A method for modeling power utilization load considering topological correlation and network constraint

Abstract

The application relates to the technical field of power systems, in particular to a power distribution and utilization load modeling method considering topological correlation and network constraints, which comprises the following steps: S1, reading system: reading power system data, analyzing the topological structure of the power system, and obtaining power generation power data of power stations; S2, data rough check: performing preliminary checking on the data; S3, data weight method based on credibility grading: giving corresponding weights to data of different sources; S4, data second step correction: combining network constraints and topological relations, constructing an objective function, optimizing power distribution, introducing node power balance, upper and lower limits of power generation, load constraints and line capacity constraints, and obtaining a power distribution and utilization load model meeting the topological correlation and network constraints through linear objective programming iteration; the application improves the stability and reliability of power system operation, and provides effective support for safe scheduling and optimal operation of the power distribution and utilization system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power systems, in particular to a power and utilization load modeling method considering topological correlation and network constraints. BACKGROUND

[0002] With the continuous growth of power demand and the increasing complexity of power systems, accurate load modeling is crucial for power system monitoring and prediction. Traditional power and utilization load modeling methods mainly rely on sensors to measure current, voltage, power and other parameters to obtain real-time load information. However, due to uneven distribution of sensors, it is difficult to accurately obtain load information in some areas. In addition, sensors may fail or be disturbed, affecting the accuracy and integrity of the data. In actual application, there are also cases where some grid node information is unknown, making it difficult for traditional load modeling methods to be applicable. Therefore, a modeling method that combines topological correlation and network constraints is needed to more comprehensively reflect the load characteristics of the power system.

[0003] Current load modeling techniques have high requirements for data integrity and usually rely on a large amount of historical load data and measurement information. In actual power system operation, due to difficulties in data collection, insufficient equipment coverage, and other problems, complete data is difficult to obtain, leading to a decrease in model accuracy, and even in the case of extreme data scarcity, an effective model cannot be constructed. In addition, existing methods mostly focus on load prediction accuracy, without fully considering the topological structure and network constraints of the power system, making it difficult for the model to accurately reflect the actual operating state of the system, thereby affecting its effectiveness in practical applications. SUMMARY

[0004] The present application provides a power and utilization load modeling method considering topological correlation and network constraints, which can effectively reduce the amount of data required and improve modeling efficiency by only using part of the disclosed power transmission system station data. This method fully considers the influence of power grid topological structure and network constraints on load power, ensuring that the constructed model strictly follows the system power flow calculation rules, thereby avoiding the problem of mismatch between the model and the actual operating conditions of the system, and improving the accuracy and reliability of the model.

[0005] A power and utilization load modeling method considering topological correlation and network constraints, comprising the following steps:

[0006] S1, reading the system: reading the topological structure, nodes, lines and generator data of the power system, and storing them in different data structures, initializing the system information, calculating using the direct current power flow model, analyzing the topological structure of the power system, and obtaining the power generation data of the station from the power market;

[0007] S2, data rough check: based on the credibility classification, the data is preliminarily checked, and the data rationality is judged in combination with the geographic information of the node, including checking the geographic conditions of the energy storage node and the power flow direction of the load node;

[0008] S3, data weight method based on credibility classification: different sources of data are given corresponding weights according to the data credibility level;

[0009] S4, data second step correction: combining network constraints and topological relations, a target function is constructed, power distribution is optimized, node power balance, upper and lower limits of power generation, load constraints and line capacity constraints are introduced, and linear target programming iteration is used to solve, and a power distribution and load model meeting the topological association and network constraints is obtained.

[0010] Optionally, the reading system in S1 comprises:

[0011] S11, data reading and system information initialization: reading the topological structure, nodes, lines and generator data of the power system, storing them in different data structures, and using the direct current flow model for calculation;

[0012] The direct current flow model is represented as:

[0013] ;

[0014] Wherein, is the node injection power, is the node voltage phase angle;

[0015] S12, element correlation analysis: the connection relationship between nodes and lines is constructed, the power grid topological structure is analyzed, the connection mode is clarified by checking the matching of nodes and lines, and the attacked lines and nodes are identified;

[0016] S13, power plant data collection: obtaining the power generation data of the power plant from the power market trading center;

[0017] S14, data credibility classification: according to the data source, the data credibility is classified into first level, second level, third level and fourth level;

[0018] S15, node classification: according to the power market transaction data, the nodes are divided into power generation nodes, load nodes and energy storage nodes.

[0019] Optionally, the data credibility classification in S14 comprises:

[0020] S141, first level credibility: the first level credibility data is derived from the winning data in the real-time bidding of the power market, and the credibility is the highest;

[0021] S142, secondary credibility: the secondary credibility data is calculated based on the characteristics of the load curve, including the transformer peak load rate of the substation and the main transformer capacity;

[0022] S143, tertiary credibility: the tertiary credibility data is obtained by power estimation based on the industry type and industry scale, by analyzing the relationship between the output of various products and the power of the plant station, and establishing a linear model using mathematical fitting method, to calculate the power demand of the related area in the unknown system, expressed as:

[0023] ;

[0024] wherein, is the power required by the production plant station j of the B type commodity when producing number of commodities, is the best slope obtained by linear fitting of the relationship between output and power, is the inherent power consumption of the production plant station;

[0025] S144, fourth credibility: the fourth credibility level data is used to fill in the power data vacancy of small substations and unknown nodes, by referring to the main transformer capacity of substations of the same voltage level, to calculate the minimum and maximum values of the main transformer capacity of the target substation, the minimum value is based on half of the single main transformer capacity, and the maximum value follows the N-1 rule, expressed as:

[0026] ;

[0027] wherein, represents the single main transformer capacity of the substation of the same voltage level;

[0028] ;

[0029] wherein, represents the maximum credible capacity of each transformer of the substations of the same level in the system.

[0030] Optionally, the data rough check in S2 includes:

[0031] S21, data classification and preliminary check: based on different credibility data sources and node types, combined with geographic information, the data of different credibility levels are preliminarily checked;

[0032] S22, data rationality check: for energy storage nodes and load nodes, check the physical law of their classification, including that pumped storage power stations in energy storage nodes have terrain drop and the power flow of load nodes is negative, if the data is not consistent with the physical law, it is determined as unreasonable data;

[0033] S23, data adjustment and correction: if high-confidence data is identified as unreasonable, the data is removed and corresponding data is supplemented from low-level confidence sources.

[0034] Optionally, the data weight method based on confidence level in S3 includes assigning weights to the first-level confidence data , to the second-level confidence data , and to the third-level confidence data , wherein .

[0035] Optionally, the second-step data correction in S4 includes:

[0036] S41, constructing a target function: the target function includes a main target of minimizing the total deviation of power plant output from the reference power value and a secondary target of minimizing the sum of the deviation ratios of each plant and the reference power value;

[0037] S42, giving a constraint condition: in the optimization process, the operation constraints of the power system are given, including node power balance constraints, upper and lower limits of power generation, load constraints, line state inequality constraints, class I information target constraints, class II information target constraints, class III information target constraints, and class IV information target constraints;

[0038] S43, linear target programming solution: a linear target programming method is used to develop iterative solution operations, based on the constructed target function and the given constraint condition, an optimal power distribution scheme is solved by using an iterative calculation method, and a power load model is obtained.

[0039] Optionally, the target function is represented as:

[0040] ;

[0041] wherein the total number of plants of the three types of confidence levels are , and , is the kth valid plant reference power value information, is the positive and negative deviation of the power flow calculation data and the kth plant reference power value information.

[0042] Optionally, the given constraint condition in S42 includes:

[0043] S421, node power balance constraint: based on Kirchhoff's current law, for each node i, the total power flowing into the node is equal to the total power flowing out of the node, and there is a power balance relationship at node i, which is represented as:

[0044]

[0045] where, represents the amount of load at the i-th node , t is the total number of nodes, represents the flow variable of the j-th line , h is the total number of lines, represents the sum of all line flows into node i, represents the sum of all line flows out of node i is the known power injection or outflow at node i;

[0046] S422, generation upper and lower limit constraints: the active power output of a generator is between its minimum and maximum allowed ranges, the constraint for the k-th generator is represented as:

[0047] ;

[0048] where, represents the active power output of a generator, represents the minimum active power output of a generator, represents the maximum active power output of a generator;

[0049] S423, load constraints: for each node i , there is an inequality constraint, represented as:

[0050] ;

[0051] where, is the lower limit value of the load adjustment amount of node i, if there is a load lower limit constraint, , if there is no such constraint, ;

[0052] is the upper limit value of the load adjustment amount of node i, there is an inequality constraint, represented as:

[0053] ;

[0054] S424, line state inequality constraints: in a power system, for each line , there is a line capacity lower limit constraint represented as:

[0055] ;

[0056] where, is the flow lower limit value of line j;

[0057] the line capacity upper limit constraint is represented as:

[0058] ;

[0059] wherein, is the flow upper limit value of line j;

[0060] S425, the first type of information target constraint: the first type of information station is located at the end of the network, and only one node is contained on the low-voltage side of the station, and the power value information of the station is embodied as the algebraic sum of the load value and the output of the end node in the power flow calculation, which is expressed as:

[0061] ;

[0062] S426, the second type of information target constraint: the second type of information station is located at the end of the network, and two nodes are contained on the low-voltage side of the station, and the power value information of the station is embodied as the sum of the power values of the two end nodes in the power flow calculation, which is expressed as:

[0063] ;

[0064] S427, the third type of information target constraint: the third type of information station is located in the middle of the network, and the low-voltage side does not directly carry load, and the power value information of the station is embodied as the sum of the branch flows of all branches from the high-voltage side node m to the low-voltage side node n in the power flow calculation, which is expressed as:

[0065] ;

[0066] wherein, refers to the line set with node n as the first node and m as the last node;

[0067] S428, the fourth type of information target constraint: the fourth type of information station is located in the middle of the network, and the low-voltage side does not directly carry load, and the power value information of the station is embodied as the sum of the branch flows of all branches from the high-voltage side node i to the two low-voltage side nodes m and n in the power flow calculation, which is expressed as:

[0068] .

[0069] The beneficial effects of the present application are:

[0070] In the present application, through the power grid topology structure, the station reference power data and the data calculation of different confidence levels, the unknown area load is accurately modeled, the dependence on real-time measurement data is significantly reduced, the model precision decline problem caused by data loss is effectively solved, in addition, through the hierarchical confidence assessment of data and the combination of data weight calculation, the reliability of data processing in the modeling process is improved, so that even in the case of limited data, a high-precision load model can be constructed.

[0071] The application, by fully considering the topology structure and network constraint of the power grid in the modeling process, constructs an optimization objective function with power flow calculation as the core, and solves it through linear objective programming, ensures that the result of the load modeling conforms to the power grid operation constraints, such as node power balance, generator output upper and lower limit, load regulation range and line power flow capacity, avoids the calculation error caused by ignoring the network constraint, in addition, by optimizing the objective function to minimize the total power deviation, the accuracy of the load modeling is improved, the model is more in line with the actual operation of the power grid, thereby improving the stability and reliability of the power system operation, and providing effective support for the safe scheduling and optimal operation of the power distribution system. BRIEF DESCRIPTION OF DRAWINGS

[0072] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only a part of the present application, and other drawings can also be obtained by those skilled in the art without any creative effort.

[0073] Figure 1 The modeling method flowchart of the embodiment of the present application. DETAILED DESCRIPTION

[0074] The present application will be described in detail below in conjunction with the drawings and specific embodiments. It should be noted that in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement them; and the drawings are only used to describe the embodiments more specifically, and are not intended to specifically limit the present application.

[0075] It should be noted that in the specification, "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like indicate that the described embodiments can include a specific feature, structure or property, but not necessarily every embodiment includes the specific feature, structure or property. In addition, when a specific feature, structure or property is described in conjunction with an embodiment, it should be within the knowledge of those skilled in the art to realize this feature, structure or property in conjunction with other embodiments (whether or not explicitly described).

[0076] Generally, the terms can be understood at least partly from the use in the context. For example, depending at least partly on the context, the term "one or more" used herein can be used to describe any feature, structure or property in the singular sense, or can be used to describe a combination of features, structures or properties in the plural sense. In addition, the term "based on" can be understood as not necessarily conveying a set of exclusive factors, but can instead, depending at least partly on the context, allow the existence of other factors not necessarily explicitly described.

[0077] As Figure 1 shown, a load modeling method considering topological association and network constraints includes the following steps:

[0078] 1. Read the system:

[0079] The data processing of the power system and the initialization operation of the system state are implemented, laying a foundation for further analysis and evaluation of the power system. Multiple aspects of the power system are considered, including nodes, lines, generators, and module elements, and the related data is processed and associated.

[0080] 1.1. Data reading and system information initialization:

[0081] During operation execution, a flag variable is assigned a specific initial value, which is intended to set the corresponding state identifier for subsequent system information processing. Through special information reading means, the information of the system is obtained, which covers important data of the system in the past operation process. The obtained information is stored in different data structures, and the number of elements of the main components in the system is counted using the corresponding statistical method, which constitutes the basic framework of the power system and provides necessary data support for further operation.

[0082] As the scale of the power system expands, the number of nodes and lines increases dramatically, and the calculation burden of AC power flow calculation will increase significantly. Using DC power flow as an approximate model can speed up the solution. DC power flow is based on some simplifying assumptions and linearizes the power flow equation in AC power system. Therefore, in the subsequent power flow calculation in this paper, the operation model of DC power flow will be used:

[0083] (1)

[0084] where is the node injection power, is the node voltage phase angle.

[0085] DC power flow ignores line resistance and approximates voltage amplitude to 1, and ignores the effect of reactive power. Therefore, when establishing the constraint condition, due to the use of DC power flow, there is no upper and lower limit constraint of node voltage and reactive power constraint, and the line capacity inequality constraint only considers active power.

[0086] 1.2. Element association analysis:

[0087] In the process of studying unknown power system, it is necessary to clearly show its topology, and create the corresponding storage structure to build the connection between nodes and lines. Through the analysis of the historical information of the system, the nodes and lines are carefully checked. According to the matching conditions met by the node and line elements, the required information is stored in a specific list in order, so that the connection of the node as the first node or the last node can be clearly shown. It helps us to grasp the connection architecture of the system and provides convenience for subsequent research. At the same time, in the process of analyzing the network topology, we can also consider some possible attacked lines and node structures, which has far-reaching significance for the safe and stable operation of the power system.

[0088] 1.3, Plant data collection:

[0089] In the study of power system, we often face complex systems with unknown information. For such systems, we can obtain the power generation data of the plant in each specific time period from the trading center of the system power market. These data can be used as the reference power of the plant, providing key basic information for subsequent system analysis and modeling. The plant reference power reflects the power generation capacity and actual power generation status of the plant at different time periods, which is of great significance to understand the power supply situation of the system. The accuracy and integrity of the plant reference power directly affect the reliability and effectiveness of subsequent research.

[0090] 1.4, Data credibility classification:

[0091] In order to more accurately use data for system analysis, it is essential to classify the credibility of data sources.

[0092] The first level of credibility: the highest level of credibility comes from the real-time bidding of the power market: the winning data. From the perspective of power plant, the core purpose of power plant participating in the power market transaction is to obtain economic benefits by selling electricity. In the transaction process, strictly delivering electricity according to the contract is the key to ensuring continuous income. Once the contract is violated, not only will the current transaction fail, but also the market reputation will be damaged, and the main source of income will be lost. From the perspective of users, users will report the required amount of electricity according to their actual expected electricity demand when participating in market bidding, considering cost control. Because if the amount is reported randomly, it will increase the cost of electricity paid to the power plant, greatly increasing the cost of electricity. Therefore, based on the interests of both parties in the market transaction, this part of data has high credibility under normal circumstances.

[0093] Secondary credibility: Although the winning data of the electricity market has important reference value, due to the complexity of the real power system operation, such as unit failure, line failure and other unexpected situations occur from time to time, and there may be speculative behavior of some market participants, these factors make the winning data of the electricity market cannot 100% accurately reflect the actual power flow of the system, there is often a certain degree of deviation between the real power flow. This shows that there is an untrustworthy possibility compared to the true data. Therefore, it is necessary to introduce a second credibility level: the power calculated based on some characteristics of the load curve. These characteristics cover the key information such as the transformer peak load rate and main transformer capacity of large or important substations in the system. Through in-depth analysis and research of these characteristics, the relevant power data can be accurately calculated.

[0094] Third level of credibility: mainly focusing on load, it is based on the industry type and the industry scale to estimate the power. In the actual power system, the end of the high-voltage distribution network is often distributed with some large similar enterprise groups. For these enterprise groups, we can use the system data we have mastered to deeply analyze the internal relationship between the output of various products and the reference power value of the plant. After a large amount of data collection, sorting and analysis, a linear relationship curve can be fitted by using mathematical fitting method.

[0095] (2)

[0096] wherein, is the power required by the production plant j of the B-type commodity when producing number of commodities, can be replaced by annual output value or monthly commodity output, is the best slope obtained by linear fitting of the relationship between output and power, is the inherent power consumption of the production plant.

[0097] This curve can accurately reflect the quantitative relationship between product output and plant reference power. In this way, when facing unknown systems, we only need to obtain the output information of the relevant products in the system, and then we can accurately calculate the power value of the unknown system at that place according to the fitted linear relationship curve. This power estimation method based on industry characteristics and scale fully considers the power consumption characteristics and laws of different industries, and provides an effective way for load data acquisition, further enriching the sources of system power data.

[0098] The fourth level of reliability: the data of the above three levels of reliability can cover most of the nodes of the system to a large extent, and the data generated presents a redundant structure, which provides a more comprehensive and reliable data basis for system analysis. However, it is inevitable that for some small substation nodes, there may be missing data, and there are also some nodes whose details are completely unknown. In order to fill in these data gaps and improve the data information of the system, we introduce the fourth level of reliability data source. The data of this level is calculated according to the voltage level and the situation of other substations of the same level. By investigating the main transformer capacity of other substations of the same voltage level, the minimum and maximum values of the main transformer capacity of the target substation are estimated. The minimum value is based on half of the capacity of a single main transformer of the same voltage level:

[0099] (3)

[0100] represents the capacity of a single main transformer of the same voltage level substation.

[0101] This is a reasonable estimate of the lower limit of the main transformer capacity of small substations based on general power system design and operation experience, under the premise of ensuring a certain power supply reliability. The maximum value is calculated according to the N-1 system operation requirement, that is, according to the maximum reliable capacity of other known substations of the same level. The N-1 criterion is an important safety criterion for power system operation, which requires the system to remain normal operation when any device in the system is out of operation.

[0102] (4)

[0103] to represents the maximum reliable capacity of each transformer of the n substations of the same level in the system.

[0104] The power data calculated in this way can provide a reasonable power estimate for small substation nodes with missing data and unknown nodes.

[0105] 1.5, node classification:

[0106] Through in-depth analysis of the transaction volume data of the electricity market, the power consumption of each load can be obtained. These data reflect the consumption of electricity at the load side, and based on this, the power consumption terminal, i.e. the load node, can be identified.

[0107] From the perspective of bidding information, the nodes participating in bidding and selling electricity to the system can be determined as power generation nodes. These nodes are the power supply sources of the power system, and their power generation capacity and stability directly affect the power supply capacity of the system. The nodes that bid to buy electricity are clearly load nodes, and the changes in their electricity demand play an important role in the supply and demand balance of the system. In addition, the nodes that indicate in the bid that they provide transmission-level services to the system are classified as energy storage nodes. Such nodes store electricity when power is abundant and release electricity when power is in short supply. Through the above analysis of power market data, the classification of numerous nodes according to their functions can be effectively achieved.

[0108] 2. Data rough check:

[0109] Based on the four-level credibility data source classification and node type classification completed in the early stage, combined with easily accessible node geographic information, preliminary checking of data from different credibility levels is carried out to determine the correctness of the data. This step is crucial to ensure data quality.

[0110] Taking energy storage nodes as an example, under the current power transmission network technology system, pumped storage is one of the main energy storage methods, and pumped storage has strict requirements on geographical conditions, requiring a certain topographic difference to realize the conversion of water potential. If a node in a very flat area is found to be classified as an energy storage node, based on the contradiction between geographical conditions and energy storage technology principles, it can be determined that this classification is obviously unreasonable. For example, for load nodes, load nodes are essentially power consumers, and their power flow should be from the system to obtain electricity, with a theoretical power value of negative. If under a certain credibility level data source, the node presents a positive power to the system, then it can be determined that this data from this credibility level data source does not meet the basic characteristics of load nodes and is unreasonable data.

[0111] When data from a high credibility source is identified as unreasonable through checking, the data needs to be removed in time, and corresponding data from a lower level of credibility source needs to be obtained. Subsequently, the data from each credibility source is repeatedly checked in combination with power market information and node geographic information until all data meet the logic and actual situation and there is no unreasonable data. On this basis, from the reasonable data that have been checked, the data with the highest credibility level that can cover all system nodes is selected, thereby constructing a credible data set for the system.

[0112] 3. Data weight method based on credibility grading:

[0113] In the process of modeling and solving power system, setting weight is a crucial operation. This is mainly because the data we rely on is not completely accurate, and there is bound to be some difference between the current real system. No matter how sophisticated and complex the algorithm is, the calculated model value is difficult to completely accurately reflect the real state of the current system. Therefore, we need to sort, check and set the corresponding weight according to the reliability of the data source. In the field of load modeling and analysis, weight allocation according to the reliability level of data source is crucial to improve the accuracy and efficiency of the model.

[0114] For the first level of data, because it comes from highly reliable channels such as real-time bidding-bidding data in the power market, it has high accuracy and reliability, so it is given the highest weight In modeling and solving, high weight makes these data dominate the calculation, minimizes error and ensures that the model result is close to the real value.

[0115] The second level of data is the power data calculated based on the characteristics of load curve, which has slightly less reliability. It is given a moderate weight This kind of data can effectively enrich the model information, while avoiding the excessive influence of potential data errors on the results.

[0116] The third level of data is mainly based on the estimation of power based on industry type and scale. For large similar enterprise groups at the end of high-voltage distribution network, the power is calculated by fitting the product output and power reference curve. This kind of data has moderate reliability and is given a lower weight It can not only supplement the load information of specific industries and improve the description of different industrial power consumption characteristics, but also achieve a good balance between calculation accuracy and efficiency.

[0117] For the fourth level of data source, only interval data is available, so it will not appear in the objective function and will not be given a weight.

[0118] The above weight satisfies the following formula:

[0119] (5)

[0120] 4、Data second step correction:

[0121] The above coarse calibration process only provides a reliable value or a reliable interval for the node data. However, the model constructed in this stage does not take into account network constraints and topological relationships. Network constraints cover key elements such as line transmission capacity limits, and topological relationships explicitly show the connection mode and structural characteristics of each node and line in the system, both of which are essential for accurately describing the operating state of the power system. Therefore, the next step of the correction work is to fully consider network constraints and topological relationships, and use optimization algorithms and mathematical models to optimize the data in all directions to further improve the accuracy of the data and the reliability of the model, so that it can more accurately reflect the actual operation of the power system.

[0122] 4.1, Constructing the planning function:

[0123] The reference power of most plant nodes is in a known state, and the main goal of system planning is to make the calculation results of the model as close as possible to the actual operating conditions. On this basis, the objective function focuses on setting the target constraint equation of the total power value. This equation allows a certain degree of deviation in the total power value, aiming to minimize the total deviation to the lowest level, thereby ensuring efficient power balance in the optimization process.

[0124] By reasonably adjusting the system power flow and considering the weight comprehensively. The objective function is the sum of two targets; the main target is to minimize the deviation between the total power value of the entire network and the total power output of the power plant; the secondary target is to minimize the sum of the deviation proportions of each plant and the reference power value.

[0125] (6)

[0126] where the total number of plant stations of the three types of reliability is , and ; is the kth valid plant reference power value information. is the positive and negative deviation of the power flow calculation data and the kth plant reference power value information.

[0127] 4.2, Given constraints:

[0128] 4.2.1, Node power balance constraint:

[0129] Based on Kirchhoff's current law, for each node i, there is a power balance, that is, the sum of the power flowing into the node and the sum of the power flowing out of the node must be equal in any case, that is, there is a power balance relationship at node i:

[0130] (7)

[0131] where, Pi represents the amount of load at the ith node , t is the total number of nodes; Pij represents the power flow variable of the jth line , h is the total number of lines; Pji represents the sum of all line power flows into node i. That is, the sum of all line power flows that transmit power to node i Pji represents the sum of all line power flows into node i. That is, the sum of all line power flows that transmit power to node i Pji represents the sum of all line power flows into node i. That is, the sum of all line power flows that transmit power to node i Pi represents the amount of load at the ith node Pi represents the amount of load at the ith node

[0132] 4.2.2, upper and lower generation constraints:

[0133] The active power output of a generator must be between its minimum and maximum allowable range. For the kth generator, there is a constraint:

[0134] (8)

[0135] Pkg represents the active power output of the generator, Pkg represents the minimum active power output of the generator, Pkg represents the maximum active power output of the generator. For different types of generators, and The values of and are determined according to the rated power, physical characteristics, and operating conditions of the generator.

[0136] 4.2.3, load constraints:

[0137] For each node i , there is an inequality constraint:

[0138] (9)

[0139] where, Pgi represents the lower limit value of the load adjustment amount of node i, if there is a load lower limit constraint, ; if there is no such constraint, .

[0140] Similarly, Pgi represents the upper limit value of the load adjustment amount of node i, there is an inequality constraint:

[0141] (10)

[0142] 4.2.4 Line State Inequality Constraints:

[0143] In power system, for line, in view of the consideration of guaranteeing power quality, effectively controlling network loss and ensuring conductor temperature in reasonable range, transmission capacity of line must be strictly restricted by upper and lower limits. This constraint condition has vital significance for maintaining stable operation of power system, optimizing power transmission efficiency and guaranteeing safety and reliability of power equipment

[0144] For each line , there is line capacity lower limit constraint:

[0145] (11)

[0146] wherein is the power flow lower limit value of line j; similarly, line capacity upper limit constraint:

[0147] (12)

[0148] wherein is the power flow upper limit value of line j.

[0149] 4.2.5 Type I Information Target Constraint:

[0150] This type of plant station is located at the end of network, and its low-voltage side contains only one node. Plant power value information is embodied in power flow calculation as the algebraic sum of load value and output of certain end node.

[0151] (13)

[0152] 4.2.6 Type II Information Target Constraint:

[0153] This type of plant station is located at the end of network, and its low-voltage side contains two nodes. Plant power value information is embodied in power flow calculation as the sum of power values of certain two end nodes.

[0154] (14)

[0155] 4.2.7 Type III Information Target Constraint:

[0156] This type of plant station is located in the middle of network, and its low-voltage side does not directly bear load. Plant power value information is embodied in power flow calculation as the sum of all branch power flows from high-voltage side node m to its low-voltage side node n.

[0157] (15)

[0158] wherein, A set of lines with node n as the first node and m as the last node.

[0159] 4.2.8, the IV type information target constraint:

[0160] The plant station is located in the middle of the network, and its low-voltage side does not directly bear the load. The power value information of the plant station is embodied in the power flow calculation as the sum of all branch power flows from the high-voltage side node i to its two low-voltage side nodes m and n.

[0161] (16)

[0162] 4.3, linear target programming solution:

[0163] Finally, the linear target programming method is used to carry out iterative solution operation. Through this process, the power load model can be obtained from an unknown system, and the topological correlation and network constraints are considered in the model.

[0164] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits are not described in detail.

[0165] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be regarded as the protection scope of the present application.

Claims

1. A method for modeling distribution load considering topological correlation and network constraints, characterized in that, The method comprises the following steps: S1, reading system: reading the data of the topology, nodes, lines and generators of the power system, and storing them into different data structures, initializing the system information, calculating by using a direct current flow model, analyzing the topology of the power system, and obtaining the power generation data of the power station from the power market; S2, data rough check: performing preliminary check on the data based on the credibility classification, judging the data rationality in combination with the node geographic information, including checking the geographic conditions of the energy storage nodes and the power flow direction of the load nodes; S3, data weight method based on credibility classification: assigning corresponding weights to the data of different sources according to the data credibility levels; S4, data second step correction: combining the network constraints and the topology relationship, constructing an objective function, optimizing the power distribution, introducing the node power balance, the upper and lower limits of power generation, the load constraints and the line capacity constraints, and solving by using linear objective programming iteration to obtain a power distribution and utilization load model meeting the topology correlation and network constraints; The data second step correction in S4 comprises: S41, constructing an objective function: the objective function comprises a main target of minimizing the total power value deviation of the power plants from the reference power value and a secondary target of minimizing the sum of the deviation proportions of each power station from the reference power value; S42, giving constraint conditions: in the optimization process, the operation constraints of the power system are given, including the node power balance constraint, the upper and lower limits of power generation constraint, the load constraint, the line state inequality constraint, the I-type information target constraint, the II-type information target constraint, the III-type information target constraint and the IV-type information target constraint, specifically comprising: The I-type information target constraint: the I-type information power station is located at the end of the network, and only one node is contained on the low-voltage side of the power station, the power value information of the power station is embodied as the algebraic sum of the load value and the output of the end node in the power flow calculation, and is expressed as: ； wherein, represents the load amount of the i-th node, respectively represent the positive and negative deviations of the power flow calculation data and the k-th power reference value information of the power station, is the k-th valid power reference value information of the power station. The II-type information target constraint: the II-type information power station is located at the end of the network, and two nodes are contained on the low-voltage side of the power station, the power value information of the power station is embodied as the sum of the power values of the two end nodes in the power flow calculation, and is expressed as: ； The III-type information target constraint: the III-type information power station is located in the middle of the network, and the low-voltage side of the power station does not directly carry a load, the power value information of the power station is embodied as the sum of all branch flows from the high-voltage side node m to the low-voltage side node n in the power flow calculation, and is expressed as: ； wherein, denotes the set of lines with node n as the first node and m as the last node; The IV-type information target constraint: the IV-type information power station is located in the middle of the network, and the low-voltage side of the power station does not directly carry a load, the power value information of the power station is embodied as the sum of all branch flows from the high-voltage side node i to the two low-voltage side nodes m and n in the power flow calculation, and is expressed as: ； S43, linear objective programming solution: the linear objective programming method is used to expand the iterative solution operation, the optimal power distribution scheme is solved by using the iterative calculation method based on the constructed objective function and the given constraint conditions, and a power distribution and utilization load model is obtained; The objective function is expressed as: ； Wherein, the total number of power stations of the three types of credibility is respectively , and , is the kth effective power station reference power value information, is the positive and negative deviation of the power flow calculation data and the kth power station reference power value information. 2.The method of claim 1, wherein, The reading system in S1 comprises: S11, data reading and system information initialization: reading the topology, nodes, lines and generator data of the power system, storing them into different data structures, and calculating by using a direct current flow model; The direct current flow model is expressed as: ； wherein, injecting power to the nodes, matrix, voltage phase angle of the nodes; S12, element correlation analysis: the connection relationship between nodes and lines is constructed, the power grid topology is analyzed, the connection mode is determined by checking the matching of nodes and lines, and the attacked lines and nodes are identified; S13, power plant data collection: obtaining power generation data of power plants from the power market trading center; S14, data credibility classification: classifying the credibility according to the data source, including first-level credibility, second-level credibility, third-level credibility and fourth-level credibility; S15, node classification: according to the power market transaction data, the nodes are divided into power generation nodes, load nodes and energy storage nodes. 3.The method of claim 2, wherein, The data credibility classification in S14 includes: S141, first-level credibility: the first-level credibility data is derived from the winning data in the real-time bidding of the power market, and the credibility is the highest; S142, second-level credibility: the second-level credibility data is calculated based on the characteristics of the load curve, including the transformer peak load rate of the substation and the main transformer capacity; S143, third-level credibility: the third-level credibility data is obtained by power estimation based on industry type and industry scale, by analyzing the relationship between the output of various products and the power of the plant, and by using a linear model established by mathematical fitting method to calculate the power demand of the related area in the unknown system, which is represented as: ； wherein, is the power required by the production plant j of the B-type commodity to produce a quantity of the commodity, is the best slope of the linear fit of the production quantity versus power relationship, is the inherent power consumption of the production plant. S144, fourth-level credibility: the fourth-level credibility data is used to fill in the power data vacancy of small substations and unknown nodes, and the minimum and maximum values of the main transformer capacity of the target substation are calculated by referring to the main transformer capacity of the substation of the same voltage level, the minimum value is based on half of the capacity of a single main transformer, and the maximum value follows the N-1 rule, which is represented as: ； wherein, represents the capacity of a single main transformer of the voltage level substation; ； wherein, representing the system's maximum credible capacity of each of the equivalent substations transformers. 4.The method of claim 3, wherein, The data rough check in S2 includes: S21, data classification and preliminary check: based on different credibility data sources and node type classification, combined with geographic information, the data of different credibility levels are preliminarily checked; S22, data rationality check: for energy storage nodes and load nodes, the physical law of classification is checked, including the fact that pumped storage power stations in energy storage nodes have terrain drop and the power flow of load nodes is negative, if the data is not consistent with the physical law, it is determined as unreasonable data; S23, data adjustment and correction: if the high credibility data is identified as unreasonable, the data is excluded, and the corresponding data is supplemented from the low-level credibility source.

5. The method of claim 4, wherein, The data weighting by credibility level in S3 includes assigning a weight to primary credibility data assigning a weight to secondary credibility data assigning a weight to tertiary credibility data wherein .

6. The method of claim 5, wherein, The given constraint conditions in S42 include: S421, node power balance constraint: based on Kirchhoff's current law, for each node i, the sum of the power flowing into the node is equal to the sum of the power flowing out of the node, there is a power balance relationship at node i, which is represented as: wherein, represents the amount of load at the i-th node represents the flow variable of the j-th line represents the sum of all line flows into node i, represents the sum of all line flows out of node i is the known power injection or outflow at node i; S422, upper and lower limit constraints of power generation: the active power output of the generator is between its minimum and maximum allowed range, the constraint of the kth generator is represented as: ； wherein Pgen, min represents the minimum active power output of the generator, Pgen, min represents the minimum active power output of the generator, Pgen, max represents the maximum active power output of the generator; S423, load constraints: for each node i with inequality constraints, denoted as: ； wherein, is the lower limit value of the load adjustment amount for node i, if there is a lower limit constraint on the load, , if there is no such constraint, ; An upper limit value of the load adjustment amount for node i has an inequality constraint, which is expressed as: ； S424, line status inequality constraints: in power systems, for each line with line capacity lower bound constraints expressed as: ； wherein, Pj is the lower flow limit value for line j; The upper limit constraint of line capacity is represented as: ； wherein, is the flow upper limit value for line j.

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