24 results about "Capacity provisioning" patented technology

The invention discloses a virtual power plant capacity configuration and regulation operation optimization method, which comprises the following steps: constructing an aggregation model and physically consistent digital twinning, establishing a linearized power distribution network model containing voltage and power flow constraints, and depicting resource efficiency and time delay characteristics; forming a time-varying uncertainty set through quantile calibration and set drift constraint; establishing a capacity-operation joint double-layer optimization model, realizing capacity configuration under the constraint of the whole life cycle cost, and obtaining a rolling scheduling strategy through distributed robust optimization; before issuing, control shielding and formalized constraint are adopted to ensure the safety of the power grid; a multi-variety collaborative quotation is generated on the market side through opportunity constraint and risk measurement; hierarchical adaptive re-optimization is realized based on a trigger criterion, and a twin model is continuously calibrated by using a hardware-in-the-loop experiment; model updating is realized by adopting federated learning and differential privacy; executing degradation control under an abnormal condition, and performing smooth rollback after recovery; and finally, the capacity and operation parameters are evaluated and corrected through performance and service life linkage.

The invention relates to the technical field of power system energy storage and automatic control, and discloses a capacity optimization method, system and device for a multi-time-scale hybrid energy storage system, and the method comprises the steps: building and solving an initial capacity optimization model based on a multi-time-scale decomposition result of new energy power fluctuation, obtaining an initial capacity configuration parameter and a multi-time scale division parameter; controlling the hybrid energy storage system to participate in power grid frequency modulation based on the initial parameters, and collecting operation performance and equipment state data; generating a performance feedback index according to the data, dynamically correcting an adjustable parameter of the capacity optimization model based on the index, re-solving the model, and processing a division parameter and a capacity configuration parameter as associated variables in the process to obtain an updated parameter; and applying the updated parameters to the system and returning to the frequency modulation operation step to form closed-loop optimization. According to the invention, collaborative dynamic optimization of the energy storage capacity and the labor division strategy is realized, the system economy is improved, the service life of equipment is prolonged, and the operation robustness is improved.

This invention relates to the field of power system technology and discloses a capacity configuration optimization method, apparatus, equipment, medium, and program product. By acquiring multiple operating models and multiple installed capacity values, this invention establishes a synthetic ammonia production capacity relationship, enabling a comprehensive understanding of the operating characteristics and possible configurations of each part of the system. This, in turn, allows for the quantification of the relationship between synthetic ammonia production capacity and various system factors. Furthermore, by establishing total investment cost and total operation and maintenance cost relationships, the cost composition of the system in terms of investment and operation and maintenance can be clearly understood. Moreover, by establishing an objective function with the goal of minimizing the sales value of synthetic ammonia and solving it using a genetic algorithm, the capacity configuration of the system can be optimized as a whole. Therefore, the capacity configuration optimization result obtained in this way can achieve optimal economic efficiency for a renewable energy hydrogen production, synthetic ammonia, and hydrogen power generation system while ensuring system safety and reliability.

The invention relates to a source network load storage collaborative planning optimization method and system. The method comprises the steps of abstracting physical entities of a source side, a power grid side, a load side and an energy storage side into virtual energy units with energy attributes, dynamic response functions and coupling weight parameters, establishing a cross-layer energy interaction relationship based on an energy flow and information flow mapping rule, and constructing a coupling graph reflecting a source-grid-load-storage multilayer structure. And performing time sequence correlation analysis on the coupling graph to obtain the coupling strength among the virtual energy units, and dividing a local optimization unit and a collaborative optimization unit according to the coupling strength to form a global collaborative planning scheme. And collecting an operation result in a scheme execution process, carrying out online correction on virtual energy unit attributes and coupling weight parameters, iteratively updating a coupling graph and a collaborative planning scheme until a preset stop condition is met, and outputting a source side capacity configuration scheme, a power grid side topology adjustment scheme, a load side adjustable resource configuration scheme and an energy storage capacity planning scheme. And the precision and the adaptability of the source-network load-storage collaborative planning can be improved.

This invention discloses a capacity configuration method for wind power-to-hydrogen systems based on VMD-MOPSO, relating to the technical field of capacity configuration for grid-connected wind power-to-hydrogen systems. The method involves obtaining a coupled mathematical model of the wind power-to-hydrogen system; decomposing the output power of the wind power generation equipment using the Variational Mode Decomposition (VMD) algorithm to determine the net load power of the energy storage system; determining the energy management strategy for the wind power-to-hydrogen system based on the operating characteristics of each device; and optimizing the system with the minimum present value of cost and the highest power supply reliability as the optimization objectives. Under the constraints of the coupled mathematical model and the energy management strategy, and with the requirement of meeting the net load power of the energy storage system, the multi-objective particle swarm optimization (MOPSO) algorithm is used to optimize the number of installations for each device to determine the target capacity configuration scheme. This method improves the system's economic efficiency and grid service capability.

The invention relates to the technical field of wind and light storage load capacity matching, in particular to a wind and light storage load capacity matching optimization system based on source network load storage integration, which comprises a data acquisition module, a data processing module, a source network load storage modeling module, a capacity matching optimization module, a real-time monitoring and scheduling module, a simulation and evaluation module and a user interface and interaction module. In a capacity matching optimization module, a particle swarm optimization algorithm and rolling time domain control optimization are fused, wind and light storage capacity configuration calculation is carried out, a PSO-RHC hierarchical fusion framework and PSO are adopted for global optimization of wind and light storage capacity configuration, and a five-dimensional weight is fused in an objective function; and the RHC executes second-level scheduling by taking the energy storage power as a single variable to obtain an optimal solution matched with the wind and light storage load capacity of the whole system, so that efficient coordination of cross-time scale optimization is realized.

The invention relates to the technical field of information and network security, and discloses a certificate capacity planning method based on time sequence prediction, which comprises the following steps: S1, multi-dimensional data acquisition and preprocessing: acquiring multi-dimensional data related to certificate capacity planning, and preprocessing the multi-dimensional data; s2, constructing and training a time sequence prediction model: constructing and training a mixed time sequence prediction model, and predicting the certificate demand quantity in a specific period in the future; s3, capacity planning optimization solution: establishing a multi-objective optimization model to obtain an optimal capacity configuration scheme; and S4, intelligent early warning and decision support: executing intelligent early warning based on the optimal capacity configuration scheme and the real-time monitoring data. According to the method, multiple models are fused, and the advantages of the models in the aspects of capture trend, seasonality, volatility and nonlinear relation are fully exerted; the prediction accuracy can be greatly improved; by establishing the multi-objective optimization model, the cost optimal configuration under the condition of satisfying multiple constraints is realized, and the resource utilization rate is effectively improved.

The application discloses a kind of unit capacity configuration method and device considering supply contribution, terminal equipment and storage medium, belong to electric power system technical field, the method is: based on the rated installed capacity of each unit, the load curve of each period in typical day and the unit on-line state of each unit in each period in typical day, the standby rate of each period in typical day is calculated;Based on the rated installed capacity of each unit, the standby rate of each period in typical day and the output curve of each unit in each period in typical day, the contribution coefficient of each unit is calculated Supply, and combining capacity redundancy coefficient, the rated installed capacity of each unit and the load curve of each period in typical day, under each constraint, with the minimum capacity compensation cost as the goal, an optimization model is constructed and solved, to obtain the installed capacity declaration value of each unit, according to which the capacity configuration of each unit is determined. Through the implementation of the present application, the problem of mismatch between unit capacity configuration and power system demand in the prior art can be solved.

The invention relates to the technical field of power system power distribution network planning and operation, and discloses a flexible power distribution network dynamic capacity configuration method and system considering power supply reliability constraint, equipment and a medium, and the method comprises the steps: collecting the basic data of a power distribution network, and building an equipment fault rate database; constructing a system state space model based on the Markov chain theory, and establishing a reliability evaluation index system; probability distribution is adopted to describe uncertainty factors, and a random scene is generated and reduced through Monte Carlo simulation; establishing a multi-objective optimization model, and solving a Pareto optimal solution set by adopting an improved genetic algorithm; and dynamic capacity configuration is implemented based on a rolling optimization strategy, a trigger adjustment mechanism is established, a configuration scheme is adjusted in real time according to a system operation state, and an optimization configuration strategy is evaluated. According to the method, the capacity dynamic optimization configuration is realized on the premise of ensuring the power supply reliability, and the economy and the operation reliability of the power distribution network planning are remarkably improved.

The invention relates to a general verification method, system and device for a storage controller and a storage medium, and relates to the technical field of prototype verification. The method is based on an FPGA (Field Programmable Gate Array) and an external DDR (Double Data Rate), and comprises the following steps: constructing a softmodel in the FPGA, constructing a BRAM (Block Random Access Memory) activation line cache pool in the FPGA, and setting the capacity configuration of the BRAM activation line cache pool according to a storage controller; calling an MIG core according to the model of the FPGA, and generating an MIG core IP according to the external DDR; a storage controller read-write verification instruction is sent by the storage controller through softmodel, and the BRAM is called to activate the line cache pool and the external DDR to execute the storage controller read-write verification instruction through the MIG core IP. The technical effect of the application is that prototype verification of the storage controller is better realized.

The application relates to the technical field of power system energy storage and automatic control, and discloses a capacity optimization method, system and device of a multi-time-scale hybrid energy storage system, which comprises the following steps: based on a multi-time-scale decomposition result of new energy power fluctuation, an initial capacity optimization model is established and solved to obtain initial capacity configuration parameters and multi-time-scale division parameters; based on the initial parameters, the hybrid energy storage system is controlled to participate in grid frequency modulation, and operation performance and equipment state data are collected; performance feedback indexes are generated according to the data, and adjustable parameters of the capacity optimization model are dynamically corrected based on the indexes, so that the model is solved again, in the process, the division parameters and the capacity configuration parameters are treated as associated variables to obtain updated parameters; the updated parameters are applied to the system and returned to the frequency modulation operation step to form a closed-loop optimization. The application realizes the collaborative dynamic optimization of energy storage capacity and division strategy, and improves the economy, equipment life and operation robustness of the system.

The invention belongs to the technical field of electric power collaborative optimization, and provides a source network load storage multi-stage collaborative configuration method and system considering multi-scale uncertainty, and the technical scheme is that capacity configuration is combined with day-ahead optimization scheduling and intra-day rolling optimization scheduling based on an information gap decision theory, and a multi-stage collaborative optimization configuration framework is constructed. The first stage adopts interval optimization, excessive conservative configuration possibly caused by traditional robust optimization is avoided while the uncertainty influence is fully considered, better balance is achieved between investment risks and cost, and the overall economical efficiency is better. The second-stage information gap decision theory optimization balances the economy and the conservative property in the day-ahead stage; rolling adjustment is achieved in the third-stage intraday stage, and real-time balance of the source load is guaranteed. And three-stage collaborative optimization copes with multi-scale uncertainty together.

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

The present application relates to the technical field of power grid reactive power compensation, and discloses a network type SVG dynamic capacity and site selection method based on load fluctuation. The method collects real-time load data and historical reactive power demand data in the power grid system, forms a load fluctuation characteristic sequence and a reactive power compensation demand characteristic sequence; performs multi-time scale correlation analysis on the above two sequences, identifies the dynamic coupling mode between load fluctuation and reactive power demand, and generates a reactive power compensation priority evaluation result; according to the load fluctuation characteristic sequence and the reactive power compensation priority evaluation result, a dynamic capacity optimization model of the network type SVG is established, and the model parameters are adaptively adjusted; the real-time collected power grid operation data is matched and analyzed with the dynamic capacity optimization model, the potential installation position and capacity configuration range of the network type SVG are determined, and a preliminary site selection and capacity configuration scheme is generated; based on the preliminary scheme, a comprehensive evaluation of technical economy is performed, and the optimal network type SVG configuration scheme is selected, which can better adapt to the dynamic operation demand of the power grid.

This invention relates to the field of power system technology and discloses a capacity configuration optimization method, device, equipment, medium, and program product. By acquiring multiple operating models, this invention can gain a deep understanding of the operating patterns and interrelationships of various components within the system. Furthermore, by setting the highest renewable energy consumption ratio as the objective and establishing an objective function based on multiple operating models and initial installed capacity values, the complex operating conditions and optimization requirements of the system can be transformed into mathematical expressions. This achieves effective integration of system information, enabling the objective function to comprehensively reflect the system's operating characteristics and constraints. Consequently, it can determine the system's renewable energy consumption under different installed capacity scales. Through particle swarm optimization, it can quickly search within a vast space of installed capacity combinations to find the combination of installed capacity that maximizes the renewable energy consumption ratio and determine the final capacity configuration optimization result, thereby improving the renewable energy consumption ratio.

The invention discloses a capacity configuration method and system for a power supply system, equipment and a medium, and the method is characterized in that the method comprises the steps: obtaining load power data, illumination resource data, and equipment operation parameters of all equipment in the power supply system; inputting the load power data, the illumination resource data and the equipment operation parameters into a multi-objective optimization function to determine a multi-objective function value corresponding to each individual in the initial population through a multi-objective optimization algorithm, and performing non-dominated sorting and congestion degree calculation on the initial population by using the multi-objective function values, the method comprises the steps of calculating a plurality of devices in a power supply system, obtaining an initial non-dominated solution set based on a calculation result, determining a target non-dominated solution set until the initial non-dominated solution set meets a preset termination condition, determining a target capacity configuration scheme based on the target non-dominated solution set, and performing capacity configuration on the plurality of devices in the power supply system based on the target capacity configuration scheme. The capacity configuration accuracy of the power supply system can be improved, and the energy utilization efficiency of the power supply system is further improved.

The application discloses a kind of reserve demand quantification method considering net load random fluctuation and equipment failure outage joint occurrence probability, comprising the following steps: step 1) considering that prediction error offsets each other, the total prediction error distribution of system is modeled;Step 2) the discrete fault loss distribution is modeled;Step 3) the cumulative distribution function of total gap random quantity is constructed;Step 4) based on the cumulative distribution function of total gap random quantity, reserve capacity demand target function is constructed;Step 5) the reserve capacity demand target function is standardized, and the reserve capacity demand standardized target function is obtained;Step 6) the reserve capacity demand standardized target function is solved, and the reserve capacity demand of system caused by prediction error is obtained.The application can more accurately depict the correlation and aggregation effect of prediction error, effectively balance the reliability and economy of reserve capacity configuration, especially suitable for system operation and market decision under the condition of high proportion of renewable energy grid connection.

The invention provides a micro-grid capacity configuration optimization method and system under an abnormal working condition, and relates to the technical field of micro-grid capacity configuration.The method comprises the steps that for a target micro-grid, a working condition system is determined by conducting abnormal working condition decomposition, and an action sequence is determined by conducting explicit modeling on an action sequence under the abnormal working condition, the decomposition standard at least comprises a working condition mode and a configuration dimension; according to the working condition system and the action sequence, deploying an initialization port in a power grid central controller, converting an uploaded abnormal working condition configuration task, and determining a task configuration demand; inputting the task configuration requirement into a capacity configurator, executing column constraint optimization generation, and determining a task configuration scheme; and performing capacity configuration control on the target microgrid according to the task configuration scheme. The technical problem that in the prior art, the capacity configuration efficiency of the micro-grid is poor is solved, and the technical effect of improving the capacity configuration efficiency of the micro-grid is achieved.

The present application discloses a resource scheduling method and apparatus for a kubernetes cluster, a storage medium, and an electronic device. The method comprises: in response to a resource scheduling request, creating a first persistent volume claim and a second persistent volume claim of a predefined type, and respectively associating the first persistent volume claim and the second persistent volume claim with a storage pool, wherein the first persistent volume claim is a native persistent volume claim of a kubernetes cluster, capacity configuration information is respectively set in the first persistent volume claim and the second persistent volume claim, and the second persistent volume claim supports the increase and decrease of the capacity configuration information; associating a pre-created pod node with the first persistent volume claim; and creating a storage volume on the basis of the first persistent volume claim, and mounting the storage volume to the pod node. By introducing a new kubernetes custom resource type, a second persistent volume claim of a predefined type is created, and the second persistent volume claim supports the increase and decrease of capacity configuration information, thereby further supporting capacity expansion and capacity reduction of a created storage volume.

Embodiments of the present application provide a semiconductor memory and a capacity configuration method for a storage block thereon. The method comprises: determining a target storage block to be configured on a memory; determining a capacity configuration parameter of the target storage block, and determining a target code from a group of codes of the target storage block, wherein the target code corresponds to a storage region to be trimmed in the target storage block; on the basis of the target code, generating a region selection signal used for selecting the storage region to be trimmed in the target storage block; and on the basis of the region selection signal, trimming the storage region to be trimmed, so as to configure the capacity of the target storage block. The present application can flexibly select the storage region to be trimmed, thereby improving the effective utilization rate of the capacity configuration for the storage block on the semiconductor memory. [Fig. 2]

This invention discloses a power regulation method, device, medium, and product for distribution networks, comprising: acquiring network topology data, first operating data, and baseline configuration parameters of flexible soft switches in the distribution network; inputting the network topology data, the first operating data, and the baseline configuration parameters into a preset decision model, aiming to minimize the sum of the configuration cost of the flexible soft switches and the line loss cost of the distribution network, and solving the model under preset constraints to obtain a target decision result, wherein the target decision result includes the target location, target capacity configuration, and target power flow allocation scheme of the flexible soft switches, and the constraints include distributed energy utilization constraints, flexible soft switch constraints, and power flow constraints; and using the target decision result to perform power regulation on the distribution network. This invention can improve the accuracy of power regulation in distribution networks.

The invention provides a capacity configuration method and a capacity configuration device for an energy storage system of a new energy station. The capacity configuration method comprises the following steps: acquiring a generated power prediction value of a new energy station in a first time period and a load prediction value associated with the new energy station; determining a standard deviation of a net load prediction error obeying normal distribution according to the generated power prediction value and the load prediction value; determining a cumulative distribution function based on the standard deviation of the payload prediction error and a predetermined expectation of the payload prediction error; determining the energy storage demand capacity of the cumulative distribution function in a preset confidence interval; and determining the capacity configuration of the energy storage system of the new energy station according to the energy storage demand capacity.

The invention provides a power grid enhanced capacity configuration optimization method and system based on multi-dimensional heterogeneous feature perception. The method comprises the following steps: firstly, extracting heterogeneity physical characteristics of distributed new energy in the aspects of installation scale, output characteristic and power quality by utilizing a clustering algorithm, and calculating an access side physical influence index and a power grid side bearing response index; secondly, calculating a source-network collaborative enhancement coefficient and a resource activation coefficient based on physical influence, stripping off the effective resource occupancy of a power grid side, and constructing a collaborative resource configuration mapping model of different voltage levels; and then, generating a power grid enhanced feedback signal based on a difference value between the resource input equivalent of each voltage level and the physical operation efficiency. The signal serves as a physical-economic coupling regulation and control threshold value and is used for closed-loop guidance of implementation of a power grid strengthening project. According to the method, the problem of power grid resource configuration imbalance caused by lack of perception of the physical characteristics of the distributed power supply is solved, the physical acceptance capability of the power grid to the distributed new energy is remarkably improved through accurate engineering guidance, and the blocking risk of the local voltage level is relieved.

The invention discloses a capacity configuration method, device and equipment of a micro-grid and a storage medium, relates to the technical field of micro-grids, and solves the problem that the capacity configuration efficiency is high when capacity configuration is carried out based on operation simulation traditionally by means of constructing dynamic load characteristic modeling, two-part electricity price capacity / demand constraint, grid-connected and off-grid scene adaptation, multi-objective optimization and the like. The problems of insufficient subdivision fields and deviation of specific scene constraints from reality in the prior art are solved, and the prior and strategy delivery of a scheduling strategy and strategy optimization in an early planning design and capacity configuration stage is realized. And capacity configuration optimization based on an actual operation strategy, a specific subdivision scene and a constraint boundary is more accurate and comprehensive compared with a traditional method.