A Probabilistic Power Flow Optimization Method and System for Distribution Networks Based on Spatiotemporal Characteristics of Source and Load

Abstract

This invention relates to the field of distribution network operation and optimization technology, and provides a probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of sources and loads. The method includes the following steps: analyzing the spatiotemporal correlation of wind power and photovoltaic output to generate output samples; predicting the spatiotemporal distribution of electric vehicle charging load; constructing a spatiotemporal demand response model guided by spatiotemporal coordinated electricity pricing, using the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as trigger conditions; employing second-order cone programming to relax AC network power flow constraints, integrating power output constraints, the spatiotemporal demand response model, and electric vehicle operation constraints to construct a probabilistic optimal power flow model; and solving the probabilistic optimal power flow model using output samples and electric vehicle charging load samples as inputs, outputting the probabilistic distribution results of the distribution network operating state. This invention can improve the fit between the probabilistic optimal power flow calculation results and actual operating conditions, providing reliable technical support for the safe and economical operation of distribution networks.

Description

Technical Field

[0001] This invention belongs to the field of distribution network operation and optimization technology, and in particular relates to a probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of source and load. Background Technology

[0002] With the large-scale integration of distributed generation (DG) power sources such as wind and solar power, as well as electric vehicles, the power distribution network is transforming into a new power system with a high proportion of renewable energy and coordinated interaction between power sources, grids, loads, and storage. This places stringent demands on the safe and economical operation and precise optimization and control of the power distribution network. Against this backdrop, probabilistic optimal power flow calculation, as a core technology for quantifying source-load uncertainties and guiding power distribution network dispatching decisions, directly determines the scientific nature of power distribution network operation and control through its computational accuracy and practicality.

[0003] Currently, probabilistic optimal power flow calculation methods for distribution networks have become a research hotspot in the industry. However, existing technologies have significant limitations when facing complex operating conditions with a high proportion of distributed generation and electric vehicle (EV) connections. Distributed generation output exhibits significant spatiotemporal correlation characteristics, while EV charging loads are influenced by factors such as road network distribution and user travel chains, exhibiting strong spatiotemporal distribution characteristics. The interaction between the two can have a complex impact on the power flow distribution of the distribution network. However, most existing probabilistic optimal power flow calculation methods either ignore the aforementioned spatiotemporal distribution characteristics, using simplified deterministic or independent probabilistic models to characterize the uncertainties on the source-load side; or they fail to incorporate the spatiotemporal demand response mechanism of EVs to explore their flexible adjustment potential, resulting in difficulties in effectively alleviating problems such as widening peak-valley differences and node voltage exceeding limits in the distribution network. At the same time, some high-precision probabilistic optimal power flow models suffer from low solution efficiency and poor convergence due to complex constraints, making it difficult to adapt to the real-time scheduling requirements of actual engineering projects.

[0004] Therefore, there is an urgent need in this field to construct a probabilistic optimal power flow calculation scheme for distribution networks that takes into account both computational accuracy and spatiotemporal characteristics of source and load, so as to accurately characterize the spatiotemporal distribution characteristics of source and load and fully explore the flexible adjustment potential of electric vehicles. Summary of the Invention

[0005] The purpose of this invention is to provide a probabilistic power flow optimization method and system for distribution networks based on the spatiotemporal characteristics of source and load, in order to solve the above-mentioned technical problems.

[0006] This invention is implemented as follows: a probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of source and load, comprising the following steps:

[0007] Analyze the spatiotemporal correlation of wind power and solar power output, generate spatiotemporal correlation output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and solar power output and conform to the spatiotemporal correlation structure.

[0008] Based on the dynamic traffic network model as a constraint, a spatiotemporal travel chain model for electric vehicles that takes into account the SOC chain is constructed to predict the spatiotemporal distribution of electric vehicle charging load and obtain electric vehicle charging load samples.

[0009] A spatiotemporal demand response model with spatiotemporal coordinated electricity pricing guidance is constructed, based on the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions.

[0010] A second-order cone programming relaxation method is used to handle the power flow constraints of the AC network. By integrating power output constraints, spatiotemporal demand response models and electric vehicle operation constraints, a probabilistic optimal power flow model is constructed.

[0011] Using power output samples and electric vehicle charging load samples as inputs, the probabilistic optimal power flow model is solved, and the probability distribution results of the distribution network operation status are output.

[0012] Furthermore, the steps of analyzing the spatiotemporal correlation of wind and solar power output, generating spatiotemporal correlated output scenarios for distributed power sources, and generating output samples that satisfy both the edge distribution characteristics of wind and solar power output and conform to the spatiotemporal correlation structure specifically include:

[0013] Based on the analysis of the spatiotemporal correlation of wind power and photovoltaic output using multivariate normal distribution and Frank-Copula function, spatiotemporal related output scenarios of distributed power sources are generated accordingly.

[0014] The reliability of the power output scenario is quantitatively verified using the ES index.

[0015] The edge distribution parameters of wind power and photovoltaic power output are fitted based on the verified power output scenario data; the wind power output is fitted with Weibull distribution parameters, and the photovoltaic power output is fitted with Beta distribution parameters.

[0016] By employing a combination of Nataf transform and inverse transform with singular value decomposition, the problem of non-positive definite correlation coefficient matrix is ​​solved, generating output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure.

[0017] Furthermore, based on the dynamic traffic network model as a constraint, a spatiotemporal travel chain model for electric vehicles (EVs) considering the SOC chain is constructed to predict the spatiotemporal distribution of EV charging load and obtain EV charging load samples. The specific steps include:

[0018] Based on the dynamic traffic network model as a constraint, a spatiotemporal travel chain model for electric vehicles that takes into account the SOC chain is constructed to characterize the full-process behavioral characteristics of electric vehicles from the starting node, through the road segments, stopping and charging in the middle to the destination.

[0019] Based on the spatiotemporal travel chain model of electric vehicles, and combined with Dijkstra's shortest path algorithm to plan the driving path, the system determines whether the SOC of electric vehicles meets the subsequent mileage requirements and triggers fast charging or slow charging decisions segment by segment, thereby predicting the spatiotemporal distribution characteristics of the charging load of the cluster electric vehicles at different times and different grid nodes.

[0020] The maximum likelihood estimation method is used to fit the parameters, and the probability distribution of charging load at each node and time period is obtained to construct a probability model of electric vehicle charging load.

[0021] The electric vehicle charging load probability model obtained by fitting is coupled and superimposed with the time series probability model of the distribution network basic load to obtain the electric vehicle charging load sample.

[0022] Furthermore, the spatiotemporal demand response model, through the leverage of electricity prices, coordinates and guides electric vehicle charging load at both temporal and spatial levels; the expression of the spatiotemporal demand response model specifically includes:

[0023] ;

[0024] in, Let be the spatiotemporal electricity price of the i-th charging station at time t; The benchmark electricity price is denoted by T(i,t) and S(i,t) are the time and space scheduling functions, respectively. The specific formula for the time scheduling function is as follows:

[0025] ;

[0026] in, Let be the electric vehicle charging load demand of the i-th charging station at time t; The average daily charging load of electric vehicles at charging station i;

[0027] The specific formula for the space scheduling function is as follows:

[0028] ;

[0029] ;

[0030] Among them, S i,t S represents the apparent power of the associated branch of charging station i at time t; i,safe P represents the safety capacity of the associated branch of charging station i; ij,t Q ij,t Let be the active and reactive power of branch (i,j) at time t, respectively; k t k s These are the time and space scheduling coefficients, respectively.

[0031] Furthermore, the objective function of the probabilistic optimal power flow model is to minimize the system operating cost; the system operating cost includes the main grid power purchase cost, grid loss cost, wind and solar curtailment cost, peak-valley difference penalty cost, demand response call cost, and electric vehicle charging cost; the constraints of the probabilistic optimal power flow model include node power balance constraints, power flow constraints, line power constraints, power output constraints, peak-valley load constraints, shiftable demand response, and electric vehicle charging station constraints.

[0032] Furthermore, the movable demand response and electric vehicle charging station constraints specifically include:

[0033] ;

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] Where, N EV P represents the number of charging stations. EV,ori,t Let t represent the electric vehicle charging load at the i-th charging station at the original time t. P represents the increase or decrease in electric vehicle charging load at the i-th charging station at time t. EV,i,t Let P be the electric vehicle charging load demand of the i-th charging station at time t; dr,i,t Let be the demand response load call amount of the i-th node at time t.

[0040] Another objective of this invention is to provide a distribution network probabilistic power flow optimization system based on source-load spatiotemporal characteristics, used to implement the aforementioned distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics, specifically including:

[0041] The output sample generation module is used to analyze the spatiotemporal correlation of wind power and photovoltaic output, generate spatiotemporal related output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure.

[0042] The load spatiotemporal distribution prediction module is used to construct an electric vehicle spatiotemporal travel chain model that takes into account the SOC chain based on the dynamic traffic network model as a constraint, predict the spatiotemporal distribution of electric vehicle charging load, and obtain electric vehicle charging load samples.

[0043] The spatiotemporal demand response model construction module is used to construct a spatiotemporal demand response model guided by spatiotemporal coordinated electricity pricing, based on the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions.

[0044] The module for constructing a probabilistic optimal power flow model is used to relax AC network power flow constraints using second-order cone programming, and integrates power output constraints, spatiotemporal demand response models and electric vehicle operation constraints to construct a probabilistic optimal power flow model.

[0045] The probabilistic optimal power flow model solution module is used to solve the probabilistic optimal power flow model by taking power output samples and electric vehicle charging load samples as inputs, and outputting the probability distribution results of the distribution network operation status.

[0046] Another object of the present invention is to provide a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the above-described probabilistic power flow optimization method for distribution networks based on source-load spatiotemporal characteristics.

[0047] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the above-described probabilistic power flow optimization method for distribution networks based on source-load spatiotemporal characteristics.

[0048] The probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of source and load provided by this invention constructs a probabilistic optimal power flow model with spatiotemporal distribution characteristics, introduces a spatiotemporal demand response mechanism for electric vehicles, and combines second-order cone programming relaxation technology to solve the model. This improves the fit between the probabilistic optimal power flow calculation results and actual operating conditions, and provides reliable technical support for the safe and economical operation of distribution networks. Attached Figure Description

[0049] Figure 1 This is the topology diagram of the 64-node road network coupled with the improved IEEE 33-node power grid in this embodiment of the invention;

[0050] Figure 2 A schematic diagram of the process for calculating the optimal power flow considering the spatiotemporal characteristics of source loads and the spatiotemporal demand response probability of electric vehicles, provided for an embodiment of the present invention;

[0051] Figure 3 This is a diagram of wind power output parameters at different times, based on the multivariate normal distribution and Copula function, considering spatiotemporal correlation in an embodiment of the present invention.

[0052] Figure 4 This is a graph showing the photovoltaic output parameters at different times, based on the multivariate normal distribution and Copula function, considering the spatiotemporal correlation in an embodiment of the present invention.

[0053] Figure 5 This is a simulation diagram of the spatiotemporal distribution of EV charging demand in the power distribution network based on the road network-spatiotemporal travel chain in an embodiment of the present invention;

[0054] Figure 6 The load curves of each charging station under demand response in this embodiment of the invention are shown.

[0055] Figure 7 The load curves of each charging station under spatiotemporal demand response in this embodiment of the invention are shown.

[0056] Figure 8 This is an 18-node voltage probability density diagram that considers the spatiotemporal characteristics of source loads but does not consider demand response in the probabilistic optimal power flow calculation in this embodiment of the invention.

[0057] Figure 9 This is an 18-node voltage probability density diagram that considers the spatiotemporal characteristics of source loads and demand response in the probabilistic optimal power flow calculation of this invention.

[0058] Figure 10 This is an 18-node voltage probability density diagram that considers the spatiotemporal characteristics of source loads and the spatiotemporal demand response of electric vehicles in the probabilistic optimal power flow calculation in this embodiment of the invention. Detailed Implementation

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0060] To address the problems in existing probabilistic optimal power flow calculations for distribution networks, such as insufficient characterization of the spatiotemporal correlation of distributed generation output, low accuracy in predicting the spatiotemporal distribution of electric vehicle charging loads, and an immature demand response guidance mechanism, which lead to significant deviations between power flow optimization calculation results and actual engineering conditions and make it difficult to support precise scheduling decisions, this invention provides a probabilistic power flow optimization method and system for distribution networks that considers the spatiotemporal demand response of electric vehicles and the spatiotemporal characteristics of source and load. The aim is to achieve deep coupling between the source and load characteristics of the distribution network and the demand response of electric vehicles, improve the accuracy of probabilistic optimal power flow calculations, and provide reliable support for the safe and economical operation of the distribution network and the optimization of scheduling strategies.

[0061] Specifically, in one embodiment of the present invention, a probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of source and load is provided, comprising the following steps:

[0062] S1. Analyze the spatiotemporal correlation of wind power and photovoltaic output, generate spatiotemporal correlation output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure.

[0063] S2. Based on the dynamic traffic network model as a constraint, construct a spatiotemporal travel chain model for electric vehicles that takes into account the SOC chain, predict the spatiotemporal distribution of electric vehicle charging load, and obtain electric vehicle charging load samples.

[0064] S3. Using the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions, a spatiotemporal demand response model with spatiotemporal coordinated electricity price guidance is constructed. Through the electricity price lever, the electric vehicle charging load is guided in a coordinated manner at both the temporal and spatial levels, thereby alleviating local overload, reducing peak-valley difference, and improving the safety and economy of power grid operation.

[0065] S4. Second-order cone programming is used to relax the power flow constraints of the AC network. Power output constraints, spatiotemporal demand response model and electric vehicle operation constraints are integrated to construct a probabilistic optimal power flow model.

[0066] S5. Using power output samples and electric vehicle charging load samples as input, solve the probabilistic optimal power flow model and output the probability distribution results of the distribution network operation status.

[0067] In this embodiment of the invention, by providing distributed power source input data that conforms to spatiotemporal characteristics, predicting the spatiotemporal distribution of electric vehicle charging load, and adjusting the load distribution through electricity price leverage, the three work together to support the calculation of probabilistic optimal power flow.

[0068] In a preferred embodiment of the present invention, the steps of analyzing the spatiotemporal correlation of wind power and photovoltaic output, generating spatiotemporal correlated output scenarios of distributed power sources, and generating output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure specifically include:

[0069] Based on the analysis of the spatiotemporal correlation of wind power and photovoltaic output using multivariate normal distribution and Frank-Copula function, spatiotemporal related output scenarios of distributed power sources are generated accordingly.

[0070] The reliability of the power output scenario is quantitatively verified using the ES index.

[0071] The edge distribution parameters of wind power and photovoltaic power output are fitted based on the verified power output scenario data; the wind power output is fitted with Weibull distribution parameters, and the photovoltaic power output is fitted with Beta distribution parameters.

[0072] The method of combining Nataf transform and inverse transform with singular value decomposition is adopted to solve the problem of non-positive definite correlation coefficient matrix. This generates output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and the spatiotemporal correlation structure, serving as reliable input data for the probabilistic optimal power flow calculation of distribution network.

[0073] In a preferred embodiment of the present invention, the steps of constructing a spatiotemporal travel chain model for electric vehicles (EVs) that considers the SOC chain based on a dynamic traffic network model as a fundamental constraint, predicting the spatiotemporal distribution of EV charging load, and obtaining EV charging load samples specifically include:

[0074] Based on the dynamic traffic network model as a constraint, an electric vehicle spatiotemporal travel chain model considering the SOC chain is constructed to accurately depict the behavior characteristics of electric vehicles throughout the entire process of "travel-stop-charge" from the starting point, through the road segments, stopping and charging in the middle to the destination, covering key behavioral parameters such as driving mileage, stop duration, and charging trigger threshold.

[0075] Based on the spatiotemporal travel chain model of electric vehicles, and combined with Dijkstra's shortest path algorithm to plan the driving path, the system determines whether the SOC of electric vehicles meets the subsequent mileage requirements and triggers fast charging or slow charging decisions segment by segment, thereby predicting the spatiotemporal distribution characteristics of the charging load of the cluster electric vehicles at different times and different grid nodes.

[0076] The maximum likelihood estimation method is used to fit the mean, variance and other parameters of the normal distribution, and the probability distribution of charging load at each node and time period is obtained to construct the electric vehicle charging load probability model.

[0077] The fitted electric vehicle charging load probability model is coupled and superimposed with the time-series probability model of the distribution network base load to obtain a complete electric vehicle charging load sample, which is used as the load input data.

[0078] In a preferred embodiment of the present invention, the spatiotemporal demand response model under the "road network-power grid" coupling uses electricity price leverage to guide electric vehicle charging load in both time and space, thereby alleviating local overload, reducing peak-valley differences, and improving the safety and economy of power grid operation; wherein, the expression of the spatiotemporal demand response model specifically includes:

[0079] ;

[0080] in, Let be the spatiotemporal electricity price of the i-th charging station at time t; The benchmark electricity price is denoted by T(i,t) and S(i,t) are the time and space scheduling functions, respectively. The specific formula for the time scheduling function is as follows:

[0081] ;

[0082] in, Let be the electric vehicle charging load demand of the i-th charging station at time t; Let be the average daily charging load of electric vehicles at charging station i; the above formula shows that, in the time dimension, by comparing the peak-valley difference of electric vehicle charging load, the load can be guided to shift from peak hours to off-peak hours using time-of-use pricing.

[0083] The specific formula for the space scheduling function is as follows:

[0084] ;

[0085] ;

[0086] Among them, S i,t S represents the apparent power of the associated branch of charging station i at time t; i,safe P represents the safety capacity of the associated branch of charging station i; ij,t Q ij,t Let be the active and reactive power of branch (i,j) at time t, respectively; k t k s These are the time and space scheduling coefficients, respectively. The above formula shows that, in the spatial dimension, by comparing the load rates of distribution network branches, the nodal electricity price signal can be used to guide users to prioritize charging stations with lower load rates.

[0087] In this embodiment of the invention, based on the spatiotemporal characteristics of the source and load, a spatiotemporal demand response mechanism for electric vehicles is embedded to fully unleash the flexible adjustment potential of electric vehicles and improve the voltage operation status of the distribution network.

[0088] In a preferred embodiment of the present invention, the objective function of the probabilistic optimal power flow model is to minimize the system operating cost; the system operating cost includes the main grid power purchase cost, grid loss cost, wind and solar curtailment cost, peak-valley difference penalty cost, demand response call cost, and electric vehicle charging cost; the constraints of the probabilistic optimal power flow model include node power balance constraints, power flow constraints, line power constraints, power output constraints, peak-valley load constraints, shiftable demand response, and electric vehicle charging station constraints.

[0089] In a preferred embodiment of the present invention, the movable demand response and electric vehicle charging station constraints specifically include:

[0090] ;

[0091] ;

[0092] ;

[0093] ;

[0094] ;

[0095] ;

[0096] Where, N EV P represents the number of charging stations. EV,ori,t Let t represent the electric vehicle charging load at the i-th charging station at the original time t. P represents the increase or decrease in electric vehicle charging load at the i-th charging station at time t. EV,i,t Let P be the electric vehicle charging load demand of the i-th charging station at time t; dr,i,t Let be the demand response load call amount of the i-th node at time t.

[0097] In a preferred embodiment of the present invention, the probabilistic optimal power flow calculation method is implemented based on the above-mentioned probabilistic optimal power flow model, specifically including: using second-order cone programming to relax the AC network power flow constraints, making them convex constraints; integrating power output constraints, demand response constraints, and electric vehicle operation constraints to construct a complete probabilistic optimal power flow model; considering the spatiotemporal characteristics of source and load, embedding the above-mentioned spatiotemporal demand response mechanism of electric vehicles to fully release the flexible adjustment potential of electric vehicles to improve the voltage operation status of the distribution network; based on multiple time-period output samples of wind power, photovoltaic power, and electric vehicle charging loads at various charging stations, substituting each sample into the deterministic optimal power flow model for calculation, obtaining the probability density function and probability distribution function of the system's minimum operating cost, voltage, power, and other output variables; using the commercial solver Gurobi to perform several 24-hour deterministic optimal power flow solutions, outputting the probability distribution results of the distribution network operation status, providing a basis for scheduling decisions. It should be noted that MATLAB 2023b and the Yalmip toolbox can be used for joint solution, with Gurobi 11.0.1 being the preferred solver, which can efficiently complete the iterative solution and result output of the probabilistic optimal power flow model, but is not limited to it.

[0098] The method provided in this embodiment of the invention has the following beneficial effects:

[0099] I. This invention provides a more reliable reference for dispatching decisions facing power system uncertainties, and significantly improves the accuracy of the source-side probabilistic model through quantitative verification using the ES index. Specifically, this method abandons the limitations of traditional models that only consider the single-dimensional temporal or spatial correlation of distributed power generation output. It constructs a wind and solar power output probabilistic model incorporating spatiotemporal correlation based on multivariate normal distribution and Copula function, and uses Nataf transform and singular value decomposition to process non-normally correlated input variables, ensuring accurate characterization of probabilistic characteristics from the data modeling level. Through comparative verification using the ES index, the model output considering the spatiotemporal dual-dimensional correlation is closer to the measured values ​​than the single-dimensional correlation model, and can more realistically represent the distribution pattern of source-side data. This overcomes the technical pain point of the probabilistic optimal power flow model, which suffers from significant deviations between calculated results and actual operating conditions due to neglecting the spatiotemporal correlation of distributed power generation.

[0100] 2. The core innovation of the method provided in this invention lies in constructing a spatiotemporal demand response model for electric vehicles under the coupling of "road network-power grid". This model breaks through the limitation of traditional demand response focusing only on the time dimension of scheduling. It combines road network constraints and user spatiotemporal travel chain models to predict the spatiotemporal distribution characteristics of electric vehicle charging load. Based on the branch load rate and node voltage level of the distribution network, a spatiotemporal coordinated electricity pricing mechanism is formulated to guide the optimized transfer of electric vehicle charging load in both time and space dimensions. Simulation verification results show that this model can effectively optimize the matching relationship between supply and demand in the distribution network, achieving a 16.6% reduction in the system load peak-valley difference. At the same time, it significantly alleviates problems such as node voltage exceeding limits and excessive branch load rate caused by the high proportion of distributed power sources and electric vehicle access, improving the power quality and operational stability of the distribution network from the perspective of source-load coordination.

[0101] 3. The method provided in this invention demonstrates significant theoretical and engineering practical value. Compared to traditional probabilistic optimal power flow models, this method integrates a probabilistic model considering the spatiotemporal characteristics of source loads with the spatiotemporal demand response constraints of electric vehicles, combines second-order cone programming relaxation techniques to construct a complete optimization model, and employs the Gurobi solver to achieve efficient convergence. This comprehensively realizes the spatiotemporal coordinated optimization of distributed power output and electric vehicle charging load. Simulation results confirm that this method, while improving the economic efficiency of distribution network operation, can effectively raise the voltage levels of each node, adapting to the operational needs of new distribution networks with high proportions of new energy and electric vehicles. This proves that this method is not only an effective tool for solving the optimal scheduling problem of distribution networks under high-penetration source load access, but also provides precise theoretical support for the planning, design, operation control, and policy formulation of new distribution networks, and has significant prospects for widespread application.

[0102] It should be noted that distributed generation refers to small and medium-sized power generation devices that are connected to the load side of the distribution network nearby. The core types include renewable energy power generation systems such as wind power and photovoltaics, as well as small gas turbines and fuel cells. A significant characteristic of this type of power source is the randomness, volatility, and spatiotemporal correlation of its output: in the time dimension, wind power output depends on real-time changes in wind speed, while photovoltaic output is affected by the temporal sequence of solar radiation intensity and weather conditions; in the spatial dimension, the output of multiple distributed power sources within the same region exhibits coordinated fluctuation characteristics, while the output of power sources in different regions shows differentiated correlation patterns due to geographical differences. The widespread penetration of distributed generation has broken the traditional "one-way power supply" pattern of the distribution network, providing clean electricity to the grid while also bringing new challenges to the power balance and voltage stability control of the distribution network.

[0103] Electric vehicle (EV) charging load refers to the electrical power drawn from the power distribution network during the charging process of electric vehicles, and it is a typical flexible and controllable load. Its core characteristic is the strong uncertainty in its spatiotemporal distribution, and its deep coupling with user travel behavior: in the temporal dimension, charging load is concentrated during nighttime home hours or daytime office / shopping mall periods, exhibiting obvious peak-valley characteristics; in the spatial dimension, the distribution of charging load is closely related to the layout of charging stations and urban road network traffic, easily forming localized load peaks in areas such as transportation hubs and residential areas. Furthermore, EV charging load has good adjustability potential; through demand response measures such as electricity price guidance and scheduled charging, the spatiotemporal transfer of load can be realized, making it an important flexible resource for smoothing out fluctuations in distributed power generation output and improving the operating efficiency of the power distribution network.

[0104] Probabilistic optimal power flow is a distribution network analysis and optimization method that integrates probabilistic statistical theory with traditional optimal power flow. It is primarily used to address the uncertainties on both the source and load sides brought about by the high proportion of distributed generation and electric vehicle (EV) integration. Unlike traditional deterministic optimal power flow, which uses fixed source and load data as input and outputs a single deterministic power flow result, probabilistic optimal power flow describes the statistical characteristics of uncertain inputs such as distributed generation output and EV charging load through probability distribution functions. Using Monte Carlo simulations, analytical methods, and commercial solvers, it solves for the probabilistic distribution characteristics of key indicators such as node voltage, branch power, and system operating costs. Its core objective is to achieve the optimal balance between the economic efficiency and safety of distribution network operation while taking uncertainty into account, providing quantitative probabilistic basis for distribution network dispatching decisions, risk assessment, and planning design.

[0105] In practical applications, such as Figure 1 The diagram shown is a topology diagram of a 64-node road network coupled with an improved IEEE 33-node power grid, specifically including:

[0106] The system's base voltage is 12.66 kV, and the base power is 10 MVA. Output power data from a wind farm and a photovoltaic power station in a certain region of Xinjiang were selected as the data source for generating the scenario. A wind farm with an installed capacity of 2 MW is connected to node 18, with cut-in wind speed, rated wind speed, and cut-off wind speed of 3 m / s, 14 m / s, and 25 m / s, respectively. A photovoltaic power station with an installed capacity of 2.5 MW is connected to node 27, with a photoelectric conversion efficiency of 64%. It is assumed that the grid, wind farm, and photovoltaic power station adopt constant power control. Specifically, the grid is: Distributed generation (DG): The system's road network comprises 64 nodes and 98 roads, and is divided into three functional zones from left to right: work zone (road network nodes 1-21), residential zone (road network nodes 22-51), and commercial zone (road network nodes 47, 52-64).

[0107] In addition, the process of power flow calculation in practical applications is as follows: Figure 2 As shown, it specifically includes:

[0108] First, basic data such as historical output of distributed generation sources, electric vehicle travel parameters, distribution network topology, and dynamic traffic network are input. On one hand, the spatiotemporal correlation of distributed generation output is characterized by multivariate normal distribution and Copula function. The marginal distribution parameters of wind power and photovoltaic output are fitted by Weibull and Beta distributions. Nataf transform and singular value decomposition are used to solve the non-positive definite problem of correlation coefficient matrix, generating distributed generation output samples that satisfy both marginal distribution characteristics and spatiotemporal correlation structure. On the other hand, a dynamic traffic network model is constructed. Electric vehicle travel paths are planned based on Dijkstra's algorithm. Charging decisions are triggered by real-time vehicle status. The normal distribution probability model of charging load is fitted and superimposed with the original load to form complete load input data. Subsequently, the hourly distributed generation output and electric vehicle charging load samples are input into the probabilistic optimal power flow model. A spatiotemporal demand response mechanism is embedded. The Gurobi solver is used to perform multi-period deterministic optimal power flow solution. After iteratively completing the 24-hour calculation, the probability density function and cumulative distribution function of key variables such as system operating cost, node voltage, and branch power are finally output statistically. This method achieves deep coupling of the spatiotemporal characteristics of distributed power sources and electric vehicle charging loads, improves the accuracy and engineering adaptability of probabilistic optimal power flow calculation, and can provide reliable quantitative support for the safe and economical operation of distribution networks and the optimization of scheduling strategies.

[0109] Using the above method, wind power scenario data with spatiotemporal correlation are fitted to obtain 24-hour wind power shape parameters and scale parameters, as shown below. Figure 3 As shown; by fitting photovoltaic scene data with spatiotemporal correlation, the shape parameters and scale parameters of 24-hour photovoltaic power are obtained as follows. Figure 4As shown in the figure. The spatiotemporal distribution of 24-hour charging demand for electric vehicles at each charging station is as follows. Figure 5 As shown in the figure, the EV charging load exhibits strong spatiotemporal distribution characteristics. Figure 6 As shown, charging station 2 (node ​​32) shifts its peak load from 13:00-16:00 to 04:00-08:00, while charging stations 8 (node ​​13) and 9 (node ​​16) shift their peak load to 22:00-24:00, thus achieving load staggering among individual charging stations. The load curves of each charging station under spatiotemporal demand response are shown below. Figure 7 As shown, in the time dimension, the peak load from 13:00 to 16:00 is further reduced and transferred to the off-peak periods from 04:00 to 08:00 and from 20:00 to 24:00. In the spatial dimension, the charging load of charging stations 8 and 9 with higher load rates on associated branches is allocated to charging stations with lower load rates. It can be seen that the coordinated scheduling of peak shaving and valley filling in the time domain and cross-station load allocation in the spatial domain can effectively achieve the spatiotemporal optimization of EV charging load.

[0110] The 18-node voltage probability density in probabilistic optimal power flow calculations considers the spatiotemporal characteristics of source loads but not the demand response, as shown in the example. Figure 8 As shown; Figure 8 The results of the probabilistic optimal power flow calculation intuitively show that the voltage of the 18 nodes is higher than 1.0 pu during the periods of 01:00-11:00 and 19:00-24:00, but the voltage drops significantly and there is a risk of exceeding the lower limit during the period of 13:00-16:00 due to the concentrated EV charging load. The lowest value is controlled at 0.95 pu.

[0111] Figure 9 The results of the probabilistic optimal power flow calculation intuitively show that by guiding electric vehicle owners to change their charging time, the overall voltage of the distribution network has been effectively increased. The voltage has rebounded to above 0.99 pu during the period from 13:00 to 16:00, which has significantly reduced the risk of voltage exceeding the limit.

[0112] The 18-node voltage probability density considering the spatiotemporal characteristics of source loads and the spatiotemporal demand response of electric vehicles in probabilistic optimal power flow calculation is as follows: Figure 10 As shown; Figure 10 The probabilistic optimal power flow calculation results intuitively and powerfully demonstrate that the distribution network probabilistic optimal power flow calculation method proposed in this invention, which considers the spatiotemporal correlation of distributed power sources and the spatiotemporal demand response of electric vehicles, has the effect of simultaneously resolving the load and power mismatch problem in both spatiotemporal dimensions. It also more comprehensively offsets the voltage disturbances caused by the large-scale access of EVs, ensuring the safe and stable operation of the distribution network and improving power quality throughout the entire time period.

[0113] In another embodiment of the present invention, a distribution network probabilistic power flow optimization system based on source-load spatiotemporal characteristics is also provided, for implementing the above-mentioned distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics, specifically including:

[0114] The output sample generation module is used to analyze the spatiotemporal correlation of wind power and photovoltaic output, generate spatiotemporal related output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure.

[0115] The load spatiotemporal distribution prediction module is used to construct an electric vehicle spatiotemporal travel chain model that takes into account the SOC chain based on the dynamic traffic network model as a constraint, predict the spatiotemporal distribution of electric vehicle charging load, and obtain electric vehicle charging load samples.

[0116] The spatiotemporal demand response model construction module is used to construct a spatiotemporal demand response model guided by spatiotemporal coordinated electricity pricing, based on the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions.

[0117] The module for constructing a probabilistic optimal power flow model is used to relax AC network power flow constraints using second-order cone programming, and integrates power output constraints, spatiotemporal demand response models and electric vehicle operation constraints to construct a probabilistic optimal power flow model.

[0118] The probabilistic optimal power flow model solution module is used to solve the probabilistic optimal power flow model by taking power output samples and electric vehicle charging load samples as inputs, and outputting the probability distribution results of the distribution network operation status.

[0119] In another embodiment of the present invention, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the above-described probabilistic power flow optimization method for distribution networks based on source-load spatiotemporal characteristics.

[0120] In another embodiment of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, and when the computer program is executed by a processor, the processor performs the above-described probabilistic power flow optimization method for distribution networks based on source-load spatiotemporal characteristics.

[0121] It should be noted that each of the above modules can be implemented as a computer program, which can run on a computer device. The computer device's memory can store the computer program that makes up each module, enabling the processor to execute each step of the above method.

[0122] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0123] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods.

[0124] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A probabilistic power flow optimization method for distribution networks based on the spatiotemporal characteristics of source and load, characterized in that, Includes the following steps: Analyze the spatiotemporal correlation of wind power and solar power output, generate spatiotemporal correlation output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and solar power output and conform to the spatiotemporal correlation structure. Based on the dynamic traffic network model as a constraint, a spatiotemporal travel chain model for electric vehicles that takes into account the SOC chain is constructed to predict the spatiotemporal distribution of electric vehicle charging load and obtain electric vehicle charging load samples. A spatiotemporal demand response model with spatiotemporal coordinated electricity pricing guidance is constructed, based on the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions. A second-order cone programming relaxation method is used to handle the power flow constraints of the AC network. By integrating power output constraints, spatiotemporal demand response models and electric vehicle operation constraints, a probabilistic optimal power flow model is constructed. Using power output samples and electric vehicle charging load samples as input, the probabilistic optimal power flow model is solved, and the probability distribution results of the distribution network operation status are output. The spatiotemporal demand response model uses electricity price leverage to guide electric vehicle charging load in a coordinated manner at both temporal and spatial levels; the expression of the spatiotemporal demand response model specifically includes: ； in, Let be the spatiotemporal electricity price of the i-th charging station at time t; The benchmark electricity price is denoted by T(i,t) and S(i,t) are the time and space scheduling functions, respectively. The specific formula for the time scheduling function is as follows: ； in, Let be the electric vehicle charging load demand of the i-th charging station at time t; The average daily charging load of electric vehicles at charging station i; The specific formula for the space scheduling function is as follows: ； ； Among them, S i,t S represents the apparent power of the associated branch of charging station i at time t; i,safe P represents the safety capacity of the associated branch of charging station i; ij,t Q ij,t Let be the active and reactive power of branch (i,j) at time t, respectively; k t k s These are the time and space scheduling coefficients, respectively. The objective function of the probabilistic optimal power flow model is to minimize the system operating cost. The system operating cost includes the main grid power purchase cost, grid loss cost, wind and solar curtailment cost, peak-valley difference penalty cost, demand response call cost, and electric vehicle charging cost. The constraints of the probabilistic optimal power flow model include node power balance constraints, power flow constraints, line power constraints, power output constraints, peak-valley load constraints, shiftable demand response, and electric vehicle charging station constraints.

2. The distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics according to claim 1, characterized in that, The specific steps for analyzing the spatiotemporal correlation of wind and solar power output, generating spatiotemporal correlated output scenarios for distributed power sources, and generating output samples that satisfy both the edge distribution characteristics of wind and solar power output and conform to the spatiotemporal correlation structure include: Based on the analysis of the spatiotemporal correlation of wind power and photovoltaic output using multivariate normal distribution and Frank-Copula function, spatiotemporal related output scenarios of distributed power sources are generated accordingly. The reliability of the power output scenario is quantitatively verified using the ES index. The edge distribution parameters of wind power and photovoltaic power output are fitted based on the verified power output scenario data; the wind power output is fitted with Weibull distribution parameters, and the photovoltaic power output is fitted with Beta distribution parameters. By employing a combination of Nataf transform and inverse transform with singular value decomposition, the problem of non-positive definite correlation coefficient matrix is ​​solved, generating output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure.

3. The distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics according to claim 1, characterized in that, Based on a dynamic traffic network model as a fundamental constraint, a spatiotemporal travel chain model for electric vehicles (EVs) considering the SOC chain is constructed to predict the spatiotemporal distribution of EV charging load and obtain EV charging load samples. The specific steps include: Based on the dynamic traffic network model as a constraint, a spatiotemporal travel chain model for electric vehicles that takes into account the SOC chain is constructed to characterize the full-process behavioral characteristics of electric vehicles from the starting node, through the road segments, stopping and charging in the middle to the destination. Based on the spatiotemporal travel chain model of electric vehicles, and combined with Dijkstra's shortest path algorithm to plan the driving path, the system determines whether the SOC of electric vehicles meets the subsequent mileage requirements and triggers fast charging or slow charging decisions segment by segment, thereby predicting the spatiotemporal distribution characteristics of the charging load of the cluster electric vehicles at different times and different grid nodes. The maximum likelihood estimation method is used to fit the parameters, and the probability distribution of charging load at each node and time period is obtained to construct a probability model of electric vehicle charging load. The electric vehicle charging load probability model obtained by fitting is coupled and superimposed with the time series probability model of the distribution network basic load to obtain the electric vehicle charging load sample.

4. The distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics according to claim 1, characterized in that, The movable demand response and electric vehicle charging station constraints specifically include: ； ； ； ； ； ； Where, N EV P represents the number of charging stations. EV,ori,t Let t represent the electric vehicle charging load at the i-th charging station at the original time t. P represents the increase or decrease in electric vehicle charging load at the i-th charging station at time t. EV,i,t Let P be the electric vehicle charging load demand of the i-th charging station at time t; dr,i,t Let be the demand response load call amount of the i-th node at time t.

5. A distribution network probabilistic power flow optimization system based on source-load spatiotemporal characteristics, used to implement the distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics as described in any one of claims 1-4, characterized in that, include: The output sample generation module is used to analyze the spatiotemporal correlation of wind power and photovoltaic output, generate spatiotemporal related output scenarios of distributed power sources, and generate output samples that satisfy both the edge distribution characteristics of wind power and photovoltaic output and conform to the spatiotemporal correlation structure. The load spatiotemporal distribution prediction module is used to construct an electric vehicle spatiotemporal travel chain model that takes into account the SOC chain based on the dynamic traffic network model as a constraint, predict the spatiotemporal distribution of electric vehicle charging load, and obtain electric vehicle charging load samples. The spatiotemporal demand response model construction module is used to construct a spatiotemporal demand response model guided by spatiotemporal coordinated electricity pricing, based on the spatiotemporal distribution of electric vehicle charging load and the load rate of distribution network branches as triggering conditions. The module for constructing a probabilistic optimal power flow model is used to relax AC network power flow constraints using second-order cone programming, and integrates power output constraints, spatiotemporal demand response models and electric vehicle operation constraints to construct a probabilistic optimal power flow model. The probabilistic optimal power flow model solution module is used to solve the probabilistic optimal power flow model by taking power output samples and electric vehicle charging load samples as inputs, and outputting the probability distribution results of the distribution network operation status.

6. A computer device comprising a memory and a processor, characterized in that, The memory stores a computer program, which, when executed by the processor, causes the processor to perform the distribution network probabilistic power flow optimization method based on source-load spatiotemporal characteristics as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the distribution network probabilistic power flow optimization method based on the spatiotemporal characteristics of source and load as described in any one of claims 1-4.

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