A demand response combined optimization method considering response reliability and main transformer load balancing

Abstract

This invention proposes a demand response resource combination optimization method that considers response reliability and main transformer load balancing. By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are incorporated into the evaluation. A heuristic optimization algorithm is used to solve the problem, resulting in a demand response resource combination. The demand response resource combination optimization model includes: an objective function that minimizes the overall network demand response resource response cost; response reliability constraints of participating resources; load balancing constraints after the demand response resource is connected to the main transformer; and logical constraints representing whether the demand response resource is included. The heuristic optimization algorithm, combined with the objective function that minimizes the demand response cost, sequentially judges the satisfaction of response reliability constraints and main transformer load balancing constraints after a certain demand response resource is included in the response, and adjusts the combination of demand response resources to provide a feasible solution to the problem.

Description

Technical Field

[0001] This invention belongs to the technical field of power systems, and in particular relates to a demand response combination optimization method that takes into account response reliability and main transformer load balancing. Background Technology

[0002] Under the new power system framework, the power balance model will shift from the traditional "source follows load" to a new model of "source-grid-load-storage interaction." User-side demand response is an indispensable regulatory resource, not only reducing overall peak load and promoting renewable energy consumption, but also providing ancillary services such as frequency regulation and reserve. With increasing system regulation requirements, demand response is no longer limited to the simple response modes of power interruption, reduction, and shifting in the initial demonstration phase; future demand response will possess bidirectional, flexible, and rapid response characteristics.

[0003] Active power regulation through user demand response has characteristics that differ from power sources and centralized energy storage: First, due to changes in response willingness and equipment status, user-side power response has high uncertainty, making it difficult to reliably meet system demand response capacity. Second, individual demand response resources have small capacities and are distributed across different main transformers. During periods of high photovoltaic power generation, power peaks generate downward adjustment demand, and concentrated demand response may lead to main transformer overload. Currently, some demand response resource optimization methods, starting from the perspective of demand response resources, guide the response behavior of demand response resources through penalty or incentive costs, indirectly improving the overall grid demand capacity execution effect. However, these methods do not take into account whether the grid can obtain response capacity with a sufficient confidence level. Furthermore, the problem of main transformer overload caused by simultaneous resource response is often overlooked. Therefore, there is an urgent need for a method that seeks a sufficient, reliable, economical, and safe combination of demand response resources starting from grid-side demand.

[0004] Optimization methods can solve for a set of parameters to achieve optimal design performance under a series of constraints, and have been widely used in system scheduling and configuration problems. However, further exploration is needed to comprehensively consider the demand response combination problem of the power grid, transform it into a mathematical optimization model, efficiently obtain optimization results, and intuitively present the demand response resource combination to better serve power grid dispatch. Summary of the Invention

[0005] In view of the defects and shortcomings of the existing technology, the present invention provides a demand response combination optimization method that takes into account response reliability and main transformer load balancing, which is used to improve the reliability, security and economy of power grid demand response resource combination scheme.

[0006] The design and implementation process of this scheme includes reading power grid and demand response resources, establishing response reliability and main transformer load balancing indicators, establishing a demand response optimization model, solving heuristic optimization algorithms, and visualization.

[0007] First, the power grid and demand response resource reading module obtains existing system demand, demand response resources, and connected transformer information from the operating power grid. The demand response resource information includes the demand response type, response power distribution model, and unit capacity response cost. The connected transformer information includes the transformer's rated capacity, power factor, and pre-demand response load rate.

[0008] Secondly, response reliability and transformer load balancing indices are constructed to evaluate the feasibility of demand response combinations. Response reliability reflects the confidence level that the resources participating in the response meet the response capacity requirements, ensuring that the demand response combination can respond reliably. The transformer load balancing index reflects whether the load of each transformer is relatively balanced when different demand response resources respond simultaneously, avoiding the risk of transformer overload during the demand response process.

[0009] Furthermore, a demand response optimization model is constructed based on the read demand response resources and main transformer information. The model includes an objective function that minimizes the overall network demand response resource response cost, response reliability constraints for participating resources, load balancing constraints after demand response resources are connected to the main transformer, and logical constraints on variables representing whether demand response resources are added.

[0010] Then, a heuristic and efficient algorithm is used to solve the demand response optimization model to obtain the demand response combination. This algorithm combines the objective function of minimizing the demand response cost, sequentially judges the changes in indicators after a certain demand response resource is included in the response in a certain order, and adjusts the combination of demand response resources. After iteration, the final demand response combination is output.

[0011] Finally, the visualization module converts the optimization model solution results into an intuitive demand response combination by matching them with the demand response database. It presents the results in both text and graphical modes, supports data export, and clearly demonstrates to dispatchers the optimal demand response invocation strategy that comprehensively considers multiple dimensions such as response reliability, main transformer load balancing, and response cost.

[0012] This invention presents a demand response combination optimization method that considers response reliability and transformer load balancing. Based on demand response resources and the operating status of connected transformers, it proposes multi-dimensional indices for response reliability and transformer load balancing, constructs a demand response optimization model, and utilizes heuristic algorithms to efficiently solve optimization problems involving a large number of 0-1 variables, obtaining demand response resource combinations. This provides dispatchers with an intuitive and clear demand response resource combination strategy. This method ensures that demand response provides response capacity at the confidence level requirements while avoiding transformer overload problems when distributed resources participate in the response, achieving economic optimization and providing the power grid with a reliable, safe, and economical demand response combination scheme.

[0013] The present invention specifically adopts the following technical solution:

[0014] A demand response combined optimization method considering response reliability and main transformer load balancing

[0015] By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are included in the evaluation, and the model is solved based on a heuristic optimization algorithm to obtain the demand response resource combination.

[0016] The demand response resource combination optimization model includes: an objective function to minimize the overall network demand response resource response cost, response reliability constraints of participating response resources, load balancing constraints after demand response resources are connected to the main transformer, and logical constraints of variables representing whether demand response resources are added.

[0017] The heuristic optimization algorithm combines the objective function of minimizing demand response cost, and sequentially judges the satisfaction of the response reliability constraint and the main transformer load balancing constraint after a certain demand response resource is included in the response in a certain order. It then performs combination adjustment of demand response resources, gives a feasible solution to the combination optimization problem to be solved, and outputs the final demand response combination after iteration.

[0018] Furthermore, the demand response resource combination optimization model specifically includes:

[0019] Optimization goal:

[0020] To minimize the response cost of the entire network's demand response resources, the objective function is shown in equation (1):

[0021]

[0022] Wherein, the superscript 'a' represents the main transformer number for demand response resource access, and the subscript 'i' represents the demand response resource sequence number; 'c' represents the unit capacity response cost parameter of the demand response resource, and 'x' is a variable representing whether the demand response resource participates in the response, with a value of 1 indicating participation and a value of 0 indicating non-participation; g i a(p) is the response power probability density function of the i-th resource connected to transformer a;

[0023] Constraints:

[0024] This includes response reliability constraints, main transformer load balancing constraints, and variable logic constraints;

[0025] The response reliability constraint refers to the ability of the resources involved in the response to meet the response capacity requirements with the required confidence level, as shown in equations (2) and (3).

[0026]

[0027] Where l and u represent the lower and upper limits of response capacity demand, respectively, α is the confidence level parameter, p represents the response power, and f(p) is the probability density function of the sum of the response powers of the resources participating in the response. This is a convolution operator; assuming that resource response behaviors are independent of each other, the probability density function of the sum of response powers is the result of convolving the probability density functions of the individual resource response powers.

[0028] The main transformer load balancing constraint is a constraint set to consider the risk of overload of the connected main transformers when responding to different demands and resources at the same time. It requires that the load rate of each connected main transformer should be as balanced as possible after the response, as shown in equations (4)-(6).

[0029]

[0030] qm≤ε (6)

[0031] Where q and m are introduced intermediate variables, and s a This represents the load power before the main transformer a responds, o a ε represents the rated capacity of main transformer a, and ε represents the upper limit of the allowable deviation of the load rate;

[0032] The variable logical constraints are represented by variable attributes, indicating that the variable is a 0-1 variable and must satisfy equation (7);

[0033]

[0034] Furthermore, the objective function of the demand response resource combination optimization model considers the unit capacity response price parameter of the demand response resources, sorted from smallest to largest, as the basis for resource selection. For each newly selected demand response resource, it is determined whether the response reliability constraint and the main transformer load balancing constraint are satisfied after the resource is included in the response, and the combination adjustment of the demand response resources is carried out. The determination of whether to include a demand response resource is based on the following three conditions.

[0035] Condition 1: The resource has not yet been included in the response, as shown in Equation (8);

[0036]

[0037] Condition 2: After the new demand response resources are added, the response reliability index is improved, that is, equations (9)-(11) hold;

[0038]

[0039] Condition 3: After the new resource is added, the load rates of different main transformers do not exceed the upper limit of the allowable deviation, that is, equation (12) holds;

[0040]

[0041] After taking reliability into account, the capacity of all demand response resources should be able to cover the upper limit of the response capacity requirement, that is, there must be a solution that satisfies equations (2) and (3). Considering the situation where the response reliability performance does not meet the requirements due to the failure to meet the main transformer load balancing constraint after traversing all resources, the method of relaxing the main transformer load balancing constraint and then searching for demand response resource combinations from the beginning is adopted; whether to relax the main transformer load balancing constraint is distinguished by the variables flag=0 and flag=1 respectively.

[0042] Furthermore, solving the demand response resource combination optimization model based on heuristic optimization algorithms specifically includes the following steps:

[0043] Demand response resources are prioritized; response cost per unit capacity of the resources is determined by parameter c. i a Sort the resources by size from smallest to largest to generate an n×2 index matrix Index, where n represents the total number of demand response resources, the first column marks the main transformer sequence number a of the resource accessed, and the second column marks the resource sequence number i of the main transformer accessed by the resource.

[0044] Initialize parameters; initialize the inclusion flag of demand response resources, the number of search rows, and the relaxation flag of the main transformer load balancing constraint;

[0045] Demand response resource search: Search for demand response resources sequentially starting from the first row of the Index matrix, and record the current search row number as count;

[0046] Determine the relaxation flag of the main transformer load balancing constraint; when the main transformer load balancing constraint is not relaxed, i.e., flag=0, the inclusion of demand response resources requires meeting conditions 1 and 2; when the main transformer load balancing constraint is relaxed, i.e., flag=1, the inclusion of demand response resources requires meeting conditions 1, 2 and 3.

[0047] Determine whether the current demand response resource should be included; if so, set the inclusion flag x. ia Mark it as 1 and move it to the next line for retrieval; if the condition is not met, move it directly to the next line for retrieval.

[0048] Determine whether the main transformer load balancing constraint needs to be relaxed; after traversing all demand response resources, determine whether the response reliability of the current demand response combination meets the standard; if it does not meet the standard, relax the main transformer load balancing constraint and search and judge the demand response resources again; if it meets the standard, output the demand response resource combination result.

[0049] Furthermore, the demand response resource combination optimization model obtains demand response resources and access transformer information from the operating power grid through the power grid and demand response resource reading module; wherein, the demand response resource information includes demand response type, response power distribution model and unit capacity response cost; the access transformer information includes the rated capacity, power factor and load factor before demand response of the transformer.

[0050] And, a demand response resource combination optimization system that takes into account response reliability and main transformer load balancing, comprising: a demand response resource combination optimization model and a calculation module;

[0051] The demand response resource combination optimization model includes: an objective function to minimize the overall network demand response resource response cost, response reliability constraints of participating response resources, load balancing constraints after demand response resources are connected to the main transformer, and logical constraints of variables representing whether demand response resources are added.

[0052] The calculation module is used to use a heuristic optimization algorithm combined with the objective function of minimizing the demand response cost to determine the satisfaction of the response reliability constraint and the main transformer load balancing constraint after a certain demand response resource is included in the response in a certain order, and to perform combination adjustment of demand response resources, give a feasible solution to the combination optimization problem to be solved, and output the final demand response combination after iteration.

[0053] Furthermore, it also includes a power grid and demand response resource reading module, used to obtain demand response resources and access transformer information from the operating power grid; wherein, the demand response resource information includes demand response type, response power distribution model and unit capacity response cost; access transformer information includes the rated capacity, power factor and load factor before demand response of the transformer, and then input into the demand response resource combination optimization model.

[0054] Furthermore, it also includes a visualization module, which is used to match the optimization model solution results output by the calculation module with the demand response database to convert them into demand response combinations and display them.

[0055] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of a demand response combination optimization method considering response reliability and main transformer load balancing as described above.

[0056] A non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a demand response combination optimization method as described above, taking into account response reliability and main transformer load balancing.

[0057] Compared with existing technologies, the present invention and its preferred solutions improve the reliability and security of demand response combinations while ensuring the economic efficiency of demand response in power grid demand response scenarios, and enhance the feasibility of demand response solutions. Attached Figure Description

[0058] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments:

[0059] Figure 1 This is a schematic diagram of the overall process of designing and implementing the method in the embodiments of the present invention;

[0060] Figure 2 This is a schematic diagram of the system adjustment requirements and demand response combination according to an embodiment of the present invention;

[0061] Figure 3 This is a flowchart of the heuristic algorithm for demand response resource combination optimization according to an embodiment of the present invention. Detailed Implementation

[0062] In the following, specific embodiments of this application will be described in detail with reference to the accompanying drawings. Based on these detailed descriptions, those skilled in the art will be able to clearly understand and implement this application. Without departing from the principles of this application, features from various embodiments can be combined to obtain new implementations, or certain features from some embodiments can be substituted to obtain other preferred implementations.

[0063] To make the features and advantages of this patent more apparent and understandable, specific embodiments are provided below, along with accompanying drawings, for detailed explanation:

[0064] This invention provides a demand response combination optimization method that considers response reliability and transformer load balancing, comprising: reading the power grid operation status to obtain information on existing demand response resources and connected transformers from the operating power grid; using response reliability and transformer load balancing indices to quantitatively evaluate the feasibility of demand response combinations; constructing a demand response optimization model to abstract the physical problem of demand response resource optimization considering response reliability and transformer load balancing into a mathematical form; using a heuristic efficient algorithm to take the capacity response cost of demand response resources as the basis for resource selection, judging the satisfaction of response reliability constraints and transformer load balancing constraints after resources are included in the response, and adjusting the combination of demand response resources, finally providing a feasible solution to the combination optimization problem to be solved; and a visualization module to convert the optimization model solution results into intuitive demand response combinations, presented in both text and graphic modes, generating demand response resource combination strategies for dispatchers to refer to.

[0065] The power grid and demand response resource reading module specifically includes: reading the type of demand response resources, response power distribution model, and unit capacity response cost of the current region to form a standardized demand response resource database; reading the main transformer information that can be accessed by the demand response resources, including the rated capacity, power factor, and pre-demand response load rate of the main transformers, and recording the connection relationship between the demand response resources and each main transformer; and regularly updating the demand response resource database and the main transformer information to reflect the latest status of the power grid.

[0066] It includes quantitative evaluation of the response reliability of demand response resources and the load balancing index of main transformers; response reliability is the confidence level of the resources participating in the response to meet the response capacity requirements; the load balancing of main transformers requires that the load rate of each connected main transformer should be as balanced as possible after the demand response.

[0067] The established model includes: (1) minimizing the response cost of the entire network demand response resources as the optimization objective; (2) response reliability constraints, main transformer load balancing constraints and variable logic constraints; the response reliability constraint is that the resources participating in the response can meet the response capacity requirements at the required confidence level; the main transformer load balancing constraint is that the allowable deviation of the main transformer load after the demand response resources are simultaneously connected is within the allowable range; the variable logic constraint characterizes whether the demand response resources participate in the response.

[0068] The algorithm solution steps include: sorting demand response resources according to the unit capacity response price parameter; selecting demand response resources according to the sorting; determining whether the response reliability is improved and whether the main transformer load balancing constraint is met after the resource is included in the response; adjusting the combination of demand response resources; selecting demand response resources according to the sorting and repeating the judgment steps; and outputting the final demand response resource combination.

[0069] The visualization module specifically includes: matching the demand response resource library and accessing the main transformer data based on the optimization model solution results, outputting the demand response resource call strategy in text and chart form, and supporting tabular data export.

[0070] The model and solution algorithm are the core design elements of this invention for solving the technical problem, while the power grid and demand response resource reading module and visualization display are optional functions. Figure 1 As shown, in a typical embodiment, a complete implementation of the above solution may generally include the following steps, but it should be noted that the numbering of these steps should not be regarded as a limitation of the present invention:

[0071] Step 1: Read Demand Response Resource Information. Read the current system response demand for this region; read the type of demand response resource, the response power distribution model, and the unit capacity response cost; read the main transformer information connected to the demand response resource, including the rated capacity, power factor, and pre-demand response load rate of the main transformer, as well as the connection relationship between the demand response resource and each main transformer.

[0072] Step 2: Construct response reliability and main transformer load balancing indices. Construct mathematical indices for response reliability based on the grid demand response capacity requirements (including upper capacity limit, lower capacity limit, and confidence level) and the power distribution probability density function of demand response resources; construct main transformer load balancing indices based on the load power before main transformer response, the rated capacity of the main transformer, and the upper limit of allowable load rate deviation.

[0073] Step 3: Construct a demand response optimization model. With the optimization objective of minimizing the overall network demand response resource response cost, add variable logical constraints, and convert the response reliability and main transformer load balancing indices into response reliability constraints and main transformer load balancing constraints.

[0074] Step 4: Solve the optimization model using a heuristic algorithm. Consider the unit capacity response price parameter based on demand response resources, sort them from smallest to largest, and use them as the basis for resource selection. For each newly selected demand response resource, determine whether the response reliability constraint and main transformer load balancing constraint are met after the resource is included in the response, and adjust the combination of demand response resources according to the indicators.

[0075] Step 5: Visualization. Based on the optimization model's solution results, the demand response resource library and access transformer data are matched, and the demand response resource mobilization strategy is presented in text and graphical form. The text displays the demand response resource number and capacity invoked under the current grid response demand; the graphical representation shows the access relationship between the demand response resources and the transformers, ensuring a clear and intuitive understanding of the demand response combination results.

[0076] More specifically, in the preferred embodiment of this work:

[0077] Step 1: Reading power grid and demand response resource information.

[0078] Demand response resources are diverse. By analyzing the types of demand response resources, their power distribution models, and unit capacity response costs in the current region, a standardized demand response resource model and database are created to accurately describe their response behavior and participate in mathematical optimization models. Based on this, the system identifies the transformer information connected to the demand response resources, including the transformer's rated capacity, power factor, and pre-demand response load rate, and records the connection relationships between the demand response resources and each transformer. Simultaneously, the system is configured to continuously collect demand response resource data, update the power response model for each resource, and update information such as grid-side active power regulation demand and transformer data to more accurately reflect the grid's demand response behavior.

[0079] Step 2: Constructing response reliability and main transformer load balancing indicators.

[0080] The power grid dispatching department calculates and issues the demand response capacity requirement based on the current power grid operation mode. Considering the uncertainty of user response behavior, the response capacity requirement is given in the range [l, u], with a required expected confidence level α. The corresponding physical meaning is that the users ultimately included in the response should be able to jointly provide a response capacity of [l, u] with a probability not less than α; this requirement is defined as the response reliability requirement. Mathematically, this means that the area enclosed by the probability density curve of the total response power and the horizontal axis [l, u] must be greater than or equal to the confidence level α. Figure 2 As shown.

[0081] The demand response capacity requirement needs to be fulfilled by demand response resources from different main transformers. Different resource combinations will lead to different costs, response reliability, and main transformer load rate performance. In practical engineering problems, it is necessary to avoid situations where demand response causes some main transformers to be severely overloaded while others still have considerable capacity. That is, the load rate deviation of different main transformers should ideally be controlled within a certain deviation limit. This requirement is called the main transformer load balancing requirement. The demand response resource optimization problem is to determine the main transformers corresponding to the participating response resources and their sequence numbers among all connected main transformers' response resources, so as to satisfy the requirements of response reliability and main transformer load balancing while seeking the optimal economy.

[0082] Step 3: Demand response optimization model construction.

[0083] Based on the demand response resource information in step one and the indicators in step two, step three abstracts and constructs a mathematical model based on the physical problem of demand response resource optimization that takes into account response reliability and main transformer load balancing.

[0084] 1. Optimization Goal:

[0085] The optimization objective of this method is to minimize the response cost of the entire network's demand response resources. The objective function is shown in equation (1):

[0086]

[0087] Wherein, the superscript 'a' represents the main transformer number for demand response resource access, and the subscript 'i' represents the demand response resource sequence number. 'c' represents the unit capacity response cost parameter of the demand response resource, and 'x' is a variable representing whether the demand response resource participates in the response; a value of 1 indicates participation, and a value of 0 indicates non-participation. g i a (p) is the response power probability density function of the i-th resource connected to transformer a.

[0088] 2. Constraints:

[0089] The constraints considered in this method include response reliability constraints, main transformer load balancing constraints, and variable logic constraints.

[0090] (1) Response reliability constraints

[0091] The response reliability constraint refers to the ability of the resources involved in the response to meet the response capacity requirements with the required confidence level, as shown in equations (2) and (3).

[0092]

[0093] Where l and u represent the lower and upper limits of response capacity demand, respectively, α is the confidence level parameter, p represents the response power, and f(p) is the probability density function of the sum of the response powers of the resources participating in the response. This is a convolution operator. Assuming that resource response behaviors are independent, the probability density function of the sum of response powers is the result of the convolution of the probability density functions of the individual resource response powers.

[0094] (2) Main transformer load balancing constraints

[0095] The main transformer load balancing constraint is a constraint set to consider the risk of overload of the main transformer when responding to different demands and resources at the same time. It requires that the load rate of each main transformer after the response should be as balanced as possible, as shown in equations (4)-(6).

[0096]

[0097] qm≤ε (6)

[0098] Where q and m are introduced intermediate variables, and s a This represents the load power before the main transformer a responds, o a ε represents the rated capacity of main transformer a, and ε represents the upper limit of the allowable deviation of the load rate.

[0099] (3) Variable logical constraints

[0100] Based on the variable attributes, a variable is characterized as a 0-1 variable and must satisfy equation (7).

[0101]

[0102] Step 4: Solve the optimization model using heuristic algorithms.

[0103] As shown in equations (1)-(7), the demand response resource optimization model is a mixed-integer optimization problem. The individual capacities of demand response resources are relatively small, and when the power grid experiences power regulation demands requiring demand response, the resulting deficit is often substantial, necessitating the introduction of a large number of 0-1 variables. As the number of 0-1 variables increases, the complexity of rigorous mathematical programming methods grows exponentially, leading to difficulties in solving the problem. This invention employs a heuristic, efficient solution algorithm to improve computational efficiency while maintaining low error in the calculation results.

[0104] Based on the objective function of the optimization model, the unit capacity response price parameter of demand response resources is sorted from smallest to largest as the basis for resource selection. For each newly selected demand response resource, the satisfaction of the response reliability constraint and the main transformer load balancing constraint after its inclusion in the response is assessed, and the combination of demand response resources is adjusted accordingly. The determination of whether to include a demand response resource is based on the following three conditions.

[0105] Condition 1: The resource has not yet been included in the response, as shown in Equation (8).

[0106]

[0107] Condition 2: After the new demand response resources are added, the response reliability index is improved, that is, equations (9)-(11) hold.

[0108]

[0109] Condition 3: After the new resource is added, the load rate of different main transformers does not exceed the upper limit of the allowable deviation, that is, Equation (12) holds.

[0110]

[0111] In practical engineering, after taking reliability into consideration, the capacity of all demand response resources should be able to cover the upper limit of the response capacity demand, that is, there must be a solution that can satisfy equations (2) and (3), but the rigidity requirement of the main transformer load balancing constraint is relatively low. This method takes into account the situation where the response reliability performance does not meet the requirements due to the failure to satisfy the main transformer load balancing constraint after traversing all resources, and handles it by relaxing the main transformer load balancing constraint and then searching for demand response resource combinations from scratch. Whether the main transformer load balancing constraint is relaxed or not is distinguished by the variables flag=0 and flag=1 respectively.

[0112] The flowchart of the demand response resource optimization heuristic algorithm is as follows: Figure 3 As shown.

[0113] The specific steps of the heuristic algorithm used in this invention are as follows:

[0114] Step 1: Demand response resource prioritization. Based on the demand response resource unit capacity response cost parameter c... i a Sort the resources by size from smallest to largest to generate an n×2 index matrix Index, where n represents the total number of demand response resources. The first column marks the main transformer sequence number a to which the resource is connected, and the second column marks the resource sequence number i corresponding to the main transformer connected to the resource.

[0115] Step 2: Initialize parameters. Initialize the inclusion flag of demand response resources, the number of search rows, and the relaxation flag of the main transformer load balancing constraint.

[0116] Step 3: Demand Response Resource Search. Starting from the first row of the Index matrix, search for demand response resources sequentially, and record the current search row number as count.

[0117] Step 4: Determine the relaxation flag of the main transformer load balancing constraint. When the main transformer load balancing constraint is not relaxed (flag=0), the inclusion of demand response resources requires meeting conditions 1 and 2; when the main transformer load balancing constraint is relaxed (flag=1), the inclusion of demand response resources requires meeting conditions 1, 2, and 3.

[0118] Step 5: Determine whether the current demand response resource should be included. If it is determined to be included, set the inclusion flag x. i a Mark it as 1 and move it to the next line for retrieval; if the condition is not met, move it directly to the next line for retrieval.

[0119] Step 6: Determine if it is necessary to relax the main transformer load balancing constraints. After traversing all demand response resources, determine whether the response reliability of the current demand response combination meets the standard. If it does not meet the standard, relax the main transformer load balancing constraints and search and evaluate the demand response resources again; if it meets the standard, output the demand response resource combination result.

[0120] Step 5: Visual presentation.

[0121] First, based on the solution results of the demand response combinatorial optimization problem, the demand response resource database is matched, the demand response resource combinations are identified, and the resources are grouped and arranged according to the main transformers connected to them, thus completing the transformation from the mathematical problem results to specific demand response combinatorial schemes.

[0122] Secondly, the demand response combination scheme is presented in text and diagram form, outputs the demand response resources participating in the response on each main transformer, and displays the confidence level of reliable response under the current combination scheme and the maximum and minimum load rate deviation of each main transformer. The results support tabular data export.

[0123] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0128] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

[0129] This patent is not limited to the above-described preferred embodiment. Anyone can derive other forms of demand response combination optimization method that takes into account response reliability and main transformer load balancing under the guidance of this patent. All equivalent changes and modifications made within the scope of this patent application shall fall within the scope of this patent.

Claims

1. A demand response combination optimization method considering response reliability and main transformer load balancing, characterized in that: By constructing a demand response resource combination optimization model, the reliability of resource response and the load balancing of multiple main transformers are included in the evaluation, and the model is solved based on a heuristic optimization algorithm to obtain the demand response resource combination. The demand response resource combination optimization model includes: an objective function to minimize the overall network demand response resource response cost, response reliability constraints of participating response resources, load balancing constraints after demand response resources are connected to the main transformer, and logical constraints of variables representing whether demand response resources are added. The heuristic optimization algorithm combines the objective function of minimizing the demand response cost, sorts the demand response resources in ascending order, and judges whether the response reliability constraints and main transformer load balancing constraints are satisfied after a certain demand response resource is included in the response. It then performs combination adjustment of demand response resources, gives a feasible solution to the combination optimization problem to be solved, and outputs the final demand response combination after iteration. The sorting process is as follows: for each newly selected demand response resource, it is determined whether the response reliability constraints and main transformer load balancing constraints are met after the resource is included in the response, and the combination of demand response resources is adjusted accordingly; the determination of whether a demand response resource is included is based on the following three conditions. Condition 1: This resource has not yet been included in the response; Condition 2: After adding new demand response resources, the response reliability index improves; Condition 3: After the resource is added, the load rates of different main transformers do not exceed the upper limit of the allowable deviation. With reliability taken into account, the capacity of all demand response resources should be able to cover the upper limit of response capacity requirements. Considering the scenario where the response reliability performance fails to meet requirements due to the main transformer load balancing constraint not being satisfied after traversing all resources, a method is adopted to relax the main transformer load balancing constraint and then search for the demand response resource combination from scratch. Whether to relax the main transformer load balancing constraint is distinguished by the variables flag=0 and flag=1 respectively.

2. The demand response combination optimization method considering response reliability and main transformer load balancing according to claim 1, characterized in that: The demand response resource combination optimization model specifically includes: Optimization goal: To minimize the response cost of the entire network's demand response resources, the objective function is shown in equation (1): Wherein, the superscript 'a' represents the main transformer number for demand response resource access, and the subscript 'i' represents the demand response resource sequence number; 'c' represents the unit capacity response cost parameter of the demand response resource, and 'x' is a variable representing whether the demand response resource participates in the response, with a value of 1 indicating participation and a value of 0 indicating non-participation; g i a (p) is the response power probability density function of the i-th resource connected to transformer a; Constraints: This includes response reliability constraints, main transformer load balancing constraints, and variable logic constraints; The response reliability constraint refers to the ability of the resources involved in the response to meet the response capacity requirements with the required confidence level, as shown in equations (2) and (3). Where l and u represent the lower and upper limits of response capacity demand, respectively, α is the confidence level parameter, p represents the response power, and f(p) is the probability density function of the sum of the response powers of the resources participating in the response. This is a convolution operator; assuming that resource response behaviors are independent of each other, the probability density function of the sum of response powers is the result of convolving the probability density functions of the individual resource response powers. The main transformer load balancing constraint is a constraint set to consider the risk of main transformer overload when different demand response resources respond simultaneously. As shown in equations (4)-(6); qm≤ε (6) Where q and m are introduced intermediate variables, and s a This represents the load power before the main transformer a responds, o a ε represents the rated capacity of main transformer a, and ε represents the upper limit of the allowable deviation of the load rate; The variable logic constraint, based on the variable attribute, represents the variable as a 0-1 variable and must satisfy equation (7); 3. The demand response combination optimization method considering response reliability and main transformer load balancing according to claim 2, characterized in that: The objective function of the demand response resource combination optimization model is used to sort the unit capacity response price parameters of the demand response resources from smallest to largest as the basis for resource selection. Condition 1 is shown in equation (8); Condition 2 corresponds to the fulfillment of equations (9)-(11); Condition 3 corresponds to equation (12) being true; 4. The demand response combination optimization method considering response reliability and main transformer load balancing according to claim 3, characterized in that: Solving the demand response resource combination optimization model based on heuristic optimization algorithms specifically includes the following steps: Demand response resources are prioritized; response cost per unit capacity of the resources is determined by parameter c. i a Sort the resources by size from smallest to largest to generate an n×2 index matrix Index, where n represents the total number of demand response resources, the first column marks the main transformer sequence number a of the resource accessed, and the second column marks the resource sequence number i of the main transformer accessed by the resource. Initialize parameters; initialize the inclusion flag of demand response resources, the number of search rows, and the relaxation flag of the main transformer load balancing constraint; Demand response resource search: Search for demand response resources sequentially starting from the first row of the Index matrix, and record the current search row number as count; Determine the relaxation flag of the main transformer load balancing constraint; when the main transformer load balancing constraint is not relaxed, i.e., flag=0, the inclusion of demand response resources requires meeting conditions 1 and 2; when the main transformer load balancing constraint is relaxed, i.e., flag=1, the inclusion of demand response resources requires meeting conditions 1, 2 and 3. Determine whether to include the current demand response resources based on the conditions; if it is determined that they should be included, mark them as included. Mark it as 1 and move it to the next line for retrieval; if the condition is not met, move it directly to the next line for retrieval. Determine whether the main transformer load balancing constraint needs to be relaxed; after traversing all demand response resources, determine whether the response reliability of the current demand response resource combination meets the standard; if it does not meet the standard, relax the main transformer load balancing constraint and search and judge the demand response resources again; if it meets the standard, output the demand response resource combination result.

5. The demand response combination optimization method considering response reliability and main transformer load balancing according to claim 1, characterized in that: The demand response resource combination optimization model obtains demand response resources and access transformer information from the operating power grid through the power grid and demand response resource reading module. The demand response resource information includes the demand response type, response power distribution model, and unit capacity response cost. The access transformer information includes the rated capacity, power factor, and pre-demand response load rate of the transformer.

6. A demand response combined optimization system considering response reliability and main transformer load balancing, characterized in that, include: Demand response resource combination optimization model and calculation module; The demand response resource combination optimization model includes: an objective function to minimize the overall network demand response resource response cost, response reliability constraints of participating response resources, load balancing constraints after demand response resources are connected to the main transformer, and logical constraints of variables representing whether demand response resources are added. The calculation module is used to use a heuristic optimization algorithm combined with the objective function of minimizing the demand response cost to judge the satisfaction of the response reliability constraint and the main transformer load balancing constraint after a certain demand response resource is included in the response in ascending order, and to perform combination adjustment of demand response resources, give a feasible solution to the combination optimization problem to be solved, and output the final demand response combination after iteration. The sorting process is as follows: for each newly selected demand response resource, it is determined whether the response reliability constraints and main transformer load balancing constraints are met after the resource is included in the response, and the combination of demand response resources is adjusted accordingly; the determination of whether a demand response resource is included is based on the following three conditions. Condition 1: This resource has not yet been included in the response; Condition 2: After adding new demand response resources, the response reliability index improves; Condition 3: After the resource is added, the load rates of different main transformers do not exceed the upper limit of the allowable deviation. With reliability taken into account, the capacity of all demand response resources should be able to cover the upper limit of response capacity requirements. Considering the scenario where the response reliability performance fails to meet requirements due to the main transformer load balancing constraint not being satisfied after traversing all resources, a method is adopted to relax the main transformer load balancing constraint and then search for the demand response resource combination from scratch. Whether to relax the main transformer load balancing constraint is distinguished by the variables flag=0 and flag=1 respectively.

7. A demand response combined optimization system considering response reliability and main transformer load balancing according to claim 6, characterized in that: It also includes a power grid and demand response resource reading module, used to obtain demand response resources and access transformer information from the operating power grid; wherein, the demand response resource information includes demand response type, response power distribution model and unit capacity response cost; access transformer information includes the rated capacity, power factor and load rate before demand response of the transformer; after reading, the data is input into the demand response resource combination optimization model.

8. A demand response combined optimization system considering response reliability and main transformer load balancing according to claim 6, characterized in that: It also includes a visualization module, which is used to match the optimization model solution results output by the calculation module with the demand response database to convert them into demand response combinations and display them.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the demand response combination optimization method as described in any one of claims 1-5, which takes into account response reliability and main transformer load balancing.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of a demand response combination optimization method that takes into account response reliability and main transformer load balancing as described in any one of claims 1-5.

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