489 results about "Electricity cost" patented technology

The invention provides a power marketing archive intelligent management method, which comprises the steps of receiving archive data through a power data acquisition interface, matching an electricity price feature code in an archive based on a real-time electricity price policy identifier, and injecting a version calibration mark into unmatched data; constructing an electricity consumption abnormity prediction model based on the user historical load mode and the environmental factors; when a query request is responded, dynamic penalty parameters are generated based on the user behavior portrait and the rule base; a quantum key distribution path is generated based on a power grid topological structure, and cascade encryption is implemented along physical connection nodes; dynamically associating user operation authority with a power grid operation risk signal, and automatically disabling core operation in a high-risk state; and analyzing archive data in a security isolation environment to drive line loss optimization, electric charge tracing and fault pre-judgment service flow in real time, and outputting feedback archive update based on the service. According to the invention, the safety, reliability and operation efficiency of power marketing archive management are improved.

The invention relates to the technical field of community new energy control, and discloses an orderly and rapid charging new energy control method, which comprises the following steps: collecting photovoltaic output data, power grid voltage / current data, user charging behavior data and battery state parameters in real time; generating a dynamic charging strategy based on the power grid load data, the time-of-use electricity price information, the photovoltaic output fluctuation range and the battery health state evaluation result; generating a corresponding local optimization strategy based on the operation state data of each charging pile; an economic index and a technical index are calculated, and when any index exceeds a preset threshold value, a strategy adjustment instruction is triggered, and a charging mode switching threshold value is updated; an equipment health index is generated by combining inverter temperature, coil coupling efficiency and insulation impedance parameters with historical fault data, and maintenance early warning is triggered when the health index is lower than a safety threshold value. According to the invention, the electricity cost of the user can be reduced on the basis of improving the multi-energy collaborative optimization capability of the charging facility in the residential area.

The invention provides a multi-stage desensitization electricity charge sensitive data protection system, belongs to the technical field of information security in the power industry, and relates to a full-life-cycle protection scheme of sensitive data such as an identity label, account information and contact information in an electricity charge service system. The problems that in the prior art, only simple masking or full-table encryption is carried out on a database layer, the desensitization dimension is single, multiple links are prone to leakage, no role fine-grained desensitization strategy exists, and exported files are difficult to trace are solved. The system comprises a data acquisition source and acquisition layer desensitization, a transmission layer encryption, a storage layer transparent encryption, an API gateway, a desensitization strategy center, a front-end dynamic mask, a watermark export, a log desensitization plug-in and an auditing log module, covers the whole process from data acquisition to log auditing, dynamically regulates and controls data display according to roles, and exports a file embedded identity watermark. And desensitizing the log and reserving operation marks. Full-link leakage prevention, fine-grained permission control and leakage traceability can be realized, and the transformation cost is low.

The invention discloses an energy storage operation optimization method and system based on hierarchical multi-agent reinforcement learning. The method comprises the following steps: firstly, initializing a hierarchical multi-agent reinforcement learning decision framework, and defining an interlayer communication synchronization mechanism; then, designing an upper layer reward function for balancing the electric charge income and the battery life loss cost, and optimizing an upper layer planning strategy; a lower layer reward function integrating current smoothness and budget tracking precision is designed, and a lower layer execution strategy is optimized; and finally, deploying the optimized upper-layer planning strategy and the optimized lower-layer execution strategy to an energy storage scheduling task, generating a charging and discharging power budget by an upper-layer planning agent in a day-ahead scheduling stage, and receiving a budget instruction and performing power adjustment by a lower-layer execution agent. According to the invention, by constructing the hierarchical decision-making architecture, coordinated optimization of short-term economic benefit and long-term life protection of the energy storage equipment is realized.

The invention provides a user electricity larceny suspicion coefficient determination method based on multi-dimensional abnormal feature analysis, and belongs to the technical field of electricity larceny prevention. According to the method, a user electricity stealing identification algorithm applied to an electricity utilization inspection system is provided, and on this basis, multi-dimensional parameters such as user longitudinal electricity utilization behavior characteristics and transverse environment correlation characteristics are creatively fused to construct a comprehensive electricity stealing characteristic index system. And after a significant deviation of an electricity consumption prediction value is found preliminarily, according to multiple characteristic quantities extracted by an index system, an abnormal weight coefficient of each characteristic quantity is dynamically determined and calculated in a combined manner, and finally an accurate electricity stealing suspicion coefficient is generated. According to the invention, potential electricity stealing behaviors can be identified more reliably, and the probability of single-index misinformation is reduced at the same time. The method emphasizes the mining of the abnormal power consumption mode of the user and the relationship between the abnormal power consumption mode and multiple factors, fully utilizes the alarm event and the electric charge settlement data of the local metering device, and significantly reduces the dependence on massive complex modeling.

The invention provides a cross-day two-stage random scheduling method for an industrial park integrated energy system, and the method comprises the steps: fitting the output fluctuation characteristics of new energy power generation equipment in a cross-day time scale based on historical data, and constructing an uncertainty scene set containing a new energy prediction error; establishing a cross-day two-stage stochastic programming model; according to the two-stage model, an optimal scheduling scheme is solved so as to minimize the overall operation cost, and the operation cost comprises the demand electric charge, the electricity purchase electric charge, the new energy power abandoning cost, the unit start-stop cost and the standby penalty cost; and on the basis of tie line power constraint and dynamic response characteristics of the multi-energy coupling equipment, feasibility verification and rolling optimization adjustment are performed on the scheduling scheme. According to the method, the new energy consumption capability of the industrial park integrated energy system under the cross-day time scale can be effectively improved, the total operation cost is reduced, and collaborative scheduling of demand cost optimization and spot market participation is realized.

The invention relates to the technical field of control and adjustment, in particular to an energy storage configuration point-based power consumption cost calculation method, which comprises the following steps of: acquiring power supply data, renewable energy output fluctuation data and computing power load fluctuation data, and generating a coupling influence feature vector; splicing the electricity price features and the coupling features to form configuration optimization input features; the input features are processed through a dual-objective optimization model, and robustness configuration parameters and economical efficiency operation parameters are output; calculating the reserve cost compensation amount of the extreme scene based on the robustness parameter, and calculating the power purchase cost and maintenance cost of the power grid based on the economical efficiency parameter; and aggregating the cost component and outputting a power consumption cost calculation result. According to the method, the interactive influence of wind-light fluctuation and load mutation is quantified through coupling features, the target conflict between robustness and economy is coordinated by adopting the Pareto algorithm, the risk compensation amount is generated in combination with a pressure test, a feature updating mechanism is fed back in a closed loop mode, and the problem that the operation cost deviates from expectation due to quantization deviation of an existing model is solved.

The invention provides an intelligent control method for a pump set, which belongs to the technical field of pump set control and comprises the following steps of: acquiring operation data and electricity price time period data of the pump set in real time; inputting the preprocessed data into a pre-trained liquid level prediction model to obtain liquid level prediction data; inputting the preprocessed data, the liquid level prediction data and the electricity price time period data into a pre-trained strategy optimization model to obtain a control instruction; the operation state of the pump set is controlled through the control instruction. Compared with the prior art, the control method has the advantages that the electricity price time period data is combined, the operation strategy of the pump set is dynamically adjusted by responding to the time-phased electricity price data, the electricity charge expenditure is reduced, and the problem of poor economical efficiency in the prior art is solved; and meanwhile, the control method is combined with liquid level prediction data to predict future working conditions, so that control parameters are adjusted, and the effects of avoiding liquid level abnormity and prolonging the service life of equipment are achieved. The system has the same beneficial effects.

The invention provides an optimal control method and system for power system carbon emission, and relates to the technical field of carbon emission optimal control, and the method comprises the steps: collecting a real-time load, a power grid carbon emission factor and time-of-use electricity price, employing a clustering algorithm to automatically divide a power utilization period, carrying out the modeling of the dynamic change of the carbon emission factor at different time periods, and carrying out the optimal control of the power system carbon emission. An energy storage charging and discharging path is optimized according to the principle of peak period discharging, smoothing and valley period charging, and energy loss and the health state of an energy storage unit in the charging and discharging process are included in carbon emission accounting; and meanwhile, a multi-target collaborative optimization model is constructed to realize tradeoff optimization of the total carbon emission and the power consumption cost, so that the time sequence characteristics of the actual production load and the power grid carbon emission factor are accurately matched, the carbon emission collection precision and the scheduling optimization level are improved, and the carbon emission and the energy consumption cost in the production cycle are effectively reduced.

The invention provides a wind and light storage micro-grid optimization scheduling method and system containing small hydropower storage, and the method comprises the steps: obtaining historical operation data, and carrying out the prediction according to the historical operation data, and obtaining a full-dimension prediction data set; according to the full-dimension prediction data set, constructing a multi-objective optimization model with the goal of minimizing residual load, minimizing power abandoning cost, minimizing cascade water abandoning amount, maximizing green energy collaborative utilization efficiency and minimizing time weighted voltage-power comprehensive error; and solving the multi-objective optimization model by adopting a hybrid optimization algorithm fused by a Hanniac optimization algorithm and an improved sparrow search algorithm to obtain a final output. According to the invention, efficient consumption of green energy can be realized, the electricity abandoning phenomenon is reduced, carbon emission is reduced, and the stability, economical efficiency and environmental protection property of a power system are improved.

The invention discloses a building air conditioner energy consumption-electric charge-comfort level intelligent control method based on MMOE and ParetoMTL, and the method comprises the following steps: (1) collecting the cooling load data of a large commercial building air conditioner, carrying out the abnormal value detection, and completing the normalization preprocessing; (2) constructing an environment-price-behavior multi-source time sequence data set and performing grading standardization; (3) dividing a training set, a verification set and a test set according to building grouping; (4) completing feature fusion and realizing task decoupling under cooperative constraint by using an MMOE mechanism; (5) jointly training a three-branch expert network, an MLP gating network and a PCGrad shared parameter optimization model; (6) generating a multi-task prediction and Pareto solution set on the test set; and (7) evaluating prediction performance and trade-off efficiency by using a plurality of indexes. According to the method, multi-target conflict, physical consistency and Pareto solution set missing difficulty can be effectively solved, and a new scheme is provided for demand response HVAC intelligent control.

The invention provides an energy management method and device for an elevator energy supply system. The method is executed by a cloud server and a local controller which work cooperatively. A cloud server obtains historical and real-time operation data of an energy supply system, predicts the available output power of a photovoltaic unit and the comprehensive power demand of an elevator load in a first time period in the future, generates an energy scheduling strategy of the first time period through optimization calculation, and issues the energy scheduling strategy to a local controller; the local controller collects the local real-time operation state of the energy supply system, selects the current execution strategy and controls the energy flow path of each unit so as to supply power to the elevator load or recover the regenerated energy of the elevator load. According to the method, the energy-saving economy can be improved, the two free energy sources including photovoltaic power generation and elevator regeneration energy are preferentially utilized to the maximum extent through cloud collaborative scheduling, the energy storage charging and discharging strategy is optimized to make full use of the commercial power peak-valley price difference, and the overall operation electric charge of an elevator system is remarkably reduced.

The invention discloses an energy control method and device based on an optical diesel storage micro-grid system and a medium. The invention relates to the field of energy management, and solves the problem of consumption of diesel generator feedback residual electricity and photovoltaic feedback electricity in a factory and the problem of reducing the demand electric charge of the factory. Whether feedback electricity is generated or not is recognized through power grid home-entry load data, once it is judged that the feedback electricity exists and energy storage does not reach the preset upper limit, energy storage charging is immediately controlled, redundant electric energy such as the feedback electricity of the diesel generator test stand and photovoltaic residual electricity is stored, and the problems that the feedback electricity cannot be connected to a grid and consequently waste is caused and photovoltaic light is abandoned are solved; when no feedback electricity exists, whether the energy storage state of charge (SOC) is lower than a preset lower limit or not is judged to avoid over-discharge, then charging is triggered in combination with the conditions that the load power is smaller than or equal to a peak clipping threshold value in a low ebb period, and low-cost energy storage is conducted through the valley electricity; and when the load exceeds a peak clipping threshold value and is in a non-low-ebb period, discharging is triggered, and the load peak demand is matched. In addition, when the load peak or the load exceeds the peak clipping threshold value, power supply is supplemented through energy storage discharge, and the cost is reduced.

The invention belongs to the technical field of data detection and processing, and provides a user electricity charge data anomaly detection method and system, and the method comprises the steps: building and employing a season sensitive baseline model to automatically learn an electricity utilization fluctuation rule of a specific season of a region based on historical electricity utilization data and meteorological data, introducing a season regulation factor into the season sensitive baseline model, and obtaining a user electricity charge anomaly detection result. And deviation correction is carried out in combination with the temperature data, so that reasonable difference of electricity utilization in different seasons is eliminated, temperature difference between south and north can be distinguished, and the false alarm rate of user electricity charge data anomaly detection is greatly reduced. Meanwhile, through year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-on-year-

The invention provides a rolling electricity price intelligent selection and comparison method suitable for industrial and commercial users. The method comprises the following steps: acquiring power consumption archive data, electric quantity data and transaction parameters of the users; based on the power consumption archive data, the electric quantity data and the transaction parameters, respectively predicting a direct purchase electricity charge prediction value, a retail electricity charge prediction value and an agent electricity charge prediction value of the user in a future preset duration during direct electricity purchase, retail package and agent electricity purchase; comparing the direct purchase electricity charge predicted value, the retail electricity charge predicted value and the agent electricity charge predicted value to obtain a comparison result including an optimal path and a corresponding saving amount; the saved amount in the comparison result is compared with a preset threshold value, and whether a path switching prompt is triggered or not is judged according to the comparison result; and if the path switching prompt is triggered, the transaction authorization document corresponding to the optimal path is generated, and the transaction authorization document is transmitted back to the power marketing system, so that the optimal path can be provided efficiently, and fine control of the electric charge cost is realized.

The invention provides a batch electric charge settlement method and device based on a multi-dimensional space-time tensor, and the method comprises the steps: receiving a query request, and obtaining the batch power consumption data of a to-be-calculated user according to the query request; generating an electricity consumption tensor according to the electricity consumption data; obtaining an electric charge rule of an area where the to-be-calculated user is located, wherein the electric charge rule is described through a DSL language; generating a condition mask matrix based on the electricity charge rule; performing condition screening according to the power consumption tensor and the condition mask matrix to obtain a screened power consumption tensor; analyzing the electricity charge rule to generate an electricity price tensor; calculating according to the electric quantity tensor and the electricity price tensor to obtain an electricity price tensor of the user to be calculated; the method can improve the efficiency of calculating the electric charge of massive users.

The invention relates to the technical field of wiring terminal boxes, in particular to an intelligent analysis type wiring terminal box and a control system thereof.The intelligent analysis type wiring terminal box comprises the intelligent analysis type wiring terminal box and the control system used for controlling the wiring terminal box, and the intelligent analysis type wiring terminal box comprises a box body and a transparent protective cover; the box body is composed of a first main body, a second main body and a third main body which are sequentially connected, the first main body is provided with a wiring communication assembly, the second main body is an insulation board, and the third main body is provided with a monitoring acquisition assembly; the control system comprises a sampling unit, a comparison unit, a processing unit, a communication unit, an acquisition terminal and an acquisition background, and all the units cooperate to realize data sampling, comparison, processing and transmission. According to the invention, the accuracy of the metering device can be guaranteed, and the working pressure of line loss index governance improvement and electric quantity and electricity charge recovery by basic units can be reduced, so that the purpose of intelligent monitoring of the metering device is achieved.

The invention belongs to the field of industrial energy optimization, and discloses a steel plate production-energy collaborative optimization method. The method comprises the following steps: firstly, determining various constraint conditions and optimization targets in a cold rolled steel coil production process; secondly, a rule-driven rolling mill distribution model is established to serve as a first-layer optimization model, contract requirements are matched with the production capacity of different rolling mills, and therefore contracts are distributed to all the rolling mills; and finally, solving the lower-layer time scheduling model, carrying out rolling calculation on the demand electric charge, and carrying out production scheduling on the contract by taking the minimization of the demand electric charge as a target and combining actual production requirements. According to the method, the actual working condition of the cold rolling mill can be well adapted while the maximum power demand of the factory is optimized.

A fleet of all-electric, battery powered industrial or commercial mobile power units (MPUs) or equipment is provided. Systems and methods for managing the fleet of equipment is also provided. Observable data, such as i) equipment performance and health data, ii) electricity cost data, iii) telematic data, and iv) scheduling data, can be used by machine learning models to optimize fleet management and decision making. The machine learning model can be configured to generate suggested actions, commands, or decisions which are transmitted to fleet managers who are responsible for activities such as i) equipment charging and maintenance, ii) equipment delivery logistics, and iii) rental pricing strategy. Feedback data from the outcome of these decisions, or the completion of the related activities, can tracked and used to update the machine learning model. Interactive portals may also be utilized by customers to make reservations and managers to manage reservations, monitor the fleet of equipment and provide support to MPUs across the product lifecycle.

The invention discloses an energy consumption risk closed-loop monitoring method, system, equipment and medium based on a brain-like inference model, and belongs to the technical field of energy digitization and intellectualization, and the method comprises the steps: collecting multi-source energy consumption data in real time, converting the multi-source energy consumption data into a pulse sequence through a pulse encoder, constructing a layered space-time memory pool to process the pulse sequence, and carrying out the real-time monitoring of the multi-source energy consumption data. And based on a neural plasticity rule, carrying out dynamic weight migration on a current risk mode and a historical risk disposal strategy, analyzing a risk conduction path, outputting risk inducements, calculating risk hormone factors, activating a dual-channel response mechanism and generating a disposal scheme by using a risk traceability tree diagram, and optimizing scheme weights through Q-learning to generate a virtual risk scene. And rehearsing the treatment scheme effect, and dynamically updating the model parameters according to the user response data and the treatment effect. According to the invention, through a multi-level cooperation and closed-loop feedback mechanism, full-process intelligentization of energy consumption risk monitoring is realized, the electric charge recovery rate is improved, and the system robustness is enhanced.

The invention relates to the field of electricity meter monitoring and management, and relates to an electricity charge management method and device based on electricity meter data, and the method comprises the steps: obtaining electricity meter image information, time-space stamp information corresponding to an electricity meter image, and historical electricity data of a user; identifying the electricity meter image information to obtain a preliminary identification result; performing fusion processing according to the preliminary identification result, the historical power consumption data of the user and the time-space stamp information corresponding to the electric meter image to obtain a credible reading report attached with a confidence level and an abnormal type label; according to the credible reading report, dynamic routing decision processing based on a confidence level and an abnormal type is carried out, and electric charge settlement processing decisions of different abnormal types are obtained; according to the invention, the intelligence and reliability of electricity charge management are effectively improved, the error rate of electricity charge settlement is reduced, and the efficiency of electricity charge settlement and the rationality of resource allocation are improved.

The invention relates to the technical field of electric power energy storage control, and discloses a multi-dimensional state driven energy storage cold energy storage optimization control system and method, and the method comprises the steps: collecting the multi-dimensional state parameters of an energy storage system and the price of an electric power market, and judging a current operation time period mode; in the active cold storage mode, target cold storage capacity is determined according to the maximum allowable heat capacity buffer margin, and cold pre-storage is completed; in the cold capacity release mode, cold capacity-heat capacity collaborative multi-stage judgment is executed, the minimum critical mass flow rate meeting the heat exchange requirement is reversely solved, the minimum critical mass flow rate is mapped to a discrete control gear of the liquid cooling circulating pump to generate a final rotating speed instruction, and meanwhile closed-loop flow correction is executed; according to the method, the minimum critical mass flow rate is accurately solved, the rotating speed of the liquid cooling circulating pump is remarkably reduced while battery thermal inertia buffering is utilized, parasitic energy consumption is greatly reduced through the cubic relation between pumping power consumption and the rotating speed, and the problem that electric charge income is counteracted due to excessive pumping is solved.

The invention discloses an intelligent computing center resource management and control method, system and device based on computing and power collaboration. The method comprises the steps of collecting computing power resource state data of an intelligent computing center, time-of-use electricity price data of an external power grid, state data of a micro-grid and state data of an energy storage system in real time; fusing the computing power resource state data, the time-of-use electricity price data, the microgrid state data and the energy storage system state data to obtain fused data; predicting the fusion data by using a pre-trained large model, and generating a computing power resource demand prediction curve and a microgrid power generation capacity prediction curve in a future set time window; then, by taking the minimization of the total power consumption cost as an optimization target, generating a power calculation cooperative scheduling strategy; and according to the calculation and power cooperative scheduling strategy, performing calculation power resource distribution, microgrid output adjustment and energy storage system charging and discharging control. The method can effectively break the separation barrier of computing power and electric power, and realizes deep cooperation of computing power scheduling and energy management.

The invention discloses a smart home energy consumption optimization control method and system, and relates to the technical field of smart home energy consumption optimization and intelligent control, and the method comprises the steps: forming standardized historical data through collection and preprocessing; training an energy consumption prediction model locally, extracting local model parameters, and aggregating multi-user parameters by using federal learning to obtain a global prediction model; and on the basis of a prediction result, a preliminary energy consumption control strategy is generated by adopting reinforcement learning, rolling optimization is carried out under power, temperature and equipment operation constraints through model prediction control, a final control instruction is obtained and issued for execution, and actual operation data backflow is used for continuously updating the model. According to the invention, the prediction precision and generalization ability are improved while privacy is protected, comprehensive optimization among energy efficiency reduction, electric charge saving, peak reduction and user comfort maintenance is realized, and a smart home energy consumption optimization control system which is accurate in prediction and reliable in control and has self-learning ability is constructed.

The invention belongs to the technical field of tunnel construction, and particularly relates to tunnel new energy operation equipment and an energy consumption control method thereof.The tunnel new energy operation equipment comprises a crawler chassis, the crawler chassis is driven by a motor, an electric telescopic arm is fixedly installed on the surface of the crawler chassis, and an external cable is slidably connected to the inner wall of the electric telescopic arm; the external cable is used for being connected with a power grid to provide electric energy for operation equipment. According to the tunnel new energy operation equipment and the energy consumption control method thereof, by arranging the energy storage driving control mechanism and the rotary conductive mechanism, seamless switching and 360-degree continuous rotation operation of the equipment in a power grid direct supply mode and a battery power supply mode are achieved when the equipment is used, and waste gas pollution in a tunnel is fundamentally eliminated. The system has the characteristics of zero emission, low noise and high energy utilization rate, the direct electric charge and ventilation energy consumption are remarkably reduced, the comprehensive energy consumption is reduced, and a brand new green and efficient equipment solution is provided for tunnel construction.

The invention discloses an electricity-hydrogen coupling system optimization operation method for promoting distributed new energy consumption, and the method comprises the following steps: S1, constructing an electricity-hydrogen coupling system, and an energy management and cooperative control strategy of the electricity-hydrogen coupling system; s2, constructing an optimal scheduling model of the electricity-hydrogen coupling system to quantitatively realize the optimal decision of the strategy; and S3, carrying out optimization solution on the optimization scheduling model of the electricity-hydrogen coupling system by adopting a scheduling optimization strategy comprising a day-ahead rolling stage and an intra-day rolling stage to obtain an optimal scheduling scheme. According to the system, surplus electric power of intermittent power sources such as photovoltaic power sources is effectively absorbed through water electrolysis hydrogen production, stored hydrogen energy is fully utilized through hydrogen energy power generation, the new energy consumption rate is greatly increased, and clean energy waste is reduced. By optimizing the operation time sequence of the electric hydrogen equipment, the purposes of peak load shifting and electric charge expenditure reduction are achieved, the utilization of clean energy is improved, meanwhile, good economic benefits are achieved, and a new technical approach is provided for cost reduction and efficiency improvement of the park.

The invention discloses a multi-press line coupling energy consumption optimization method and system. The method comprises the following steps: collecting real-time operation state data and real-time power data of each device of the stamping production line; establishing a hybrid logic dynamic model based on the data, and describing a relation between a discrete working state of the equipment and a continuous energy consumption dynamic state and a material flow sequence constraint; performing event-triggered or periodic-triggered system-level rolling optimization by using an upper coordinator to minimize the total system electricity charge cost including time-sharing electricity-degree electricity charge and demand electricity charge as a target, and solving an optimal working state sequence of each device in a future time domain; issuing the sequence to a local controller of the equipment; and the local controller generates and executes a smooth control instruction based on the equipment dynamics model. The system comprises a sensing layer, an optimization decision-making layer and an execution layer. On the premise that the production takt is guaranteed, the energy consumption and demand peak value of the whole line are reduced, and the energy efficiency management level is improved.

The invention discloses a multi-source data fused new energy power electricity charge intelligent accounting and energy efficiency analysis system, relates to the technical field of new energy power V2G networks, and aims to solve the problems that the existing system cannot effectively fuse cross-regional heterogeneous V2G network multi-source data, complement sparse data and guarantee privacy, so that the multi-agent electricity charge allocation precision is insufficient, and the efficiency of the system is low. The system comprises a multi-region dynamic data acquisition module for acquiring real-time multi-source data and historical data of a cross-region heterogeneous V2G network, and transmitting the real-time multi-source data and historical data to a federal transfer learning processing module; and the federal transfer learning processing module is used for performing feature extraction, cross-regional common feature transfer and heterogeneous data calibration on the multi-source data, and synchronously realizing data full-life-cycle privacy protection through the self-adaptive privacy protection module. The method has the advantages that the cross-regional V2G network multi-subject electricity charge accounting precision is improved, and fair settlement support is provided for commercial operation.

This invention discloses a photovoltaic greenhouse management system based on light energy consumption optimization, specifically relating to the field of photovoltaic greenhouse light and energy consumption coordinated control technology. It addresses the problem in existing photovoltaic greenhouses where light control is disconnected from photovoltaic output, electricity prices, and power distribution capacity, making it difficult to simultaneously guarantee crop light requirements and reduce overall electricity costs. By introducing topology modeling, light demand and light-energy response modeling under unified time synchronization conditions, and superimposing energy budgeting and coordinated optimization control, the adjustable power of multiple greenhouses, crop light targets, photovoltaic output, and time-of-use electricity prices are unified into a single decision framework. This allows for dynamic allocation of supplemental lighting power and shading opening under power distribution capacity constraints, prioritizing photovoltaic absorption and avoiding high electricity prices. This enables multiple greenhouses to simultaneously meet light targets and control electricity costs, solving the problem of existing technologies struggling to balance crop needs and energy consumption constraints.

The invention discloses an energy management strategy and system based on cloud end and edge end cooperation. An energy management platform is responsible for monitoring and controlling operation of the whole system; the master control EMS is responsible for receiving the operation strategy issued by the platform, giving an operation scheme in real time, controlling all energy storage devices in the system according to the adjusted scheme, collecting data of the EMS of each station and transmitting the data to the platform; the site EMS is responsible for receiving the operation scheme of the master control EMS, controlling execution units such as a PCS according to the operation scheme, collecting data of the execution units and transmitting the data to the master control EMS; the process control system is used for controlling the operation power of the PACK battery pack and collecting related data of the PACK battery pack; and the battery system is used for controlling the on-off of a relay of the PACK battery pack and collecting related data of the PACK battery pack. The whole system can realize analysis of previous electricity utilization records of the client, and help the client to select an optimal payment scheme according to the local electricity fee charging regulations of the client.