207 results about "Capacity planning" patented technology

The invention relates to the technical field of order management, and discloses a supply chain-oriented intelligent order management method, which comprises the steps of obtaining corresponding multi-modal data through an order demand flow, a production equipment state, logistics sensor dynamic information and an inventory topological graph; analyzing relevance between orders and equipment based on a space-time diagram convolutional network, and generating a capacity allocation scheme; calculating a logistics path planning scheme, predicting a stock stockout risk and generating a replenishment suggestion; if the high-priority order exists, inserting a productivity plan and adjusting an equipment process chain; if resource conflicts occur, dynamically allocating resources; if the path risk value exceeds the threshold value, standby path switching is triggered; adjusting weighting parameters through an adaptive federation algorithm, generating a global strategy and issuing the global strategy to the client; the client dynamically adjusts local configuration and uploads execution effect data in real time; and if abnormity is detected, triggering global strategy regeneration and updating the model through federated learning increment. According to the invention, efficient management of supply chain orders can be realized.

The invention relates to the technical field of cloud computing, in particular to an intelligent capacity planning method and system in a hybrid cloud environment. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, heterogeneous resources in a hybrid cloud are modeled by using a declarative description language, a dependency relationship among the resources is identified, and a directed acyclic graph (DAG) is constructed; the resource operation tasks are divided in batches, and the tasks without the dependency relationship are executed in parallel; synchronizing the running state of each resource in real time, and performing state consistency verification and abnormity marking; triggering a retry mechanism when an exception occurs; and after the resource arrangement process is completed, summarizing data in the whole process, and performing performance evaluation and strategy optimization on resource operation. According to the method and the system for intelligent capacity planning in the hybrid cloud environment, the use condition of resources can be monitored and analyzed in real time, the resource demand can be accurately predicted, a reasonable resource scheduling strategy can be generated, manual intervention is reduced, and elastic expansion and optimal configuration of cloud resources are realized.

The invention discloses a multi-level storage control method based on access popularity, and relates to the technical field of information, the method comprises the following steps: building a storage resource monitoring module, and collecting data volume change, read-write frequency and capacity occupation proportion information of each node from a storage system in real time; the method comprises the following steps: collecting original data, carrying out preliminary cleaning and formatting processing on the collected original data to obtain a standardized resource state data set, comparing predicted data with operation parameters of a current storage system, and if a predicted demand exceeds a current capacity limit, automatically generating a capacity expansion scheduling task to obtain a final resource optimization configuration scheme; according to the multi-level storage control method and device based on the access popularity, intelligent monitoring, load balancing and capacity planning of storage resources are achieved, the resource utilization efficiency and expandability of a storage system are improved, and an effective solution is provided for stable operation and performance optimization of a large-scale storage system.

A unit capacity planning method and system based on a carbon emission reduction path optimization model. The method comprises: acquiring wind-power photovoltaic power data and electric heating load data (S1); constructing a carbon emission reduction path optimization model on the basis of the wind-power photovoltaic power data and the electric heating load data (S2); and performing hierarchical solution on the carbon emission reduction path optimization model to obtain an optimal energy unit capacity planning scheme (S3).

The invention discloses an energy storage capacity optimal configuration method for multi-dimensional constraint modeling, particularly relates to the technical field of energy storage capacity planning, and is used for solving the problems of feasible solution space fragmentation and low global optimization efficiency caused by heterogeneous constraint conflicts in the prior art. The method comprises the following steps: acquiring multi-dimensional constraint data such as continuous technical parameters, operating conditions and discrete policies and rules of the energy storage system; quantizing conflict strength between constraints and distributing dynamic weights; converting the discrete rule into a continuous penalty function to construct a fusion constraint model; analyzing the solution space topological structure and monitoring the dynamic change of the solution space topological structure to generate a connectivity graph and an evolution trend graph; generating a self-adaptive search strategy based on the atlas; outputting a global optimal solution set and generating a verifiable energy storage capacity configuration scheme; and through dynamic weight distribution and solution space connectivity guidance, the multi-constraint collaborative optimization capability is significantly improved.

The invention relates to the field of low-altitude communication network planning methods, in particular to a 5G three-dimensional coverage and capacity planning design method for low-altitude economy, which comprises the following steps: performing airspace range limitation and application scene division on a target airspace, establishing a mobile model of a low-altitude terminal, and providing dynamic input for simulation; the method comprises the following steps: establishing a dual-frequency heterogeneous network, and determining base station addresses covering targets at different heights and meeting different signal station spacing; performing three-dimensional coverage and quality simulation on the established dual-frequency heterogeneous network, performing network KPI evaluation on the simulation until the standard is reached, and outputting a network deployment planning scheme; deploying according to a network deployment planning scheme, predicting low-altitude service flow, and adjusting a beam direction and a PRB reservation ratio to realize on-demand allocation of resources; and carrying out actual measurement on the deployed network, collecting an air measurement index, comparing a comparison deviation between the air measurement index and a simulation result, and carrying out fine adjustment on the network according to the comparison deviation. According to the invention, diversified communication requirements are met, and the success rate of terminal access is improved.

A data-driven production capacity prediction method and apparatus for an automated production line, relating to the technical field of data processing. On the basis of various types of historical feature data of a surface-mount production line device, key features affecting production capacity are extracted via mutual information, and a PSO-ELM prediction model is constructed, thereby enabling intelligent prediction of production line capacity, and laying a foundation for refined and intelligent scheduling optimization and production capacity planning. The present invention fully takes into account dynamic disturbance event data to correct the prediction model, thereby effectively improving the accuracy of the prediction model.

The invention discloses a power distribution system planning operation collaborative optimization method integrated with multi-flexibility resources. Comprising the steps of obtaining a reference data set of a power distribution system, and fitting wind power and photovoltaic edge distribution to construct a joint probability model; and fitting the relationship between the energy storage discharge depth and the capacity attenuation, establishing a full life cycle cost model, and outputting equivalent operation cost parameters changing along with the operation strategy. Based on wind-solar combined distribution parameters, operation cost parameters and load electricity price prediction, a multi-temporal-spatial-scale scene set is generated by adopting Latin hypercube sampling, and a typical scene is obtained through clustering and scene reduction. And constructing a two-stage robust capacity planning model in combination with the typical scene and the equivalent operation cost. The model is solved through a main-sub problem decomposition coordination strategy, an installation and capacity configuration scheme is obtained, and integrated collaborative optimization of long-term planning and short-term operation of the power distribution system is achieved. According to the invention, the safety, economy and flexibility of the power distribution system in a high-volatility environment can be effectively improved.

This disclosure pertains to computer-assisted methods, apparatus, and systems for forecasting network performance responsive to network reconfigurations in large scale communication networks.

The invention relates to an electro-hydrogen conversion new energy base capacity planning configuration optimization method, belongs to the technical field of new energy, and solves the technical problems of seasonal imbalance, excessive conservative traditional robust optimization, imperfect evaluation system and insufficient multi-energy flow coupling optimization in existing new energy base capacity configuration. Comprising the steps of obtaining historical data in a new energy base scheduling period; generating a typical scene set based on the historical operation data, constructing a confidence interval of the wind and light output uncertainty of the new energy base, and obtaining a confidence robustness gamma value; constructing an electricity-hydrogen collaborative capacity optimization model with the goal of maximizing the comprehensive efficiency of the new energy base power system; converting the electricity-hydrogen collaborative capacity optimization model into a robust optimization model based on the confidence interval and the confidence robustness gamma value of the wind-solar output uncertainty; and solving the robust optimization model by using a CPLEX solver to obtain a new energy base capacity planning configuration scheme. And reliable low-carbon operation of the new energy base is realized.

Systems, devices, and methods are provided for training and / or inferencing capacity planning using a machine learning model. A first time series may be provided as an input to a machine learning model, which may be an Auto Regressive Integrated Moving Average (ARIMA)-based forecasting model. The machine learning model may be trained solve a conditional maximum likelihood problem by performing quantile regression. The machine learning model may forecast one or more innovations using Monte-Carlo simulations. The machine learning model may generate, as an output, a value that corresponds to an amount of computing resources that is predicted, over a second time series, to be sufficient to satisfy a threshold level of availability or quality.

The invention relates to a source network load storage capacity planning method and system based on a multi-agent game, and the method comprises the steps: constructing a multi-agent game behavior data set, analyzing the dynamic interaction of a strategy through employing a game theory model, and obtaining the strategy preference and interaction mode of each agent; constructing a source network load storage collaborative planning model, and determining an initial capacity configuration scheme of each main body; extracting a benefit distribution proportion of each main body from the initial capacity configuration scheme, and adjusting capacity configuration by adopting a multi-objective optimization algorithm to obtain an optimized capacity planning scheme; analyzing the influence of peak demand fluctuation and transmission capacity limitation on the scheme by adopting a Monte Carlo simulation method to obtain a dynamic adaptability index of the scheme; and adjusting game theory model parameters to obtain updated strategy preferences and interaction modes, adjusting capacity configuration through iterative calculation, and generating a new capacity planning scheme. Compared with the prior art, the method has the advantages of comprehensively considering multi-main-body strategy dynamic interaction, improving the feasibility of the planning scheme and the like.

Techniques are disclosed for managing aspects of a dedicated region cloud at a customer location (a “DRCC”). A DRCC may comprise cloud infrastructure components provided by a cloud provider and hosted by computing devices located at the customer's (a “cloud owner's”) location. Services of the central cloud-computing environment may be similarly executed at the DRCC. The DRCC may include a service configured to collect, store, and / or present data corresponding to the cloud infrastructure components via one or more interfaces (e.g., interfaces provided to the cloud provider and / or the cloud owner). Data collected within the DRCC (e.g., capacity and usage data, etc.) may be provided and accessible to the central cloud at any suitable time. Obtaining such data enables the user to ascertain various operational aspects of the DRCC, while enabling the system and / or user to execute various DRCC-specific operations regarding capacity planning, health and performance, change management, and the like.

The invention relates to the technical field of energy storage system-containing ship power control, in particular to a ship hybrid energy storage system capacity planning method based on multilayer power distribution, which comprises the following steps of: S1, establishing a power generation side operation constraint of an all-electric ship hybrid energy storage system; s2, constructing a ship hybrid energy storage system robust capacity planning model with the goal of minimizing the investment cost of the ship hybrid energy storage system and the operation cost of the ship microgrid; s3, solving the robust capacity planning model of the ship hybrid energy storage system to obtain the optimal capacity of the ship hybrid energy storage system; and S4, taking the optimal capacity as the design capacity of the ship hybrid energy storage system, performing power distribution under the multilayer time scale in combination with the actual propulsion power demand to obtain an optimal power distribution scheme, and realizing power distribution of each component of the ship hybrid energy storage system through the optimal power distribution scheme. According to the invention, the safety and stability of all-electric ship navigation can be ensured.

A system and method are disclosed for performing rough cut capacity planning. The method includes receiving supply chain transaction data as transaction tables, generating a base plan and generating updated transaction tables, denormalizing the base plan and the updated transaction tables, generating supply chain network flow paths and supply chain network data, solving a rough cut capacity planning problem based at least in part on the supply chain network flow paths, the supply chain network data and simulation data, repeating at least the generating and solving until business goals of the rough cut capacity planning meet a threshold, and updating the simulation data based on an upsert process. The method further includes relaxing supply chain network constraints, inverting the supply chain network, and traversing a perturbation in the supply chain network constraints as demands in a reverse direction towards customer nodes.

The invention belongs to the technical field of pumped storage, and discloses a comprehensive evaluation method for the operation capability of a pumped storage unit in different power grid forms and a related device. The method comprises the steps of obtaining operation data of a pumped storage power station to be evaluated and data of a power grid where the pumped storage power station is located; based on the operation data of the pumped storage power station and the data of the power grid where the pumped storage power station is located, constructing a pumped storage power station multi-type regulation capability evaluation system in different power grid forms; and based on the pumped storage power station multi-type regulation capability evaluation system in different power grid forms, carrying out comprehensive quantitative evaluation on the regulation capability of the pumped storage power station, and obtaining a dynamic priority score of each unit in the to-be-evaluated pumped storage power station. According to the invention, comprehensive quantitative evaluation of the regulation capability of the power station is realized, and a scientific decision basis is provided for capacity planning, operation strategy optimization and multi-type energy storage collaborative scheduling of the pumped storage power station.

This invention relates to a capacity planning method for microgrid energy storage devices, comprising the following steps: establishing a multi-state transition operation model for a reversible solid oxide battery, wherein the multi-state operation includes the reversible solid oxide battery being in a power generation state, an electrolysis state, a thermal standby state, and a shutdown state; constructing a two-layer capacity planning model for the energy storage device, wherein the upper layer constructs an objective function with the objective of minimizing the sum of the energy storage device investment cost, the equipment replacement cost, and the microgrid's total life cycle operating cost, and the lower layer constructs an objective function with the objective of minimizing the microgrid's total life cycle operating cost; combining the reversible solid oxide battery multi-state transition operation model, setting constraints on the upper-layer objective function and the lower-layer objective function; solving the upper-layer objective function and the lower-layer objective function under the constraints to obtain a capacity planning scheme for the reversible solid oxide battery, hydrogen storage device, and thermal storage device.

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 provides a hydrogen-electricity hybrid energy storage capacity optimization method and device applied to an integrated energy system, and belongs to the field of integrated energy configuration optimization. The method provided by the invention comprises the following steps: adopting a self-adaptive time sequence compression algorithm to perform self-adaptive grouping, weighted fusion and iterative updating processing on annual original time sequence data of the integrated energy system, and compressing to obtain representative time sequence data; based on the representative time sequence data, constructing a capacity planning model, and solving to obtain a capacity pre-planning value of the system core equipment; the capacity pre-planning value is substituted into the original time sequence data, the capacity planning model is reused, the hydrogen energy storage capacity is checked and corrected through full-time sequence energy balance simulation, and the final capacity configuration of the system core equipment is obtained; and based on the final capacity configuration, simulating system operation, extracting an electric and thermal power unbalance residual sequence, extracting a residual fluctuation scene and a corresponding probability, respectively constructing independent optimization models of electric energy storage and thermal energy storage, and solving to obtain the optimal capacity of the electric energy storage and the thermal energy storage.

The invention relates to a layered and partitioned distributed power supply and energy storage two-stage planning method considering source storage load, belongs to the technical field of distributed power supply and energy storage capacity planning in a power distribution network, and aims at solving the problem of consumption caused by access of high-proportion renewable energy to the power distribution network and improving the power supply reliability of a power system. The power system operation cost is reduced; the method comprises the following steps: firstly, dividing a voltage regulation area of each distributed power supply on the basis of an electrical distance structural index based on active-reactive voltage sensitivity, secondly, carrying out two-stage planning on the basis of partitioning, constructing a two-stage planning model by taking minimum network loss and optimal economical efficiency as targets, and carrying out solving by adopting a distance-based improved ant lion algorithm; and finally, solving based on an IEEE33 node example, and verifying the effectiveness of the two-stage planning. The method has the advantages that the power complementation characteristic is fully played, the two-stage planning model is constructed, and the power distribution network operates economically, safely and reliably.

The invention provides a shared energy storage and power transmission line cooperative configuration method, and belongs to the technical field of intelligent power grids. According to the method, power system data is collected and preprocessed, an uncertainty model of new energy output and load is established, and multiple operation scenes are generated. And constructing a collaborative optimization configuration model taking system annual comprehensive cost minimization as a target function, and considering energy storage and power transmission line related constraint conditions. And solving the model by adopting an improved particle swarm optimization algorithm to obtain an energy storage configuration scheme (installation position, capacity configuration and charging and discharging strategies) and a power transmission line planning scheme (extension path, capacity planning and upgrading reconstruction measures). And comprehensively evaluating the scheme by using a multi-index comprehensive evaluation system. By adopting the shared energy storage and power transmission line collaborative configuration method, the new energy consumption capability can be effectively improved, the blockage of the power transmission line can be relieved, the economical efficiency and reliability of the system can be improved, and the method is suitable for planning and operation of a power system with large-scale new energy access.

The invention discloses a self-pick-up distribution method based on customer self-pick-up behavior dynamic modeling and a space-time replenishment strategy, and the method comprises the steps: obtaining customer self-pick-up behavior data, building a model based on truncation normal distribution, and determining a probability density function and a probability function; establishing a self-pickup cabinet available capacity and replenishment model, calculating the residual capacity at each moment, and determining replenishment time and vehicle waiting time and cost; a self-pickup distribution mathematical model with the minimum operation cost as a target function is constructed, and self-pickup cabinet site selection, capacity planning, replenishment decision making and vehicle path optimization are carried out; and designing a two-stage heuristic algorithm for solving, generating self-pickup cabinet site selection, capacity grade configuration, replenishment opportunity decision and vehicle path planning schemes, and realizing operation cost optimization. Through dynamic modeling and a space-time replenishment strategy, the space-time utilization rate of the self-service cabinet is improved, the operation cost is reduced, and an efficient solution is provided for express terminal distribution.

The application discloses a capacity planning method and device of a wind-solar-hydrogen storage micro-grid system, and relates to the technical field of renewable energy micro-grid planning. The method comprises the following steps: obtaining a target function and parameter constraint conditions of the wind-solar-hydrogen storage micro-grid system; the target function is determined according to total fixed costs and total operating costs of the wind-solar-hydrogen storage micro-grid system; the parameter constraint conditions comprise photovoltaic and wind power installed capacity output constraints, energy storage boundary and energy change rate constraints, maximum hydrogen consumption rate constraints, system feasibility constraints, continuous operation constraints and timing consistency constraints, and robustness constraints; the target function and the parameter constraint conditions are solved to obtain the installed capacity of a photovoltaic power generation system and a wind power generation system, and the storage capacity of an electrochemical energy storage system and the rated power capacity of a hydrogen fuel cell system. The method improves the accuracy of micro-grid system capacity planning and realizes the balance between economy and reliability.

The application discloses a multi-dimensional constraint modeling energy storage capacity optimization configuration method, and particularly relates to the technical field of energy storage capacity planning, and is used for solving the problems of feasible solution space fragmentation and low global optimization efficiency caused by heterogeneous constraint conflicts in the prior art; the method is characterized in that: multi-dimensional constraint data of a continuous technical parameter, operation condition and discrete policy rule of an energy storage system are acquired; the conflict intensity between constraints is quantified and a dynamic weight is distributed; the discrete rule is converted into a continuous penalty function to construct a constraint model; the topology structure of a solution space is analyzed and its dynamic change is monitored to generate a connectivity atlas and an evolution trend atlas; an adaptive search strategy is generated based on the atlas; a global optimal solution set is outputted and a verifiable energy storage capacity configuration scheme is generated; and through dynamic weight distribution and solution space connectivity guidance, the multi-constraint collaborative optimization capability is significantly improved.

The invention relates to a fused salt heat storage coupling gas generator set capacity configuration and day-ahead scheduling optimization method, belongs to the technical field of power system optimization scheduling, and aims to solve the problems of capacity configuration and day-ahead scheduling of a fused salt heat storage coupling gas generator set system in a steel mill. According to the method, a double-layer optimization model is established based on day-ahead scheduling and capacity planning of a fused salt heat storage coupled gas generator set system, the maximum net present value of the whole life cycle of a heat storage system serves as a target function on the upper layer, and optimal capacity configuration is screened through a grid search algorithm; and the lower layer takes the maximum daily operation net income of the coupling system as an objective function, adopts a mixed integer linear programming solver, solves an optimal solution on the premise of meeting all physical constraints, and further obtains an optimal day-ahead operation strategy of the coupling system. Reference is provided for construction investment of related projects, decision basis is provided for actual production scheduling of the system, and economic benefits of steel mills are guaranteed.

The application belongs to the technical field of power distribution network planning, and particularly relates to a full-peer power distribution system equipment capacity planning method, device, equipment and storage medium. Data information of a to-be-planned full-peer power distribution system is acquired; based on the data information, a full-peer power distribution system planning model is constructed with the minimum annual comprehensive cost of the full-peer power distribution system as an objective function and the intelligent soft switch, distributed photovoltaic and energy storage capacity as variables; based on multiple power distribution networks and the full-peer power distribution system planning model, a distributed planning model is constructed; and based on the distributed planning model and a preset constraint condition, the planning of the full-peer power distribution system equipment capacity is determined. The capacity of the intelligent soft switch, distributed photovoltaic and energy storage equipment in the full-peer power distribution system is reasonably planned, the operation efficiency and overall stability of the power distribution system are improved, and the solving efficiency of the planning model is improved.

The invention discloses a capacity planning method, apparatus and device, and a storage medium. The method comprises the steps of obtaining current operation data of a data center and a preset capacity application efficiency evaluation factor; scoring each preset candidate capacity planning strategy by adopting a Bellman optimal equation according to the current operation data and the capacity application efficiency evaluation factor to obtain a scoring result; determining a target capacity planning strategy from the candidate capacity planning strategies according to the scoring result; and carrying out capacity planning in the data center by adopting the target capacity planning strategy according to the received capacity planning demand. Based on a Bellman optimal equation, according to the current operation data of the data center and a preset capacity operation efficiency evaluation factor, the candidate capacity planning strategy is comprehensively scored, and based on a scoring result, an optimal target capacity planning strategy is obtained, so that capacity planning requirements of different users can be met according to different user capacity planning requirements. And the optimal capacity planning can be realized in the data center by calling the target capacity planning strategy.

The invention provides a communication base station energy system, a capacity planning method and related equipment, and belongs to the technical field of communication infrastructures. The system comprises a direct-current light stacking system, a storage battery pack, a hydrogen fuel cell, a hydrogen storage bottle group, an intelligent combiner box and a switching power supply, wherein the hydrogen storage bottle group is connected with the hydrogen fuel cell and is used for providing hydrogen for the hydrogen fuel cell; the hydrogen fuel cell and the direct current light stacking system are respectively connected with the intelligent combiner box, and the hydrogen fuel cell and the direct current light stacking system supply power to equipment of a communication base station through the intelligent combiner box; the intelligent combiner box and the storage battery pack are merged into a switching power supply together, and the switching power supply is used for controlling the intelligent combiner box and the storage battery pack to supply power to equipment of the communication base station.

The application discloses a network type energy storage power station capacity planning and configuration optimization method based on multi-source data, relates to the technical field of configuration optimization, and comprises the following steps: calculating the cavity length and cavity height of a subsurface cavity based on the sound wave response signal of a corridor ground surface structure; calculating the cavity risk coefficient of a target corridor based on the cavity length and cavity height; calculating the updated discharge depth based on the cavity risk coefficient and the planning discharge depth in an electric station design parameter table; generating a shadow band section of the target corridor, marking the energy storage units of the energy storage power station as high-risk units or low-risk units based on the shadow band section; calculating the high-risk power sum of all the high-risk units, and calculating the high-risk capacity sum of all the high-risk units based on the updated discharge depth. The application can improve the reliability of the operation safety of the power station.

The invention relates to the technical field of comprehensive energy system planning and operation, and discloses a wind-solar-hydrogen-ammonia-alcohol integrated collaborative optimization planning method, system and equipment and a medium. The method comprises the following steps: constructing an integrated structure comprising a wind-solar power generation unit, an electrolytic hydrogen production unit, an air separation nitrogen production unit, a synthesis ammonia unit, a methanol synthesis unit, a carbon capture unit and a hydrogen storage unit; an electricity-hydrogen-nitrogen-carbon-ammonia-alcohol multi-energy flow network topological structure is established; constructing a capacity planning layer-operation simulation layer double-layer optimization model by taking the lowest total cost of the whole life cycle of the system as a target, and dynamically introducing carbon emission cost in an operation layer; and solving the model by adopting an improved multi-target genetic algorithm, and outputting the optimal planning capacity and the hour-level operation plan of each unit. According to the method, multi-energy flow collaboration, cross-period optimization and economic-low carbon collaboration of the wind-solar-hydrogen-ammonia-alcohol system under multiple uncertainties are realized, and the flexibility, the economical efficiency and the low carbon property of the system are improved.