538 results about "Energy balancing" patented technology

The invention relates to a distributed energy management system for prolonging the cycle life of a lithium battery. The system comprises an acquisition unit used for acquiring battery state characteristic data of single batteries in a lithium battery pack; the identification unit is used for carrying out charge and discharge abnormal mode identification on the battery state characteristic data to obtain charge and discharge abnormal event data; the decomposition unit is used for performing hierarchical characteristic decomposition and multi-dimensional fusion based on the charging and discharging abnormal event data to obtain a cycle life attenuation characteristic matrix; and the equalization control unit is used for carrying out life equalization control based on the cycle life attenuation characteristic matrix to obtain a target charge and discharge coefficient. According to the invention, the local over-charge and over-discharge phenomena are effectively inhibited, the service life loss of the battery pack is reduced, and the energy balance adjustment of the multi-node control unit is realized.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention discloses a new energy project intelligent planning method and system based on a large language model, and relates to the technical field of new energy planning, and the method comprises the steps: constructing a new energy project planning knowledge base; the new energy project planning knowledge base comprises basic data required by project planning and is used for providing data support for the optimization model; based on the training data in the new energy project planning knowledge base, performing field adaptability training on the large language model to obtain a special large language model in the new energy project planning field; and based on a project demand input by a user, analyzing project parameters through the special large language model, inputting an analysis result into the optimization model, and generating a planning scheme of a new energy project. According to the method, automatic conversion from a natural language demand to a professional planning scheme is realized, the accuracy of energy balance constraint is ensured through the topological structure diagram of the energy system, a flexible switching optimization target of economic benefits and environmental benefits is supported, and finally intelligent generation and evaluation of the planning scheme are realized.

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 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 source network load storage oriented regional collaborative scheduling method and system, and relates to the technical field related to power resource scheduling, and the method comprises the steps: traversing target region source network load storage to carry out real-time data collection, and constructing a source network load storage multi-dimensional data set to carry out multi-scale layered optimization; operation constraint conditions are set, limitation is carried out in combination with a power grid topological structure, a cooperative scheduling instruction set is determined, cooperative control feedback is carried out, and regional power scheduling feedback parameters are generated; and multi-channel dynamic correction is carried out, and a cooperative scheduling optimization strategy is generated to carry out energy balance cooperative scheduling on the source network load storage in the target area. The technical problems that in the prior art, intermittent fluctuation of new energy is difficult to consume, the collaboration of all links of source network load storage is poor, the dispatching flexibility of a power system is insufficient, and the resource configuration efficiency is low are solved. The technical effects of optimizing regional energy resource allocation and improving the clean energy consumption level, the power grid operation stability and the source grid load storage cooperative response capability are achieved.

The invention provides an energy storage intelligent monitoring method and system for an industrial and commercial energy storage power station, and relates to the technical field of intelligent monitoring, and the method comprises the steps: determining a current impact characteristic and a voltage fluctuation characteristic of an energy storage unit during the switching of a charging state and a discharging state through an operation record of the energy storage power station; extracting the energy conversion rate of the energy storage unit in the charging and discharging state switching process, and determining the transient energy entropy change of the energy storage unit during state switching in the charging and discharging state switching process according to the energy conversion rate and the current impact characteristics; performing compensation cost constraint on the energy inverse difference of the energy storage unit during charging and discharging state switching through transient energy entropy change and voltage fluctuation characteristics to obtain a state constraint parameter of energy compensation of the energy storage unit during state switching; according to the method and the device, the energy balance of the energy storage power station in the charging and discharging state switching process can be realized, so that the operation stability of the energy storage power station is improved.

The invention discloses a multi-target computing power unloading system oriented to an unmanned aerial vehicle cooperative cluster, and aims to realize comprehensive optimization of task scheduling efficiency, energy balance and unloading intelligence on the premise of satisfying service real-time performance. The system firstly senses the operation state of each unmanned aerial vehicle in a cluster, extracts multi-dimensional information such as task data volume, residual electric quantity, local computing power and adjacent cooperative node resources, constructs a system state space, and defines action spaces such as local processing, edge unloading and cooperative unloading. And then, designing a multi-target fusion reward function, carrying out weighted integration on key indexes such as task time delay, electric quantity balance and node residual energy, dynamically adjusting reward feedback in combination with service delay constraint, and guiding a reinforcement learning agent to converge to a better unloading strategy.

The invention discloses a steam pipe network decoupling steady-state optimization method and device, a medium and a program product, and relates to the technical field of smart city guarantee. The method comprises the following steps: determining node pressure distribution data and flow distribution data when a mass and momentum balance condition is satisfied according to fluid distribution data of a pipe network simulation model corresponding to a physical structure and an actual working condition of a steam pipe network; and determining node enthalpy data meeting an energy balance condition. Determining a condensation phase change node according to the node pressure distribution data and the node enthalpy data, associating the steam enthalpy of the condensation phase change node to a saturation state under the current pressure, and updating a current fluid distribution parameter according to the determined condensation water volume, and taking the parameters of the pipe network simulation model finally reaching the dynamic balance state as the operation state parameters of the steam pipe network under the target operation condition. The problem that the steam pipe network parameters cannot be accurately determined in the prior art can be solved, and the running state parameters of the steam pipe network can be accurately determined.

The invention discloses a fire-fighting fire risk assessment and early warning method based on an AI large model, and relates to the technical field of fire risk assessment and early warning, and the method employs the collaborative design of a dual-rate time axis encoder and a deformable convolutional layer, retains the transient characteristics of a voltage, captures the gradual change trend of the temperature, and remarkably improves the early abnormality detection capability. A feature extraction window is intelligently adjusted along with the thermal runaway stage, the problem of time-effect mismatching caused by a fixed time window is solved, and continuous tracking from fine precursor capture to violent change is achieved; through electrochemical coupling and energy balance constraint, a lithium battery thermal runaway mechanism is converted into priori knowledge of model training, irrational prediction of a pure data driving model is avoided, and interpretability and reliability of early warning decision are enhanced.

The invention discloses a public building cold load short-time prediction method fusing physical information, and the method comprises the steps: collecting and preprocessing the historical cooling capacity, indoor environment, outdoor weather and equipment operation state data of a public building at a fixed time interval, and obtaining multi-dimensional input features; respectively establishing a workday sub-model and a holiday sub-model according to workday and holiday scene division; the workday sub-model and the holiday sub-model jointly form a cold load prediction model, the workday sub-model adopts a long short-term memory (LSTM) network, and the holiday sub-model adopts a light gradient elevator (Light GBM); a physical constraint loss function based on building energy balance and heat conduction residual error is introduced in the training process, and the physical constraint loss is fused into a total loss function according to a weighting coefficient so as to constrain that the output of each sub-model accords with the law of energy conservation and thermal inertia; monitoring the prediction error MAPE in real time and performing online calibration; and outputting a short-time cold load prediction result.

The invention relates to the technical field of metal smelting, in particular to a multi-gun top-blowing continuous blowing intelligent system and method. The system comprises a data acquisition module, an analysis processing module and a prediction feedback module. The method comprises the steps that the temperature, the liquid level and the material state in the furnace are monitored in real time and transmitted to the central data processing platform in real time for intelligent feedback control; dimensionality reduction is conducted on sensing data in the smelting process, the least principal components with the contribution rate exceeding 80% are extracted, and dimensionality reduction processing is achieved; carrying out thermal analysis by utilizing a material and energy balance equation, constructing a theoretical model of the blowing process, capturing a long-term dependency relationship, and predicting dynamic parameter change in the smelting process; and an interaction model of the intelligent agent and the smelting environment is constructed, a control strategy is updated in real time, and production parameters are adaptively adjusted. The problem of low-altitude pollution caused by converter blowing is solved, the defect of low efficiency of intermittent operation of current smelting is overcome, and a continuous and stable production process is realized.

Sustainable enterprise-wide energy balancing is presented herein, which describes a series of processes to make choices in favor of a sustainably sourced energy mix, grid reliability, and / or to prioritize critical systems in times of a catastrophe. An example method comprises receiving a group of input data representing a standardized phrase, wherein the group of input data is received from first equipment associated with an electric power generation and transmission operator entity; parsing the standardized phrase to identify an initiating event; in response to identifying the initiating event, determining that an initiating threshold value associated with the initiating event has fallen below a defined risk threshold value; and executing, on remote second equipment, a flow of customer defined processes representing a group of runbooks.

The invention discloses a heat exchange station circulating pump characteristic adaptive optimization control method and system, and relates to the technical field of circulating pump control, and the method comprises the steps: building a building terminal heat transfer model and a building energy balance model based on the temperature distribution of the surface of a radiator, and then building a building load prediction model; performing numerical modeling on the performance curve of the heat exchange station circulating pump, and establishing a circulating pump performance model; based on a depth deterministic strategy gradient algorithm, establishing a neural network control model for self-adaptive control of the circulating pump of the heat exchange station; establishing a state, an action and a reward function of an agent of the neural network control model; and training the neural network control model to obtain a trained control model, and performing real-time control on the heat exchange station circulating pump based on the trained control model. The technical problems that a traditional control method of the circulating pump is low in operation efficiency and serious in energy waste are solved.

The invention discloses a power distribution equipment voltage quality adjustment control method, device and equipment, and the method comprises the steps: collecting voltage waveform data in the operation process of power distribution equipment, and carrying out the feature extraction of the voltage waveform data, and constructing a voltage feature library; identifying a resonance voltage mode based on the voltage feature library, performing energy density analysis on the resonance voltage mode to position a resonance energy collection point, generating a resonance compensation signal, and converting resonance energy into adjusting power; performing voltage distribution evaluation on the resonance enhancement control domain, identifying an energy surplus region and an energy deficient region, and constructing a bidirectional energy channel between the two regions to generate an energy balance configuration table; carrying out oscillation characteristic analysis on the energy balance configuration table, and implementing oscillation type voltage regulation at a periodic regulation point; carrying out key path identification on the oscillation adjustment parameters, and optimizing a processing sequence to generate an adjustment time sequence table; and extracting execution instructions of each node according to the adjustment time sequence table to generate a hierarchical control instruction set, thereby realizing active perception, intelligent analysis and accurate regulation and control of voltage quality.

The invention relates to the technical field of power system control, in particular to a direct-current capacitor self-synchronizing grid-connected control strategy method, system, equipment and medium, a direct-current capacitor energy dynamic model is established based on an actual energy balance relation, a direct-current voltage signal is acquired in real time, and a synchronous compensation signal is calculated based on the acquired direct-current voltage signal; performing phase compensation and gain adjustment on the synchronous compensation signal through a correction link based on the calculated synchronous compensation signal, adjusting the internal frequency of the inverter based on the synchronous compensation signal after phase compensation and gain adjustment, controlling the DC voltage through the internal frequency of the inverter, and establishing a self-synchronization control model; and based on the self-synchronization control model, establishing an adaptive control algorithm, generating a damping coefficient and a virtual inertia coefficient through the adaptive control algorithm, and dynamically adjusting control parameters according to the damping coefficient and the virtual inertia coefficient.

The invention belongs to the technical field of photovoltaic network construction, and discloses a pre-synchronization start control strategy of a photovoltaic network construction type converter. According to the method, an energy balance relation between a photovoltaic array and output power of an inverter of a virtual synchronous machine (VSG) is used for replacing charging and discharging of an energy storage battery on a system, and a photovoltaic virtual synchronous machine (PV-VSG) pre-synchronization slow start control strategy is designed. The strategy can enable the photovoltaic inverter to have the capability of independently supporting the bus voltage and frequency, and can avoid the grid connection failure caused by the destructive influence of the system power shortage caused by external disturbance on the DC bus voltage at the starting moment of the energy-storage-free photovoltaic grid connection system. According to the strategy, PV-VSG network construction control in a power reserve mode is considered, and smooth grid connection of the photovoltaic on-load system is realized. In the step of stabilizing the voltage of the direct current bus, sliding mode control is adopted to replace traditional PI control, and the robustness of a network construction system is improved.

The invention discloses an elevator energy-saving control system based on an auxiliary power supply module, which relates to the field of elevator energy-saving control and comprises a battery module, the auxiliary power supply module and an energy-saving control module, the battery module is connected with a circuit breaker, the circuit breaker is connected with at least one contactor set used for being connected with at least one elevator in a one-to-one correspondence mode, the same bypass contactor is connected between the circuit breaker and all the contactor sets, and the bypass contactor is connected with an anti-reverse diode in parallel. The mains supply and the battery module are respectively connected with the auxiliary power supply module; the auxiliary power supply module is connected with the energy-saving control module; and the energy-saving control module is respectively in communication connection with the circuit breaker, the contactor group, the bypass contactor, the battery module and the auxiliary power supply module. The charging and discharging energy balance of the battery module can be realized, and the problems of conversion electric quantity loss and high cost caused by the use of a conversion module are avoided.

The invention discloses an isolated high-voltage direct-hanging energy storage converter and energy balance integrated control method. The method comprises the following steps: S1, acquiring SOC and voltage of each battery cluster in an energy storage converter; s2, establishing a controlled energy channel between the battery clusters, and performing self-balancing transfer on energy between the adjacent battery clusters according to voltage and SOC differences between the battery clusters; s3, selecting an expansion phase shift mode according to the power flow direction between the battery clusters, and selecting a phase shift ratio under the constraint of transmission power to reduce current stress and backflow power; s4, generating a modulation coefficient of a shared bridge arm according to decoupling inductive current inner loop proportion tracking; performing PR current closed loop on the main bridge arm, and generating a switching sequence of each switching tube of the shared bridge arm by adopting unipolar carrier phase-shifting modulation; and S5, outputting the modulation coefficient of the shared bridge arm and the switching sequence of each switching tube, driving the isolated bidirectional DC-DC converter and each H-bridge module to work, and realizing grid-connected energy bidirectional flow and voltage and current index satisfaction.

The invention discloses an intelligent electric vehicle queue optimization method based on congestion prediction and DRL. The method comprises the following steps: (1) modeling a queue position optimization problem of an electric vehicle queue into a mathematical model which takes energy balance as a target and is constrained by a traffic environment; and (2) solving the mathematical model in the step (1) by adopting a DRL method based on a TRPO algorithm to obtain an optimal queue adjustment strategy of the electric vehicle queue. In the step (2), congestion state information in an external traffic environment is obtained through an LSTM traffic prediction and FCM method, and the congestion state information, the remaining electric quantity of each vehicle in the motorcade and the accumulated driving distance are jointly used as state input of a TRPO algorithm; according to the TRPO algorithm, a strategy network and a value network are adopted to respectively obtain a strategy for adjusting the queue position, and the return expectation in the current state is evaluated. The TRPO algorithm dynamically adjusts the updating step length of the strategy through the KL divergence updated by the constraint strategy, and guarantees the stability and convergence in the strategy updating process.

The invention discloses a method for processing carbon flux data obtained by a vortex correlation method. The method comprises the steps of data acquisition, original data preprocessing, data post-processing, data interpolation, data storage and additional description. The method comprises the following steps: capturing a signal by adopting an eddy correlation method, and recording original data in real time through a Smart Flux collector; in the preprocessing stage, invalid data, abnormal values, rainfall influence data and the like are removed by using Eddypro software, and necessary instrument error and inclination correction is carried out; in the post-processing stage, Eddypro and TOVI software are combined for further processing, including energy balance correction, quality control and the like; for missing data, adopting a linear interpolation method and other interpolation methods; the processed data can be stored in an Excel mode, an R language mode or a programming mode. According to the method, the carbon flux data quality can be effectively improved, and a solid foundation is provided for subsequent scientific research and data analysis.

The invention discloses an enterprise lease-oriented equipment credit evaluation system and method, relates to the technical field of equipment data processing, and aims to synchronously quantify the running temperature, power consumption and heat dissipation state of equipment by forming an original thermal state data set Hraw and avoid one-sided judgment caused by traditional single-parameter monitoring. Based on a thermal state feature set Hset of a temperature stability index Tstb, a power consumption fluctuation spectrum index Pspc and a heat dissipation anomaly integral Qint, a three-dimensional feature space reflecting equipment operation stability and energy balance can be constructed, a thermal state feature template set Htpl is further formed through clustering, and dynamic reference establishment in different operation modes is achieved. And comparing the real-time feature vector with the thermal state feature template set Htpl to calculate a thermal balance index Hc, and generating an equipment credit score Sc in combination with a historical credit mean value score Sh, so that quantitative credit of enterprise equipment use behaviors can be realized.

The invention provides an ocean satellite Internet of Things multi-modal data narrow-band transmission scheduling method and device, belongs to the technical field of ocean satellite Internet of Things, and innovatively provides a multi-modal data narrow-band transmission scheduling strategy for challenges of ocean observation equipment in an energy-limited and dynamic satellite channel environment. The core of the method comprises the following steps: constructing a multi-modal data dynamic transmission model, and carrying out data value evaluation based on space-time intelligence; designing a congestion perception and hierarchical congestion control strategy to guarantee data timeliness; the transmission fairness is optimized in combination with the data value weight factor, the PAoI and the Jain fairness index; an energy dynamic evolution model is constructed, and long-term energy revenue and expenditure balance of equipment is guaranteed; based on a deep Q network and imitation learning, in combination with a security reinforcement learning framework, information timeliness, energy consumption and congestion are jointly optimized. According to the method, the deep and far sea observation and detection data transmission time delay, energy consumption and the congestion occurrence rate are remarkably reduced, and the information timeliness and the multi-modal data transmission scheduling fairness are improved.

The invention relates to an industrial park-based comprehensive energy flow direction on-line monitoring system and method, and the system comprises an energy modeling module which is used for representing instrument information, carrying out the modeling of collection labels and collection frequencies of different types of instruments, constructing nodes of different energy layered structures, carrying out the modeling of a directed flow direction relation between the nodes, and carrying out the modeling of the directed flow direction relation between the nodes; binding the logic relationship between the node and the instrument; the data acquisition module is used for performing data acquisition through an instrument, performing edge processing and encrypted transmission through communication equipment, and storing the data in a database; the energy monitoring module is used for monitoring and counting the state of the instrument and monitoring the energy flow direction of the enterprise in real time according to the directed flow direction of the node; and the statistical analysis module is used for providing energy balance analysis for different links of the enterprise energy in the conversion and use process according to the nodes, evaluating the energy consumption loss degrees of the different links, visualizing the nodes with abnormal loss and giving an alarm. Through the real-time and accurate calculation of the energy loss among the nodes at all levels, the backtracking of the nodes with abnormal loss and the timely pushing of the alarm information, the enterprise can timely discover the problems occurring in the energy supply, and can quickly find out the corresponding solution, reduce the energy consumption loss and guarantee the robustness of the energy flow network.

The invention discloses an actual evapotranspiration calculation method based on multi-source remote sensing data. Comprising the following steps: S1, acquiring multi-source remote sensing data; S2, based on an energy balance principle; S3, adopting a dynamic data fusion algorithm based on machine learning; S4, constructing an actual evapotranspiration calculation model adapted by regions; the method can accurately estimate the actual evapotranspiration of the heterogeneous region through multi-source remote sensing data fusion and a dynamic calculation model, is suitable for scenes such as agricultural areas, wetlands, urban green lands and forests, keeps high calculation precision and adaptability under various weather conditions, and has a wide application prospect. The problems of low resolution, data missing, lack of regional adaptability, low calculation efficiency and the like in the prior art are effectively solved, the precision, adaptability and efficiency of evapotranspiration calculation are improved, and more efficient technical support is provided for large-scale water resource management, ecological assessment and agricultural production.

The embodiment of the invention provides a thermal power plant waste heat gradient utilization management method and system, belongs to the field of energy and power engineering, and aims at solving the problems that in the prior art, waste heat gradient matching is unreasonable, the low-temperature waste heat recovery efficiency is low, and the system integration degree is insufficient. The method comprises the following steps: constructing a multi-physics field coupling digital twinborn model and performing real-time correction; establishing a dynamic matching matrix of the residual heat source and the heat consumption load based on balance analysis; solving a Pareto optimal operation scheme through a multi-objective optimization model; and generating and issuing a cooperative control instruction. The system comprises a panoramic data sensing module, a digital twin modeling module, a balance and optimization decision module and a cooperative control instruction generation module. Through global optimization and dynamic regulation and control, the waste heat utilization efficiency, the economical efficiency and the equipment reliability are remarkably improved.

The invention discloses a self-adaptive photovoltaic energy storage comprehensive optimization method and system, and belongs to the technical field of photovoltaic energy storage optimization control. According to the method, a system operation model is established based on historical and real-time operation data of a photovoltaic unit, an energy storage unit and a load side, a hybrid robust-stochastic optimization framework is introduced, a collaborative optimization model with energy balance and system stability as targets is constructed, and an initial control parameter set is solved; in the system operation process, a power fluctuation mode and energy storage response characteristics are identified through a multi-layer agent algorithm, and a dynamic energy storage control instruction is generated and corrected; and according to the cross-cycle change trend of the operation performance index, adaptively adjusting the weight and step length of the control parameter, and realizing strategy evolution and dynamic closed-loop optimization. According to the invention, cooperative operation of photovoltaic and energy storage can be realized under complex operation conditions, and the energy utilization rate and the operation stability are improved.

The invention relates to an anti-reflux control method based on time interval distribution. The method comprises the following steps: acquiring a multi-source fused operation data set; obtaining a trend prediction result set according to the operation data set; according to the trend prediction result set, dividing the prediction time window into a plurality of continuous control time periods, and based on the power generation prediction value, the load prediction value and the uncertainty index of each time period, calculating a corresponding time period countercurrent risk level, and generating a time period risk assessment result; obtaining a time period budget allocation table according to a time period risk assessment result; according to the time period budget distribution table, performing time-division power adjustment on the photovoltaic inverter, the energy storage unit and the controllable load, and realizing smooth transition between adjacent time periods through a cross-time period energy balance mechanism; and monitoring the grid-connected point power and the equipment operation state in real time, and when power deviation or load sudden change is detected, dynamically correcting the current output according to the residual capacity of the time period budget allocation table. According to the invention, the photovoltaic anti-reflux control effect can be improved.

The invention provides a multi-source complementary intelligent power supply management method and system in a mobile square cabin. The method comprises the following steps: collecting real-time state data of multi-type energy supply equipment, and generating a comprehensive energy management data set; based on the comprehensive energy management data set, dynamically evaluating real-time efficiency by adopting an interval optimization algorithm based on an evolutionary strategy, and generating an energy combination strategy report by applying a Markov decision process; based on the energy combination strategy report, adopting a gradient descent optimization algorithm to adjust working parameters of the multi-type energy supply equipment, and applying a real-time energy balance technology to generate an optimized energy configuration scheme; and based on the optimized energy configuration scheme, monitoring the operation condition of a power supply system, optimizing working parameters, and generating an intelligent power supply management parameter set. According to the technical scheme, the intelligent power supply management effect can be effectively improved.

The invention discloses an industrial park integrated energy system optimization method and system, and belongs to the technical field of building and energy system design optimizing.By determining constraint conditions of energy balance constraint, equipment capacity constraint and equipment operation constraint, load data in an industrial park are used as initial conditions, design parameters and meteorological data are input, and the optimal energy balance constraint, equipment capacity constraint and equipment operation constraint are determined; taking the hydrogen doping ratio as a decision variable, constructing a comprehensive energy system optimization model and determining a target function; solving the objective function by taking minimization of annual total cost, carbon emission and energy consumption of the integrated energy system as an objective, and outputting a Pareto solution set; and performing multi-target sorting on the Pareto solution set, screening out an optimal solution corresponding to the decision variable, and obtaining a real-time power condition of each device corresponding to the optimal solution. According to the comprehensive energy system optimization model constructed by the method, the use proportion of hydrogen energy can be adjusted under different operation conditions, so that the optimal energy efficiency and environmental protection effect are achieved.