271 results about "Energy load" patented technology

The invention discloses a multi-domain collaborative flexible load schedulable potential evaluation and energy management method, and relates to the technical field of park energy management, and the method comprises the steps: obtaining space-time multi-source data of a smart park, constructing a graph neural network power consumer clustering model based on iterative self-organization analysis, and generating a power consumption behavior portrait of a power consumer; based on power utilization parameters of building air conditioners, electric vehicles, park ponds and energy storage batteries in the smart park, the schedulable potential of the multi-element flexible resources is evaluated; and based on the hybrid neural network and the Harris eagle optimization algorithm, constructing a load prediction model, and predicting various types of energy loads in a future time period. The invention aims to establish an accurate model and method, accurately evaluate the schedulable potential of different types of flexible loads in different scenes, realize efficient utilization, energy conservation and emission reduction and optimal configuration of park energy, and improve the overall energy management level and operation efficiency of the park.

The invention discloses an energy optimization decision-making system based on cloud computing, and belongs to the technical field of energy optimization, and the method specifically comprises the steps: collecting energy consumption data and environmental parameter data of energy consumption equipment, carrying out the preprocessing, extracting space-time joint feature vectors of the energy consumption data and the environmental parameter data of the energy consumption equipment, and carrying out the calculation of the space-time joint feature vectors; constructing a space-time collaborative prediction model of dynamic heterogeneous graph fusion, predicting energy load in a future preset time period, establishing a multi-target optimization model including economic cost, carbon emission and equipment loss, and solving the multi-target optimization model to generate an optimal scheduling instruction; according to the method, millisecond-level data analysis and scheduling optimization are realized through the dynamic heterogeneous graph fused space-time collaborative prediction model and cloud computing, and the method is suitable for large-scale complex scenes and cross-domain collaborative scheduling.

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

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 relates to the technical field of energy load prediction distribution optimization, in particular to an energy terminal-oriented dynamic load prediction distribution optimization method and system. The method comprises the following steps: generating a load prediction value by using a long short-term memory network based on acquired terminal load data; constructing a load priority matrix and a load distribution conflict rule set, and constructing a multi-factor integration constraint model in combination with the optimization parameters of the load predicted value; dynamically correcting the model based on the load prediction value; an integer programming problem is solved, and a distribution instruction of each terminal is obtained; and performing execution control based on the distribution instruction. The distributed acquisition unit supports multiple communication modes, acquires and normalizes various types of terminal data, loads a virtual value compensation model for checking abnormal nodes, guarantees data integrity and reliability, improves the processing capability of the system for multi-source heterogeneous data, and enhances compatibility and robustness.

The invention discloses an integrated energy load prediction method and system based on modal decomposition and TCN-Transform fusion, and aims to solve the key problems of low prediction precision, insufficient utilization of meteorological factor and load correlation, insufficient optimization of a model structure and the like in integrated energy system load prediction. The method comprises the following steps: comprehensively acquiring electric load, cold load, thermal load and various meteorological data, acquiring different types of data by adopting a special device, and then preprocessing the data; using a maximum information coefficient correlation analysis method to screen remarkably related meteorological features; determining an optimal decomposition parameter in combination with variational mode decomposition and a crown porcupine optimization algorithm; a prediction model fusing TCN and Transform advantages is constructed, and the structure is optimized according to load prediction characteristics; the precision and stability of load prediction of the integrated energy system are remarkably improved, the relation between the integrated energy load and external factors is reflected more comprehensively, and a reliable load prediction basis is provided for optimized operation and management of the integrated energy system.

The invention discloses a classification aggregation identification method based on adjustable load response performance, and the method comprises the steps: constructing index order-based classification according to a integrating degree index and an access standard, and dividing adjustable load resources managed by a virtual power plant into a main energy load resource set, a frequency modulation load resource set, a standby load resource set and a comprehensive load resource set; and aggregating the classified resources by using a self-organizing mapping (SOM) clustering method based on feature vectors, and performing external characteristic identification analysis on an aggregate based on maximum flexibility. According to the method, the adaptation degree of the adjustable load resources and the power transaction products is fully considered, corresponding integrating degree indexes are set, classification is carried out in a bit order sorting mode, and the adjustable load resources in different categories are aggregated by adopting a self-organizing mapping neural network (SOM) clustering method based on feature vectors, so that the power transaction products are classified. And finally, evaluating the external characteristics of the polymer by adopting an external characteristic identification mode with maximized flexibility.

The invention relates to the technical field of resource configuration optimization, in particular to a virtual power plant resource optimal configuration method and system. The method comprises the following steps: obtaining energy load storage resource data corresponding to each power generation unit in a virtual power plant, and carrying out power generation power prediction analysis and energy storage equipment charge and discharge state analysis on each power generation unit in the virtual power plant; the energy power generation power corresponding to each power generation unit and the charging and discharging operation state data of the energy storage equipment are obtained; performing resource operation load demand prediction analysis on the corresponding power generation units to obtain resource operation load demand prediction quantities corresponding to the power generation units; and carrying out resource configuration optimization target analysis on each power generation unit in the virtual power plant to obtain a virtual power plant power generation unit resource configuration optimization target and resource optimization configuration processing so as to generate a virtual power plant power generation unit resource optimal configuration scheme. According to the invention, efficient optimal configuration of each power generation unit resource in the virtual power plant can be realized.

The invention discloses a comprehensive energy low-carbon optimization scheduling method and system based on a master-slave game. The method comprises the following steps: constructing a hydrogen-containing comprehensive energy system model, and inputting data of an external energy supply side, energy conversion and energy storage equipment and a load side to obtain initialized system operation parameters; building a user side model based on the model, generating an initial electricity / heat load demand, calculating a user alternative energy consumption rate and dynamically adjusting an electricity / heat energy conversion ratio in combination with a price difference of real-time electricity and heat prices and user energy consumption behavior characteristics, and forming a new electricity / heat load distribution scheme; the integrated energy operator generates an electricity / heat price signal and formulates a dual excitation strategy, the user aggregator adjusts the user energy load and response strategy according to the signal and the strategy and feeds back the strategy to the integrated energy operator, and the user aggregator and the integrated energy operator adjust bidirectional feedback iteration to solve Stackelberg balance through the electricity / heat price signal and the load to obtain an optimal scheduling scheme. According to the invention, economic low-carbon operation and benefit win-win between subjects can be realized.

The invention discloses a multi-element energy storage collaborative distributed energy system operation regulation and control method, and the method comprises the steps: considering an optimal scheduling strategy of a power-energy type electricity storage, heat storage, cold storage and other technologies for cooperatively regulating and controlling a multi-energy load curve, and constructing a generalized technical model of energy equipment and system operation constraints; designing an energy system operation data preprocessing mechanism and a weight determination model under a given historical window; an energy system scheduling sequence of bipartite dynamic regression coordinated electric heating unit operation is provided, and an economic operation planning model is constructed; the model is applied to a historical window, optimal parameters of key regulation and control nodes are searched and determined through a heuristic algorithm so as to guide future time window scheduling and complete scheduling closed loop, and hyper-parameters of the method, such as historical and future window vision fields, time nodes and weights, are determined based on multiple scheduling closed loop behaviors so as to complete method closed loop. According to the method, a commercial solver is not needed, the economic operation rule of the distributed system is directly mined from historical operation, the method is used for determining the on-off and output plan of each device of the system in a future time sequence, and engineering application is facilitated.

The invention discloses a wind power-containing electric power system source load collaborative scheduling method considering the participation of a smelting type high-energy load, and the method comprises the steps: analyzing the scheduling demands of a wind power-containing electric power system, and building a model considering the load of an electric arc furnace and the wind power output uncertainty based on the schedulable characteristics of the load of the electric arc furnace; a data driving method is adopted to generate a large-scale scene, the uncertainty superposition influence of the electric arc furnace load and the wind power output is analyzed, and the reserve capacity needed by the electric power system for coping with uncertainty fluctuation is determined; a smelting type high-energy load self-adaptive electricity price mechanism considering wind power output change and source load fluctuation is provided, and iron and steel enterprises are stimulated to participate in demand response; and establishing an optimal scheduling model giving consideration to interests of multiple parties, and solving an optimal scheduling scheme by adopting an artificial bee colony algorithm. According to the method, economic benefits and social responsibilities of smelting type high-energy load enterprises are considered, support is provided for the enterprises to participate in consumption of blocked wind power, and new energy consumption and economic and stable operation of a power system are facilitated.

The invention discloses a carbon emission dynamic prediction method and system based on multi-energy load data. The method comprises the following steps: collecting and preprocessing carbon emission, energy consumption and multi-energy load consumption historical data of a park, calculating a joint correlation coefficient, and screening energy types; calculating an initial carbon emission-energy conversion matrix and an energy-multi-load conversion matrix; constructing a carbon emission coupling objective function, and solving and updating a corresponding conversion matrix by alternately fixing a carbon emission-energy and energy-multi-energy load conversion matrix; establishing a carbon emission coupling model, calculating a predicted carbon emission error, constructing a predicted error correction objective function, and correcting a conversion matrix; and S4, predicting the multi-energy load consumption in the (M + 1) th month and correcting errors, and predicting the carbon emission in the (M + 1) th month in combination with the conversion matrix corrected in the step S4. According to the method, the conversion relation between the carbon emission and the multi-energy load can be dynamically adjusted, and the accuracy of carbon emission prediction and the response capacity to actual working condition changes are remarkably improved.

The invention discloses a multi-energy load prediction method considering multiple uncertainties and suitable for an integrated energy system, and belongs to the technical field of energy load prediction. According to the method, a multi-task learning framework is used for multi-load prediction, the complex coupling relation among electricity, heat and hydrogen loads is captured, and the prediction precision of each prediction task is enhanced; considering the influence of uncertainty factors on prediction, specifically including input uncertainty and model parameter uncertainty, respectively performing uncertainty modeling on the input uncertainty and the model parameter uncertainty, and quantifying the uncertainty risk of the uncertainty factors on load prediction; and in combination with an attention mechanism, the model is helped to pay attention to important time step feature information, and unnecessary information interference is reduced. According to the multi-load prediction method provided by the invention, the coupling relationship between the loads can be captured under the condition that uncertainty factors are considered, and accurate prediction of the multi-energy loads is realized.

The invention provides a multi-energy load prediction method and device for an integrated energy system, and belongs to the field of load prediction. The method comprises the steps that target data in a preset time period are acquired, and the target data comprise load data and climate data of various energy sources; performing clustering processing and variational mode decomposition on the load data of the multiple energy sources to obtain first processing data; performing correlation analysis on the first processing data and the climate data to obtain second processing data; inputting the second data into a feature extraction model to obtain high-dimensional time features of the various energy sources, and inputting the high-dimensional time features into a pre-constructed load prediction model to obtain predicted loads of the various energy sources; wherein the feature extraction model comprises an attention mechanism module, and the load prediction model comprises a time attention mechanism. According to the invention, the accuracy of multi-energy load prediction can be improved.

The invention discloses a virtual power plant robust optimization scheduling method based on source load uncertainty, and belongs to the technical field of energy management. The method comprises the following steps: step 1, power supply side uncertainty modeling; step 2, load uncertainty modeling is carried out; 3, carrying out two-stage random robust optimization scheduling modeling; and step 4, solving the two-stage random robust optimization scheduling model. The method has the following beneficial effects: (1) according to the two-stage random robust optimization scheduling method, the economic scheduling of each unit meeting the multi-energy load is realized; and (2) according to a two-stage random robust optimization scheduling strategy, a scheduling scheme with the minimum cost is obtained in the worst scene. And (3) economic dispatching strategies are made for various uncertain conditions by adjusting robust optimization parameters, and the environmental adaptability of the virtual power plant is improved. And (4) in the energy market balance power deviation of the virtual power plant, the economical efficiency is more remarkable.

A system for automating matching of energy load with energy generation for energy consumers, utilities, and gird operators, comprising a processor and a memory containing instructions configuring the processor to collect, from a grid network connecting load centers and generation sources, energy datasets including energy attribute records and energy load data, match each load center to the generation sources based on the energy datasets, matching further comprises generating actionable energy models, each one of the actionable energy models representing a configurable allocation of the energy attribute records, based on the energy datasets, and matching the load centers and generation sources as a function of at least one actionable energy model, populate an allocation report for each load center, and transmit the allocation report to an external device in communication with the processor.

The invention discloses a regional integrated energy system optimization method and system, and the method comprises the steps: S1, constructing a multi-energy demand response model, and carrying out the linkage scheduling among multi-energy loads based on the multi-energy demand response model; s2, designing and optimizing a stepped carbon transaction mechanism; and S3, performing multi-objective optimization and cooperative operation. According to the invention, through a three-layer optimization architecture of multi-energy demand response, stepped carbon transaction and multi-target cooperation, economic, low-carbon and efficient cooperation optimization of the regional integrated energy system is realized, and the blank of the traditional technology in the aspects of multi-energy flow linkage scheduling, dynamic carbon price excitation and new energy refined consumption is filled.

The invention provides a privacy protection multi-energy load day-ahead probability prediction method based on a diffusion model, and belongs to the field of multi-energy load prediction of an integrated energy system. Comprising the following steps: S1, acquiring respective energy consumption load data by a local main body, and preprocessing and normalizing the data; s2, dividing the energy consumption data into historical loads and labels, and respectively training two groups of self-encoders-self-decoders; s3, the local main body uploads the latent variables obtained after coding to the cloud; s4, grouping and integrating latent variables by the diffusion model of the cloud, and constructing a joint probability prediction model; and S5, during model reasoning, after a latent variable generated by the cloud is downloaded locally, a final predicted value is obtained through decoding by a self-decoder. According to the method, a prediction framework based on longitudinal federated learning is designed for a multi-energy load probability prediction scene related to a cross-energy form in an integrated energy system, and a diffusion model capable of predicting multi-energy load joint probability distribution is constructed by considering a coupling relationship among multi-energy loads.

The invention discloses a comprehensive energy scheduling model construction method and system based on an alternating direction multiplier method, and relates to the technical field of power systems. A comprehensive energy load comprehensive demand response model, an electric vehicle ordered charging model and a virtual heat storage heat supply network energy flow calculation model are combined to construct a comprehensive energy system optimization scheduling model; carrying out distributed solution on the interconnected integrated energy system by adopting an alternating direction multiplier method to obtain a multi-region distributed self-collaborative optimization scheduling strategy; and dividing the integrated energy system into a plurality of main bodies, and carrying out distributed solution by adopting an alternating direction multiplier method so as to realize a global target of lowest operation and maintenance cost of each main body and obtain a distributed self-collaborative optimization scheduling strategy among the multiple resource main bodies. According to the method, an optimal scheduling model is solved by adopting an alternating direction multiplier method, so that similar effects can be realized through interaction of necessary boundary variables (boundary power) among main bodies.

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

The invention discloses a load management operation method for comprehensive energy optimal configuration, and relates to the technical field of energy management, and the method comprises the following specific steps: microclimate partition division: carrying out the comprehensive field exploration of a target region, collecting the landform, building layout and vegetation coverage data, analyzing the influence of each factor on the microclimate formation, and carrying out the calculation of the microclimate; according to the method, a partition division basis is determined according to the difference between airflow and heat caused by topographic relief, the influence of a building on sunlight and ventilation and local microclimate characteristics of vegetation construction, and a linkage model comprehensively considering the relationship among a microclimate partition state, a building adjustment behavior and an energy load is established; the complex relation among microclimate parameter changes, building adjustment action changes and energy load changes is accurately reflected, meanwhile, according to state changes after building adjustment, the energy management system can dynamically optimize an energy configuration scheme, unnecessary energy transmission and conversion links are reduced, and the energy utilization rate is improved. And deep optimization configuration of comprehensive energy of the whole area from generation, transportation to use is realized.

The invention discloses a multi-energy system carbon emission accounting method and system based on a block chain, and the method comprises the steps: obtaining multi-energy load data, and designing a multi-energy system frame according to the multi-energy load data; based on a multi-energy system framework, designing an emission model and a physical model of carbon emission of different energy sources on a user side, and obtaining carbon emission data of the different energy sources on the user side; a block chain technology is introduced to verify, consensus and store carbon emission data. According to the invention, the accuracy, real-time performance and transparency of carbon emission accounting are improved; and the credibility of the data is enhanced through the block chain technology. And important contributions are made for promoting global carbon emission reduction and sustainable development targets.

The invention relates to a multi-energy-load scene generation method and device based on a diffusion model. Multi-energy load data and condition information are input into a time-space and cross-dimension decomposition reconstruction network of a classifier-free condition diffusion architecture; projecting the multi-energy load data into time sequence embedding and cross-dimension feature embedding; coding the condition signal and the diffusion step number together to obtain a condition embedding vector; the reconstruction network comprises a time channel and a feature channel, a global context relationship is captured by an encoder, and condition information is fused based on a condition embedding vector through a decoder; wherein an STAR module is arranged behind the decoder of the feature channel; and integrating the output of the time channel and the output of the feature channel, and then carrying out layer-by-layer denoising to generate a multi-energy-load scene. Compared with the prior art, the method has the advantages that autocorrelation, cross correlation and dislocation correlation among cold, hot and electric loads of the building group and renewable energy output are considered, and high-fidelity scene generation under condition control is realized.

The invention relates to the technical field of energy scheduling, and discloses a centralized smart energy load regulation and control system based on a public building, which comprises a power supply unit, a central control unit, a data acquisition unit, a scheduling execution unit and an equipment classification unit, the central control unit comprises a data processing module and an energy consumption demand prediction module. By classifying the equipment and evaluating the energy efficiency, the energy consumption characteristics and efficiency levels of different equipment can be clearly known, an accurate regulation and control strategy can be formulated for each category, the equipment with low energy efficiency can be emphatically optimized, the overall energy utilization efficiency can be improved, demand prediction is carried out by virtue of previous energy consumption data, the regulation and control are more prospective, and the energy efficiency of the equipment is improved. The energy consumption trend of different time periods and seasons can be predicted in advance, so that excessive investment on electric power infrastructures is avoided, the electric power cost is saved, limited electric power resources are preferentially allocated to key equipment and important areas, and optimal allocation of the resources is realized.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

A crowdsourced energy system includes a plurality of distributed energy resources managed by crowdsourcees of the system, a power network to which the distributed energy resources are connected, and a system operator that manages energy trading transactions and energy delivery within the system, the system operator operating at least one computing device configured to: obtain day-ahead peer-to-peer energy trading transaction requests from crowdsourcees for energy to be delivered from the distributed energy resources, estimate day-ahead energy load and solar forecasts, determine optimal power flow for the delivery of energy, and schedule delivery of energy from the distributed energy resources across the power network based upon the energy trading transaction requests, the estimated forecasts, and the determined optimal power flow.

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

The invention relates to the field of integrated energy system management, and discloses an IES load-energy price coupling prediction method and system based on mixed attention, and the method comprises the steps: determining a multi-dimensional management feature through a multi-dimensional management feature extraction unit according to integrated energy management information; wherein the comprehensive energy management information comprises renewable energy output information, multi-energy load information and energy price information in a time sequence; according to the multi-dimensional management features, through a mixed attention unit, determining fusion management features of the multi-dimensional management features under the channel attention weight and the time attention weight; and according to the fusion management features, through a fusion output unit, determining a renewable energy output prediction value, a multi-energy load prediction value and an energy price prediction value. The multi-dimensional management feature extraction unit comprises a multi-column convolutional neural network unit and a long-short term memory network unit. According to the invention, the integrated energy system can be managed and dispatched to plan and operate under the optimal target.

The invention provides a distributed energy management system and method for industrial automation, and relates to the technical field of energy management, and the method comprises the steps: collecting the operation state data of different types of energy in distributed energy from an industrial equipment terminal; local energy state analysis is carried out on the operation feedback instruction by using a parallel chain technology to obtain collaborative allocation boundaries of different energy types, and static scheduling constraints of the distributed energy under each operation condition are constructed; performing energy efficiency anomaly identification on an energy efficiency scheduling strategy during distributed energy scheduling to obtain an interconnection scheduling trend of the distributed energy under source network load storage dynamic coordination, and further determining a correction scheduling hierarchy during graded energy load change; and performing accurate load matching by using the intelligent transformer area fusion terminal to perform quantitative planning on distributed energy operation. According to the invention, dynamic cooperative scheduling can be carried out on the distributed energy system in an industrial automatic operation mode, so that the response speed of energy hierarchical management is improved.

The invention provides an energy distribution optimization method and device, electronic equipment and a storage medium, and can be applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring energy purchase cost data and energy supply attribute data of an energy supplier in a target scheduling period and energy demand attribute data and energy constraint attribute data associated with each energy load area; the energy purchase cost data, the energy supply attribute data, the energy demand attribute data and the energy constraint attribute data are processed based on an energy distribution prediction model, energy distribution information is obtained, and the energy distribution prediction model comprises a first-stage distribution sub-model constructed based on a multi-objective optimization function and a constraint condition set; and determining a target energy distribution scheme corresponding to the energy load area according to the energy distribution information. Cooperative optimization of key targets of cost, reliability and timeliness is realized in a target scheduling period, and the overall robustness and feasibility of an energy distribution scheme in a complex and uncertain environment are remarkably improved.