23 results about "Long term planning" patented technology

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 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 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.

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.

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.

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 invention relates to the technical field related to energy storage operation management, in particular to an operation strategy optimization method and system for energy storage equipment, and the method comprises the steps: setting strategy issuing parameters, and carrying out the operation strategy layered optimization management of the energy storage equipment through an energy storage strategy issuing architecture of annual granularity layering, monthly granularity layering and daily granularity layering. The technical problem that the operation efficiency of the energy storage equipment in different time periods is difficult to maximize due to the fact that connection among long-term planning of an annual strategy, medium-term adjustment of a monthly strategy and short-term execution of a daily strategy is not smooth is solved. According to the method, a hierarchical and refined collaborative optimization mechanism for constructing an annual granularity, monthly granularity and daily granularity three-level energy storage strategy is realized, and the technical effect of seamless connection of long-term planning, middle-term adjustment and short-term customization is realized.

The invention relates to a multi-dimensional security constraint-oriented source network collaborative planning method, apparatus and device, and a storage medium. The method comprises the steps of constructing a typical scene set based on basic data, and obtaining an extreme scene set and an extreme scene probability distribution uncertainty set; establishing a three-stage source network collaborative planning model, and introducing a frequency safety constraint based on frequency change rate limitation, a short-circuit current constraint based on node self-admittance and a direct-current short-circuit ratio constraint based on a multi-infeed short-circuit ratio; and decomposing and solving the three-stage source network collaborative expansion planning model, performing iteration to obtain an optimal planning scheme, and enabling the optimal planning scheme to meet the short-circuit ratio and short-circuit current constraint by utilizing a topological post-verification and constraint generation process. Compared with the prior art, the problem that traditional robust planning is too conservative is avoided while multiple types of safety requirements are considered, coordinated balance of economy and safety in source network planning is achieved, and the method is suitable for medium-and-long-term planning of high-proportion renewable energy sources and an alternating-current and direct-current hybrid system.

The invention discloses a power system scheduling method, device and equipment and a storage medium, and belongs to the technical field of power system scheduling. The method comprises the following steps: constructing a system planning and operation model; constructing a medium and long term runoff uncertainty optimization model based on an information gap decision theory, and determining a runoff uncertainty robust radius bearable by the system through a risk avoidance strategy; the method comprises the following steps: constructing a short-term wind and light uncertainty robust optimization model based on adaptive distributed robust optimization, and constructing a wind and light uncertainty set by adopting a state-driven adaptive Wasserstein distance; carrying out cooperative solution on the system planning and operation model, the medium and long-term runoff uncertainty optimization model and the short-term wind and light uncertainty robust optimization model by adopting a hierarchical optimization framework comprising three hierarchies to obtain a solution result; and outputting a medium and long-term planning scheme and a short-term operation strategy. According to the embodiment of the invention, the operation safety of the power system can be effectively improved.

The invention discloses a unified optimization method for physical toughness and operation configuration of an electric power system. The method comprises the following steps: establishing a double-layer computing architecture comprising an upper-layer power grid physical system model and a lower-layer energy community model; generating a random scene set and a robust uncertainty set; executing a numerical optimization process under a two-stage random-robust hybrid optimization framework: in the process, determining a physical topology reinforcement parameter in a long-term planning stage, determining a physical repair scheduling parameter in a short-term operation stage, and carrying out mathematical coupling on the two stages through technical constraints; and carrying out convergence solution on the unified optimization process. According to the method, a power grid operator can consider the physical toughness constraint and economic cost of coupling short-term emergency repair scheduling under the attack of the worst case when carrying out planning decision-making of long-term physical topology reinforcement, so that an engineering decision-making scheme with the highest physical security level and the optimal economic benefit is output.

This invention discloses a method for coordinated and optimized configuration of energy storage power stations in the medium and long term, belonging to the field of energy storage power station planning technology. The method includes: comprehensively considering the economic benefits during the construction of the planned energy storage power station, as well as the grid status, environmental factors, rated power, and capacity configuration of the completed energy storage power station, to construct a two-layer multi-objective optimization model and corresponding constraints; under the constraints, using a preset multi-objective optimization model solving algorithm, solving the multi-objective optimization model to obtain a Pareto optimal solution set for the energy storage power station configuration scheme; screening the schemes in the Pareto optimal solution set to obtain the optimal scheme, and configuring the planned energy storage power station with the obtained optimal scheme to achieve the optimal planning and configuration of the energy storage power station capacity. Using the technical solution of this invention can provide rational suggestions for the investment and construction of energy storage power stations.

The invention discloses a large-scale wind-solar-thermal storage base scheduling decision-making and optimal configuration method and system based on hierarchical reinforcement learning. The method comprises the steps that historical data are collected and preprocessed; a probability prediction model based on Transform is constructed, and wind and light power quantile prediction is output; designing a multi-time scale state space including long-term planning, short-term scheduling and real-time control; a hierarchical deep reinforcement learning framework is established, a high layer adopts a TD3 algorithm to carry out capacity configuration, a middle layer adopts an MADDPG algorithm to carry out day-ahead scheduling, and a low layer adopts a PPO algorithm to carry out real-time control; key constraints such as system inertia and short-circuit ratio are embedded through a projection method and a penalty term; training the network by adopting a priority experience playback and course learning strategy; deploying an online rolling optimization and incremental learning mechanism; according to the method, the high-dimensional state space of a large-scale base can be effectively dealt with, collaborative optimization of investment and operation is realized, and the system supporting capacity under the high proportion of new energy is guaranteed.

The application discloses a power system distribution robust scheduling optimization method based on optimal configuration, relates to the field of power system robust scheduling, and comprises the following steps: coupling modeling of power transmission network expansion planning, DPFC and energy storage devices, and solving the model to generate an optimal configuration scheme; constructing a dynamically updatable uncertainty set, combining the optimal configuration scheme, and adopting a two-stage distribution robust optimization method to obtain a day-ahead scheduling scheme; based on the day-ahead scheduling scheme, using a hybrid robust and adaptive model predictive control framework, rolling optimization is used to generate real-time control parameters of the DPFC and fast power adjustment instructions of the energy storage; the application combines the optimal configuration, the two-stage distribution robust optimization and the hybrid robust adaptive model predictive control, realizes the collaborative optimization of long-term planning and short-term scheduling, and solves the problems that the scheduling scheme is insufficient in robustness, and economy and real-time adaptability are difficult to be considered.

The invention discloses a multi-time scale energy storage locating and sizing optimization method and system, and the method comprises the steps: building a long-term planning layer model; establishing a day-ahead scheduling layer model, and optimizing energy storage charging and discharging power, standby allocation and cross-node energy allocation; establishing a real-time security layer model, and realizing energy storage rapid output control through linearizing frequency-inertia constraint and voltage-reactive power-harmonic agent constraint; a master-slave decomposition method is adopted, a planning layer is taken as a main problem, a scheduling layer and a security layer are taken as sub-problems, and a Benders coordination strategy is utilized for iterative solution to obtain an energy storage site selection and volume determination scheme under multiple time scales; and comprehensively evaluating the economy, safety, energy self-consistency rate and electric energy quality of the energy storage full life cycle, and determining an optimal configuration result. The technical problem that an existing energy storage configuration method is difficult to consider economy, safety and operation coordination of the system can be solved, and full-life-cycle optimal configuration of locating and sizing of the energy storage system in the high-voltage-class power grid is achieved.

The invention discloses a multi-energy system planning method based on two-stage collaborative optimization, and belongs to the field of multi-energy system planning, and the method comprises the steps: obtaining the decommissioning of a coal-fired power plant, the investment of renewable energy sources, the extension of a power transmission line, the planning of a power-to-gas station, a gas pipe network, and other related parameters, and combining with the topology of a power network; and a power grid extension planning model with the goal of minimizing the average carbon emission reduction cost is constructed, system operation and long-term planning constraints are considered, and a coal-fired power plant decommissioning, renewable energy investment and power transmission line extension plan is optimized and generated. And determining candidate site selection of the power-to-gas station according to the carbon emission intensity, constructing a power-to-gas station site selection and volume determination model by taking the maximization of the total profit as a target, optimizing and generating a power-to-gas station site selection and volume determination and gas pipe network expansion plan, and finally completing joint planning of the multi-energy system. According to the invention, the problem of low resource configuration efficiency caused by independent planning of different energy systems in the prior art can be solved.

The invention provides an open-pit-to-underground transportation simulation optimization method and system, and relates to the technical field of production decision making and optimizing.The method comprises the steps that a representative year is selected from an open-pit mining stage, an open-pit and underground combined mining stage and an underground mining stage according to a preset long-term plan; decomposing the long-term planning of each representative year into a plurality of short-term plans; constructing a plurality of production scenes by combining mine production characteristics corresponding to each representative year, production parameters contained in the short-term planning and a plurality of preset development transportation schemes; establishing a discrete simulation model based on the plurality of production scenes and pre-collected transportation equipment data and facility data; and for each production scene, multi-objective optimization iteration is respectively carried out, that is, the optimization model generates equipment scheduling information according to the current iteration state, and the discrete simulation model carries out simulation calculation according to the equipment scheduling information. According to the method, the economy and production stability of a transportation scheme developed in the transition stage can be improved.

The application provides a new energy station multi-time scale hierarchical cascade operation and maintenance scheduling method and system for coping with extreme weather, first, multi-source data is acquired and the current scene is identified according to the multi-source data; the multi-source data is input into the mechanism model corresponding to the current scene to obtain extreme weather quantization results; then the data is input into a multi-time scale hierarchical cascade decision model to generate a decision scheme; wherein the multi-time scale hierarchical cascade decision model has long-term, medium-term and short-term execution layers and a cross-layer coordination hub; the cross-layer coordination hub is used for cooperatively adjusting the parameters of the long-term planning layer, the medium-term scheduling layer and the short-term execution layer according to the extreme weather quantization results and the mechanism model state. The application precisely quantizes the influence of extreme weather by using the mechanism model, and then dynamically cooperatively adjusts the parameters of the multi-time scale hierarchical cascade decision model by using the quantization results and the model state, thereby effectively improving the adaptability of the operation and maintenance of the new energy station under extreme weather.