Distributed source-load end-to-end transaction optimization control method considering user willingness

Abstract

The present invention belongs to the field of new energy consumption and power trading, and specifically relates to a distributed source-load end-to-end transaction optimization control method taking into account user intentions. An original source-load end-to-end model is established; based on the original source-load end-to-end model, a distributed source-load end-to-end optimization control model with user intentions is established; based on the distributed source-load end-to-end optimization control model with user intentions in the second step, a distributed end-to-end optimization control model with network constraints and user intentions is established. By using an endogenous application load vector matrix, the intermediate flow calculation is simplified and it is ensured that bilateral transactions meet network constraints. The augmented Lagrange multiplier method is used to include network constraints in the objective function, reducing the constraints of the objective function; the introduction of user intentions is conducive to the reasonable control of transactions.

Description

Technical Field

[0001] The present invention belongs to the field of new energy consumption and power trading, and specifically relates to a distributed source-load end-to-end transaction optimization control method taking user wishes into account. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Currently, the power system is facing increasing operational pressure. With rising energy consumption, traditional fossil fuel-based power generation is increasingly unable to meet electricity demand. Renewable energy sources offer advantages such as large reserves, low pollution, and recyclability. Effectively developing these resources will help promote sustainable energy development. However, large-scale centralized renewable energy generation faces challenges such as large power generation fluctuations and difficulty absorbing them. Distributed generation, with its greater flexibility and ease of absorption, balances power demand amidst power fluctuations and is more suitable for the development of new power systems. Therefore, the rational integration and coordination of distributed energy resources can effectively alleviate pressure on the power grid and minimize the frequency and impact of power outages.

[0004] To unlock the potential of distributed generation, in addition to implementing new energy subsidy policies, end-to-end trading is currently the best solution. Distributed source-load end-to-end trading allows producers and users to directly participate in the transaction process, eliminating the need to rely on traditional independent operators. This allows for greater initiative and operability, fosters more competitive markets, and generates greater benefits for distributed generation agents. However, distributed generation capacity is generally small, transaction cycles are short, and the transaction process is characterized by multiple actors.

[0005] Currently, there are still two problems in the existing distributed source-load end-to-end transaction optimization control method.

[0006] 1) Due to the constraints of the distribution network, energy trading is not feasible, thus disrupting the stable operation of the power system;

[0007] 2) Energy trading does not take into account the preferences of multiple users, making it difficult to reasonably control transactions. Summary of the Invention

[0008] The present invention proposes a distributed source-load end-to-end transaction optimization control method that takes user preferences into account, in order to solve the problems in the existing technology that energy trading is infeasible due to the constraints of the distribution network, and that energy trading does not take into account the preferences of multiple users, making it difficult to reasonably control the transaction.

[0009] To achieve the above object, the present invention proposes the following technical solutions:

[0010] A distributed source-load end-to-end transaction optimization control method taking user intention into account comprises the following steps:

[0011] The first step is to establish an end-to-end source-load primitive model;

[0012] The second step is to establish a distributed source-load end-to-end optimization control model with user intentions based on the original source-load end-to-end model;

[0013] The third step is to establish a distributed end-to-end optimization control model with network constraints and user preferences based on the distributed source-load end-to-end optimization control model with user preferences obtained in the second step.

[0014] The fourth step is to input the grid topology data and the user data on the node to obtain the distributed source-load end-to-end transaction volume, and adjust the power supply system based on the obtained distributed source-load end-to-end transaction volume.

[0015] Preferably, the first step is to establish the original source-load end-to-end model in the following specific steps:

[0016] Suppose the network consists of a set of nodes and a set of lines Composition; and are the number of nodes and the number of lines, respectively;

[0017] A collection of end-to-end market agents ;in , It is a collection of power plants, is a collection of consumers, is the number of agents participating in the market, L is the set of lines;

[0018] Introducing an incidence matrix Represents the correspondence between nodes and agents, let , we get the original source-load end-to-end model, specifically:

[0019] ;

[0020] ;

[0021] ;

[0022] ;

[0023] ;

[0024] ;

[0025] in is the vector of node power injection, is the total power vector of the agent transaction; if the agent Connect to the node ,but ,otherwise If the agent Not with agent To trade, ; Representation Agent In satisfying the constraints The total power of transactions, and are the upper and lower limits of the total transaction power, l ij represents the capacity of the power flow line, and Indicates the upper and lower limits of the power flow line capacity.

[0026] Preferably, agent When selling electricity, Is positive, the formula is:

[0027] .

[0028] Preferably, agent When consuming electricity, is negative, the formula is:

[0029] .

[0030] Preferably, in the second step, the specific steps of establishing a distributed source-load end-to-end optimization control model with user intentions are as follows:

[0031] Introducing user willingness coefficient , user willingness coefficient Electrical distance to the agent Proportional: ; It is the unit distance cost of electricity sales transaction across the grid;

[0032] trend ,in is the value of the current, is the reactance matrix of the line , ;matrix is an incidence matrix; if the line The power flow for the node To Node ,but If the line The power flow for the node To Node ,but ;otherwise ;

[0033] Function of voltage angle ;Balance node ,in ;

[0034] The power flow of the line is expressed as ,or ;in , is the load vector matrix;

[0035] The power flow constraint is expressed as ,in , Is an association matrix that describes the relationship between agents and nodes. If the agent i Connect to the node j ,but ,otherwise ;

[0036] The distributed source-load end-to-end optimization control model with user willingness is obtained as follows:

[0037]

[0038]

[0039]

[0040]

[0041]

[0042]

[0043] .

[0044] Preferably, and satisfy .

[0045] Preferably, the third step is to establish a distributed end-to-end optimization control model with network constraints and user preferences. The specific steps are:

[0046] Make the symbol , the power flow constraint of the line is:

[0047]

[0048] Introducing slack variables in inequalities and It is definitely worth removing:

[0049] ;

[0050] The augmented part of the Lagrangian is:

[0051] ;

[0052] Slack variables 、 and Lagrange multipliers 、 The updates are as follows:

[0053] ;

[0054] ;

[0055] ;

[0056] ;

[0057] According to the augmented part of Lagrange, we have:

[0058] ;

[0059] get:

[0060] ;

[0061] set up , ,get:

[0062] ;

[0063] The distributed end-to-end optimization control model with network constraints and user preferences is obtained:

[0064]

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074] .

[0075] Preferably, a scaled form of the alternating direction multiplier method is used to obtain the augmented part of the Lagrangian.

[0076] The present invention is beneficial in that:

[0077] By introducing user intentions, a distributed end-to-end optimization control model with network constraints and user intentions is established to achieve reasonable control of transactions based on the preferences of multiple users.

[0078] By using the inherent application load vector matrix, we can simplify the intermediate power flow calculation and ensure that the bilateral transactions meet the network constraints. , so that the power flow constraint of the line is expressed as In this way, the voltage angle The need to be an optimization variable;

[0079] The augmented Lagrange multiplier method is used to include network constraints into the objective function, reducing the constraints of the objective function;

[0080] The scaling alternating direction multiplier method is used to optimize the control of distributed source-load end-to-end transactions. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0082] Figure 1 A flow chart of a distributed source-load end-to-end transaction optimization control method taking user preferences into account;

[0083] Figure 2 Schematic diagram of the distributed source-load end-to-end transaction optimization control model taking into account user preferences; DETAILED DESCRIPTION

[0084] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0085] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0086] Example 1:

[0087] See also Figure 1 As shown, the present invention provides a distributed source-load end-to-end transaction optimization control method taking into account user wishes, and the specific steps include:

[0088] Step 1: Assume that the network consists of node B and line L. ,line ;in and are the number of nodes and the number of lines, respectively;

[0089] Set up a collection of end-to-end market agents ;in , It is a collection of power plants, is a collection of consumers, is the number of agents participating in the market, and L represents the set of lines.

[0090] Introducing an incidence matrix Represents the correspondence between nodes and agents, let ,in is the vector of node power injection, and P is the total power vector of agent transactions. Connect to the node ,but ,otherwise .

[0091] Establish the original source-load end-to-end model, specifically:

[0092] Establish the power balance constraints of the agents:

[0093]

[0094] If the agent Not with agent To trade, Assuming the agent When selling electricity, is positive; when consuming electricity, is negative;

[0095] Establishing an agent Power constraints for transactions:

[0096]

[0097] The matrix P collects all possible bilateral transactions within the source-load peer-to-peer community. Representation Agent In meeting the agent The power constraint of the transaction limits the total power of the transaction, and are the upper and lower limits of the total transaction power respectively. n Is a set of trading partners Internal Agent All transactions The result of the sum.

[0098] Establish power flow constraints for the route:

[0099]

[0100]

[0101] in To pass the line The trend, and is the voltage angle at both ends of the line, are line parameters. The transmission constraints are given by Execution, where and are the upper and lower limits of the trend respectively.

[0102] The source-load end-to-end transaction model integrates bilateral transactions between multiple source-load agents and can be described in the form of an optimization problem, whose goal is to minimize the total cost.

[0103] According to the above constraints, the original source-load end-to-end model is:

[0104] (2.2)

[0105] (2.3)

[0106] (2.4)

[0107] (2.5)

[0108] (2.6)

[0109] (2.7)

[0110] (2.8)

[0111] (2.9)

[0112] In this way, the matrix P describes a transaction graph. Equation (2.2) guarantees the power balance of each transaction in the system. The model can be extended to integrate production users as agents without specifying the overall transaction sign. The introduction of the matrix P in the original source-load end-to-end model makes it possible to personalize the price of each transaction, reflecting the nature of the source-load end-to-end. Network constraints in the form of (2.7)-(2.8) are the traditional representation of operational requirements. However, it is not suitable for applications in source-load end-to-end optimization control. To address this problem, we propose a method based on the load vector matrix to ensure that bilateral transactions do not violate network constraints.

[0113] Step 2: Establish a distributed source-load end-to-end optimization control model with user willingness;

[0114] Introducing user intentions , the current work explores the willingness to trade with neighboring agents, reflecting the trend of supporting local power generation and eliminating unnecessary power transfer. Electrical distance to the agent Proportional, that is . is the unit distance cost for a specific transaction, The larger the value of The stronger the willingness to trade. Under the conditions, select symbol.

[0115] The power flow is expressed in the form of a matrix ,in is the value of the current, is the reactance matrix of the line , .matrix Is an association matrix that describes the relationship between the power flow between nodes. The power flow for the node To Node ,but If the line The power flow for the node To Node ,but ;otherwise .

[0116] Using the same sign, the node power injection can be described as a function of the voltage angle, Then select the balance node ,in The power flow of the line can be expressed as a function of the node power injection , or in another form ,in , is a loading vector matrix.

[0117] The power flow constraint is expressed as ,in , Is an association matrix that describes the relationship between agents and nodes. If the agent i Connect to the node j ,but ,otherwise .

[0118] The source-load end-to-end optimization control model with user willingness is:

[0119] (2.10)

[0120] (2.11)

[0121] (2.12)

[0122] (2.13)

[0123] (2.14)

[0124] (2.15)

[0125] (2.16)

[0126] Formulas (2.11)–(2.15) are consistent with the original end-to-end source-load model. The main goal of the modified algorithm is to incorporate user preferences by introducing constraint (2.16) responsible for line limitations and regularizing the objective function. This algorithm serves as a unified optimization-based end-to-end trading framework. No corrections or off-site intervention are required. This setting is applicable to different types of preferences and can be used to investigate the impact of energy policies.

[0127] Step 3: Establish distributed end-to-end optimization control with network constraints and user preferences

[0128] The alternating direction multiplier method is used to solve problem (2.10) and the network constraints are included in the objective function via augmented Lagrangian. Using the notation ,in Load vector matrix, Describing the correspondence between nodes and agents, the power flow constraint of the line is:

[0129] (3.1)

[0130] Then, starting from the absolute value of the power flow, we introduce the slack variable of the inequality and remove the absolute value to obtain the following form:

[0131] (3.2)

[0132] Based on the alternating direction multiplier method in a scaled form, the augmented part of the Lagrangian is:

[0133] (3.3)

[0134] Slack variables 、 and Lagrange multipliers 、 The update is as follows

[0135] (3.4)

[0136] (3.5)

[0137] (3.6)

[0138] (3.7)

[0139] Continuously deriving the first term of (3.3), we can obtain

[0140] (3.8)

[0141] The expression can be written as: (3.9)

[0142] in , .Next step

[0143] (3.10)

[0144] The first term of the distribution between agents When using the following form

[0145] (3.11)

[0146] when hour,

[0147] (3.12)

[0148] Similarly, we can repeat the same process for the second term to get:

[0149] (3.13)

[0150] (3.14)

[0151] The distributed end-to-end transaction model under network constraints and user preferences is:

[0152] (3.15)

[0153] (3.16)

[0154] (3.17)

[0155] (3.18)

[0156] (3.19)

[0157] (3.20)

[0158] (3.21)

[0159] (3.22)

[0160] (3.23)

[0161] (3.24)

[0162] (3.25)

[0163] in , and The expression has the following form:

[0164] (4.1)

[0165] (4.2)

[0166] (4.3)

[0167] (4.4)

[0168] Equations (4.1)-(4.4) describe the distribution among agents and A possible method.

[0169] The available transaction results are:

[0170] P n =（P nl ……P nNΩ ） (5.1)

[0171] The fourth step is to input the grid topology data and the user data on the node to obtain the distributed source-load end-to-end transaction volume, and adjust the power supply system based on the obtained distributed source-load end-to-end transaction volume.

[0172] This paper proposes an optimal control method for end-to-end source-load transactions based on a scaled alternating direction multiplier method that takes into account user preferences and network constraints. The overall optimization control objective of this method is to minimize the total cost of the electricity market and maximize the absorption of renewable energy. Line network constraints are represented by a built-in load vector matrix, and network constraints are incorporated into the objective function using the augmented Lagrange multiplier method. The alternating direction multiplier method decomposes the original optimization problem into several easily solvable sub-optimization problems, which are then solved iteratively. This effectively improves algorithm execution efficiency and enhances data reliability. The current work explores the preference for trading with neighboring agents, demonstrating a trend toward supporting local generation and eliminating unnecessary power transfers. Under this logic, the preference coefficient is proportional to the electrical distance between agents. Furthermore, in the absence of network constraints, line overloads are likely to occur, ultimately rendering transactions infeasible. The proposed optimal control method enables the proper operation of distributed end-to-end source-load transactions without requiring additional corrective action, making it attractive for practical implementation.

[0173] In order to achieve the above object, the present invention adopts the following technical solutions:

[0174] (1) By using an endogenous applied load vector matrix, we simplify the intermediate power flow calculation and ensure that bilateral transactions meet network constraints. We introduce a notation that allows the power flow constraints of the line to be expressed in this way, eliminating the need to use the voltage angle as an optimization variable.

[0175] (2) The augmented Lagrange multiplier method is used to include network constraints into the objective function, reducing the constraints of the objective function;

[0176] (3) Introducing user willingness β, which refers to the preference for transacting with a specific agent compared to other agents. The current work explores the preference for transacting with neighboring agents, reflecting the tendency to support local power generation and eliminate unnecessary power transfers. Under this logic, the preference coefficient is proportional to the power distance between agents.

[0177] (4) The scaling alternating direction multiplier method is used to optimize the control of distributed source-load end-to-end transactions.

[0178] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0179] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0180] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0182] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0183] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A distributed source-load end-to-end transaction optimization control method taking into account user intentions, characterized in that: The steps include: The first step is to establish an end-to-end source-load primitive model; The second step is to establish a distributed source-load end-to-end optimization control model with user intentions based on the original source-load end-to-end model; The third step is to establish a distributed end-to-end optimization control model with network constraints and user preferences based on the distributed source-load end-to-end optimization control model with user preferences obtained in the second step. The fourth step is to input the grid topology data and the user data on the nodes to obtain the distributed source-load end-to-end transaction volume. The power supply system is adjusted based on the obtained distributed source-load end-to-end transaction volume. The first step is to establish the original source-load end-to-end model. The specific steps are: Suppose the network consists of a set of nodes and a set of lines Composition; and are the number of nodes and the number of lines, respectively; A collection of end-to-end market agents ;in , It is a collection of power plants, is a collection of consumers, is the number of agents participating in the market, L is the set of lines; Introducing an incidence matrix Represents the correspondence between nodes and agents, let , we get the original source-load end-to-end model, specifically: ； ； ； ； ； ； in is the vector of node power injection, is the total power vector of the agent transaction; if the agent Connect to the node ,but ,otherwise If the agent Not with agent To trade, ; Representation Agent In satisfying the constraints The total power of transactions, and are the upper and lower limits of the total transaction power, l ij represents the capacity of the power flow line, and Indicates the upper and lower limits of the power flow line capacity, and is the voltage angle at both ends of the line, P represents a matrix that collects all possible bilateral transactions within the source-load end-to-end community, is the line parameter, Gather for trading partners.

2. A distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 1, characterized in that: acting When selling electricity, Is positive, the formula is: 。 3. The distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 1 is characterized in that: acting When consuming electricity, is negative, the formula is: 。 4. The distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 1 is characterized in that: In the second step, the specific steps of establishing a distributed source-load end-to-end optimization control model with user intentions are as follows: Introducing user willingness coefficient , user willingness coefficient Electrical distance to the agent Proportional: ; It is the unit distance cost of electricity sales transaction across the grid; trend ,in is the value of the current, is the reactance matrix of the line , ;matrix is an incidence matrix; if the line The power flow for the node To Node ,but If the line The power flow for the node To Node ,but ;otherwise ; Function of voltage angle ;Balance node ,in ; The power flow of the line is expressed as ,or ;in , is the load vector matrix; The power flow constraint is expressed as ,in , Is an association matrix that describes the relationship between agents and nodes. If the agent i Connect to the node j ,but ,otherwise ; The distributed source-load end-to-end optimization control model with user willingness is obtained as follows: Indicates the upper limit of the current.

5. A distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 4, characterized in that: and satisfy .

6. A distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 4, characterized in that: The third step is to establish a distributed end-to-end optimization control model with network constraints and user preferences. The specific steps are as follows: Make the symbol , the power flow constraint of the line is: Introducing slack variables in inequalities and It is definitely worth removing: ； The augmented part of the Lagrangian is: ； Slack variables 、 and Lagrange multipliers 、 The updates are as follows: ； ； ； ； According to the augmented part of Lagrange, we have: ； get: ； set up , ,get: ； The distributed end-to-end optimization control model with network constraints and user preferences is obtained: 、 Respectively represent lines l Slack variables and Lagrange multipliers for the kth iteration of the first set of power flow constraints.

7. A distributed source-load end-to-end transaction optimization control method taking user intention into account according to claim 6, characterized in that: The augmented part of the Lagrangian is obtained by using the alternating direction multiplier method in a scaled form.

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