3177 results about "Supply energy" patented technology

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 present disclosure relates to the technical field of intelligent monitoring and management of power systems, and specifically relates to an intelligent collaborative power consumption regulation and control method, apparatus and system for source-grid-load-storage, an electronic device and a storage medium. Said system comprises an energy regulation and control center and energy regulation and control units provided in microgrids; the energy regulation and control units use a temporal attention mechanism-based LRCN dual-layer network combined model to predict power consumption amounts, so as to generate power consumption surpluses and shortages within a future preset time; and on the basis of the power consumption surpluses and shortages and latest current electricity prices of the microgrids, the energy regulation and control center uses a fusion multi-objective algorithm based on a Pareto front curve and a fuzzy algorithm to generate a microgrid collaborative power consumption regulation and control solution, and sends the regulation and control solution to the energy regulation and control units for execution, so as to ensure the balance of energy supply and demand of the microgrids. Therefore, the present disclosure achieves efficient, intelligent and refined energy management for microgrid clusters, reducing energy consumption and costs, and providing solid support for sustainable development of microgrids.

The invention relates to an electric energy comprehensive management control method for high and low load working conditions, and belongs to the field of energy supply system control. The method comprises the following steps: acquiring current operation information of an energy supply system, including a working condition mode, a target power demand and a battery state of charge (SOC) value, and further judging a working condition type and calculating a total power demand value of the system; on the basis, a power distribution scheme of a power generation unit and a battery is judged according to an SOC value and a power demand: under a normal working condition, the balance of power generation and battery charging and discharging is realized by setting an SOC threshold interval, and under an emergency working condition, the maximum rate discharging of the battery is dynamically controlled or the charging current is limited to cope with load fluctuation. According to the system, the bus voltage and the SOC are maintained in a safe range through a closed-loop regulation mode, so that the stability and the response capability of the energy supply system are improved.

The invention discloses a distributed power supply storage and charging integrated system, and belongs to the technical field of power system management. The system comprises a clean energy access module, an energy storage control scheduling module, an intelligent charging control module, an energy management scheduling module, a communication network interconnection module and an equipment state monitoring module, the battery state is dynamically evaluated from multiple dimensions, and a matching strategy is called through a real-time environment sensing mechanism; the charging and discharging path and rate are more in line with the actual load demand and the energy supply and demand state of the system, the battery circulation loss is reduced, the energy utilization rate is maximized, the non-critical load reduction power of the user is dynamically guided, and the specific charging demand of the user in the future time period is dynamically predicted. Therefore, the system has the capability of allocating energy resources in advance, centralized coordination is carried out on the multiple charging piles, imbalance of the energy resources in spatial distribution is avoided, the scheduling flexibility of the whole system is improved, and the self-adaptive capability of the system to complex power consumption behaviors is enhanced.

The invention discloses a pipe network health real-time monitoring system and an evaluation method. According to the invention, through real-time health monitoring and dynamic deduction capability, the flexibility and safety of energy supply are significantly improved. The healthy twin module constructs a digital mirror image of the full life cycle of energy equipment, solar hydrogen production efficiency fluctuation, hydrogen storage tank pressure change and fuel cell attenuation trend can be accurately tracked, and risk points such as electrolytic cell abnormal hydrogen evolution and proton exchange membrane aging can be recognized in advance. The all-weather transparent monitoring enables the system to dynamically adjust the ratio of hydrogen production to power generation, the fuel cell is automatically switched to supply power in cloudy and rainy days, continuous energy supply is guaranteed, and the system can generate a coping plan in advance by simulating extreme scenes such as sudden drop of photovoltaic output and hydrogen leakage, so that the system is convenient to use. The energy interruption risk caused by sudden equipment failure is greatly reduced, meanwhile, the service life of key components is prolonged, the maintenance cost of a distributed energy network is reduced, and meanwhile the overall energy efficiency utilization rate is also improved.

The present application relates to a multi-objective scheduling method and apparatus for an urban power grid, a computer device, a storage medium, and a computer program product. The method comprises: connecting hydrogen energy vehicles to an original urban power grid operation system to obtain an updated urban power grid operation system; determining an electrical-hydrogen coupling unit from the updated urban power grid operation system; establishing a corresponding mathematical model for each subunit in the electrical-hydrogen coupling unit; determining a first objective function on the basis of operation cost parameters of respective modules of the updated urban power grid operation system, determining a second objective function on the basis of energy supply parameters of each subunit of the electrical-hydrogen coupling unit, and generating a multi-objective optimization and scheduling model on the basis of the first objective function and the second objective function; and solving the multi-objective optimization and scheduling model to obtain a target scheduling strategy.

The invention discloses an airport energy balance management method, system and device and a medium, and relates to the field of energy management. The method comprises the following steps: constructing an energy topological graph according to multi-source data; predicting energy load demands of the airport in different time periods according to the energy topological graph, determining the supply amount of renewable energy in different time periods according to the predicted illumination intensity and wind power, and determining a time-sharing energy supply and demand balance probability according to the energy load demands and the supply amount; determining a preliminary energy management plan according to the cost parameter, the time-sharing energy supply and demand balance probability and the equipment operation constraint, wherein the preliminary energy management plan comprises an energy storage system charging and discharging time sequence, a standby generator starting and stopping plan and a power grid electricity purchasing proportion; and executing the preliminary energy management plan through the digital twin of the airport to obtain first energy execution data, and adjusting the preliminary energy management plan through the difference between the first energy execution data and the second energy execution data at the current moment. By implementing the technical scheme provided by the invention, the energy cost is reduced.

The invention provides an industrial carbon footprint dynamic prediction method fusing multi-source data, which comprises the following steps: step 1, collecting multi-source data, core data including electricity consumption, converted standard coal consumption, key industrial process product yield and carbon emission, and three types of extended data including new energy consumption data, process upgrading data and external data; 2, performing dynamic modeling according to the multi-source data obtained in the step 1, including dynamic carbon emission factor calculation and a process-environment coupling equation; 3, an industrial carbon-energy-economy multi-objective optimization model of dynamic coupling new energy consumption is constructed, minimization of carbon emission and operation cost is taken as an objective function, and constraint conditions comprise production requirements, energy supply and policy compliance; and 4, performing optimization solution on the target function of the multi-target optimization model by adopting an NSGA-II algorithm, and outputting a carbon emission-cost tradeoff curve.

The invention discloses an air conditioner supply and demand collaborative optimization scheduling method, which relates to the technical field of air conditioner supply and demand scheduling, and comprises a sensing prediction module, an acquisition comparison module, a deviation correction driving module, a control adjustment module, a cold source control module, a strategy adjustment module and an anomaly detection module. When the abnormality detection module detects that the air conditioner terminal breaks down, the control adjusting module enhances the power of the peripheral equipment to fill the cooling capacity gap, if the water chilling unit breaks down, the cold source control module starts the standby unit to switch the cooling capacity output path, and when the cooling capacity is insufficient, the control adjusting module is in linkage to reduce the air volume of the low-priority area to preferentially guarantee cooling of the core area; meanwhile, a strategy adjusting module automatically matches a predefined strategy chain according to the fault type, for example, global average temperature control is switched when temperature control fails, a load balancing strategy is triggered to be distributed to a standby unit when equipment is overloaded, and the strategy is gradually rolled back to the optimal strategy based on historical data after the fault is repaired; and strategy conflicts and equipment fluctuations are avoided through progressive improvement of cold energy supply.

The invention relates to the technical field of smart home, and discloses a smart home energy supply and demand collaborative optimization system based on reinforcement learning. The system comprises an energy data acquisition module, a reinforcement learning decision module, a supply and demand collaborative optimization module, a real-time regulation and control execution module and a system performance monitoring module. The energy data acquisition module is used for acquiring various energy and environment data and processing exceptions; the reinforcement learning decision-making module constructs a state and action space and generates a regulation and control strategy in combination with a reward function; the supply and demand collaborative optimization module performs multi-objective optimization on energy distribution; the real-time regulation and control execution module regulates and controls the equipment according to the instruction priority; and the system performance monitoring module tracks the state and performs feedback optimization. In addition, an abnormal mode recognition module and a multi-agent cooperation module are further arranged and used for recognizing energy consumption abnormity and achieving equipment cooperation respectively. The system can effectively optimize household energy supply and demand, reduce cost, and improve energy utilization rate and user satisfaction.

The invention provides a flexible preparation system and method for sustainable aviation fuel, and the system comprises a green power supply module which is used for outputting fluctuating green electric energy; the process execution device is connected with the green power supply module and comprises five process modules, namely an air carbon capture module, a co-electrolysis module, a Fischer-Tropsch synthesis module, a hydrogen production module and a hydrogenation refining module; the data acquisition module is used for acquiring the temperature, the pressure, the liquid level, the raw material input flow and / or the product output flow of each process module in the process execution device; and the decision control module is used for predicting the future power supply condition of the green electricity based on the historical weather data of the green electricity location, adjusting the distribution of the power in each process module according to the future power supply condition, and adjusting the temperature, pressure and / or raw material input flow in each process module. According to the invention, the cost, the reliability, the operation and maintenance performance, the product quality and the green power utilization rate can be substantially improved at the same time, and the flexible and stable operation of the system under the green power condition is realized.

The invention discloses a power supply method and a power supply system for on-line monitoring equipment of a lightning arrester, and the method specifically comprises the steps: activating a multi-source energy module according to a preset priority order based on the operation state and environment parameters of the lightning arrester, and supplying power to the on-line monitoring equipment; based on the output characteristics of the multi-source energy module and the residual electric quantity of the on-line monitoring equipment, a constant voltage-constant current hybrid power supply strategy in a conventional mode or an anti-interference power supply strategy in an abnormal mode is executed on the on-line monitoring equipment; and processing the historical leakage current, environmental temperature and humidity and vibration spectrum characteristics of the lightning arrester acquired by the online monitoring equipment through the LSTM prediction model, predicting the energy supply trend of the online monitoring equipment in the preset future time, and dynamically adjusting the working parameters of the online monitoring equipment according to the energy supply trend. According to the invention, stable power supply of the lightning arrester on-line monitoring equipment in a complex environment is realized, and the power supply reliability and the equipment availability are improved.

The invention discloses a balance scheduling system for energy management and a balance scheduling method thereof, and relates to the technical field of energy management scheduling, and the balance scheduling system comprises an energy data acquisition module, an energy demand trend analysis module, an energy supply and demand dynamic prediction module, an energy balance scheduling decision module, a scheduling execution module and a performance monitoring module. After the energy supply and demand dynamic prediction module integrates real-time external factor data of the energy data acquisition module and a long-term historical rule of the energy demand trend analysis module, a reference prediction baseline based on a long-term trend is generated, and then a dynamic correction value is superposed to respond to short-term disturbance. And meanwhile, through an error traceability mechanism for separating a trend baseline from external correction, the module performance can be optimized in a targeted manner, if an error is derived from long-term trend deviation, a trend analysis model is calibrated, and if external data is lagged, priority adjustment of a data acquisition module is triggered, so that system self-diagnosis and prediction precision closed-loop improvement are realized.

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 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 belongs to the technical field of anti-explosion electric cabinets, and particularly discloses an anti-explosion electric cabinet for new energy supply, which comprises an anti-explosion electric cabinet and an active cooling and fire extinguishing system, and is characterized in that the active cooling and fire extinguishing system comprises a centralized cooling and fire extinguishing unit, a confluence controller, a control module, a plurality of pressure pipelines and a plurality of temperature feedback devices; a plurality of working parts in the explosion-proof electric cabinet are respectively provided with a monitoring unit; the explosion-proof electric cabinet has the advantages that the monitoring unit is arranged in the explosion-proof electric cabinet, real-time monitoring of the environment of the electric cabinet is achieved, and especially under the abnormal conditions of high temperature and the like, the active cooling and fire extinguishing system can rapidly respond, automatically increase the spraying amount of the fire extinguishing material and improve the fire extinguishing effect. The system integrates a passive pressure relief structure, an active explosion venting system, an inerting explosion suppression system and automatic water mist spraying, multi-level safety protection is provided, and safety and stability of electric power distribution facilities, especially an electric cabinet and a distribution box in a new energy grid-connected system in a complex environment are ensured.

The invention discloses a dynamic resource allocation system and method for smart travel. The system comprises a multi-source data acquisition module, a data fusion module, a prediction module, a framework construction module, an algorithm optimization module, a feedback module and a resource allocation module. According to the method, environment data, energy consumption data, renewable energy supply data and tourist flow data in a plurality of sub-regions of the tourist attraction are collected, and fusion and deep learning prediction are carried out on the data, so that future energy consumption, energy supply and flow data are obtained. And a reinforcement learning scheduling framework is constructed, and intelligent scheduling of the charging pile, the electric sightseeing vehicle, the lighting equipment and the monitoring equipment is realized. And carrying out global optimization on the resource scheduling scheme by adopting a speed pause particle swarm optimization algorithm so as to output a resource allocation scheme with optimal energy consumption. The scheduling scheme is further optimized based on user feedback data, and the dynamic adaptability of resource configuration is ensured. According to the invention, the energy utilization efficiency in the smart travel scene is improved, and the operation cost is reduced.

The invention discloses a fuel cell coupled flow battery energy supply system and an operation control method thereof. The energy supply system comprises a basic energy supply unit, an energy storage peak regulation unit, a renewable energy power generation unit, an electrolytic hydrogen production unit, a hydrogen storage unit, a first-stage waste heat recycling unit, a second-stage waste heat recycling unit and a heat storage unit. The basic energy supply unit is used for providing a basic power load of the target building group; the energy storage peak regulation unit is used for providing fluctuation power load of the target building group; the first-stage waste heat recycling unit receives first-stage waste heat generated by power generation of the fuel cell and is used for indoor heating or indoor refrigeration. And the second-stage waste heat recycling unit directly receives the first-stage waste heat or second-stage waste heat from the first-stage waste heat recycling unit and is used for preheating the electrolyzed water and / or the hydrogen storage unit. By arranging the first-stage waste heat recycling unit and the second-stage waste heat recycling unit, waste heat is utilized in a multi-stage mode according to requirements, and the waste heat utilization rate is high.

The unmanned aerial vehicle hydrogen-electricity hybrid power system comprises a vehicle body, a hydrogen fuel cell system, a power cell system and an energy management system are arranged in the vehicle body, and electric energy output by the hydrogen fuel cell system and the power cell system supplies power to a motor through the energy management system; the motor controls the spiral blades, the rotating speed of the motor is independently adjusted, and the posture and movement of the unmanned aerial vehicle are controlled by changing the direction and size of the lifting force; the energy management system is used for performing energy conversion during air speed change and braking of the unmanned aerial vehicle, converting mechanical energy into electric energy, inputting the electric energy into the power battery system, and meanwhile, converting the electric energy to judge whether the unmanned aerial vehicle has a high-power energy supply demand currently so as to adjust energy supply; according to the invention, the service life of the galvanic pile monomers can be prolonged, so that the service life of the hydrogen-electricity hybrid power system module can be prolonged; according to the energy management method, unnecessary energy loss is reduced, and the endurance time of the unmanned aerial vehicle is further prolonged.

The invention relates to the technical field of energy storage management, and particularly discloses a control method and platform for a distributed energy storage system of an expressway, and the method comprises the steps: collecting battery state parameters, and combining a battery equalization judgment result as BMS feedback information for output; the target EMS collects and analyzes BMS feedback information to determine a key charging and discharging strategy of the battery pack, and the strategy is executed when the strategy is feasible; a strategy optimization mode of collaborative operation of strategy safety degradation and a system joint compensation mechanism is adopted to optimize and protect infeasible key charging and discharging strategies; the target EMS collects and analyzes PCS collection data, determines a dynamic anti-countercurrent control strategy and executes the dynamic anti-countercurrent control strategy; the high-speed distributed energy storage system performs environment safety monitoring on the system, performs abnormal response according to the monitoring condition, can ensure that the high-speed distributed energy storage system can flexibly adjust charging and discharging behaviors, effectively prevents current from flowing back to a power grid, improves the intelligent control level and the operation efficiency of the system, and provides powerful support for energy supply and stable operation of the expressway.

The invention relates to the technical field of control systems, and particularly discloses an electric vehicle and renewable energy source integrated energy management measurement and control system and method, and the system comprises an acquisition module, an optimization control module, a control execution module, a constraint module, and a fault tolerance module. The acquisition module acquires photovoltaic, wind power and electric vehicle load data through a distributed sensor network, and the control execution module issues an instruction through a multi-priority message queue and performs closed-loop feedback; the constraint module decouples space-time constraints through causal derivation, and updates strategy network parameters in combination with federated learning; and the fault-tolerant module performs dynamic fault tolerance based on the Bayesian network and the generative adversarial network. The method covers multi-source data processing, hierarchical optimization instruction generation, closed-loop verification and anti-interference training processes, solves the problem of optimization path deviation caused by difficult solution of high-dimensional mixed integer programming and time-space correlation interference, and realizes dynamic balance of energy supply and demand and low-carbon operation.

The invention belongs to the technical field of charging pile thermoelectric control, and particularly relates to a high-power liquid cooling charging pile charging power optimization scheduling method and system. The method comprises the following steps: collecting operation state parameters of a plurality of liquid cooling charging piles; identifying a target pile body in a charging state or a standby state; calculating a theoretical energy supply upper limit based on an electrothermal model; combining the vehicle charging demand, the priority, the battery state and the power grid load to construct a power scheduling model and execute distribution; power adjustment and redistribution are carried out when cooling abnormity or connection faults are detected; and finally, a scheduling control instruction is generated and executed in real time. The system comprises an information acquisition module, a state identification module, a model calculation module, a power distribution module, a dynamic adjustment module and an instruction issuing module. According to the invention, efficient, safe and dynamic power scheduling control of the high-power liquid cooling charging pile in a multi-device parallel connection scene is realized.

The invention relates to the technical field of downhole operation control, in particular to a downhole operation platform control system and a crane, and the system comprises a task identification module, a demand index module, a power sequencing module, a rhythm switching module and a command reconstruction module. According to the method, through accurate recognition of the structure state and parameter linkage, accurate judgment of the initial operation stage is achieved, false starting and false action of the platform are avoided, coupling analysis between operation data can dynamically set the scheduling priority, it is guaranteed that key tasks are executed in advance, coordination is improved, energy supply conditions are judged through power parameter fusion, and paths are distributed according to needs; the energy utilization efficiency is enhanced, stage rhythm conversion achieves stable switching based on action states and logic mapping, action disjunction is prevented, finally, control instructions are rearranged through feedback time delay and an execution sequence, an efficient and ordered response mechanism is formed, the overall operation efficiency, stability and the intelligent level are improved, and the risk of manual intervention and operation imbalance is reduced.

The invention discloses an urban energy network intelligent allocation method and system, and the method comprises the steps: obtaining building energy consumption data and energy supply network topology information, building a supply and demand matching reference, recognizing a load peak value dense time period and a peak clipping and valley filling potential region, and exporting transferable load data to form a regional supply and demand distribution diagram; generating a peak shifting adjustment space in combination with the user response delay duration and the equipment start-stop period; performing gap matching analysis on the regional supply and demand distribution map and the peak shifting adjustment space, creating a hierarchical regulation instruction set, and differentially generating an instant response instruction and a delay response instruction; a regulation and control coordination parameter is formed through coordination configuration of the peak shifting revenue coefficient and the instant response instruction; finally, a regulation and control execution range is determined, cost benefit evaluation is implemented, an urban-level energy scheduling execution scheme is formed, accurate matching and efficient scheduling of urban energy supply and demand are achieved, and comprehensive and efficient scheduling decision support can be provided for application scenes such as an intelligent power grid, regional heat supply and comprehensive energy service.

The invention discloses a zero-carbon park energy supply optimization method and system based on big data analysis. The method comprises the following steps: S1, constructing a unified park energy operation data set; s2, performing feature extraction and data fusion on the park energy operation data set by using a big data processing technology to generate an energy supply and demand prediction result at a future moment; s3, constructing a distributed energy storage regulation and control optimization model of the zero-carbon park; s4, solving the distributed energy storage regulation and control optimization model of the zero-carbon park by using an improved hybrid optimization algorithm, and generating a plurality of distributed energy storage regulation and control strategy candidate solutions; and S5, evaluating the distributed energy storage regulation and control strategy candidate solution, selecting an optimal distributed energy storage regulation and control strategy which is most matched with the current park energy supply and demand state according to a preset fitness evaluation index, and taking the optimal strategy as a regulation and control scheme. According to the method, the energy utilization efficiency can be effectively improved in a complex dynamic environment, the energy storage loss and carbon emission are reduced, and the intelligent energy supply optimization target of the zero-carbon park is achieved.

The invention relates to the technical field of logistics transportation management, and discloses a vehicle transportation management method based on a digital technology, and the method comprises the following steps: collecting the energy state data of a vehicle in real time through a vehicle monitoring module; transmitting the collected vehicle data to a big data platform by using an Internet of Things technology, analyzing the data, and generating an early warning signal of an energy state; when the early warning signal is triggered, determining an optimal supply path according to the current position of the vehicle and traffic information, and arranging an energy supply service in advance; and through an intelligent scheduling module, according to real-time traffic information, vehicle load and destination distance factors, a transportation route and task distribution of the vehicle are dynamically adjusted. Through real-time monitoring and analysis, the system can automatically trigger a supply early warning signal when the energy state is lower than a safety threshold value, so that non-planned parking caused by energy exhaustion is avoided, and the accuracy of energy management and the operation reliability are remarkably improved.

The invention discloses a two-phase cold plate liquid cooling ORC waste heat recovery and photovoltaic cooperative energy supply system and method, and relates to the technical field of energy management and waste heat utilization. The two-phase cold plate liquid cooling ORC waste heat recovery and photovoltaic cooperative energy supply system and method comprises the following steps that S1, thermal parameters and electric power states related to cooperative energy supply are collected, and a thermoelectric cooperative input data set is constructed and preprocessed; s2, the supply and demand difference of the high-temperature steam in the main condensation branch and the ORC branch is recognized; s3, coordinating a photovoltaic power supply structure and a cooperative energy supply sequence, and judging the matching degree of cooperative energy supply to a load; and S4, evaluating a cooling, heating and power cooperation and double-source coupling state in combination with a load matching condition and an output behavior. The problems that in an existing two-phase cold plate liquid cooling structure, high-temperature steam is not recycled, photovoltaic power generation does not participate in linkage energy supply, the power source is single, waste heat waste is serious, and energy collaboration is insufficient are solved.

The invention, which relates to the technical field of energy system regulation and control, provides a seasonal multi-scene-driven adaptive regulation and control system for a park integrated energy system, comprising a seasonal feature intelligent extraction module, a multi-scene plan library construction module, a three-party non-cooperative game model module and an adaptive execution module. The seasonal feature intelligent extraction module is used for decomposing historical load data, extracting seasonal feature parameters and generating an energy supply priority sequence; the multi-scene pre-arranged plan library construction module is used for constructing a regulation and control pre-arranged plan for coping with electricity price fluctuation, equipment faults, extreme weather and production scheduling, and realizing multi-target dynamic balance; the three-party non-cooperative game model module is used for defining strategy spaces and revenue functions of an energy supply side, an energy consumption side and an energy storage side; according to the method, through dynamic operation of the seasonal feature vector and the load incidence matrix, accurate adjustment of the energy supply priority is achieved, the refrigeration power supply response time in summer is shortened, and the waste heat recovery efficiency in winter is improved.

The invention provides a building energy consumption real-time monitoring and carbon emission evaluation method based on digital twinning, and the method comprises the steps: obtaining regional energy supply real-time data and building group load demand data, predicting an energy supply fluctuation trend and a load change trend, and obtaining a short-term supply and demand dynamic prediction result; adjusting the operation mode of the building group combined cooling heating and power system in real time according to the mode switching optimization scheme, dynamically matching the output change of the distributed energy, and feeding back the output change to the digital twinborn model; carbon emission data is extracted from the optimized load distribution scheme, a digital twinborn model is used for predicting the total carbon emission amount and peak value change trend, the threshold value and constraint of carbon emission control are adjusted, and a final energy scheduling scheme is generated; and obtaining operation data according to the final energy scheduling scheme, calculating the energy utilization efficiency and the carbon emission reduction amount of the building group through an energy efficiency evaluation method preset in the digital twin model, and obtaining an execution effect evaluation result of the scheduling scheme.