28 results about "Mixed integer quadratic programming" patented technology

The invention discloses an offshore wind power plant micro-siting optimization method considering regular layout. The method comprises the following steps: firstly, obtaining wind data, wind power plant parameters, fan parameters and wake flow parameters; performing multi-angle rotation on the wind power plant based on an equivalent rotation coordinate method, constructing a gridding mixed integer quadratic programming model, and performing rotation rule layout optimization (RRLO-1) to obtain an initial fan layout; on the basis of the initial layout, a hybrid algorithm (GA-ISSA) of a genetic algorithm and a sparrow search algorithm is adopted, the position of a draught fan and the rotation angle of the wind power plant are finely adjusted, and fine rule layout optimization (RRLO-2) is completed; and finally, the wake flow influence is calculated through a Cosine wake flow model and a quadratic sum superposition model, a fan power curve is processed in a piecewise linearization mode, and the final layout is output by taking the maximization of the total generating capacity as the target. According to the method, a fan position configuration scheme meeting the engineering rule layout requirement is provided for early-stage planning of the offshore wind plant, the wake effect is reduced, the power generation efficiency is improved, and development of the offshore wind power industry and low-carbon target implementation are facilitated.

The invention discloses a network construction type energy storage locating and sizing optimization method considering new energy multi-station short circuit ratio improvement, and relates to the technical field of power system and energy storage system planning. Proposing an accurate modeling method of the new energy multi-station short-circuit ratio considering the resistance influence; based on the characteristics of a network construction type energy storage independent voltage source and the characteristics of short-time high-rate overload capacity, a relation model between new energy multi-station short-circuit ratio improvement and network construction type energy storage site selection capacity is provided; based on a convex optimization theory and a mixed integer quadratic programming method, a network construction type energy storage locating and sizing optimization model considering new energy multi-station short-circuit ratio improvement is constructed. The new energy multi-station short-circuit ratio under the weak power grid is improved, the addressing and sizing scheme optimization of network-building type energy storage is realized, and the operation safety of a new energy unit and the reliability of new energy power transmission under the weak power grid area are improved.

A medium- and long-term optimal scheduling model taking maintenance and ecological scheduling for a cascade hydropower station into consideration. The model comprises: setting common scheduling constraints, maintenance constraints and ecological scheduling constraints, taking the maximization of cascade power generation as a goal, transforming the constraints into recognizable mathematical expressions, and establishing an optimal scheduling model for a cascade hydropower station; solving the model to generate an initial cascade power station scheduling process, calculating the average water level for time periods in the cascade power station scheduling process, using as a corridor a region that ranges from K meters above the average water level to K meters below the average water level, taking the intersection of the corridor with a normal water storage level and a dead water level, and using the intersection as a new corridor; within the range of the corridor, respectively approximating a water consumption rate and an expected output as polynomial functions, which are of degrees no greater than 2, of a reservoir-out flow, a reservoir water level and a downstream station water level, establishing a mixed-integer quadratic programming model, and executing a solution to obtain a new cascade power station scheduling process; and repeatedly performing iterative calculation until an end condition is met, and thereby obtaining a final cascade hydropower station scheduling scheme and an optimal occasion for the development of maintenance and ecological scheduling.

The invention provides a green electricity hydrogen production system optimization method and system considering multi-tank service life balance, and belongs to the technical field of new energy and electrochemistry, the method comprises the steps of obtaining market data, power grid data and downstream hydrogen load demand data in a preset scheduling period, and obtaining initial state data of a plurality of electrolytic tanks deployed in a hydrogen production station; constructing a mixed integer quadratic programming optimization model with the goal of maximizing the comprehensive benefit of the system; the model is solved, and the optimal scheduling strategy of the start-stop state and the operation power of each electrolytic cell in each time period in the scheduling period is obtained; and converting the optimal scheduling strategy into a control instruction, and issuing the control instruction to a controller of a hydrogen production station to complete collaborative optimization of the green electricity hydrogen production system. The method aims at solving the problems that in an existing green electricity hydrogen production system scheduling method, the calculation cost of a nonlinear model is high, the nonlinear model is difficult to apply to an actual large-scale system, and short-term market income and long-term equipment life cannot be effectively coordinated and balanced.

The invention discloses a multi-vehicle cooperative trajectory planning optimization method based on a safe corridor, and belongs to the field of automatic driving, and the method comprises the following steps: representing a discretized unmanned vehicle space-time corridor based on the real-time pose information of unmanned vehicles in a vehicle group and the rectangular contour information of the unmanned vehicles; constructing a target cost function according to the geometric information of the space-time corridor and the motion time of the driverless vehicle; boundary constraints of a space-time corridor are established according to poses of the unmanned vehicles in the vehicle group, and collision avoidance constraint conditions are established in combination with position information of the space-time corridor of the unmanned vehicles; a mixed integer quadratic programming model is established according to the target cost function and the multi-constraint condition, and a global optimal solution, namely space-time safety corridor position information and motion time information, is obtained through solution calculation; and constructing a driverless vehicle trajectory optimization problem according to the safe continuous corridor obtained by solving, and generating a driverless vehicle motion trajectory through calculation and optimization. According to the method, the fastest passing track of the unmanned vehicle under the collision-free basis can be obtained, and the overall passing efficiency of multiple vehicle groups is improved.

The invention provides a pre-disaster and post-disaster two-stage power grid toughness optimization method and system for extreme weather, and aims to improve the stability and self-healing capability of a power grid in an extreme weather event. The method comprises the following steps: firstly, identifying potential risk factors in advance by monitoring the operation state of a power distribution network and combining weather forecast information; in the pre-disaster stage, a mixed integer quadratic programming (MIQP) optimization method is adopted to carry out optimal distribution on power grid resources so as to minimize load loss and power interruption risks. When an extreme weather event occurs, the system dynamically adjusts resource allocation according to real-time data through a model predictive control (MPC) optimization method. The system integrates a monitoring and data collection module, a pre-disaster planning module, a post-disaster recovery module, a scheduling and execution module and a user interface module, and through cooperative work of the modules, reliability and stability of a power system under extreme weather conditions are ensured.

The application relates to the technical field of system-in-package, and discloses a functional chip SIP (System in Package) system-in-package method and system. The method constructs a reduced-order discrete thermal state predictor through multi-order exponential attenuation fitting, generates a DVFS gear-noise hazard degree spectrum mapping table based on a synchronous switching noise spectrum and an ADC noise sensitivity curve, generates a multi-DVFS working condition robust grounding network topology by optimizing a narrow bridge connection structure parameter by using a sequential quadratic programming, quantizes equivalent noise interference amounts of each DVFS gear on an ADC chip, and jointly optimizes DVFS gear selection and power consumption upper limit distribution under a rolling time domain mixed integer quadratic programming framework, simultaneously performs online model correction through exponential weighted moving average and Kalman filtering, and realizes cooperative satisfaction of thermal constraints and noise constraints.

The invention discloses a power system risk assessment device and method based on extended dimension adaptive sparse grid interpolation and a medium, and relates to the field of power system risk assessment, the device comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, a processor executes a computer program to realize a power system risk assessment method based on extended dimension adaptive sparse grid interpolation, and the method comprises the following steps: firstly, extending DASGI by using a relaxation technology, so that the DASGI can effectively approach a complex risk assessment function which takes a load shedding amount as an output, has strict non-negativity, and gathers a large number of results near a zero value; based on the constructed EDASGI proxy model, an analytic relationship between the unit commitment and the system risk is established; the iterative recursion solving process of the RSUC is converted into a mixed integer quadratic programming problem, efficient and unified solving is achieved, and the efficiency and precision of risk decision making in a high-dimensional uncertainty scene are remarkably improved.

A distributed energy storage station monitoring system relates to the technical field of energy storage station control and comprises an equipment layer and a plurality of energy storage units, and each unit is provided with an intelligent terminal and an edge computing unit. The communication layer is used for hybrid networking and supports three-channel redundancy switching and quantum key distribution; in the station control layer, an Acrel-1000DP monitoring host is deployed, and an IEC-61850 protocol conversion module and a Hyperledger Fabric block chain evidence storage system are integrated; the optimization engine is used for generating a dynamic charging and discharging strategy based on mixed integer quadratic programming and reinforcement learning; the optimization engine is based on the mixed integer quadratic programming and reinforcement learning algorithm, the optimal charging and discharging strategy is dynamically generated by integrating multiple factors, the charging and discharging power is flexibly adjusted, the purposes of peak load shifting and the like are achieved, and the operation efficiency and economic benefits of the energy storage system are improved; the station control layer monitoring host has a comprehensive monitoring alarm function, masters the operation state of the energy storage station in real time, deals with abnormity in time, and guarantees safe operation.

Confined areas present an opportunity for early deployment of autonomous vehicles due to the absence of non-controlled traffic participants. In this disclosure, two approaches for coordination of multiple AVs in confined sites. Each approach computes speed-profiles for the AVs such that collisions are avoided in cross intersections, merge-split zones and narrow road sections. Specifically, this is done by solving an optimal control problem where the motion of all vehicles is optimized jointly. The order in which the vehicles pass the crossings is determined through the solution of a Mixed Integer Quadratic Program. Through simulation results, the capability of the algorithm is demonstrated in terms of performance and observance of collision avoidance constraints.

The application discloses a kind of topological relation checking method, system, computer device and medium for data missing station area, the method includes: obtaining the voltage data and electric quantity data of each distribution transformer and each user of low-voltage station area;Using matrix decomposition method to the voltage data, electric quantity data of each user is completed;According to the voltage data of each distribution transformer, using cosine similarity to group station area;According to the voltage data of each distribution transformer and each user, determine the group attribution of each user based on station area grouping;According to the electric quantity data of distribution transformer and user, the user in each group is solved user transformer relationship by quadratic programming, obtains the file of each station area.The application according to the voltage data and table code data of low-voltage station area distribution transformer and low-voltage user, mines its correlation, judges the station area attribution of low-voltage user by mixed integer quadratic programming algorithm, to realize station area topological relation checking.

The application discloses a low-altitude unmanned aerial vehicle route setting method and device, acquires airspace data; the airspace data is standardized, and standard airspace data is obtained; the standard airspace data is input into a space-time graph neural network, and an airspace use heat map is obtained; based on the airspace use heat map and airspace structure data, a mixed integer quadratic programming model is constructed and solved, and an initial route network is obtained; the initial route network is iteratively optimized through a neighborhood search algorithm, and a final main route network is obtained. Through the space-time graph neural network, the dynamic airspace heat map is generated, so that the main route network can be dynamically adjusted according to the flow fluctuation, thereby solving the problems of low efficiency in the peak period and idle resources in the valley period in the high-density area; meanwhile, based on the route network generated by the heat map and the optimization algorithm, the unmanned aerial vehicle can autonomously plan a branch route at the end of the main route, get rid of the response lag of centralized scheduling, and realize distributed flexible response to low-frequency and sudden areas.

The application provides a power stability control strategy decision method and system based on mechanism and data fusion driving, and belongs to the technical field of power system optimal dispatching; the method comprises the following steps: a comprehensive space-time characteristic state vector of a power grid is extracted in combination with a graph convolution network; a limited Markov decision framework is constructed, a state vector is analyzed based on a branch competition Q network, an advantage function of each control node is calculated in parallel through multiple independent branches, and a preliminary joint control strategy is obtained; a mixed integer quadratic programming model is constructed by taking the preliminary joint control strategy as an optimization target and a physical mechanism safety boundary as a constraint, an active-voltage sensitivity matrix is extracted based on a Jacobian matrix to quantize a voltage safety boundary, and a branch and bound method is used for solving and outputting a final control strategy; and the application can effectively cope with the uncertainty of new energy by fusing data driving and a mechanism model, and can directly meet the safety and stability defense demand of a large-scale new energy base sending-out power grid.

The invention discloses a data center and pumped storage power station linked electric calculation integrated economic dispatching method, which comprises the following steps of: establishing power balance and operation constraints including photovoltaic power, wind power, thermal power, energy storage, power grid interaction and pumped storage charging and discharging on the basis of 24-hour load and wind and light output prediction; calculation force space transfer, time transfer (peak clipping back-filling) and cold storage charging and discharging strategies are introduced into the data center, load controllable adjustment is achieved through cold water equivalent offset and task inventory constraint, and peak clipping and valley filling are completed together with pumping and storage two-way adjustment. A mixed integer quadratic programming model is constructed by taking electricity purchasing and selling, unit and scheduling cost as targets, the optimal output and scheduling strategy of each time period is solved by adopting CPLEX, and the system economy and the renewable energy consumption capability are improved.

The present invention discloses a multi-vehicle collaborative trajectory planning optimization method based on a safety corridor, belonging to the field of autonomous driving, which includes the following steps: representing a discretized spatio-temporal corridor of driverless vehicles based on the real-time pose information and rectangular contour information of the driverless vehicles in the vehicle group; constructing an objective cost function with the geometric information of the spatio-temporal corridor and the movement time of the driverless vehicles; establishing boundary constraints of the spatio-temporal corridor according to the poses of the driverless vehicles in the vehicle group, and establishing collision avoidance constraint conditions in combination with the position information of the spatio-temporal corridor of the driverless vehicles; establishing a mixed-integer quadratic programming model according to the objective cost function and multiple constraint conditions, and obtaining the global optimal solution through calculation, that is, the position information and movement time information of the spatio-temporal safety corridor; constructing a driverless vehicle trajectory optimization problem according to the obtained safe continuous corridor, and generating a driverless vehicle movement trajectory through calculation and optimization. The present invention can obtain the fastest passing trajectory of driverless vehicles without collision, and improve the overall passing efficiency of multiple vehicle groups.

The application relates to a taxiway path optimization method and system for reducing potential conflicts in a flight area, belonging to the technical field of airport scheduling methods, which considers potential conflicts between aircrafts and aircrafts and between aircrafts and ground service vehicle routes, and constructs a mixed integer quadratic programming model for aircraft taxiway path optimization, with a main target of minimizing potential conflicts between aircrafts and aircrafts and between aircrafts and vehicles, and a secondary target of minimizing ground service vehicle driving distances; the aircraft taxiway path optimization model is solved, and an optimal solution is output, including aircraft taxiing paths and ground service vehicle service paths, types and quantities of potential conflicts, and the results can be used for reanalysis by adjusting parameters, thereby providing reference for airport aircraft and vehicle scheduling decision makers. Compared with the prior art, the application considers the safety of ground traffic and the convenience of passengers, and optimizes the allocation of aircraft landing and taking-off aprons and runways and the taxiing paths in an all-round way, which helps to guarantee the safety of taxiways and improve the operation efficiency of airports.

The present invention discloses a parallel strategy search method for efficient training of large artificial intelligence models. The method comprises the following steps: inputting a large model; obtaining system execution performance information and the large model's execution performance information; using a cost model to estimate the execution time and storage overhead of the large model on the current system; establishing and solving a mixed integer quadratic programming mathematical model for automatically searching for parallel training strategies for the large model, taking the time spent on each iteration of pipeline parallel training as the optimization target; and outputting the optimal parallel training strategy for the large model. The method supports pipeline parallelism, data parallelism, tensor parallelism, and optimizer parallelism, has a large strategy space, and shortens search time. It can be applied to single-machine multi-GPU parallel training of large models and distributed training of multi-machine clusters, thereby improving the training efficiency of large models.

The invention provides a method capable of stabilizing the negative pressure of a combustion chamber and accurately measuring smoke components, which comprises the following steps that: a system controller obtains a signal of smoke flow Qy generated by combustion in a hearth of the combustion chamber; a mixed integer quadratic programming algorithm is adopted to obtain a vacuum pump combination scheme with the lowest vibration amplitude and each vacuum pump operation scheme in the combination scheme according to the principle that the smoke flow distribution step length is 10% of Qy; the system controller controls the multistage vacuum pumps according to the obtained vacuum pump combination scheme with the lowest vibration amplitude and the vacuum pump operation scheme, so that the hearth pressure is regulated and controlled to the target pressure to stabilize the negative pressure of the combustion chamber; the vacuum pumps extract smoke generated by combustion in the combustion chamber hearth and then send the smoke to the smoke analyzer for component analysis, and the smoke analyzer is arranged on the smoke component measuring port. The invention further provides a system based on the method. According to the invention, by optimizing the combination and operation scheme of the vacuum pump, the influence of vibration and non-uniform exhaust rate on flame form and pollutant generation is weakened, and the flue gas components are accurately measured.

The invention belongs to the technical field of power system optimization scheduling, and particularly relates to a new energy-thermal power collaborative scheduling method considering a section dynamic power flow limit, which comprises the following steps of: constructing a multi-region new energy output space-time correlation model, describing space-time correlation by combining a Copula function, and generating a typical scene through clustering reduction; the method guarantees the comprehensiveness of scene coverage and balances the calculation efficiency, achieves the collaborative optimization of the economic cost and the environmental protection target through defining a core parameter and variable system and constructing a target function matrix, and achieves the real-time updating of the dynamic limit along with the fluctuation of the new energy output through building a multi-dimensional constraint system containing the section dynamic power flow constraint. And finally, a CPLEX solver is combined with an adaptive differential evolution algorithm to efficiently solve a mixed integer quadratic programming problem, so that scientific and reliable technical support is provided for optimal scheduling of a power system, and the economical efficiency, the safety, the environmental protection property and the new energy consumption efficiency of scheduling are comprehensively considered.

A vehicle-to-building charging / discharging method, V2B, may include inputting input data comprising at least one of electric vehicle information relating to charging and discharging of batteries of electric vehicles, building information relating to energy usage of a building, and electric vehicle supply device information, EVSE, relating to the EVES connected to the electric vehicles and charging and discharging a battery, setting a scheduling model using the input data and an objective function, outputting optimization data using the scheduling model, and performing charging / discharging scheduling of the electric vehicles using the optimization data, wherein setting a scheduling model comprises setting the scheduling model using mixed integer linear programming, MILP.if a number of electric vehicles is not greater than a number of EVSE, and adjusting the scheduling model using mixed-integer quadratic programming, MIQP, if the number of electric vehicles is greater than the number of EVSE.

The invention discloses a heat pump load non-intrusive identification method and system based on reactive power characteristics and operation constraints, and the method comprises the steps: obtaining a time sequence of a user, and the time sequence comprises an active power time sequence and a reactive power time sequence; establishing a load feature library of the heat pump, wherein the load feature library comprises a typical active power sequence and a typical reactive power sequence of the heat pump; establishing a mixed integer quadratic programming model, and determining parameters of the mixed integer quadratic programming model; determining constraint conditions of the mixed integer quadratic programming model; and solving the mixed integer quadratic programming model based on the constraint condition, the time sequence and the load feature library, outputting a heat pump start-stop state sequence, and determining the start-stop time of the heat pump based on the heat pump start-stop state sequence.

The invention discloses an automatic switching control method for a photovoltaic energy storage microgrid. The method comprises the following steps: S1, establishing a photovoltaic power generation model, an energy storage system dynamic model and a power balance equation; s2, defining a multi-objective optimization function including power grid interaction cost, energy storage SOC balance and equipment life loss; s3, collecting photovoltaic output, load demand and energy storage SOC data in real time, and predicting a photovoltaic and load curve in a future time domain; s4, solving a rolling optimization problem through mixed integer quadratic programming, and generating an energy storage charging and discharging instruction, power grid interaction power and a load switching decision; and S5, executing the optimal control instruction at the current moment, updating the system state and rolling to the next control period.

The invention discloses a cooperative control method for a multi-modal energy storage system, and relates to the technical field of energy storage control, and the method comprises the steps: S1, carrying out the data collection, obtaining multi-modal initial data and a real-time data set, and carrying out the preprocessing of the multi-modal initial data, and obtaining synchronous multi-modal data; s2, performing preliminary feature extraction and embedded mapping on the synchronous multi-modal data, and performing feature fusion and vector batch normalization processing through an InTense fusion network to obtain a final fusion feature vector; s3, contribution degree calculation is carried out based on the final fusion feature vector, and a modal contribution proportion set is obtained; s4, establishing a linear regression model, and calculating through a weighted least square method to obtain predicted load data; s5, in combination with the modal contribution proportion set, performing optimization calculation on the mixed integer quadratic programming model through a particle swarm optimization algorithm to obtain an energy storage charging and discharging strategy; and S6, converting the energy storage charging and discharging strategy to obtain regulation and control instruction data, and completing real-time control.

The invention provides a Saggoc new energy large-base scheduling method considering transmission capacity of internal and external lines, and the method comprises the steps: taking the minimization of the total operation cost of a system as a target function, and constructing an optimal scheduling model which considers the transmission capacity constraint of an internal power transmission network of a large base and the transmission capacity constraint of an extra-high voltage outgoing DC channel at the same time; and solving the optimal scheduling model by using a mixed integer quadratic programming algorithm to generate a scheduling plan considering both security and economy. According to the method, the stability, the robustness and the comprehensive operation benefit of the large-base power delivery system are improved.

The invention discloses a detection signal noise reduction method and system based on an electrochemical workstation, and relates to the technical field of detection signal noise reduction. The method comprises the following steps: acquiring an electrochemical detection curve and preset parameters in an electrochemical detection process, performing area fine division on the electrochemical detection curve to obtain fine areas, and labeling feature information; based on the fine region and the feature information, constructing a linkage association relationship model through a complex network theory method; performing noise recognition on the fine region based on the linkage association relationship model to obtain a noise recognition result in the region, and performing local noise reduction to obtain a local noise reduction result; and acquiring the global trend constraint of the electrochemical detection curve, and performing secondary global noise reduction on the local noise reduction result by using a mixed integer quadratic programming algorithm to obtain a global noise reduction result. The global noise reduction result is generated through correlation analysis, and the noise reduction of the electrochemical detection signal is realized.

The application discloses a deep stall recovery control method and system based on a hybrid model predictive control, relates to the technical field of flight control and flight safety, and solves the problem that an airplane is difficult to recover after entering a deep stall and exceeding a stall angle of attack by establishing a quadratic objective function of post-predictive control attitude stability, using a hybrid system and a quadratic objective function predictive control strategy, on-line solving a mixed integer quadratic programming, obtaining a current optimal control input and state estimation, and then through rolling optimization, obtaining a final stall recovery control sequence.

The application discloses a kind of electrolytic cell cluster state transition considered electric hydrogen coupling market equilibrium analysis method, belong to new energy technology field, belong to new energy technology field.This method obtains renewable energy output, power system load, power grid topology, thermal power unit offer, frequency modulation demand, hydrogen market demand, hydrogen production system parameter and electrolytic cell cluster state transition parameter etc.Basic data;Build electrolytic cell cluster state transition aggregation model and renewable energy hydrogen production system economic optimization decision model, joint power energy-frequency modulation joint clearing model and hydrogen market hydrogen purchase utility model, form equilibrium analysis model;Through to lower model establishment optimality condition and using strong duality transformation, obtain single-layer mixed integer quadratic programming model and solve, obtain the equilibrium result of power side, hydrogen production side and hydrogen market side.This application realizes electric-hydrogen cross-market coordinated equilibrium analysis under the premise of not tracking electrolytic cell, accurately depicts the coupling influence of power market, frequency modulation resource and hydrogen market demand after hydrogen production system is connected to power grid.

The present application belongs to the technical field of heavy haul train group operation optimization, and discloses a speed curve collaborative optimization method and system for heavy haul train group operation, comprising the following steps: for the speed curve optimization problem of heavy haul train group operation under temporary speed limit conditions, a mixed integer quadratic programming model is constructed, and a collaborative optimization method based on target-oriented multi-agent reinforcement learning is designed, aiming to cope with random interference, realize flexible adjustment of train group operation marshalling, maximize overall operation efficiency, reduce operation cost, and ensure the dynamic stability of train group operation. According to the present application, a collaborative optimization technology is used to generate an optimized reference curve for each train in the queue, thereby improving the adaptability of train group operation to random interference, and the target-oriented multi-agent reinforcement learning significantly shortens the solution time, and realizes the comprehensive goal of safe, efficient, energy-saving and stable operation of train group under the condition of sudden interference.