257 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.

Described herein are devices, systems, methods, and processes for optimizing network traffic distribution across multiple paths in a manner that is energy-efficient and environmental sustainability-aware. This may be achieved by leveraging time-series analytics and capacity planning based on seasonalities. Data associated with the Layer 3 topology of the network can be collected. Bandwidth can be pre-reserved on an energy-aware traffic engineering tunnel. The time-series data can be used to build a capacity plan based on the seasonalities. Nodes may be clustered based on usage patterns and network utilization seasonality. The data can be used to make decisions about when and where to combine or shut down paths for energy efficiency, while maintaining optimal network performance. A hysteresis mechanism may be incorporated to avoid oscillation when changing active links. Power savings can be achieved by fully turning off or depowering certain network components when they are not needed.

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 system is described that combines outputs of network simulations, which are used for capacity planning, with empirical data of network element failure probabilities, to produce what traffic utilization is needed for each network device interface to achieve a specified availability target. Utilization of each interface can be captured for each failure case simulated. From this failure data, the utilization distribution for each link can be produced, across all failure cases. This utilization-by-failure data from simulation is combined with the empirical mean-time between failure (MTBF) and mean-time to recovery (MTTR) of each failure from the span reliability data set. This allows for a prediction of what percentage of time the interface is utilized. Thus, a relationship is established between the capacity provisioned and the percentage of time that the capacity will be uncongested. Network devices can then be added to the network to establish the availability target.

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 a cross-regional energy storage type virtual power plant multi-scene capacity planning method and system. The method comprises the steps of obtaining thermal power generating unit data, power grid load data and wind power photovoltaic installation data in a conventional operation scene and an unconventional operation scene of each region as historical data; sampling the historical data to generate a capacity planning scene, and reducing the capacity planning scene through clustering to obtain the occurrence probability of each scene and the output parameter of each scene after clustering; according to the energy storage investment cost, the system operation cost, the flexibility mutual aid service cost and the occurrence probability of each scene, constructing a target function with the lowest system comprehensive cost and the maximum new energy consumption as targets; constructing constraint conditions of the target function according to the output parameters of each scene; solving energy storage configuration capacities under different capacity planning scenes according to the constructed objective function and constraint conditions; and outputting the optimized energy storage configuration capacity planning strategy of each region and each time period.

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 network device determines at least one of throughput variation and latency variation associated with at least one flow transiting a network slice of a mobile network. The network device compares the at least one of the throughput variation and the latency variation with performance requirements of the network slice, and determines, based on the comparing, network slice resources needed for capacity planning of the network slice. The network device adds or removes network slice resources from the network slice based on the determined network slice resources.

The invention relates to the technical field of smart power grids, and discloses a new energy equipment capacity configuration method and system based on a graph neural network, and the method comprises the steps: constructing a corresponding directed weighted graph structure based on the topological data of a target new energy equipment group; updating the directed weighted graph structure based on the real-time operation data of the target new energy equipment group to obtain a corresponding time-varying composition structure; performing parameter adjustment on the first multi-modal graph neural network model based on the time-varying composition structure to obtain a second multi-modal graph neural network model; performing multi-modal feature fusion on the time variation composition structure based on a second multi-modal graph neural network model to obtain a corresponding embedded vector set; and on the basis of the embedded vector set, a capacity planning model is constructed by taking the matching of the generating capacity of the target new energy equipment group with the power demand as a target, and the capacity planning model is solved to obtain a distributed capacity configuration strategy of the target new energy equipment group. The reliability of capacity planning can be effectively improved.

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.

The invention discloses a capacity configuration method and system based on participation of energy storage in spot joint market income, and the method comprises the steps: building a capacity planning model framework based on participation of an energy storage power station in electric power spot joint market income based on consideration of uncertainty conditions of wind power output; establishing a bidding model that the energy storage power station participates in the day-ahead energy-frequency modulation combined market under a master-slave game bidding strategy, and calculating the optimal configuration of an economic benefit guide energy storage power station capacity planning model; converting the bidding model into a single-layer equilibrium constraint mathematical programming problem according to a strong duality theory and a KKT condition; and solving the global energy storage power station capacity planning model by adopting a mode of nesting a CPLEX solver in a particle swarm algorithm. The method has the beneficial effects that the economic benefit of the energy storage power station can be maximized through scientific capacity configuration and a market bidding strategy, the market competitiveness of the energy storage power station is effectively improved, and wide application of an energy storage technology in a modern power system is promoted.

The invention provides a resource index prediction method and device based on a Transform framework and a medium, and the method comprises the steps: carrying out the pre-training of a PatchTST model through employing the historical data of resource indexes, such as a CPU, a memory, and the like, improving the future values of the prediction indexes of a conventional PatchTST model into the future distribution of the prediction indexes, obtaining a prediction mean value and a prediction variance, and carrying out the prediction of the prediction mean value and the prediction variance. And further converting into prediction value output, prediction interval upper bound output and prediction interval lower bound output. Compared with a traditional time sequence prediction method based on a Transform architecture, the method has the advantages that the prediction accuracy is high, the training time is short, the upper and lower bounds of a prediction interval can be output, and the predicted upper and lower bounds can be used for representing a confidence interval of a predicted value. According to the invention, richer and more accurate guidance can be provided for intelligent operation and maintenance scenes such as dynamic capacity expansion and contraction, capacity management, capacity planning and the like.

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 relates to a service and hardware resource matching method and device, electronic equipment and a storage medium, and belongs to the technical field of capacity planning. The method comprises the following steps: acquiring a first resource usage amount of N types of resources and a service availability target value within a service unit time length, wherein N is an integer greater than or equal to 1; for any type of resources in the N types of resources, determining the reference resource usage amount of any type of resources based on the first resource usage amount of any type of resources and the availability target value; predicting a first resource prediction use amount of any kind of resources at the current moment and a second resource prediction use amount of the service at the next moment; based on the first resource prediction usage, the second resource prediction usage and the reference resource usage, obtaining a target resource usage of the service for any kind of resources; and obtaining a target hardware resource configuration matched with the service based on the target resource usage of the N types of resources. Therefore, according to the scheme, the service can reach the availability target value, and the hardware cost is saved.

The invention relates to the technical field of new energy storage application, and provides a photovoltaic and hydrogen energy storage multi-time scale capacity planning method considering demand response, comprising the following steps: constructing a user response model according to user power demand response; constructing a hydrogen energy storage system model, including constructing an electrolytic cell model and a fuel cell model; according to the user response model and the hydrogen energy storage system model, establishing a photovoltaic-hydrogen storage system multi-time scale capacity planning model considering the system cost and the system benefit, wherein the photovoltaic-hydrogen storage system multi-time scale capacity planning model comprises an objective function and constraint conditions; and calculating the capacity plan of hydrogen energy storage based on the photovoltaic-hydrogen storage system multi-time scale capacity planning model. According to the method, the capacity planning model is constructed by considering the demand response and combining different characteristics of the hydrogen energy storage system under multiple time scales, so that the accurate constant volume of the hydrogen energy storage capacity is realized, and the large-scale redundant configuration of hydrogen production-storage equipment is avoided.

The invention discloses a virtual power plant double-layer extension planning method considering virtual energy storage, and the method employs double-layer optimization to formulate an energy storage planning problem which comprises an upper layer and a lower layer, the upper layer is a planning problem and aims at minimizing the cost of energy storage configuration, and the lower layer is an operation problem and is used for optimizing the operation strategy of a VPP to achieve the maximization of operation benefits. The connection between the planning layer and the operation layer is realized by transmitting a capacity planning scheme of the ESS, in the upper-layer planning problem, the capacity of the ESS is a decision variable to obtain a planning scheme with the optimal cost, in the lower-layer operation problem, the output of scheduling resources is set as a decision variable, and the operation limitation of the scheduling resources is considered to realize the target of maximizing the operation benefit; the invention relates to the technical field of virtual energy storage. According to the virtual power plant double-layer extension planning method considering virtual energy storage, the advantage of maximizing the operation benefit while reducing the energy storage cost is realized, and the economical efficiency and the flexibility of the virtual power plant are improved.

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.