A method for ensuring emergency power supply in a distribution area

Abstract

The present invention relates to an emergency power supply method for a distribution substation, comprising the following steps: obtaining user profile information of a target distribution substation, and dividing the power supply priority of each user according to the user profile information; obtaining power supply and demand data under a disaster scenario, analyzing the coupling relationship between the disaster scenario and the power supply and demand data, and analyzing the power balance of the target distribution substation at different stages of the disaster; designing a phased photovoltaic and energy storage coordinated control strategy based on the power balance analysis results, and supplying power to each user's electrical equipment in combination with the power supply priority of each user when a disaster occurs.

Description

Technical Field

[0001] The present invention relates to an emergency power supply method for a distribution station area, belonging to the technical field of emergency protection of distribution networks. Background Art

[0002] To address the challenges of disaster prevention and control, some regions have implemented power hedging mechanisms. This involves initiating preventive power outages across a region when extreme weather occurs, potentially impacting the reliability of power supply to important distribution station areas (DSAs), such as hospitals, schools, businesses, and industries.

[0003] Addressing DSA emergency power supply in disaster-prone areas involves three main phases: emergency preparedness, impact assessment, and emergency power supply. During the emergency preparedness phase, power supply companies establish a disaster emergency power supply system and develop pre-emptive DSA emergency power supply plans to mitigate the impact of disasters on DSA power supply. During the impact assessment phase, quantitative indicators are used to analyze the impact of disasters on DSA operations, providing a basis for formulating DSA emergency power supply strategies. During the emergency power supply phase, network reconstruction, island operation control, and multi-energy collaborative optimization are used to ensure DSA emergency power supply in disaster scenarios.

[0004] There are various existing DSA emergency supply guarantee methods. However, there is currently a lack of systematic evaluation methods for the security targets of different types of DSA, making it difficult to accurately formulate differentiated supply guarantee strategies. In addition, due to the complex coupling relationship between the sporadic nature of disasters, the randomness of photovoltaic power, and the differences in security needs, an effective source-load-storage coordinated operation mechanism has not yet been established, which seriously restricts the further improvement of DSA emergency supply guarantee capabilities. Summary of the Invention

[0005] In order to solve the problems existing in the above-mentioned prior art, the present invention proposes a method for emergency power supply in distribution substations. First, based on the DSA load importance classification, the emergency power supply demands of multiple types of DSAs under disasters are quantitatively analyzed to clarify the power supply priorities of different users; secondly, the complex coupling relationship between disaster scenarios and power supply and demand data is analyzed; finally, a phased DSA power balance analysis method is constructed to achieve accurate matching of supply and demand, and a phased photovoltaic and energy storage collaborative control strategy is designed to meet the diverse DSA power supply demands.

[0006] The technical solutions of the present invention are as follows:

[0007] In one aspect, the present invention provides a method for ensuring emergency power supply in a distribution area, comprising the following steps:

[0008] Obtain user profile information of the target distribution area and prioritize the supply of each user based on the user profile information;

[0009] Obtain power supply and demand data under disaster scenarios, analyze the coupling relationship between disaster scenarios and power supply and demand data, and analyze the power balance of target distribution substations at different stages of the disaster;

[0010] Based on the power balance analysis results, a phased photovoltaic and energy storage coordinated control strategy is designed, and power is supplied to each user's electrical equipment in the event of a disaster, taking into account the supply priority of each user.

[0011] As a preferred embodiment, the step of dividing the supply guarantee priority of each user according to the user profile information includes:

[0012] Extract multiple characteristic indicators reflecting the user's sensitivity to power outages through user profile information;

[0013] Assign weights to each characteristic indicator;

[0014] Calculate the score of each user's power outage sensitivity based on the weighting results and the characteristic values ​​of each user's characteristic indicators extracted from the user profile information;

[0015] The power supply priority is divided according to the score of each user's power supply interruption sensitivity.

[0016] As a preferred embodiment, in the step of weighting each characteristic indicator:

[0017] The entropy weight method is used to assign weights to each characteristic index.

[0018] As a preferred embodiment, the step of prioritizing power supply according to the power outage sensitivity score of each user includes:

[0019] Clustering is performed based on the score of each user's power outage sensitivity;

[0020] Based on the clustering results, the power outage sensitivity scores of all users in each cluster are obtained, and the evaluation benchmark scores for each cluster are calculated based on them.

[0021] The supply guarantee priority of each cluster is determined based on the size of the evaluation benchmark scores of each cluster, and the corresponding supply guarantee priority is assigned to all users in each cluster.

[0022] As a preferred embodiment, obtaining power supply and demand data in a disaster scenario includes:

[0023] The target distribution substation area's power load forecast data under disaster scenarios, photovoltaic power generation forecast data, and energy storage system charging and discharging capabilities serve as power supply and demand data;

[0024] Among them, electricity load forecast data and photovoltaic power generation power forecast data are obtained through machine learning algorithms.

[0025] As a preferred embodiment, the step of analyzing the coupling relationship between the disaster scenario and the power supply and demand data includes:

[0026] Introducing contingency variables that induce the occurrence of the corresponding disaster scenario;

[0027] Extract the random variables of the supply guarantee resources and the differential variables of the supply guarantee demand from the power supply and demand data, where the random variables of the supply guarantee resources are the random variables of photovoltaic power, and the differential variables of the supply guarantee demand are the differential variables of electricity load;

[0028] Carry out coupling analysis, specifically:

[0029] The Copula function is used to calculate the joint distribution function of three variables among the contingency of inducing factors, the randomness of supply resources, and the diversity of supply demand.

[0030] As a preferred embodiment, the steps of analyzing the power balance of the target distribution station area at different stages of the disaster are specifically as follows:

[0031] Divide the entire disaster period into different stages;

[0032] A dynamic programming approach will be used to solve the power balance problem for target distribution substations at different stages. The first objective function of the dynamic programming approach is constructed with the goal of ensuring that the power provided by guaranteed supply resources can maximize the power demand. Guaranteed supply resources include photovoltaic and energy storage systems.

[0033] By dynamically recursively solving the first objective function, the optimal solution for the power provided by the supply resources at different stages is obtained;

[0034] The power balance of the target distribution station area in each stage is obtained by recursively calculating the optimal solution of the power provided by the stage-by-stage supply guarantee resources.

[0035] As a preferred embodiment, the steps of designing a phased photovoltaic and energy storage coordinated control strategy based on the power balance assessment results and the supply priority of each user are as follows:

[0036] A second objective function is constructed to minimize the cost of ensuring power supply to the target distribution area when a disaster occurs, based on the power balance of the target distribution area at each stage and the capacity constraints of the photovoltaic and energy storage systems.

[0037] Solve the second objective function and obtain the control strategies of photovoltaic and energy storage systems at each stage.

[0038] On the other hand, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, the method for emergency power supply in a distribution area as described in any embodiment of the present invention is implemented.

[0039] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for ensuring emergency power supply in a distribution station area as described in any embodiment of the present invention.

[0040] The present invention proposes a phased DSA emergency power supply control method based on photovoltaic and storage collaboration. First, the user's power supply sensitivity is identified from multiple dimensions, and the importance of user supply security is classified. By predicting the power load and photovoltaic power generation power under disasters, and combining the charging and discharging capabilities of the energy storage system, a power supply and demand coupling model under disaster scenarios is established, revealing the inherent relationship between the sporadic nature of disasters, the randomness of photovoltaic output, and the differences in user supply security needs. Based on the time scale, the DSA power balance is dynamically evaluated, the power balance situation at different stages is analyzed, and by optimizing the photovoltaic and storage collaborative control strategy, the optimal supply security for users of different levels is ensured under limited supply security resources. This method can effectively improve the DSA emergency power security time and reduce the DSA emergency power security cost.

[0041] Additional aspects and advantages of the present invention will be set forth in the following description, and some of them will be obvious from the description, or may be learned by practicing the present invention. In addition, the various aspects and advantages of the present invention may be realized and obtained by the method steps and combinations particularly pointed out in the appended claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention;

[0043] Figure 2 Schematic diagram of the process of generating photovoltaic power generation data and electricity load data samples through a GAN network in one embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] It should be understood that the step numbers used herein are only for convenience of description and are not intended to limit the order in which the steps are to be executed.

[0046] It should be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] The terms “include” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0048] The term "and / or" refers to and includes any and all possible combinations of one or more of the associated listed items.

[0049] See also Figure 1 This embodiment provides a method for ensuring emergency power supply in a distribution area, which specifically includes the following steps:

[0050] S100, based on the power supply company's marketing system and electricity consumption information collection system, obtains user profile information of the target distribution station area and real-time measurement data such as current and voltage of the meter at 96 points every day. Combined with the multi-dimensional factors in the user profile, it identifies the user's power supply sensitivity characteristics and completes the user's power supply importance analysis; then classifies the user's power supply and divides the user's power supply priority to provide a basis for DSA user power supply when a disaster occurs.

[0051] S200. Obtain electricity load forecast data and photovoltaic power generation power forecast data for the target distribution station area under disaster scenarios, and analyze the charging and discharging capabilities of the energy storage system to obtain electricity supply and demand data under disaster scenarios; among which, the electricity load forecast data and photovoltaic power generation power forecast data are obtained through machine learning algorithms.

[0052] On this basis, the coupling relationship between disaster scenarios and power supply and demand data is analyzed to reveal the intrinsic connection between disaster sporadicity and power supply and demand data.

[0053] S300. Analyze the power balance of the target distribution substation at different stages of the disaster. Design a phased photovoltaic and energy storage coordinated control strategy based on the power balance analysis results. Combined with the power supply priority of each user, adopt a phased photovoltaic and energy storage coordinated control strategy to supply power to each user's electrical equipment when a disaster occurs. Finally, evaluate the superiority of the control strategy implementation. If it is not optimal, readjust the power supply priority of each user. The superiority of the control strategy implementation can be evaluated by efficiency, economy, effectiveness, adaptability, sustainability, etc.

[0054] In one embodiment, in step S100, the step of dividing the supply guarantee priority of each user according to the user profile information includes:

[0055] S101, extract multiple characteristic indicators reflecting the user's sensitivity to power outage through user profile information; among them, the user type, credit record, user, etc.

[0056] The system collects user attribute data for DSA user supply priority analysis, including level, supply guarantee frequency, and other archival information, supporting multi-dimensional evaluation. Real-time data such as voltage, active power, reactive power, and current from user meters collected at 96 points daily provides accurate power load and operating status data for DSA disaster emergency supply control strategies.

[0057] Specifically, the user's power outage sensitivity refers to the severity of the impact of power outages on multiple aspects within a DSA power supply area, reflecting the user's important position in regional activities.

[0058] In this embodiment, the DSA user power outage sensitivity includes three dimensions and 10 characteristic indicators, as shown in Table 1:

[0059] Table 1 DSA user power supply interruption sensitivity index table

[0060]

[0061] S102. The development levels of different regions vary significantly, resulting in varying weights for the characteristic indicators of DSA users' sensitivity to power outages. Therefore, to accurately assess the sensitivity of DSA users to power outages in each region, it is necessary to assign weights to each characteristic indicator based on regional characteristics.

[0062] The weighting methods that can be used include: entropy weight method, principal component analysis (PCA), factor analysis, CRITIC method, grey relational analysis, etc.

[0063] S103. Based on the weighting results and the characteristic values ​​of each user's characteristic indicators extracted from their user profile information, a power outage sensitivity score is calculated for each user to determine a comprehensive score for each user's sensitivity to power outages. Each user is assigned a quantitative score reflecting their sensitivity to power outages, thereby providing a decision-making basis for the power supply security importance analysis. In the power supply security importance analysis, the power outage sensitivity score directly reflects the importance of the user. A higher score indicates a higher sensitivity to power outages and a greater importance for the user; conversely, a lower score indicates a lower importance for the user.

[0064] S104: Prioritize power supply according to the power outage sensitivity scores of each user. The priority level can be:

[0065] Divided by score range:

[0066] The power outage sensitivity scores of each user can be divided into different score segments, such as high, medium, and low, or even more detailed grades. This method is simple and intuitive, easy to understand and operate.

[0067] By percentage:

[0068] Based on the distribution of each user's power outage sensitivity score, users are divided into the top X% high-scoring group, the middle Y% medium-scoring group, and the remaining low-scoring group. This method can more accurately reflect the differences between users.

[0069] Cluster analysis:

[0070] Use a clustering algorithm (such as the K-means algorithm) to cluster the power outage sensitivity scores of each user to form different power outage-sensitive user groups.

[0071] In one embodiment, in step S102, the feature indicators are weighted using the entropy weight method (EWM). The entropy weight method (EWM) is a weight adjustment method based on information entropy. In EWM, the weight of the feature indicator is determined by the size of the information entropy. When the information entropy is larger, the dispersion of the corresponding feature indicator is smaller, and the weight of the corresponding feature indicator is also smaller; conversely, when the information entropy is smaller, the weight of the corresponding feature indicator is larger.

[0072] The specific steps of using the entropy weight method to weight the characteristic indicators are as follows:

[0073] Normalize the raw data:

[0074] ;

[0075] in, is the original data, specifically The sample in The indicator value on the characteristic indicator, It is The maximum value of the characteristic index, It is The minimum value of the characteristic index; is the standardized data, specifically The sample in Standardized data on characteristic indicators;

[0076] Calculate the proportional coefficient of each sample under each characteristic index:

[0077] ;

[0078] in, Indicates the The sample in The proportional coefficient under the characteristic index;

[0079] Calculate the first or Information entropy of characteristic indicators e SL.η for:

[0080] ;

[0081] in, n SL is the number of characteristic indicators.

[0082] Calculate the or The information entropy redundancy of characteristic indicators g SL.η for:

[0083] ;

[0084] Calculate the or The weight of the characteristic index w SL.η for:

[0085] .

[0086] Based on the above embodiment, in step S103, the formula for calculating the score value of each user's power outage sensitivity is: b SL.η Specifically:

[0087] ;

[0088] in, For the or The index value of a characteristic index.

[0089] In one embodiment, in step S103, the steps of performing user supply guarantee importance analysis are as follows:

[0090] Assume that the target distribution area has m SL Users are evaluated using the characteristic indicators in Table 1 above to obtain the index value of each characteristic indicator. The importance of user supply in the target distribution area can be evaluated using the matrix D express:

[0091] ;

[0092] in, Evaluate the scores of various indicators for the first user in DSA; is the mth SL The evaluation scores of each indicator of each user; the evaluation scores of each indicator can be calculated by weighting the weight of the feature indicator calculated in the above step S102 and the indicator value of the corresponding feature indicator.

[0093] In one embodiment, for step S104, clustering is used to prioritize the supply guarantee. This embodiment specifically uses the K-Medoids algorithm for clustering. The K-Medoids algorithm is a clustering algorithm based on data partitioning and is an improved version of the K-Means algorithm. It can effectively solve the problem of sensitivity to outliers in K-Means clustering. The K-Medoids algorithm is different from the K-Means algorithm in determining the center point. K-Medoids uses the point with the smallest distance between each center point and other points in the cluster as the center to improve the ability to cluster abnormal data points. Therefore, this embodiment uses the K-Medoids algorithm to classify user supply guarantees;

[0094] The cost function is used to evaluate the quality of user supply guarantee classification, the optimal cluster division and center point are solved by repeated iteration, and the clustering error of Euclidean distance is used to evaluate the quality of user supply guarantee classification clustering results. K-Medoids algorithm user supply guarantee clustering error d SSE for:

[0095] ;

[0096] in, x KE Provide users with sample points in the clustering data space; k KE Guaranteed number of randomly selected objects in the clustering dataset for users; O KE.j For the j Cluster centers.

[0097] The specific implementation process is as follows:

[0098] A100, randomly selected from the user supply guarantee data set k KE An object is used as the initial representative object of the user supply guarantee classification;

[0099] A200, calculating the Euclidean distance between the remaining user guaranteed objects and the representative object, and dividing them into the cluster of the closest user guaranteed objects according to the distance;

[0100] A300, randomly select a non-representative object O rand ;

[0101] A400, using non-representative objects O rand Replace user supply clustering j Cluster centers O KE.j The total cost of the cost function uses the user-guaranteed clustering error d SSE calculate;

[0102] A500, if the user's power sample exchange cost value is negative, then use O rand replace O KE.j , and form a new object set;

[0103] A600. Repeat steps A200-A500 until the total cost value does not change.

[0104] According to the clustering results, users are classified and the average score of the power outage sensitivity assessment of each type of users is used as the assessment benchmark score. d KE The calculation formula is as follows:

[0105] ;

[0106] in, m KE is the number of DSA users in this category after clustering using the K-Medoids algorithm; d pcs.j For the j The DSA user power outage sensitivity evaluation score of each user can be specifically obtained by using the formula for calculating the score value of the power outage sensitivity of each user in step S103 in the above embodiment.

[0107] Each cluster is ranked by its benchmark score, and its priority level is determined. This priority level is then assigned to all users within each cluster. Specifically, user priority is determined based on the benchmark score of each cluster: higher scores give higher priorities, while lower scores give lower priorities.

[0108] Because disasters rarely occur, the time for emergency power outages is shorter than normal, resulting in a relatively limited amount of available data samples for the power load and photovoltaic power generation in the DSA area during disaster scenarios. Therefore, in one embodiment, in step S200, the method for obtaining power load forecast data and photovoltaic power generation forecast data using a machine learning algorithm is specifically as follows:

[0109] The small sample learning method is used to predict the electricity load and photovoltaic power generation in the DSA area, specifically:

[0110] Generative adversarial networks (GAN) are used to convert existing photovoltaic power generation and DSA electricity load or generate new data to expand the sample size of the power and load forecasting model training set, thereby improving the model's prediction accuracy and generalization ability for photovoltaic power generation and DSA electricity load.

[0111] Through adversarial training between the generator and the discriminator, GAN can generate synthetic data that closely resembles the distribution of real data, which has the advantage of generating data close to real data. Therefore, GAN is suitable for data enhancement of photovoltaic power generation and DSA power load data in small sample scenarios.

[0112] In the process of GAN generating photovoltaic power generation and DSA power load data, the generator receives random noise of photovoltaic power generation and DSA power load as input and converts it into data close to the distribution of real data of photovoltaic power generation and DSA power load; the discriminator receives real data and generated data and tries to distinguish between the two. Through continuous optimization, the generator gradually generates more realistic data, while the discriminator continuously improves its discrimination ability. In the photovoltaic power generation and DSA power load data generation task, GAN generates high-quality synthetic data through adversarial training, effectively expanding the training sample set, thereby improving the performance of the prediction model. The specific process is as follows: Figure 2 shown.

[0113] After obtaining sufficient sample data, PV power generation and DSA power load are forecasted. For PV power generation forecasting, the input data consists of meteorological data and PV component data. Meteorological data includes solar irradiance, humidity, and temperature, derived from 1x1 square kilometer regional data from Numerical Weather Prediction (NWP). PV component data includes PV module conversion rate, PV module tilt angle, and PV combination loss, derived from the PV management system. For DSA power load forecasting, the input data includes meteorological data, historical load data, and economic data. The meteorological data is the same as for PV power generation forecasting; the historical load data comes from the electricity consumption information collection system; and the economic data is GDP data.

[0114] The prediction model uses an LSTM (Long Short-Term Memory) neural network. The LSTM consists of an input gate, a forget gate, a memory unit, and an output gate. In this embodiment, the input gate is used to input meteorological and PV module data for PV power forecasts, and meteorological data, historical load data, and economic data for DSA power load forecasts. The forget gate optionally records input data for PV power and DSA power load forecasts. The memory unit is used to store and update information within the LSTM. The output gate outputs the PV power and DSA power load forecast data.

[0115] Photovoltaic power generation prediction data output by LSTM p PV.pred for:

[0116] ;

[0117] Among them, tan is the hyperbolic tangent activation function used by the forget gate in the LSTM for photovoltaic power generation and DSA power load prediction: c The activation function in LSTM for photovoltaic power generation and DSA power load prediction, c Sigmoid function can be used; p PV.hist is the historical photovoltaic power generation data; p PV.FG The photovoltaic power data after passing through the forget gate of the LSTM network; β met It is the meteorological data of NWP; β eff PV module data.

[0118] DSA power load forecast data output by LSTM p EL.pred for:

[0119] ;

[0120] in, p EL.hist It is the historical electricity load data of DSA; p EL.FG The DSA power load data passes through the forget gate of the LSTM network; β eff It is the GDP data of the region where DSA is located.

[0121] In one embodiment, in step S200, analyzing the charge and discharge capacity of the energy storage system specifically involves analyzing energy storage loss, which refers to power loss caused by physical and chemical factors during the charge and discharge process of the energy storage device. Energy storage loss reduces the overall efficiency of the DSA emergency power supply.

[0122] Energy storage loss p ES.loss Specifically:

[0123] ;

[0124] in, p ES.chg It is the internal resistance loss caused by the energy storage system during the charging and discharging process; p ES.sdl The loss is caused by the self-reaction of active substances in the energy storage system; p ES.cv It is the loss of AC-DC conversion in the energy storage system.

[0125] In one embodiment, in step S200, the step of analyzing the coupling relationship between the disaster scenario and the power supply and demand data includes:

[0126] Introducing contingency variables that induce the occurrence of the corresponding disaster scenario;

[0127] Extract the random variables of the supply guarantee resources and the differential variables of the supply guarantee demand from the power supply and demand data, where the random variables of the supply guarantee resources are the random variables of photovoltaic power, and the differential variables of the supply guarantee demand are the differential variables of electricity load;

[0128] Taking wildfire disaster scenarios as an example, during emergency power supply assurance during wildfire disasters, when a power supply company receives a wind warning of force 7 or higher from the local meteorological bureau, or detects winds of force 7 or higher through micro-meteorological devices installed on transmission lines, it will immediately initiate emergency power outages and restore power after the wind subsides. Therefore, winds of force 7 or higher are a triggering factor for wildfire disasters. By exploring the coupling between the sporadic occurrence of winds of force 7 or higher, the randomness of photovoltaic power generation, and the variability of power load, we can provide a basis for emergency power supply assurance strategies in wildfire disaster scenarios, thereby improving power supply capacity and reducing the risk of power outages for critical users during wildfire disasters.

[0129] Copula function is a method to describe the correlation structure and dependency of multiple random variables. Copula function associates the joint distribution function with the marginal distribution functions of other variables to analyze their correlation. Due to the sporadic nature of gale of level 7 and above, the randomness of photovoltaic power and the inconsistent dimension of security demand, this embodiment selects Copula function to couple the sporadic nature of gale of level 7 and above, the randomness of photovoltaic power and the security demand, and calculates the three-variable joint distribution function between the three. H corr for:

[0130] ;

[0131] in, f wind ( p wind ) is the marginal distribution function of the sporadic variable of gale force 7 and above; f PV ( p PV ) is the marginal distribution function of the random variable of photovoltaic power; f EL ( p EL ) is the marginal distribution function of the electricity load difference variable; s It is a ternary Copula function, which connects the three marginal distribution functions of the sporadic occurrence of gale force 7 and above, the randomness of photovoltaic power and the difference in electricity load to form a ternary joint distribution function.

[0132] In the specific implementation, it is necessary to first model the sporadic nature of gale force 7 and above, the randomness of photovoltaic power and the difference in electricity load, and obtain their respective marginal distribution functions. f wind ( p wind ), f PV ( p PV )and f EL ( p EL ). Then, by selecting the appropriate ternary Copula function s , connect the three marginal distribution functions to construct the three-variable joint distribution function H corr This step can accurately describe the Copula function of the correlation and dependence between variables to ensure the accuracy and reliability of the joint distribution function.

[0133] In one embodiment, in step S300, the step of analyzing the power balance of the target distribution station area at different stages of the disaster is specifically as follows:

[0134] S311. Divide the entire disaster period into different phases. According to statistics on power outages during DSA disasters, the DSA emergency power supply period typically lasts from several hours to several days. During this period, the continuity of power supply is directly related to the maintenance of production and livelihoods in the region. Since photovoltaic power generation is affected by solar radiation, and user load is influenced by electricity consumption behavior, it varies significantly in different time periods. For example, photovoltaic power generation is higher during daytime when solar radiation is strong, and is almost zero at night. The output power of energy storage depends on its charging and discharging status, capacity limitations, and scheduling strategy. User load is determined by user production and living habits. Therefore, to more accurately respond to the dynamic changes in power supply and demand, it is necessary to divide the disaster emergency power supply period into multiple phases and conduct a detailed analysis of the power supply and demand balance in each phase. By assessing the power balance between DSA user demand and the power supply capabilities of photovoltaic and energy storage, targeted power scheduling strategies can be formulated to optimize resource allocation and ensure stable operation of the power system during DSA wildfire disasters.

[0135] S312. Solve the power balance problem for the target distribution area at different stages using dynamic programming. Dynamic programming (DP) is an operations research method that optimizes multi-stage decision-making processes. DP decomposes the power balance problem for the target distribution area into several subproblems, such as the photovoltaic control subproblem, the energy storage control subproblem, and the user power demand control subproblem. The DSA power balance solutions for these subproblems are then used to solve the DSA power balance solution for the entire stage.

[0136] Among them, the first objective function of the dynamic programming method is constructed with the goal of ensuring that the power provided by the supply resources can meet the electricity demand to the maximum extent; the supply resources include photovoltaic and energy storage systems; the first objective function z DP for:

[0137] ;

[0138] in, n T The period for emergency supply guarantee; p EL.pred.j For the j The DSA power load demand for each time period is obtained through the power load forecast data obtained in step S200.

[0139] S313. By dynamically recursively solving the first objective function, the optimal solution for the power provided by the supply resources at different stages is obtained, and the supply strategy for the optimal solution is obtained. V DP.t for:

[0140] ;

[0141] in, p EL.pred.t is the DSA power load demand in the current period; V PV.ES.t+1 A strategy to provide power to the largest photovoltaic and energy storage system from the next period to the final emergency power supply moment.

[0142] S314, and recursively calculate the optimal solution of the power provided by the supply guarantee resources in each stage to obtain the power balance status of the target distribution station area in each stage.

[0143] In one embodiment, in step S300, the steps of designing a phased photovoltaic and energy storage coordinated control strategy based on the power balance assessment results and the power supply priority of each user are specifically as follows:

[0144] S321, based on the power balance of the target distribution area at each stage and the constraints of photovoltaic and energy storage system capacity, the target distribution area can ensure the cost of supply when a disaster occurs. C tot The second objective function with minimum as the goal is:

[0145] ;

[0146] in, n op The number of phases of emergency supply guarantee for DSA disasters; p ld.k 、 p pv.k 、 p es.k Respectively k DSA user load, photovoltaic power generation power, and energy storage discharge power during each supply guarantee phase; c ld.k 、 c pv.k 、 c es.k Respectively k Cost functions of DSA users, photovoltaic power generation, and energy storage discharge in each supply guarantee phase; p pv.l.k 、 p es.l.k Respectively k The lower limits of photovoltaic power generation and energy storage discharge power in each supply guarantee phase; p pv.h.k 、 p es.h.k Respectively k The upper limit of photovoltaic power generation power and ES discharge power in each supply guarantee stage.

[0147] Among them, the cost of photovoltaic power generation consists of cpv for:

[0148] ;

[0149] in, c pv.inv The unit investment cost per kilowatt-hour of electricity generated for PV construction; c pv.om is the operating and maintenance cost per PV kilowatt-hour of electricity generated.

[0150] Energy storage discharge cost structure c es for:

[0151] ;

[0152] in, c es.cyc is the cost of using the ES cycle life; c es.chg The internal resistance loss cost caused by the ES charging and discharging process; c es.sdl The loss cost caused by the self-reaction of active substances inside ES; c es.cv is the power loss cost of ES AC / DC conversion. C es.inv The unit investment cost of kilowatt-hour discharge for ES construction;

[0153] Costs for DSA users c ld Composition:

[0154] ;

[0155] in, c ld.elecp The electricity price cost for DSA users.

[0156] S322: In each supply guarantee phase, repeatedly solve the second objective function and execute the above steps S312 to S313 at the same time to obtain the control strategy for the coordination of photovoltaic and energy storage systems in each phase ( V DP.t ).

[0157] The present application also proposes an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for ensuring emergency power supply in a distribution area as described in any embodiment of the present invention is implemented.

[0158] The present application also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for ensuring emergency power supply in a distribution station area as described in any embodiment of the present invention.

[0159] In the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can represent: a, b, c, a and b, a and c, b and c or a and b and c, where a, b, c can be single or multiple.

[0160] Those skilled in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented using a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0161] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0162] In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), magnetic disk or optical disk, and other media that can store program code.

[0163] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for ensuring emergency power supply in a distribution area, characterized in that: The following steps are involved: Obtain user profile information of the target distribution area and prioritize the supply of each user based on the user profile information; Obtain power supply and demand data under disaster scenarios, analyze the coupling relationship between disaster scenarios and power supply and demand data, and analyze the power balance of target distribution substations at different stages of the disaster; Design a phased photovoltaic and energy storage coordinated control strategy based on power balance analysis results, and provide power to each user's electrical equipment in the event of a disaster, taking into account each user's power supply priority. The acquisition of power supply and demand data in disaster scenarios includes: The target distribution substation area's power load forecast data under disaster scenarios, photovoltaic power generation forecast data, and energy storage system charging and discharging capabilities serve as power supply and demand data; Among them, electricity load forecast data and photovoltaic power generation forecast data are obtained through machine learning algorithms; The step of analyzing the coupling relationship between the disaster scenario and the power supply and demand data includes: Introducing contingency variables that induce the occurrence of the corresponding disaster scenario; Extract the random variables of the supply guarantee resources and the differential variables of the supply guarantee demand from the power supply and demand data, where the random variables of the supply guarantee resources are the random variables of photovoltaic power, and the differential variables of the supply guarantee demand are the differential variables of electricity load; Carry out coupling analysis, specifically: The Copula function is used to calculate the joint distribution function of three variables among the contingency of inducing factors, the randomness of supply resources, and the diversity of supply demand.

2. A method for ensuring emergency power supply in a distribution area according to claim 1, characterized in that: The step of dividing the supply guarantee priority of each user according to the user profile information includes: Extract multiple characteristic indicators reflecting the user's sensitivity to power outages through user profile information; Assign weights to each characteristic indicator; Calculate the score of each user's power outage sensitivity based on the weighting results and the characteristic values ​​of each user's characteristic indicators extracted from the user profile information; The power supply priority is divided according to the score of each user's power supply interruption sensitivity.

3. A method for ensuring emergency power supply in a distribution area according to claim 2, characterized in that: In the step of weighting each characteristic indicator: The entropy weight method is used to assign weights to each characteristic index.

4. The method for ensuring emergency power supply in a distribution area according to claim 2, characterized in that: The step of prioritizing power supply according to the power outage sensitivity scores of each user includes: Clustering is performed based on the score of each user's power outage sensitivity; Based on the clustering results, the power outage sensitivity scores of all users in each cluster are obtained, and the evaluation benchmark scores for each cluster are calculated based on them. The supply guarantee priority of each cluster is determined based on the size of the evaluation benchmark scores of each cluster, and the corresponding supply guarantee priority is assigned to all users in each cluster.

5. The method for ensuring emergency power supply in a distribution area according to claim 1, characterized in that: The steps for analyzing the power balance of the target distribution station area at different stages of the disaster are as follows: Divide the entire disaster period into different stages; A dynamic programming approach will be used to solve the power balance problem for target distribution substations at different stages. The first objective function of the dynamic programming approach is constructed with the goal of ensuring that the power provided by guaranteed supply resources can maximize the power demand. Guaranteed supply resources include photovoltaic and energy storage systems. By dynamically recursively solving the first objective function, the optimal solution for the power provided by the supply resources at different stages is obtained; The power balance of the target distribution station area in each stage is obtained by recursively calculating the optimal solution of the power provided by the stage-by-stage supply guarantee resources.

6. The method for ensuring emergency power supply in a distribution area according to claim 5, characterized in that: The steps for designing a phased photovoltaic and energy storage coordinated control strategy based on the power balance analysis results are as follows: A second objective function is constructed to minimize the cost of ensuring power supply to the target distribution area when a disaster occurs, based on the power balance of the target distribution area at each stage and the capacity constraints of the photovoltaic and energy storage systems. Solve the second objective function and obtain the control strategies of photovoltaic and energy storage systems at each stage.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the distribution station area emergency power supply method according to any one of claims 1 to 6 is implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method for ensuring emergency power supply in a distribution area as described in any one of claims 1 to 6 is implemented.

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