44 results about "Constraint programming model" patented technology

The invention discloses a coupled numerical meteorological and hydrological aggregate forecasting reservoir scheduling risk decision method comprising the steps that the numerical meteorological and hydrological aggregate forecasting model of the reservoir basin is established, and the flood process of the basin is forecasted in a rolling way; the uncertainty of hydrological aggregate forecastingis assessed by using the Bayes model averaging method, the Bayes posteriori probability is reckoned and the probability of the flood scene in the future is updated in real time; a flood scene tree isconstructed by using the method based on the probability distance on the basis of the hydrological aggregate forecasting result and branch cutting of the flood scene tree is performed; and a reservoiroptimal scheduling random change constrained programming model is established, the optimal scheduling decision of the reservoir is solved by using the optimization method and the decision risk is assessed. With application of the method, the forecast period of hydrological forecasting can be effectively extended and the forecast precision can be enhanced, and the uncertainty between modes and different data assimilation schemes is comprehensively considered; and the method is suitable for medium-and-short-term real-time reservoir scheduling and can significantly enhance the reliability of thereservoir scheduling decision.

The invention discloses a dynamic random scheduling control method for a power distribution network containing a micro-grid. The method is a dynamic random scheduling control method of comprehensively considering the minimal planned operation cost of the micro-grid and the distribution network. The method comprises the following steps: firstly, respectively building chance-constraint programming models for the minimal expected planned operation cost of the micro-grid and the distribution network, and determining the chance-constraint programming models by a particle swarm optimization algorithm and a two-point estimation method; searching a power exchange value with the minimal comprehensive expected planned operation cost by the particle swarm optimization algorithm according to a power exchange control interval, between the micro-grid and the distribution network, obtained by respective optimization results of the micro-grid and the distribution network, so as to determine the optimal dynamic random scheduling control scheme of the micro-grid and the power distribution network; and finally validating the practicability by an improved IEEE 33 node standard system algorithm.

The invention relates to a reliability optimization method of a distributed power supply system. In the method, the random uncertainty of a load forecasting value and the fuzzy uncertainty of the renewable energy type DG (Distributed Generation) output are mainly considered, the load forecasting value is represented by a random variable in normal distribution, the renewable energy type DG active output is represented by a trapezoidal fuzzy variable, and the minimization of an expected value of the sum of the investment and operating cost and the outage loss compensation cost of the distributed power supply system is taken as a target function, so as to establish a hybrid chance-constrained programming model based on reliability optimization. In comparison with the scheme obtained by the conventional method, the method provided by the invention has stronger adaptability to the uncertainty of future factors, and can largely reduce compensation investment after the event and reduce loss and waste.

The invention discloses an opportunity constraint planning-based commercial park comprehensive energy system optimization scheduling method. The method comprises: firstly, constructing a commercial park comprehensive energy system model, and on the basis of modeling of energy conversion of all elements in a park, considering uncertainty of new energy output and load, and establishing a commercialpark energy optimization scheduling model based on opportunity constraint planning. The model aims at minimizing the operation cost. An improved immune genetic algorithm and a stochastic simulation hybrid algorithm are adopted for solving, quantitative indexes are established for accompanying unbalance risks in an opportunity constraint planning model, reference is expected to be provided for comprehensive energy system operation scheduling for balancing economy and reliability, and the method has important guiding significance for actual scheduling of the system.

The invention provides a power resource management method for opportunistic array radar multi-target tracking in a clutter environment. The power resource management method comprises the steps of measuring measurement origin uncertainty caused by clutters by means of an information reduction factor, expressing RCS of targets by using random variables, establishing a random chance-constraint processing model, relaxing the random chance-constraint processing model by adopting a conditional value at risk, converting the random chance-constraint processing model into a certainty model which is easy to solve, further solving the certainty model by adopting a Lagrangian multiplier method, regarding a Bayesian Cramer Rao Lower Bound as a measurement standard for power distribution so as to solvethe optimal power distribution at a next moment, and adopting a probability data association filter for tracking the targets in the clutter environment. The power resource management method considersthe target tracking clutter environment, adopts the information reduction factor for measuring magnitude of clutter influence, further considers a target information uncertainty caused by the target RCS, introduces the random chance-constraint processing model, comprehensively considers the relationship between resources and tracking precision, and makes the model closer to reality.

The invention discloses a DG-included power distribution network planning method based on a Voronoi graph. A DG-included power distribution network chance constraint planning model is built according to the power grid operation condition, wherein the model includes components such as investment and operation cost, and planning is constrained in a flexible constraint mode. For the load point distribution issue, a greedy algorithm based on the Voronoi graph is put forward and approaches the optimal value by seeking the local minimum value in every iteration. The DG-included power distribution network planning method adopts a genetic algorithm, codes the position of a transformer substation before optimization and testifies the meeting condition of chance constraint in every optimizing process.

The invention discloses a grid planning method considering the difference of the risk tolerance of regional distribution networks. The grid planning method comprises the following steps that aiming atthe problems of large difference of the risk tolerance of the distribution networks in town regions and large load fluctuation of the town regions in a rural power grid reconstruction project, a non-parametric kernel density estimation method is used for expressing the uncertainty of the load; the difference of ability of the distribution networks in withstanding the operational risks caused by the uncertainty of the load in the town regions is taken into account, and a risk tolerance evaluation index system of the regional distribution networks is established by being combined with the characteristics of the distribution networks in the town regions; the combination weighting method based on the analytic hierarchy process and the entropy weight method is adopted to evaluate; and a chance-constrained programming model for circuit upgrading and selection is established, and according to evaluation results of the risk tolerance of the distribution networks in the town regions, the confidence level of chance constraint conditions is selected in a differentiation mode, so as to achieve the coordination of the risk tolerance and investment costs of the regional distribution networks and achieve lean investment.

The invention discloses an electric system active power real-time dispatching method considering wind power prediction error. The electric system active power real-time dispatching method comprises the following steps of describing random distribution character of the wind power prediction error by using Laplace distribution; by taking economic efficiency optimum and wind curtailment minimum as two targets, introducing a chance constraint condition; solving active power flow probability distribution of a system on the basis of a probabilistic power flow method for Latin hypercube sampling; establishing a buffer generator set active power optimum allocation model considering the wind power prediction error, and solving a chance constraint planning model by adopting an improved genetic algorithm. According to the electric system active power real-time dispatching method disclosed by the invention, the wind power prediction error is considered during real-time dispatching, power unbalance and flow off-limit brought to the system by wind power output offset can be avoided, and the safe operation of the system is guaranteed; moreover, while the wind power prediction error is assimilated, the wind power accepting ability is increased by the system.

The invention discloses an optimized operation method for a heat and power cogeneration micro grid system of a solar thermal power station. According to the technical scheme adopted by the invention,the method comprises the following steps: firstly proposing the solar thermal power station introduced into the heat and power cogeneration micro grid system to assist system operation, and constructing a new micro grid system architecture by combining energy conversion equipment such as a wind power station, an electric energy storage device and a heat pump; then taking a retail electricity priceand a retail heat price as signals and guiding users in the micro grid system to participate in demand response, and building a demand response model for an electrical load and a heat load based on aprice elasticity matrix; on the basis, considering uncertainty of wind power and solar power outputs, and building a fuzzy chance constrained planning model by taking maximization of running benefitof a micro grid as a goal; and then simplifying the model, finally forming a mixed integer linear programming problem, and solving by adopting an efficient commercial solver CPLEX. The method disclosed by the invention considers the uncertainty of the wind power and solar power outputs and maximizes the running benefit of the micro grid system.

The invention is suitable for the field of photovoltaic power generation technology, and provides a distributed photovoltaic power generating grid-connecting planning method and terminal equipment. The method comprises the steps of establishing a photovoltaic probability model and a load probability model; calculating an initial time sequence sample of each preset time period according to median Latin hypercube sampling, the photovoltaic probability model and the load probability model; calculating power distribution network node voltage probability distribution according to a multiple-integration approximation method, a Gram-Charlier grade number, a probability trend equation and the initial time sequence sample; and establishing a photovoltaic grid-connecting opportunity restraining planning model according to the power distribution network node voltage probability distribution. The distributed photovoltaic power generating grid-connecting planning method and the terminal equipment have advantages of improving distributed photovoltaic permeability, reducing integrated economic cost in a long-term planning process of the power distribution network, and improving voltage level in ashort-period operation process of the power distribution network.

The invention is applicable to the technical field of distribution network planning and discloses a programming method and system for distributed photovoltaic grid-connected penetration level. The method includes the steps of determining photovoltaic power output and load output as random variables, and establishing a comprehensive probabilistic model of the photovoltaic power output and a probabilistic model of the load output; using Latin hypercube sampling method based on the principle of equal probability transformation and a square root method to generate mutually independent random vectors; determining a chance constrained programming model when the sum of penetration levels of distributed photovoltaic power supplies accessed to a distribution network system is the maximum, using a Cuckoo search algorithm based on a particle swarm algorithm to solve the chance constrained programming model, and obtaining the distributed photovoltaic grid-connected maximum penetration level; and according to the distributed photovoltaic grid-connected maximum penetration level, programming the photovoltaic grid-connected level of each node in the distribution network system. The method and system of the invention consider the characteristics of the randomness and volatility of the photovoltaic power output, improve the voltage distribution level of the nodes, and ensure the safe and stableoperation of the distribution network.

The invention relates to a wind-hydrogen system joint optimization modeling method based on opportunity constraint planning, and the method comprises the steps: starting from the perspective of benefits of wind-hydrogen system investors, applying equal electric quantity-equal electric quantity; according to the method, the wind power fluctuation cost before and after optimization is calculated, the investment project capital profit margin (ROE) maximization is taken as the target, the uncertainty of wind power is considered, and an opportunity constraint planning model of wind power plant power transmission project and hydrogen system configuration joint optimization is constructed.

The invention relates to an active power distribution network multi-target day-ahead optimization scheduling method based on wind and light randomness. The method comprises the following steps: establishing a fan output prediction model and a photovoltaic output prediction model by adopting Weibull distribution and Beta distribution respectively; respectively establishing a demand response model based on price and excitation; according to the opportunity constraint planning theory, expressing a target function containing random variables and constraint conditions in a form meeting a certain confidence coefficient, performing calculating by using a random simulation technology, and establishing an opportunity constraint planning model; generating a large number of wind and light output scenes according to the theory of a scene method, obtaining fewer scenes after sorting and reduction, and establishing a scene method model according to an expected objective function and constraint conditions; and proposing economical efficiency, safety and reliability indexes of the active power distribution network, and respectively solving the opportunity constraint programming model and the scene method model by using an MOEA/D algorithm. According to the method, the day-ahead multi-target random optimization scheduling problem of the active power distribution network containing wind, light, storage and demand responses is well solved.

The invention discloses an active distribution network planning method for improving a distributed generation consumption capacity, characterized by comprising the following steps of S1, inputting distribution network information; S2, predicting an electric vehicle access demand; S3, in consideration of the construction cost and the running cost, defining an objective function to minimizing the total cost, and by using the distributed generation and energy storage as variables, carrying out source-grid-storage coordination planning and constructing a coordination planning model; S4, solving the model; S5, calculating a distributed generation consumption rate [omega]; and S6, outputting a multi-dimensional planning comparison result. The method has the beneficial effects of minimizing the overall construction operation cost, considering the grid consumption capacity, realizing the source-grid-charge-storage coordination planning, using a multi-time-scale and multi-constrained planning model, and considering the difference of distributed generation permeability and storage access scale in different stages, and can be applied to the planning and design of county distribution network with different distributed generation permeability.

The invention provides a hybrid optimization method for flexible assembly workshop scheduling. The hybrid optimization method comprises the following steps: S1, determining known information and algorithm parameters of flexible assembly workshop scheduling; s2, adding a label to the workpiece for subsequent use; s3, generating an initial population of feasible solutions by utilizing the known information of the problem, and encoding the schedule of each feasible solution in the population in the form of a task sequence; s4, selecting some individuals from the population through competition, performing crossover and mutation gene operation on the individuals, and performing search in neighborhoods of the known feasible solutions by changing a processing plan and machine selection to obtainnew feasible solutions; s5, for each feasible solution in the population, optimizing the processing sequence by using a constraint programming model to form a new solution and update the population; and S6, repeating the steps S4 and S5, and performing iteration until a termination condition, namely the total running time or the total number of iterations, reaches a preset limit.

The invention discloses a comprehensive energy station stochastic programming method and system based on a dynamic carbon transaction model, and the method comprises the steps: analyzing the fluctuation aggregation characteristics of a carbon transaction income price sequence, and establishing a carbon transaction price prediction model through an index generalized autoregression condition heteroscedasticity model; establishing a comprehensive energy station stochastic programming model considering wind/light/load uncertainty, establishing a wind/light/load uncertainty stochastic distributionmodel, processing random variables through opportunity constraint programming, and converting the stochastic opportunity constraint programming model into an equivalent deterministic model; inputtingdata such as supply side energy types, annual energy requirements, equipment parameters and typical daily operation maximum power requirements, adopting a mixed integer nonlinear programming solver for solving, and outputting a programming result for analysis. The capacity configuration scheme and the annual operation time of the equipment can be obtained at the same time, and the service life ofthe equipment is prolonged to the maximum extent while the park energy supply safety is met.

The invention relates to a co-scheduling method for multi-line electric buses in a single parking lot, in particular to a co-scheduling method for multi-line electric buses in a single parking lot. The invention aims to solve the problem that the service lives of electric buses purchased in the same batch are different. The method comprises the steps that 1, K electric buses are arranged on I lines of the same starting station, and N shifts need to be operated every day; 2, the travel time of the shift n is a random variable; 3, calculating charging waiting time and charging service time; 4, calculating the charging cost after the shift number en is finished; 5, calculating the energy consumption of the travel of the electric bus; 6, calculating the operation intensity difference; 7, establishing an opportunity constraint planning model; 8, converting an objective function; 9, converting the opportunity constraint programming model into a deterministic model; 10, solving the deterministic model; and 11, outputting an optimal solution of the deterministic model. The method is applied to the field of electric bus operation scheduling.

The invention relates to the technical field of electric vehicle charging networks, and discloses a multi-target electric vehicle charging network planning method. Comprising the steps that S1, planning boundary conditions are given; s2, establishing a charging network multi-target opportunity constraint planning model which considers the shortest average charging driving distance of the electric vehicle and the lowest construction cost of the charging network at the same time; s3, designing a chromosome coding scheme and crossover and mutation operators, solving the charging network multi-target opportunity constraint programming model by adopting a non-dominated genetic algorithm based on a feasibility rule, and giving a Pareto solution set of the charging network multi-target opportunity constraint programming model; and S4, starting from a solution with the lowest investment cost in the Pareto solution set, determining an optimal charging network planning scheme according to a marginal investment income maximum principle. The average driving distance from the electric vehicle to the charging station and the construction cost of the charging network are reduced.

The invention provides a power grid adaptive emergency control method based on on-line parameter identification. The method relates to a fault diagnosis analytical model based on chance constrained programming, a fault diagnosis technology considering electric quantity information, voltage security and stability adaptive emergency control based on synchronous measurement information, and a low-frequency low-voltage load reduction amount on-line tuning technology based on a massively parallel processing platform. The invention provides a chance constrained programming model and method for power system fault diagnosis, achieves on-line application functions, and solves problems that the conventional method has a poor capability of processing uncertain factors and is low in fault tolerance.

The invention discloses a batching optimization method and system based on nonlinear opportunity constraints. The method comprises: fully considering the influence of uncertainty of various mineral element components of the zinc concentrate; taking the lowest unit cost of zinc in the mixed zinc concentrate as a target; establishing a nonlinear opportunity constrained programming burdening optimization model; obtaining corresponding mean values and variances of various zinc element contents and impurity element contents when various zinc concentrates are warehoused to solve an optimal solutionof the nonlinear chance-constrained programming model. Compared with an existing batching method, the mixed zinc concentrate batching method has the advantages that the quality of the prepared mixed zinc concentrate is more stable, and the cost of the used zinc concentrate raw material is lower.

The invention discloses a variational Bayesian feedback optimization-based batching optimization method. The method comprises the following steps: establishing a distribution parameter optimization model according to a test value of a last round of batching process of zinc concentrate, optimizing parameters of the distribution parameter optimization model through a variational Bayesian method, and substituting the posterior probability distribution obtained through optimization into the distribution parameter optimization model; matching by combining a nonlinear chance constraint programming model, and acting on a batching process; and feeding back a new round of test value to the distribution parameter optimization model. According to a feedback test value, a variational gauss method is adopted to optimize and adjust each component of each ore storage bin, and the problem of uncertainty of zinc concentrate components of each ore storage bin is solved, so that the proportion is optimized, and the batching quality is improved.