162 results about "Energy planning" patented technology

Disclosed in the present invention is a source-grid-load-storage bi-level collaborative planning method for a power distribution network. The method comprises carrying out planning prediction on source-grid-load-storage data of a power distribution network to obtain planned year load data; on the basis of power grid configuration and energy planning constraint conditions, by using the maximization of the total revenue of a main operator as an objective function, constructing a power grid planning model, wherein the objective function comprises an energy supply operator objective function, a power distribution network operator objective function, an energy storage operator objective function and a load aggregator objective function; on the basis of power generation resource operation costs, constructing an electric energy market simulation clearing model; using the power grid planning model as an upper-level planning layer, using the electric energy market simulation clearing model as a lower-level planning layer, inputting the planned year load data into the electric energy market simulation clearing model, and iteratively solving a bi-level collaborative planning model to obtain an optimal power grid planning scheme. The embodiments implement bi-level collaborative planning of the market and a power grid, and improve the safety and stability of the power grid.

The invention discloses an energy planning method for a low-carbon economic comprehensive energy system based on multi-agent deep reinforcement learning. The method comprises the following steps: constructing a comprehensive energy system structure including multiple energy forms such as wind power, photovoltaic, electricity-to-gas, combined heat and power generation, energy storage and the like; establishing a carbon emission intensity model of each subsystem based on an energy input and output relationship, and constructing a segmented carbon tax function to quantify the carbon cost; a double-layer two-stage Nash optimization model is designed, and the game relationship between collaboration between subsystems and external energy interaction is considered; the optimization problem is converted into a Markov decision process, and a state, an action and a reward function are defined; a multi-agent TD3 algorithm model is constructed and trained, and strategy stability and robustness are improved through a differential evolution mechanism; and deploying the trained strategy model in an actual system to realize self-adaptive energy planning and scheduling for the carbon economic target. The method has the characteristics of high adaptability, high carbon benefit and excellent intelligent cooperation capability.

The invention relates to the technical field of energy planning, and particularly discloses a source-storage-transmission combined planning method considering carbon emission responsibility allocation, and the method comprises the steps: building a carbon emission responsibility double-layer allocation strategy; based on a carbon emission responsibility double-layer allocation strategy, establishing a source-storage-transmission combined planning model considering carbon emission responsibility allocation; and solving the source-storage-transmission combined planning model considering the carbon responsibility allocation to obtain a system source-storage-network optimal combined planning scheme. According to the method, carbon emission responsibilities are scientifically shared according to the principle of'who causes, who undertakes, who governs and who benefits', the effect of the carbon emission responsibilities in the planning process is effectively considered, an energy storage system is additionally arranged, the system flexibility is improved, and renewable energy consumption is promoted.

The invention discloses a power load curve clustering method, system and equipment based on an improved density peak value clustering algorithm and a storage medium, and belongs to the field of intelligent power distribution networks, and the method comprises the steps: carrying out the sampling segmentation of power load data, constructing a daily load curve set, carrying out the standardization processing of each daily load curve, and generating a standardized sample set; based on the standardized sample set, calculating a sample distance relation of the daily load curve set, and constructing a corresponding distance matrix; establishing a local density estimation model according to the distance matrix, and calculating a density estimation value of each sample curve; according to the local density estimation value of each sample curve and the relative distance between each sample curve and other samples in the distance matrix, constructing a clustering decision value set, and selecting a plurality of sample curves with clustering decision values higher than a threshold value as clustering centers; and by taking the clustering center as a reference, sequentially distributing the residual sample curves to the class cluster to which the corresponding clustering center belongs according to the density estimation value and the distance relationship between the residual sample curves and each clustering center, and completing sample clustering division. According to the method, the problem of misclassification when the density difference of the power load curve set is too large is solved, the sample density difference when the data density difference is too large is accurately represented, the clustering precision of the load curve is effectively improved, reliable power consumer social attribute identification is provided for a power supply side, and an energy planning strategy is better implemented.

The invention discloses a power load prediction method based on an adaptive multi-scale hypergraph neural network, and the method comprises the following steps: obtaining subsequences of different time scales based on power load data, generating an initialized node feature, an initialized hyperedge feature and a point-edge incidence matrix, and executing node constraint and hyperedge constraint; modeling high-order interaction among fine-grained features through a hypergraph convolution attention mechanism to obtain updated node features, and modeling high-order interaction among coarse-grained features through the attention mechanism to obtain updated hyperedge features; and fusing the updated node features and hyperedge features to carry out power load prediction, and carrying out model training and application. According to the method, high-order interaction among multi-scale features can be adaptively modeled, inherent time sequence changes in each scale can be distinguished, the accuracy of power load prediction is improved, and the method has wide application prospects in the fields of power system operation, energy planning, energy efficiency management and the like.

The invention discloses a surface mine low-carbon energy planning method and system and computer equipment, and the method comprises the steps: calculating the historical total energy consumption and historical total carbon emission of a surface mine; inputting the historical total energy consumption and the historical total carbon emission into a time sequence prediction model to obtain a time sequence prediction result of the total energy consumption and the total carbon emission under the reference scene; calculating a total energy consumption prediction value and a carbon emission prediction value under the reference scene based on the total energy consumption and total carbon emission time sequence prediction result and a preset production plan coefficient; and based on the carbon emission reduction value, the total energy consumption prediction value and the carbon emission prediction value of the green low-carbon scene, calculating the total carbon emission of the green low-carbon scene, and performing low-carbon energy planning according to the total carbon emission of the green low-carbon scene to obtain a low-carbon energy planning path. According to the invention, combined application of various low-carbon, zero-carbon and negative-carbon technologies can be automatically planned, energy and carbon emission low-carbon transformation paths and development trends of enterprises can be simulated, and low-carbon transformation decision making of strip mine enterprises can be assisted.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

An operation and maintenance adjustment method based on the light abandoning amount of a photovoltaic power station comprises the following steps: obtaining historical operation and maintenance data of the photovoltaic power station, and determining the power limiting time type of the photovoltaic power station; obtaining power data of the photovoltaic power station, wherein the power data comprises an AGC value, inverter actual time sequence power, inverter theoretical time sequence power, inverter historical actual time sequence power and inverter historical theoretical time sequence power; inputting the power data into a prediction model, and obtaining a light abandoning amount through the prediction model; and adjusting the equipment of the photovoltaic power station according to the light abandoning amount and the power limiting time type. Through the analysis of the light abandoning amount and the power limitation, the method plays an important role in the aspects of power supply and demand balance, energy planning, energy conservation and emission reduction, emergency response, evaluation effect and the like, and facilitates the realization of sustainable energy development and power supply stability in the photovoltaic power station.

The invention discloses a photovoltaic power generation capacity prediction method and system based on a solar radiation model, and the method comprises the steps: obtaining and preprocessing the historical meteorological data of a region where photovoltaic power generation is located, analyzing the historical meteorological data, and determining the key features related to solar radiation in the historical meteorological data; building a solar radiation model based on the data corresponding to the key features and a preset neural network model, and predicting solar radiation prediction data in a period of time in the future through the solar radiation model; standard electric energy conversion characteristics of the photovoltaic cell under solar radiation are obtained, and stage electric energy conversion characteristics of the photovoltaic cell in different solar radiation stages are determined based on the solar radiation prediction data and the standard electric energy conversion characteristics; and predicting the photovoltaic generating capacity of different solar radiation stages based on the stage electric energy conversion characteristics and the solar radiation prediction data. The photovoltaic generating capacity is predicted based on the solar radiation model, the photovoltaic generating capacity in a period of time in the future can be accurately predicted, and important support is provided for energy planning and management.

The invention provides a dual-time-scale optimization scheduling system and method considering diesel storage insurance supply and new energy consumption, and the method comprises the steps: collecting a plurality of data in real time, and outputting a historical data set and real-time monitoring data to a scheduling module; based on the wind and light output predicted values and load predicted values of the day before dispatching and the dispatching day, a multi-target optimization model is constructed, an energy storage charging and discharging plan, a diesel generator start-stop plan and an electric vehicle dispatching instruction are output, and cross-day energy transfer is achieved; on the basis of a model prediction control principle, 15 minutes are taken as a scheduling step length, a day-ahead scheduling plan is corrected in a rolling manner in combination with ultra-short-term source load prediction data, and prediction deviation is stabilized; controlling charging and discharging switching of the energy storage system, dynamic adjustment of the charging power of the electric vehicle and the operation state of the charging pile; through cooperative operation of day-ahead pre-scheduling and intra-day rolling optimization, source-load balance and low-carbon operation are realized. According to the scheduling system, long-period energy planning and short-period dynamic correction are both considered, and the self-consistency and low-carbon property of the off-grid charging station are improved.

The invention is suitable for the technical field of energy planning and configuration, and provides a distributed light heat storage coupling system configuration method and device, and the method comprises the steps: obtaining the heat load data of a non-central heating target region in a heating period and the equipment data of a distributed light heat storage coupling system; based on the equipment data of the distributed optical heat storage coupling system, constructing an electric-thermal coupling model; and based on the heat load data in the heating period, taking the minimum annual comprehensive cost of the distributed optical heat storage coupling system in the whole life cycle as an upper-layer target and the minimum annual operation cost as a lower-layer target, and solving the electric-thermal coupling model by using a particle swarm algorithm to obtain an optimal configuration result of the capacity of each device in the distributed optical heat storage coupling system. According to the method, the heat load data in the heating period can be fully considered in the configuration process, it is ensured that the system can meet the actual heating requirement of a user, and the capacity configuration optimal scheme of the distributed light heat storage coupling system in different scenes is determined through the bilevel programming model.

The invention provides a polar region clean energy system staged capacity optimization method and device and a readable storage medium, and belongs to the field of new energy planning. The problems of energy storage capacity redundancy and low hydrogen energy conversion efficiency existing in a traditional capacity planning method are solved; comprising the following steps that a staged optimization model is constructed based on seasonal characteristics, the staged optimization model comprises a first-stage model and a second-stage model, the first-stage model considers day-to-day balance of one year, the second-stage model only considers intra-day balance, and total cost minimization is taken as a target; inputting related parameters, and calling the first-stage model to solve annual capacities of wind power, photovoltaic and hydrogen energy storage and a standby threshold value of a diesel generator; calling the second-stage model day by day to obtain power and capacity of electrochemical energy storage and hydrogen energy storage; performing global capacity decision and outputting final capacity configuration of each device; the method is applied to clean energy system capacity planning.

The invention belongs to the technical field of energy planning, and discloses a coal mining area green electricity direct connection planning method and system, and the method comprises the steps: integrating geographic space information, obtaining a three-dimensional geographic space information database, carrying out the processing through a land recycling evaluation model, obtaining a thermodynamic diagram, and carrying out the quantitative analysis of the thermodynamic diagram, and obtaining the power generation amount. Processing and fusing by using a space-time convolutional neural network and a distributed intelligent sensing model to generate a meteorological database; analyzing by using a wind power and photovoltaic space-time output model to obtain a photovoltaic combined sample; processing by using the load prediction model to obtain a load prediction curve set; a source-load time sequence mismatch quantitative model is input for analysis, and an energy storage collaborative configuration scheme is obtained; inputting a multi-target line optimization model for analysis to obtain a direct connection line topological structure diagram; and performing data-driven optimization decision optimization to obtain a source storage load collaborative optimization model, and performing coal mining area green electricity direct connection planning through the source storage load collaborative optimization model. The green electricity is obtained through direct connection, and the cost is reduced.

The invention discloses a polar region environment-oriented two-stage micro-siting optimization method for a wind generating set, and relates to the technical field of polar region renewable energy planning and wind power generation, the optimization method comprises the following steps: polar region environment parameter initialization and scene modeling; the first stage is grid primary selection based on mixed integer linear programming; the second stage is coordinate refinement based on a multi-population genetic algorithm; and scheme output and efficiency evaluation. According to the method, a sector reweighting mechanism of extremely cold correction parameters is realized through reweighting of biochemical parameters and scenes in an environment special for a polar region, the extremely cold correction parameters are directly embedded into a wind regime scene set and a power model, and the problem that a conventional land model can obviously underestimate or overestimate single-machine power and wake flow propagation distance under extremely low temperature and low roughness conditions, so that the reliability of the model is improved is solved. Therefore, the power prediction and wake flow loss evaluation are more practical under the polar region conditions such as the south pole, so that the engineering reliability of site selection decision and the accuracy of generating capacity prediction are improved.

The invention relates to the technical field of new energy vehicle charging planning management, and discloses a collaborative planning method and management system for new energy, and the system comprises a new energy planning parameter collection module, a new energy planning analysis module, and a new energy planning management feedback module. Intelligent and accurate analysis of the number of new energy vehicle charging piles required theoretically in the target geographic area is carried out according to the mean value of the charging access times of the new energy vehicle in the target geographic area in combination with an intelligent recognition algorithm and scientifically set standard data of the new energy vehicle charging piles with different charging access times; the planning management quality of the new energy automobile charging pile is improved; the current actual number and the planned new number of the new energy vehicle charging piles in the target geographic area are accurately collected based on numerical analysis, digital processing of the planning management result of the new energy vehicle charging piles in the target geographic area is achieved, and the applicability of planning management of the new energy vehicle charging piles is improved.

The invention relates to the technical field of photovoltaic energy storage, in particular to a photovoltaic energy storage direct current intelligent microgrid monitoring management system and method, and the method comprises the steps: predicting the predicted photovoltaic power generation amount of a parking shed in a period of time in the future based on weather forecast parameters and historical power generation data; predicting a charging curve based on historical data; calculating an insufficient generating capacity curve based on the charging curve and the predicted photovoltaic generating capacity of the parking shed; the energy storage equipment is divided into a standby state and a charging state, and when the value of the insufficient generating capacity curve is a positive value, the working states of the two kinds of energy storage equipment are switched; when the insufficient power generation curve is a positive value, generating a power grid supply curve based on the insufficient power generation curve; calculating a dynamic charging price based on the power grid supply curve; and sending the dynamic charging price to the user side. Therefore, the generating capacity and the power consumption can be predicted, corresponding energy planning is performed in advance, and the working efficiency is improved.

The invention relates to an electric heating integrated energy system optimization planning method and device considering seasonal heat storage and a medium, and the method comprises the following steps: constructing a seasonal heat storage model based on a heat charging and discharging state and an internal thermal power state, the internal thermal power state comprising a weekly state and an intra-period state, and being obtained through the prediction of a deep neural network; based on the seasonal heat storage model, an electric heating integrated energy system coordinated planning model is built, and the electric heating integrated energy system coordinated planning model takes the sum of minimum annual investment cost and operation cost as a target function and builds constraint conditions based on investment constraints and operation constraints; and carrying out hybrid linear transformation on the electric heating integrated energy system coordinated planning model by adopting a large m method, and solving to obtain an optimal planning scheme of the electric heating integrated energy system. Compared with the prior art, the method has the advantages that the electric heating comprehensive energy system can be accurately modeled, the nonlinear optimization problem of a traditional physical modeling method is solved, and scientific and efficient energy planning is achieved.

The invention relates to the technical field of energy management, and provides an integrated energy system collaborative planning method and device suitable for a park and considering uncertain factors. Mathematical models are constructed for the wind turbine generator, the cold load, the thermal load and the electric load respectively, and an operation scene set is constructed; clustering each kind of scene feature data in the operation scene set to obtain a plurality of data class groups of each kind of scene feature data; classifying the scenes according to the corresponding relationship between the scene feature data and the scenes in the data class groups to obtain a plurality of scene class groups corresponding to each scene feature data; determining a plurality of scene class groups to which the current park planning belongs; determining an intersection of a plurality of scene class groups to which the current park planning belongs; and according to the cost data of each scene in the intersection, planning integrated energy system distributed energy supply equipment and energy supply equipment construction site selection. According to the method, finer multi-energy planning design can be realized, and the multi-energy planning efficiency is improved.

The embodiment of the invention relates to the technical field of energy planning, and discloses a hydrogenation network distribution planning method and device based on multi-source data, and a storage medium, and the method comprises the steps: firstly obtaining a preliminarily planned hydrogenation network route (i.e., an initial hydrogenation station topological map), obtaining a plurality of initial candidate hydrogenation station addresses through spatial mapping; adjusting the initial candidate address according to a multi-source data set of industry, traffic, environment, economy, safety and the like to obtain a target candidate address, and screening out a target hydrogenation site address according to preset conditions including rigid (such as distance limitation with a peripheral sensitive area) and flexible (such as single-station service radius limitation) constraints; and finally, generating a hydrogenation network distribution circuit diagram based on the target addresses. Therefore, according to the technical scheme of the invention, multiple factors can be comprehensively considered, so that the site selection of the hydrogenation sites is more scientific and reasonable, the accurate matching of hydrogen energy supply and terminal requirements can be realized, and the efficiency of hydrogenation network distribution planning can be improved.

The invention belongs to the technical field of wind and light load probability prediction, and particularly discloses a wind and light load probabilistic balance method considering hydrogen energy two-stage planning, comprising the following steps: constructing a joint probability distribution model of wind power, photovoltaic output and load demand, and defining a feasible region; based on a joint probability distribution model of wind power, photovoltaic output and load demand and a feasible region, first-stage hydrogen energy monthly planning is carried out with the goal of minimizing wind-abandoning, light-abandoning and electricity-abandoning quantity; and with minimization of the total cost of the system as a target and with the monthly planning total amount of the first-stage hydrogen energy as a constraint condition, second-stage hydrogen energy hourly scheduling is carried out, and wind and light load probabilistic balance is realized. The problems that the randomness and volatility of wind and light output are not fully considered in an existing method, the wind and light abandoning rate is high, energy waste is serious, system planning and real-time scheduling are usually separated in an existing planning and scheduling method, and dynamic association cannot be established between monthly energy storage configuration and hourly output optimization are solved.

The invention relates to the technical field of energy planning, and discloses an uncertain scene generation and planning method based on source load matching and time sequence characteristics, which effectively reduces the operation performance deviation and improves the engineering implementability of a stochastic planning scheme. According to the method, a representative meteorological year data set, a reference capacity optimization variable set and a whole-year wind-light load initial scene time set are determined through historical data of a target area in previous years; dividing the sample points in each hour into a residual power state region set and a power shortage state region set based on positive and negative source charge difference values; respectively clustering the two types of sets hour by hour by adopting a distance clustering algorithm to obtain an annual typical scene set and a corresponding first probability set; and further generating a year-round disordered boundary sequence, and performing optimization by taking the representative meteorological year data set time sequence as a benchmark to form a year-round scene boundary with time sequence characteristics. According to the method, the source load difference and the time sequence characteristics are effectively reserved, the system reliability evaluation accuracy and the planning implementability are improved, and the operation deviation is reduced.

The invention discloses an energy structure planning method and system based on an entropy flow dynamic model, and relates to the field of comprehensive energy planning, and the method comprises a first model building module which is used for building a decision matrix and a state matrix, and building an evolution model based on a system dynamics principle; the second model construction module is used for constructing a target function which takes system entropy generation as an optimization target and takes policy implementation cost and system orderliness as constraint conditions according to the entropy balance equation and the thermodynamic force-flow relationship, and constructing a maximum entropy generation model based on the target function and an evolution model; and the path output module is used for performing iterative calculation by utilizing an outer layer nested optimization algorithm based on the maximum entropy generation model to obtain an optimal policy combination of each time node in a preset planning period, and outputting an energy planning maximum entropy path. According to the method, system dynamics and an entropy theory are fused, source-network-load-storage full-link interaction is described, and a scientific and reliable benchmark reference is provided for low-carbon transformation of an energy system.

A new energy planning method based on new energy development planning and consumption model, including: 1. Constructing a new energy development demand model based on the carbon constraint target and basic power balance situation of the new energy planning area; 2. Constructing a power sufficiency and new energy consumption level model under various power source combinations; then constructing a comprehensive evaluation index model based on the power sufficiency and new energy consumption level model; 3. Constructing a new energy capacity utilization efficiency model based on the historical annual power generation data of the new energy planning area; 4. Constructing an investment return model based on the cash flow and initial investment within the project life cycle; 5. Constructing a new energy development planning and consumption model using the above models, and conducting new energy planning based on the new energy development planning and consumption model. The present invention proposes a new energy development planning and consumption model, which is suitable for energy planning departments, power companies, etc., and provides a solid scientific basis for energy structure adjustment and carbon emission control.

The invention discloses a multi-energy complementary system site selection method based on dynamic weight and geographic information system (GIS) cooperation, and belongs to the technical field of renewable energy planning. According to the method, a technical framework of'multi-dimensional data acquisition-dynamic weight optimization-collaborative decision verification 'is constructed, so that the core problems of weight staticizing, space-time splitting, insufficient robustness and the like in a traditional site selection method are solved. According to the technical scheme, an analytic hierarchy process and a space-time dynamic correction mechanism are innovatively combined, and self-adaptive weight optimization of multi-dimensional criteria such as climate resources, power grid access and environmental constraints is achieved; through fusion of spatial clustering analysis and Monte Carlo simulation, a site selection verification system considering geographical proximity and system collaboration is established. According to the method, the dynamic response capability of site selection decision to policy change and technical evolution is remarkably improved, the scientificity and implementation reliability of a multi-energy complementary system planning scheme are ensured, and an intelligent decision support tool is provided for efficient deployment of renewable energy sources in a novel power system.

The invention discloses a cooperative computing resource optimal configuration method based on a MapReduce framework. The method comprises the following steps: establishing an implementation scene and initializing the battery electric quantity and channel gain of equipment; at the beginning of each time slot, a reinforcement learning algorithm is utilized to solve an advance time energy planning problem according to the current state, and a computing resource optimization problem, an unloading decision problem and a transmission rate control problem are solved at an AP end at each time slot while the optimal equipment energy consumption is obtained; the optimal time allocation and the optimal transmission power decision are obtained by the equipment end, and the equipment energy and the channel information are updated by each piece of equipment. According to the method, the MapReduce framework is combined with the cooperative computing system, a system model of the edge computing system and the battery on the long-time scale is established, the used electric quantity can be planned in advance, and the purposes of saving energy, reducing emission and reasonably utilizing energy are achieved.

The present invention relates to the technical field of energy planning, and specifically discloses a source-storage-transmission joint planning method considering carbon emission responsibility sharing. The method includes: establishing a two-layer carbon emission responsibility sharing strategy; based on the two-layer carbon emission responsibility sharing strategy, establishing a source-storage-transmission joint planning model considering carbon emission responsibility sharing; solving the source-storage-transmission joint planning model considering carbon responsibility sharing to obtain an optimal joint planning scheme for the system's source-storage-network. According to the principle of "who causes, who bears, who governs, and who benefits", this method scientifically shares carbon emission responsibilities, effectively considers the role of carbon emission responsibilities in the planning process, adds energy storage systems, improves system flexibility, and promotes the consumption of renewable energy.

The invention belongs to the technical field of electric power, and discloses a roof distributed photovoltaic installation potential evaluation method based on deep learning, which is particularly suitable for being applied to an urban environment. The problem that in the prior art, the prediction precision of the power consumption demand of a user is insufficient is solved, and a composite prediction model combining variational mode decomposition and a Crossform network is disclosed. According to the model, firstly, VMD is used for preprocessing original power utilization data, modal components of different frequencies are effectively separated out, noise interference is reduced, and data quality is improved; and further analyzing and capturing a long-term dependency relationship in the time sequence through a Crossform network, thereby realizing high-precision prediction of the power consumption demand. The method improves the accuracy and practicability of evaluation, can be widely applied to different urban environments, and provides scientific basis and technical support for urban energy planning. The invention further discloses a comprehensive photovoltaic installation potential evaluation framework.

The present invention relates to the field of energy management technology, and provides a method and device for collaborative planning of an integrated energy system taking into account uncertain factors applicable to a park. Mathematical models are constructed for wind turbines, cooling loads, heating loads, and electrical loads respectively, and an operation scenario set is constructed; the characteristic data of each scenario in the operation scenario set are clustered respectively to obtain multiple data clusters of each scenario characteristic data; the scenarios are classified according to the correspondence between the scene characteristic data and the scenarios in the data clusters to obtain multiple scenario clusters corresponding to each scenario characteristic data; the multiple scenario clusters to which the current park plan belongs are determined; the intersection of the multiple scenario clusters to which the current park plan belongs is determined; and the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned according to the cost data of each scenario in the intersection. The present invention helps to achieve more refined multi-energy planning and design, and improves the efficiency of multi-energy planning.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The embodiment of the invention provides an energy regulation and control system, a regulation and control method and electronic equipment, and belongs to the technical field of data processing. The energy regulation and control system comprises an Internet of Things platform module used for collecting various equipment data of an oil field station; the data platform module is used for carrying out data preprocessing on the various equipment data; the algorithm platform module is used for performing energy regulation and control based on the various equipment data after data preprocessing by using an operational research optimization algorithm or a machine learning method; and the business platform module is used for performing correlation analysis on the various equipment data based on a correlation analysis method and determining an operation strategy of the oil field station based on a correlation analysis result. The embodiment of the invention is used for solving the defects that an existing scheme lacks a comprehensive and multi-dimensional energy planning system, so that the system cost is reduced, and the load efficiency of an oil field station is improved.