678 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 technical field of data processing, and discloses a power distribution method based on AI artificial intelligence and an intelligent power distribution cabinet. The method comprises the following steps: carrying out real-time data acquisition on key nodes of the power distribution system through an intelligent sensor network to obtain original operation data of the power distribution system, and carrying out cleaning and standardization processing on the original operation data to obtain a standardized data set; performing analysis modeling on the standardized data set through multi-level hybrid model training to obtain an AI analysis model of the power distribution system; processing and analyzing the power distribution data collected in real time to obtain a load prediction result and a dynamic balance strategy; performing abnormal mode identification and multi-path reasoning on the system operation parameters to obtain equipment health state evaluation and maintenance suggestions; and based on the equipment health state assessment and maintenance suggestions, performing multi-objective optimization calculation on renewable energy source supply and power consumption demands to obtain an intelligent energy scheduling scheme. According to the invention, the efficiency and accuracy of the power distribution method based on AI artificial intelligence are improved.

A system and method for directing energy use within a facility comprising charging stations for charging a fleet of electric vehicles, using an energy optimisation engine. The energy optimisation engine receives energy supply characteristics and facility operational energy consumption characteristic of energy assets associated with the facility over a first time period, and electrical vehicle operational requirements for the electric vehicles during a second period; computes energy distribution instructions for the first time period and satisfying a designated energy condition corresponding at least in part with the energy supply characteristics; and directs operation of the charge stations or the energy assets in accordance with the energy distribution instructions.

The invention discloses a smart home energy management optimization method in an Internet of Things environment. According to the method, the Internet of Things technology is utilized, intelligent identification and classified management are carried out on equipment, multiple energy management strategies are preset, and a user can conveniently and autonomously adjust; energy data is monitored in real time through a sensor and an intelligent electric meter, an energy consumption rule is mined by using a big data analysis technology, and a demand trend is predicted; constructing an intelligent energy dispatching center, and formulating a dynamic energy distribution scheme by using an optimization algorithm; a mobile phone application and an intelligent control panel are developed, and a user is supported to check data, control equipment and carry out personalized customization; and meanwhile, equipment firmware upgrading and remote management are realized. According to the invention, energy consumption can be reduced, cost can be controlled, equipment collaboration and energy distribution efficiency can be improved, user experience can be enhanced, system maintenance and upgrading are facilitated, and intelligent household energy management is more efficient, intelligent and flexible so as to adapt to diversified household requirements.

An energy scheduling system of a high-power liquid cooling charging pile belongs to the technical field of liquid cooling charging piles, and comprises a power scheduling module, a liquid cooling management module, a data acquisition and monitoring module, an energy storage and energy recovery module, a communication and interaction module, an intelligent scheduling module and a remote management and analysis module. Through the intelligent scheduling and energy optimization technology, power distribution can be adjusted in real time according to the vehicle demand and the power grid state, overload is avoided, and the charging efficiency is improved; and secondly, an advanced liquid cooling management technology is adopted, and temperature monitoring and cooling liquid flow rate adjustment are combined, so that heat balance and stable operation of the equipment under a high-load condition are ensured, the service life of the equipment is prolonged, and energy waste is reduced. In addition, the system can store redundant electric energy and release the electric energy in the peak load period of the power grid through the energy storage and energy recovery module, so that the power grid pressure is effectively reduced, and the energy utilization rate is also 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 relates to the technical field of energy scheduling, and discloses an intelligent optimization scheduling method of an informatization energy management system, which comprises the following steps of: data acquisition and fusion: acquiring real-time data of each node in an energy system through distributed Internet of Things equipment and a multi-mode sensor network, and fusing and processing the acquired data to obtain a data fusion result; a high-quality data set in a unified format is generated; and prediction modeling and demand response: based on user behavior analysis and a machine learning algorithm, establishing a demand response model, and constructing a multi-stage energy demand and supply prediction model including short-term, medium-term and long-term time dimensions. Through combination of a collaborative optimization algorithm and a distributed decision-making mechanism, a global optimal energy scheduling scheme is realized on the basis of local optimization of each distributed energy unit, high collaboration and optimal resource allocation among the energy units in a complex energy network are ensured, and the energy scheduling efficiency is improved. Therefore, the energy utilization efficiency and the operation stability of the whole system are improved.

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 discloses a power supply management method and system based on a new energy charging pile, and relates to the technical field of power supply management, and the method comprises the steps: collecting power supply source data, calculating a charging pile power supply risk, calculating a risk weight through an entropy weight method, and constructing a two-stage LSTM model to predict photovoltaic power generation and EV demand data. And determining the power supply priority of the charging pile based on the risk weight, constructing a dynamic energy distribution model, determining the power distribution of a power supply source by using a Pareto optimization algorithm, and carrying out charging power distribution by using a stable matching algorithm according to a utility function. According to the method, influence degrees of photovoltaic power generation fluctuation risks, energy storage performance risks and power grid price fluctuation risks are quantified through entropy calculation, importance of risk indexes is accurately reflected as weights, data optimization of an input layer of a double-stage LSTM model is guided, the risk indexes are brought into priority calculation, the power supply weight of high-risk energy is reduced, and the power supply efficiency is improved. And power supply fluctuation or cost increase caused by high risk is avoided.

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 relates to the field of carbon emission management, energy distribution and optimization regulation and control, in particular to an industrial enterprise carbon emission allocation method based on regional energy and carbon platform supervision data. The method comprises the following steps: step 1, data acquisition and preprocessing, including data acquisition, data preprocessing and data analysis; 2, initial carbon quota distribution, wherein a multi-level decision-making model is established, and the initial carbon quota of an enterprise is calculated; 3, a dynamic adjustment mechanism is provided, wherein the dynamic adjustment mechanism comprises the steps of obtaining power data feedback, calculating a quota adjustment coefficient and dynamically adjusting a quota; step 4, accumulation of double-control index data, including accumulation of the double-control index data and accumulation of carbon emission intensity data; and 5, continuously optimizing the model. According to the method, the flexibility of carbon quota distribution is improved, normal production of enterprises is ensured, meanwhile, the total carbon emission control target is not exceeded, and the overall efficiency of carbon emission management is improved by introducing a multi-level decision model and a feedback mechanism.

The invention relates to the technical field of energy regulation and control, in particular to an inter-building energy regulation and control method based on the Internet of Things. The method comprises the following steps: predicting and judging whether the actual energy consumption of a building is abnormal or not; when the actual energy consumption is predicted and judged to be abnormal and the maximum energy consumption deviation value is positive, external climate data is associated to determine an execution mode of power dispatching; when the maximum energy consumption deviation value is negative, obtaining an identification type result of the current time period; and determining whether to execute power dispatching control based on the identification type result. By monitoring the energy consumption deviation value of each building in real time, the energy consumption deviation condition of the building in the to-be-monitored range is determined, energy consumption abnormity is found in time, power distribution is optimized, energy waste is reduced, external climate data and building types are associated to dynamically adjust a power dispatching strategy, the energy consumption requirement of the building is judged more accurately, and the energy consumption of the building is improved. Therefore, different environment and energy scheduling requirements can be met, power distribution can be rapidly adjusted, and the stability of energy supply can be ensured.

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 energy management system based on a cloud and edge integrated optical storage system. The energy management system comprises a cloud module and an edge module, the EMS controller is used for collecting information of the optical storage equipment module and the load module and uniformly sending the collected data to the cloud module; the cloud module predicts future photovoltaic power generation power / electric quantity through a photovoltaic power generation prediction model by receiving data acquired by the EMS controller; predicting a future load through the load prediction model; analyzing peak and valley time periods of the electricity price through the electricity price management model, and sending prediction data to the edge module; and the edge module carries out energy management through the control logic of energy management according to the battery SOC, the chargeable amount, the photovoltaic predicted power and the generating capacity, and superposition peak-valley arbitrage and safe anti-countercurrent logic. Through the arrangement, the whole energy management system has high intelligence and automation levels, energy distribution and use are optimized, and the energy utilization efficiency is improved.

The invention discloses a distributed energy scheduling management method and system for a virtual power plant, and relates to the technical field of distributed energy management, and the method comprises the steps: collecting electricity price information and operation information, carrying out the cleaning and preliminary processing of data, guaranteeing the data quality, and carrying out the data management; a machine learning algorithm is utilized to predict load information, electricity price and related energy price in a virtual power plant range, a hybrid optimization model is constructed and solved, optimal power generation, power utilization and energy storage operation plans for different power transaction markets are generated, efficient configuration of resources is realized, and the optimal power generation, power utilization and energy storage operation plans are obtained based on operation information data and prediction result data. According to the method, the operation information data and the prediction result data are acquired, the distributed energy units are recombined, an optimal resource combination for different market transactions is formed, a scheduling strategy for optimally configuring the resource combination is executed, and a real-time scheduling optimization strategy is generated according to the latest operation state information. And dynamic optimal configuration of the distributed energy unit is realized.

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 discloses a hybrid pumped storage power station seasonal regulation characteristic-considered water-wind-light-storage complementary scheduling method, which relates to the technical field of energy scheduling, and comprises the following steps: additionally building a pump station between built conventional cascade hydropower stations to build a hybrid pumped storage power station which has pumped storage and runoff power generation functions; the hybrid pumped storage power station and the cascade water-wind-light power station are co-constructed to form a water-wind-light-storage complementary system; constructing a water-wind-light-storage complementary long and short term nested scheduling model considering seasonal regulation characteristics of the hybrid pumped storage power station, and taking maximum power generation benefits as a model objective function; based on a dynamic programming successive approximation algorithm, a pumping power generation working condition conversion model is introduced to construct a model solving method; solving the scheduling model through a model solving method, and obtaining a water-wind-light-storage complementary power generation plan of the seasonal regulation characteristics of the hybrid pumped storage power station; and while the short-term peak shifting benefit and the new energy consumption level are improved, the long-term water storage and energy storage effects of abundant storage and dry supplement of the hybrid pumped storage power station are brought into full play.

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 relates to the technical field of mine energy loss cost statistics, in particular to a cost statistics method based on mine energy loss balance, which comprises the following steps: deploying multiple types of sensors in mine equipment and areas to collect data, and carrying out anomaly detection, interpolation supplement and classified storage processing; constructing an energy consumption model by integrating equipment power, load and environmental factors, accounting electric power, fuel and total energy cost according to the energy consumption model, and analyzing the proportion; a genetic algorithm, a particle swarm algorithm and an LSTM model are used to optimize equipment parameters and an energy distribution path, and energy demand and cost are predicted; developing a visual interface to display energy consumption and cost data, generating a decision report, and pushing the decision report to a manager; comparing actual and predicted data regularly, collecting equipment, process and market information, and optimizing an energy loss and cost accounting model and algorithm; according to the method, fine energy management is realized, the utilization efficiency is improved, the cost is reduced, scientific decision support is provided for mine energy management, and the system adaptability and sustainability are enhanced.

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.

A system has means for globally coordinating distribution of energy between subsystems in a battery electric vehicle based on a computed energy consumption trajectory for a predetermined route of the battery electric vehicle. The subsystems include at least an electric powertrain, a cooling system, and a cabin air conditioning system. The means for performing the global coordination has system inputs and is associated with a coordination system layer above an optimization system layer and a component control system layer. The means for performing the global coordination is arranged to distribute energy available in a battery of the vehicle to the subsystems in a coordinated manner based on a coordination function using the system inputs based on a cost function considering a must / want energy to be provided to the subsystems via the optimization system layer so as to maximize a user satisfaction for a user of the vehicle while driving and at the same time reach a driving destination on the predetermined route with a predetermined remaining state of charge of the battery.

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.