2695 results about "Power demand" patented technology

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

Disclosed in the present invention is an optimized dispatching method for a virtual power plant, which method is applied to a virtual power plant dispatching control center in a virtual power plant dispatching system. The method comprises: predicting next-day wind power and photovoltaic output values and power demand conditions of electric vehicles in a load aggregator on the day ahead; acquiring a next-day output scheme reported by each output unit in a virtual power plant dispatching system, wherein the next-day output scheme is determined by each output unit on the basis of a day-ahead electricity selling price, electric-vehicle charging and discharging strategies and the output characteristics and costs of other output units; and performing coordinated optimization on the basis of the next-day output schemes reported by the output units and a virtual power plant optimized dispatching model which takes demand responses and environmental costs into account, so as to obtain a final next-day output plan, and issuing the final next-day output plan to each output unit, such that each output unit performs execution on the basis of the final next-day output plan. The present invention makes load dispatching in a virtual power plant more flexible.

The invention discloses an energy storage and power grid coordination control system based on photovoltaic preferential energy supply, and relates to the technical field of power system dispatching automation, and the method comprises the steps: collecting the output power of a photovoltaic module, the charge state value of an energy storage unit and a load power demand in real time; dynamically calculating a photovoltaic and energy storage power distribution weight based on a preset photovoltaic priority energy supply strategy, and determining target output power of a photovoltaic module and an energy storage unit; when photovoltaic and energy storage cannot meet load requirements, power grid access control is automatically judged and triggered, so that continuity and stability of power supply are guaranteed. The problems that in the prior art, in the power dispatching process of an optical storage integrated system, dynamic reflection of the photovoltaic priority energy supply principle is lacked, the power distribution mode is not flexible, the power grid access response lags behind, and self-adaptive adjustment of dynamically adjusting the output current according to the real-time operation state is lacked are solved.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

Disclosed is a system and a method for blockchain-based virtual power plant management, relating to the field of virtual power plants. The system mainly includes three modules: an event-driven based demand rapid response and flexible resource optimal allocation module configured to adjust power supply and demand in real time according to power demand information and available resource information of an industrial park; a blockchain-based data collaboration module configured to establish a data sharing and transaction environment among various participants of a virtual power plant; and a visualization management module configured to provide a user interface for displaying, in real time, operational status and power transactions of the virtual power plant in the user interface. The system can effectively manage virtual power plants, enhancing the efficiency and transparency of power supply.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention provides an intelligent computing power scheduling method in a distributed computing environment, and relates to the technical field of distributed computing, and the intelligent scheduling method specifically comprises the steps of collecting resource information data, constructing a resource and task model, evaluating a computing power demand, formulating an adjustment strategy, distributing and scheduling the computing power, monitoring and adjusting in real time, and feeding back and optimizing. Through comprehensive modeling and dynamic computing power evaluation of computing nodes and tasks and in combination with multiple intelligent scheduling strategies, accurate allocation of computing power resources can be realized, the problems of resource waste and node load imbalance are effectively avoided, the overall utilization rate of resources in a distributed computing environment is remarkably improved, and the computing power of the distributed computing environment is improved according to the characteristics and requirements of the tasks. The execution nodes are reasonably selected, the resource allocation is optimized, the task correlation is considered, the data transmission overhead is reduced, the task execution speed can be increased, the task completion time can be shortened, and the processing capacity and response speed of the system are improved.

The invention discloses an edge computing power sharing method, an edge computing power sharing device, edge computing power sharing equipment and a medium, and the edge computing power sharing method comprises the following steps: connecting edge nodes through a decentralized P2P network, constructing a network connection module, and realizing computing power resource sharing among the nodes; collecting and monitoring computing power resource state data of each edge node through a computing power resource management module, judging nodes in an idle state through an evaluation algorithm, and adding computing power resources of the nodes into a shared pool; the node matching module is used for screening out an optimal matching node from the shared pool by utilizing a preset screening algorithm according to task demand information released by a computing power demand side; and the distribution execution module sends the task instruction to the selected optimal node from the cloud, and after the task is executed, the node releases the computing power resource and performs expense settlement through the charging module. According to the method, the utilization efficiency of computing power resources is optimized through decentralized node management and an intelligent matching mechanism, so that edge computing resources are more easily integrated and utilized.

The invention relates to the technical field of ship power systems and energy management. The invention provides an energy optimization management method and system for an extended-range hybrid power ship. The method comprises the following steps: collecting ship multi-source heterogeneous data in real time, and constructing a multi-source heterogeneous data set; based on the multi-source heterogeneous data set, constructing a load prediction model based on a time sequence convolutional network and long and short term memory fusion, and dynamically outputting propulsive power demand probability distribution in a future navigation period; a multi-objective optimization model is established, and an improved multi-objective genetic algorithm is adopted to generate a Pareto frontier solution set; and on the basis of a rolling time domain optimization framework, in combination with real-time navigation situation awareness data, carrying out online correction on the Pareto solution set, and generating and executing an optimal energy distribution instruction set. The problems of an existing extended-range hybrid power ship energy management technology in the aspects of working condition adaptability, multi-energy coupling efficiency, real-time state sensing and predicting capacity and emission and economical efficiency balance are solved.

The embodiment of the invention provides an intelligent dynamic load regulation and control method and system for an intelligent power grid. Wherein power parameters of key nodes are collected in real time through time sequence analysis and a deep learning algorithm, future power demand fluctuation is predicted, and a power grid topological structure model is constructed to identify the key nodes and a potential influence range; a genetic algorithm, particle swarm optimization and other multi-objective optimization algorithms are adopted to generate specific operation instructions for different power utilization areas or devices, and optimization objectives comprise minimization of energy cost, maximization of renewable energy utilization rate and guarantee of power supply stability; converting the strategies into guidance suggestions, pushing the guidance suggestions to a terminal user, collecting feedback information, generating a preference model by applying a reinforcement learning algorithm, and selecting an optimal strategy for implementation based on user acceptance and feasibility evaluation; risks and abnormal conditions are detected through the Bayesian network, and the strategy is adjusted in time. According to the technical scheme provided by the embodiment of the invention, the efficient distribution of power resources and the energy utilization efficiency in the smart power grid are improved.

Disclosed in the present invention are a method and system for full-range participation of energy storage charging and discharging in power grid frequency regulation. The method comprises: collecting operating power and frequency data of a power grid, so as to construct a full-range droop coefficient; setting constraints for an upper limit value and a lower limit value of a frequency dead band; obtaining an actual output power instruction value by means of a constraint function constructed on the basis of a theoretical power instruction value and a charging / generated power rated value, and controlling the power grid to operate on the basis of the actual output power instruction value; setting a hysteresis range on a power instruction of an energy storage system, and when a power demand of the power grid exceeds an upper limit threshold value of the hysteresis range, switching the energy storage system from a charging mode to a discharging mode; and when the power demand of the power grid is lower than a lower limit threshold value of the hysteresis range, switching the energy storage system from the discharging mode to the charging mode. In the present invention, when the frequency of the power grid is increased, generated power is reduced until power is absorbed from the power grid, and when the frequency of the power grid is reduced, the absorption of power from the power grid is reduced until electrical power is delivered to the power grid, thus achieving full-range participation of energy storage in power grid frequency regulation control, and satisfying regulation requirements of power grids.

A charging method based on intelligent dynamic power allocation. The method comprises: acquiring the current SOC, a real-time power demand, a rated power and infrared position detection information of an electric vehicle; on the basis of the current SOC, the real-time power demand, the rated power and the infrared position detection information of the electric vehicle, and in combination with a dynamic average power allocation strategy, a user first-come-first-served allocation strategy, a reverse SOC threshold value incentive strategy and an overall-power utilization maximization strategy, performing power allocation and generating a corresponding control instruction; and controlling the output of a charger topology switch on the basis of the control instruction. Further disclosed is a multi-gun charger apparatus using the charging method. The charging method includes a moderate power allocation strategy and takes into account the demands of both a charger control system and users, and can effectively improve the overall charging efficiency, reduce the charging time, and improve the utilization rate of charging piles.

The invention relates to the technical field of power management, and provides a power distribution method based on BMS power control and related equipment thereof. The method comprises the following steps of: performing multi-mode synchronous acquisition and self-adaptive preprocessing on detected battery pack global state data to obtain a battery state tensor, and performing unsteady-state signal decomposition and time-space feature extraction on a received load power demand signal to obtain a power component matrix; performing multi-constraint dynamic planning on the battery state tensor and the power component matrix to obtain an initial power distribution strategy, performing dynamic correction in combination with a deep reinforcement learning model to obtain an adaptive distribution strategy, and performing multi-layer circuit equalization optimization on the adaptive distribution strategy to obtain a power regulation instruction set; and finally, performing multi-physics field coupling verification and fault-tolerant reconstruction on the power regulation instruction set to obtain an execution signal sequence. According to the invention, through data fusion, demand prediction, strategy optimization and deep learning, power distribution is accurately regulated and controlled according to the battery state and the load power demand.

The invention relates to the technical field of electric power regulation and control, in particular to a method and system for controlling an electric power energy storage system, and the method comprises the following steps: obtaining the voltage and current when energy storage equipment is charged, analyzing the voltage fluctuation and current change trend, recognizing a fluctuation point, and calculating the fluctuation amplitude and frequency. And judging the deviation degree between the fluctuation data and the normal operation data of the equipment to obtain an equipment stability index. According to the invention, by monitoring the voltage and current of the energy storage device during the charging period and deeply analyzing the fluctuation characteristics of the electrical parameters, the evaluation accuracy of the stability of the device, the accurate fluctuation point identification and the calculation of the fluctuation amplitude and frequency are significantly improved, and the behaviors of the device in different charging and discharging periods are allowed to be more accurately judged. And the residual capacity of the energy storage equipment is matched with the actual power demand, so that the power distribution efficiency is effectively improved.

The invention discloses an electric forklift multi-mode driving switching method and system based on multivariable cooperation, and relates to the technical field of industrial vehicle intelligent control. The method is used for solving the problems of one-sided working condition sensing, switching oscillation and safety fault tolerance deficiency in multi-mode driving. The method comprises the following steps: firstly, generating a dynamically weighted working condition feature vector through multi-source sensing data fusion, and quantifying a stability risk, an energy consumption constraint and a power demand; secondly, based on a stability risk trigger steering safety envelope, arbitrating a multi-mode priority and correcting a torque upper limit in combination with a fuzzy rule to form an anti-conflict execution sequence; then, torque distribution and a steering angle acceleration instruction are optimized in a rolling mode in the safe envelope boundary, center-of-gravity shift is synchronously restrained, the track precision is improved, and energy consumption is reduced; finally, the control authority is transferred adaptively according to the operation intensity of a driver, when the decision is abnormal, the load-ramp rule base is degraded, an instruction is transferred through an S-shaped curve, and a torque balancing strategy is executed at the high position of a pallet fork to maintain stable.

The invention belongs to the technical field of wind power generation, and discloses a wind driven generator multi-drive variable pitch control method based on big data, which comprises the following steps of: constructing a multi-source data acquisition network covering an environment, a unit, a power grid and a driver, and combining preprocessing means such as denoising and standardization and feature extraction methods such as principal component analysis; high-quality input is provided for a short-term prediction model; the model can accurately pre-judge wind conditions and power requirements in the future 10-30 minutes, and is matched with a global optimization algorithm to dynamically adjust a variable pitch strategy, so that the wind driven generator can still keep stable power output under complex working conditions of gust, power grid fluctuation and the like, and particularly has outstanding performance in areas with unstable wind resources. According to mechanical coupling characteristics of the multi-drive variable pitch system, mechanical parameters such as a gear transmission gap and shafting rigidity are fully included in global optimization, and power output, mechanical fatigue and load distribution are balanced through a multi-objective optimization algorithm.

The invention relates to the technical field of computing power resource scheduling, and particularly discloses a green data center computing power demand prediction and energy consumption control method, system and device. Firstly, computing power information and environmental parameters of a data center are collected in real time based on edge equipment; afterwards, a time sequence trend is predicted by using an LSTM neural network, a CNN network is fused to extract computing power features, an LSTM-CNN hybrid model is constructed, repeated training is performed through an Adam optimization algorithm, and the model can accurately pre-judge a future computing power demand; and in combination with analysis of historical energy consumption and real-time monitoring data, an energy consumption analysis model is established to evaluate power consumption conditions under different computing power requirements. Based on the prediction result and the energy consumption analysis, the working state of the server cluster is dynamically adjusted, the load is scheduled, the energy use is optimized, the energy efficiency ratio of the data center is continuously improved by verifying and adjusting the energy consumption strategy, the overall operation cost and the energy consumption are remarkably reduced, and the development requirements of green energy conservation are met.

The invention discloses a virtual power plant path optimization system and method based on an electricity-carbon combination model, belongs to the technical field of virtual power plants, and aims to solve the problem of how to realize carbon emission minimization while ensuring stable operation of a power grid. The method comprises the following steps: collecting target data in real time through interfaces of power generation equipment, energy storage equipment, a load end and carbon emission monitoring equipment; performing data preprocessing on the collected target data, analyzing an energy consumption mode and a carbon emission trend based on the preprocessed target data, predicting a future power demand and a carbon emission level, and judging whether an abnormal trend exists or not; constructing a power-carbon combination model, and optimizing a power production path by using an objective function and constraint conditions; when an abnormal trend is detected, a scheduling scheme is formulated by optimizing a power production path; and executing the scheduling scheme, scheduling the power resources and the power generation equipment, and controlling coordinated operation among the power generation unit, the energy storage equipment and the power grid load in the virtual power plant.

The invention provides a reconfigurable energy storage system for online dynamic replacement of a fault battery and a control method. Firstly, a multi-level reconfigurable battery array framework is constructed, each battery module is integrated with an independent three-port power switch unit and a wireless communication node, and self-management and quick response of the battery modules can be achieved. The bottom layer battery cluster adopts MOSFET matrix topology, and the FPGA controls PWM signals to dynamically adjust the series-parallel connection structure of the batteries. And the central controller calculates an optimal connection pair in real time based on the power demand, and controls the contactor to establish a new path. When multiple modules fail, the system automatically activates three-layer collaborative reconstruction, the faulty module is switched to a bypass through a three-port switch, and a central controller periodically refreshes a charging and discharging plan according to a system state, so that the cycle efficiency of the system is improved, and the service life attenuation rate of a battery array is reduced. According to the invention, online dynamic replacement of the faulty battery is realized, the operation stability and efficiency of the system are improved, and the maintenance cost and downtime are effectively reduced.

The invention discloses a hybrid micro-grid load control method, system and device and a storage medium, and the method comprises the steps: carrying out the feature extraction of historical operation data in a hybrid micro-grid, and obtaining a power factor distribution set of multiple types of loads; performing time sequence analysis on the power factor distribution set, and performing fusion processing on an increment information sequence obtained based on an analysis result to obtain load dynamic power characteristic description data; performing parameter correction on the load power simulation model based on the load dynamic power characteristic description data to obtain a first digital twinborn model; performing secondary correction on the first digital twinborn model based on the load power characteristic subset to obtain a second digital twinborn model; and inputting the real-time operation data into the second digital twinborn model to obtain second reactive power demand data, and executing a load control instruction of the hybrid microgrid generated by the second reactive power demand data. According to the method provided by the invention, the operation stability of the hybrid microgrid is improved.

The invention discloses a power supply circuit, a power supply method, a server, a product and a medium, and relates to the technical field of server power supply, the total power demand of the server is dispersed through the parallel design of multiple power panels, the bus current of a single board connected to a first voltage is reduced to the safe bearing range of an MOS tube, the impact risk is reduced, and the service life of the server is prolonged. The electronic fuse is matched with the single-board power to set an overcurrent protection threshold value, and the short-circuit protection response time is shortened; each board is independently provided with a power supply brick to realize second voltage localization conversion and single board output current reduction, a multi-board output end combined power supply design is combined, a heat source is dispersed to eliminate heat accumulation, the through-current capability is multiplied through a parallel path, and the heat dissipation and electrical design feasibility of the circuit board is improved. The technical problems of MOS tube over-limit, overcurrent protection delay, circuit board heat accumulation and difficult through-flow caused by large current brought by a power supply in a high-power server are solved, and the technical effects of enhancing power supply reliability and optimizing energy efficiency are achieved.

The invention relates to the technical field of intelligent power supplies, in particular to an intelligent power supply for an unattended station. Comprising a multi-source power supply scheduling unit; an intelligent energy storage management unit; a double-link communication unit; the double-link communication unit is used for constructing a multi-redundancy communication link of wired, wireless and power line carriers, and the double-link communication unit is based on link quality data, adopts an automatic switching strategy to guarantee data transmission continuity, preprocesses monitoring data by using an edge side data clustering algorithm, and realizes offline data caching and transmission flow optimization. Through a multi-source energy dynamic scheduling strategy, according to photovoltaic real-time output, commercial power time-of-use electricity price and load electricity demand, power supply paths of photovoltaic, commercial power and a storage battery can be dynamically optimized, the consumption proportion of clean energy is improved, the dependency degree of high-cost commercial power is reduced, a complex energy structure of photovoltaic, commercial power and storage battery multi-energy complementation is effectively adapted, and the energy utilization rate is improved. And power supply flexibility is enhanced.

The invention relates to the field of energy storage power supply management, in particular to a power supply management method, system and equipment based on a household energy storage power supply system. The method comprises the following steps: collecting a household power utilization monitoring data stream, carrying out equipment use behavior preference mining and multi-scale state transition deep learning, and generating a user equipment behavior prediction model; mining power utilization behaviors of power utilization terminals one by one based on the household power utilization monitoring data flow, performing multi-time-point power utilization demand prediction based on a user equipment behavior prediction model, and constructing a multi-time-point power utilization demand prediction map of the user; and obtaining meteorological environment parameters of a current user family area, performing climate disturbance demand adjustment on the power demand prediction maps at the multiple time points based on the meteorological environment parameters, and constructing a climate disturbance power demand prediction map. According to the invention, through dynamic user demand changes, the energy efficiency management level and the energy allocation precision of long-term operation of the energy storage system are improved.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention provides a pressure monitoring method for cooperative work of multiple charging piles, and the method comprises the steps: obtaining real-time transient power data from a local node of a power grid, drawing a transient power curve according to the transient power collected in real time, and determining a triggering reason for the sharp increase of a power demand through analyzing the change trend of the transient power curve; determining the electromagnetic interference aggravation degree according to the climbing rate parameter, analyzing the electromagnetic wave characteristics during the operation of the inverter according to the electromagnetic interference aggravation degree, adjusting the frequency range of the inverter control signal according to the electromagnetic wave characteristics, and determining the optimal configuration of interference suppression; and if the charging pile cluster operates in the preset load balancing and resource utilization efficiency interval, dynamically updating the power scheduling priority and the load response rate in combination with the real-time operation condition and the available capacity monitoring data of the power grid.

The invention relates to the technical field of ship control, and particularly provides an energy optimization method and device for a pure electric tug, a storage medium and a control system. According to the method, the operation navigation data, collected in real time, of the pure electric tug can be input into the pre-constructed operation classification model, and the real-time operation state is obtained. If the real-time operation state of the pure electric tug is a heavy load dragging state, determining the current propulsion power demand and the total power demand of a propulsion system in the pure electric tug, and inputting the operation navigation data, the current propulsion power demand and the total power demand into a pre-constructed power distribution model, and performing power distribution according to a power distribution coefficient output by the model. Therefore, the power distribution response can be quickly and accurately made based on the intelligent power distribution model, and the intelligent ship is realized. Moreover, electric energy supply of the propulsion system can be guaranteed preferentially, so that sufficient power supply of the propulsion system can be guaranteed, and the operation efficiency of the pure electric tug can be improved.

The invention provides an elastic computing power scheduling method and device based on space-time prediction and a medium, and belongs to the technical field of cloud computing and edge computing. The method comprises the following steps: acquiring a historical load, a satellite cloud picture, meteorological data, a burst flow mark, an energy price and a green electricity proportion in real time; constructing a fusion prediction model which comprises a multi-modal feature coding module, a space-time attention fusion module and a prediction output layer; a multi-objective optimization function is constructed, cost, delay and resource fluctuation are minimized, and green rewards are maximized; solving a task allocation variable and a resource adjustment amount by adopting mixed integer programming; the actual green electricity utilization rate is monitored, and the green reward coefficient is dynamically adjusted to correct prediction and scheduling deviations. According to the method, the computing power demand fluctuation of different areas in the future time period can be predicted in advance, so that resource allocation is dynamically adjusted, resource idleness and waste are reduced, and the utilization rate of computing power resources is remarkably improved.

The invention relates to the technical field of Ethernet power supply, and discloses a POE power supply power control method, device and equipment and a storage medium. The method comprises the steps of obtaining port electrical parameter data, device temperature data and historical power supply operation data of a target POE power supply, and performing multi-dimensional calibration compensation, multi-window time-frequency decomposition, dynamic and static feature extraction and feature hierarchical mapping on the port electrical parameter data based on the device temperature data; performing classification boundary construction and load feature mapping conversion on the mapping result to obtain a load classification mapping table, and performing progressive prediction of multi-level power supply parameters and power demand integration based on historical power supply operation data to obtain a power demand prediction result; and performing multi-constraint optimization calculation on the power demand prediction result, generating a power distribution strategy, and based on the power distribution strategy, controlling the POE power supply to perform multi-power closed-loop control, and generating a power control result. According to the invention, efficient and accurate control of the POE power supply power is realized.

The invention discloses a low-carbon optimal scheduling method based on source-grid-load-storage integration, and relates to the technical field of energy scheduling, and the method comprises the steps: obtaining historical power demands, renewable energy power generation, energy storage equipment states and power grid load data based on an environment sensor of a source-grid-load-storage integrated system; obtaining historical time sequence characteristic parameter data of the operation mode of the source-network-load-storage integrated system, and determining an interval vector range of the operation mode of the source-network-load-storage integrated system; acquiring real-time data; obtaining a real-time operation vector matrix; matching degree verification is carried out according to the real-time operation vector matrix and the interval vector range of the operation modes of the source-network-load-storage integrated system, and the operation mode with the maximum matching degree is screened out to serve as a scheduling scheme. According to the invention, through source-grid-load-storage integrated operation mode matching, the scheduling scheme is optimized, and the use efficiency of the power grid load and the energy storage equipment is improved.

The invention provides an intelligent power supply management system based on multi-energy complementation, which relates to the technical field of intelligent power supply management and comprises a multi-energy supply module, a data acquisition module, a multi-source data fusion module, a scheduling optimization module, a demand prediction and response module, a supply safety evaluation module, an energy storage and load balancing module and an energy scheduling module. The multi-energy supply module comprises a solar energy supply sub-module, a wind energy supply sub-module, an energy storage system and a traditional power grid access sub-module, and by combining multi-source data and an advanced algorithm, the precision of power load prediction is remarkably improved. Compared with a traditional prediction method based on historical data, the method comprehensively considers the influence of various external factors such as climate change, holidays and festivals, and can more accurately predict the power demand fluctuation. The system can dynamically adjust the load prediction model in real time, improves the reliability of power supply, and can effectively cope with challenges caused by demand changes, environment changes and emergencies.