454 results about "Robust optimization" patented technology

The invention discloses a regional integrated energy system operation robust optimization method considering electricity-to-gas conversion and uncertainty, and the method comprises the steps: carryingout the detailed modeling of an electricity-to-gas conversion process according to the principle of an electrochemical reaction, and analyzing the coupling relation between different energy forms ina matrix mode based on an energy center model; establishing a regional comprehensive energy system operation optimization model considering the uncertainty of various load prediction powers, and establishing a two-stage robust optimization model according to a polyhedral uncertain interval; and decomposing the two-stage robust optimization model into a main problem and a sub-problem, converting the sub-problem into an optimization problem of a single target, and carrying out iterative solution to obtain a comprehensive energy system robust optimization scheme. According to the method, the potential of complementary mutual relief and flexible scheduling among multiple energy forms is fully excavated, wind curtailment can be reduced, and the operation economy and flexibility of the comprehensive energy system are improved; and the contradiction between the operation risk and the cost can be coordinated according to the actual condition of the regional integrated energy system.

An improved resource allocation system comprising a reliability decision engine (323), which allocates the portfolio's assets as required for the desired reliability portfolio. The reliability decision engine including two reliability decision engines, a basic reliability decision engine (325) and a robust reliability decision engine (327). The use of robust optimization makes it possible to determine the sensitivity of the optimized portfolio. Scenarios can be specified directly by the user or automatically generated by the system in response to a selection by the user. Inputs (329, 331) are applied to basic the basic reliability decision engine (325) and inputs (311) are applied to robust reliability decision engine (327).

The present invention relates to system and method for providing near real-time DR events and price signals to the customer end-points to optimally manage the available DR resources. The system utilizes bottom up load forecasting for accurate individualized forecasts for customer loads in the presence of dynamic pricing signals. For better efficiency and reliability of grid operation the system utilizes advanced machine learning and robust optimization techniques for real-time and “personalized” DR-offer dispatch.

A method and system are provided that minimize wafer or package level test time without adversely impacting yields in downstream manufacturing processes or degrading outgoing quality levels. The method provides optimization by determining, a priority, the most effective set of tests for a given lot or wafer. The invention implements a method using a processor-based system involving the integration of multiple sources of data that include: historical and realtime, product specific and lot specific, from wafer fabrication data (i.e., process measurements, defect inspections, and parametric testing), product qualification test results, physical failure analysis results and manufacturing functional test results. These various forms of data are used to determine an optimal set of tests to run using a test application sequence, on a given product to optimize test time with minimum risk to yield or product quality.

The invention relates to the technical field of wireless communication, and provides a method for joint optimization of schedule and resource allocation based on a genetic algorithm, which is applied in a CoMP communication system. The method includes steps of: S1, encoding chromosome; S2, initializing setting; S3, computing fitness value; S4, determining whether the optimal solution is better than an elite: if yes, updating the elite and executing S5, and if no, turning to S5; S5, determining whether a predetermined generation of population has been generated or not, if no, executing S6, otherwise, turning to S8; S6, participating reproduction process to generate two offspring chromosome individuals; S7, determining whether a predetermined number of offspring chromosome individuals have been generated, if yes, turning to S3 to compute again; otherwise keeping on reproduction; S8, performing schedule and resource allocation according to a solution corresponding to the elite. Under the condition of satisfying schedule limitation and power limitation, the method can, by uniting schedule and resource allocation, efficiently optimize system performance with less computing complexity.

The invention discloses a dispatching method for achieving robust operation of an electrical power system. The dispatching method comprises the steps that S1, original data information is obtained; S2, under a certain confidence coefficient level, an upper limit and a lower limit of a mean value of day-ahead, intra-day and real-time wind power generation forecast errors, an upper limit and a lower limit of day-ahead, intra-day and real-time photovoltaic power generation forecast errors, and an upper limit and a lower limit of day-ahead, intra-day and real-time load forecast errors are obtained; S3, a day-ahead dispatching plan, a robust safe operation range corresponding to the day-ahead dispatching plan, an intra-day dispatching plan, a robust safe operation range corresponding to the intra-day dispatching plan, a real-time dispatching plan and a robust safe operation range corresponding to the real-time dispatching plan are obtained. According to the method, the rolling coordination technologies of forecast information, current operation information and historical operation information are considered simultaneously, the robust safe operation ranges of the system are obtained, and therefore the dispatching plans are not limited to a unique preset value, and flexible dispatching in the robust ranges can be achieved. The obtained dispatching plans can be used for coping with stochastic volatility of new energy power generation better, and safety and economical efficiency are both considered.

A method for sound source localization in a digital system having at least two audio capture devices is provided that includes receiving audio signals from the two audio capture devices, computing a signal-to-noise ratio (SNR) for each frequency band of a plurality of frequency bands in a processing frame of the audio signals, determining a frequency band weight for each frequency band of the plurality of frequency bands based on the SNR computed for the frequency band, computing an estimated time delay of arrival (TDOA) of sound for the processing frame using the frequency band weights, and converting the estimated TDOA to an angle representing sound direction.

The invention provides a method for preventing a conflict between moving load balance and moving robustness optimization function, which adopts the main idea that when the moving load balance operation is carried out in an LTE-Advanced network, the adjustment of the moving load balance to a switching threshold needs to be limited in the tolerance range of the moving robustness optimization so as to prevent various switching problems caused by inaccurately adjusting the switching parameter by the moving load balance.

A method for computing a preferred set of prices for products in a subset of a plurality of products is provided. Generally, initial prices for a plurality of products are stored. A subset of products of the plurality of products is designated, where the number of products in the subset of products is less than the number of products in the plurality of products. A demand model is created based on Bayesian modeling. Prices for the products in the subset of products are optimized, while maintaining the initial prices of products of the plurality of products that are not in the subset of products, wherein the optimization uses a grid method to determine the optimized prices.

The invention discloses an iterative learning trajectory tracking control and robust optimization method for a two-dimensional motion mobile robot. The method includes the steps that firstly, a kinetic equation of a two-dimensional motion mobile robot discrete non-linear motion system model is established; a discrete non-linear state space expression is established; a P type open-closed loop iterative learning controller based on the iterative learning control technology is established; then the robust convergence of the established discrete non-linear control system is theoretically analyzed; then parameter item splitting is conducted on control gains of the P type controller, meanwhile, a quadratic performance index function based on controller parameters is designed, and the purpose is to optimize the control parameters; finally, monotone convergence characteristics of output errors and parameter selection conditions generated when a control algorithm acts on a controlled system are analyzed and optimized, and the two-dimensional motion mobile robot can rapidly track an expected motion trajectory at high precision. The method has the advantages that the robust optimization iterative learning controller is suitable for tracking control in an ideal state and suitable for trajectory tracking tasks under the condition that interference exists outside. A designed iterative algorithm is simple and efficient, introduction of a large number of additional parameter variables is not needed, and engineering realization is easy.

Resource allocation techniques for robust optimization of a set of assets. In these techniques, a user defines or selects scenarios that model investment conditions including normal and/or extreme conditions. The set of assets is optimized across the scenarios to produce weights for the assets in the set that optimize the worst-case value of the assets. A resource allocation system is disclosed which first selects a reliable set of assets for optimization and then optimizes the reliable set of assets. Optimization of the set of assets may involve robust or non-robust optimization, many different kinds of constraints and/or multiple constraints, different objective functions, and different adjustments for the objective functions. Selection of the set of assets and selection of the kind of optimization, of the constraints, of the objective function, and of the adjustments to the objective function is done using a graphical user interface.

The invention relates to a microgrid energy management method based on two-stage robust optimization, and the method comprises the following steps: constructing a microgrid electrical equipment basicmodel; constructing an uncertainty set for uncertain variables; constructing a microgrid two-stage robust optimization model: taking 0/1 variables and a continuous variable and an uncertain variable as a first-stage optimization variable and a second-stage optimization variable, taking the minimum operation cost of a microgrid as an optimization target, taking the operation restriction of all equipment and the uncertainty set as the constraints, and building a two-stage robust optimization model of the microgrid; solving the two-stage robust optimization model: decomposing the two-stage modelinto a main problem and a subproblem, and obtaining the dual problems of the problems through employing the strong dual theory and the linearization theory; employing a mixed integral linear planningmethod for the alternate solving of the dual problems of the main problem and the subproblem.

Handover (HO) statistics and handover issue events which are due to non-mobility causes are not included in the statistics fed into mobility robustness optimization (MRO). The non-mobility causes may include, e.g., load balancing, retracting users to prepare for cell maintenance or restart/reconfiguration and cell outage including compensation means. Radio link failure (RLF), handover failure (HOF), and handover oscillations (HOosc) that are due to non-mobility cause are excluded from the statistics upon which mobility robustness optimization is based. Non-mobility causes are also differentiated from mobility causes when reporting key performance indicators to an operator.

The invention discloses a method for detecting and switching a failure scene based on mobile terminal information, which mainly solves the problems of the inaccuracy and the incompleteness of the detection for switching the failure scene in the mobile robustness optimization in the conventional SON. The method mainly comprises the following contents: 1, modifying reestablishment connection request information, adding a former cell field, and adding options of coverage hole and other RLF to a request connection reason field; and 2, receiving the reestablishment connection request information by using a target base station; reading a value of the request connection reason field, and if the value is the other RLF, determining that the link failure is resulted from irrationality switching of the parameter; reading the former cell field, and if the value is 0, switching the failure scene in a mode that the switching is triggered too late; if the value is the same as a local cell ID, switching the failure scene in the mode that the switching is triggered to early; and if the value is different from the local cell ID, then switching the failure scene in the mode that the failure scene isswitched to an error cell. The method is applied to parameter optimization of a cellular network for improving network performance.

The invention provides a modeling method for a production scheduling problem distribution set robust model, which belongs to the field of production scheduling and operations research. A distributed set robust optimization method based on an uncertain distribution function set is adopted to model the production scheduling problem, the model is composed of an objective function and constraint conditions, the objective of the model is to search for an optimal robust scheduling sequence, and thus, in a condition in which the total flow time of the sequence obeys the worst distribution in the processing time, the minimum condition risk value is realized. In the case of solution, the model is decomposed to an assignment subproblem and an integer two-order cone programming subproblem, and a minimum average processing time priority criterion and two Cauchy relaxation algorithms designed by the invention are adopted to optimize the model and obtain the optimal solution. The distribution set robust optimization method is applied to the production scheduling problem, the method is more applicable to the actual production condition compared with the existing robust modeling method, and in a condition of ensuring the system performance, a decision risk is reduced.

The invention discloses an adaptive robust scheduling optimization method for a virtual power plant. The method adopts adaptive robust scheduling optimization to process the output uncertainty of renewable energy sources, and considers the day-ahead and real-time two-stage scheduling of the virtual power plant. A model established with the adaptive robust scheduling optimization method is a three-layer optimization model. In order to solve the problem, the method comprises the following steps that: firstly, importing an auxiliary variable, dividing the model into a single-layer main problem and double-layer sub problems; secondly, through a duality theory, converting the double-layer sub problems into the single-layer problem; and finally, adopting a column sum constraint generation method, and solving the main problem and the sub problems through alternating iteration until the gap of two problems is converged into an acceptable range. Compared with statistic robust optimization, themethod is characterized in that the balance situation of the regulation strategy and the real-time market of each polymerization unit in the virtual power plant after the output of the renewable energy sources is obtained is considered, the fluctuation of renewable energy sources can be effectively stabilized, and the economic benefit of the virtual power plant is improved.

A computer-based profit optimization model is provided that takes account of supply-side and demand-side factors in optimizing profit for an organization. The model takes into account which parts the organization uses to assemble various products. Demand curves are used to characterize the quantity of each product that will be demanded for various classes of product and classes of customer. Supply model data is used to determine which mix of products can be sold in view of parts availability. Using the demand model and supply model data, the optimization model can recommend a set of prices to use for selling the organization's products to various customers and at various shipment times. The model ensures that the organization has sufficient resources available to produce the products and enforces user-supplied business rules and other constraints.

A cooperative game-based micro-grid optimal dispatching method for charging, exchanging and storing integrated power plants includes modeling photovoltaic power, wind power and electric vehicle powerexchange demands by using robust optimization considering electric vehicle power exchange demand and stochastic characteristics of photovoltaic and wind power generation; the optimal dispatching modelof upper micro-grid with minimum power supply cost being constructed; the life loss cost model and operation loss model of integrated power plant being established; constructing the optimization model of lower-level charging-replacing-storage integrated power station with the objective function of maximizing CSSIS profit; the optimal dispatching model of charge-exchange-storage integrated microgrid based on cooperative game being constructed. The model is solved by non-dominated sorting genetic algorithm with elite strategy (NSGAII) combined with solver CPLEX. The invention provides the theoretical basis and technical support for the construction of the charging and replacing infrastructure of the urban electric vehicle, and is beneficial to improving the economic benefits of the operation of the electric network, the micro-grid and the charging and replacing power station.

The invention provides a virtual power plant combined heat and power scheduling robust optimization model. A model aggregation unit comprises a distributed generating set, a wind turbine generator set, a photovoltaic set, a combined heat and power (CHP) set, a boiler, electric energy storage, heat energy storage, an electric load and a heat load. Participation of the CHP set in the SRM (Spinning Reserve Market) situation is considered. Aiming at the facing uncertain problem of a virtual power plant (VPP) and resulting risks, robust optimization (RO) is utilized to process uncertainty of the EM electricity price, the SRM electricity price, the wind power capacity, the photovoltaic capacity, the electric load and the heat load, and risk quantification indexes are established, and thus robustness and economical efficiency of a RO model are balanced. The model provided by the invention well solves the existing combined heat and power scheduling optimization model establishment problem of the VPP when participating EM and SRM at the same time, and improves flexibility of decision making, and thus the profit of the VPP is increased. Meanwhile, the introduction of the RO model effectively reduces system risks, and thus the effective reference is provided for a decision maker to select a proper robust factor.

The invention discloses a microgrid robust optimization scheduling method in consideration of component frequency characteristics. In consideration of the uncertain fluctuation characteristics of windpower and photovoltaic power output, a microgrid robust optimization scheduling module in consideration of the frequency response characteristics of various components is established. The robust optimization scheduling module is solved by using a Benders decomposition method. An original problem is decomposed into a sub problem and a main problem to be alternately iterated to obtain a robust optimization scheduling scheme. The objective function of the robust optimization model achieves minimum total operating cost of the microgrid under an extreme scenario with maximum network loss. The constraints include active balance constraint, diesel set operating characteristics, energy storage device operating characteristics, line frequency characteristics, load frequency characteristics, voltage safety constraint, and frequency security constraints. The obtained scheduling scheme can ensure that the system cannot exceed a frequency limit within the uncertain fluctuation range of distributedwind power and photovoltaic power output, and ensure the frequency safety of the microgrid.

The invention discloses a robust optimization-based combined cooling heating and power supplying type microgrid operating method. The operating method comprises the following steps of step 10), establishing a combined cooling heating and power supplying type microgrid economic operation target function; step 20), establishing a certainty constraint in combined cooling heating and power supplying type microgrid operating constraint; step 30), establishing uncertainty constraint in the combined cooling heating and power supplying type microgrid operating constraint; and step 40), solving an optimization model to obtain system operating control variable, and transmitting corresponding instructions to each equipment in the system, wherein the optimization model comprises the target function established in the step 10), the certainty constraint established in the step 20) and the uncertainty constraint established in the step 30). By adoption of the operating method, adverse impact caused by uncertainties of renewable energy and load can be eliminated.

The invention provides a mobile robustness optimization enhancing method (USP_MRO: User Specific Parameters in MRO) in a Long Term Evolution ADSL (LTE-A) system, comprising the following steps: introducing UE specific handoff parameters aiming to a certain characteristic which is specific to UE and influences the handoff performance in mobile robustness optimization, and maintaining different handoff parameters for the UE with different characteristics by each base station; except for common handoff parameters, also informing a plurality of UE specific handoff parameters to the UE by eNB after accessing a certain UE to eNB; and selecting a proper UE specific handoff parameter for use by the UE according to self characteristics during handoff. The invention considers the UE characteristics influencing the handoff effect into the mobile robustness optimization by introducing the UE specific handoff parameters in the mobile robustness optimization, thereby greatly increasing the success probability of handoff and preferably realizing the mobile robustness optimization.

The invention discloses a distributed optimal operation method considering the uncertainty for an active distribution network, which comprises the steps of firstly, building an uncertainty model of photovoltaic output by using a robust optimization method in consideration of the uncertainty of the photovoltaic output; secondly, building an optimal scheduling model of the active distribution network, wherein the model takes the minimum operation cost as an objective function and comprehensively considers power flow constraints, safe operation constraints and output constraints of adjustable andcontrollable resources; then building an optimal scheduling model of multiple microgrids connected to the distribution network, wherein the model takes the minimum operation cost of the microgrids asan objective function and adds the problem of renewable energy consumption serving as a penalty function to the objective function; and modifying the distribution network model and the microgrid model based on a Lagrange function in consideration of a coupling relationship between the distribution network and the microgrids in tie-line power so as to build a distributed optimal scheduling model of the active distribution network.

The invention discloses a bus dynamic departure scheduling optimization method based on a genetic algorithm. The method specifically comprises the steps that information of vehicles and passengers running on a single line is collected before the starting moment of a planning period; a passenger flow arrival rate function is obtained according to the real-time data and prediction, and parameters needed by model calculation are determined at the starting moment of the planning period; a scene-based dynamic bus departure scheduling robust optimization model is established by taking minimization of an expected value of total waiting time of passengers as an objective function under the condition that scenes on a single line are different; a genetic algorithm is designed for solving, and according to the probability of subjective occurrence of each scene in the preference adjustment model and the magnitude of a regret value in the model constraint, different solutions for selecting an optimal departure scheme are obtained. According to the method, the problem of dynamic bus departure scheduling under the condition that the passenger arrival rate is uncertain is solved, the waiting time of passengers is shortened, the potential risk of bus operation is reduced, and the safety and the stability of a bus system are improved.