1089 results about "Energy management system" patented technology

A data-driven dynamic energy management system for the adaptive coordination of renewable and conventional energy sources, consisting of: a processing unit configured to perform real-time calculations to optimize the generation, storage, and distribution of electrical energy by continuously analyzing operational data, forecasting future energy demand, and generating control instructions to match available generation resources with forecasted consumption demand; a storage unit connected to the processing unit, configured to store records of historical energy production and consumption, environmental data, operating thresholds and learned model parameters, and to provide said data as input for the forecasting and optimization routines performed by the processing unit; a multitude of IoT-based monitoring units, each comprising at least one sensor configured to measure instantaneous parameters of generation, storage level, consumption rate and environmental conditions, with each monitoring unit being configured to periodically transmit measurement packets to the processing unit via a secure communication network; a forecasting unit implemented in the processing unit, configured to process historical and real-time data to create forecast curves for demand and generation using statistical and probabilistic forecasting techniques, and to dynamically update the weights of the forecasting model in response to observed deviations between forecasted and actual output; an optimization control unit implemented in the processing unit and configured to evaluate the outputs of the forecasting unit together with current operational data to determine a set of optimized control variables representing the target generation contribution of each energy source, and to pass these targets to a lower-level controller for execution; a controller that is communicatively connected to the processing unit and the multiple energy generation sources and is configured to regulate the operation of each source by adjusting the activation state, output level and operating priority based on the control signals received from the processing unit; an energy storage management unit comprising at least one battery array and a power conditioning circuit, configured to receive control instructions from the processing unit, store excess generated energy, release stored energy when forecasted demand exceeds available generation, and report charging and discharging characteristics in real time to the processing unit for continuous recalibration; an alarm and notification control unit connected to the processing unit, configured to continuously compare storage levels and generation reserves with stored operating thresholds, trigger predefined responses when critical or abnormal conditions are detected, and transmit acoustic, visual, and digital remote alerts to designated operators; a user interface terminal connected to the processing unit, configured to display real-time generation statistics, demand forecasts, energy storage status, and system alerts, and to accept operator-defined parameter inputs that are transmitted to the processing unit for recalibration of forecast or optimization parameters; and a secure server interface configured to synchronize operational logs, learning data, and performance indicators with a remote monitoring or analysis server for centralized monitoring, long-term data analysis, and distributed decision support.

The invention discloses an optical storage and charging integrated micro-grid energy management system, relates to the technical field of micro-grid energy management, and is used for solving the problems of low energy scheduling efficiency and insufficient stability in an optical storage and charging system. The system comprises a power generation monitoring module, an energy storage management module, a charging load control module and an energy coordination module. The photovoltaic power generation state and environmental parameters are monitored in real time through a multi-type sensor network, and the running state of the photovoltaic module is judged; based on battery monitoring data and photovoltaic output characteristics, a differential energy storage management strategy is formulated; the charging power is dynamically distributed in combination with the energy state to realize charging and discharging intelligent regulation and control; by converging multi-source information, source storage and load interaction is coordinated to cope with different power states; accurate monitoring, intelligent scheduling and efficient cooperation of the photovoltaic, storage and charging integrated micro-grid are realized, and the energy utilization efficiency and the operation stability are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an intelligent flight path planning and energy management system and method for a long-endurance fixed-wing unmanned aerial vehicle. Comprising an environment sensing unit; the flight path planning unit is used for planning a flight path meeting task requirements, safety requirements and energy constraints based on the flight environment information of the unmanned aerial vehicle acquired by the environment sensing unit and pre-stored performance parameters of the unmanned aerial vehicle; and the energy management unit realizes energy dynamic management and optimization according to the real-time energy state of the unmanned aerial vehicle, the flight task and the planning result of the flight path planning unit. The flight path planning unit can call working condition energy consumption data such as navigational speed and height output by the energy management unit in real time, and dynamically adjust the weight of the path to avoid high-energy-consumption flight segments; the energy management unit synchronously and intelligently adjusts a main / standby battery charging and discharging strategy to optimize energy distribution according to a task time sequence (such as waypoint priority and track curvature) of a track.

The invention discloses an unmanned aerial vehicle cruising method and system based on a deep learning artificial intelligence image recognition algorithm. According to the method, an unmanned aerial vehicle carrying an improved YOLOv7-SwinT target recognition model collects real-time image data of an inspection area, and the model fuses a single-stage target detection architecture of YOLOv7 and a visual feature extraction network of Swin Transform. Progressive target detection is realized by adopting a three-level recognition architecture, wherein the progressive target detection comprises primary anomaly detection based on lightweight CNN, intermediate accurate positioning in combination with an attention mechanism and advanced target classification of multi-sensor data fusion. And the system combines the electric quantity of the unmanned aerial vehicle, the environmental condition and the task priority according to the identification result, generates a dynamic inspection path through an adaptive path planning algorithm, and realizes multi-vehicle collaborative operation by using an intelligent task allocation algorithm. In the inspection process, sensor data are processed in real time through edge computing equipment, and charging scheduling is optimized by adopting an intelligent energy management system. The target recognition precision and the cruising efficiency of the unmanned aerial vehicle in a complex environment are remarkably improved, and the method is suitable for application scenes such as electric power inspection and security monitoring.

The invention provides an energy digitization platform resource scheduling method based on cloud edge cooperative computing, which comprises the following steps: acquiring real-time supply and demand data, an energy price signal and network topology information from a distributed energy management system, and preprocessing to obtain a structured dynamic supply and demand scene data set meeting a unified format requirement; aiming at a dynamic supply and demand scene data set, respectively detecting the fluctuation frequency and amplitude of an energy price on different time scales by adopting a time sequence analysis method, detecting the change condition of a network topology structure in real time by adopting a network analysis technology, and extracting key parameters reflecting scene dynamic characteristics from the change condition; and extracting a scheduling demand of cross-regional energy flow from the adjusted edge node permission configuration, and optimizing a cross-regional energy flow path in combination with real-time inter-regional supply and demand difference data and network state evaluation to obtain a globally optimized cross-regional energy scheduling scheme.

The invention discloses an electric power control method based on a diesel generator, commercial power and photovoltaic grid connection. The method comprises the following steps: acquiring operation data of commercial power, a diesel generator and photovoltaic-energy storage through an energy management system, constructing a multi-target dynamic optimization model, and distributing power by taking the lowest comprehensive operation cost, the highest power supply reliability and the minimum carbon emission as targets; the power analysis and regulation unit adopts a double-loop mechanism of outer-loop Kalman filtering prediction compensation and inner-loop model prediction control, synchronizes the voltage, frequency and phase of the side to be connected with the grid and the power grid side, and smoothly switches the power change rate of each energy source in the process through a self-adaptive sliding mode control strategy; and the grid-connected switching unit intelligently pre-judges the grid-connected switching opportunity by using a long short-term memory (LSTM) network and fuzzy logic fusion model. According to the invention, the energy utilization efficiency and the power supply stability are improved, multi-energy intelligent dynamic regulation and control are realized, the comprehensive operation cost of the system is effectively reduced, and reliable power supply under complex working conditions is ensured.

The embodiment of the invention provides a digital simulation modeling method and device of a tandem type hybrid electric propulsion system, computer equipment and a medium, and relates to the technical field of simulation modeling, and the method comprises the following steps: constructing a turboshaft prime mover model, constructing a generator model, connecting the turboshaft prime mover model and the generator model in series to generate a generator set, the power generation groups are connected in parallel to construct a turbine power generation system; constructing a propulsion system; constructing a battery energy storage system; constructing a power transmission and distribution system, and respectively connecting the power transmission and distribution system with a turbine power generation system, a propulsion system and a battery energy storage system; and constructing a comprehensive control and energy management system, and outputting a digital simulation modeling result through a turbine power generation system, a power transmission and distribution system and a battery energy storage system. According to the scheme, a simulation modeling method based on multi-physics field coupling double-power-source dynamic response and energy flow regulation and control provides reliable modeling technical support for engineering application of a tandem type hybrid electric propulsion system.

An intelligent energy management system, includes: a drive device including an engine configured to output power to a wheel of the vehicle, a drive motor configured to output power to the wheel, and an electric generator connected to the engine and driven by the engine to generate electricity; a power battery configured to supply electricity to the drive motor and charged with an alternating current outputted from the electric generator or the drive motor; and a control device configured to acquire multi-domain data fusion information, predict, according to the multi-domain data fusion information, a route-specific vehicle energy consumption corresponding to a preset travel route, plan, according to a road section-specific vehicle energy consumption corresponding to each road section, a target SOC corresponding to each road section, and control, according to the target SOC and an actual vehicle demand corresponding to each road section, the drive device.

The invention discloses a wind power plant energy management system and a dispatching optimization system, and belongs to the field of wind power generation. The system comprises a data acquisition module, a wind speed prediction module, a wake effect analysis module, a power prediction and distribution module, an optimization scheduling module, a dynamic adjustment module, an energy efficiency evaluation module and a communication control module which work cooperatively. Through multi-source data fusion and dynamic collaborative optimization, the comprehensive performance of the wind power plant is remarkably improved, on the operating efficiency level, the system combines the space-time convolutional neural network and the multi-target optimization algorithm, high-precision prediction of minute-level wind speed and optimal distribution of whole-field power are achieved, energy loss caused by the wake effect is effectively reduced, and the wind power generation efficiency is improved. The output strategy is dynamically adjusted according to the health state of the fan, and the fatigue loss of the equipment is delayed while the generating capacity is maximized; on the power grid adaptability level, a self-learning mechanism based on the frequency disturbance qualified rate is introduced, and frequency modulation response parameters are optimized in real time.

The invention discloses an intelligent Internet of Things home energy management system, and relates to the technical field of intelligent homes, the system comprises a multi-dimensional data sensing module which uses a sensor and an intelligent equipment interface to collect human physiological data, obtains environment and external data through an environment sensor and a weather API, and sends the environment and external data to a cloud server; historical physiological records, environmental regulation logs and user feedback data are called, timestamps and space coordinate information are added to all kinds of data at the same time, and the data are converted into data vectors and uploaded to a system database; through the multi-dimensional data sensing module, human body physiological data, environmental data and external data can be comprehensively collected, and a physiological state prediction model and a health risk identification model exclusive to a user are constructed in combination with historical records and user feedback, so that the personalized level of health management is improved, and the health risk of the user is improved. And through a chaos embedding biological rhythm prediction formula and a dynamic Bayesian health risk assessment formula, accurate prediction of the physiological state of the user and identification of the health risk are realized.

The invention discloses a state monitoring and real-time evaluation system and method for an energy storage power station, and relates to the field of energy storage power station monitoring, and the method comprises the steps: collecting multi-source heterogeneous data of the energy storage power station through constructing a hierarchical multi-scale sensing system; the collected multi-source heterogeneous data is preprocessed; inputting the processed data into the first model and the second model, carrying out classification judgment on the state of the energy storage battery, and outputting an evaluation result; the assessment result is transmitted to a safety early warning unit, the early warning unit maps and judges the state assessment result according to a preset risk grading rule, and early warning grade information and a risk label are output; and transmitting an evaluation result and an early warning result to an energy management system, and performing visual display and early warning prompt on the real-time state. An evaluation method combining a battery behavior mechanism and an artificial intelligence algorithm is introduced, dynamic prediction and trend analysis of key indexes are realized, and the accuracy and adaptability of the second model are improved.

The invention provides a safe communication method and system for a V2G charging pile and an energy storage system and a storage medium. The system comprises an energy management system EMS, a charging pile EVSE, a vehicle battery management system BMS and an energy storage battery management system BMS. A session key is established through certificate bidirectional authentication and ECDH negotiation, encryption and anti-replay are realized based on AES / SM4-GCM and additional authentication data, and digital signature verification and REST / TLS transmission are performed on a cross-domain instruction; and when the cloud is unreachable, the EVSE locally executes cooperative control and virtual droop voltage stabilization, or the EMS adopts model prediction control to issue an optimal charging and discharging track. According to the protocol, the confidentiality, integrity and availability of V2G communication and control and the stability of the micro-grid are improved.

The invention discloses an energy management system and method for a charging station, and relates to the technical field of management analysis, and the method comprises the steps: carrying out the charging demand space-time distribution prediction based on the operation data of the charging station, and generating a charging load distribution prediction map; topological structure analysis is carried out on charging station power grid connection nodes to obtain power grid topological parameters, and charging and discharging strategy optimization is carried out in combination with real-time electricity price fluctuation data and energy storage system state data to generate an optimal charging and discharging strategy; planning a charging and discharging path of the energy storage system based on the optimal charging and discharging strategy to obtain a charging and discharging path, and adjusting the charging and discharging path according to a power grid load balancing index to generate a power grid safety interaction strategy; and inputting the power grid safety interaction strategy into the charging station energy management digital twin model for strategy verification to obtain an optimized energy management strategy, and issuing the optimized energy management strategy to a charging station control terminal for execution. The method has the effect of improving the energy management efficiency of the charging station.

The invention relates to the technical field of energy systems, and discloses an energy system intelligent decision-making method, which comprises the following steps: acquiring four-flow data of an industrial energy system, and constructing a four-flow parameter model based on a production flow topological graph; constructing a multi-agent reinforcement learning environment, taking the four-flow parameter model as a state space of the multi-agent reinforcement learning environment, and determining an action space and a reward function of each agent; performing domain adjustment on the large language model based on the industrial energy system knowledge base; through combination of a multi-agent reinforcement learning model and a large language model, a decision strategy is dynamically adjusted according to real-time data feedback, and decision feasibility verification is performed based on a physical mechanism model. According to the method, the four-flow integrated parameter model is constructed, and a multi-agent reinforcement learning and large language model cooperation mechanism is introduced, so that the problems of data splitting and the like in a traditional energy management system are effectively solved, and the intelligent level of energy system decision making is improved.

The invention discloses a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization method and a photovoltaic-energy storage-charging multi-stage scheduling and market bidding optimization device. The method comprises the following steps: constructing a data-driven random environment model reflecting photovoltaic output, electricity price fluctuation and charging load uncertainty by adopting a mode of combining time sequence clustering and a non-homogeneous Markov chain based on historical operation data; modeling a scheduling and bidding problem of the optical storage and charging integrated station into a multi-stage Markov decision process model which comprises day-ahead decision and joint optimization of multiple intra-day rolling adjustment; a deep reinforcement learning algorithm is utilized to train the network, and a strategy regulation and control network which can adapt to various uncertain scenes and meet equipment physical constraints is obtained; and deploying the trained strategy regulation and control network in an energy management system to realize global coordinated scheduling and bidding of the optical storage and charging integrated station. According to the method, the economic benefit is remarkably improved, the robustness is greatly enhanced, the decision is globally coordinated and optimized, the real-time decision capability is strong, and the expandability and portability are good.

The invention discloses a diesel-storage intelligent micro-grid system. A diesel generator charges an energy storage unit or cooperatively supplies power when power supply is insufficient; the battery energy storage unit stores electric energy and supplies power to a load; the micro-grid energy management system dynamically coordinates the output proportion, and a harmonic suppression module suppresses harmonic interference; the load prediction module predicts load power change; the transient response control module deals with transient power impact; the power distribution system is connected to a main power supply and is switched to a diesel generator power supply mode when power supply of the main power supply does not meet requirements; when the diesel generator supplies power, the bidirectional converter converts electric energy into charging current of the energy storage unit and inhibits harmonic interference; when the energy storage unit supplies power, the direct current is converted into the alternating current required by the load. According to the invention, seamless switching of multiple energy sources, active stabilization of power fluctuation, flexible conversion of electric energy forms, optimization of energy distribution efficiency and comprehensive improvement of the survivability of the micro-grid can be realized.

The invention provides a hybrid vehicle energy management system and method based on a traffic state, a storage medium and a computer program product, and the system comprises the steps: constructing a congestion prediction model based on a deep learning model, the congestion prediction is used for predicting the traffic flow and the driving speed of a future time period according to the historical traffic flow data and the historical driving speed data; calculating a first congestion index according to the predicted traffic flow in the future period; calculating a second congestion index according to the predicted driving vehicle speed sequence of the future time period; performing weighted calculation on the first congestion index and the second congestion index to obtain a comprehensive traffic congestion index in a future time period; traffic jam types are divided according to the interval where the comprehensive traffic jam index is located; and determining an energy management mode and / or a target SOC of the hybrid vehicle based on the traffic jam type. According to the invention, multi-source data are collected and analyzed in real time, congestion is accurately quantified by using a deep learning algorithm, an energy distribution strategy of vehicles is dynamically adjusted, and the energy utilization efficiency is improved.

Certain aspects of the present disclosure relate to a local energy management system (LEMS) at local mixed power generating sites for providing grid services and grid service applications. The LEMS generally serves as a local power control agent for facilitating energy management at the local site level by controlling and leveraging a plurality of local assets deployed at the local site, and combining a plurality of generated power from each site which acts as its own virtual power plant for delivering grid services to the grid. In addition, the LEMS has the ability to effectively handle and fulfill energy and electrical objectives of the grid services, including regulation or demand response objectives from the grid, by conveying operational set points that control the power charge and discharge at each local asset in order to meet those objectives.

The invention discloses an energy consumption management system and method for a data center, and relates to the technical field of data center thermal management, and the method comprises the steps: carrying out the noise reduction of a structured data set through quantum harmonic oscillator coupling wavelet transform, and generating a real-time three-dimensional thermal distribution diagram through a Kriging spatial interpolation algorithm, solving a Navier-Stokes equation by combining the magneto-rheological grid wind resistance value, and extracting a heat flow vector; injecting pulse voltage into the microcapsule phase-change material layer according to the dual-channel execution instruction set to trigger phase-change heat absorption, reducing the viscosity of a target path by using a space gradient magnetic field, and generating a three-dimensional wind speed vector field; the real-time three-dimensional thermal distribution diagram is generated through the Kriging space interpolation algorithm, high-precision monitoring and analysis of the complex thermal environment of the data center are achieved, the accuracy of local hot spot recognition is improved, and therefore the energy utilization efficiency and the cooling effect are remarkably improved, and the purposes of reducing energy consumption and improving the operation stability and energy efficiency of the data center are achieved.

A system for AI-supported cybersecurity and regulatory compliance in energy distribution networks, consisting of: a hardware-embedded data acquisition module configured to intercept, capture, and time-stamp operational data streams and to control data traffic from SCADA (Supervisory Control and Data Acquisition) systems, AMI (Advanced Metering Infrastructure) systems, and energy management systems (EMS) via multiple communication protocols without operational latency; an FPGA-based deep packet inspection unit coupled with the data acquisition module, wherein the FPGA firmware is configured to perform line rate filtering, protocol decomposition and metadata extraction of the acquired data and forwards preprocessed packet data to an AI processing unit; an AI processing unit consisting of a multi-core central processing unit (CPU), a dedicated AI accelerator selected from a graphics processing unit (GPU) or a tensor processing unit (TPU), and a volatile memory buffer; a response orchestration module that is communicatively coupled with network management devices and operations controllers, wherein the response orchestration module is configured to perform automated security and compliance remediation measures, including network isolation of compromised segments, enforcement of protocol encryption, and privilege revocation; and an immutable audit logging subsystem configured to record all detected events, compliance assessments, and corrective actions in a blockchain-based distributed ledger, with each log entry cryptographically anchored with a secure hash value and digitally signed with keys stored in a secure hardware enclave.

The invention relates to a photovoltaic low-carbon park smart energy management system and method based on digital twinning. Accurate mapping and simulation analysis of a park energy system are realized by constructing a digital twinborn model, dynamic optimization scheduling of energy equipment is realized by adopting a multi-agent cooperative control algorithm, an energy optimization strategy is formulated by taking low carbon as a target, the health state of the equipment is predicted by utilizing machine learning, and an intelligent operation and maintenance plan is generated. And decision support is provided through energy big data analysis. The system integrates multi-source data, and efficient utilization and low-carbon operation of park energy are realized through the steps of digital twin modeling, intelligent agent collaboration, low-carbon scheduling, equipment prediction, big data analysis and the like. Practical application shows that the system can improve the photovoltaic efficiency by 8%, reduce the energy cost by 18%, reduce the carbon emission intensity by 30%, and significantly improve the park energy management level and the low-carbon degree.

The invention discloses an industrial and commercial energy storage control method and system based on countercurrent and demand, and the method comprises the steps: collecting the operation data of industrial and commercial energy storage and a power grid, and obtaining plan data from an energy management system; based on the operation data, whether the battery management system breaks down is judged, if yes, a safe shutdown mode is switched into, and the charging and discharging power is set to be zero; judging whether countercurrent or demand overrun exists, if so, switching to a countercurrent charging mode, and adjusting the discharging power in real time; if the demand exceeds the limit, switching into a demand discharge mode, and adjusting the discharge power in real time; and if the battery management system has no fault, no countercurrent and no unnecessary quantity overrun, switching into a plan mode, and obtaining and executing charging and discharging power based on plan data. Operation modes are switched according to priorities through real-time data, so that the safety of an industrial and commercial energy storage system is ensured under complex working conditions, certain economy is ensured, and coordinated control of the energy storage system is realized.

A multiport converter designed to supply direct current loads comprising one or more energy sources, a battery, a multiport DC-DC converter connected to the energy sources and to a load, an IoT device for communication and control of the system. The multiport DC-DC converter further comprises an energy management system for selectively controlling the charging or discharging of the battery and the amount of energy directed to the load based on predictions of energy generation from the energy sources, based on a load demand and based on optimized scenarios. The multiport converter is used in green hydrogen generation plants powered by renewable energy sources, such as wind and photovoltaic energy; and a system capable of self-managing and ensuring better use of energy from the sources and better efficiency of green hydrogen generation.

The invention discloses an electric vehicle energy management method and system based on vehicle speed prediction, and relates to the technical field of electric vehicle energy management, and the method comprises the steps: collecting vehicle driving data to construct a state vector, constructing a quantile LSTM vehicle speed distribution predictor, and predicting the quantile vehicle speed track of each step in the future; mapping the quantile vehicle speed track into traction power distribution through longitudinal dynamics, and carrying out battery power, current and SOC quantile rolling prediction; predicting and defining predicted time domain energy consumption based on the battery power quantile so as to construct an MPC optimization objective function of the CVaR risk constraint; constructing a deterministic constraint set based on a quantile prediction trajectory, defining a terminal risk cost according to a predicted terminal state, and merging the terminal risk cost into an MPC optimization target to obtain a final target function; the robustness and adaptability of the electric vehicle energy management system are obviously improved, especially in complex and uncertain road and traffic environments.

The invention provides a multi-element hybrid energy storage system for supporting a new energy station. The multi-element hybrid energy storage system comprises a wind / light new energy station home weather forecast system, a wind / light new energy station home weather forecast system, a wind / light new energy station, a wind / light new energy station, an intelligent energy management system and a plurality of energy storage systems, wherein the multiple energy storage units jointly form a multi-element hybrid energy storage system; the wind / light new energy station is connected to the intelligent energy management system; the multi-element hybrid energy storage system is connected to the intelligent energy management system; the wind / light new energy station is directly connected to the multi-element hybrid energy storage system and provides charging power for the multi-element hybrid energy storage system; the multi-element hybrid energy storage system is connected to a traditional large power grid, and off-peak electricity is used for providing charging power for the ternary hybrid energy storage system. The sensing information flow output by the wind / light new energy station and the intelligent energy management system is bidirectional information flow. According to the invention, the energy utilization efficiency and the new energy consumption capability can be improved.

The invention discloses an intelligent micro-grid electric energy quality adjusting system based on hybrid energy storage, and the system comprises a hybrid energy storage unit which is used for storing and releasing adjusting electric energy; the converter module is connected with the hybrid energy storage unit and a power grid and is used for realizing conversion of alternating-current and direct-current electric energy and injection of compensation energy; the intelligent control unit is in communication connection with the converter module and the hybrid energy storage unit and is used for detecting power quality abnormity in real time and generating a compensation control instruction; the energy management system is in communication connection with the intelligent control unit and is used for formulating a long-term charging and discharging strategy of the hybrid energy storage unit based on the multi-source information; the communication and interface module is used for realizing data interaction between the units and between the system and an external network; the intelligent control unit is configured to dynamically distribute the output proportion according to the real-time electric energy quality state, the load demand and the energy storage state. The system is high in response speed, wide in adaptability, low in cost, high in integration level and convenient to expand and maintain; and meanwhile, the electric energy quality is greatly improved.

The invention relates to the technical field of energy management, in particular to a dynamic energy management method and system for a central air-conditioning system based on a large model, and the method specifically comprises the steps: outputting a short-term load demand prediction curve in a rolling manner through a time sequence depth prediction model; carrying out inversion calculation on equivalent heat capacity and equivalent heat resistance of the building material through a physical information neural network, and quantifying thermal inertia parameters of the building material in real time; evaluating a phase change delay time coefficient of the building material; obtaining a photovoltaic power generation power prediction curve of a power grid system and a power grid time-of-use electricity price signal, and determining an optimal energy charging and discharging time window of the building material; and adopting a multi-objective decision algorithm to decide and generate an optimal operation strategy of the central air-conditioning system. The problem that in the prior art, an energy management system matched with the thermal dynamic characteristics of a building is lacked is solved.

The invention provides a load tracking control method for an energy storage system in an oil field scene, which realizes cooperative control of constant-power operation of a generator and rapid compensation of energy storage. The system comprises the following mechanisms: a diesel generator; the energy storage system comprises an energy storage converter PCS and an energy storage battery pack; the AI sampling module is used for respectively sampling implementation voltage and current data of the diesel generator and the load through a voltage transformer PT and a current transformer CT; the EMS system side power calculation module comprises an energy management system power calculation module and a power conversion system; and an oil field load; an output end of a diesel generator and an output end of an energy storage converter PCS are converged to a load output line and then are connected to an oil field load, an AI sampling module also samples voltage and current data of the load output line, the diesel generator and an energy storage system supply power to the oil field load at the same time, and an engine operates in a high-efficiency interval. And the energy storage system bears instantaneous power fluctuation.

The invention discloses a self-adaptive adjustment method and system based on an energy management system, and relates to the technical field of energy management, and the method comprises the following steps: collecting multi-dimensional heterogeneous data including weather conditions, economic situations, social activities, user behavior patterns and energy price fluctuations; according to the method, the multi-dimensional heterogeneous data is fused, the space-time attention mechanism and the graph neural network are utilized to construct the high-precision prediction model, ultra-short-term to medium-and-long-term energy demand dynamic prediction is realized, the problem of insufficient multi-factor fusion is solved, the hierarchical response strategy is triggered based on the prediction result, and the prediction efficiency is improved. Distributed energy storage, a flexible load and a production end are linked to perform dynamic power adjustment, strategy autonomous optimization is realized through reinforcement learning, the adaptive adjustment ability to a complex scene is improved, an adaptive adjustment closed loop is formed through real-time error calculation and model parameter correction, and adjustment parameters and model weights are corrected in real time, so that the real-time performance of the system is improved. And the stability and accuracy of long-term operation of the system are ensured.

The invention discloses a production and manufacturing enterprise cluster chain type interaction carbon emission reduction method based on dynamic electrical carbon factors, and relates to the technical field of industrial carbon emission reduction and energy management systems. According to the method, a four-layer decision-making framework of a global coordination layer, a cluster coordination layer, an enterprise execution layer and an equipment control layer is constructed, and dynamically updated electric carbon factors are introduced, so that accurate identification, carbon emission accounting and collaborative optimization of enterprise clusters on a product chain are realized. The core of the method is that carbon quota allocation, production scheduling and resource allocation are carried out among hierarchies by utilizing a multi-objective and double-layer optimization model, so that the total carbon emission of a cluster is minimized while the economic benefit is ensured. A dynamic monitoring and feedback mechanism is also established, the power carbon factor weight can be adjusted and a resource allocation plan can be triggered according to real-time data such as the proportion of clean energy of the power grid, closed-loop regulation from a macroscopic strategy to microscopic operation is formed, the overall carbon footprint of an industrial chain is effectively reduced finally, and the participation barrier of small and medium-sized enterprises is broken.