16 results about "Uncertain set" patented technology

The application discloses a conformal calibration electricity price prediction method for computer-aided collaborative decision-making, and belongs to the field of power distribution network optimization; the method is applied to a collaborative system containing flexible resources on the computing power side and the power side, and comprises the following steps: in a training stage, a neural network is trained by using historical characteristic information, a conformal threshold is determined based on a conformal prediction theory, and a historical electricity price box type uncertainty set is constructed; the set is input into a differentiable convex optimization layer which is reconstructed from a robust scheduling model dual, and a total actual operation cost is taken as a loss function to perform decision focusing back propagation training on the prediction network. In a day-ahead scheduling stage, a trained prediction network and the conformal threshold are used to generate a calibrated day-ahead electricity price box type uncertainty set, and the set is input into the differentiable convex optimization layer again to obtain optimal scheduling decision variables containing the states of the two types of flexible resources and to issue control instructions. The application reduces the comprehensive operation cost of the system under the premise of guaranteeing uncertainty statistical coverage.

The present application belongs to the field of comprehensive energy planning, and provides a kind of comprehensive energy system planning robust optimization configuration method and system.The method comprises, obtaining the resource condition and load demand parameter of comprehensive energy system, establish the IES optimization configuration model with the maximum internal rate of return as optimization target;Establish the uncertain set model of wind and light output and cold and heat load demand parameter, based on IGDT combined with IES optimization configuration model, construct IES robust optimization model facing investment income;Simplify IES robust optimization model, solve the maximum uncertainty fluctuation radius and the uncertain scene parameter of the corresponding worst scenario;Under the condition that the uncertain scene parameter is determined, the optimization configuration scheme of maximizing investment income is solved;According to different expected income deviation calculation forms IES optimization configuration scheme set, adopt fuzzy decision, select optimal scheme.Maximize the investment rate of return as optimization configuration target, and based on information gap decision, under the premise of reaching expected income, solve configuration scheme.

The invention discloses a multi-type standby resource collaborative optimization method and device considering source-load uncertainty, and the method comprises the steps: generating a plurality of new energy and load prediction error sub-databases based on data clustering, generating a rhombic convex hull uncertainty set based on the sub-databases, generating a standby demand curve set of a limit scene, and generating a system operation standby constraint; constructing a multi-type standby resource collaborative optimization model and a multi-type standby resource scheduling operation model which take minimum day-ahead stage cost and intra-day stage cost as targets and take system operation standby constraints as constraint conditions; and solving the multi-type standby resource collaborative optimization model to obtain the standby demand capacity of the system at each moment and a corresponding configuration scheme of the multi-type standby resources. According to the method, accurate quantitative evaluation of the system operation standby capacity demand and optimal configuration of standby resources are realized, and the new energy consumption is effectively promoted and the scheduling operation economy is improved while the sufficient standby margin of the system is guaranteed.

The invention discloses an active power distribution network optimization scheduling and risk assessment-oriented adjustable uncertainty set construction method, which comprises the following steps of: firstly, considering the correlation among various renewable energy sources, and constructing a maximum uncertainty set of the renewable energy sources based on variance, covariance and confidence factors; secondly, defining an adjustable uncertainty set in the maximum uncertainty set, and further constructing a risk fuzzy set considering a load reduction risk and a power abandoning risk; then, establishing an operation risk model based on a distributed robust optimization method, and converting the risk model into a solvable finite dimension linear optimization problem by introducing dual variables; and finally, drawing an operation risk curve by using a piecewise linearization method to realize coordination and balance of robustness and economy in uncertainty set selection. According to the method, the adaptive capacity of the power distribution network to renewable energy fluctuation can be effectively improved, and the economical efficiency and reliability of system operation are optimized.

The invention provides a dual-RIS-assisted robust optimization method for security and covert communication, and relates to the technical field of communication. The dual-RIS-assisted robust optimization method for security and covert communication comprises the following steps: S1, system modeling and index definition; s2, modeling an uncertainty set of the imperfect CSI, and establishing a robust constraint; and S3, constructing a unified optimization problem P1 of'worst case KL divergence minimization '. According to the invention, under the same power and energy budget, the robust scheme significantly reduces the KL divergence in the whole CSI error upper bound range, is always close to a perfect CSI reference, and is obviously superior to a non-robust baseline; and meanwhile, the total safety rate is kept higher and more stable. Therefore, the method can be improved at the same time when the detected probability is low and the confidential information is difficult to eavesdrop.

The present application relates to the technical field of administration, in particular to a security personnel intelligent scheduling system and method based on hierarchical architecture. The system adopts five-layer architecture of data management layer, feature calculation layer, intelligent core layer, decision support layer and user interaction layer to realize business logic decoupling. The method core includes: converting requirements into structured portraits through a project analyzer; the intelligent scheduling engine adopts a multi-objective evolutionary algorithm integrating robust optimization, generates a resilient scheduling scheme capable of resisting the worst absence scenario by defining an uncertain set of personnel availability; the dynamic scheduler adopts an online algorithm with a guaranteed competitive ratio to provide real-time decisions with bounded performance for unpredictable temporary tasks. The scheme realizes the change from post-event remediation to pre-event prevention, guarantees business continuity, and provides quantifiable performance guarantees for scheduling decisions in dynamic environments.

The invention discloses a distribution network flexible resource day-ahead robust scheduling method under a source load multivariate fluctuation risk, and belongs to the field of power system optimization operation. The core innovation of the method lies in that an uncertainty set model considering internal and external source load fluctuation risks is constructed, and a two-stage decision-making mechanism and a solving method thereof are designed: resources such as an interconnection switch, an on-load voltage regulation transformer gear, an energy storage charging and discharging mode and the like are pre-allocated in a day-ahead stage; in the second stage, multi-type energy storage and transferable load power scheduling are optimized, and a self-adaptive robust framework coping with the worst scene is formed. On the basis, an optimization model is decomposed into a main problem and a risk stabilization sub-problem, non-convex features brought by endogenous uncertainty are processed by adopting McCormick envelope, and iterative solution is performed through a column constraint generation algorithm. According to the method, through collaborative optimization of source-load double-side flexible resources, wind power, photovoltaic and load multivariate fluctuation risks are effectively stabilized, and the operation economy and flexibility are remarkably improved while the safety robustness of the power distribution network is guaranteed.

The application discloses a kind of water resource collaborative robust scheduling method considering supply and demand double uncertainty, comprising: the risk exposure contribution rate of risk of system risk of inflow and water demand is evaluated, and asymmetric inflow and water demand robust uncertainty set is constructed accordingly;Supply and demand risk coupling amplification index is calculated, based on the index, the joint uncertainty space is screened and reconstructed, and a joint scenario tree is generated;Scenario tree is input into water resource hierarchical collaborative scheduling model, inter-period optimization is carried out using global resource allocation layer, and stage risk quantification parameter representing risk constraint strength of each stage is determined;The parameter is used as cross-layer risk constraint by using local scheduling decision layer, and a dynamic scheduling strategy that meets the requirements of global risk control is generated.The application effectively solves the problems of over-conservatism and extreme risk defense failure in the scheduling process through risk scenario construction driven by physical mechanism and cross-layer risk explicit conduction.

The application discloses a data-driven robust optimization scheduling implementation method based on a multi-affine strategy, obtains a scheduling strategy by establishing a two-stage robust optimization model and solving, adopts a self-organizing mapping neural network to cluster a wind power sample data set to obtain a multi-uncertainty set, and then solves the robust optimization model based on the multi-affine strategy, transforms the optimization model by adopting the multi-affine strategy, and combines a dual principle to convert the original optimization model into a linear programming problem for solving, so as to realize data-driven robust optimization scheduling. The application is based on a robust optimization method, improves the conservativeness of uncertainty factor modeling by establishing a multi-uncertainty set, and improves the economy of the obtained scheduling strategy by introducing the multi-affine strategy, can ensure the optimization model solving efficiency, effectively depicts the fluctuation range of the uncertainty factor, and improves the power grid operation efficiency while ensuring the power grid safety.

This invention discloses a unit combination method based on historical data under renewable energy penetration, comprising the following steps: S1. Constructing standby constraints in the unit combination problem; S2. Constructing a prediction error dataset consisting of prediction errors at various times within the data collection period; S3. Dividing the error dataset into two parts: a dataset and a set of uncertainties; S4. Dividing the dataset into uncertainties and constructing an uncertain set, and estimating its shape parameters; S5. Transforming robust constraints with uncertainties into deterministic constraints for direct solution; S6. Reconstructing the uncertain set and introducing a transformation function to transform and solve the optimization problem. This invention reduces the impact of prediction errors on solving the unit combination problem by using historical data of renewable energy prediction errors, effectively improving energy utilization.

The invention belongs to the technical field of task scheduling of multi-core processors, and discloses a task allocation method and device for a multi-core processor and a vehicle. The task allocation method comprises the steps that S1, a system task is divided into n subtasks, and a directed acyclic graph model is constructed with the subtasks as nodes, establishing a multi-objective optimization model according to the execution cost of the sub-tasks, the communication cost between the sub-tasks, the constraint conditions and the optimization objective; s2, on the basis of the multi-objective optimization model, introducing an uncertainty set to represent the uncertainty of the cost, and establishing a multi-objective robust optimization model considering the uncertainty of the cost; and S3, solving the multi-target robust optimization model by adopting a double-layer multi-target optimization algorithm to obtain Pareto front edges, and selecting an optimal allocation scheme from the Pareto front edges as a task allocation scheme of the multi-core processor. By applying the technical scheme of the invention, the robustness can be improved, the inter-core communication consumption is reduced, and the load balancing is realized.

This invention relates to a two-stage robust planning method for demand-side capacity that considers both cost and flexibility resources, belonging to the field of power system operation and maintenance technology. By introducing an uncertainty adjustment parameter, this invention constructs a box-shaped uncertainty set with flexibly adjustable conservatism, effectively mitigating the inherent drawbacks of overly conservative traditional robust optimization methods. This allows the model to dynamically adjust the boundary of the uncertainty set according to the different risk tolerances of the actual system, thus achieving a refined trade-off between economy and reliability during the planning stage. The two-stage robust optimization model constructed in this invention, considering the electricity-carbon coupling mechanism, incorporates carbon trading costs as an important component of operating costs into the objective function, effectively responding to the needs of the national green and low-carbon energy strategy. The improved column generation and interior point hybrid algorithm designed in this invention proposes an adaptive scenario selection mechanism and a gradient-guided initialization strategy, significantly shortening the solution time while ensuring the global optimality of the solution.

This invention provides a novel method for worst-case performance-optimal robust beamforming. First, through analysis, the following important conclusions are obtained: In the design of a worst-case performance-optimal beamformer, the constraint of the uncertainty set can be given by the Capon spectrum search grid spacing. Based on this, and combined with a method for reconstructing the interference noise covariance matrix, the novel robust beamforming method of this invention is obtained. Because the interference noise covariance matrix is ​​reconstructed, the target signal component is removed, and the constraint of the uncertainty set for worst-case performance is determined based on the analysis, the novel method of this invention can ensure the design of an optimal beamformer under worst-case performance conditions. Furthermore, for the uncertainty set constraint method in robust adaptive beamforming technology, the novel method of this invention also provides a general and universal method for calculating the uncertainty set constraint, which is of great significance.

The embodiment of the invention provides a multi-place inventory network optimization method and device, equipment and a storage medium, and relates to the technical field of display. The method comprises the following steps: acquiring demand correlation and historical data, constructing a first support set or a second support set based on the correlation, constructing at least one first uncertainty set or second uncertainty set corresponding to the support set according to the historical data, acquiring scheduling parameters of an inventory network, and constructing an inventory optimization model based on the scheduling parameters and the uncertainty sets, and reconstructing the constraint condition set as a linear constraint condition set by using the first uncertainty set, or reconstructing the constraint condition set as a second-order cone programming condition set by using the second uncertainty set, and solving the target function based on the linear constraint condition set or the second-order cone programming condition set to obtain inventory optimization data. For demand uncertainty, different support sets are constructed according to correlation driving, so that an inventory optimization model is not limited to a single constraint of a moment condition any more, the correlation is subjected to hierarchical processing, and the inventory cost is optimized.

The invention belongs to the technical field of balance analysis and intelligent scheduling of a power system, and relates to a hydropower flexible interval construction method and system considering uncertainty of new energy, and the construction method comprises the steps: 1, obtaining basic technical data of the power system containing the new energy; 2, constructing a day-ahead electric power and electric quantity balance analysis model, and establishing a water and electricity flexible interval value function of the system; 3, constructing a two-stage robust optimization model based on the decision dependence uncertainty set; dividing problems into main problems, and iteratively solving sub-problems; 4, multi-objective automatic optimization based on mixed integer linear programming; according to the method, on the premise of ensuring that the space-time boundary of hydroelectric resources is not out of limit, the response capability of the system to uncertain factors is improved, the problem that connection between a real-time clearing result and a medium-and-long-term decomposition strategy is unbalanced is effectively solved, meanwhile, the complexity of a real-time clearing model is further reduced, and the overall clearing efficiency is improved.

The present application relates to the technical field of power system dispatching operation, and particularly relates to a power grid adaptive dispatching optimization method for new energy access, comprising: extracting probability distribution characteristic parameters of new energy and load prediction error; generating a power interval prediction sequence for defining a source and load uncertainty set; constructing a double-layer optimization dispatching model; solving the double-layer optimization dispatching model to output unit start-stop plan, base point output plan, spinning reserve capacity requirement and real-time adjustment instruction; calculating real-time power deviation, feeding back the real-time power deviation to update the probability distribution characteristic parameters, and adaptively adjusting the weight coefficient of the robustness penalty term based on the comparison result of the deviation and the current robustness constraint boundary to form a closed-loop feedback. Through deep fusion of prediction information and strengthening of online adaptive adjustment capability, not only the economy and safety of new energy power grid dispatching are improved, but also solid technical support is provided for the intelligentization of the power system.