborative authentication and tracing method and system for green power environment rights and interests, and the method comprises the steps: carrying out the accumulation calculation of self-generation and self-use green power data, green power transaction data and green power certificate data of all new energy data centers, and obtaining the total amount of green environment rights and interests; configuring an association relationship among the authentication account, the accounting sub-account and the basic account according to the block chain smart contract to obtain an account hierarchy association rule; based on an account level association rule and a green consumption total amount proportion of each new energy data center, decomposing a green environment right total amount recorded by the authentication account to an accounting sub-account and a basic account of the corresponding new energy data center, and performing real-time uplink evidence storage on full life cycle data in a right decomposition and conduction process; and backtracking corresponding original measurement data, uplink time and participant information based on the full life cycle data of block chain evidence storage so as to trace the source of the full link of green environment rights and interests.

The invention provides a full-time multi-scene clearing and pricing method suitable for inter-provincial multi-channel centralized bidding. The method comprises the following steps: dividing seller market subjects participating in a centralized bidding transaction into new energy seller market subjects and traditional energy seller market subjects; based on historical prediction data, a transaction period is divided into a new energy large-power generation period set, a traditional energy large-power generation period set and a common output period set, and differential clearing strategies are respectively implemented: clearing and transaction path marginal pricing are only performed on corresponding types of energy in the new energy or traditional energy large-power generation period; and introducing a green power excitation item to the target function in the common output time period, and improving the new energy consumption level by adjusting an excitation factor. According to the method, the scientificity and pertinence of market design are improved, the cross-provincial and cross-regional resource optimization configuration efficiency is optimized, and the low-carbon and economical targets of system operation are cooperatively achieved.

The utility model provides a heat pump coupling system which comprises a heat pump module used for providing cooling and heating loads; the radiation refrigeration module is used for providing a cold load; the power supply module comprises a photovoltaic module, an energy storage module and a power grid module, is connected with the heat pump module through a switch assembly, and is used for supplying power to the heat pump module; and the control module is used for controlling the switch assembly according to the electric quantity provided by the photovoltaic module, the electric quantity of the energy storage module and the required electric quantity of the heat pump unit, and controlling the power supply mode of the heat pump module. Solar energy, stored energy and the power grid are used for supplying power to the heat pump unit, the radiation refrigeration technology assists in providing cooling loads, effective utilization of clean energy of solar energy and air energy is fully achieved, electricity consumption of the power grid is reduced, meanwhile, the cooling load consumption, the operation cost and the initial investment cost of the heat pump are reduced, and the energy-saving and environment-friendly effects are achieved. And green electricity utilization, energy conservation and carbon reduction are realized.