67 results about "Uncertain set" patented technology

The invention discloses a new energy generation power considered electric power dispatching moment uncertainty distribution robustness optimization method comprising the moment uncertainty and belongs to the fields of electric power dispatching and uncertainty optimization. The new energy contained electric power dispatching moment uncertainty distribution robustness optimization method comprises step 1, establishing a new energy generation power moment uncertainty set; step 2, establishing an electric power dispatching moment uncertainty distribution robustness optimization method model by the moment uncertainty distribution robustness optimization method; step 3, transforming the model into a semi-definite program through the Lagrangian duality principle. According to the new energy contained electric power dispatching moment uncertainty distribution robustness optimization method, the similar and different characteristics of the distribution obtained by long-term statistics and the distribution obtained by short-term statistics are considered starting from the angle of the uncertainty of the moment so as to provide the safe dispatching scheme; the effect of the multi-electric-field correlation on a system is convenient to consider; the dispatching scheme is provided for sequences which do not exist in the new energy generation power probability distribution.

An environment-economic robust dispatching method for power system based on classified uncertain sets is disclosed. The method constructs uncertain sets of wind power, photovoltaic power and load based on classified probability opportunity constraints. Furthermore, the robust multi-objective optimal dispatching model of power system environment economy based on classified uncertain sets is proposed, which takes robustness as the objective of collaborative optimization and considers economy and environmental protection comprehensively, so as to realize multi-objective optimal decision. The invention fully takes into account the randomness distribution characteristics differences of wind power, photovoltaic power and load, and realizes accurate description of robustness of optimized dispatching scheme. For the first time, robustness is taken as the objective of collaborative optimization, which eliminates the subjectivity of preset robustness (or confidence), and leads to more reasonablerobustness and higher overall satisfaction.

The invention discloses a robust optimizing method for a power generation plan under an uncertain environment. Uncertain parameters are determined according to the overall requirements in the power generation plan compilation under the uncertain environment; an uncertain optimization model of a power generation plan is established by using robust optimization; uncertain optimization problems are converted into definite optimization problems; an adjustable middle variable result is calculated; and an active power of an ordinary power generation set is resolved so as to generate the result of the power generation plan ultimately. According to the robust optimizing method for the power generation plan under the uncertain environment, the robust optimization is applied to the power generation plan in which the uncertainty is considered; the power generation plane obtained by the optimization is adaptive to all possible operation scenes in an uncertain set and has an extremely strong robust property, thus the engineering can be realized conveniently.

An active power distribution network intelligent soft switch robust optimization modeling method taking uncertainty into consideration includes: according to a selected power distribution system, inputting active power distribution system structure and parameters; according to the active power distribution system structure and parameters, building an active power distribution network intelligent soft switch certainty optimization model; performing second-order cone model conversion on the active power distribution network intelligent soft switch certainty optimization model, and acquiring an active power distribution network intelligent soft switch certainty optimization model compact form; according to the active power distribution network intelligent soft switch certainty optimization model compact form, setting an uncertain set of a distributed power source and load, and on this basis, building an active power distribution network intelligent soft switch robust optimization model taking uncertainty into consideration. By the method, randomness and volatility of the distributed power source and the load are fully considered to build the active power distribution network intelligent soft switch robust optimization model taking uncertainty into consideration, and a column sum constraint generation algorithm is adopted for solution to acquire an intelligent soft switch robust running strategy.

The invention discloses an active distribution network robust active and reactive power coordination optimization method based on time series scene analysis, which comprises the following steps: establishing an active distribution network active and reactive power coordination optimization deterministic model; The uncertain factors are analyzed and the scene is generated, and then the clustering method is used to cluster the similar scenes. A two-stage robust optimization model is established, and the original problem is transformed into a single objective function model which only contains the main problem. A two-stage robust optimization model is used to control the active distribution network. The invention considers the uncertain factors caused by the output power of the distributed generation and the load fluctuation in the active distribution network, characterizes the system uncertainties through an uncertain set mode, and improves the stability and reliability of the system operation. Through ARMA model combined with K-Means clustering technology reduces the computational complexity of the problem by reducing the size of the scene.

The invention provides a simulation modeling and scheduling method for an urban drainage system. The method comprises the following steps: establishing a drainage network static database based on a geographic information system; carrying out grid region division and constructing an urban drainage system model; introducing an Adam algorithm into the urban drainage system model, repeatedly and iteratively optimizing the calculated value and the monitored value of the urban drainage system model, and correcting the error of the urban drainage system model; obtaining an urban ponding area distribution map and a ponding liquid level condition; constructing a robust optimization scheduling model which takes the minimum sum of the current economic scheduling cost and the adjustment cost under real-time operation as a target; and using a robust optimization scheduling model to obtain a value in the uncertain set to obtain a determined feasible solution, and combining the pump station scheduling index to obtain a final pump station scheduling plan. Precise scheduling of the pump station can be achieved, the error between the theoretical value and the actual measured value of the urban drainage model is corrected, and energy consumption is reduced.

The invention provides a wind-water-fire joint robust unit combination method. Firstly, an uncertain set is established for the uncertainty of wind power output, and the uncertainty of the wind poweroutput is described in three aspects of a wind power output prediction interval, a time smoothing effect and a spatial cluster effect. Then, a mixed integer linear programming model for water and power dispatching is built by using a linearization method, and the model is integrated in a robust unit combination model considering wind and power dispatching to obtain a wind-water-fire joint robust dispatching model. Finally, a robust optimization model in two phases is solved by adopting a C&CG method. Compared with a traditional robust unit combination model, the wind-water-fire joint robust unit combination model has the advantages that the operation cost is reduced, and the starting number and the running time of thermal power units are reduced, so that the carbon emission is reduced, andthe environmental benefit is achieved; the wind power consumption capacity of a system is increased due to the addition of water power; and the problem of a unit combination containing cascade hydropower stations can be solved within a reasonable time range.

The invention discloses a green data center energy-saving task scheduling strategy based on robust optimization. The green data center energy-saving task scheduling strategy is mainly used for solvingthe problems of high energy consumption, high electricity charge and high pollution of a data center. The green data center energy-saving task scheduling strategy deploys solar cell panels for the data center, and the data center can be powered by solar energy and a traditional power grid in a hybrid manner. In order to solve the characteristics of randomness, discontinuity and instability of solar power generation, the green data center energy-saving task scheduling strategy designs a novel and flexible uncertainty model, defines an uncertain set to limit fluctuations of solar power generation amount by introducing reference distribution, considers the electricity price difference and time-varying characteristic of geographic distributed computing nodes, designs the reasonable task scheduling strategy, and allocates requests submitted to the data center by users to computing nodes and time periods with high solar output and low electricity price for processing, so as to cost the lowest electricity charge and achieve the purposes of saving energy and protecting the environment.

Aiming at the problems caused by the randomness and fluctuation of a large number of distributed power sources to a distribution network after the distributed power sources are connected to the distribution network, the invention provides a distribution network optimal regulation method considering the interaction between sources and loads and the randomness of the output power of the distributedpower sources, thereby optimizing the operation of the distribution network while guaranteeing the safe and reliable operation of a system. Firstly, the randomness of renewable energy output is characterized in the form of an uncertain set, and the set is reduced by an extreme scenario method, and a random model is transformed into a deterministic model. According to demand response technology, price incentives are used to realize source and load interaction with electricity. Finally, a double-layer regulation model is used to coordinate the regulation capacity of renewable energy with a traditional regulation means of the distribution network to reduce the network loss and voltage fluctuation of the distribution network, reduce the regulation times of traditional equipment, and optimize the operation of the distribution network.

The invention discloses a novel robust self-adaptive wave beam forming method under DOA mismatch conditions, mainly solves problems of array radar target detection performance loss under serious mismatch of direction of arrival. The achieving steps are: 1. a orthogonal matrix U is obtained; 2. a rotating vector r is obtained; the expected steering vector is estimated under serious mismatch of direction of arrival conditions; 3. weighted vector under serious mismatch of direction of arrival is estimated; 4. a robust wave beam forming method optimization problem is formed based on a generalized rank signal model; 5. weighted vector is calculated, the novel robust self-adaptive wave beam forming method estimates the weighted vector of expected signal within large uncertain sets, then the robust wave beam forming method based on the generalized rank signal model is used to obtain the robustness to compete other types of mismatch, which can achieve the maintenance of main lobe under the premise of no losing degree of freedom under serious mismatch conditions and the higher output signal and interference to noise ratio is obtained to improve the performance of array radar during target object detection.

The invention discloses a microgrid energy two-stage robust optimization method and system. The method comprises the steps: taking operation equipment and a load in a microgrid as constraints, and taking the minimization of the operation cost as a target to construct an island-type microgrid energy scheduling model; constructing an uncertainty set for uncertainty variables in the scheduling model;taking the switching variable as a first-stage variable, taking other energy optimization decision variables as second-stage optimization variables, taking the minimum operation cost as an optimization target, and taking the operation limitation and the uncertainty set of each equipment as constraints to construct a two-stage robust optimization model based on an expected scene; and respectivelysolving a first-stage model and a second-stage model in an alternate iteration mode by adopting a column and cut generation algorithm until convergence to obtain energy optimization results such as micro-grid power generation and power utilization plans. The method and the system are used for solving the problems of conservative decision, poor economy and the like in the prior art, the safe and stable operation of the island-type microgrid in an uncertain environment can be ensured, and the economic operation of the system also can be maintained.

The invention provides a wind power integration spare setting method and device considering compressed-air energy storage. The method comprises the steps of according to wind power historical data, predicting wind power processing, and constructing a wind power uncertain set; obtaining generation capacity constraint, power balance constraint, transmission flow constraint and spare quantity which apower system needs to meet at each time interval; obtaining power output constraint and power storage constraint of a compressed-air energy storage system at each time interval; obtaining the minimumcost objective function of the power system; constructing a robust and spare setting model considering the compressed-air energy storage; taking advantage of mixed integer linear programming to convert and a C&CG solution algorithm to solve the robust and spare setting model, and obtaining a robust and spare setting strategy considering the compressed-air energy storage. The wind power integration spare setting method considering the compressed-air energy storage has the advantages that the characteristics of equivalently improving system rotation spare by the compressed-air energy storage are made full of, the system safety is improved, and the stability level is improved.

The invention discloses a robust optimization scheduling method considering a wind power multivariate correlation ellipsoid set. The method comprises the steps of obtaining edge probability distribution of a wind power true value and a wind power prediction value according to historical data of a wind power plant; initializing a cyclic variable; calculating a covariance matrix; calculating parameters of multivariate normal condition distribution; sampling the condition distribution to obtain an actual value sample; constructing a multi-ellipsoid set; calculating a multi-ellipsoid integrity index and a high-efficiency index with the dimension TR of historical data on the dth day; traversing all dates, and calculating comprehensive indexes; traversing all TR to obtain an optimal correlationtime scale; constructing a multi-dimensional ellipsoid uncertainty set according to the latest day-ahead prediction value; and based on the multi-dimensional ellipsoid uncertainty set, constructing amicrogrid two-stage robust scheduling model, and finding an economic optimal scheduling scheme in the worst uncertainty scene. The method is used for solving the uncertainty of wind power in a microgrid, improving the economy of a robust scheduling scheme and reducing unbalanced power.

The embodiment of the present invention provides a method and system for calculating the capacity of a wind farm energy storage device. The method comprises the steps of: obtaining a standard reference distribution function and all approximate reference distribution functions of the wind power output according to the historical data of wind power output of the wind farm; obtaining the KL distance between each approximate reference distribution function and the standard reference distribution function, and constituting an uncertain set composed of all the approximate reference distribution functions having KL distances not exceeding a preset KL distance; based on the uncertain set, establishing a distributed robust energy storage device planning model of the wind farm; acquiring the capacity of the energy storage device according to the distributed robust energy storage device planning model of the wind farm. The embodiment of the invention establishes the distributed robust energy storage device planning model. The distributed robust optimization does not require an accurate probability distribution function, so the result is more robust in the statistical sense and avoids a situation of putting a large quantity of resources to deal with an extreme scene with an extremely low probability, thereby achieving a low conservativeness.

The invention discloses a robust unit combination method based on a multi-band uncertain set in the technical field of power system dispatching automation. By analyzing the wind power and load historical data provided by Elia, a temporal autocorrelation exists between wind power and load prediction errors, a prediction error time correlation constraint is proposed, and the constraint is approximated to a linear constraint by using the discrete characteristics of the uncertain set. The time correlation constraint and the multi-band uncertainty set are combined to accurately capture uncertain variables of fluctuation levels at different time periods. In addition, the robust unit combination is solved by using a Benders decomposition method and a C&CG method. A test result shows that the method can effectively reduce the conservativeness of the uncertain set, and the economic efficiency of the system operation is improved while the robustness of a UC result is ensured.

The invention discloses a method for energy-efficient transmission of robustness in a cognitive network. According to the method for energy-efficient transmission of the robustness in the cognitive network, channel state information of relevant channels is measured through the receiving end of a secondary user and is fed back to the sending end of the secondary user; the sending end of the secondary user carries out measurement multiple times to determine an uncertain set of the gain of each channel; the secondary user establishes a model according to a robustness optimizing method to maximize secondary user energy efficiency; the poorest channel gain is solved, and a service quality requirement constraint of a main user is converted into a convex constraint; the optimal sending power is solved according to a power distribution algorithm, transmission is carried out on each channel according to the optimal sending power, and the optimal transmission power of the robustness is solved when the channel state information is uncertain. The method for energy-efficient transmission of the robustness in the cognitive network can be used for cognitive network communication and has the advantages that the maximization of the energy efficiency of the secondary user is guaranteed under the circumstance that the channel state information is uncertain, interference power at the receiving end of the main user is strictly controlled, the service quality of the main user is guaranteed, and negative influence of the uncertainty of the channel state information on the performance of the cognitive network is effectively eliminated.

The invention discloses a robust optimization scheduling method and device for a comprehensive energy system, and the method comprises the steps: carrying out the clustering of a wind power predictionerror sample set through an infinite-dimension Gaussian mixture model, and obtaining a data-driven wind power prediction error uncertainty set; establishing a robust optimization scheduling model ofthe electrical comprehensive energy system according to the wind power prediction error uncertainty set, wherein the robust optimization scheduling model comprises a day-ahead plan scheduling stage and a real-time operation scheduling stage; solving a day-ahead plan scheduling stage to obtain a decision-making unit start-stop and output interval and a natural gas supply amount interval; solving the real-time operation scheduling to obtain the output value of the decision-making unit and the gas supply amount of the natural gas. According to the invention, the problem of IEGS coordination optimization in an uncertain environment of wind power output is solved.

The invention discloses a new energy uncertain set modeling method based on spatial-temporal correlation, and belongs to the technical field of electrical engineering. The method comprises the following steps of firstly, collecting historical data of all new energy field station output, wherein historical data of each day is a historical scene; then solving a minimum closed-package high-dimensional ellipsoid capable of surrounding all historical scenes; selecting a plurality of vertexes of the ellipsoid as the top points of the uncertain set, namely, obtaining the expression of the generalizedpolyhedron defined by the vertexes to describe the uncertain set, and finally introducing a zooming coefficient modification uncertain set. According to the method, a new uncertain set is formed by convex hulls surrounded by limit scenes of a limited number, and can be easily expanded to the existing two-stage robust optimization method. The number of the limit scenes and the number of the randomvariables are linearly increased, and the solving speed of the robust optimization method is guaranteed. The method has the advantages that the economy of the set can be adjusted by modifying coefficient according to the actual demand.

The invention provides a frequency domain passive cavitation imaging and frequency compound imaging method based on feature space adaptive beamforming, including the steps: performing Fourier transform on a time domain cavitation signal and performs phase shift and array element apodization processing; constructing a covariance matrix and obtaining the weighting vector of adaptive beamforming according to the normalized steering vector and covariance matrix; decomposing the covariance matrix into the feature space, and projecting the weighted vector onto the signal subspace and then carrying out passive cavitation imaging in the whole frequency domain; constructing a cavitation artifact ratio index and selecting the optimal steering vector uncertain set parameters according to the resultsof passive cavitation imaging in full frequency domain under different steering vector uncertain set parameters, so as to realize passive cavitation imaging and frequency composite imaging in different sub-frequency domains. The invention can greatly suppress imaging artifacts, thereby improving spatial resolution of passive cavitation imaging in frequency domain, can carry out comprehensive characterization on any plurality of frequency components, and is suitable for real-time monitoring of multiple focused ultrasonic therapy applications.

The invention provides a forward collision-preventing sonar neritic steady high-resolution azimuth estimating method. According to the forward collision-preventing sonar neritic steady high-resolution azimuth estimating method, the vector optimization is combined with second-order cone programming; a source vector which is relevant with a neritic multi-path structure is decomposed; an uncertain set of the errors of the source vector is constructed; and the accurate azimuth estimation can be carried out on a target through carrying out effective array response constraint on elements in the uncertain set. By using the method provided by the invention, the neritic multi-path influence and uncertain environmental factors are adequately considered; a high-resolution azimuth estimating method is prevented from generating obvious performance decrement and a spectral peak splitting phenomenon in a neritic environment; the dependence on underwater environmental parameters is effectively overcome; the forward collision-preventing sonar neritic steady high-resolution azimuth estimating method has quite strong tolerance for uncertain factors such as channels, environments and the like; and the steadiness of a high-resolution algorithm is effectively improved.

The invention provides a two-stage N-K robust fault constrained unit combination method considering fault probability. Based on a fault uncertainty set and probability criteria, a two-stage robust fault-constrained unit combination model with the goal of minimizing shutdown cost and operating cost under a prediction scenario is established, the two-stage method combining the Benders decompositionmethod and the column and constraint generation (C&CG) method is utilized for problem solving, the scheduling cost in the basic scenario is minimized through the proposed model, moreover, that the determined robust unit combination can be adaptively and safely adjusted under uncertain generator and transmission line faults is guaranteed. The method is advantaged in that the proposed model can effectively guarantee robustness of the unit combination under N-K faults in the basic scenario, considering the fault probability of generators and transmission lines can effectively eliminate extreme fault scenarios with less probability of occurrence, conservative property of the uncertain set is reduced, and economical property of robust optimization is improved.

The invention discloses a power system scheduling method and device considering wind power probability distribution uncertainty. The method comprises the following steps of: constructing a wind power probability distribution uncertainty set; based on the wind power probability distribution uncertainty set, defining operation risk cost; based on the operation scheduling cost and the operation risk cost of an power system, taking the pursuit of the lowest total operation cost of the power system as a target, constructing a robust economic scheduling model considering the wind power probability distribution uncertainty; determining an optimization constraint condition of the robust economic scheduling model; solving the robust economic scheduling model considering the wind power probability distribution uncertainty to obtain an optimal decision variable; and realizing the scheduling of the power system based on the optimal decision variable. Automatic optimization of the system operation risk level and the wind power admissible range under the condition of uncertain wind power probability distribution is realized. For a formed complex nonlinear optimization model, the original model is converted into a linear programming problem of iterative solution on the basis of the characteristics of the wind power probability distribution uncertainty set and the characteristics of actual operation of the system.

The invention relates to a cascade hydropower robust optimal scheduling method based on a random security domain. The method comprises the following steps: 1) according to uncertainty and time sequence correlation of incoming water of medium / long-term cascade hydropower, establishing the random security domain, that is, a multiband uncertain set with incoming water time sequence correlation beingtaken into consideration; 2) according to the multiband uncertain set, constructing a robust two-stage model with robustness and economy being coordinated; 3) calculating a cross-year cascade hydropower pre-scheduling model in the first stage of the robust two-stage model to obtain a pre-scheduling plan of a hydropower station and reservoir capacity under a predication condition; and 4) convertinga re-scheduling model in the second state of the robust two-stage model into the max-min mathematical optimization problem, and judging robust feasibility of the pre-scheduling plan, and finally, obtaining a scheduling plan having robustness through feedback correction coordination optimization. Compared with the prior art, the method has the advantages of being precise and reliable, consideringincoming water time sequence correlation and robustness and economy coordination and the like.

The invention discloses an optimal power flow calculation method containing wind power access based on robust cone programming, and belongs to the technical field of power system dispatching automation. The method comprises the following steps: according to prediction data of day-ahead wind power output and load, taking minimum power generation cost of a unit as a target function, describing uncertainty of wind power output by an uncertain set in a polyhedron form, introducing alternating current power flow constraint to carry out safety check, and constructing a robust cone programming model based on alternating current power flow; performing cone relaxation on the non-linear constraint, and processing a non-linear term by using an auxiliary variable substitution method so as to obtain a cone programming model; in order to solve the uncertainty optimization problem, using a C&CG algorithm to decompose the model into a main problem in a basic scene and a sub-problem in an uncertain scene, substituting unit commitment decisions solved in the basic scene into safety verification in the uncertain scene, performing dual transformation of inner layer optimization, solving a dual cone programming model, and obtaining a globally optimal solution.

The invention relates to a day-ahead intra-day scheduling method considering multi-type demand response uncertainty, and belongs to the technical field of power system optimal scheduling. The uncertainty generated by the demand response due to the randomness of the willing of a participating user can influence economy of power system scheduling. According to different action mechanisms of the electricity price type demand response and the incentive type demand response, uncertainty models are established for the electricity price type demand response and the incentive type demand response respectively: firstly, describing the electricity price type demand response quantity by using fuzzy variables, and creating an uncertainty set of the interruptible load actual interruption quantity by using a robust optimization theory; then, according to the difference between the two demand side management response speed, allocating the two demand side management response speeds and other output resources into a day-ahead day-ahead two-stage decision model; based on the fuzzy chance constraint programming theory and the multi-stage robust optimization theory, using a bat algorithm and an entropy weight method for solving;.

The invention relates to a Spark platform-based uncertain set frequent item mining method, and belongs to the field of data mining. According to the method, a novel UWEEP-tree structure is put forwardon the basis of a Spark big data framework to process data sets in parallel without repeatedly scanning the data sets and generating plenty of candidate sets, so that the algorithm execution efficiency is greatly improved; and meanwhile, both the survival probabilities and weight values of uncertain data items are considered, so that frequent items more according with user demands are mined and anew thought is provided for the uncertain set frequent item mining method.

The invention relates to an intra-day optimization operation strategy for providing stacking service by load side energy storage. A 'look-ahead-value function approximation' mixed intra-day operation strategy of an online rolling optimization two-stage robust approximation dynamic programming model is provided and can optimize a power reference point in real time in a finite time window. In a day-ahead stage, a post-decision state approximate value function is introduced by applying an approximate dynamic programming thought so as to represent long-term expected net benefits in different time period states, and offline training is carried out on the approximate value function by utilizing a differential learning algorithm. In the intra-day operation process, the online rolling optimization two-stage robust approximation dynamic programming model dynamically obtains the power reference point of each time period on the basis of the rolling update prediction of the electricity price and the load, a frequency uncertainty set and a long-term time domain approximate value function. The strategy provided by the invention not only can effectively guarantee the frequency regulation capability of the load side energy storage, but also can quickly evaluate the long-term influence of real-time decision through the offline training-online application mode of the approximate value function, and well balance global economic benefits and online operation overhead.

The invention discloses a method for analyzing an uncertain wind power AC-OPF problem via a data driven robustness model. The method comprises the following steps that 1) historical data is introduced, and parameters including a ball-box uncertain set, the confidence level, the risk degree, the iteration step, the iteration frequency and a radius parameter of the box-ball uncertain set of wind power generation are set; 2) an ellipsoid uncertain set is used to solve a dual problem of the given problem and find a robustness optimal solution; 3) an uncertain area is calculated; 4) the uncertain area replaces the uncertain set, and a safety approximation of optimal trend of the AC power grid is calculated; 5) if the safety approximation is greater than or equivalent to the risk degree, a result is output, and the algorithm is ended, and otherwise, a next step is turned to; 6) a new radius parameter is set, the iteration counted number is updated, and the step 2) is returned to. The data driven robustness is used, so that the economic performance is higher on the premise that the operational safety and stability of the power system are ensured.

The invention discloses a wireless resource allocation method in an imperfect channel state information fading channel, and the method comprises the following steps: (1) considering the influence of achannel estimation error, and modeling the estimation error of channel state information into an uncertain set by using an ellipsoid approximation method; (2) establishing a joint optimization problem by using a weighted summation method; (3) based on an on-line learning method, respectively proposing a time delay sensitive data scheduling method aiming at two conditions of channel estimation error boundary determination and uncertainty. The scheduling algorithm based on online learning provided by the invention can solve the problem of channel state information estimation errors, and improves the performance of the system in the aspects of energy efficiency, transmission delay and packet loss rate.