66 results about "Probabilistic load flow" patented technology

The invention discloses a power distribution network risk assessment method based on a photovoltaic output prediction error, and the method comprises the steps: constructing an isolated tree of an isolated Sessen algorithm, carrying out the fusion processing of the historical data of mass distributed photovoltaic output, and correcting the abnormal value of the photovoltaic output; a probability distribution model of a photovoltaic output prediction error is constructed based on a photovoltaic output typical scene, a power distribution network photovoltaic typical output scene is divided based on an adaptive K-means clustering algorithm, and distributed photovoltaic output is subjected to ultra-short-term prediction by adopting BiLSTM according to meteorological conditions to obtain a photovoltaic output prediction curve. Carrying out modeling on the photovoltaic output prediction error by adopting KDE to obtain a probability distribution curve of the photovoltaic output prediction error; in combination with a semi-invariant method and a Gram-Charger expansion series method, probability distribution of voltage of each node of the power distribution network is obtained through probabilistic load flow calculation; and calculating the node voltage out-of-limit probability of the power distribution network, and evaluating the voltage out-of-limit risk of the power distribution network. According to the method, the key uncertainty factors in the change of the running state of the power distribution network can be determined.

An electrothermal coupling probabilistic load flow calculation method considering time correlation characteristics of an input variable comprises the following steps: introducing an adaptive window width reflection kernel density estimation method to fit wind speed data, and establishing an uncertainty model of the input variable; a Sobol random sequence obeying [0, 1] uniform distribution is generated, a random number sequence considering time correlation is generated by using a position sorting method according to input actual measurement data, and finally a wind speed sequence sample is obtained through a cubic spline interpolation method. Inputting structural parameters of a power system, introducing a heat balance equation, and establishing an electrothermal coupling probabilistic load flow calculation model by being combined with a probabilistic load flow calculation model; and initializing the model, inputting a wind temperature sequence of each scene, and carrying out electrothermal coupling probabilistic load flow calculation by utilizing a Monte Carlo simulation method distribution based on a Sobol sequence. Uncertainty modeling is carried out according to a model output result, the result is evaluated and analyzed, and a conclusion is obtained. The method is of great significance in analyzing the operation condition of the power system and further guaranteeing safe and stable operation of the power system.

The invention discloses a power distribution network operation risk assessment method and device, electronic equipment and a storage medium, and relates to the technical field of power systems or other related fields, and the method comprises the steps: obtaining the charging power data of N electric vehicles in a target time period, N being a positive integer; node load flow calculation is carried out based on the charging power data, and probabilistic load flow expressions of N electric vehicles are established; based on the load flow calculation result and the probabilistic load flow expression, establishing a Gaussian mixture distribution body to obtain a Gaussian mixture probability model; and acquiring real-time charging power data of the N electric vehicles, and performing operation risk assessment on the target power distribution network based on the real-time charging power data and the Gaussian mixture probability model to obtain an assessment result. According to the invention, the technical problem of low evaluation efficiency and accuracy caused by high calculation complexity of power distribution network operation risk evaluation when the electric vehicle accesses the power grid in a large scale in the prior art is solved.

The invention relates to the technical field of distributed energy management control under the condition of large-scale access of a micro-grid group, in particular to a micro-grid group optimization configuration method, device and equipment for supporting reactive compensation of a distribution network and a medium, and the method comprises the steps: building a chance constraint reactive power optimization model by considering the random characteristics of grid connection of a large-scale micro-grid group; the optimal installation capacity of the reactive power compensation device is solved through a random simulation particle swarm algorithm; based on the optimal installation capacity, probability distribution of system node voltage and line power flow is calculated by applying a semi-invariant probabilistic power flow method, and confidence level constraint is set for a result of the probability distribution to ensure that a planning result meets a predetermined confidence interval requirement, so that the equivalent annual cost generated by system operation is reduced, and the planning efficiency is improved. The overall economic benefit of the system is improved, and the probabilistic load flow calculation method based on the semi-invariant method converts complex convolution operation into simple arithmetic operation between semi-invariants, so that the complexity of the calculation process is reduced.

The invention discloses a non-parametric probabilistic load flow calculation method considering complex uncertainty for a power system. The method comprises the following steps: firstly, constructing source-load nonparametric probability distribution based on a multivariate Gaussian mixture model; then, constructing an alternating current power flow model under a polar coordinate system in the power system, and performing linear expansion on the alternating current power flow model under the polar coordinate system in the power system at a mean point of source-load non-parametric probability distribution by adopting a first-order Taylor series approximation method to obtain a system operation simplified model based on alternating current power flow linearization of the power system; and finally, according to the linear transformation property of the Gaussian distribution, obtaining the probability distribution of the power flow distribution of the power system under each Gaussian component, and in combination with the total probability law, carrying out weighted summation on the probability distribution under each Gaussian component according to the weight to obtain the non-parametric probabilistic power flow probability distribution of the power system. The method provided by the invention has stronger generalization ability and accuracy, and has important reference significance for operation uncertainty quantification of the power system under complex uncertainty.

The invention discloses a power distribution network optimization scheduling method and system based on multi-target deep reinforcement learning, and relates to the technical field of power distribution network optimization operation, and the method comprises the steps: building a power distribution network operation model and a probabilistic load flow model considering the photovoltaic output and load demand uncertainty, completing the probabilistic load flow calculation under the uncertainty, and obtaining a power distribution network optimal scheduling model; analyzing to obtain a probability density function of node voltage and line power flow of the power distribution network; introducing a utility function based on preference, establishing voltage out-of-limit and line overload risk indexes considering severity weight, and constructing a risk-economic collaborative power distribution network multi-objective optimization operation problem; the decision process of the problem is modeled into a multi-target Markov decision process, a decomposition-based multi-target deep reinforcement learning algorithm is adopted, learning and training of the decision process are performed on reinforcement learning agents, a Pareto strategy set is obtained, an optimal operation strategy is screened out, and optimization regulation and control are performed on the power distribution network. And collaborative optimization of the operation risk and cost of the power distribution network system is realized.

The invention discloses a deep neural network probabilistic load flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and confidence levels, and determining state variables and orders; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; a node voltage vector is used as input, branch admittance is used as physical information to be embedded into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, a Jacobian matrix is solved, and a sensitivity matrix is calculated; calculating and aggregating semi-invariants, and transmitting the semi-invariants to a state variable; and after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and calculating a fluctuation interval in combination with a confidence level under opportunity constraint to realize quantitative evaluation. According to the method, the probability load flow calculation complexity is reduced, the limitation of a traditional method in processing the network topology complexity problem is solved, and quantitative evaluation of the probability load flow is realized.

The invention discloses a power system probabilistic power flow method and system based on polynomial proxy and graph parallelism, and belongs to the technical field of power system planning and operation optimization. The method comprises the following steps: establishing a probability distribution model of a bus load prediction error to obtain sample distribution of predicted power; representing the output response quantity of the system as an input polynomial function, and establishing an initial polynomial agent model; determining an optimal configuration point set; mapping all the selected configuration points to an input sample space to obtain a small number of sample points of an input variable; performing deterministic load flow calculation on the sample points to obtain a sample vector of output response; calculating an undetermined coefficient in the proxy model according to the configuration point matrix and the sample vector; and solving the statistical characteristic quantity of the power flow response to obtain the probability distribution of the response. According to the calculation method provided by the invention, the probability distribution of the power flow response can be accurately and efficiently estimated, and convenience is provided for probabilistic power flow calculation of a novel power system under the access of a distributed power supply.

The application discloses a kind of analytic expression probabilistic power flow calculation method, device and storage medium.The application is determined after the mapping relationship between the injection power of node and the operating state of power system by linear power flow model, using first-order polynomial fitting correction method to revise the operating state of power system, make the operating state of the power system after revision be closer to real operating state, according to the mapping relationship between the injection power of node and the operating state of power system after revision, update linear power flow model, carry out analytic expression probabilistic power flow calculation using updated linear power flow model, to effectively improve the accuracy of probabilistic power flow calculation.

The invention provides an urban power distribution network probabilistic load flow calculation method for large-scale access of demand side resources, and belongs to the technical field of power system power distribution network analysis and optimization. The method comprises the following steps: constructing a dynamic Copula model in combination with space-time sequence analysis, introducing a Markov chain, and carrying out multi-dimensional uncertainty coupling modeling; a probabilistic power flow-multi-objective optimization model is constructed, and energy router cooperative control is carried out based on distributed model predictive control; sparse expansion is carried out by adopting error feedback adaptive sampling and sparse Bayesian learning in combination with polynomial chaos expansion, and probability power flow is calculated through OpenDSS; establishing a three-phase probabilistic power flow model and a DG-EV-DR collaborative probability model; and evaluating the extreme scene risk, and visualizing the result through a digital twin platform. According to the method, the accuracy and practicability of distribution network probabilistic load flow calculation in a demand side resource large-scale access scene are effectively improved, and a risk quantification basis is provided for power grid dispatching.

The invention relates to a multi-source scene-driven power distribution network probabilistic load flow calculation method and system, and the method comprises the steps: obtaining the historical data of 24 hours per day in a complete year of load and photovoltaic, and carrying out the clustering division based on a comprehensive similarity index, thereby obtaining a plurality of optical load typical daily output scene sets and the probability distribution thereof; processing data of each scene set to obtain an expected value and a variance of optical charge power; combining the network topology of the power distribution network, taking an expected value as a node injection amount, and performing probabilistic load flow calculation by adopting a two-point estimation method; and carrying out statistical analysis on a calculation result to obtain related statistical data of branch power and node voltage in each typical scene. According to the method, the typical scene probability distribution set is generated by using historical data, the estimation point selection precision and the calculation result reliability are improved, the calculation times are reduced, the efficiency is improved, the actual power grid operation behavior can be better reflected, decision support is provided for a power grid operator, resource allocation is optimized, the cost is reduced, and renewable energy utilization is promoted.

The invention discloses a time sequence probabilistic power flow power supply capability assessment method, system and device and a medium, and relates to the technical field of power supply capability assessment. The method comprises the following steps: firstly, setting a boundary condition of a Gaussian kernel function, constructing a self-adaptive diffusion kernel density estimation model of wind and light output, deriving a diffusion kernel function by adopting a progressive integral mean square error method to select a self-adaptive optimal bandwidth, and improving the local adaptability of the wind and light output model; and secondly, constructing a wind power and photovoltaic time sequence joint probability distribution model by using a Copula function theory, obtaining time sequence wind and light output samples with correlation, and finally performing power supply capability evaluation through a proposed probabilistic power flow power supply capability calculation method considering wind power and photovoltaic time sequence and correlation. The calculation accuracy of the probabilistic power flow power supply capability can be improved.

The invention provides a power distribution network voltage out-of-limit probability prediction method considering load fluctuation and photovoltaic output, and relates to the technical field of power distribution network safety. The method comprises the following steps: constructing a time-varying Gumbel-Copula joint distribution function according to a Spearman rank correlation coefficient, obtained by calculation, of a load and photovoltaic output; based on a time-varying Gumbel-Copula joint distribution function, probability distribution of the load and probability distribution of the photovoltaic output are reconstructed through a Hermite polynomial, and parallel probabilistic power flow solving is carried out; and calculating the voltage out-of-limit probability of each node according to the result of the parallelization probabilistic power flow solution. According to the method, dynamic coupling characteristics of different time periods are considered, a time-varying Gumbel-Copula joint distribution function is introduced, probability distribution of loads and probability distribution of photovoltaic output are reconstructed through a Hermite polynomial, the influence of intermittent superposition of load fluctuation and photovoltaic output can be overcome, and real-time prediction of the voltage out-of-limit probability can be accurately completed in extreme weather.

The invention discloses an integrated energy system situation awareness method based on a message passing graph-long short-term memory neural network, and the method comprises the following steps: firstly, obtaining a load data set and topological information of an integrated energy system from a historical database, constructing a probabilistic load flow calculation physical model of the integrated energy system, and calculating to obtain a load flow data set of the integrated energy system; secondly, taking load data and branch admittance as input, taking node voltage amplitude and phase angle as output, and using a deep neural network multi-topology joint training method to train the DNN; and finally, constructing a topological graph data set of the integrated energy system, obtaining an accurate feature vector of each node based on an MPNN layer, and taking an output node voltage of the MPNN layer as an input of an LSTM layer to realize accurate situation prediction of the integrated energy system.

The invention relates to the technical field of power control, in particular to an active regulation space quantitative evaluation method considering active voltage coupling influence, and the method comprises the steps: firstly constructing a steady-state power flow equation containing DC control characteristics, correcting a Jacobian matrix through introducing a commutation bus power correction, and building a voltage active sensitivity index; probabilistic load flow calculation is carried out in combination with a new energy output probability model, and a voltage weak node is accurately identified by using an impedance mode margin index; and generating a PV curve by adopting a continuous power flow method, superposing a static voltage safety constraint condition, and performing intersection operation on a unit output interval and a reserve capacity interval in a safe operation range to obtain an effective active power regulation space, thereby accurately quantifying the actual regulation capability on the premise of ensuring the voltage safety, and further realizing high-precision voltage regulation. And the safety and the economical efficiency of system operation are effectively improved.

A novel probabilistic load flow calculation method considering wind temperature long-range correlation characteristics comprises the following steps: firstly, based on actually measured wind temperature data, generating a wind temperature long-range trend sequence by using a moving average method, and determining a sample scale by using a Pearson correlation coefficient method; secondly, adopting a Latin hypercube sampling Monte Carlo simulation method to generate a random wind temperature sequence, and performing correlation processing through Nataf transformation to obtain wind temperature data under multiple scenes; secondly, initializing a probabilistic load flow model, inputting wind speed and line parameters of the wind power plant, and performing conventional probabilistic load flow calculation to obtain initial current of each branch; and then, calculating the line temperature in different scenes by using a heat balance equation. And finally, calculating an impedance correction factor according to the line temperature, correcting an impedance parameter of the power transmission line, performing probabilistic load flow calculation again, and analyzing and evaluating a result. According to the method, long-range correlation characteristics of wind temperature are effectively considered, and the accuracy and reliability of probabilistic load flow calculation are improved.

The present invention relates to a probabilistic power flow method and system for power systems based on polynomial surrogate and graph parallelism, belonging to the technical field of power system planning and operation optimization. It includes establishing a probability distribution model of the bus load prediction error to obtain the sample distribution of the predicted power; representing the output response quantity of the system as a polynomial function of the input and establishing an initial polynomial surrogate model; determining the optimal set of collocation points; mapping all selected collocation points to the input sample space to obtain a small number of sample points of the input variables; performing deterministic power flow calculations on the sample points to obtain the sample vector of the output response; calculating the undetermined coefficients in the surrogate model according to the collocation point matrix and the sample vector; and solving the statistical characteristic quantities of the power flow response to obtain the probability distribution of the response. The calculation method provided by the present invention can accurately and efficiently estimate the probability distribution of the power flow response, facilitating the probabilistic power flow calculation of a new power system under the access of distributed power sources.

The invention discloses a long-process simulation deduction continuous section construction method and system, and belongs to the technical field of power grid simulation. The method comprises the steps that the total duration of long-process simulation deduction is subdivided into a plurality of calculation time points at equal intervals, and then a power grid structure, regulation and control operation and meteorological prediction data in the total duration of long-process simulation deduction are obtained; the transient fault probability and the disaster-caused fault probability of the whole-network equipment are calculated respectively, and then a whole-network equipment fault probability model is constructed; calculating whole-network probabilistic power flow distribution according to a whole-network equipment fault probability model by taking the whole-network equipment transient fault probability and the disaster-caused fault probability as input, and determining the running state of the whole-network equipment according to the whole-network probabilistic power flow distribution; performing real-time load flow calculation to obtain a simulation deduction calculation section of a calculation time point; and finally obtaining a long-process simulation deduction continuous section sequence in the simulation time period. According to the method, a power grid operator can better formulate an operation and maintenance strategy, resource configuration is optimized, and the safety and stability of the power grid are improved.

The application discloses a deep neural network probability power flow calculation method and system, and belongs to the field of power system analysis. The method comprises the following steps: acquiring power system parameters and a confidence level, determining state variables and order; establishing and linearizing a nonlinear power flow equation to obtain a Jacobian matrix; taking a node voltage vector as input, embedding branch admittance as physical information into a deep neural network model based on a multi-head self-attention mechanism and semi-invariant guidance, solving the Jacobian matrix, and calculating a sensitivity matrix; calculating and aggregating semi-invariants, and transferring the semi-invariants to state variables; after standardization, obtaining a distribution function by using a six-order Cornish-Fisher series, and under an opportunity constraint, combining the confidence level to calculate a fluctuation interval and realize quantitative evaluation. The application reduces the complexity of probability power flow calculation, solves the limitations of traditional methods in dealing with network topology complexity problems, and realizes quantitative evaluation of probability power flow.

The invention discloses a probabilistic power flow analysis method and system based on a source-load interaction mechanism, and the method comprises the steps: employing a source-load interaction relation recognition model to recognize a source-load interaction relation based on the source-load data corresponding to a target power grid, and enabling the model to be obtained through the iterative training of a large model; probabilistic power flow analysis is carried out based on the source-load interaction relation and the source-load data; each round of training comprises the following steps: acquiring sample source load data and a label thereof; sample source load characteristics of the sample source load data are determined through a large model, and the sample source load characteristics are mapped into interaction relation characteristics; obtaining target prompt information matched with the interaction relation characteristics in a database; using a large model to generate a second sample source-load interaction relationship based on the target prompt information and the interaction relationship characteristics; the current round of training is performed on the large model based on the similarity between the first sample source load interaction relationship and the second sample source load interaction relationship, so that the analysis result precision can be improved, and more accurate reference information is provided for planning and scheduling of a power system.

The invention discloses an energy storage power station collaborative planning method and system based on uncertainty propagation analysis. In order to solve the problems that an existing energy storage planning method is indefinite in physical mechanism and insufficient in interpretability, the planning method comprises the steps that operation data of a power system are obtained, and an analysis alternating current probabilistic power flow model used for establishing an analysis relation between system voltage statistical characteristics and line power statistical characteristics is constructed based on the operation data; constructing a propagation matrix representing the uncertainty propagation characteristics of the system based on the analysis of the AC probabilistic power flow model; performing spectral analysis on the propagation matrix to identify one or more dominant propagation modes which play a dominant role in the uncertainty of the system; and based on the one or more dominant propagation modes, determining a planning scheme of at least one type of energy storage power station, the category of the planning scheme including the access position capacity of the energy storage power station. According to the method, energy storage configuration is guided by using an uncertainty physical propagation mechanism, and the risk control effect, the calculation efficiency and the engineering interpretability of a planning scheme are remarkably improved.

The invention relates to a probabilistic load flow calculation method for new energy and load correlation, and the method specifically comprises the steps: building a probabilistic model of new energy and load based on the distribution types and parameters of the new energy and load, inputting network structure parameters, the probabilistic model information of the new energy and load, and correlation coefficient information, the method comprises the following steps of: setting a median Latin hypercube sampling scale and an iteration number marking variable, then sampling mutually independent random variables such as new energy and load and random variables such as new energy and load with a correlation so as to generate random samples, and finally, based on a Newton-Raphson method, calculating a maximum iteration number of the new energy and the load so as to obtain a maximum iteration number of the new energy and the load. Performing power flow calculation on the obtained random samples in sequence, finally performing statistics on power flow results such as node voltage, line transmission power and line loss rate obtained by each power flow calculation, drawing a probability density curve, and performing probability power flow calculation of new energy and load correlation by using the probability density curve; the method has the advantages of being accurate in calculation, fast and efficient.

The invention provides an electric power system operation risk assessment method and system, and the method comprises the steps: carrying out the probabilistic load flow calculation based on a probability density function corresponding to the output data of an electric power system through employing a Latin hypercube algorithm, and obtaining the probabilistic load flow data of the electric power system; according to the probabilistic power flow data of the power system, calculating a corresponding operation risk assessment index value; performing risk assessment on the power system according to the operation risk assessment index value; according to the method, the Latin hypercube algorithm is adopted in the probabilistic load flow calculation process of the power system, the uncertainty in the grid connection process can be considered, the process of probabilistic load flow calculation can be simplified, the calculation precision and the sampling efficiency are improved, and then a more accurate probabilistic load flow calculation result is obtained; the accuracy of power system operation risk assessment is improved.

The application discloses an asymmetric medium-voltage three-phase distribution network probability power flow calculation method containing single-phase photovoltaic access, and comprises the following steps: 1, based on historical power data of photovoltaic, weight values are obtained by an improved entropy weight method, and then the output probability density function of distributed photovoltaic is obtained by LightGBM prediction; 2, for the medium-voltage three-phase distribution network with single-phase photovoltaic access, the uncertainty of photovoltaic output and access position is considered, the neutral point voltage is alternately iterated with Newton method, and a three-phase distribution network power flow model is constructed; 3, integral points and weights of point estimation method are determined, space transformation is carried out by Nataf inverse transformation, and power flow distribution is obtained from the three-phase distribution network power flow model; each order statistical moment is calculated, Gram-Charlier series fitting is adopted, and the probability distribution of the distribution network system is obtained. The application considers the influence of uncertain single-phase photovoltaic access to obtain the probability power flow distribution, and has positive significance for the study of power flow, probability optimization problem, system operation and safety evaluation of the neutral point not directly grounded distribution network system.

The present invention discloses a method, device, electronic device, storage medium and program product for probabilistic power flow analysis of a flexible interconnected distribution network under vehicle-grid interaction. The method comprises: obtaining uncertainty variable parameters of a distribution network system to be analyzed, and performing photovoltaic power generation output probability analysis, wind power generation output probability analysis, power load probability analysis and electric vehicle charging demand probability analysis based on the uncertainty variable parameters; standardizing the above analysis results to obtain a unified power mismatch equation and a state correction function, and further obtaining a fuzzy linearization equation of the uncertainty variable; obtaining the series of output variables based on random variable estimation, processing the fuzzy linearization equation of the uncertainty variable, obtaining a characteristic vector of probability density and a quantile of the variable, and further constructing a probabilistic power flow equation; performing probabilistic power flow analysis based on the probabilistic power flow equation to obtain a probabilistic power flow analysis result, thereby improving the accuracy of the probabilistic power flow analysis.

The invention discloses a probabilistic power flow algorithm based on a linear power flow model and a total probability formula, and aims at a scene that a conventional probabilistic power flow calculation method based on semi-invariant cannot be carried out due to the ill-conditioned Jacobian matrix in probabilistic power flow analysis of an active power distribution network. And an optimal state variable function is selected to improve the calculation precision. A total probability formula is introduced in probability calculation, N output interval combinations are obtained by performing segmentation processing on the new energy unit, and a joint conditional probability density function and each-order semi-invariant of the new energy unit under the N output interval combinations are calculated. The method comprises the following steps of: calculating a semi-invariant of a state variable based on a linear power flow model under each output interval combination, finally fitting a distribution function by adopting a Gram-Charlier (GC) series to obtain N conditional probability distribution functions of the state variable, and aggregating by adopting a total probability formula to obtain a complete probability density function. According to the method, the precision of probabilistic power flow analysis is improved, and the technical method has relatively high implementability.

The application relates to the technical field of optimal scheduling of distributed power grids, and specifically discloses a two-stage optimal scheduling method for distributed power grids based on a probabilistic power flow and a WOA-PPSO algorithm, which comprises the following steps: constructing a probabilistic power flow model, calculating the probability distribution characteristics of node voltage and line flow in the power grid containing wind power, photovoltaic power and load randomness based on the semi-invariant method and Gram-Charlier series expansion. The application introduces the probabilistic power flow modeling into the optimization constraint of the scheduling algorithm, and responds to the uncertainty that may exist in the power grid in real time; by constructing a two-stage scheduling algorithm, the global optimal scheduling plan is generated based on the WOA algorithm in the day-ahead scheduling, and the deviation caused by the uncertainty is quickly responded on the basis of the optimal scheduling plan in the real-time scheduling of the day; and the result of the day-ahead scheduling is taken as the reference starting value of the real-time scheduling of the day, and the projection method is combined with the PSO algorithm to construct the PPSO algorithm, so that the solving speed in the real-time scheduling of the day is maximally improved.

The invention provides an active power distribution network accurate reconstruction optimization method and device, and the method comprises the steps: generating random sample data corresponding to the output of a distributed power supply and the load characteristics of a power grid according to the operation parameters of an active power distribution network, and generating a probabilistic power flow input variable set of the active power distribution network according to the random sample data; performing probabilistic load flow calculation based on the input variable set to obtain probabilistic load flow index information of each node voltage and line load flow; generating an electric energy quality comprehensive evaluation index of the active power distribution network according to the probabilistic power flow index information; establishing a multi-target optimization model for accurate transformation of the active power distribution network based on the comprehensive evaluation index of the power quality by taking the highest and minimum transformation cost of the power quality as a target; and solving the multi-objective optimization model by adopting an improved particle swarm algorithm to obtain a precise reconstruction scheme of the active power distribution network. According to the technical scheme provided by the invention, refined and efficient transformation decision of the active power distribution network under high uncertainty is realized.

The application discloses a kind of AC-DC hybrid system probabilistic power flow calculation method considering new energy boundedness and correlation, comprising the following steps: 1) obtaining AC-DC hybrid system basic information, determine the scaling factor, and utilize scaling unscented transformation method to select sample points on standard normal distribution;2) calculate boundary parameter, convert the selected sample points to original probability space, obtain original probability space sample points;3) establish the power conversion model of wind farm and photovoltaic power station and the deterministic power flow model of AC-DC hybrid system;4) input wind speed, light intensity into the power conversion model of wind farm and photovoltaic power station, obtain wind power and photovoltaic output data;5) input load data, wind power and photovoltaic output data into the deterministic power flow model of AC-DC hybrid system, carry out probabilistic power flow analysis and calculation.The application can analyze high-dimensional probabilistic power flow considering new energy boundedness and correlation when large-scale new energy is accessed to AC-DC hybrid power grid.

Disclosed in the present disclosure are an apparatus and method for managing voltage of a hybrid distribution network. According to an aspect of the present disclosure, provided is a method for managing voltage of a hybrid distribution network, the method comprising: receiving monitoring information related to a hybrid distribution network; carrying out probabilistic power flow calculation on the basis of the received monitoring information; determining, on the basis of a result of the probabilistic power flow calculation, whether a voltage violation occurs in the distribution network; on the basis of whether the voltage violation occurs, selectively determining whether the maximum voltage or the minimum voltage in the distribution network violates a voltage management range; and on the basis of whether the maximum voltage or the minimum voltage in the distribution network violates the voltage management range, selectively deriving a droop gain by using an optimization model.