248 results about "Energy dispatch" patented technology

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 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 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.

The invention relates to the technical field of ship energy consumption control, and particularly discloses an FSRU ship energy consumption and carbon emission intelligent management and control system based on route big data, and the system comprises a route big data collection unit which is used for obtaining the multi-source data of an FSRU ship in the full-route-segment navigation process, and transmitting the multi-source data to an FSRU ship. The multi-source data at least comprises ship operation data, route environment data, carbon capture data and waste heat data; wherein the FSRU ship is a floating type storage regasification device ship; and the data preprocessing unit is connected with the airline big data acquisition unit and is used for cleaning the multi-source data and outputting preprocessed data. According to the method, the staged cooperative control model is established, the unique working condition requirements of the FSRU ship in different operation stages such as berthing, loading and unloading, offshore cruising and open sea navigation can be accurately identified and adapted, the limitation of a traditional single control strategy in variable scenes is overcome, and refinement and dynamics of energy distribution and emission control are achieved.

The invention relates to the technical field of harmonic suppression, and particularly discloses a ship power grid harmonic active suppression and energy management method and system, and the method comprises the steps: collecting the basic electric parameters of a ship power grid, the operation parameters of energy equipment, and the transient characteristics of a nonlinear load; and based on the collected multi-dimensional data, utilizing a deep learning hybrid model containing an attention mechanism to predict the harmonic trend and the energy demand of the ship power grid, and performing online iteration updating on model parameters according to a prediction error. According to the invention, active harmonic suppression and energy management are deeply coupled, so that conversion from passive compensation to prospective intervention is realized; the system can dynamically coordinate the strategy of harmonic treatment and energy distribution based on accurate prediction of the harmonic trend and energy demand of the power grid, and significantly improves the overall energy efficiency and operation economy of the ship power grid on the premise of ensuring that the electric energy quality strictly reaches the standard.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.

The invention relates to the technical field of new energy management and edge computing, and particularly discloses a heterogeneous computing power aware new energy cluster collaborative scheduling method and system. According to the method, information such as static attributes and dynamic loads of various energy devices and control units in a cluster is collected through a lightweight probe, and a dynamically updated heterogeneous computing power resource portrait is constructed after processing; analyzing the structured scheduling request, and combining a cluster physical layout to construct a space-time-capability-dependency-computing power-energy consumption five-dimensional coupling adaptation degree model; searching an optimal scheduling plan through a hybrid optimization algorithm, wherein the optimal scheduling plan covers energy distribution, energy storage scheduling and transmission path planning; after the stability is verified by the digital twin sandbox, an instruction is issued; real-time monitoring is carried out during operation, and computing power-physical joint rescheduling is triggered. Intelligent perception, accurate prediction, simulation verification and dynamic collaborative optimization of the heterogeneous computing power resources of the new energy cluster are realized, and the overall efficiency, stability and adaptive capacity of an energy system are remarkably improved.

The present invention discloses a system and method for optimizing energy distribution in renewable energy plants. The system (100) comprises a solar power plant or a wind power plant to generate electrical energy. A renewable energy controller (108) monitors real-time energy output and manages energy flow to a grid (112). The renewable energy controller (108) optimizes charging cycles based on forecasts. The renewable energy controller (108) generates dispatch schedules through an intelligent bidding unit (114), considering market prices and grid conditions, while ensuring grid code compliance. A battery digital twin unit (122) is operatively connected to the renewable energy controller (108), to simulate future states of the plant based on historical data, real-time operational data, and weather forecasts, providing inputs for the intelligent bidding unit (114) to optimize energy dispatch and storage.

The invention relates to the technical field of building energy consumption management, in particular to a building heating ventilation air conditioner intelligent control method for energy efficiency optimization, and the method comprises the steps: dividing a building into a plurality of independent temperature control regions, and deploying multifunctional sensor nodes; acquiring real-time environment data, personnel data and weather forecast data; the ultra-short-term load prediction model is maintained to predict the cold / heat load demand of the area in the future 15 minutes, and a cold / heat load demand prediction value is obtained; reporting the cold / heat load demand prediction value to a central processing unit in a standardized data format; the central processing unit optimizes the energy distribution of the whole building through a multi-objective optimization algorithm based on the prediction requirements of all the regions, and obtains a resource scheduling list in the next 15-minute period; and according to the resource scheduling list, a regulation and control instruction is issued to equipment in each independent temperature control area. Therefore, the problems of response lag, extensive control and the like in the prior art are solved.

A system, method, and receiver control architecture for a Symbolic Resonant Energy Transfer Network (SRETN) configured to distribute electrical power wirelessly across planetary-scale distances under cryptographic, economic, and biological safety governance. The system comprises a distributed mesh of terrestrial resonant transmission towers and orbital relay satellites configured to deliver energy via directed magnetic resonance coupling. A Symbolic Energy Kernel governs authorization and transmission, preventing energization of any transmission vector unless a secure cryptographic energy handshake is completed. The handshake verifies: (i) authenticity and compliance of a requesting receiver device; (ii) real-time solvency of an associated decentralized energy ledger account; and (iii) clearance of the transmission path through active radar and lidar-based biological exclusion scanning. Energy is transmitted as discrete, tokenized packets, each cryptographically metered and acknowledged by the receiver prior to continuation. The system enables a wireless, infrastructure-light energy distribution paradigm in which electrical power is accessed on demand with transaction-level auditability and enforced safety constraints.

The invention provides a mobile kitchen resource scheduling and energy optimization method and system based on multi-modal perception, relates to the technical field of mobile kitchen management, and covers the working state of cooking equipment, the food material storage environment, the energy supply dynamic state, the cooking task queue and other data by obtaining a mobile kitchen multi-scene perception data set. A resource energy interference knowledge graph is constructed based on mobile kitchen multi-scene perception data to represent the mutual interference relationship of each link. And determining a dynamic adaptive priority of resource scheduling and energy allocation according to the resource and energy interference knowledge graph, and executing collaborative allocation processing according to the dynamic adaptive priority to generate a resource and energy collaborative allocation scheme. And in the cooking task execution process, the knowledge graph is updated and the collaborative allocation scheme is adjusted by using real-time sensing data, so that a dynamically optimized mobile kitchen resource scheduling and energy optimization scheme is formed, and the resource utilization efficiency and the energy utilization efficiency of the mobile kitchen are effectively improved.

The invention discloses an intelligent scheduling method and system for a new energy load at a vertical take-off and landing point, and belongs to the technical field of new energy scheduling, and the method specifically comprises the steps: obtaining multi-main-body energy demand data through a power supply circuit device, and carrying out the distributed storage and desensitization processing; combining the processed multi-subject energy demand data with the real-time state information of the battery of the electric energy storage system, and calculating the dynamic energy distribution weight of each partner by adopting an improved Shapley value algorithm; on the basis of the weight and the real-time task dynamic state of each partner, a new energy scheduling rule is constructed, a control instruction is generated according to the new energy scheduling rule, new energy resources are scheduled and allocated, and a result is fed back to the electric energy storage system and each partner; according to the invention, the intelligent level, fairness, system energy efficiency and adaptive ability of new energy scheduling in a multi-partner scene are effectively improved.

The invention relates to the technical field of photovoltaic power generation, in particular to a regional autonomous regulation and control method and system for distributed photovoltaic groups and user side energy storage. The method comprises the following steps: obtaining photovoltaic group output data and user side load data; performing energy balance evaluation according to the photovoltaic group output data and the user side load data to determine a regional energy gap; performing energy storage autonomous adjustment analysis based on the regional energy gap to obtain energy storage autonomous adjustment data; performing peak clipping and valley filling identification based on the energy storage autonomous adjustment data to obtain peak clipping and valley filling data; positioning an energy mutual aid path by using peak clipping and valley filling data; performing power grid stability detection based on the energy mutual aid path to obtain power grid stability data; predicting a critical instability state according to the power grid stability data; and adjusting energy storage parameters according to the critical instability state to obtain energy storage adjustment parameters. Based on the photovoltaic power generation technology, the energy dispatching precision and the power grid operation stability of the regional photovoltaic and energy storage system are improved.

The invention relates to the technical field of double-source power supply, in particular to an energy management method and device for a double-source power supply vehicle, computer equipment and a medium. The method comprises the following steps: S1, acquiring various types of operation data of the double-source power supply vehicle in a sliding time window; s2, predicting the predicted energy consumption of the dual-source power supply vehicle in a prediction time domain by using the trained energy consumption prediction model based on the multiple types of operation data; s3, determining availability information of a contact network for supplying power to the double-source power supply vehicle in the prediction time domain, and battery constraint conditions and pantograph constraint conditions of the double-source power supply vehicle; s4, taking the predicted energy consumption, the availability information, the battery constraint condition and the pantograph constraint condition as constraint conditions of the target function, and generating an energy configuration strategy by minimizing the target function; the energy configuration strategy comprises an energy distribution strategy of the vehicle-mounted battery and the pantograph, a pantograph operation strategy and a vehicle-mounted battery charging and discharging strategy. By adopting the method, energy switching can be carried out in time.

Systems and methods for allocating energy including distributing and receiving energy using a mobile energy storage (MEES) system at locations of a power supplier in an energy market system by a user. Determine allocating amounts of energy for the MEES system and for each time interval for all time intervals for an upcoming operating time period based on a set of uncertain parameters using an optimization model. Base on calculating an objective function using uncertain parameters. Update the objective function using constraints. While optimizing the objective function for a value hierarchy associated with energy and reserve bidding scenarios for the user and delivery scheduling for the MEES system based upon the stored user risk preferences. Control scheduling of the MEES system between the locations of the power supplier, according to allocating of the amount of the electrical energy for the MEES system at the locations of the power supplier.

The invention discloses a multi-scene-oriented electric hydrogen ammonia system operation optimization method and system, and belongs to the technical field of energy and control. The method comprises the following steps: performing wind and light scene generation on wind and light energy historical output data to obtain a multi-dimensional wind and light output scene library covering different output characteristics; by taking the scene library as an input, constructing and solving a multi-body operation optimization model of the electricity-hydrogen-ammonia integrated system comprising a source side, a network side, a hydrogen side, a nitrogen side, a storage side, an ammonia side and a market side, and obtaining an overall optimal energy interaction and scheduling strategy of the system; and finally, based on the strategy, performing multi-stage energy distribution on hydrogen production by water electrolysis, gas storage, nitrogen production and Harberg-Borschner synthesis ammonia sections. According to the invention, the problems of poor adaptability to wind and light output characteristics, insufficient multi-main-body cooperation and low energy distribution efficiency in the prior art are effectively solved, and safe, economical and efficient cooperative operation of the electro-hydrogen ammonia system in multiple scenes is realized.

This application relates to the field of energy dispatching, and in particular to a method and system for distributed energy storage dispatching based on multi-energy collaboration. The method includes: acquiring a set of factory order information; based on this, extracting and fusing multi-dimensional production value features to generate a dynamic production value assessment information set; based on this, combined with preset energy cost constraints, calculating dynamic electricity value thresholds and making global energy dispatching strategy decisions to generate an energy dispatching strategy information set; acquiring a set of energy supply influencing factors; based on the energy dispatching strategy information set and the energy supply influencing factors set, performing multi-timescale rolling optimization and global preset energy allocation to generate an energy allocation execution instruction set; based on this, issuing and executing dispatching instructions and dynamic production compensation for production lines and energy storage layers, generating and outputting a multi-energy collaborative dispatching report. This application can improve the economic efficiency of factories during energy dispatching.

The present invention relates to a method for operating an energy distribution system (2), wherein the energy distribution system (2) is connected to a vehicle (12) and to another consumer (14). The method comprises determining (I) an available amount of power that can be distributed by the energy distribution system (2). The method comprises detecting (III) the charged state of the vehicle (12). The method comprises determining (IV) a reserve power to charge the vehicle (12) at a later time. The method comprises allocating (VII) a maximum consumption power to the other consumer (14), taking the reserve power into account. The present invention further relates to an energy management device (4) for carrying out this method, and to an energy distribution system (2) with such an energy management device (4).

This application discloses a data-driven energy dispatch fault diagnosis method, device, and medium, mainly relating to the field of fault diagnosis technology. It addresses the problems of existing solutions neglecting the multimodal distribution characteristics of energy data, failing to effectively capture the complex coupling relationships between the source-grid-load-storage links, lacking awareness of dispatch strategies, and being sensitive to noise interference. The method includes: obtaining a graph convolutional hidden representation matrix and then calculating a sparse dictionary matrix; optimizing the graph convolutional sparse coding objective function, solving for the sparse coding coefficients, and outputting a feature mining data matrix; calculating a dispatch strategy matching degree matrix based on the feature mining data matrix, using introduced dispatch plan data and real-time operational constraints; fusing the operational status classification vector and the dispatch strategy matching degree matrix to obtain predicted fault categories; and iteratively training a diagnostic model based on the predicted fault categories and labeled fault categories until a well-trained diagnostic model is obtained.

The invention discloses a self-adaptive energy management system and method suitable for a grazing-light-storage complementary system, and relates to the technical field of energy management, and the system comprises a multi-dimensional data sensing module which collects various types of data and transmits the data to a preprocessing storage module, and the data are stored after being denoised and normalized; the system state evaluation module predicts the generated power, evaluates the energy storage state and predicts the load demand; the self-adaptive strategy generation module generates an energy scheduling strategy, and the energy scheduling strategy is executed by the scheduling strategy execution module; the deviation feedback correction module evaluates deviation, and if a threshold value is exceeded, strategy correction is triggered; according to the invention, by integrating multi-dimensional data perception and a dynamic algorithm, the data precision and processing efficiency of the grazing-light-storage complementary system are improved, a reliable basis is provided for state evaluation and strategy generation, the scheduling deviation is reduced, and the energy utilization rate is improved; meanwhile, in combination with multiple models and a collaborative optimization mechanism, strategy dynamic closed-loop optimization is realized, and the adaptive capacity and the operation stability of the system are enhanced.

The invention relates to the technical field of power dispatching, in particular to an expressway direct-current micro-grid energy dispatching method and device, and the device comprises a local dispatching module which is connected with a distributed execution module, an emergency response module, an operation refining module and a scene switching module. According to the welding device, a plurality of sheet-metal aluminum veneers can be sequentially welded, so that the cut openings at the corners of the folded edges of the aluminum veneers are tighter, the tightness degree after welding is improved, the welding point positions of all the aluminum veneers can be kept consistent, the welding precision is improved, and the welding quality is improved. The system has the functions of efficient and stable power supply guarantee, scene adaptation and precise regulation and control, emergency response and safety prevention and control.

This invention discloses a multi-source collaborative optimization control method for smart microgrids, relating to the field of energy technology. The method includes: connecting to the user side to predict the power supply demand of the target microgrid in a preset time zone; acquiring a hybrid energy supply module containing multiple distributed power sources; collecting historical power supply logs of the distributed power sources and generating relevant time sequences; performing energy curtailment rate analysis based on this to generate indicators and decisions; optimizing energy storage units after determining that the indicators meet the standards; and then performing multi-source collaborative power supply control according to preset demand. This method solves the technical problems of existing new energy power generation uncertainties that are difficult to accurately predict, and unreasonable energy storage module configurations that lead to significant energy losses during power generation, storage, and distribution, resulting in low energy utilization efficiency and unstable power supply quality. By optimizing and integrating new energy power generation, energy storage modules, and energy distribution in the microgrid, it achieves the technical effect of reducing energy losses and ensuring efficient and high-quality power supply to the microgrid.

The invention relates to the technical field of power distribution network control, in particular to an emergency generator set starting control method and system based on energy dispatching optimization, and the method comprises the steps: carrying out the voltage distribution calculation of a to-be-controlled power distribution network based on a historical operation period set, and obtaining a plurality of load node voltage amplitude sets, selecting a plurality of emergency risk nodes according to the plurality of load node voltage amplitude sets, calculating the number of emergency generators according to the current power distribution network vacancy power and the emergency rated generation power, and setting an emergency generator starting position set according to the number of emergency generators and the plurality of emergency risk nodes, according to the method, the accuracy and the response efficiency of emergency power generation of the power distribution network can be improved, and the voltage instability risk of the power distribution network is reduced.

The application belongs to the technical field of energy optimization, and discloses a distributed energy optimization decision system based on cloud computing; comprising: a data receiving module, which is used for receiving historical power generation data collected by a resource end and historical load data collected by a user end by a cloud platform; a region division module, which is used for dividing a distributed power grid into regions according to the historical power generation data and the historical load data by the cloud platform; a data prediction module, which is used for predicting future power generation data and future load data according to the historical power generation data and the historical load data by the cloud platform; and an energy optimization module, which is used for formulating an energy dispatching optimization strategy by using an optimized fish optimization algorithm according to the future power generation data and the future load data by the cloud platform; the application fully gives play to the advantages of cloud computing, realizes intelligent management and automatic operation of the distributed power grid, reduces energy consumption and cost in the energy dispatching process, and improves the stable operation and sustainable development capacity of the distributed power grid.

The invention relates to the technical field of power dispatching, and provides a digital twin power energy dispatching system based on distributed control, and the system comprises a data collection layer which is used for collecting the operation data of a plurality of energy nodes distributed in a target power grid in real time and the environment data of the environment where each energy node is located; the digital twinborn layer is used for updating a pre-constructed digital twinborn model based on each operation data and each environment data so as to enable the updated digital twinborn model to be mapped with each energy node in real time, and performing analogue simulation and state prediction of power grid operation based on the digital twinborn model to generate simulation prediction data; and the distributed scheduling layer is used for calculating a scheduling instruction set by adopting a distributed optimization algorithm based on the simulation prediction data so as to perform power energy scheduling on the target power grid based on the scheduling instruction set. Safe and economic dispatching of the power grid is achieved, and the intelligent level and the operation economical efficiency of the power grid are remarkably improved.

The invention relates to the field of energy scheduling, in particular to an off-network state-oriented service area integrated energy system energy scheduling method, which comprises the following steps of: acquiring load operation data of a service area in an off-network state, and establishing a load model of a road service area integrated energy system; obtaining a new energy supply type of the service area, and constructing various energy supply equipment models of the service area in an off-grid state so as to establish an energy supply model of the road service area integrated energy system; constructing a single-target optimization scheduling model; and performing a whale optimization algorithm on the single-target optimization scheduling model, and performing optimization according to a penalty term to obtain an energy scheduling scheme. According to the service area integrated energy system energy scheduling method oriented to the off-network state, power supply guarantee of the key load is maximized, normal operation of equipment with high priority level in the off-network state of the road service area is guaranteed, and the overall energy utilization efficiency is improved.

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

The invention relates to the technical field of elevator group control and energy distribution, in particular to an elevator energy distribution system based on Internet of Things data acquisition, which comprises a state sensing module used for acquiring real-time operation data, passenger call data and power grid constraint instructions of an elevator group; a power cost prediction module for predicting and determining a scheduling power cost in response to a passenger call; the power hard constraint verification module is used for verifying whether the scheduling power cost meets the instantaneous power upper limit or not; the coupling dispatching cost calculation module is used for obtaining the estimated elevator waiting time of the elevator; the coupling dispatching cost of the elevator is calculated; the group control scheduling decision module is used for determining and assigning the elevator with the lowest coupling scheduling cost to respond to the passenger call; the passenger call is forcibly delayed; according to the method, prediction drift caused by factors such as equipment aging and friction change is effectively overcome, and the continuous reliability of power hard constraint verification is ensured.

The application discloses a low-carbon energy dispatching system of wisdom park source network load storage cooperation, relates to the wisdom park energy dispatching technical field, and the system includes the data acquisition module and gathers the distributed power output, the energy storage charge and discharge state and the load power demand time series data; the prediction analysis module is associated with the power output data and the weather forecast data, generates the short-term output prediction curve; the net load calculation module calculates the difference value of supply and demand, generates the net load fluctuation curve; the energy storage dispatching module plans the energy storage charge and discharge plan by overall planning the peak valley arbitrage and the frequency modulation standby capacity; the load management module generates the load reduction instruction containing the reduction time length and the compensation price by combining the adjustable load response potential and the net load low valley period. The system can improve the energy storage utilization efficiency and the power grid stability, reduce the influence of load reduction on the park power consumption, realize the wisdom park energy low carbonization and fine collaborative scheduling.

According to the energy transaction method and device based on the block chain, the electronic equipment and the storage medium provided by the invention, decentralized, automatic, transparent and credible energy transaction can be realized through energy transaction based on the block chain; through matching optimization calculation based on the first preference parameter, the energy supply quantity, the second preference parameter and the energy demand quantity, energy distribution can be more reasonable, personalized demands of users can be realized, and the satisfaction degree of the users is improved.