487 results about "Energy dispatch" patented technology

The invention relates to the technical field of energy scheduling, in particular to a multi-modal data fusion method and system based on energy scheduling and a storage medium. The method comprises the following steps: obtaining multi-modal data, and carrying out abnormal fluctuation feature extraction to obtain a space-time fusion abnormal feature labeling set; deducing a multi-objective optimization path according to the space-time fusion abnormal feature labeling set to obtain a dynamic scheduling decision map; performing edge node game equilibrium calculation according to the dynamic scheduling decision map to obtain a trusted scheduling verification chain; performing digital twinborn constraint optimization on the trusted scheduling verification chain to obtain a closed-loop scheduling digital twinborn body; compiling a dynamic scheduling instruction set based on the closed-loop scheduling digital twin to obtain an anti-disturbance energy scheduling strategy library; and obtaining real-time energy supply and demand data, and performing scheduling deviation tracing on the real-time energy supply and demand data according to the anti-disturbance energy scheduling strategy library to obtain an energy distribution decision. According to the invention, the efficiency and reliability of energy scheduling can be improved.

The invention relates to the field of digital twinning technology and intelligent energy management, and discloses an industrial park dynamic resource regulation and control method based on digital twinning, which comprises the following steps of: constructing a digital twinning platform: acquiring operation states and environmental data of various devices in a park in real time through an Internet of Things sensor network, establishing a digital twin model of each device, and updating the virtual state of the device in real time through data transmission; real-time data of the devices are input into the platform, the state of each device is dynamically updated, synchronization of the digital twin model and the physical device is ensured, and accurate simulation of the current state of the device is provided. According to the invention, by integrating the park management system and the closed-loop control system, real-time collaborative scheduling of park equipment and energy management is realized, energy distribution is optimized, and balanced utilization of various energies is ensured; meanwhile, real-time feedback and error correction are realized through a closed-loop control mechanism.

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The invention discloses a micro-grid dynamic coordination control method and system for distributed energy, and relates to the technical field of micro-grid energy management. The method comprises the steps of 1, collecting multi-source micro-grid data in real time, performing edge calculation and data preprocessing operation, and performing early judgment of an island mode; 2, converging the structure and state of each micro-grid, dynamically constructing and updating a micro-grid topological graph, analyzing the structure change and health condition of the micro-grid topological graph, and evaluating the comprehensive risk of micro-grid nodes; 3, calculating the actual distributable power of each type of loads, and carrying out autonomous control and elastic mode switching; and step 4, on the basis of the actual distributable power of each type of loads, evaluating the collaborative energy scheduling capability of the micro-grid in real time, performing role identification and implementing an optimization strategy. The problem of island emergency scheduling response lag caused by sudden failure of a main network due to some extreme events along with continuous expansion of the access scale of distributed energy and a micro-grid is solved.

The invention relates to the technical field of energy scheduling, and discloses a smart city energy dynamic scheduling system and method based on big data analysis, and the method comprises the following steps: the operation state of a transformer substation, the basic parameters of a charging pile and regional load prediction data are collected in real time, data cleaning and abnormal value filtering are carried out; constructing a complete power grid-charging facility dynamic information base; calculating the power supply margin of each region based on the capacity loss of the faulty transformer substation, establishing a weight scoring system of charging pile power adjustment in combination with the charging demand urgency, and determining the reduction or recovery priority of each charging load; and generating a charging pile power adjustment instruction through a multi-target optimization model, and iteratively correcting a power distribution scheme and generating a final scheduling instruction set by taking minimization of user satisfaction loss as a target while meeting the power grid capacity. According to the invention, by constructing a dynamic response mechanism and a multi-target collaborative optimization model, accurate and rapid regulation and control of the traffic load are realized in the scene of sudden power shortage of the power grid.

The invention provides a smart park Internet of Things distributed energy intelligent analysis and control method and device, and relates to the technical field of smart park energy management, and the technical scheme is characterized in that the method comprises the steps: obtaining energy consumption data, meteorological data, distributed energy power generation data and user behavior data of each region of a smart campus; generating a basic load prediction value and an event-driven load prediction value according to the acquired data; making a day-ahead energy scheduling plan; when a deviation exists between the actual energy consumption condition and the predicted value, adjusting the energy scheduling plan by adopting a rolling scheduling strategy; and executing the adjusted energy scheduling plan, and controlling the distributed energy system and the energy consumption equipment to optimize campus energy utilization. The smart park Internet of Things distributed energy intelligent analysis and management and control method and device provided by the invention have the advantages of accurately predicting complex and changeable energy consumption demands and utilizing the distributed energy to the maximum extent.

The invention relates to the technical field of energy dispatching, in particular to a power dispatching optimization method based on optical energy storage, which comprises the following steps: extracting photovoltaic output and charge state fluctuation based on partitions, synchronously identifying abnormal intervals, analyzing direction matching and energy connectivity, screening abnormal units, and extracting linkage units in combination with load and power utilization characteristics. Evaluating power flow deviation, determining an unbalance level, matching a response list, and outputting an automatic linkage scheduling adjustment instruction set. According to the method, photovoltaic output and energy storage state fluctuation are identified in partitions, the accuracy of capturing an abnormal space-time coupling phenomenon is enhanced, the identification depth of scheduling imbalance hidden danger is improved, regional load distribution and power utilization behavior consistency judgment are combined, dynamic load feature evaluation of abnormal units is completed, the abnormal units are scheduled in a layered mode according to risk levels, and the scheduling efficiency is improved. The scheduling range is accurately adjusted, and the response precision and the handling capacity of the scheduling mechanism to the dynamic difference of the load side under new energy fluctuation are improved.

The invention relates to the technical field of smart home, and discloses a smart home energy supply and demand collaborative optimization system based on reinforcement learning. The system comprises an energy data acquisition module, a reinforcement learning decision module, a supply and demand collaborative optimization module, a real-time regulation and control execution module and a system performance monitoring module. The energy data acquisition module is used for acquiring various energy and environment data and processing exceptions; the reinforcement learning decision-making module constructs a state and action space and generates a regulation and control strategy in combination with a reward function; the supply and demand collaborative optimization module performs multi-objective optimization on energy distribution; the real-time regulation and control execution module regulates and controls the equipment according to the instruction priority; and the system performance monitoring module tracks the state and performs feedback optimization. In addition, an abnormal mode recognition module and a multi-agent cooperation module are further arranged and used for recognizing energy consumption abnormity and achieving equipment cooperation respectively. The system can effectively optimize household energy supply and demand, reduce cost, and improve energy utilization rate and user satisfaction.

The invention provides a multi-park energy consumption prediction scheduling method based on digital twinning and a control system, and systematically solves the problem of the pain point of multi-park energy consumption management by constructing a technical chain from data perception to closed-loop optimization. The method comprises the following steps: firstly, by constructing a global unified digital twinborn model, standardized integration of dispersed and heterogeneous park assets and data is realized, and the problem of information islands is solved; secondly, prediction is carried out by adopting federated learning, cross-park knowledge sharing and joint modeling are realized on the premise of ensuring data privacy and security of each park, and the prediction precision of a single park under the condition of limited data is remarkably improved; and finally, through a'prediction-decision-execution-update 'closed-loop process, traditional passive and static energy consumption management is converted into active and dynamic prediction scheduling, so that the energy consumption peak can be stabilized prospectively, the energy distribution can be optimized, and the comprehensive energy consumption cost and carbon emission can be effectively reduced.

The invention provides an AI-based carbon asset and carbon inspection management method and platform, and belongs to the technical field of carbon emission management, and the method comprises the steps: inputting the operation activity data of a target object into a preset energy efficiency analysis model, and obtaining an energy efficiency analysis result; based on the energy efficiency analysis result, performing energy flow modeling and analysis on the operation activity data of the target object to obtain an energy flow analysis result; the energy flow analysis result is input into a preset energy efficiency balance optimization model, an optimized energy distribution scheme and carbon emission prediction values, predicted based on the energy distribution scheme, of all links are obtained, and the energy efficiency balance optimization model is configured as follows: under the condition that the production constraint condition of the target object is met, the energy distribution scheme is optimized; optimizing energy distribution by taking minimization of the total carbon emission as a target; comprehensively calculating the carbon emission predicted value of each link to obtain a target total carbon emission predicted value of the target object; and generating a corresponding target carbon asset management strategy according to the target total carbon emission prediction value, the carbon transaction market data and the carbon sink data.

The invention provides an energy distribution method and system for an unmanned aerial vehicle charging pile, and the method comprises the steps: firstly collecting historical operation parameters and real-time working condition parameters of an energy storage unit, and evaluating the health state of a battery; then acquiring and integrating charging tasks of all charging piles to form a task profile including average required power and total charging duration; predicting a health state attenuation value of the energy storage unit based on the current health state and the charging task; calculating the health loss cost of completing the charging task by combining the battery health state and the attenuation value; establishing an optimization model with the goal of minimizing the sum of the power grid electricity purchasing cost and the battery health loss cost, and solving to obtain an optimal energy distribution strategy under the condition of meeting the charging requirement and the system operation constraint condition; and finally, generating a control instruction, regulating and controlling the energy flow among the power grid interface, the energy storage unit and the charging pile, and executing an optimal strategy. The effects of efficiently distributing energy, reducing the total operation cost and prolonging the service life of energy storage equipment are achieved.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention relates to the technical field of energy storage modules, in particular to a direct-current power supply energy storage system suitable for various household appliances. According to the method, the current change value and the power fluctuation range of the energy storage unit are analyzed, statistics of the power change amplitude and the time interval is combined, the work cycle rule of the energy storage unit is defined, the power difference between the partitions is extracted through the Markov chain model, and real-time balance of the partition power supply state is achieved by gradually optimizing path distribution. And resource waste caused by power fluctuation is effectively reduced. Through gradual correction of a real-time output power value and a target power value, dynamic power matching of an energy storage unit is realized, the flexibility and accuracy of energy distribution are improved, voltage fluctuation trend is monitored and adjusted through a variational mode decomposition technology, voltage stability control of an energy storage module is realized, the reliability of an energy storage system is optimized, and the energy storage efficiency is improved. And the overall stability and the energy utilization efficiency are improved by matching the output power and the frequency adjustment range.

The invention relates to the technical field of power dispatching, in particular to a building project power resource intelligent dispatching method and system, and the method comprises the following steps: obtaining instantaneous power demand data of electric equipment in a building construction region, calculating a load density value of each region according to the power consumption of the equipment in each construction region, and calculating a load density value of each region; and obtaining regional load density data. According to the method, the load density value of each region is calculated by using the instantaneous power demand data of the electric equipment in the construction region, and the discharge power of the energy storage equipment is adjusted in combination with the current charging state and the dischargeable power of the energy storage unit, so that the energy storage equipment can dynamically adapt to the fluctuation characteristics of the construction load; and by combining the peak-valley electricity price floating value and the load state of the energy storage equipment, the charging and discharging switching threshold value of the energy storage equipment is set, so that the electric energy regulation and control in the peak-valley period are more flexible, the peak electricity purchasing cost is reduced, and the economical efficiency of energy dispatching is improved.

The invention belongs to the technical field of electric equipment supervision, and discloses a digital twinning-based park electric equipment supervision system and a digital twinning-based park electric equipment supervision method. The method comprises the following steps: acquiring physical parameters of each device, real-time operation parameters acquired by a sensor and environmental parameters of each device, inputting the data into a digital twinborn model construction platform, establishing a digital twinborn model of the device, and meanwhile, setting data updating frequency to ensure that the model can reflect the actual state change of the device in time; operation data in the model are obtained in real time, state coefficients of all devices are obtained through the operation data, the operation conditions of all the devices are analyzed according to the state coefficients, maintenance personnel are helped to rapidly position and solve problems, and then an optimal energy distribution scheme is obtained according to the operation conditions and energy consumption conditions of all the devices; and optimal utilization of energy is realized.

The invention discloses an energy scheduling method, device and equipment based on a virtual power plant, and a storage medium. The method comprises the following steps: obtaining corrected predicted power of distributed renewable energy sources of the virtual power plant; a distribution optimization model of the virtual power plant is constructed, the distribution optimization model comprises a target function and constraint conditions, the target function takes response cost, energy storage cost, regulation and control cost and carbon transaction cost of the virtual power plant as targets, and the constraint conditions are charge and discharge characteristics of a distributed power supply; in the actual operation process, the corrected and predicted power is input into the distribution optimization model, solving optimization is carried out through a path optimization algorithm in an uncertain environment, and a scheduling strategy is obtained; and performing energy scheduling on the power distribution network accessed to the virtual power plant according to the scheduling strategy, thereby realizing cooperative and clustered operation of various power resources in the virtual power plant, improving the operation efficiency of the power distribution network, reducing energy waste, and improving the reliability of overall energy supply.

The invention relates to the technical field of building intelligent control and energy saving, and discloses a building equipment intelligent control optimization management system based on a green low-carbon building. The system comprises five subsystems, wherein an environmental parameter sensing subsystem is used for acquiring temperature and humidity gradients, illumination intensity distribution and personnel activity thermodynamic diagram data inside and outside a building through a distributed sensing network to form a multi-dimensional environmental state vector; the equipment operation state acquisition subsystem acquires an operation power curve, an energy efficiency conversion rate curve and an equipment health degree index of heating ventilation air conditioning, illumination and renewable energy equipment in real time; the dynamic load prediction subsystem establishes an energy consumption load space-time distribution prediction model according to historical data time sequence relevance; the control strategy generation subsystem is used for generating an optimized instruction set containing an equipment start-stop time sequence, a power regulation gradient and an energy distribution weight in combination with the current environment vector and a predicted load; the execution terminal adapts the subsystem to convert the instruction into a device compatible signal and calibrate a response delay.

The invention provides a workshop-based energy management and intelligent scheduling optimization system, and the system comprises a data collection module which is used for obtaining the energy consumption data and equipment state data of production equipment, environment sensors and a power grid in a workshop in real time; the data preprocessing module is used for carrying out abnormal value correction, multi-source data alignment and normalization processing on the original data; the energy prediction module predicts future workshop energy consumption demand and energy supply fluctuation through an improved long short-term memory network; the scheduling optimization module is used for constructing a multi-constraint optimization model based on the energy consumption prediction result and generating an equipment start-stop and energy distribution scheme; the dynamic control module is used for adjusting equipment operation power and a power grid interaction strategy in real time according to the optimization scheme; the energy efficiency evaluation module is used for calculating an energy utilization rate, carbon emission intensity and a scheduling scheme stability index; and the feedback correction module is used for iteratively updating the prediction model and the optimization parameters according to the actual operation data and the evaluation result. The intelligent level of energy management can be improved.

The invention discloses a smart factory energy efficiency optimization method, system and device based on the industrial Internet of Things, and relates to the technical field of the industrial Internet of Things, and the method comprises the steps: collecting production energy consumption data based on equipment operation parameters and production plan information; constructing a factory digital twinborn model, and determining an equipment start-stop time sequence and an energy distribution strategy; deploying edge computing nodes, monitoring the vibration spectrum of the equipment, generating a state prediction map in a time sliding window, and performing adaptive compensation optimization; and meanwhile, an energy flow network is drawn up based on the equipment start-stop time sequence and the energy distribution strategy, and energy dynamic adjustment is carried out. The technical problems of low energy utilization efficiency and insufficient equipment operation stability of a smart factory in the prior art are solved, and the technical effects of realizing smart factory energy efficiency optimization based on the industrial Internet of Things and improving the energy utilization efficiency and the equipment operation stability are achieved.

The invention relates to an electric energy dispatching method for virtual power plant multi-subject system game, which comprises the following steps: acquiring and constructing a Wasserstein distance fuzzy set of electricity price and renewable energy output based on historical power plant data, and respectively establishing a superior power grid electricity price predicted value and a fuzzy set constraint of renewable energy output; establishing a VPP multi-subject collaborative transaction optimization model among an operator, an electric vehicle charging station and a producer, and reconstructing the VPP multi-subject collaborative transaction optimization model based on a Wasserstein distance fuzzy set and a maximum harsh lower bound method; and for the reconstructed VPP multi-agent collaborative transaction optimization model, establishing a game equilibrium model based on a non-cooperative game theory, and solving through a stationary point optimization method to obtain an electric energy scheduling scheme among multiple agents of the virtual power plant. Compared with the prior art, the method has the advantages that the robustness of game decision results is improved, and the flexibility and intelligence of electric energy utilization are improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an electrical manufacturing execution management system based on cloud computing, which comprises a scheduling optimization module, an energy management module, an equipment health and state monitoring module and a production efficiency improvement module. According to the method, accurate scheduling of production resources is realized by capturing electrical production task progress, equipment use conditions, process parameters and energy consumption data, then the operation mode, time and speed of equipment are regulated and controlled, resource allocation is more flexible, the energy consumption data are analyzed by relying on cloud computing, the energy use mode and equipment load are optimized, and the energy utilization efficiency is improved. Effective energy distribution is realized, energy efficiency is improved, equipment faults can be predicted in advance in combination with an equipment health assessment model, a maintenance period is adjusted according to an equipment state, production interruption caused by equipment shutdown is avoided, deep analysis is performed on production scheduling, then a production sequence and resource distribution are adjusted, production efficiency is improved, and production efficiency is improved. And the stability and the flexibility of the production line reach a higher level.

The invention relates to the technical field of micro-grid control, in particular to a source-grid-load storage and charging integrated control method and system based on a micro-grid, and the method comprises the steps: considering the current SOC, health state and charging and discharging rate of a battery energy storage system, and evaluating the available capacity and charging and discharging capability of the battery energy storage system; according to the predicted charging load curve, the photovoltaic output curve and the energy storage state evaluation result, taking peak load shifting, photovoltaic consumption and charging station power supply guarantee as targets, generating a scheduling strategy by using an optimization algorithm, and determining a power distribution relationship among a photovoltaic system, a battery energy storage system and a charging station in each time period; and executing a scheduling strategy generated by the energy management module through three stages of regulation and control units, wherein the three stages of regulation and control units are respectively used for limiting grid-connected power fluctuation of the micro-grid, optimizing local energy distribution and island switching, regulating active power and reactive power and carrying out harmonic suppression. And stable operation of the micro-grid and stability of electric energy quality are ensured.

The invention discloses a multi-energy micro-grid power supply system based on a hydrogen fuel cell and a control method. The micro-grid power supply system comprises an intelligent grid-connected switch, an electrical load, an isolation transformer, an energy storage converter, a hydrogen fuel cell system, an energy storage lithium battery system and a local energy management system. The control method comprises the steps that when a power grid loses power or a PCC switch is switched off, an energy storage converter PCS is automatically switched to an off-grid operation mode or is switched to an off-grid operation mode according to an off-grid command, and stored electric energy is continuously used for on-load operation; when the power grid recovers or is converted into grid connection in a planned manner, the PCS is automatically synchronized to a state which is synchronous with the power grid or according to a grid connection command, and the intelligent static switch is closed to be converted into grid connection operation. According to a coordination control strategy among the PCS converter, the intelligent grid-connected switch and the energy storage battery and an SOC grading strategy between the fuel battery and the energy storage battery, functions of millisecond-level fast switching of grid-connected and off-grid operation, variable load fast energy scheduling control of a load and the like are realized.

The invention discloses a multi-type energy scheduling method based on a trusted data space, and the method comprises the steps: accessing a multi-type energy provider through a block chain node, and constructing a cross-domain trusted data space; obtaining energy data of each party; processing to obtain an energy data set; calling a federated learning model, training a load prediction neural network, and predicting to obtain a future load demand; constructing a multi-target optimization model by taking the maximum consumption efficiency and the minimum energy abandoning rate of each type of energy as targets, and solving the multi-target optimization model based on the predicted future load demand and the output curve of each type of energy to obtain an optimal scheduling strategy; and storing the optimal scheduling strategy on the chain, and obtaining the optimal scheduling strategy on the chain by each type of energy providers to perform energy scheduling. A decentralized trusted data space is constructed through a block chain technology, data source traceability and non-tampering operation are realized, a centralized platform trust risk is avoided, and data privacy zero leakage during cross-domain sharing is ensured by adopting privacy calculation.

The invention discloses a dynamic signal synchronization control method and system for an energy scheduling network, and relates to the technical field of dynamic signal control, and the method comprises the steps: carrying out the analysis and disassembly of the energy scheduling network, and determining signal transmission delay data and signal transmission error data; configuring a first energy distribution vector; configuring a second energy distribution vector; fusing the first energy distribution vector and the second energy distribution vector, and configuring a regulation and control strategy matrix of an energy stable distribution level and a signal synchronization correction parameter; and performing multi-node cooperative control, and feeding back a regulation and control effect to the coordinated optimization network to iteratively optimize the attention weight. According to the invention, the technical problems of poor network coordination and weak anti-interference capability caused by low signal synchronization precision and unstable distribution of the energy scheduling network in the prior art are solved, and the technical effects of realizing dynamic synchronization and stable distribution of the signals in the energy scheduling network and improving the coordination and anti-interference capability of the network are achieved.

The invention discloses a smart factory energy efficiency optimization method and system, electronic equipment and a storage medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring energy consumption parameters and energy consumption associated variables of a plurality of production units in a target factory; for each production unit, dividing the energy consumption parameters into energy consumption data sequences corresponding to a plurality of time scales according to a preset time interval, and adjusting a reference energy consumption threshold value based on the energy consumption association variable to obtain a target energy consumption threshold value corresponding to the production unit; calculating an energy consumption trend characteristic value of the production unit according to the energy consumption data sequence corresponding to each time scale, and determining an energy distribution level of the production unit in combination with the energy consumption trend characteristic value of the production unit and a target energy consumption threshold value; and dividing the available energy quota corresponding to each production unit based on the energy distribution level of each production unit and the total energy quota of the target factory. By implementing the technical scheme provided by the invention, the effect of improving the efficiency of energy efficiency optimization of the smart factory is achieved.

A method for generating an optimized regional architecture for efficient national transport is provided. The method integrates vehicle powertrain architectures, operations logistics, and energy pathways to optimize both behind-the-fence and public access energy dispensing solutions supported by local distributed energy resource equipment and centralized fuel sourcing. More specifically, the method provides an optimal regional architecture for electrified national transport modeling. The method assimilates critical data for seasonal operating scenarios to provide a regional specific constrained-optimal infrastructure deployment solution. As discussed herein, the present invention provides local government agencies, industry end users, energy suppliers, and equipment providers with a flexible planning tool to navigate the deployment of electrified freight transportation systems in view of localized constraints.

The invention discloses a virtual power plant energy management method and system based on an air conditioning load, and relates to the field of energy management, and the management method comprises the steps: obtaining power consumption data and integrated operation information data in a virtual power plant management range, carrying out the statistical division of the operation information data according to an operation rule and a potential distribution model, and carrying out the statistical division of the operation information data; generating a batch monitoring management set; constructing a virtual power plant electric energy transaction framework based on the batch monitoring management set, analyzing an electric power balance relationship between an electric quantity supply and demand difference and a virtual power plant, and outputting an energy scheduling result in a virtual power plant management range; and the air conditioner load data serve as demand response resources, the energy dispatching result is optimized, an air conditioner control issuing instruction is generated, the air conditioner control issuing instruction is recorded and decomposed, and the control state of the air conditioner is output. Through optimal scheduling of the air conditioner load, the demand of the air conditioner load can be reduced in the peak period of power demand, so that the load pressure of the system is effectively reduced, and excessive energy consumption is avoided.

The invention discloses a light storage charging station electric vehicle ordered charging control method based on bilevel planning, and the method comprises the steps: firstly constructing a bilevel planning model of a light storage charging station, and enabling the upper layer planning model to be used for optimizing the overall energy scheduling of the charging station; the lower-layer planning model performs optimization scheduling on the charging behavior of the electric vehicle in the charging process according to the energy distribution result of the upper-layer planning model; and then, iteratively solving the bilayer planning model by adopting a dynamic alternating direction multiplier method, taking the output of the upper layer planning model as the input constraint of the lower layer planning model, and feeding back the result to the upper layer planning model by the lower layer planning model until the target function value of the upper layer planning and the lower layer planning converges or reaches a preset number of iterations, and the orderly charging power of the electric vehicle is obtained, and a control strategy is dynamically adjusted in case of operation deviation. According to the method, through collaborative optimization of upper-layer planning and lower-layer planning, the overall operation benefit of the power system is optimized, and a flexible charging strategy is provided for an electric vehicle user.

The invention relates to the technical field of energy data management and control, and discloses a smart park energy data dynamic management and control system. The energy data acquisition module of the system acquires and preprocesses the operation data flow in the park in real time. The energy feature analysis module performs multi-dimensional feature extraction on the data stream, and identifies feature vectors such as peak load and steady-state operation. The event trigger engine generates an energy management event signal based on conditions such as feature vector and threshold comparison, timing pattern matching and the like. The parameter node identification module analyzes an event signal time sequence and positions key nodes such as equipment start and stop and load sudden change. The offset calculation module calculates the offset of the energy parameter relative to the historical reference for the key node. The similar parameter derivation module derives a migratable parameter set containing general control parameters and scene adaptive parameters according to the offset and a historical database. And the dynamic regulation and control module performs real-time adjustment such as power distribution and equipment scheduling on park energy distribution by using the set.