Power distribution and utilization load modeling method considering topological correlation and network constraint

Abstract

The invention relates to the technical field of electric power systems, in particular to a power distribution and utilization load modeling method considering topological correlation and network constraint, which comprises the following steps: S1, a reading system: reading data of an electric power system, analyzing a topological structure of the electric power system, and acquiring generated power data of a plant station; s2, data coarse verification: carrying out preliminary verification on the data; s3, a data weighting method based on credibility grading: endowing data of different sources with corresponding weights; s4, second-step correction of the data: combining the network constraint and the topological relation, constructing an objective function, optimizing power distribution, introducing node power balance, upper and lower limits of power generation, load constraint and line capacity constraint, and obtaining a power distribution and utilization load model conforming to the topological relation and the network constraint through linear objective planning iterative solution; according to the invention, the operation stability and reliability of the power system are improved, and effective support is provided for safe scheduling and optimized operation of the power distribution and utilization system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and particularly to a modeling method for distribution and consumption loads considering topological correlation and network constraints. Background Art

[0002] With the continuous growth of power demand and the increasing complexity of power systems, accurate load modeling has become crucial for power system monitoring and prediction. Traditional distribution and consumption load modeling methods mainly rely on sensors to measure parameters such as current, voltage, and power to obtain real-time load information. However, due to uneven sensor distribution, it is difficult to accurately obtain load information in some areas. In addition, sensors may malfunction or be interfered with, affecting the accuracy and integrity of data. In practical applications, there are also cases where information about some grid nodes is unknown, making traditional load modeling methods difficult to apply. Therefore, a modeling method that can combine topological correlation and network constraints is needed to more comprehensively reflect the load characteristics of power systems.

[0003] Current load modeling technologies have high requirements for data integrity and usually rely on a large amount of historical load data and measurement information. In the actual operation of power systems, due to problems such as difficult data acquisition and insufficient equipment coverage, complete data is difficult to obtain, resulting in a decline in model accuracy. Even in the case of extremely scarce data, an effective model cannot be constructed. In addition, most existing methods focus on load prediction accuracy and do not fully consider the topological structure and network constraints of power systems, resulting in models that are difficult to accurately reflect the actual operating state of the system, thus affecting their effectiveness in practical applications. Summary of the Invention

[0004] The present invention provides a modeling method for distribution and consumption loads considering topological correlation and network constraints, which can effectively reduce the data demand and improve the modeling efficiency when only using partial publicly available data of transmission system substations. This method fully considers the influence of the grid topological structure and network constraints on load power, ensures that the constructed model strictly follows the system power flow calculation rules, thus 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 modeling method for distribution and consumption loads considering topological correlation and network constraints includes the following steps:

[0006] S1, Read the system: Read the data of the topological structure, nodes, lines, and generators of the power system, store them in different data structures, initialize the system information, perform calculations using the DC power flow model, analyze the topological structure of the power system, and obtain the generator power data of the substations from the power market;

[0007] S2, Coarse data verification: Based on the credibility grading, conduct preliminary verification on the data, and judge the data rationality in combination with the node geographical information, including checking the geographical conditions of energy storage nodes and the power flow direction of load nodes;

[0008] S3, Data weight method based on credibility grading: Assign corresponding weights to data from different sources according to the data credibility level;

[0009] S4, Second-step data correction: Combine network constraints and topological relationships to construct an objective function, optimize power distribution, introduce node power balance, generation upper and lower limits, load constraints, and line capacity constraints, and iteratively solve through linear objective programming to obtain a power distribution and utilization load model that conforms to topological association and network constraints.

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

[0011] S11, Data reading and system information initialization: Read the topological structure, nodes, lines, and generator data of the power system, store them in different data structures, and calculate using the DC power flow model;

[0012] The DC power flow model is expressed as:

[0013] P = Bθ;

[0014] where P is the node injection power, B is the susceptance matrix, and θ is the node voltage phase angle;

[0015] S12, Element correlation analysis: Construct the connection relationship between nodes and lines, analyze the power grid topological structure, clarify its connection method by checking the matching situation of nodes and lines, and at the same time identify the attacked lines and nodes;

[0016] S13, Substation data collection: Obtain the generation power data of substations from the power market trading center;

[0017] S14, Data credibility grading: Conduct credibility grading according to the data source, including first-level credibility, second-level credibility, third-level credibility, and fourth-level credibility;

[0018] S15, Node classification: According to the power market trading volume data, divide the nodes into generation nodes, load nodes, and energy storage nodes.

[0019] Optionally, the data credibility grading in S14 includes:

[0020] S141, First-level credibility: The first-level credibility data comes from the winning bid data in the real-time bidding of the power market, with the highest credibility;

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

[0022] S143, Tertiary credibility: The tertiary credibility data is obtained by estimating the power based on the industrial type and scale. By analyzing the relationship between the output of various products and the power of the plant station, and using the mathematical fitting method to establish a linear model, the power demand of the relevant area in the unknown system is estimated, expressed as:

[0023] P B,j = α * X j + C j ;

[0024] Among them, P B,j is the power required for the production plant station j of Class B products to produce X j quantity of products. α is the best slope obtained by linearly fitting the relationship between output and power, and C j is the inherent power consumption of the production plant station;

[0025] S144, Quaternary credibility: The quaternary credibility data is used to fill the power data gaps of small substations and unknown nodes. By referring to the capacity of the main transformer of substations with the same voltage level, the minimum and maximum values of the capacity of the main transformer of the target substation are estimated. The minimum value is based on half of the capacity of a single main transformer, and the maximum value follows the N-1 criterion, expressed as:

[0026] S min = 0.5 * S single ;

[0027] Among them, S single represents the capacity of a single main transformer of substations with this voltage level;

[0028] S max = max{S1, S2, S3, ……, S n};

[0029] Among them, S1, S2, S3, ……, S n represent the maximum credible capacities of the transformers of the 1, 2, 3, …, n substations of the same level in the system respectively.

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

[0031] S21, Data classification and preliminary verification: Based on different credibility data sources and node types for classification, combined with geographical information, the data of different credibility levels is preliminarily verified;

[0032] S22, Data Rationality Check: For energy storage nodes and load nodes, check the physical laws of their classifications, including that pumped-storage power stations in energy storage nodes have terrain elevation differences and the power flow direction of load nodes is negative. If the data does not conform to the physical laws, it is determined as unreasonable data;

[0033] S23, Data Adjustment and Correction: If highly reliable data is determined to be unreasonable, this data is excluded, and corresponding data is supplemented from a lower-level reliability source.

[0034] Optionally, the data weight method based on reliability classification in S3 includes assigning weight M1 to first-level reliability data, weight M2 to second-level reliability data, and weight M3 to third-level reliability data, where M1 >> M2 >> M3.

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

[0036] S41, Constructing the objective function: The objective function includes the main objective of minimizing the deviation between the total network power value and the sum of the power outputs of power plants in the reference power value, and the secondary objective of minimizing the sum of the deviation ratios of each substation to the reference power value;

[0037] S42, Given constraint conditions: During the optimization process, the operating constraints of the power system are given, including node power balance constraints, generation upper and lower limit constraints, load constraints, line state inequality constraints, type-I information objective constraints, type-II information objective constraints, type-III information objective constraints, and type-IV information objective constraints;

[0038] S43, Solving linear goal programming: Use the linear goal programming method to perform iterative solving operations. Based on the constructed objective function and the given constraint conditions, use the iterative calculation method to solve the optimal power distribution plan and obtain the distribution and utilization load model.

[0039] Optionally, the objective function is expressed as:

[0040]

[0041] where P ∑+ P ∑- are the positive and negative deviations between the total network power value and the sum of the power outputs of power plants in the reference power value. The total numbers of substations of the three types of reliability are n if,1 、n if,2 and n if,3 , P B,k is the reference power value information of the kth valid substation, P S+，k , P S-,k are the positive and negative deviations between the power flow calculation data and the reference power value information of the kth substation.

[0042] Optionally, the given constraints in S42 include:

[0043] S421, node power balance constraint: Based on Kirchhoff's current law, there is power balance for each node i, that is, 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, expressed as:

[0044]

[0045] where P Load,i represents the load of the i-th node (i = 1, 2, 3…, t), t is the total number of nodes, and P j represents the power flow variable of the j-th line (j = 1, 2, 3…, h), h is the total number of lines. represents the sum of the power flows of all lines flowing into node i. represents the sum of the power flows of all lines flowing out of node i, and P Load ,i represents the load of node i, and b i is the known power injection or outflow at node i;

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

[0047]

[0048] where P G,k represents the active power output of the generator. represents the minimum active power output of the generator. represents the maximum active power output of the generator;

[0049] S423, load constraint: For each node i ∈ N, there is an inequality constraint, expressed 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, then If there is no such constraint, then

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

[0053]

[0054] S424, Line status inequality constraint: In the power system, for each line \(j\in L(m,n)\), the lower limit constraint of line capacity is expressed as:

[0055]

[0056] where, is the lower limit value of the power flow of line \(j\);

[0057] The upper limit constraint of line capacity is expressed as:

[0058]

[0059] where, is the upper limit value of the power flow of line \(j\);

[0060] S425, Class-I information target constraint: Class-I information substations are located at the network end, and there is only one node on its low-voltage side. The substation power value information is reflected 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] -ΔP Load，i +ΔP G，i +P Load，i -P G，i +P S+，k +P S-,k =P B,k ;

[0062] S426, Class-II information target constraint: Class-II information substations are located at the network end, and there are two nodes on its low-voltage side. The substation power value information is reflected as the sum of the power values of two end nodes in the power flow calculation, which is expressed as:

[0063]

[0064] S427, Class-III information target constraint: Class-III information substations are located in the middle of the network, and their low-voltage sides do not directly carry loads. The substation power value information is reflected as the sum of the power flows of all branches pointing from the high-voltage side node \(m\) to its low-voltage side node \(n\) in the power flow calculation, which is expressed as:

[0065]

[0066] where, \(L(n,m)\) refers to the set of lines with node \(n\) as the head node and \(m\) as the end node;

[0067] S428, Class-IV information target constraint: Class-IV information substations are located in the middle of the network, and their low-voltage sides do not directly carry loads. The substation power value information is reflected as the sum of the power flows of all branches pointing from the high-voltage side node \(i\) to its two low-voltage side nodes \(m,n\) in the power flow calculation, which is expressed as:

[0068]

[0069] Advantages of the present invention:

[0070] In the present invention, through the power grid topology structure, substation reference power data, and data calculation with different credibility levels, accurate modeling of the load in unknown areas is achieved, significantly reducing the dependence on real-time measurement data, effectively solving the problem of model accuracy decline caused by data loss. In addition, by grading the credibility of the data and combining with data weight calculation, the reliability of data processing in the modeling process is improved, enabling the construction of a high-precision load model even under limited data conditions.

[0071] In the present invention, by fully considering the topology structure and network constraints of the power grid during the modeling process, an optimization objective function with power flow calculation as the core is constructed and solved through linear objective programming, ensuring that the results of load modeling meet the power grid operation constraints, such as node power balance, upper and lower limits of generator output, load adjustment range, and line power flow capacity, avoiding calculation errors caused by ignoring network constraints. In addition, by optimizing the objective function to minimize the total power deviation, the accuracy of load modeling is improved, making the model more in line with the actual operation of the power grid, thereby enhancing the stability and reliability of the power system operation and providing effective support for the safe dispatching and optimal operation of the distribution and utilization system. Description of the Drawings

[0072] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only those of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0073] Figure 1 It is a schematic flow chart of the modeling method according to the embodiment of the present invention. Detailed Embodiments

[0074] The present invention will be described in detail below in conjunction with the drawings and specific embodiments. At the same time, it should be noted here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments. For some well-known technologies, those skilled in the art can also adopt other alternative methods for implementation; moreover, the drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0075] It should be noted that in the specification, the indication of "an embodiment", "embodiment", "exemplary embodiment", "some embodiments", etc. means that the described embodiments may include specific features, structures or characteristics, but not necessarily every embodiment includes such specific features, structures or characteristics. Additionally, when describing a specific feature, structure or characteristic in combination with an embodiment, implementing such feature, structure or characteristic in combination with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the relevant art.

[0076] Generally, terms can be understood at least in part from their use in context. For example, at least in part depending on the context, the term "one or more" as used herein can be used to describe any feature, structure or characteristic in a singular sense, or can be used to describe a combination of features, structures or characteristics in a plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey a set of exclusive factors, but rather can alternatively, at least in part depending on the context, allow for the existence of other factors that are not necessarily explicitly described.

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

[0078] 1. Read the system:

[0079] Implement data processing of the power system and initialization operations of the system state, laying a foundation for subsequent in-depth analysis and evaluation of the power system. Multiple aspects in the power system are comprehensively considered, including elements such as nodes, lines, generators, and modules, and their related data are processed and associated.

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

[0081] During the operation execution process, a flag variable is assigned a specific initial value, and this operation aims to set the corresponding status identifier for subsequent system information processing. System information is obtained through specialized information reading means, and this information covers important data during the past operation of the system. The obtained information is stored in different data structures, and corresponding statistical methods are used to count the number of elements of the main components within the system, and these elements constitute the basic architecture of the power system, providing the necessary data support for subsequent further operations.

[0082] As the scale of the power system expands, the number of its nodes and lines increases sharply, and the computational 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 simplified assumptions and linearizes the power flow equations in the AC power system. Therefore, in the subsequent power flow calculations in this article, the operation model of DC power flow will be adopted:

[0083] P = Bθ (1)

[0084] Where P is the nodal injection power, B is the susceptance matrix, and θ is the nodal voltage phase angle.

[0085] The DC power flow ignores the line resistance, approximates the voltage magnitude to 1, and ignores the impact of reactive power. Therefore, when establishing constraint conditions subsequently, due to the use of DC power flow, there are no upper and lower limits constraints on nodal voltage and reactive power constraints, and the line capacity inequality constraint only considers active power.

[0086] 1.2. Element Relevance Analysis:

[0087] In the process of studying an unknown power system, it is necessary to clearly display its topological structure, and a corresponding storage structure will be created to build the connection relationship between nodes and lines. Through the analysis of the system's historical information, nodes and lines are carefully inspected. According to the matching conditions satisfied by the node and line elements therein, the required information is stored in a specific list in an orderly manner, so as to clearly show the connection situation when the node is the head node or the end node. This helps us 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 line and node structures that may be attacked, which is of great significance for ensuring the safe and stable operation of the power system.

[0088] 1.3. Substation Data Collection:

[0089] In power system research, a complex system with most information unknown is often faced. For such a system, from the trading center of the system's power market, the generating power data of substations at each specific time period can be obtained. These data can be used as the reference power of substations, providing key basic information for subsequent system analysis and modeling. The substation reference power reflects the generating capacity and actual generating status of substations at different times, which is of great significance for understanding the power supply situation of the system, and its accuracy and integrity directly affect the reliability and effectiveness of subsequent research.

[0090] 1.4. Data Credibility Classification:

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

[0092] First-level credibility: The first-level data with the highest credibility comes from the real-time bidding in the power market: winning bid data. From the perspective of power generation companies, when participating in power market transactions, their core goal is to obtain economic benefits by selling electric energy. During the transaction process, delivering electric energy strictly in accordance with the contract is the key to ensuring their continuous revenue. Once the contract is violated, not only will the current transaction fail, but it may also damage their market reputation and thus lose their main source of income. From the perspective of users, when participating in market volume reporting, due to cost control considerations, they will declare the required amount of electric energy based on their actual expected electricity demand. Because if they report the volume randomly, it will lead to an increase in the fees they pay to the power plant, greatly increasing their electricity costs. Therefore, based on the interest-driven nature of both sides of the market transaction, this part of the data has relatively high credibility under normal circumstances.

[0093] Second-level credibility: Although the winning bid data in the power market has important reference value, due to the complexity of the operation of the real power system, unexpected situations such as unit failures and line failures occur from time to time, and there may also be speculative behaviors of some market participants. These factors make the winning bid data in the power market unable to accurately reflect the actual power flow situation of the system by 100%, and there is often a certain degree of deviation from the true power flow of the system. This indicates that there is a possibility that some bid data is untrustworthy compared to the real data. Therefore, it is necessary to introduce a second credibility level: the power estimated based on some characteristics of the load curve. These characteristics cover key information such as the peak load rate of transformers and the main transformer capacity of large substations or important substations in the system. Through in-depth analysis and research of these characteristics, relevant power data can be estimated more accurately.

[0094] Third-level credibility: It mainly focuses on the load aspect and is obtained by estimating the power based on the industrial type and scale. In the actual power system, there are often some large clusters of similar enterprises distributed at the end of the high-voltage distribution network. For these enterprise clusters, 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 substation. Through a large amount of data collection, collation and analysis, using the method of mathematical fitting, a linear relationship curve can be fitted.

[0095] P B,j = α * H j + C j (2)

[0096] Among them, P B,j is the power required for production plant j of Class B products to produce X j quantity of products. X j can be replaced by the annual output value or the monthly product output. α is the best slope obtained by linearly fitting the relationship between output and power, and C jis the inherent power consumption of the production plant and substation.

[0097] This curve can accurately reflect the quantitative relationship between product output and the reference power of the plant and substation. In this way, when faced with an unknown system, we only need to obtain the output information of the relevant products in the system, and we can accurately estimate the power value at that location in the unknown system based on this fitted linear relationship curve. This power estimation method based on industrial characteristics and scale fully considers the electricity consumption characteristics and laws of different industries, provides an effective way to obtain load data, and further enriches the source of system power data.

[0098] Fourth-level credibility: The data of the above three credibility levels can cover most nodes of the system to a large extent, and the generated data shows a redundant structure, which provides a relatively comprehensive and reliable data basis for system analysis. However, inevitably, for some small substation nodes, data may be missing, and there are also some nodes whose details are completely unknown. To fill these data gaps and improve the data information of the system, we introduce the data source of the fourth-level credibility. The data at this level is used to estimate power based on the voltage level and the situation of other substations of the same level. By examining 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. Among them, the minimum value is estimated based on half of the single-unit main transformer capacity of this voltage level:

[0099] S min = 0.5 * S single (3)

[0100] S single represents the single-unit main transformer capacity of the substation of this voltage level.

[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, on the premise of ensuring a certain power supply reliability. The maximum value is estimated according to the N-1 system operation requirement, that is, determined according to the maximum credible capacity of other known substations of the same voltage level in the system. The N-1 criterion is an important safety criterion for power system operation, which requires that the system can still operate normally when any one device in the system is taken out of operation.

[0102] S max = max{S1, S2, S3, ……, S n} (4)

[0103] S1 to S n represent the maximum credible capacities of the transformers of the n substations of the same level in the system.

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

[0105] 1.5. Node classification:

[0106] By deeply analyzing the trading volume data of the power market, the power consumption of each load can be obtained. These data reflect the consumption of electricity on the load side. Based on this, the electricity consumption terminals, that is, load nodes, can be identified.

[0107] From the perspective of bidding information analysis, nodes that participate in bidding and sell electric energy 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. Nodes that bid to purchase electric energy are clearly load nodes, and the changes in their electricity consumption demands play an important role in the supply-demand balance of the system. In addition, nodes that indicate providing transmission-level services to the system in the bidding are classified as energy storage nodes. Such nodes store electric energy when the power is abundant and release electric energy when the power is short. Through the above analysis of the power market data, the classification of numerous nodes according to their functions can be effectively achieved.

[0108] 2. Coarse data verification:

[0109] Based on the previously completed classification of data sources with four levels of credibility and the classification of node types, combined with the easily accessible geographical information of nodes, a preliminary verification of the data from different credibility-level sources is carried out to judge the correctness of the data. This link is crucial for ensuring data quality.

[0110] Taking energy storage nodes as an example, in the current technical system of the transmission grid, pumped storage is one of the main energy storage methods, and pumped storage has strict requirements for geographical conditions and needs to have a certain elevation difference to realize the conversion of water potential energy. If it is found that a node in a very flat area is classified as an energy storage node, based on the contradiction between the geographical conditions and the energy storage technology principle, it can be determined that this classification is obviously unreasonable. Another example is for the case of load nodes. Load nodes essentially consume electric energy, and the power flow direction should be to obtain electric energy from the system, and the power value should theoretically be negative. If, under a certain credibility-level data source, this node shows a situation of providing positive power to the system, then it can be considered that the data generated by this credibility-level data source for this node does not conform to the basic characteristics of load nodes and belongs to unreasonable data.

[0111] When data from a high-credibility source is found to be unreasonable after verification, the data must be removed in a timely manner and the corresponding data must be obtained from a lower-level credibility source. Subsequently, the data from each credibility source is repeatedly and roughly verified by continuously combining power market information and node geographic information until all data are consistent with logic and actual conditions and there is no unreasonable data. On this basis, from the verified reasonable data, the data that can cover all system nodes and has the highest credibility level is selected to build a system of credible data sets.

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

[0113] In the process of modeling and solving the power system, setting weights is a crucial operation. This is mainly because the data we rely on is not completely accurate, and there must be certain differences between it and the current real system. No matter how sophisticated and complex the algorithm is, the calculated model value is difficult to fully and accurately reflect the true state of the current system. Therefore, we need to sort, verify and set corresponding weights according to the credibility of the data source. In the field of power distribution load modeling and analysis, weight allocation based on the credibility level of the data source is crucial to improving model accuracy and calculation efficiency.

[0114] For the first level of credibility data, because it comes from highly reliable channels such as real-time bidding and winning bid data in the power market, it has extremely high accuracy and reliability, so it is given the highest weight M1. When modeling and solving, the high weight makes these data dominate the calculation, minimize the error, and ensure that the model results are close to the real value.

[0115] The second level of credibility data is the power data calculated based on the load curve characteristics, which is slightly less reliable. Giving it a moderate weight M2 can effectively use this type of data to enrich model information while avoiding excessive impact of potential data errors on the results.

[0116] The third level of credibility data is mainly based on the estimated power of industry type and scale, such as the large-scale similar enterprise group at the end of the high-voltage distribution network, which is calculated by fitting the relationship curve between product output and plant reference power. This type of data has moderate reliability and is given a lower weight M3. It can not only supplement the load information of specific industries, improve the model's description of the power consumption characteristics of different industries, but also achieve a good balance between calculation accuracy and efficiency.

[0117] For the fourth level credibility source, its nodes only have interval data, so they will not appear in the objective function and will not be assigned weights.

[0118] The above weights satisfy the following formula:

[0119] M1>>M2>>M3 (5)

[0120] 4. Second correction of data:

[0121] The above rough verification process only provides a credible value or a credible interval for the node data. However, the model constructed in this stage has not taken into account the network constraints and topological relationships. Network constraints cover key elements such as line transmission capacity limits, and topological relationships define the connection methods and structural characteristics of each node and line in the system. Both are crucial for accurately describing the operating state of the power system. Therefore, the next second correction work is to, on the basis of fully considering network constraints and topological relationships, use optimization algorithms and mathematical models to perform a comprehensive optimization process on the data, so as to further improve the accuracy of the data and the reliability of the model, enabling it to more accurately reflect the actual operating conditions of the power system.

[0122] 4.1. Construction of the planning function:

[0123] The reference power of most substation nodes is in a known state. The main goal of system planning is to make the calculation results of the model closest to the actual operating conditions to the greatest extent. On this basis, the constructed objective function focuses on setting the objective constraint equation for the total power value. This equation allows a certain degree of deviation in the total power value, aiming to minimize the total deviation to ensure efficient power balance in the system during the optimization process.

[0124] By reasonably adjusting the system power flow and considering the weights comprehensively. The objective function is the sum of two parts; the main objective is to minimize the deviation between the total power value of the whole network and the sum of the power outputs of power plants in the reference power value; the secondary objective is to minimize the sum of the deviation ratios between each substation and the reference power value.

[0125]

[0126] Among them, P ∑+ P ∑- are the positive and negative deviations between the total power value of the whole network and the sum of the power outputs of power plants in the reference power value; the total numbers of substations with three types of credibility are n if,1 , n if,2 and n if,3 ; P B,k is the information of the reference power value of the kth valid substation. P S+，k , P S-,k are the positive and negative deviations between the power flow calculation data and the information of the reference power value of the kth substation.

[0127] 4.2. Given constraint conditions:

[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 is always equal to the sum of the power flowing out of the node in any case. That is, there is a power balance relationship at node i:

[0130]

[0131] where P Load,i represents the load of the i-th node (i = 1, 2, 3…, t), and t is the total number of nodes; P j represents the power flow variable of the j-th line (j = 1, 2, 3…, h), and h is the total number of lines; represents the sum of the power flows of all lines flowing into node i. That is, the power flows P j on all lines transmitting power to node i are added up to obtain the total power flowing into node i. represents the sum of the power flows of all lines flowing out of node i. P Load,i represents the load of node i, which affects the power balance at node i. b i is the known power injection or outflow at node i. The meaning of this equality constraint is that the power flowing into node i (including line power flow and load adjustment) minus the power flowing out of node i is equal to the known power injection or outflow at node i, thus ensuring that the power at node i is in a balanced state at any moment, which is one of the basic requirements for the stable operation of the power system.

[0132] 4.2.2. Generation upper and lower limit constraints:

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

[0134]

[0135] P G,k represents the active power output of the generator, represents the minimum active power output of the generator,

[0136] represents the maximum active power output of the generator. For different types of generators, and values are determined according to the rated power, physical characteristics and operating conditions of the generator.

[0137] 4.2.3. Load constraints:

[0138] For each node i ∈ N, there is an inequality constraint:

[0139]

[0140] Among them, is the lower limit value of the load adjustment amount of node i. If there is a load lower limit constraint, then If there is no such constraint, then

[0141] Similarly, The upper limit value of the load adjustment amount of node i has the inequality constraint:

[0142]

[0143] 4.2.4. Line status inequality constraint:

[0144] In the power system, for a line, considering ensuring power quality, effectively controlling network losses, and ensuring that the conductor temperature is within a reasonable range, the transmission capacity of the line is necessarily strictly constrained by upper and lower limits. This constraint condition is of crucial significance for maintaining the stable operation of the power system, optimizing power transmission efficiency, and ensuring the safety and reliability of power equipment.

[0145] For each line j ∈ L(m,n), there is a line capacity lower limit constraint:

[0146]

[0147] Among them is the lower limit value of the power flow of line j; similarly, the line capacity upper limit constraint:

[0148]

[0149] Among them is the upper limit value of the power flow of line j.

[0150] 4.2.5. Class I information target constraint:

[0151] This type of substation is located at the end of the network, and its low-voltage side contains only one node. The substation power value information is reflected as the algebraic sum of the load value and the output of a certain end node in the power flow calculation.

[0152] -ΔP Load，i +ΔP G，i +P Load，i -P G，i +P S+，k +P S-,k =P B,k (13)

[0153] 4.2.6. Class II information target constraint:

[0154] This type of substation is located at the end of the network, and its low-voltage side contains two nodes. The substation power value information is reflected as the sum of the power values of two certain end nodes in the power flow calculation.

[0155]

[0156] 4.2.7. Class III Information Target Constraint:

[0157] This type of substation is located in the middle of the network, and its low-voltage side does not directly carry load. The substation power value information is reflected in the power flow calculation as the sum of the power flows of all branches from the high-voltage side node m to its low-voltage side node n.

[0158]

[0159] Among them, L(n, m) refers to the set of lines with node n as the head node and m as the end node.

[0160] 4.2.8. Class IV Information Target Constraint:

[0161] This type of substation is located in the middle of the network, and its low-voltage side does not directly carry load. The substation power value information is reflected in the power flow calculation as the sum of the power flows of all branches from the high-voltage side node i to its two low-voltage side nodes m and n.

[0162]

[0163] 4.3. Linear Goal Programming Solution:

[0164] Finally, use the linear goal programming method to carry out iterative solution operations. Through this process, a power distribution and consumption load model can ultimately be obtained from an unknown system, and this model has taken into account topological associations and network constraints.

[0165] The present invention covers any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of the present invention. For the public to have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, and those skilled in the art can fully understand the present invention without these detailed descriptions. Additionally, well-known methods, processes, flows, components, and circuits are not described in detail to avoid unnecessary confusion to the essence of the present invention.

[0166] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A modeling method for distribution and utilization load considering topological association and network constraints, characterized in that Including the following steps: S1, Reading system: Read the data of the topology, nodes, lines and generators of the power system, store them in different data structures, initialize the system information, calculate using the DC power flow model, analyze the topology of the power system, and obtain the generator power data of the power plants from the power market; S2, Coarse data verification: Conduct preliminary verification on the data based on the credibility grading, and judge the data rationality in combination with the geographical information of the nodes, including checking the geographical conditions of the energy storage nodes and the power flow direction of the load nodes; S3, Data weighting method based on credibility grading: Assign corresponding weights to the data from different sources according to the data credibility level; S4, Second-step data correction: Combine the network constraints and topological relationships, construct the objective function, optimize the power distribution, introduce node power balance, generation upper and lower limits, load constraints and line capacity constraints, and obtain the distribution and utilization load model that meets the topological association and network constraints through iterative solution of linear objective programming.

2. The method for modeling the distribution and utilization load considering topological association and network constraints according to claim 1, wherein, The reading system in S1 includes: S11, Data reading and system information initialization: Read the data of the topology, nodes, lines and generators of the power system, store them in different data structures, and calculate using the DC power flow model; The DC power flow model is expressed as: P = Bθ; where, P is the node injection power, B is the susceptance matrix, and θ is the node voltage phase angle; S12, Element correlation analysis: Construct the connection relationship between nodes and lines, analyze the grid topology, clarify its connection method by checking the matching situation of nodes and lines, and at the same time identify the attacked lines and nodes; S13, Power plant data collection: Obtain the generator power data of the power plants from the power market trading center; S14, Data credibility grading: Conduct credibility grading according to the data source, including first-level credibility, second-level credibility, third-level credibility, and fourth-level credibility; S15, Node classification: Divide the nodes into generation nodes, load nodes and energy storage nodes according to the power market transaction volume data.

3. A method for modeling the distribution and utilization load considering topological association and network constraints according to claim 2, characterized in that, The data credibility grading in S14 includes: S141, First-level credibility: The first-level credibility data comes from the winning bid data in the real-time bidding of the power market, with the highest credibility; S142, Second-level credibility: The second-level credibility data is estimated based on the characteristics of the load curve, including the peak load rate of the transformers in the substations and the main transformer capacity; S143, Third-level credibility: The third-level credibility data is obtained by power estimation based on the industrial type and scale. By analyzing the relationship between the output of various products and the power of the power plants, and using the mathematical fitting method to establish a linear model, the power demand of the relevant areas in the unknown system is deduced, expressed as: P B,j = α * X j + C j ; Among them, P B,j is the power required by production plant j of Class B goods when producing X j quantity of goods, α is the best slope obtained by linearly fitting the relationship between output and power, and C j is the inherent power consumption of the production plant; S144, Fourth-level credibility: The fourth-level credibility data is used to fill the power data gaps of small substations and unknown nodes. By referring to the main transformer capacity of substations of the same voltage level, the minimum and maximum values of the main transformer capacity of the target substation are deduced. The minimum value is based on half of the single main transformer capacity, and the maximum value follows the N-1 criterion, expressed as: S min = 0.5 * S single ; Among them, S single represents the capacity of a single main transformer in a substation of this voltage level; S max = max{S1, S2, S3, ……, S n}; Among them, S1, S2, 3, ……, S n represent the respective maximum credible capacities of the transformers of the 1st, 2nd, 3rd, …, nth substations of the same level in the system.

4. A method for modeling the distribution and utilization load considering topological association and network constraints according to claim 3, characterized in that The coarse data verification in S2 includes: S21, Data Classification and Preliminary Verification: Based on different data sources and node types with different credibility levels, combined with geographical information, preliminary verification is carried out on data with different credibility levels. S22, Data Rationality Check: For energy storage nodes and load nodes, check the physical laws of their classification, including that the pumped-storage power station in the energy storage node has a terrain drop and the power flow direction of the load node is negative. If the data does not conform to the physical laws, it is determined as unreasonable data. S23, Data Adjustment and Correction: If the high-credibility data is determined to be unreasonable, the data is excluded, and corresponding data is supplemented from the data source with a lower credibility level.

5. A method for modeling the distribution and utilization load considering topological association and network constraints according to claim 4, characterized in that The data weight method based on credibility grading in S3 includes assigning weight M1 to the first-level credibility data, weight M2 to the second-level credibility data, and weight M3 to the third-level credibility data, where M1 >> M2 >> M3.

6. The method for modeling the distribution and utilization load considering topological association and network constraints according to claim 5, characterized in that The second-step data correction in S4 includes: S41, Constructing the objective function: The objective function includes the main objective of minimizing the deviation between the total power value of the whole network and the sum of the power outputs of power plants in the reference power value, and the secondary objective of minimizing the sum of the deviation ratios of each substation to the reference power value. S42, Specifying the constraint conditions: During the optimization process, the operating constraints of the power system are specified, including node power balance constraints, generation upper and lower limit constraints, load constraints, line state inequality constraints, type-I information objective constraints, type-II information objective constraints, type-III information objective constraints, and type-IV information objective constraints. S43, Solving by linear goal programming: The linear goal programming method is used to carry out iterative solution operations. Based on the constructed objective function and the specified constraint conditions, the optimal power distribution scheme is solved by using the iterative calculation method to obtain the distribution and utilization load model.

7. A method for modeling the distribution and utilization load considering topological association and network constraints according to claim 6, characterized in that The objective function is expressed as: where P Σ+ P Σ- are the positive and negative deviations of the sum of the power outputs of power plants in the total network power value and the reference power value, and the total numbers of substations with three types of credibility are n if,1 , n if,2 and n if,3 , P B,k is the reference power value information of the kth valid substation, P S+，k , P S-,k are the positive and negative deviations between the power flow calculation data and the reference power value information of the kth substation.

8. A method for modeling the distribution and utilization load considering topological association and network constraints according to claim 7, characterized in that, The specified constraint conditions in S42 include: S421, Node power balance constraint: Based on Kirchhoff's current law, there is power balance for each node i, that is, the sum of the power flowing into the node is equal to the sum of the power flowing out of the node. The power balance relationship at node i is expressed as: Among them, \(P\) Load,i represents the load of the \(i\)-th node (\(i = 1, 2, 3, \cdots, t\)), where \(t\) is the total number of nodes, and \(P\) j represents the power flow variable of the \(j\)-th line (\(j = 1, 2, 3, \cdots, h\)), where \(h\) is the total number of lines, represents the sum of the power flows of all lines flowing into node \(i\), represents the sum of the power flows of all lines flowing out of node \(i\), and \(P\) Load ,i represents the load of node \(i\), and \(b\) i is the known power injection or outflow of node \(i\); S422, Generation upper and lower limit constraint: The active power output of the generator is between its minimum and maximum allowable ranges. The constraint for the kth generator is expressed as: Among them, P G,k represents the active power output of the generator, represents the minimum active power output of the generator, represents the maximum active power output of the generator; S423, Load constraint: For each node i ∈ N, there is an inequality constraint, expressed as: wherein, is the lower limit value of the load adjustment amount of node i. If there is a load lower limit constraint, then If there is no such constraint, then is the upper limit value of the load adjustment amount of node i, and there is an inequality constraint, which is expressed as: S424, Line state inequality constraint: In the power system, for each line j ∈ L(m,n), there is a line capacity lower limit constraint, expressed as: Among them, is the lower limit value of the power flow of line j; The line capacity upper limit constraint is expressed as: Among them, is the upper limit of the power flow of line j; S425, Type-I information objective constraint: The type-I information substation is located at the end of the network, and there is only one node on its low-voltage side. The substation power value information is reflected as the algebraic sum of the load value and the power output at the end node in the power flow calculation, expressed as: -ΔP Load，i +ΔP G，i +P Load，i -P G，i +P S+，k +P S-,k =P B,k ; S426, Type-II information objective constraint: The type-II information substation is located at the end of the network, and there are two nodes on its low-voltage side. The substation power value information is reflected as the sum of the power values of two end nodes in the power flow calculation, expressed as: S427, Class III Information Target Constraint: Class III information substations are located in the middle of the network. Their low-voltage sides do not directly carry loads. The substation power value information is reflected in the power flow calculation as the sum of the power flows of all branches from the high-voltage side node m to its low-voltage side node n, expressed as: where L(n, m) refers to the set of lines with node n as the head node and m as the end node; S428, Class IV Information Target Constraint: Class IV information substations are located in the middle of the network. Their low-voltage sides do not directly carry loads. The substation power value information is reflected in the power flow calculation as the sum of the power flows of all branches from the high-voltage side node i to its two low-voltage side nodes m and n, expressed as:

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