3159 results about "Energy system" patented technology

The invention discloses a park energy router multi-agent coordination control method based on a dynamic game model, and relates to the technical field of energy internet optimization control. The method comprises the following steps: according to real-time meteorological data and historical load fluctuation characteristics, identifying multi-energy supply and demand time sequence conflicts, constructing a multi-subject game framework, quantifying dynamic coupling strength of meteorological parameters and load fluctuation, and generating a dynamic constraint boundary; then, combining dynamic constraints and operation states, dynamically adjusting economic, reliability and low-carbon index weights, superposing a risk penalty term of a future supply and demand difference value, and constructing a coordination revenue function; when the photovoltaic output and the thermal load change rate exceed threshold values, an emergency game mode is triggered, Nash equilibrium is solved through a distributed optimization algorithm, and a coordination control instruction is output; and finally, collecting execution data, calculating a supply and demand deviation correction rate and an income balance degree, adjusting a coupling weight and a constraint boundary based on feedback, and realizing multi-subject benefit dynamic balance and stable and efficient operation of the park energy system.

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

The invention relates to an offshore energy platform coordinated scheduling method based on multi-energy complementation and hierarchical optimization, which combines multi-energy complementation characteristic modeling, multi-target opportunity constraint optimization and rolling optimization, and realizes offshore multi-energy coordinated scheduling by constructing a hierarchical decoupling optimization and control system. Based on prediction and historical data of multiple types of energy such as offshore wind power, photovoltaic energy and tidal energy, complementarity and flexibility of the energy are quantified, high-quality data support is provided for scheduling optimization, a day-ahead layered optimization model containing renewable energy priority consumption and flexible standby configuration is constructed, and a medium-and-long-term output strategy is formulated. Output of various energy sources is dynamically adjusted through a rolling optimization mechanism, and flexible response to renewable energy fluctuation is achieved. And finally, second-level frequency and voltage support is realized by using a virtual synchronous machine and droop control, and the self-adaptive capability of the system is enhanced. According to the invention, the cooperative regulation capability and operation stability of the offshore platform multi-energy system can be effectively improved, and the dependence on a traditional standby power supply is reduced.

Disclosed in the present invention are a low-carbon operation method and system for an integrated electric-thermal energy system under a demand response. The method comprises: collecting data in a combined heat and power generation unit, establishing a low-carbon demand response model on a demand side, and constructing an integrated electric-thermal energy system model; on the basis of the integrated electric-thermal energy system model, establishing an optimal scheduling model that satisfies an operational constraint condition and aims to maximize the revenue of an energy supply side; establishing a carbon emission flow model to calculate a carbon emission flow, mapping the carbon emission of the energy supply side to a load side, and establishing the low-carbon demand response model on the demand side by maximizing the consumer surplus as an objective; and using a genetic algorithm to solve the optimal scheduling model that aims to maximize the revenue of the energy supply side, and the low-carbon demand response model on the demand side, obtaining a low-carbon optimization scheduling plan of the integrated electric-thermal energy system, and implementing scheduling of the integrated electric-thermal energy system. According to the present invention, the carbon emission of the integrated electric-thermal energy system is reduced, the cost of energy conservation and emission reduction is reduced, and the low carbon performance and the economical efficiency are both considered.

The invention provides a rebalance dynamic adjustment method applied to a multi-source system and a service system, and relates to the technical field of smart energy, and the method comprises the steps: collecting a real-time operation data set of the multi-source energy system, covering energy supply, load demand and system operation state features, carrying out the dynamic supply and demand deviation feature extraction, and carrying out the dynamic adjustment of the dynamic supply and demand deviation features; a feature set containing supply, demand fluctuation and system stability deviation is generated, then a pre-trained multi-source collaborative prediction model is called, and a multi-source collaborative prediction result containing energy supply and load demand trend prediction features is generated based on the feature set; and dynamic adjustment strategy matching is carried out according to a multi-source collaborative prediction result and real-time operation data, and a strategy set of energy output adjustment, energy storage charging and discharging, load priority distribution and the like is obtained. And finally, executing an adjustment strategy and obtaining feedback data, optimizing parameters of the multi-source collaborative prediction model based on the adjustment strategy, triggering closed-loop adjustment operation, and realizing rebalance dynamic adjustment of the multi-source system.

The invention relates to the technical field of energy scheduling, in particular to an energy system capacity optimization and mobile resource space-time decoupling cooperative scheduling method, system and device under extreme disaster disturbance and a medium. Load change parameters are coupled to form a disaster-causing response database; renewable energy output fluctuation and user load demand deviation are converted into fuzzy variables, and a capacity configuration optimization model is established in combination with a disaster-causing response database to generate multi-energy complementary capacity configuration; mobile resources are introduced to establish an elastic lifting scheduling model, and an elastic lifting scheme is generated by implementing damaged road network topology reconstruction of the mobile resources, space-time transfer chain modeling and action state coordinated regulation and control. According to the method, collaborative decision-making of capacity optimization of equipment and dynamic scheduling of mobile resources is driven through the disaster-causing response database, and dual elastic gains of fixed resource optimization and after-disaster quick response of the mobile resources under extreme disaster events are achieved.

The invention provides an intelligent scheduling and control method and device for an integrated energy system. According to the method, power, gas and heat resource operation data are acquired, multi-scale layered modeling is performed according to a time scale and a space scale, and a power resource state space model, a gas resource flow continuity model, a heat resource heat balance model and a multi-energy coupling characteristic constraint model are established; carrying out feature extraction and dimension reduction representation by adopting a deep auto-encoder network; cooperative training of multiple groups of cognitive models is carried out through a split hierarchical federal learning framework, and a global intelligent model is obtained; constructing a neural architecture search network with a hybrid bionic learning rule, setting a hierarchical scheduling target, and generating a hierarchical intelligent scheduling strategy; and a fault-tolerant control mechanism is constructed, and error detection and correction of operation deviation are realized. According to the invention, multi-time scale collaboration, collaborative learning under multi-device group privacy protection and high-reliability fault-tolerant control are realized, and the operation efficiency and reliability of the integrated energy system are remarkably improved.

The present disclosure discloses an optimization control method for an integrated energy system based on a physical-informed neural network, which comprises the following steps: S1, constructing an a solar-electricity-heat-gas integrated energy system optimization control model; S2, generating a node connection relation matrix based on the network topology structure of the integrated energy system; S3, constructing a deep graph neural network model with physical-informed fusion; S4, constructing a loss function of the deep graph neural network model with physical-informed fusion; and S5, training a physical-informed neural network model according to the historical operation data to be used for system optimization control. The present disclosure can effectively deal with the influence of uncertainty of renewable energy and unexpected situations on the energy system, thereby ensuring the safe and stable operation of the integrated energy system.

The invention discloses a distributed energy collaborative scheduling optimization method based on edge computing. According to the method, a plurality of edge computing nodes are deployed in a distributed energy system, a multi-protocol compatible OPC UA communication channel is constructed through protocol conversion middleware to collect data, and after the edge computing nodes clean and normalize the data, a preliminary scheduling scheme is generated through an improved genetic algorithm; the improved genetic algorithm is optimized through cooperation of a deep reinforcement learning model and an adaptive attenuation mechanism. And uploading the preliminary scheduling scheme to a cloud end, and obtaining a global optimal scheduling strategy through a multi-target particle swarm optimization algorithm. And the cloud carries out credible evidence storage on the global optimal scheduling strategy abstract value through an alliance chain smart contract, and establishes a PoA consensus mechanism. And when the communication is interrupted, the edge computing node starts the local emergency scheduling module, and incremental data synchronization is performed after the communication is recovered. The distributed energy scheduling optimization problem is effectively solved, the energy utilization efficiency is improved, and the system stability and reliability are enhanced.

The invention provides a smart park Internet of Things distributed energy intelligent analysis and control method and device, and relates to the technical field of smart park energy management, and the technical scheme is characterized in that the method comprises the steps: obtaining energy consumption data, meteorological data, distributed energy power generation data and user behavior data of each region of a smart campus; generating a basic load prediction value and an event-driven load prediction value according to the acquired data; making a day-ahead energy scheduling plan; when a deviation exists between the actual energy consumption condition and the predicted value, adjusting the energy scheduling plan by adopting a rolling scheduling strategy; and executing the adjusted energy scheduling plan, and controlling the distributed energy system and the energy consumption equipment to optimize campus energy utilization. The smart park Internet of Things distributed energy intelligent analysis and management and control method and device provided by the invention have the advantages of accurately predicting complex and changeable energy consumption demands and utilizing the distributed energy to the maximum extent.

The invention provides a computing-electric-thermal coupled data center integrated energy system collaborative optimization method, and relates to the field of data center energy-saving management and control, and the method comprises the steps: firstly obtaining real-time data collected by a data center; secondly, a data center load model is constructed based on the relation among real-time data quantification calculation power, electric power and heating power; constructing a collaborative optimization model according to the data center load model, and solving the collaborative optimization model to obtain an optimization result; and finally, adjusting calculation task distribution, a power supply strategy and cooling system operation parameters according to an optimization result to form a calculation-electricity-heat coupled data center integrated energy system collaborative optimization strategy. According to the calculation-electricity-heat coupled data center comprehensive energy system collaborative optimization method, collaborative optimization is carried out on the data center energy system through multi-energy complementation, and energy conservation and efficiency improvement of the data center are achieved.

The invention relates to the field of distributed energy, in particular to a distributed energy intelligent allocation method based on Internet of Things big data in a smart city, which comprises three steps of data acquisition, processing and output. Firstly, photovoltaic, energy storage and charging pile real-time data are obtained through a multi-protocol analysis engine, and meteorological cloud charts, traffic thermodynamic diagrams and building energy consumption historical data are combined. And secondly, constructing a dynamic weight model by utilizing a space-time joint attention mechanism, generating a global wind and light output prediction network through federated learning, constructing an elastic scheduling strategy library by adopting a multi-target dynamic game algorithm, optimizing a scheduling model on a digital twin platform, and finally generating a real-time scheduling scheme through a two-stage increment optimization architecture. The method effectively improves the efficiency, economical efficiency, reliability and environmental protection property of an energy system, provides a powerful tool for urban managers, guarantees stable, economical and clean energy supply for residents, and promotes sustainable development of smart cities.

To provide a system and an apparatus for implementing a mobile energy system.SOLUTION: In an aspect, a mobile energy system includes a cabinet having an interior space defined by an outer surface of the cabinet, and the interior space can include a battery storage area configured to receive a battery energy storage system having at least 2 kWh of storage capacity. The mobile energy system can also include an item storage area configured to receive and store one or more other items, a connector configured to connect the battery energy storage system located in the battery storage area to an external power source, and a charging cable configured to connect the battery energy storage system to a charging port of an electric vehicle.SELECTED DRAWING: Figure 4

The invention provides a minute-level intelligent scheduling method and system for hydrogen-based energy prepared from renewable energy, and relates to the technical field of energy management and intelligent scheduling, and the method comprises the steps: obtaining data of power generation equipment, hydrogen production equipment and a hydrogen storage system, adaptively adjusting the length of a dynamic time window based on the power generation fluctuation frequency, and generating a fluctuation feature sequence; and constructing a minute-level intelligent scheduling model, optimizing a hydrogen production power regulation instruction by utilizing a mutual feedback correction parameter, calculating an optimal power transfer path when an abnormality is detected, and executing stepped load reduction turn-off. The operation stability of the hydrogen production system can be improved, efficient utilization of generated power is achieved, and the operation risk of the system is reduced.

The invention discloses an energy management three-dimensional visualization method and system based on digital twin and Internet of Things, and relates to the technical field of energy management visualization, and the method comprises the steps: obtaining multi-source heterogeneous energy data of an energy management object; constructing a virtual mirror image model corresponding to the physical energy system based on the multi-source heterogeneous energy data and the digital twin model; generating a three-dimensional visual scene based on the virtual mirror image model so as to map the geographic position distribution of the physical energy system, and presenting the real-time energy consumption state, the energy flow transmission path and the environmental parameter change of the equipment; receiving a user interaction instruction, carrying out interaction operation on the target area or equipment, calling the associated multi-source heterogeneous energy data, and carrying out superposition display on the associated multi-source heterogeneous energy data; energy consumption abnormity early warning information or an equipment regulation and control strategy is generated in combination with the energy management optimization rule, and the equipment regulation and control strategy is input into the physical energy system to be executed. The invention provides an energy management method capable of realizing multi-source data fusion and accurate mapping of a physical system.

The invention provides a wind-light-water multi-energy system short-term scheduling decision-making method considering source load uncertainty. The method comprises the following steps: acquiring a source load scene set with relevance by applying a hierarchical normalized flow vine Copula generator based on an hour-by-hour source load time sequence; carrying out scene reduction on the generated source load scene set by adopting a density sensitive synchronous reduction algorithm, and extracting a typical source load scene subset; inputting the typical source load scene subset into a rigid-elastic polymorphic constraint transfer double-layer scheduling model, and solving the model by adopting a double-channel elite iterative algorithm to obtain a multi-target solution; and carrying out risk feature decoupling and risk feature tensor construction on the multi-target solution, thereby obtaining an optimal scheduling scheme by adopting an entropy-risk topological mapping decision model. According to the method, the economical efficiency, the safety and the decision-making credibility of short-term scheduling of the system are improved.

The invention provides an integrated energy system dynamic scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: constructing an agent for the scheduling of an integrated energy system based on a Markov decision interaction architecture, and the agent comprises a state space, an action space and a reward function; dividing the integrated energy system into multiple environments with different characteristics in different time periods by using K-means clustering; capturing environmental characteristics of the integrated energy system in a scheduling period by using a prediction model, and matching an intelligent agent of a corresponding environment; setting an economical depth deterministic strategy gradient model for each environment to carry out interactive learning and decision making, and forming a distributed learning-distributed execution architecture; and carrying out dynamic scheduling on the integrated energy system by utilizing the distributed learning-distributed execution architecture. Compared with other deep reinforcement learning methods and traditional optimization methods, the method provided by the invention can obtain a more economic and stable scheduling result.

The invention relates to the technical field of micro-grid energy dispatching, in particular to a micro-grid energy dispatching carbon emission reduction system based on reinforcement learning dual-objective optimization. The data acquisition module is used for acquiring information such as equipment energy consumption data, environmental parameters and carbon emission coefficients in the micro-grid in real time; the data processing module is used for cleaning acquired data, extracting features and performing standardization processing; the intelligent scheduling module is used for extracting information features and a deep reinforcement learning algorithm, optimizing by using a model to obtain an optimization strategy, and forming a scheduling instruction set; the execution feedback module is used for dynamically scheduling the operation state of equipment in the micro-grid according to the scheduling instruction set; the man-machine interaction module provides a user interface and is used for displaying the carbon quota use condition and the system operation state and allowing a user to input an adjustment demand; compared with the prior art, the energy system of the park is used as a unified network, mutual influence of multiple systems is considered, accurate scheduling is achieved, environmental protection and high efficiency are achieved, and the operation cost is reduced.

The invention discloses a multi-park cross-space-time comprehensive energy consumption multi-objective optimization method and system based on deep learning, and the method comprises the steps: carrying out the feature extraction of data in the operation of a multi-park energy system through an extended long-short term memory network model, and constructing a multi-park multivariate energy efficiency evaluation index model according to the extracted multi-dimensional energy consumption features; evaluating the correlation degree of the operation process of the multi-park integrated energy system, and constructing an energy complementary relation matrix; according to the multivariate energy efficiency evaluation index model and the energy complementary relation matrix, constructing a multi-park system multi-target optimization model; solving the optimal solution of the optimization model under the operation constraint through a Transform-based multi-target particle swarm optimization algorithm, and obtaining an energy operation scheduling strategy of the integrated energy system of the plurality of parks; user interaction feedback is adopted in optimization, so that the result better meets the actual demand and target of the user; according to the method, cross-space-time comprehensive energy multi-target optimization of multiple parks can be realized, and the comprehensive energy utilization efficiency of the parks is improved.

The invention discloses an integrated energy system real-time regulation and control method and system considering spot market electricity price, and belongs to the technical field of integrated energy system operation regulation and control optimization. The method comprises the following steps: solving output power, electrochemical energy storage charging power, electrochemical energy storage discharging power and to-be-adjusted load capacity of a CCHP unit at a moment t according to a dynamic electricity price of an electric power spot market at a moment t-1, and solving power generation cost, power transmission blocking cost and marginal cost of loss of the unit; and summing the auxiliary service cost constructed according to the auxiliary service demand fluctuation quantity at the moment t to obtain the dynamic electricity price of the electric power spot market at the moment t, constructing a real-time optimization regulation and control model of the integrated energy system, and solving the model in combination with a model prediction control theory and a machine learning algorithm to obtain an optimal regulation and control strategy of the integrated energy system. The economical efficiency, the reliability, the timeliness and the accuracy of regulation and control of the comprehensive energy system in the electric power spot market are improved, and the response will of a user is improved.

The invention relates to the field of power systems, in particular to a multi-energy complementary power supply network cooperative control method and system. The method comprises the following steps: collecting dynamic characteristic data of wind power, photovoltaic power, energy storage and load through a sensor group, and generating a multi-energy state characteristic matrix with a timestamp; and matching the feature matrix with a dynamic response feature library, and outputting a dynamic feature parameter set. Based on the set, a multi-objective optimization model is constructed, second-level frequency deviation suppression, minute-level tie line power stabilization and hour-level economic dispatching are covered, and a cooperative control parameter set is generated. And generating a thermal power generating unit compensation amount, an energy storage charging and discharging sequence and an interruptible load priority list. After execution, power grid deviation data are collected, a dynamic deviation value is calculated, and when a threshold value is exceeded, feature library parameters are corrected and loop optimization is carried out. According to the invention, the problem of insufficient cooperative control of the multi-energy system in the prior art is solved, and the stability and economy of the power grid are improved.

The invention provides a harbor district energy double-layer coordination optimization method oriented to multiple interest subjects. The method comprises the steps of performing fusion processing and data cleaning on real-time operation data, historical operation data and environmental parameter data of a harbor district energy management system; performing multi-scale decomposition and feature importance evaluation by adopting a time sequence decomposition algorithm; probability modeling and multi-scale prediction are carried out on the basis of feature recognition uncertainty factors; constructing an upper-layer government regulation and control model and a lower-layer harbor district-ship east game model, and calculating a balance coefficient; and performing monthly strategy optimization, weekly scheduling optimization and hour-level real-time control based on the balance coefficient. By constructing a complete technical chain of data processing, feature extraction, prediction modeling, game optimization and real-time control, dynamic balance of benefits of the government, the harbor district and the ship eastern is realized, and the operation efficiency and the control precision of the harbor district energy system are remarkably improved.

Lowest cost usage scheduling of an energy system during a time interval of interest is achieved by utilizing two components of learning and optimization. First, a number of learning approaches including Linear Least Square Regression (LLSR), Auto Regressive Integrated Moving Average (ARIMA), and Multi-Layer Perceptron Deep Learning (MLPDL) are used to forecast energy production and storage of the energy producing components of a given group of energy components as a function of historical production data and weather data collected from weather and geo reports. Then, an optimization problem is formulated to create the optimal schedule of energy usage during time intervals of interests for the given group of energy components subject to scheduling and equipment constraints. Two alternative solutions, namely Simplex Branch and Bound (SBB) and Interior Point Branch and Bound (IPBB) are presented to solve the optimization problem. Accordingly, integrated iterative methods, programs, and systems are described aiming at minimizing the cost of energy consumption for the given group of energy components within time intervals of interest.

Provided are a method, a system, a storage medium, and an electronic device for selecting a combination of equipment in an elementary energy full chain that realizes the conversion and interaction of various types of energy and improves the flexibility of the energy system. [Solution] The method obtains basic technical and economic index parameters of equipment in the hydrogen energy full chain, classifies the basic technical and economic index parameters based on the performance differences of different types of equipment in the hydrogen energy full chain, performs a comparative analysis of each type of equipment in the hydrogen energy full chain according to the classification, and based on the results of the comparative analysis, comprehensively considers the performance difference indexes and operating characteristics of the equipment, establishes constraints for the classification indexes corresponding to each equipment, determines one of the classification indexes as part of the target function, finds the optimal results under different renewable energy penetration rates, and obtains a rule for selecting equipment combinations in the hydrogen energy full chain in a high-proportion renewable energy system.

The invention discloses a distributed energy system source load coordinated optimization method based on a quasi-potential game method, and the method comprises the steps: constructing a distributed energy system model, inputting system parameters, and predicting renewable energy power generation and initial load demands. In a source side optimization stage, a leader layer potential function of a quasi-potential game is established by taking minimization of source side cost # imgabs0 # as a target, and an initial power generation plan is generated by comprehensively considering economical efficiency and carbon emission constraints; and then, based on a scheduling result, calculating a carbon potential epsilon t of each node and a dynamic carbon emission factor # imgabs1 # of each stage, optimizing a load side response based on an LCDR scheme, constructing a local potential function of a follower layer, reflecting a relationship between a user profit maximization target and carbon emission, and adjusting user behaviors through a distributed decision. And the updated load demand is fed back to the source side, and the source side optimizes the output plan of each unit based on the updated load, so that an iterative process of source side potential function optimization-load side equilibrium response is formed, and an optimal scheduling strategy and scheduling result of the energy supply side are obtained. According to the framework, the global consistency requirement of a traditional potential game is relaxed, independent optimization of source-load two sides under the guidance of respective potential functions is allowed, and a Nash equilibrium state is finally achieved only by ensuring monotonous convergence of total potential energy of a system in an iteration process. According to the method, the convergence advantage of the potential game is reserved, the method is also adapted to the characteristics of a source-load heterogeneous decision subject, efficient consumption of renewable energy and collaborative optimization of carbon emission are realized through bidirectional transmission of the carbon potential signal, and the overall efficiency of the system is remarkably improved.

The invention relates to an electricity-carbon cooperative scheduling optimization method and device for a low-carbon park integrated energy system, and the method comprises the steps: carrying out the cooperative prediction of a multi-state parameter through employing a panoramic situation deduction model, and generating a panoramic dynamic situation scene set; establishing an electricity-carbon cooperative scheduling model considering a carbon transaction mechanism, and deeply embedding the real-time carbon cost into a target function to carry out Pareto optimization of economic cost and carbon emission cost; an electricity-carbon cooperative scheduling model is converted into a standard mixed integer linear programming model, a situation deduction-day-ahead optimization-rolling correction hierarchical calculation framework is adopted to decompose a cooperative scheduling optimization problem to different time scales for decision making, and a global optimization plan is made on the day-ahead layer based on a panoramic dynamic situation. Deviation is corrected on line through rolling optimization in the intraday layer; and the integrated energy system executes the corrected scheduling plan. Compared with the prior art, the method has the advantages that the consumption rate of renewable energy sources can be remarkably increased and carbon emission can be effectively reduced while the operation economy of the system is ensured.

The invention relates to a low-carbon park comprehensive energy system multi-agent collaborative carbon responsibility allocation method, which comprises the steps of collecting multi-source data of a park, and performing multi-energy flow optimization scheduling by taking the minimum total operation cost of a comprehensive energy system as a target; constructing a dynamic carbon flow distribution matrix by utilizing a bidirectional carbon flow dynamic correction model considering carbon flow transfer caused by an energy storage charging and discharging process; a load carbon responsibility decoupling distribution model is adopted to decouple the total load into a plurality of sub-load categories, and different carbon responsibility distribution coefficients are given to each category of sub-load basis; the method comprises the following steps: performing multi-agent collaborative optimization by taking each benefit party in a park as a game participant, taking a Nash bargaining as a collaborative optimization framework, taking maximization of utility gains of all participants as an objective function, and taking a Shapley value as a carbon responsibility distribution basis, and obtaining a carbon responsibility distribution scheme of each agent in combination with a user carbon responsibility distribution coefficient. Compared with the prior art, the method ensures fairness, efficiency and operability of a carbon responsibility allocation scheme.

The invention relates to a virtual power plant scheduling method considering source-load coupling and multi-agent game. The method comprises the following steps: establishing a multi-temporal-spatial-scale model of a multi-energy system in a virtual power plant; the uncertainty of the source load is quantified; on the basis of quantification of source load uncertainty and a multi-temporal-spatial-scale model, a multi-subject scheduling model under a master-slave game framework is constructed, and in the multi-subject scheduling model, a VPP operator serves as a leader, and the market is regulated and controlled through dynamic electricity price and heat price; an energy supply aggregator, a user side aggregator and a carbon processing aggregator are used as followers, unit output, load adjustment and carbon processing strategies are optimized respectively, the two parties perform multi-stage dynamic gaming to form balance, and the user side aggregator comprises CSRLA and EVA; and solving the multi-subject scheduling model to obtain a virtual power plant scheduling optimization strategy. Compared with the prior art, the method has the advantages of realizing dynamic balance of interests of multiple parties, privacy protection and the like.

The invention provides a self-adaptive algorithm optimization control system and method for intelligent energy management. The system comprises a data acquisition unit which acquires operation data of various energy devices in real time; the model prediction unit predicts renewable energy output and load demand within a preset time based on a machine learning algorithm by using historical data and operation data collected in real time to obtain a prediction result; the optimization engine unit constructs a multi-objective optimization model according to the prediction result and the operation data, solves the multi-objective optimization model by adopting a heuristic intelligent algorithm, and generates an optimal energy scheduling strategy; the adaptive control unit analyzes the optimal energy scheduling strategy into a control instruction which can be executed by each energy device, issues the control instruction to an execution unit, and monitors the execution state and system response of the energy device at the same time; and the execution units adjust respective operation states according to the control instruction, and cooperatively realize optimal scheduling of the whole system. The method can dynamically adapt to changes of an energy system, and the energy utilization rate is increased.

The invention provides a deep sea energy system life prediction method, and relates to the technical field of life prediction, and the method specifically comprises the steps: collecting the electrical characteristics of a battery and deep sea environment stress, and carrying out the standardization processing; constructing a deep sea environment degradation model, and constraining the physical characteristics based on the deep sea environment model; inputting the data into a physical branch network to extract consistency constraint features and physical initial priori; the standardized data, the physical enhancement features and the consistency constraint features are input into a lightweight data branch network, and deep residual information formed after the lightweight data branch network is pre-trained is transmitted to a physical branch network; and the RUL priori estimation output by the physical branch network and the data correction item output by the data branch form a final prediction, the final prediction is input into a Bayesian framework to derive posteriori distribution, and uncertainty quantization is carried out. According to the technical scheme, the problems of low service life prediction accuracy and relatively high computing resources of the deep sea energy system in the prior art are solved.