50 results about "Long term planning" patented technology

The invention discloses a new energy medium and long term stochastic programming method and system coupled with an electro-hydrogen water regulation power supply, and the method comprises the steps: building a power supply planning model with the minimum planning operation cost as an optimization target; bringing the new energy power supply and energy storage adjusting equipment operation model and the new energy consumption rate upper and lower limit constraint expression into a power supply planning model; in consideration of the randomness of fluctuation at two sides of a source and a load in power supply planning, based on the obtained power system operation scene set S and the power supply planning model, dividing into an investment decision-making layer and an operation simulation layer according to a time period influenced by a decision, and constructing a power supply planning two-stage stochastic optimization model based on a typical scene; and solving the power supply planning two-stage stochastic optimization model under a medium and long term planning framework, and determining the investment planning installed capacity of the adjusting power supply and the new energy in the planning level year. According to the invention, economy of medium and long term planning and power supply capacity configuration of new energy can be improved, and reasonable consumption and flexible adjustment of new energy electric power are promoted.

The invention discloses a power system distribution robust scheduling optimization method based on optimal configuration, and relates to the field of power system robust scheduling, and the method comprises the steps: carrying out the coupling modeling of a power transmission network extension plan, a DPFC and an energy storage device, and solving a model to generate an optimal configuration scheme; constructing an uncertainty set capable of being dynamically updated, and obtaining a day-ahead scheduling scheme by adopting a dual-stage distribution robust optimization method in combination with the optimal configuration scheme; on the basis of a day-ahead scheduling scheme, a hybrid robust and adaptive model prediction control framework is utilized, and a real-time control parameter of the DPFC and a rapid power adjustment instruction of energy storage are generated through rolling optimization; according to the invention, by combining optimal configuration, dual-stage distribution robust optimization and hybrid robust adaptive model prediction control, collaborative optimization of long-term planning and short-term scheduling is realized, and through real-time model parameter updating and dynamic uncertainty processing, real-time scheduling is realized. The problems that a scheduling scheme is insufficient in robustness, and economical efficiency and real-time adaptability are difficult to consider are solved.

The invention discloses a method and system for enabling a large model to have spatial reasoning and long-term planning capabilities, and belongs to the technical field of artificial intelligence, and the method is implemented by the following steps: space-relation-graph conversion: converting complex spatial description into a graph structure which can be efficiently processed by a computer; adaptive reasoning: combining the language understanding ability of the large language model and the structure perception ability of the graph neural network to realize a reasoning mechanism; curriculum reinforcement learning: through curriculum reinforcement learning, a progressive learning strategy is used, so that the system can gradually master spatial reasoning tasks from simple to complex; by means of meta-cognitive monitoring, the system can evaluate the decision-making quality of the system in real time and adjust the decision-making quality when necessary; training and optimization: adopting an end-to-end training mode, and combining supervised learning and reinforcement learning to realize optimization. According to the method, the performance and robustness of the model in a complex space task are remarkably improved, and the adaptability and interpretability of the model are enhanced.

The invention discloses a multi-energy load prediction and power distribution network planning method based on data driving. The method comprises the following steps of 1, collecting and sorting historical data and real-time data for load prediction and power distribution network planning; 2, constructing a model for predicting a multi-energy load based on GCN-LSTM deep learning by combining GCN and LSTM; 3, introducing the Bayesian theorem into the model for predicting the multi-energy load based on GCN-LSTM deep learning constructed in the step 2, and establishing a load prediction model; 4, establishing a modeling thought of power distribution network extension planning based on MDP, dividing a planning period into a plurality of stages, defining a current state and an optional action in each stage, and selecting an optimal decision for realizing a long-term planning target according to the current state and the optional action; and 5, on the basis of the step 4, constructing a power distribution network extension planning model based on the MDP, and obtaining the optimal action and the corresponding reward of each stage by solving the power distribution network extension planning model based on the MDP.

The invention discloses a medium and long term planning method for a low-carbon park integrated energy system. The method comprises the steps of park basic data collection, energy demand prediction, energy resource evaluation, energy supply system construction, energy storage planning, carbon emission accounting and emission reduction strategy making, economic and environmental benefit evaluation, planning scheme optimization and determination and the like. According to the method, through a multi-objective optimization algorithm, an optimal planning scheme is solved by taking the lowest energy system cost, the lowest carbon emission and the highest energy supply reliability as objectives. According to the invention, optimization objectives of energy system cost minimization, carbon emission minimization and energy supply reliability maximization can be realized, so that low-carbon development and environmental benefit improvement of a park are promoted.

The application discloses a kind of based on Copula function's urban water, energy and environmental system optimization decision management method, comprising: S1. the Copula joint distribution function of water environment capacity and available energy amount in urban water is constructed;S2. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is established;S3. the resource optimization allocation and joint risk management model of urban water, energy and environmental system is solved;The method realizes the optimization allocation and joint risk management of urban water-energy-environment system water and energy under uncertain conditions.Provide urban water-energy-environment system resource optimization allocation scheme and long-term planning scheme of sewage treatment process under different risk scenario combinations on the basis of maximum economic benefit, and help decision makers better realize the trade-off between joint default risk and system benefit, to realize urban risk controllable and water-energy-environment collaborative development provides scientific method.

The invention relates to the technical field of integrated energy systems, and particularly provides a multi-energy-flow scheduling method and system for a regional integrated energy system, and the method comprises the steps: S1, carrying out the unified modeling of a DC micro-grid, a heat supply network and a natural gas network based on a circuit comparison method, and obtaining a network model of each energy network; s2, constructing a direct-current micro-grid island hierarchical control strategy; and S3, according to the state parameters of the regional integrated energy system, performing dynamic load flow calculation on the direct-current microgrid under the island hierarchical control strategy based on a decomposition load flow method and the network model of each energy network, performing steady-state load flow calculation on the heat supply network and the natural gas network, and generating a scheduling instruction, so that multi-energy-flow coupling response can be accurately captured, and the scheduling efficiency is improved. According to the method, the model inconsistency error is reduced, the unit calculation burden is reduced, the expandability and fault tolerance of the system are improved, and high-precision support is provided for optimal scheduling and long-term planning.

The invention discloses a multi-temporal-spatial-scale optimization operation strategy of a distributed integrated energy system, and aims to solve the operation optimization problem of the distributed energy system in high volatility and complementarity. The method is specifically realized through the following technical scheme: firstly, constructing a multi-energy coupling model of the integrated energy system, and realizing refined modeling of equipment units and user side demand response; secondly, based on renewable energy source day-ahead prediction data, performing day-ahead scheduling by adopting an LSTM model, and constructing an energy supply and demand balance mechanism; renewable energy fluctuation is coped with through a real-time scheduling strategy, and real-time energy balance is guaranteed. In addition, comprehensive energy system long-time scale optimization considering equipment capacity planning is provided, and the overall cost of the system is reduced. And finally, solving the model by adopting a particle swarm optimization algorithm to realize joint optimization of long-term planning and real-time scheduling of the integrated energy system. The operation efficiency and economical efficiency of the distributed integrated energy system are effectively improved, and the method is suitable for integration and management of energy systems of various scales.

The present invention belongs to the technical field of power system operation control, and relates to a method for optimizing unit operation strategies under the coordination of an electric energy market and a capacity market, comprising: 1. constructing a decision optimization model for upper-level power generation enterprises to participate in an electric energy spot market and an electric capacity market, 2. constructing a lower-level electric energy spot market clearing model, 3. constructing a lower-level electric capacity market clearing model, 4. converting the lower-level market clearing problem into a KKT optimality condition, 5. linearizing the model, and 6. calling a Gurobi solver to solve the linearized mixed integer linear programming problem; the present invention fully considers the coordinated operation relationship between the electric energy spot market and the electric capacity market, realizes the short-term optimization of the electric energy market and the long-term planning of the electric capacity market complement each other, enhances the linkage between markets, further improves the overall market efficiency, and effectively guarantees the reliability margin of the system.

The application provides a renewable energy supply-distribution-demand-storage system space-time coordination configuration method, relates to the technical field of intelligent energy, and comprises the following steps: acquiring data of multiple regions; calculating time mismatch degrees, space mismatch degrees and space-time coupling mismatch degrees of the regions; decomposing an optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension; constructing multi-objective optimization functions of the time layers; decomposing the optimization problems of the time layers in a space dimension; alternately solving local optimization sub-problems and global coordination problems of the regions by using a distributed iteration method; outputting a configuration result of a current time layer when the algorithm converges; executing a decision of a first time period by each time layer and taking the decision as a constraint condition of a next time layer to obtain power generation installed capacities, power transmission line capacities, energy storage capacities and operation strategies of the regions. The application can improve a renewable energy consumption rate and reduce system operation costs.

The invention provides a power supply and power transmission channel capacity optimization method, system, equipment and medium, and the method comprises the steps: carrying out the iteration solving of a long-term capacity planning model and a short-term operation simulation model in a double-layer model through a double-layer dynamic iteration mechanism based on a new energy and load typical power curve of a set region, and obtaining a new energy and load typical power curve; obtaining a power supply and power transmission channel capacity optimization configuration scheme; the long-term capacity planning model is constructed on the basis of optimization targets of the minimum total power supply cost in a planning period, the highest power supply reliability under the uncertainty of new energy power generation and the maximum new energy consumption amount; the short-term operation simulation model considers changes of power generation cost of a power supply in a power system, new energy power abandoning penalty cost and load shedding penalty cost, considers multiple optimization targets, deeply couples medium and long-term capacity planning with short-term operation simulation, can adapt to new energy fluctuation and multi-time scale coupling requirements, and improves the simulation efficiency. And thus, medium-and-long-term planning requirements are met.

The invention discloses an intelligent power grid dynamic planning method and system based on multi-dimensional space-time coupling, and belongs to the technical field of power grid planning, and the method comprises the following steps: carrying out original data collection, carrying out multi-dimensional modeling, and carrying out dynamic planning; according to the method, short-term scheduling and long-term planning can be dynamically coordinated through multi-dimensional modeling, cross-regional resource complementation can also be achieved, insufficient or waste of local resources is reduced, and the dynamic characteristics of the power grid are accurately captured through high-resolution spatio-temporal data. According to the method, the uncertainty of wind and light output prediction errors, load fluctuation and the like can be effectively handled, the model can be dynamically planned, flexible resources can be reserved, extreme weather or equipment faults can be handled, time and space separation is broken through, resource utilization efficiency maximization is achieved, and overall economical efficiency, reliability and low-carbon performance are cooperatively improved.

The invention discloses a method for calculating a recommended maintenance grade of hydropower main equipment, and relates to the field of industrial internet platforms, equipment maintenance and hydropower equipment. The method aims at solving the problems that an existing maintenance decision lacks long-term planning and special project requirements are not considered sufficiently, equipment maintenance grades are intelligently recommended by integrating multiple dimensions, and according to equipment state evaluation data, equipment states are matched through score state evaluation and dimension state evaluation; secondly, in combination with a rolling maintenance plan, equipment maintenance interval duration and associated items, a recommended maintenance level is automatically determined by utilizing a rule engine; and finally, calculating the equipment maintenance period based on the maintenance grade and the extended straight line construction period of the equipment associated project. The automation and intelligence of the maintenance decision of the hydropower main equipment are realized, and the scientificity and accuracy of the maintenance decision are improved; the application of the method can significantly improve the maintenance efficiency and effect of the hydroelectric equipment, ensures the long-term stable operation of the equipment, reduces faults and downtime caused by improper maintenance, and has important practical value.

The application discloses a kind of carbon constraints under regional power system planning collaborative optimization method and system, method includes: the unit product cost of each department technical path of calculation terminal, the technical path occupancy rate is predicted using Logit selection model and inventory replacement model, and terminal energy consumption and carbon emissions are summarized and calculated;Based on terminal energy consumption, power load curve is constructed, and system operation simulation data is obtained by solving power production model;Multi-year power supply and demand side evolution path optimization model with minimum total cost in the whole cycle as the target is constructed, which is decomposed into long-term planning main problem and short-term operation sub-problem using Benders decomposition, under the condition of introducing total carbon, carbon intensity and department carbon emission constraint, the optimal evolution path meeting carbon constraint is output by iteratively solving through investment scheme transmission and feedback optimality cut or feasibility cut between main problem and sub-problem.The application realizes the collaborative optimization of supply and demand sides under carbon constraint.

The invention relates to a reverse deduction power system planning method, which comprises the following steps of: obtaining a long-term annual total installed capacity target in a long-term planning layer; in the middle-term coordination layer, an adjusted long-term annual total installed capacity target is obtained; in the short-term verification layer, outputting a final long-term annual total installed capacity target; the long-term planning layer, the middle-term coordination layer and the short-term verification layer jointly form a long-term-middle-term-short-term three-layer coupling deduction framework, and iterative optimization circulation of verification is operated from bottom to top. According to the method, a long-term-middle-term-short-term three-layer coupling deduction framework is constructed, reverse solution is carried out with the carbon peak reaching target year as rigid constraint, the problem that long-term strategy and short-term operation are disjointed frequently occurring in traditional electric power planning is effectively solved, the planning result has perspectiveness, and the method is suitable for large-scale popularization and application. The scientificity and the performability of the planning scheme are obviously improved; the total economic cost of low-carbon transformation of the whole power industry is effectively reduced, and collaborative improvement of economy and reliability is achieved.

The invention discloses a method for determining a chemical flooding block popularization benefit oil price limit, and solves the problems that a chemical flooding scheme is long in economic evaluation time consumption and cannot meet medium and long term planning in tertiary oil recovery. The method comprises the following steps: acquiring a chemical flooding potential block well pattern adjustment mode and investment allocation; chemical flooding economic evaluation and development parameters are obtained; establishing a chemical flooding enhanced recovery efficiency economic limit model, and calculating enhanced recovery efficiency economic limits under different development modes, different oil prices and different single well controlled reserves to obtain an economic limit chart; based on the development parameters of the chemical flooding potential block, when the recovery efficiency improving technical level of the chemical flooding potential block is equal to the recovery efficiency improving economic limit, according to the economic limit chart, interpolation is carried out to obtain the popularization benefit oil price of the chemical flooding potential block; the benefit oil prices are sorted from small to large, and the development and popularization sequence is optimized. According to the method, the popularization economic benefits and the popularization sequence of different blocks can be determined, so that a scientific and reasonable decision basis is provided for medium-and-long-term planning of tertiary oil recovery.

The invention provides an urban power system toughness improvement method and device based on vehicle network interaction, and the method constructs a two-stage stochastic optimization model with the minimum total cost of the system as a target, and covers the long-term planning and operation scheduling of the urban power system in a disaster scene. The method can improve the toughness of an urban power system in an extreme weather long-term scene, achieves the collaborative optimization of multi-dimensional costs such as power generation capacity expansion, line reinforcement, operation maintenance and load reduction, effectively reduces the comprehensive cost, and enhances the dynamic response capability of the system. A fusion channel of vehicle-network interaction and urban power grid toughness planning is opened, and a technical path capable of being popularized is provided for safe, flexible and economical operation of a power system under future climate uncertainty.

According to the micro-grid system planning capacity configuration method and system based on double-layer optimization, a deep Q network reinforcement learning method is adopted, and a capacity configuration scheme of each decision period is determined according to a state space and an action space; forming a capacity configuration scheme of the long-term planning period by the capacity configuration schemes of all decision periods in the long-term planning period; determining a capacity configuration scheme of each short-term planning period in the current decision period according to the target model by adopting a mathematical planning method under the constraint condition of the target model and the constraint of the upper limit of the configuration capacity of each short-term planning period in the current decision period; using the updated decision period reward function to update the economic index of the long-term planning period capacity configuration scheme; and when the updated economic index is smaller than the economic index before updating, feeding back the updated reward function of which the decision cycle is implemented so as to update the capacity configuration scheme of the long-term planning cycle, and solving the problem of planning capacity configuration of the micro-grid system under the uncertainty of multiple time scales.

The invention belongs to the technical field of model construction, and particularly relates to an HRP dynamic planning optimization model construction method based on a DIP medical insurance payment mode, which comprises the following specific steps: determining a target of a model and defining key variables; collecting various data indexes, and cleaning and preprocessing the data; selecting variables having significant influence on the target, and defining constraint conditions of the model; a multi-stage decision model is established, long-term planning is decomposed into short-term decision points, a mathematical model is constructed by applying a dynamic planning theory, and the relationship between variables is quantified; and selecting a dynamic programming algorithm to solve the model so as to realize algorithm writing program codes for calculation. The method achieves a remarkable effect in the aspect of improving the operation efficiency of the hospital. Resource allocation is more reasonable, cost control is enhanced, and service efficiency is remarkably improved. In addition, patient satisfaction investigation shows that the overall evaluation of the patient on hospital services is improved, especially in the aspects of service processes and medical quality.

The invention discloses a collaborative operation method for a data center cluster and shared energy storage, and aims to solve the problems of disjunction of long-term planning and short-term operation, difficulty in multi-agent collaboration, conservative uncertainty response, insufficient service quality guarantee and the like of a green power direct connection data center cluster in energy management. The invention provides a two-stage double-layer collaborative management framework. In the first stage, a robust optimization model driven by data is constructed, and the capacity of the shared energy storage and the capacity of the gas turbine are optimally configured. And in the second stage, an asymmetric Nash bargaining game model is constructed, and distributed solution is carried out by using an alternating direction multiplier method so as to realize multi-agent cooperative operation and fair income distribution. According to the method, the cross-time-scale coupling decision problem is effectively solved, synergistic interaction among multiple subjects is realized, and the overall economy and the renewable energy consumption rate of the system are improved.

The present invention relates to a source-grid-load coordinated expansion planning method considering the optimization of combined hydropower, wind-solar and direct current transmission, belonging to the technical field of integrated energy system planning and operation. The method carries out detailed modeling of cascade hydropower stations and demand response, and takes into account the operational constraints of direct current transmission. The method utilizes demand response resources to alleviate the intermittent output of wind and solar power stations on the power supply side, proposes a load-side demand response service planning model to reduce the system's long-term planning costs, and establishes a source-grid-load coordinated planning model. The method establishes an opportunity constraint model to ensure the system's operational safety and the effective absorption of wind and solar power output, and utilizes the strong extension formula of the sample average approximation method to transform the opportunity constraint model into a mixed integer linear programming problem for solution.

A distributed photovoltaic accommodation capacity quantitative evaluation method, system and device considering uncertainty, the method comprising: generating a source and load uncertainty scenario suitable for long-term planning of a power distribution network, comprehensively considering the occurrence probability of various typical scenarios within a period based on an uncertainty method, aggregating clusters through improved K-Means mean clustering, and reducing scenarios of clusters through an SBR algorithm based on Kantorovich distance to obtain various typical scenarios and their probabilities; constructing a future simultaneous period load and distributed photovoltaic joint scenario set, and solving the voltage out-of-limit probability and comprehensive line loss rate of each load node by using a joint scenario probability power flow calculation method; establishing a distributed photovoltaic accommodation capacity quantitative evaluation model considering uncertainty, relaxing the voltage constraint, taking the maximum access amount of distributed photovoltaic as the target, and carrying out optimization solution of the maximum accommodation capacity of distributed photovoltaic based on the source and load uncertainty scenario.

A capacity configuration method and system for microgrid system planning based on double-layer optimization adopts a deep Q-network reinforcement learning method to determine the capacity configuration scheme for each decision cycle according to the state space and action space; the capacity configuration scheme for the long-term planning cycle is composed of the capacity configuration schemes of all decision cycles in the long-term planning cycle; a mathematical programming method is adopted to determine the capacity configuration scheme for each short-term planning cycle in the current decision cycle according to the constraint conditions of the target model and the upper limit of the configuration capacity of each short-term planning cycle in the current decision cycle; the economic index of the capacity configuration scheme for the long-term planning cycle is updated using the updated decision cycle reward function; when the updated economic index is less than the economic index before the update, the updated reward function of the implemented decision cycle is fed back to update the capacity configuration scheme for the long-term planning cycle, thereby solving the capacity configuration problem of microgrid system planning under multi-time scale uncertainty.

The invention relates to an intelligent soft switching planning method and device for a power distribution network, equipment, a medium and a product. The method comprises the steps of generating an intelligent soft switch planning strategy set according to power distribution network parameters, to-be-selected positions of intelligent soft switches in the power distribution network and unit capacity cost of each planning period by obtaining power distribution network parameters of each planning period of the power distribution network, and determining the intelligent soft switch according to the power distribution network parameters and the intelligent soft switch planning strategy set. According to the method, a first income model of an operator and a second income model of a power distribution network are constructed, and a target intelligent soft switch planning strategy of each planning period is determined according to the first income model and the second income model, so that different requirements of different interest subjects in intelligent soft switch planning can be considered; medium and long term planning can be carried out on the intelligent soft switch in different periods, and the characteristic that the source load increases year by year in the power distribution network is considered, so that the utilization efficiency of equipment resources is improved, and the return on investment of the intelligent soft switch is improved.

The invention relates to a rapid unit commitment model construction method based on scheduling and unit aggregation coupling. The method comprises the following steps: S1, constructing a traditional unit commitment model; s2, introducing line transmission capacity limitation and a direct current power flow model into a traditional unit combination model; s3, aggregating the large-scale units with the same type of operation characteristics, and carrying out output characteristic and climbing start-stop constraint modeling on an aggregation set; s4, introducing a relaxation hypothesis of an economic scheduling only model, and coupling constraint conditions of unit aggregation and scheduling only; and S5, completing construction of a rapid unit commitment model based on scheduling and unit aggregation coupling, and performing piecewise linearization solution on the model. According to the method, rapid operation simulation of a large number of thermal power generating units can be achieved, the method is suitable for a novel electric power system with high renewable energy permeability and a scene where long-term operation simulation of renewable energy volatility needs to be considered, and powerful support is provided for long-term planning and daily scheduling of the electric power system on the premise that calculation precision is guaranteed.

The invention provides a distributed energy storage dynamic configuration method and system. The method comprises the steps that a present value coefficient is obtained through calculation according to the number of years corresponding to each energy storage planning stage; constructing an initial target function by taking minimization of the sum of the energy storage operation cost and the building cost as a target, and integrating present value coefficients of the operation cost and the building cost into the initial target function to obtain a final target function; calculating an objective function value according to the improved genetic algorithm, obtaining individual fitness according to the objective function value, and respectively calculating a crossover probability and a mutation probability according to the individual fitness; performing crossover operation according to the crossover probability, performing mutation operation according to the mutation probability, obtaining a minimum objective function value under the current iteration after completion, and judging whether the minimum objective function value under the current iteration is smaller than the minimum objective function value under the last iteration; and outputting a final minimum objective function value. According to the embodiment of the invention, the core problem that the existing static model cannot match the long-term planning demand can be solved.

The present application relates to the technical field of elastic power system planning optimization, and particularly relates to a power supply structure optimization method and system for energy transformation and elasticity improvement, comprising: constructing and solving a short-term unit commitment model according to collected power system data to obtain unit output and line power flow information; inputting the unit output and line power flow information into a constructed two-stage long-term power system planning model considering extreme events to obtain unit capacity expansion demand and the most serious extreme event scenario of the power system, and inputting the same into the short-term unit commitment operation model to take the same as a standard thermal power unit to be upgraded for unit upgrading to obtain unit expansion results. The present application realizes a unit structure optimization scheme combined with long-term planning and short-term operation by taking into account long-term expansion planning and short-term operation, thereby laying a foundation for subsequent elastic power system planning optimization research.

The invention relates to the technical field of power grid carbon business planning, in particular to a dynamic prediction method, system and device for medium and long term planning of power grid enterprise carbon business and a storage medium. Collecting multi-source heterogeneous data, carrying out power grid service tagging processing, and constructing a carbon service exclusive database; based on a hierarchical policy analysis system, combining four-level evaluation index quantitative analysis to the cooperative influence of international rules, domestic policies and regional detailed rules on the power grid carbon business; establishing a scene suitability evaluation system, performing multi-dimensional evaluation scoring on carbon business cases outside the region, screening high-quality cases, and extracting reusable experience; performing basic trend fitting through a time sequence model, and combining a machine learning model with dynamic factors to construct a hybrid prediction model; and setting a fluctuation threshold value based on the dynamic factor library, triggering correction when the factor change exceeds the threshold value, and dynamically adjusting the prediction result. The problems of insufficient policy analysis, unscientific case reference, inaccurate prediction method and non-closed-loop landing execution are solved.

The invention provides a renewable energy supply-transmission-demand-storage system space-time collaborative configuration method, and relates to the technical field of smart energy, and the method comprises the steps: obtaining the data of a plurality of regions; calculating a time mismatch degree, a space mismatch degree and a space-time coupling mismatch degree of each region; decomposing the optimization problem into a long-term planning layer, a medium-term scheduling layer and a short-term real-time layer in a time dimension; constructing a multi-objective optimization function of each time layer; the optimization problem of each time layer is decomposed in the spatial dimension; alternately solving the local optimization sub-problem and the global coordination problem of each region by adopting a distributed iteration method; when the algorithm converges, outputting a configuration result of the current time layer; and executing the decision of the first time period by each time layer and taking the decision as a constraint condition of the next time layer to obtain the power generation installed capacity, the power transmission line capacity, the energy storage capacity and the operation strategy of each region. The renewable energy consumption rate can be improved, and the system operation cost can be reduced.

The present application relates to a method, device, computer equipment, storage medium and computer program product for establishing a power grid production project. The method includes: obtaining multiple long-term planning basis information and multiple short-term planning basis information for the power grid; determining the equipment configuration strategy of the target planning cycle based on the multiple long-term planning basis information, and generating an equipment problem library based on the multiple short-term planning basis information; summarizing the equipment configuration strategy and the equipment problem library, and determining at least one target power grid production project from the power grid production project set based on the power grid production project access judgment standard; packaging each target power grid production project according to the preset production project packaging rules to obtain a packaged project set to complete the power grid production project establishment task. The use of this method can improve the efficiency of production project establishment.