61 results about "Uncertain set" patented technology

The invention provides a power distribution network optimization method based on hierarchical robust control and dynamic decision, which realizes multi-time scale coordination control and real-time adaptive control by constructing a hierarchical architecture of a minute-level equipment layer, an hour-level region layer and a day-preceding-level system layer and combining a dynamic information decision mechanism. The method comprises the steps of preprocessing measurement information in a grading manner, establishing a multi-target robust optimization model, decomposing the model into a local quick response and global coordination problem, constructing a time-varying uncertainty set and embedding mixed integer robust optimization, and realizing the collaboration of millisecond-level correction and hour-level scheduling by adopting a second-order cone relaxation technology. And the weight is dynamically adjusted and optimized through the information decision module, so that the robustness and the operation efficiency of the system are improved.

The invention discloses a two-stage fault recovery method for improving the elasticity of a power distribution system, and the method comprises the steps: constructing a typhoon space-time evolution model, and generating a prediction fault scene; simulating the distribution line in the predicted fault scene by using a Monte Carlo simulation method to generate a plurality of fault scenes; based on the information entropy value of the fault scene and the fault multiple number, an uncertainty set is constructed, and then a robust optimization model is constructed; solving the robust optimization model, and determining a pre-layout scheme of the mobile energy storage system and the static var compensator in a pre-disaster worst fault scene; a multi-source collaborative post-disaster multi-period multi-target fault recovery model is constructed; the fault recovery model realizes recovery of a power-loss load and a fault line of a power distribution system through network reconstruction and optimal scheduling of a mobile energy storage system, a static var compensator, a maintenance team and an electric vehicle. According to the invention, the overall elasticity of the power distribution system is obviously improved, the capability of resisting disasters is enhanced, and the capability of rapidly recovering power supply is also improved.

The embodiment of the invention provides a virtual power plant cooperative control method and system and electronic equipment, and relates to the technical field of power distribution Internet of Things. The method comprises the steps of obtaining a control target; calculating a fluctuation coefficient according to historical data of the global state parameters, and determining an uncertain set; establishing a robust optimization model based on the uncertainty set, and solving the robust optimization model to obtain an optimal solution of a robust objective function; training a Bayesian deep reinforcement learning model by using the historical data; inputting the current state data of the global state parameters into a trained Bayesian deep reinforcement learning model, generating an optimal action, and calculating a corresponding confidence coefficient; and selecting one of the optimal action and the optimal solution of the robust objective function to generate a first control instruction according to the relationship between the confidence coefficient and a preset confidence coefficient threshold value, and issuing the first control instruction to a low-layer intelligent agent. According to the implementation mode provided by the invention, the virtual power plant can be promoted to develop in a more refined, intelligent and autonomous direction.

The invention discloses a narrowband adaptive digital beam forming and optimization design, which comprises the following steps of: providing an optimized robust adaptive beam forming method on the basis of the idea of covariance matrix correction and guide vector estimation: firstly, reconstructing an IPNCM matrix, establishing an optimal interference covariance matrix in an interference signal angle sector by applying the idea of an uncertain set, and then establishing an optimal interference covariance matrix in an interference signal angle sector; a noise covariance matrix is established by applying characteristic decomposition of a matrix, so that an IPNCM matrix is obtained, the influence of an expected signal in the covariance matrix is reduced to the maximum extent, the suppression capability of an algorithm on interference noise under the condition of low SNR is ensured, the situation that the algorithm misjudges the expected signal as interference for suppression under the condition of high SNR is avoided, and then the noise suppression capability of the algorithm is improved. A new constraint condition is added on the basis of the RCB algorithm, and it is ensured that the guiding vector converges to an expected signal instead of an interference signal, especially under the condition of low SNR. An optimized robust Capon algorithm is provided by using a Capon power spectrum, an uncertainty set idea and convex optimization, and the provided algorithm has higher robustness and can ensure effectiveness.

The invention discloses a multi-stage toughness planning method and equipment for an unexpected power-traffic coupling system. The method comprises the following steps: establishing a toughness multi-stage planning model by taking the minimum total investment and operation cost in all planning periods as a target; constructing constraint conditions of power-traffic coupling under multi-stage toughness planning; establishing an uncertain set considering natural disasters, and converting the uncertain set into a multi-stage robust model according to the toughness multi-stage planning model; and solving to obtain a planning strategy. According to the invention, the coordinated operation mechanism of the power-traffic coupling system is described, and the strategic layout and capacity expansion of the charging stations in a plurality of planning periods and the reinforcement of the power line are determined at the same time. The decision of each period is based on the result and insight of the previous moment, and the strategic and adaptive development process is ensured. In order to cope with uncertain disasters occurring in each planning stage, the provided model is remodeled into a multi-stage robust optimization model.

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 application discloses a data-driven urban power grid multi-stage operation scheduling method and system, and establishes a mathematical model of a power distribution system coordination scheduling problem containing multiple energies; based on the obtained mathematical model of the power distribution system coordination scheduling problem, a linear budget constraint generation method based on historical data is used to construct a budget uncertainty set; based on the obtained budget uncertainty set, a multi-stage robust optimization method based on an implicit decision strategy is used to determine a coordination scheduling scheme; and the coordination scheduling of multiple resources and devices is adaptively optimized in a rolling time domain mode.

The invention discloses a post-disaster power distribution network recovery method based on communication-physical coupling modeling. The post-disaster power distribution network recovery method comprises the following steps: 1) coupling modeling: modeling a physical power distribution network and a communication network thereof together; 2) modeling a local distributed robust optimization model: based on an uncertain set defined by adopting a Wasserstein distance, constructing a regional-level distributed robust optimization model with optimization objectives of maximizing a load recovery amount and a load value and minimizing power generation cost and communication cost; 3) consistency variable design and region coupling: different regions establish consistency constraints through shared variables of shared boundary nodes to ensure the coordination consistency of key boundary states in distributed solution; 4) distributed iterative solution: performing parallel solution on the local optimization problem of each region by adopting an alternating direction multiplier method, and realizing global consistency through neighborhood shared boundary variables; and 5) scheduling result execution and feedback: issuing a final recovery decision of each region to field equipment for execution, and performing monitoring and iterative feedback on a recovery state.

This invention provides a robust optimization scheduling method for microgrids that considers segmented multi-interval uncertainty sets. Based on a Gaussian mixture model, the source-load power and error data for wind, solar, and load are piecewise fitted. This is combined with a box-type interval uncertainty set to establish a multi-interval uncertainty set for each power segment, used to describe the uncertainties of wind, solar, and load. With the goal of minimizing microgrid operating costs and carbon emission costs, a robust optimization scheduling model for microgrids that considers segmented multi-interval uncertainty sets and carbon emissions is constructed. A column and constraint generation algorithm is used to solve the robust optimization scheduling model for microgrids. This invention addresses uncertainty modeling and carbon emission issues in microgrids, reduces the conservatism of robust optimization scheduling strategies, and improves the economic efficiency of microgrid scheduling.

The invention discloses a risk-driven high-proportion new energy power system electric quantity reserve decision-making method and system, and belongs to the technical field of power system operation. The method comprises the following steps: carrying out operation risk definition of a VRE admission domain for a power system, and learning a mapping relation between an uncertain set boundary and an operation risk for the power system by using a conservative sparse neural network CSNN after definition; constructing a risk-driven adaptive uncertainty set; based on the linearization model and the adaptive uncertainty set, establishing a risk look-ahead robust scheduling framework through offline training and online mapping modes; and using the risk prospective robust scheduling framework to identify the operation risk of the power system through an online mapping mode, generating a current power reserve decision based on the operation risk, and executing the power reserve decision. The method has good expandability and engineering feasibility, and can be directly integrated to an existing scheduling platform.

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 a data-driven spatial-temporal correlation uncertainty set construction method, and the method comprises the steps: obtaining a first data set according to the actual value and predicted value of the power generation power of a renewable energy unit in a historical time period; and based on the first data set, constructing an uncertainty set of temporal correlation constraint-spatial correlation constraint-residual constraint for the robust optimization model to call. According to the method, accurate modeling is carried out on the spatial-temporal correlation of the renewable energy unit by constructing a'time-space-residual 'multi-dimensional constraint system.

The invention discloses a demand side capacity two-stage robust planning method considering cost and flexible resources, and belongs to the technical field of power system operation and maintenance. According to the method, uncertainty adjustment parameters are introduced, a box type uncertainty set capable of flexibly adjusting conservative property is constructed, the inherent defect that a traditional robust optimization method is too conservative is effectively relieved, the model can dynamically adjust the boundary of the uncertainty set according to different bearing capacities of an actual system to risks, and the robustness of the system is improved. Therefore, refined balance of economy and reliability can be realized in the planning stage. According to the constructed two-stage robust optimization model considering the electricity-carbon coupling mechanism, the carbon transaction cost is taken as an important component of the operation cost to be incorporated into the target function, and the requirements of the national green low-carbon energy strategy are effectively responded. According to the improved column generation and interior point hybrid algorithm designed by the invention, a self-adaptive scene screening mechanism and a gradient guide initialization strategy are provided, the solving time is remarkably shortened, and meanwhile, the global optimality of understanding is ensured.

The invention relates to the technical field of new energy prevention and control, and discloses a novel uncertainty set representation design method for preventing new energy uncertainty risks. The method comprises the steps of collecting operation data from a new energy system in real time, and generating a multi-dimensional time sequence data set; carrying out multi-scale characteristic decomposition on the data set, identifying an uncertainty association mode in combination with historical similar scene information, and generating an uncertain factor set with confidence; constructing a dynamic uncertainty set model, optimizing boundary parameters by using a multi-scale feature decomposition result, and updating constraint conditions in real time; inputting real-time data into a model for verification, identifying risk events, marking levels, and feeding back the risk events to a scheduling decision-making system for collaborative analysis of a risk influence range; and performing reverse optimization on the scheduling decision system based on a risk identification result, identifying and verifying a risk mitigation strategy and an adjustment scheme, and determining an optimal decision path and implementation steps. The method can dynamically capture the uncertainty of the new energy, and effectively improves the operation safety and stability of the new energy system.

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 application discloses a kind of virtual power plant planning-operation two-stage distribution robust optimization methods based on scene driving, comprising: S1, based on the master-slave game framework of power supply, load and energy storage three parties, capacity configuration model and master-slave game model are constructed;S2, considering the correlation and uncertainty of wind turbine, photovoltaic output, construct the uncertainty set of wind light output probability distribution, and generate multiple typical scenarios by sampling and clustering algorithm;S3, under the driving of typical scenario, the confidence interval of uncertainty set is comprehensively norm-constrained, and the distribution robust optimization model of virtual power plant considering uncertainty is established in planning and operation two stages;Wherein, distribution robust optimization model contains capacity configuration model and master-slave game model;S4, based on the column and constraint generation algorithm of modified Kriging model, distribution robust optimization model containing master-slave game model is solved.Utilizing the application, the influence of uncertainty on virtual power plant planning rationality and operation economy can be reduced.

The invention provides a power system unit commitment optimization method considering new energy probabilistic reserve, and the method comprises the steps: constructing a decision-dependent uncertain set based on wind power data before and after wind curtailment of a wind power plant; constructing a first-stage robust unit combination model by taking the maximum value of the day-ahead scheduling cost and the cost required for real-time scheduling in various possible scenes in the uncertain set as an objective function; constructing a second-stage robust unit commitment model by combining constraint conditions of the first-stage robust unit commitment model and taking real-time scheduling cost as a target function; and coupling the first-stage robust unit commitment model and the second-stage robust unit commitment model to obtain a new energy power system unit commitment optimization model. According to the method, all available standby resources including the new energy can be more effectively scheduled and utilized, the actual utilization rate and the absorption level of the new energy are improved, and the capacity of the whole power system for coping with uncertainty impact is enhanced.

The application discloses a kind of distributed robust optimization method, equipment and storage medium considering railway power grid flexibility, for high-altitude area power grid access impact traction load and fluctuation new energy, the flexibility requirement uncertainty set based on Wasserstein distance is constructed by analyzing the flexible demand of high-altitude railway along power system, the potential risk size caused by flexibility requirement uncertainty to system is quantitatively evaluated, the risk cost when flexibility shortage is determined, according to the risk cost when flexibility shortage and flexibility resource operation cost, the distributed robust optimization model based on Wasserstein distance is constructed, the model is converted into processable optimization problem using the approximation framework based on conjugate function, with high reliability, improve the flexibility adjustment capability of circuit, improve the balance of robustness and economy of optimization scheduling of system by reducing the number of constraints.

The invention discloses a virtual power plant regulation and control method based on distributed energy and flexible load aggregation, and belongs to the technical field of power system operation and control. A cooperative control architecture comprising a resource layer, an aggregation layer, an optimization layer, a control layer and a market layer is constructed, and distributed optimization is realized by improving an ADMM algorithm; according to the method, opportunity-constrained programming and an ellipsoid uncertainty set are used for processing wind and light uncertainty, a GRU and a Dueling DQN are adopted to form a deep reinforcement learning load response mechanism, a resource layer completes equipment access and multi-frequency sampling, an aggregation layer clusters to generate an equivalent model and constructs a low-delay communication network, an optimization layer constructs a multi-objective function, and the multi-objective function is combined with an MIQP and a robust algorithm for solving. The control layer executes regulation and control through MPC rolling optimization and a priority strategy, and the market layer realizes transaction and income sharing. According to the method, the wind and light absorption rate is increased by 15%-20%, the calculation efficiency is 30% or above, and the method is suitable for a high-proportion renewable energy scene.

The application relates to the technical field of electric power, and discloses a virtual power plant dynamic resource optimization scheduling method and system. The application solves the identification deviation problem caused by parameter coupling, and improves the robustness of a scheduling strategy to parameter uncertainty. An equivalent parameter combination method reduces the model dimension, so that multi-parameter joint estimation has computational feasibility. A multi-time scale optimization framework realizes dynamic cooperation of a parameter model and a scheduling strategy, and avoids scheduling deviation caused by parameter update lag in a traditional method. A coverage rate calibration mechanism ensures the matching degree of an uncertainty set and an actual error distribution, and provides a reliable data basis for robust optimization. The application realizes accurate modeling and identifiable optimization of key parameters of a virtual power plant, and solves the model misalignment problem caused by parameter redundancy or unidentifiability in a traditional method. Through generation of a candidate identifiable parameter set, the feasibility of subsequent experimental design and parameter estimation processes is ensured.

The invention provides a multi-granularity energy consumption characteristic parameter decoupling method and system based on a constraint uncertainty set, and the method comprises the steps: dividing the energy consumption characteristic of an industrial load into a plurality of intervals according to the energy consumption distribution and condition category probability, constructing a corresponding convex hull, and forming an uncertainty set based on a multi-interval convex hull; constructing a worst expectation four-layer robust optimization model by using the uncertainty set based on the energy consumption uncertainty and the condition category of the industrial load on different time scales; a Bregman alternating direction multiplier method is adopted to solve the non-convex problem of the worst expectation four-layer robust optimization model; and on the basis of the solution of the non-convex problem, performing continuous updating and decoupling optimization on the worst expectation four-layer robust optimization model by adopting a column and constraint generation algorithm of an alternating iteration strategy so as to realize decoupling of the multi-granularity energy consumption characteristics of the industrial load. According to the invention, the flexibility and adaptability of energy management are enhanced, and the energy utilization efficiency is improved.

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 relates to the technical field of administrative management, in particular to a security personnel intelligent scheduling system and method based on a layered architecture. The system adopts a five-layer architecture of a data management layer, a feature calculation layer, an intelligent core layer, a decision support layer and a user interaction layer to realize business logic decoupling. The core of the method comprises the following steps: converting a demand into a structured portrait through a project analyzer; the intelligent scheduling engine adopts a multi-objective evolutionary algorithm fused with robust optimization, and generates a flexible scheduling scheme capable of resisting the worst absence scene by defining an uncertain set of personnel availability; and the dynamic scheduler adopts an online algorithm with competition ratio guarantee to provide real-time decisions with bounded performance for unpredictable temporary tasks. According to the scheme, shift arrangement is changed from post-event remedy to pre-event prevention, and quantifiable performance guarantee is provided for scheduling decisions in a dynamic environment while service continuity is guaranteed.

The invention discloses a self-adaptive beam forming method based on hybrid uncertain set IPNCM reconstruction, and belongs to the technical field of beam forming, and the method comprises the steps: employing steering vector random errors and phase errors of all interference signals to carry out modeling as a hybrid uncertain set with circular and interval constraints; estimating an interference signal steering vector and power on each uncertainty set, and reconstructing an interference noise covariance matrix through characteristic decomposition; then, replacing a sample covariance matrix with the reconstructed interference noise covariance matrix, and obtaining RAB beam weighting based on the interference noise covariance matrix on an expected signal steering vector uncertainty set, so that the robustness of the adaptive beam forming method is improved; and the influence of the interference signal steering vector random error on the performance of the adaptive beam forming method is reduced.

The present invention relates to a multi-period collaborative scheduling method for oil refining and utility systems under uncertain conditions. The method comprises the following steps: Step S1: acquiring operating data and price data for purchased thermal coal and electricity; Step S2: predicting the boundaries of an uncertainty set based on the price data, thereby constructing a dynamic uncertainty set; Step S3: establishing a multi-period collaborative scheduling model for the refinery production system and utility systems under the dynamic uncertainty set; Step S4: introducing the model using a distributionally robust optimization method based on the dynamic uncertainty set; Step S5: solving the multi-period collaborative scheduling model for oil refining and utility systems under uncertain conditions using a robust dual reconstruction approach, and outputting a production plan. Compared to existing technologies, the present invention has advantages such as improving the robustness and conservatism of the production plan output by the model.

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.