2171 results about "Energy system" patented technology

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.

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.

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 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 discloses a morning orbit computing power satellite, and belongs to the field of aerospace technology and space infrastructure. The satellite comprises a satellite platform and a computing force load arranged on the satellite platform. The satellite platform is provided with a condensation type energy system and a space pump drive fluid loop system. The condensation type energy system adopts an expandable condensation type solar cell array to generate power, switches a power supply mode according to an illumination period and an earth shadow period of a morning and night orbit, and supplies power to computing force loads and platform equipment. The space pump drive fluid loop system adopts an expandable flexible radiation cooling plate for heat dissipation, heat dissipation is conducted on computing force loads through a fluid loop, and a heating assembly is arranged to prevent a working medium from being frozen under the low-temperature working condition. Through the expandable structure and the orbit environment adaptability design, continuous energy supply and effective heat management are provided for the computing power load in the morning and night orbit environment, and high-power-consumption computing power load in-orbit stable operation is supported.

The invention provides an urban virtual power plant operation method and device based on an intelligent agent architecture, relates to the field of artificial intelligence, and solves the problem that in the prior art, operation strategies related to a virtual power plant are mostly based on static rules and manually set optimization models. And rapid and efficient response is difficult to realize in a multi-participant, multi-constraint condition and dynamic electricity market environment. The method comprises the following steps: collecting multiple pieces of first data in an urban energy system in an urban virtual power plant; performing arrangement and task scheduling on the first data on the basis of data types required by the plurality of agents to execute tasks, determining the first data required to be processed by each agent, and calling the plurality of agents to process the first data required to be processed; and generating a regulation and control decision and a market transaction strategy of the urban virtual power plant based on the processing results output by the plurality of agents and the operation constraint conditions. The method is used in the operation process of the urban virtual power plant.

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 relates to a source-load multi-subject hierarchical collaborative optimization method based on a fused niche. According to the scheme, multi-subject modeling, a master-slave game mechanism, niche evolution optimization and an interlayer feedback coordination strategy are combined. Firstly, a unified mathematical model of various power supplies and loads in a source-load system is constructed, and optimization targets and constraints of the unified mathematical model are defined. Secondly, introducing a master-slave game (Stackelberg) mechanism, and simulating a dynamic game behavior of source first-onset and load response; thirdly, a niche evolution algorithm is adopted to improve the search diversity and the global optimal solution obtaining capability; and finally, through an interlayer feedback mechanism, realizing mutual guidance and correction of a game result and an evolution process, and outputting a global consistent scheduling scheme. According to the method, the optimization precision, the operation coordination and the scheduling robustness of the source-load system in a complex and changeable environment can be effectively improved, and the method is suitable for multi-source and multi-load collaborative optimization scenes such as a micro-grid and an integrated energy system.

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 hydrogen energy park planning method and system based on a heuristic algorithm, and relates to the technical field of smart energy management, and the method comprises the steps: constructing a mathematical model of a park comprehensive energy system which is composed of a renewable energy power generation unit, an energy conversion device, a carbon capture and gas power device and a multi-type energy storage unit; based on the mathematical model, with the minimum annual comprehensive cost of the park comprehensive energy system as a target, establishing a cost target function, setting constraint conditions, and formulating an energy management strategy; and according to an energy management strategy, solving according to an improved taboo horse racing optimization algorithm THRO, and planning the hydrogen energy park according to a solving result. According to the invention, not only is the interior of the exterior of the environment realized, but also the maximization of economic benefits is realized.

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 belongs to the technical field of power system optimization scheduling, and discloses a mathematical model dual-drive-based port intelligent scheduling method, which comprises the following steps of inputting real-time operation data of a port logistics system and an energy system; on the basis of a deep reinforcement learning model, operation data is adopted for training, a state-action-reward mapping relation is established, and a data-driven preliminary scheduling strategy is generated; based on the operation data, constructing a model-driven traffic distribution-user balance optimization model, and obtaining an energy pricing strategy capable of minimizing the operation cost; constructing a multi-agent collaborative decision framework to coordinate a preliminary scheduling strategy and a traffic distribution-user equilibrium optimization model, and realizing iterative optimization of dual-drive strategy collaboration; dynamically selecting an optimal sub-heuristic strategy by adopting a dual deep Q network structure based on an iterative optimization result; the problems of insufficient strategy generalization ability and poor dynamic environment adaptability in the prior art are solved.

The invention discloses a low-carbon economic operation optimization method for an electricity-hydrogen-heat comprehensive energy system, and belongs to the crossing field of intelligent energy systems and artificial intelligence, and the method comprises the following steps: 1, building a low-carbon economic dual-target operation optimization problem model of the electricity-hydrogen-heat comprehensive energy system; 2, constructing a dual-objective operation optimization problem solving framework with a third-generation non-dominated sorting genetic algorithm as a core; 3, modeling a population evolution decision problem in the genetic algorithm into a Markov decision process, and constructing a deep reinforcement learning agent and population evolution environment interaction mechanism; 4, training the intelligent agent by adopting a double-depth Q network algorithm, and outputting a genetic action according to an environment state; 5, obtaining a new filial generation in combination with a selection strategy associated with the reference points and genetic actions output by the intelligent agent; the step 4 and the step 5 are iteratively evolved until the maximum iteration generation number is reached, and the operation cost can be reduced under the same carbon emission condition.

The invention discloses a building energy system multi-type demand response operation strategy optimization method and system, and relates to the technical field of building energy management and demand response optimization control, and the method comprises the steps: collecting building user side multi-source information, building a building cooling load prediction structure, and bringing the structure into an optimization scheduling model input system. Constructing a building energy system optimization scheduling equipment model; collecting power grid side information, and dynamically loading an optimal scheduling model for a time-of-use electricity price scene and a peak clipping scene based on judgment of power grid demand response type information of the next day; analyzing an optimization problem of a unified objective function in a double-type response scene, introducing a power reservation coefficient based on a time-of-use electricity price scene, and controlling flexible resource retention; a mixed integer linear programming algorithm is adopted to solve the multi-scene optimization scheduling model, and a corresponding strategy, scheme and power configuration plan are generated; according to the method, flexible dynamic regulation and control and unified scheduling in a multi-response scene are realized, and the strategy adaptability and collaboration are improved.

The invention relates to the technical field of comprehensive energy optimization scheduling, in particular to a shared energy storage optimization scheduling method and system considering island operation. According to the shared energy storage optimization scheduling method, a double-layer optimization framework of a double-layer energy storage optimization model is adopted, and island operation constraints of regional integrated energy subsystems are introduced on the optimization basis of minimizing the operation cost of a multi-regional integrated energy system; a real-time shared energy storage scheduling scheme obtained by solving a double-layer energy storage optimization model not only can optimize the operation cost of the multi-region integrated energy system based on historical operation data and real-time operation data, but also can meet the island operation emergency energy constraint requirement of a region integrated energy subsystem; the power grid disconnection risk caused by the real-time load change of the system is handled, the power supply reliability of the system under the extreme working condition is improved, and then the robustness of the energy storage dynamic correction strategy of the multi-region integrated energy system is improved.

The invention provides an energy coordination control system and scheduling method for multi-energy complementary new energy storage, and belongs to the technical field of new energy storage and energy technology management. The data acquisition and processing module is connected with a current and voltage sensor, a power transmitter, a meteorological station sensor and a battery management system sensor, and is used for acquiring voltage, current, power, state of charge and environmental data of photovoltaic power, wind power, energy storage and load through the sensors; and sliding window filtering and wavelet de-noising processing are carried out through the embedded processor. The method has the beneficial effects that the uncertainty of wind and light output is effectively processed by establishing a layered distributed optimization architecture and adopting a method of combining multi-objective optimization and robust optimization, and the consumption rate of renewable energy sources can be remarkably improved, the net load fluctuation can be reduced and the energy consumption can be reduced on the premise of meeting various system operation constraints. Therefore, dependence on conventional fossil energy is reduced, and the economic operation level and the environmental protection benefit of the whole energy system are improved.

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 discloses a hydrogen-containing integrated energy system robust optimization scheduling method considering a dynamic green certificate-carbon emission collaborative transaction mechanism. The method comprises the following steps: establishing the dynamic green certificate-carbon emission collaborative transaction mechanism considering reward and punishment characteristics; establishing a hydrogen-containing comprehensive energy system equipment model according to a hydrogen-containing comprehensive energy system architecture; determining a target function and constraint conditions according to the dynamic green certificate-carbon emission collaborative transaction mechanism considering the reward and punishment characteristics and the hydrogen-containing integrated energy system equipment model so as to establish a hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism; and solving the hydrogen-containing integrated energy system robust optimization scheduling model considering the dynamic green certificate-carbon emission collaborative transaction mechanism based on a robust linear optimization theory. According to the method provided by the invention, the running flexibility of the hydrogen-containing comprehensive energy system is improved, and meanwhile, the running economy and the carbon emission level of the system are improved.

The invention discloses an intelligent operation and maintenance method and system for an energy management platform, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: analyzing data through employing an NLP technology, generating a multi-objective optimization function, constructing a user behavior portrait, adjusting the weight parameters of the multi-objective function through employing a reinforcement learning algorithm in combination with an Actor strategy network, and carrying out the operation and maintenance of a user. An energy scheduling scheme is obtained by using an improved bald eagle search algorithm and a prey escape energy mechanism, a digital twinborn model of mapping of a physical space and a virtual space is constructed, and the energy scheduling scheme is optimized and implemented by combining the digital twinborn model with a soft constraint mechanism. Through fusion of NLP technology semantic analysis and a reinforcement learning algorithm, a multi-target scheduling strategy is dynamically optimized, and an improved bald eagle search algorithm and a digital twin model are combined to realize simulation verification and closed-loop feedback, so that the intelligence and optimization level of energy system operation is improved, and the stability, economy and low-carbon operation capability of the system are also improved.

A power distribution network-multi-integrated energy system distributed optimization scheduling method considering interval uncertainty and multi-agent game comprises the following steps: representing fluctuation characteristics of a distributed power supply and a load by adopting an interval number, and establishing a multi-agent game model with robustness; a dynamic electricity price transaction mechanism based on Nash bargaining is constructed, the income requirements of all subjects in the DN-MIES system are met, and system benefit balance is achieved; the MVA-ATC algorithm is proposed based on the interval information of the source load uncertainty to carry out distributed iterative solution, and the main body privacy is guaranteed while the solution efficiency is improved. For the problem of benefit distribution after the MIES alliance is accessed to the power distribution network, the invention provides a DN-MIES mixed game distributed optimization method considering multi-subject benefits, so that benefit distribution of the power distribution network accessed to multiple IESs is realized, and benefit balance during cooperative operation of the power distribution network and the multiple IESs is effectively guaranteed; meanwhile, an MVA-ATC distributed solving algorithm is provided by utilizing interval uncertainty and an ATC algorithm, so that the solving efficiency of the model is improved, and meanwhile, the fluctuation of the source load is stabilized.

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 aims to provide a solid hydrogen storage fuel cell underwater vehicle hybrid power system coupled with a water management system, and belongs to the field of hybrid power. According to the invention, the hydrogen fuel cell is combined with a common cell, sufficient, stable and efficient power supply is provided for the underwater vehicle, the limitation of a traditional single energy system in the aspects of cruising ability, power output, energy density and the like is solved, and the comprehensive performance of the underwater vehicle is remarkably improved. A water supply and return integrated circulating water pump is adopted, and efficient and rapid circulation of water inside and outside the underwater vehicle is achieved by integrating water supply and drainage functions. The solid hydrogen storage material is adopted to release hydrogen, one part of hydrogen is conveyed to the fuel cell and participates in reaction to provide power output needed by the underwater vehicle, the other part of hydrogen is recycled and assists the hydrogen storage material in injection, and the underwater closed circulation mode of the underwater vehicle is achieved. And efficient operation and effective utilization of energy are ensured.

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.