6789 results about "Power consumption" patented technology

The present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

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.

Embodiments of the invention disclose a method, a device and a system for processing a wake-up lock, and belong to the field of computer technology. The method comprises the following steps: detecting whether a wake-up lock is in a working state, and if so, obtaining a wake-up parameter corresponding to the wake-up lock, wherein the wake-up parameter comprises an arithmetic operation corresponding to the wake-up lock and an application program corresponding to the wake-up lock; judging whether the arithmetic operation corresponding to the wake-up lock requires a sensor of a terminal; if not, generating a wake-up transfer request according to the wake-up parameter, sending the wake-up transfer request to a server, and releasing the wake-up lock, wherein the server is used for executing the arithmetic operation corresponding to the wake-up lock; and receiving a wake-up message sent by the server, and waking up the terminal according to the wake-up message. By adopting the method disclosed by the invention, after a display screen of the terminal is closed, the system time occupancy of the application program can be reduced, the power consumption of the terminal is reduced in the case that the display screen of the terminal is closed, and the standby time of the terminal is prolonged.

The invention relates to an energy management method, device and equipment based on an air conditioning system and a medium. The method comprises the steps that in a preset period, a target cold storage capacity calculation model matched with the current working condition is selected from a plurality of candidate cold storage capacity calculation models according to the pre-obtained photovoltaic power generation power, the air conditioner system load and the change trend of the environment temperature; the environment temperature and the electric energy are input into the target cold storage capacity calculation model, and the cold storage capacity, output by the target cold storage capacity calculation model, of a phase change cold storage material in the air conditioner system is obtained; based on the cold storage capacity, the photovoltaic power generation power and pre-obtained planned electricity consumption probability distribution, analysis processing is conducted, and an energy management strategy of the air conditioning system is obtained; the energy management strategy is used for guiding the air conditioning system to adopt commercial power supply or photovoltaic power supply, whether to utilize the phase change cold storage material for cold storage or not and whether to start a compressor to assist in executing the cold storage process or not. By adopting the method, the power consumption cost can be reduced.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention belongs to the technical field of intelligent hardware control methods, and discloses a low-power-consumption intelligent hardware control method which comprises the following steps: acquiring working state parameters and user interaction information of intelligent hardware, and generating an equipment state feature vector; judging whether a dormancy condition is met or not according to the equipment state feature vector; when the sleep condition is satisfied, sending a network communication agent instruction; the coprocessor configures an internal timer and awakens signal output after receiving the instruction; the master control stores a system operation state, configures a wake-up source and then enters a deep sleep state; the coprocessor cuts off the power supply of the inactive module; when a timer of the coprocessor reaches a preset network heartbeat cycle, a wake-up signal is sent to the master controller; and the master control recovers the system state, updates the feature vector and re-decides whether to sleep or not. And through multi-dimensional state perception and a master-slave cooperation architecture, and through refined power management and a dynamic reevaluation mechanism, the equipment power consumption is obviously reduced on the premise of ensuring the user experience.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention discloses a single-chip microcomputer hierarchical dormancy control method based on dynamic power consumption prediction, and relates to the technical field of single-chip microcomputer low power consumption control, and the method specifically comprises the steps: building a mapping relation between a load current and a clock frequency; setting a dynamic dormancy threshold value; constructing a dynamic power consumption prediction model; a dormancy strategy is designed, module-level power consumption management and single-chip microcomputer function-based power consumption management are adopted, the callable frequency or the maximum operation duration of each function is distributed according to task priorities and battery residual energy consumption, and single-chip microcomputer peripherals are triggered through events and are independently awakened; and executing the sleep strategy, automatically awakening the single chip microcomputer according to a preset awakening condition, and realizing abnormity positioning and processing based on threshold comparison and power consumption monitoring in the process of executing the sleep strategy. According to the invention, module-level power consumption management and single-chip microcomputer function-based power consumption management are realized, the limitation of traditional global dormancy is broken, the need of overall reset during wakeup is avoided, and the response speed is improved while the energy consumption is reduced.

The invention discloses an electricity larceny identification method based on graph calculation, and particularly relates to the technical field of electricity utilization anomaly detection of an electric power system. Historical power consumption data and a power supply topological relation of power consumers are collected, and a multi-dimensional behavior graph model fusing behavior characteristics and structural information is constructed; performing structure disturbance analysis on each node in the graph, calculating information entropy change before and after node removal, performing attention fusion on a time sequence behavior feature of the node and a structure disturbance vector, constructing a joint feature vector, and enhancing feature expression through spectral clustering and linear reconstruction; a behavior propagation field and a disturbance adjustment mechanism are introduced into the graph to form a disturbance response graph, and an abnormal gathering area is identified through path energy analysis and focusing area fitting; calculating confidence scores of the nodes and outputting a suspicious user list; the method can realize efficient identification of electricity stealing behaviors with strong concealment and complex transmissibility, and has the advantages of high precision, strong interpretability and wide application scene adaptability.

The invention relates to the technical field of electric energy meter data analysis, and discloses a power consumption data analysis operation and maintenance method and system of an electric energy meter. The method comprises the following steps of: acquiring and preprocessing electricity utilization state data, namely synchronously acquiring voltage, current and power data, and performing timestamp alignment, data cleaning and resampling; classifying according to preset peak, flat and valley time intervals, and extracting time-frequency domain characteristics such as voltage fluctuation amplitude and current change rate in each interval to generate a set; detecting time period switching frequency, and marking an abnormal interval if the time period switching frequency exceeds a threshold value; generating an abnormal feature subset according to similarity decomposition of historical anomalies and current features; correcting the abnormal feature subset based on the electrical and time sequence parameter association relationship; and reversely backtracking a potential fault source along the fault association map, determining an operation and maintenance priority according to the number of abnormal parameters, position depth and the like, and generating an operation and maintenance instruction. The system comprises corresponding modules such as data acquisition and processing. According to the scheme, data can be accurately analyzed, a fault source is positioned, and the operation and maintenance efficiency is improved.

The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.

The invention discloses a metering box abnormal power consumption behavior diagnosis method and system based on edge calculation, and belongs to the technical field of power monitoring, and the method comprises the steps: collecting the voltage, current and time mark data of a metering box in real time through an edge end, synchronously obtaining the physical state information of a box door state and the like, generating a space-time feature matrix through an electric parameter space-time coupling analysis method, and carrying out the calculation of the time-space feature matrix; inputting an electric parameter feature mask pruning algorithm to obtain an optimized feature set, triggering an edge-sensing sleep wake-up linkage device when current abnormity is monitored, and collecting magnetic field data; the edge end calculates a total-branch electric energy difference value, combines the magnetic field data and a box door state, and constructs an electricity consumption abnormity classification determination tree through a metering error-electricity stealing behavior coupling diagnosis method to distinguish electricity stealing behaviors; an intermittent abnormal trajectory splicing algorithm is adopted to process fragmented abnormal data to generate an electricity stealing trajectory, and the electricity stealing trajectory is fed back to an electric parameter space-time coupling analysis method to dynamically update the space-time feature weight; according to the invention, the abnormity diagnosis precision and real-time performance are improved, and the energy consumption is reduced.

A system includes control circuitry for identifying, during operation in a first operating mode wherein electrical power is limited by protection devices, an indicator to enter a second operating mode. The indicator corresponds to power consumption of the electrical system exceeding a power capacity of the electrical system, nearing, achieving, or exceeding a limit. In response to the indicator, the system operates in the second operating mode by retrieving reference information comprising load preferences and limits, and managing one or more loads, sources, or both to modify power consumption, power capacity, or both based on the indicator and based on the reference information. The system communicates with loads or sources, and manages branch circuit using controllable elements to prevent power consumption from exceeding power capacity. The system modifies setpoints, turns devices on or off, schedules device operation or otherwise manages the electrical system based on predetermined preferences.

The invention relates to the technical field of unit power failure detection, in particular to an AC power supply input power failure detection method and system. The AC power supply input power failure detection method comprises the following steps: comparing a characteristic voltage value with a preset threshold value; triggering a sampling frequency adjustment mechanism according to the preliminary detection signal, increasing the sampling rate according to the voltage fluctuation frequency by adopting an adaptive algorithm, and obtaining high-precision sampling data through real-time calculation; for the optimized power consumption control scheme, acquiring current system state data, coordinating quick response capability and low power consumption optimization through a state machine algorithm, and outputting a multi-stage interaction strategy signal; adjusting a dynamic threshold value of capacitance voltage division design according to the multi-stage interaction strategy signal, and predicting voltage fluctuation by adopting a Kalman filtering algorithm to obtain smoothed detection sensitivity data; and analyzing the misjudgment rate reduction degree through a threshold fine tuning algorithm in combination with the abnormal fluctuation characteristics and the partial pressure state, and outputting a final power failure detection result.

The invention relates to the technical field of power grid data analysis, in particular to a power consumer anomaly detection and risk assessment method, system and device based on spatial-temporal feature fusion and a storage medium. The method comprises the following steps: acquiring multi-source power consumption data of various power consumers, constructing a space-time correlation feature extraction model, and establishing a user behavior space-time coupling relationship through time sequence analysis and spatial distribution characteristic analysis; constructing a multi-dimensional risk mapping model based on a long short term memory network and a convolutional neural network, establishing a nonlinear mapping relation from feature information to a risk assessment result, and identifying an abnormal power consumption behavior; establishing a distributed node credibility evaluation system, and performing credibility quantitative analysis on the data nodes in combination with a fuzzy comprehensive evaluation method; and based on an abnormal detection result and credibility evaluation, intelligent analysis and dynamic early warning of the electric charge management and control risk are realized. The time-space coupling characteristic of power consumption data can be fully mined, and high-precision identification of abnormal power consumption behaviors and accurate detection of non-technical power loss are realized.

The invention relates to a virtual power plant optimization operation method and system based on data center shared energy storage and load space-time migration, and the method comprises the steps: quantifying the electric energy utilization efficiency of a data center according to the power consumption of IT equipment, the power consumption of refrigeration equipment and the power consumption of other auxiliary equipment in a data center power consumption model; in the load space-time migration model, delay processing time calculation is carried out on batch processing loads, and the load migration amount across the data centers is calculated among the data centers; dynamically distributing the energy storage capacity of each data center in the shared energy storage model, and sharing the energy storage investment cost by adopting a Shapley value; in the double-layer optimization model, the upper-layer model generates an electricity price signal and a demand response instruction according to the wind and light output prediction data, the real-time electricity price of the power grid and the initial load demand of each data center, and transmits the electricity price signal and the demand response instruction to the lower-layer model; and the lower-layer model feeds back the obtained data center response and the electrical load to the upper-layer model. Compared with the prior art, the method has the advantages of high collaboration, high efficiency, high consumption and the like.

The invention discloses a distributed photovoltaic and energy storage cooperative power distribution network voltage fluctuation suppression method and system, and relates to the technical field of voltage fluctuation suppression, and the method comprises the following steps: building an inter-node disturbance propagation accessibility matrix based on the real-time operation data of a power distribution network, and recognizing a voltage disturbance path set; constructing a disturbance irreversible structure chart based on the voltage disturbance path set, and identifying a one-way amplification chain; according to historical disturbance data of a unidirectional amplification chain source node, calculating a disturbance excitation probability and generating a disturbance active propagation domain; constructing an energy storage intervention buffer area in the disturbance active propagation domain; in the energy storage intervention buffer area, the reverse blocking capability of energy storage is evaluated by simulating disturbance propagation, and a collaborative strategy activation condition is generated. Through disturbance path simulation evaluation, energy storage redundancy configuration and power consumption are reduced, and the suppression efficiency and adaptability of the system are improved.

The invention relates to the technical field of power systems, and discloses a voltage regulation system of reactive power compensation equipment, which is characterized in that harmonic interference is filtered and eliminated through a voltage reactive power sensing unit, and accurate power grid parameters are acquired; a load trend pre-judgment unit analyzes the historical load curve and the real-time change trend to generate a future pre-judgment signal; the dynamic compensation calculation unit combines the current power grid state with the prediction to calculate the optimal compensation capacity and generate a switching instruction, after the hybrid switching actuator receives the instruction, a thyristor is triggered firstly to realize inrush current-free conduction, a contactor is switched to maintain low power consumption after the current is stable, and meanwhile, the compensation state monitoring unit performs closed-loop adjustment on parameter deviation, so that the optimal compensation capacity is obtained. The multi-dimensional protection unit prevents overvoltage, overcurrent and overheating, the double-path power supply guarantees continuous operation when the main power supply is abnormal, the intelligent communication unit realizes data uploading and remote cooperation, quick response switching of reactive compensation equipment and guarantee of safety of a large-scale power grid are realized, and inrush current impact in the switching process is effectively inhibited.

The invention provides a full-link electricity consumption monitoring method, system and equipment based on intelligent Internet of Things, and relates to the technical field of power system management. The method comprises the following steps: acquiring operation data acquired by an intelligent sensor group deployed at a power network node in real time, and executing localized carbon flow accounting according to the operation data through an edge computing node; constructing a joint probability model based on Monte Carlo simulation and a deep belief network, and generating a node carbon flow density matrix under multiple scenes; dynamically updating an electricity-carbon conversion coefficient according to real-time energy structure data, and calculating node-level carbon emission: optimizing a power grid topology and an energy storage scheduling strategy by adopting a double-delay depth deterministic strategy gradient algorithm TD3 and taking a carbon flow density matrix as a constraint condition; and generating an intelligent report including a carbon footprint thermodynamic diagram, emission reduction potential evaluation and block chain evidence storage. According to the invention, the capability of multi-target collaborative optimization of the security and economy of the power grid can be improved.

The invention discloses a power grid dispatching method based on emergencies and a graph structure. The method comprises the following steps: establishing a baseline load prediction model by collecting historical electrical load data; recognizing emergencies influencing the power utilization demand in real time; and in combination with real-time power consumption behavior data, constructing a time-varying power consumption disturbance factor, and dynamically correcting load prediction. Geographic distribution and historical power generation data of new energy power stations such as wind power and photovoltaic power stations are acquired, a power generation graph structure model is constructed, the power generation amount is independently predicted by adopting a long-short-term memory network, and a prediction result is corrected in combination with a graph structure. And based on the corrected load and power generation prediction, considering the capacity and the energy state of the energy storage system, optimizing multi-time-sequence-scale energy scheduling, and formulating an energy storage charging and discharging and generator set operation scheme. According to the method, the dynamic influence of emergencies and the spatial relevance of new energy are fused, the load prediction precision and the new energy power generation prediction accuracy are remarkably improved, more reasonable power grid dispatching is achieved, and the safety and stability of a power grid are enhanced.

The invention relates to the field of server systems, and discloses a server cabinet system, which comprises a real-time power monitoring module used for collecting server current data at a frequency of once per second, calculating real-time power and dynamically adjusting power distribution; the intelligent prediction and optimization module is used for predicting future demands and generating an optimal distribution strategy based on the normalized historical power consumption data and load data; and the redundant power supply switching module comprises a main power supply and a standby power supply converted by the lithium battery pack through an inverter, and is used for switching to the standby power supply when the voltage of the main power supply is lower than 180V for 10ms. According to the method, the elastic power supply strategy is generated by fusing the prediction demand and the two-dimensional weight of the service priority, the continuous operation of the core service is ensured, the prior art depends on a single priority label to allocate resources, the burst load and the long-term stability are difficult to balance, and the problem of resource waste caused by separation of the two is solved.

The invention discloses a liquid cooling adaptive control method and system based on a single-phase immersion liquid cooling data center, and relates to the technical field of liquid cooling control, and the method comprises the steps: reading real-time power consumption data of the data center, and synchronously starting a temperature monitoring sensor to read chip temperature data and environment temperature data; configuring a short-time predictor by using the historical load sequence, and executing information gain ratio and prediction uncertainty analysis of preset candidate granularity; performing matching evaluation of single-phase immersion liquid cooling and the data center by using the calibrated prediction granularity; and if the triggering threshold value is met, self-adaptive optimization under local-global control coordination constraint of single-phase immersion liquid cooling control is executed, and a liquid cooling response scheme is output. The technical problems that in the prior art, the cooling capacity of single-phase immersion liquid cooling is low in matching degree with the load requirement, response lags behind, and energy consumption control is insufficient are solved, and the technical effects of self-adaptive optimization control based on real-time and prediction data, energy consumption reduction and heat dissipation efficiency and temperature control precision improvement are achieved.

The invention discloses an electricity consumption data anomaly detection method and device based on a space-time dual-hybrid expert model, and belongs to the technical field of electric energy metering. The method comprises the following steps: acquiring original power consumption data, preprocessing the original power consumption data, generating a time sequence set, and building a time dimension hybrid expert model and a channel dimension hybrid expert model based on the time sequence set; for a multivariable time sequence anomaly detection task, obtaining target power consumption data, taking the target power consumption data as input of a time dimension hybrid expert model and a channel dimension hybrid expert model, and performing time and space dependency relationship fusion on an output result to obtain a fusion result; and on the basis of a preset comprehensive anomaly score, according to a fusion result, performing anomaly detection, and judging whether the target power consumption data is abnormal or not. According to the implementation of the method, the extraction capability of the model on the normal mode in the high-dimensional complex time series data and the robustness of anomaly judgment are remarkably improved.

The invention relates to the field of intelligent power grids, and discloses an electric power marketization operation optimization method and system based on user-side resource aggregation, and the method comprises the following steps: a), recognizing and classifying user-side resources, specifically including distributed power generation, energy storage equipment and adjustable loads, forming a user-side resource database, and facilitating the subsequent management and scheduling; b) formulating transaction rules of market participants, and determining participation conditions, rights and obligations of household users, small commercial users and large industrial users to ensure fair participation of various users in the market; and c) automatically executing the transaction through the smart contract. Through user side resource aggregation, the method can effectively integrate distributed power generation, an energy storage system and an adjustable load, optimizes supply and demand balance of power, and enables a user to adjust a power utilization strategy in different demand time periods through a real-time monitoring and dynamic pricing mechanism, thereby reducing resource waste and improving the overall efficiency of a power system.

The invention discloses a power distribution network hierarchical cooperative operation control method for distributed photovoltaic local absorption, relates to the technical field of electrical engineering, and aims at solving the problem of local electric energy absorption of a power distribution area containing a large amount of distributed photovoltaic and flexible resources. Global scheduling is carried out on flexible resources and distributed photovoltaic in a transformer substation area on the basis of a day-ahead-day centralized optimization strategy and hierarchical-partition coordination control, an optimization result is applied to a real-time control stage, and optimized scheduling instructions are distributed from top to bottom; and the optimal operation of distributed photovoltaic local absorption in the transformer area is realized. Therefore, by adopting the power distribution network hierarchical collaborative operation control method for distributed photovoltaic local consumption, a novel power system in a complex operation mode can be accurately analyzed, and the photovoltaic consumption capability of the power distribution network is improved by collaborative optimization of flexible resources and an operation scheme of a distributed power supply; and the operation economy of the novel power system is improved.

The invention belongs to the field of star flash mouse communication, and discloses a star flash mouse low-power-consumption communication system and method based on Polar code and SLE collaborative optimization, the system adopts a layered architecture design, collaborative optimization of energy efficiency and performance is realized through deep coupling of a physical layer and a link layer, and the performance of the system is improved. The core of the system is composed of three functional units including a Polar code coding module, an SLE control engine and a cross-layer optimization interface, and a complete closed-loop control system is formed. According to the invention, through deep cooperation of the Polar code and the SLE mechanism, the overall power consumption is reduced by 42.7% compared with the traditional scheme; according to the Polar code dynamic construction method based on track prediction, an ultra-low bit error rate of 10 <-5 > magnitude can still be kept in a 2.4 GHz complex interference environment, and in combination with signal-to-noise ratio self-adaptive coding parameter adjustment, it is ensured that transmission delay within 0.5 ms is always kept from a low-speed office scene to a high-speed electronic sports scene; in the aspect of engineering implementation, the method has the capability of quickly adapting to hardware platforms of different manufacturers, and a complete solution with high performance and low cost is provided for large-scale commercial use of the satellite flash technology in the consumer electronics field.