1566 results about "Energy system" patented technology

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 the technical field of energy system control, and discloses a distributed cooperative fault-tolerant control method for a multi-energy-station cooling and heating system, and the method comprises the steps: enabling each energy station to measure and calculate a residual error based on a physical model and a sensor, forming a normalized health index, and carrying out the neighborhood broadcasting; carrying out robust anomaly judgment by utilizing a neighborhood median and a median absolute deviation, and isolating an abnormal site; cost coefficients are automatically generated for available sites according to equipment maneuverability, and control redistribution of minimum disturbance is solved and implemented through distributed consistency optimization; an exponential weighting updating mechanism of a residual error sequence is adopted to carry out online self-adaption on a noise baseline and a judgment threshold value so as to realize closed-loop self-calibration; when sensor abnormity, equipment errors or individual station faults occur in the multiple energy stations which work cooperatively, distributed rapid identification is achieved, faults are isolated, energy supply is redistributed with minimum disturbance, system control requirements are met, and equipment safety constraints are kept.

The invention discloses an autonomous decision-making system of a multi-level intelligent agent in an urban area and an operation method thereof. Each end-side decision-making unit broadcasts an equipment state variable to a side-side decision-making unit; the side decision-making unit determines a global reference state and generates a control instruction of each end decision-making unit; after the equipment executes the control instruction, each end-side decision-making unit updates an equipment state variable according to the real-time operation data of the equipment, and when an abnormal event is judged to occur, a side decision-making unit predicts a decision-making variable of each piece of equipment based on a collaborative optimization model of an equipment target and an urban area target; the cloud regulation and control platform predicts a global consistency variable based on the collaborative optimization model of all the side decision units; predicting a regulation and control instruction of each side decision-making unit according to a global dynamic optimization target under a system operation constraint; and the side decision-making unit decomposes the regulation and control instruction into the control instruction of each end decision-making unit, so that the problems that the global cooperative capability of the urban regional integrated energy system is weak and cooperative scheduling is not timely under extreme events are solved.

The invention discloses a multi-objective optimization method and system for a regional integrated energy system, and the method comprises the steps: constructing a multi-source input sequence sample; inputting a multi-source input sequence sample into the wind power and photovoltaic prediction model for processing, obtaining a current wind power and photovoltaic output optimization prediction result, calculating the current wind power and photovoltaic output optimization prediction result and a really constructed sample, obtaining a combined loss function value to train the model, obtaining the trained wind power and photovoltaic prediction model, processing the sample collected in real time, and obtaining a wind power and photovoltaic output prediction result. Outputting current wind power and photovoltaic output optimal values; and constructing an online optimization model of the integrated energy system, performing online optimization solution on the constructed model by an accelerated particle swarm optimization algorithm to obtain a control strategy of the energy system, issuing the control strategy to the energy equipment, and performing multi-target optimization scheduling. According to the method, the wind power and photovoltaic prediction model is combined with the accelerated particle swarm optimization algorithm, prediction errors are fully considered, and the stability and flexibility of the regional integrated energy system are enhanced.

The invention discloses a source network load storage real-time scheduling method for MADRL-GAN collaborative optimization, and the method comprises the steps: firstly constructing a carbon pollution collaborative optimization model, and converting the model into a solvable convex problem through function linearization and mixed integer conversion; further mapping the model into a multi-agent reinforcement learning model, dividing agents according to an electrical coupling degree, and designing a state and action space containing a dual-Critic reward mechanism; thirdly, learning system uncertainty distribution by using a generative adversarial network, generating diversified scenes to train a reinforcement learning model, and obtaining a preliminary strategy; and finally, correcting the strategy through the generative adversarial network, generating a final scheduling action through a strategy mixing mechanism, and realizing distributed real-time optimal scheduling. According to the method, the problems of multi-target collaboration, high-uncertainty scheduling and real-time distributed decision making in a high-proportion renewable energy system are effectively solved.

The invention relates to the technical field of energy prediction, in particular to an energy short-term load prediction method and system based on SE-Block improved Transform. The method comprises the steps of performing reversible normalization preprocessing based on acquired multi-element load sequence data; carrying out feature extraction and fusion on the preprocessed data by utilizing improved cross-scale interaction Patching, wherein the feature extraction and fusion comprise multi-scale feature extraction, cross-scale interaction alignment, residual error correction and dynamic fusion; and performing feature screening on the fused features based on a channel attention mechanism, wherein the feature screening comprises feature response based on improved SE-Block and non-linear interaction of context vectors. Aiming at the non-stationarity of the actual load caused by the influence of meteorological conditions and user behaviors, the model accurately depicts the fluctuation details of the load curve by automatically eliminating the noise interference among multiple variables, and the robustness of the model in the multi-element load prediction of the integrated energy system is reflected.

The invention provides a park integrated energy system low-carbon scheduling method combining demand response and carbon emission flow, and belongs to the technical field of energy system low-carbon scheduling. Establishing a directed weighted carbon flow network model based on a graph theory, tracking a carbon emission transmission path by adopting a maximum flow minimum cut theorem and a proportional allocation principle, constructing a space-time coupled dynamic carbon flow state space model, and performing state estimation by adopting a Kalman filtering algorithm; the carbon emission responsibilities are distributed based on a Shapley value method, a stepped carbon transaction cost function is established, an optimal scheduling strategy is solved through a double-layer iterative optimization framework, and the technical problem that the carbon emission responsibilities are difficult to distribute reasonably due to the fact that a park integrated energy system cannot accurately track a carbon emission transmission path when electric heat gas multi-energy flow coupling is considered is solved.

The invention belongs to the technical field of power supply and distribution, and particularly relates to a hydrogen-lithium hybrid power supply multi-mode uninterruptible power supply system, a control method and a storage medium, and the system comprises a hydrogen energy power supply vehicle, a lithium battery energy storage unit, a power conversion subsystem PCS, a voltage phase tracking module, a static change-over switch STS and a control unit. According to the invention, through cooperative work of the lithium battery and the hydrogen fuel cell, millisecond-level rapid switching during commercial power failure is realized, continuity of load power supply is ensured, and the problem of starting delay of a traditional hydrogen energy system is solved. Intelligent cooperative control of hydrogen energy and lithium batteries is realized according to different scenes, energy distribution is optimized, and the operation efficiency and stability of the system are improved. The control unit monitors the operation state of each part of the system in real time, calculates the dynamic efficiency and grid-connected / off-grid stability control quantity of the system, generates a control signal according to the dynamic efficiency and the grid-connected / off-grid stability control quantity, coordinates the work of each part, and ensures that the system can operate efficiently and stably in various operation modes.

The invention relates to the technical field of hydrogen load capacity configuration, in particular to a multi-scene hydrogen load comprehensive energy system double-layer capacity optimal configuration method, which comprises the following steps of: firstly, constructing a multi-scene model containing strong, weak, discontinuous and non-hydrogen loads based on the time sequence characteristics of hydrogen loads in an industrial park; then a comprehensive energy system mathematical model covering wind and light power generation, hydrogen energy conversion, multi-type energy storage and cogeneration units is established; then, constructing a double-layer capacity optimization configuration model of which the upper layer takes the whole life cycle cost minimization and the lower layer takes the operation cost minimization and the system stability optimization as targets; and finally, solving a Pareto leading edge by adopting a non-dominated sorting genetic algorithm with an elitist strategy, making a decision by utilizing an approximate ideal solution sorting method, and screening out a capacity configuration scheme with optimal comprehensive performance. The problem that a traditional configuration scheme is difficult to adapt to multi-scene hydrogen load fluctuation is effectively solved, and the economical efficiency, the stability and the renewable energy consumption capacity of the system are remarkably improved.

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.

A multi-energy coupling aggregator feasible region rapid identification method relates to the technical field of comprehensive energy system operation optimization, and comprises the following steps: firstly, establishing an optimization operation model which aims at minimizing the total operation cost and covers electricity-gas-heat multi-energy coupling and equipment operation constraints; secondly, on the basis of the model, defining an interactive energy feasible region (IEFR) and a flexible climbing feasible region (FRFR) to be identified; then, a max-min robust optimization model used for depicting a feasible region boundary is constructed, and the max-min robust optimization model is converted into a mixed integer second-order cone programming MISOCP model capable of being efficiently solved through a strong dual theory and a KKT condition; and finally, carrying out iterative solution by adopting a polyhedral projection algorithm based on a dichotomy and a plane cutting mechanism, and rapidly obtaining an accurate boundary of the IEFR and the FRFR.

The invention discloses a super-large plane array type space computing power center, and belongs to the field of space technologies and space infrastructures. The space computing power center is deployed in a morning and evening orbit and comprises a plurality of computing power satellites which are physically docked in orbit through a docking mechanism. Each computing power satellite comprises a satellite platform, and a computing power load, a condensation type energy system and a space pump drive fluid loop system which are arranged on the satellite platform, and a docking mechanism for inter-satellite in-orbit physical docking is also assembled on the satellite platform. A plurality of computing power satellites form a planar array type rigid whole body through the docking mechanism, and energy scheduling among the light condensation type energy systems is realized based on the docking mechanism. According to the space computing power center, through multi-satellite physical docking and energy collaborative scheduling, the energy utilization efficiency and the power supply reliability of the system are improved.

The invention discloses a building group energy-saving management and control method and system based on big data analysis, and relates to the field of big data analysis, and the method comprises the steps: carrying out the standardization processing of the real-time visitor flow data of a commercial district, and the subentry energy consumption data, time information and environmental parameters of the commercial district and a residential district, and constructing a current scene feature vector; screening matched similar historical scene samples, and generating a commercial district people flow prediction curve in a future target time period through weighted fitting calculation; screening matched historical regulation and control records, and performing weighted calculation to obtain a mean value as a historical reference regulation and control parameter; generating a candidate parameter combination according to a preset step length, calling the corresponding total energy consumption and the indoor temperature of the residential area, and selecting an energy-saving regulation and control instruction; and adjusting operation parameters of the commercial district energy system according to the energy-saving regulation and control instruction. According to the building group energy-saving management and control method and system based on big data analysis, the problem that the use comfort of a residential area is poor due to the fact that the energy consumption fluctuation of a commercial area is too large is solved.

The invention discloses a multi-energy load prediction method based on feature screening and multi-model fusion, and belongs to the field of electric power and comprehensive energy load prediction. The invention provides a three-stage hybrid learning prediction framework. In the first stage, dynamic feature screening is achieved through a recursive feature elimination cross validation method based on expert knowledge constraints, key meteorological elements and multi-energy load time sequence features are reserved, and redundant feature interference is reduced. In the second stage, a multi-task long-short-term memory network is constructed, and coupling relation modeling and collaborative prediction of cold, heat and electricity multi-energy loads are achieved through sharing time sequence characteristic representation and a task exclusive output structure. And in the third stage, a random forest is adopted to carry out nonlinear correction on the residual error of the sub-model, so that the precision and robustness of prediction in sudden disturbance and local non-stationary scenes are improved, the prediction error is effectively reduced, and the stability of multi-energy load prediction is improved. And reliable support is provided for optimized operation, scheduling decision and renewable energy consumption of the park integrated energy system.

The invention discloses a multi-energy fusion system cooperative scheduling method and device in a port dynamic scene, and relates to the field of port energy system optimization scheduling, and the method comprises the steps: constructing a dynamic coupling relation model according to port data; according to the energy system power of each area at the current time point, a multi-objective optimization model is constructed, and objective functions of the multi-objective optimization model include operation cost minimization, total carbon emission minimization, energy storage equipment service life maximization, energy storage equipment power optimization and energy storage equipment power optimization. The constraint conditions of the multi-objective optimization model are power balance, energy storage charge state, electric hydrogen equipment capacity, berth distribution continuity and load demand priority; according to the multi-target optimization model and the port data of each region at the current time point, determining a target collaborative scheduling operation strategy of each region at the current time point, the target collaborative scheduling operation strategy comprising a multi-energy output distribution scheme, a load response instruction and an equipment operation parameter; and executing the target collaborative scheduling operation strategy of each region at the current time point.

The invention discloses a multi-energy system closed-loop prediction control method, system, equipment and medium, and the method comprises the steps: carrying out the decomposition, dimension reduction and nonlinear modeling of the time sequence characteristics and environmental influence factors of photovoltaic output through a photovoltaic power prediction model, and outputting a future multi-period photovoltaic power prediction sequence; establishing a state equation and an output equation, integrating equipment operation constraints, and constructing a hydrogen-containing energy storage state space model; inputting local load power and a future multi-period photovoltaic power prediction sequence into the hydrogen-containing energy storage state space model, and solving an objective function through a rolling optimization algorithm to obtain a future multi-period optimal scheduling scheme; and applying a first hour control instruction of the optimal scheduling scheme to an actual system, and proportionally superposing the deviation between an actual measurement value and a historical prediction value to a photovoltaic power prediction sequence of a next period through a feedback correction item to realize closed-loop control. According to the method, the photovoltaic local consumption rate can be improved, and the power grid fluctuation rate is reduced.

The invention provides a multi-agent reinforcement learning regional energy collaborative scheduling method and system, and belongs to the field of regional energy system scheduling. Coupling degrees and a coupling degree matrix between agents are constructed; inputting the observation vector into a strategy network to obtain a decision action; individual basic rewards and system economic rewards are calculated, and constraint reference rewards are constructed; individual differentiation basic rewards are calculated, and rewards are distributed; inputting the decision action into a physical quantity prediction network, calculating a physical consistency reward, obtaining a final reward and a global reward, and calculating a target return; splicing observation vectors and decision actions of all agents, splicing global joint observation vectors and joint action vectors, inputting the spliced vectors into a value network, and training; inputting the local state set into the trained strategy network, outputting a scheduling instruction, inputting the scheduling instruction into the trained value network, and outputting an evaluation result; the problems of depiction rigidness of an intelligent agent coupling relation, lack of a cooperative benefit distribution mechanism and insufficient decision physical consistency are solved.

The invention discloses a comprehensive energy optimization scheduling method considering combustion of gas turbines with different hydrogen doping ratios, and belongs to the technical field of gas turbines, and the method comprises the steps: constructing a source-network-load-storage integrated combined cooling heating and power comprehensive energy system which covers an energy production layer, an energy conversion layer, an energy storage layer and an energy consumption layer; wherein in the energy consumption layer, electric load is supplied by gas turbine power generation, wind and light direct supply and energy storage discharge in a combined mode, heat load is borne by gas turbine waste heat, a gas-fired boiler and a heat storage system together, the coverage rate of a sensitive area is increased through the ergodicity of a chaotic system, population aggregation is avoided through initial value sensitivity, and it is ensured that all optimization schemes cover a key decision interval; meanwhile, in the algorithm evolution process, through a self-adaptive crossover variation mechanism, early enhancement of sensitive area exploration to capture efficiency inflection points and later fine development and positioning of an optimal solution, the problem of key solution omission of a traditional algorithm is thoroughly solved, and a more accurate hydrogen doping ratio reference is provided for system operation.

The invention discloses an optimal scheduling method based on a multi-energy complementary micro-grid and an active power distribution network, which belongs to the technical field of power grid scheduling and comprises the steps of S1, existing scheduling problem identification including multi-energy system systematic defect analysis, source load fluctuation adaptability defect quantification, distributed energy consumption bottleneck identification and economy-reliability constraint conflict modeling; according to the optimal scheduling method based on the multi-energy complementary micro-grid and the active power distribution network, the defects of insufficient multi-energy coordination, poor source load fluctuation adaptation and the like are diagnosed firstly, and then an energy storage multi-space-time coordination, wind-light output prediction and flexible load response model is constructed; through hierarchical optimization, multi-energy flow-power distribution network linkage, multi-target weight dynamic adaptation and dynamic safety margin adjustment landing, the distributed energy consumption rate and the multi-energy cooperation efficiency can be effectively improved, the power supply reliability, economy and environmental protection performance are balanced, the toughness of an electric power system to deal with uncertainty is enhanced, and the power supply efficiency is improved. And an efficient scheduling scheme is provided for novel power system construction.

The invention provides a distributed photovoltaic and energy storage load collaborative optimization operation method and system. The method comprises the following steps: acquiring multi-source data and processing the multi-source data by adopting an improved wave sorting algorithm; constructing a CNN and LSTM hybrid model to predict photovoltaic output; applying a single-index time FPT algorithm and combining with a cactus shrinkage principle to predict load demand and electricity price fluctuation; establishing a collaborative optimization model taking economy and photovoltaic consumption rate as double targets; carrying out distributed iterative optimization by adopting a single-index time FPT algorithm based on a cactus shrinkage principle; and generating a multi-time-scale control instruction and issuing the multi-time-scale control instruction to each device to realize cooperative control. Through the innovative data processing method and optimization algorithm, the efficient cooperative operation of the distributed energy system is realized, the photovoltaic consumption rate and the economic benefit are improved, and the practical value is relatively high.

The invention discloses an energy system and method for coupling calcium-based thermochemical heat storage and carbon capture. The energy system comprises a calcination unit, a carbon dioxide pretreatment storage unit, a calcium oxide heat exchange storage unit, a carbonation reaction unit and a heat utilization unit which are arranged based on a boiler. The calcining unit takes green valley electricity as energy to drive calcining reaction to realize heat storage, and generated CaO is processed by the calcium oxide heat exchange storage unit and then is stored for use in a heat release stage; generated COs are stored for standby application after passing through the carbon dioxide pretreatment storage unit; in the heat release stage, CaO and the desulfurized flue gas are subjected to heat release carbonation reaction in the carbonation reaction unit, and heat is released; caCO3 generated by the carbonation reaction can be used for the next heat storage stage; and the heat released by the carbonation reaction is conveyed to the heat utilization unit for heat exchange. Through coupling of calcium-based thermochemical heat storage and carbon capture, green electricity and off-peak electricity are fully utilized, and efficient storage and gradient utilization of energy are achieved.

The invention discloses a power grid dispatching method in an energy storage participation scene. The method comprises the following steps: establishing a master-slave game system taking an energy storage end of a demand side of a power distribution network as an upper layer and an electrical load end of the demand side as a lower layer, wherein the master-slave game system comprises an upper-layer energy storage model and a lower-layer electrical load model; inputting the scheduling capacity and the scheduling ratio of the energy storage end into the upper-layer model, outputting electric quantity transmission cost parameters of the energy storage end and the electric load end, inputting the electric quantity transmission cost parameters into the lower-layer model, outputting the transmission electric quantity of the energy storage end and the electric load end after processing, and inputting the transmission electric quantity into the upper-layer model for circulation until a variable reaches a steady-state value, and obtaining the scheduling amount to realize the scheduling of the power grid. According to the method, dynamic collaborative optimization of energy storage and power utilization load side resources can be realized, the problems of low resource utilization rate and insufficient model precision in the prior art are solved, the overall optimization and scheduling flexibility of an energy system is remarkably improved, and the technical blank in the field of collaborative optimization and dynamic interaction in the prior art is filled.

The invention discloses a waste heat recovery strategy analysis method and related equipment, and the method comprises the steps: firstly obtaining the flow and inlet temperature of each waste heat medium, and calculating the theoretical waste heat recovery power hour by hour in combination with environmental parameters and discharge temperature limitation; an energy conversion model is established based on the power and the equipment performance parameters, and a cascade framework is constructed according to the waste heat grade; and finally, by taking system energy efficiency maximization as a target, establishing an optimization model containing multiple types of constraints for solution, and obtaining an optimal equipment configuration and operation strategy. According to the cascade structure, efficient matching of waste heat and equipment is achieved, and recycling waste caused by mismatching is avoided; variable heat utilization requirements can be dynamically responded through hourly calculation and load constraint, and accurate space-time matching of waste heat and the requirements is achieved; the multi-constraint model enhances the multi-target optimization capability and makes up for the optimization deficiency in the prior art; the system planning process solves the problem of systematicness shortage of an existing method, the waste heat potential is effectively excavated, and the efficiency of the waste heat utilization comprehensive energy system is improved.

The invention is suitable for the technical field of electrochemical energy storage, and provides a multi-source cooperative control method and device, computer equipment and a computer program product, and the method comprises the steps: obtaining basic parameters of a power grid, a hybrid energy storage system and a new energy power generation system; obtaining prediction parameters of the electric power energy system based on the historical working conditions and the prediction model; according to the preset initial investment amount, the cycle life of the hybrid energy storage system and the basic parameters and working conditions of the electric power energy system, calculating the unit energy charging and discharging cost of the hybrid energy storage system; based on the unit energy charging and discharging cost, the time step length, the power grid billing time-of-use electricity price and the prediction parameters of the hybrid energy storage system and the planned charging and discharging power of the hybrid energy storage system, an objective function with the optimal economical efficiency as an objective is constructed, the objective function is solved, and the optimal planned charging and discharging power of the hybrid energy storage system in each time period is obtained. According to the method, multi-dimensional data is combined, an objective function with the optimal economical efficiency is constructed and solved, and a hybrid energy storage charging and discharging strategy is automatically obtained.

The invention provides a comprehensive energy self-adaptive scheduling method based on meteorological prediction, and aims to solve the problems that an existing energy system lags in response to meteorological disturbance and is insufficient in load regulation precision, and the comprehensive energy self-adaptive scheduling method comprises the following steps: fusing second-level meteorological disturbance data, electrical load power data, energy storage device charge state data and power grid boundary power flow data; constructing a disturbance response feature set, and establishing a time correlation mapping structure between a disturbance variable and a load variable; according to the method, a disturbance trend is predicted based on a mapping structure, an index set of disturbance direction, intensity and sensitive sections is formed, a dynamic regulation capability vector is generated in combination with a current load state, a scheduling optimization model meeting multiple operation constraints is constructed on the basis, a cooperative control strategy set is output, and the dynamic regulation capability vector is obtained. Finally, the control strategy is converted into a control instruction, the control instruction is accurately acted on a power generation device, an energy storage device and a load device, second-level dynamic self-adaptive scheduling of the comprehensive energy system on meteorological disturbance is achieved, and the system response capacity, stability and energy utilization efficiency are improved.

The invention discloses a hydrogen energy park scheduling method based on an improved cloud drift optimization algorithm, and relates to the technical field of intelligent development and scheduling of a comprehensive energy system, and the method comprises the following steps: building a target function with the minimum economic cost and carbon processing cost of the park comprehensive energy system as a target; and after wind and light power supply constraints, energy storage equipment constraints, power balance constraints, carbon sequestration capacity constraints, carbon capture constraints, gas turbine output and climbing constraints, gas boiler constraints, fuel cell constraints and power-to-gas constraints are set, an improved cloud drift optimization algorithm is used for solving, and the hydrogen energy park is scheduled. According to the invention, the bottleneck of insufficient regulation capability of a single energy network is broken.

The invention provides a comprehensive energy system multi-element load prediction method based on double-layer decomposition and reconstruction and TECNFormer, and the method comprises the steps: obtaining standardized input data, meteorological factors and time characteristics, which are obtained through the preprocessing of a historical multi-energy load sequence of a comprehensive energy system, wherein the historical multi-energy load sequence comprises an electric load sequence, a cold load sequence and a heat load sequence; performing double-layer modal decomposition on the standardized input data, and classifying the standardized input data into different types of dynamic components; respectively inputting the dynamic components into a time sequence enhanced convolution module, and extracting multi-scale local features by combining multiple heterogeneous differential convolution operators with causal convolution; taking a TECNFormer composed of a time sequence enhanced convolution module and an improved long sequence prediction network as a unified shared feature extraction layer, combining the improved long sequence prediction network with a bidirectional long-short-term memory network and a sparse attention mechanism to capture long-range dependence and local details, and obtaining joint modeling features; on the basis of a hard shared network architecture, a multi-task branch is arranged at an output end, and electric, cold and heat load prediction results are synchronously output.

The invention discloses a multi-stage coordinated scheduling method and system for a hydrogen-electricity coupling comprehensive energy system, and the method comprises the steps: solving an initial scheduling scheme according to a day-ahead scheduling model of the hydrogen-electricity coupling comprehensive energy system, the initial scheduling scheme comprises the unit output of a thermal power generation unit in the hydrogen-electricity coupling comprehensive energy system, the renewable energy grid-connected proportion of a renewable energy power station and the electricity and hydrogen purchasing amount of a hydrogen production center; monitoring the safety confidence of the hydrogen-electricity coupled comprehensive energy system within specified time after the hydrogen-electricity coupled comprehensive energy system executes the initial scheduling scheme, if the safety confidence is greater than a preset threshold value, ending and exiting, and otherwise, skipping to the next step; and an intra-day correction network based on an MAPPO algorithm is adopted to correct the day-ahead scheduling model, and a corrected scheduling scheme is issued to the hydrogen-electricity coupling integrated energy system for execution. According to the method, the economical efficiency, the safety and the calculation efficiency performance of the hydrogen-electricity coupling system in a complex and uncertain environment can be guaranteed.

The invention discloses a modeling and scheduling method and device for a dynamic load and a shared distributed energy storage system under shared power conversion, and relates to the technical field of shared power conversion and distributed energy storage systems, and the modeling and scheduling method comprises the steps: building a multi-target optimization scheduling model based on user power conversion information and power conversion demand prediction data under a shared power conversion scene; and constructing a collaborative solving mechanism, solving the comprehensive scheduling model, and obtaining and selecting an optimal shared energy storage scheduling scheme. The optimal scheduling model comprehensively considers the economic cost of optimal scheduling and the operation and scheduling balance of the shared distributed energy storage system, so that the cost of configuring energy storage by a single user can be reduced, the operation coordination and load balance capability of the whole system can be optimized and improved, good resource sharing and system coordination effects are embodied, and the user experience is improved. The method is suitable for building a flexible energy system with a user side as a core in the future.