A method and system for generating a node load response curve under power transmission and distribution coordination

Abstract

This invention belongs to the field of power transmission and distribution systems, and discloses a method and system for generating nodal baseline load response under transmission and distribution coordination. It integrates the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, iteratively updates the incentive unit price to obtain the optimal value, and linearizes the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator. Finally, based on the linearization result and the optimal unit price, it solves the game equilibrium to generate the nodal baseline load response. The nodal marginal electricity price can characterize the differences in the geographical distribution of nodes, thus transforming a single incentive signal into a differentiated and precise incentive signal. Simultaneously, the linearization process eliminates the limitations of nonlinear terms on commonly used optimizers through mathematical transformation. This enables the demand response mechanism to adapt to the actual operating needs of the power grid under high renewable energy penetration, improves the accuracy of incentives and the feasibility of optimization, effectively enhances the overall effect of demand response, and promotes its practical application.

Description

Technical Field

[0001] This invention belongs to the technical field of power transmission and distribution systems, and particularly relates to a method and system for generating node quasi-line load response under transmission and distribution coordination. Background Technology

[0002] As the global energy transition progresses, the penetration rate of renewable energy in transmission and distribution systems continues to rise, leading to increasingly prominent issues of wind and solar power curtailment, which seriously affect the resource utilization efficiency and supply-demand balance stability of the power system. Demand response, as a key means to address these problems and optimize power system operation, aims to guide users to proactively change their electricity consumption behavior through price signals or incentive mechanisms during power system operation. This includes adjusting electricity consumption time, power consumption, or consumption methods, thereby achieving power supply-demand balance and improving system operating efficiency. In recent years, related technical fields have proposed demand response schemes tailored to load profiles at different node locations. While these schemes have considered network constraints at the transmission and distribution system levels, in large-scale demand response scenarios, the incentive prices received by each node from the upper layer remain consistent, failing to fully adapt to the distributed characteristics of the power grid.

[0003] Existing demand response technologies still have significant shortcomings, making it difficult to meet the precise operational needs of power systems with high renewable energy penetration. On the one hand, while existing research has confirmed a correlation between user load curves and grid power balance, in-depth and systematic research has not been conducted on the intrinsic relationship between user demand curves and nodal marginal prices. Current demand response mechanisms generally ignore the characteristic differences arising from geographical distribution variations among nodes, resulting in overly simplistic incentive signals that are detached from the actual grid distribution and fail to provide precise incentives to users at different nodes, thus affecting the overall effectiveness of demand response. On the other hand, the response quantities generated by distribution system operators to demand response incentives issued by transmission system operators contain nonlinear terms. Commonly used optimizers such as Gurobi have limitations in solving nonlinear constraints, making it difficult to directly solve these nonlinear terms. This hinders the smooth progress of the demand response mechanism optimization process and restricts its practical application.

[0004] It is evident that existing demand response mechanisms suffer from problems such as a single incentive signal and the inability to directly solve nonlinear terms in the response quantity using common optimizers. These issues make it difficult to adapt to the actual operational needs of the power grid under high renewable energy penetration rates, thus hindering the application and promotion of demand response technology. Summary of the Invention

[0005] This invention provides a method and system for generating node quasi-linear load response under transmission and distribution coordination. This method can effectively solve the problems of the existing demand response mechanism, such as the simplification of excitation signals and the inability to directly solve the nonlinear terms in the response quantity using common optimizers. It can adapt to the actual operation requirements of the power grid under high renewable energy penetration, and promote the application effect and popularization of demand response technology.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A method for generating nodal baseline load response under transmission and distribution coordination includes: The marginal electricity price at each node is incorporated into a pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, thereby obtaining the optimal incentive unit price. The two-layer Steinberg game model includes three decision-making entities: the transmission system operator, the distribution system operator, and the user, which are used to characterize the hierarchical master-slave decision-making interaction relationship among the three decision-making entities. In the interaction between transmission system operators and distribution system operators, the exponential and product nonlinear terms in the demand response incentive service quantity of distribution system operators are linearized to obtain a linearized demand response incentive service quantity; wherein, the linearization process incorporates the nodal marginal electricity price; the demand response incentive service quantity of distribution system operators is calculated based on the optimal incentive unit price; Based on the linearized demand response incentive service volume and the optimal incentive unit price, the game equilibrium state of the two-level Steinberg game model is solved to generate the nodal guideline load response under transmission and distribution coordination.

[0007] Furthermore, before incorporating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, the process also includes: Constructing a two-level Steinberg game model and obtaining the marginal electricity price at each node; The specific steps for constructing a two-layer Steinberg game model are as follows: With the goal of minimizing the total cost of transmission system operators as the upper-level game objective, and the goal of minimizing the total cost of distribution system operators and maximizing the electricity consumption benefits of users as the lower-level game objectives, a two-level Steinberg game model is established, consisting of a master-slave game between transmission system operators and distribution system operators, and a master-slave game between distribution system operators and users. Power balance constraints, equipment output constraints, and line flow constraints are set for transmission system operators, distribution system operators, and users, respectively, with the incentive unit price serving as the core interaction parameter of the two-level Steinberg game model. The calculation method for the marginal electricity price at the node is as follows: The first method: Calculate the marginal electricity price at each node by taking the partial derivative with respect to the node load; The second method involves calculating the marginal electricity price at the node by taking the partial derivative of the unit's output.

[0008] Furthermore, the specific expression of the two-layer Steinberg game model is as follows: The objective function of the power transmission system operator is:

[0009] In the formula, It is the minimum total cost for power transmission system operators; This refers to the cost of generating electricity from conventional generating units; It is the cost of renewable energy curtailment for power transmission system operators; It is the incentive unit price issued by the power transmission system operator to the power distribution system operator; It is the demand response incentive service volume of the power distribution system operator; Demand response incentives for power distribution system operators The specific formula is as follows:

[0010] In the formula, It is the actual electricity consumption of the power distribution system operator node at time t; It is the Euclidean distance between the actual electricity consumption curve of the nodes of the distribution system operator and the guideline of the transmission system operator; This indicates the similarity between the actual electricity consumption curves of the nodes of the distribution system operator and the baselines of the transmission system operator. It is the similarity penalty coefficient; Represents a node; The constraints on power transmission system operators include: Adjustable balance, non-adjustable constraint:

[0011] Output constraints of conventional units:

[0012] Renewable energy output constraints:

[0013] Unit ramp-up constraints:

[0014] Power flow constraints in transmission system lines:

[0015] In the formula, This is the output of the gth conventional unit. It is a collection of generator sets; This is the output of the rth new energy unit. It is a set of adjustable resources; It is the power consumption of inelastic loads. It is a collection of inelastic loads; Represents the set of scheduling periods; Represents a set of transmission lines; This is the minimum output of the g-th conventional unit; This is the maximum output value of the g-th conventional unit; It is the upper limit of the downward ramp rate of the g-th conventional generator unit; It is the active power output of the g-th conventional generator unit at time t; It is the gth conventional generator unit at t One moment of effort; It is the upper limit of the upward ramp rate of the g-th conventional generator unit; It is the minimum allowable transmission power of the l-th transmission line; This is the maximum allowable transmission power of the l-th transmission line; It is the power transfer distribution factor from the r-th renewable energy resource to the l-th line; It is the power transfer distribution factor from the g-th conventional generator unit to the l-th line; It is the power transfer distribution factor from the i-th distributed generation device to the l-th line; It is the power transfer distribution factor from the j-th inelastic load to the l-th line; The objective function of the power distribution system operator is:

[0016] In the formula, It is the minimum total cost for power distribution system operators; It is the cost of renewable energy curtailment for power distribution system operators; It is the service volume that incentivizes responding to user demand; It is the incentive unit price issued by the power distribution system operator to the user; k represents the user; The constraints on power distribution system operators include: Adjustable balance, non-adjustable constraint:

[0017] Output constraints of conventional units:

[0018] Renewable energy output constraints:

[0019] Power flow constraints in power distribution system:

[0020] In the formula, It refers to the active power output of renewable energy within the distribution network; It refers to the active power of inelastic loads within the distribution network; It is the active power consumption of users within the distribution network; It is the active power of the tie line of the i-th node of the power distribution system operator; It is the maximum active power of the tie line of the i-th node of the power distribution system operator; It is the distribution-side power transfer distribution factor from the r-th renewable energy source to the l-th distribution line; It is the distribution-side power transfer distribution factor from the i0th distribution system operator tie-line node to the lth distribution line; It is the power transfer distribution factor on the distribution side from the k-th user to the l-th distribution line; It is the distribution-side power transfer distribution factor from the j-th inelastic load to the l-th distribution line; The user's objective function is:

[0021] In the formula, It refers to the user's comfort when using electricity; It is the sum of the user's electricity comfort benefits throughout the entire time period; User constraints include: Constraint 1:

[0022] Constraint 2:

[0023] In the formula, It is the maximum allowable power consumption of users in the distribution network at time t; This is the user's total electricity consumption.

[0024] Furthermore, in the step of integrating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, the specific steps for initializing the incentive unit price are as follows: The power distribution area corresponding to the power distribution system operator is divided into several zones according to the power grid nodes; The marginal electricity price of each power grid node is used as the initial incentive unit price for the transmission system operator in the corresponding partition, thus completing the initialization of the incentive unit price. Specifically, the initial incentive unit price of a node is mapped to its marginal electricity price, as shown in the following formula:

[0025] In the formula, It is the initial incentive unit price of the node; It is the marginal electricity price of node i; Therefore, the initial incentive price for the corresponding block node in region M is:

[0026] In the formula, k represents the number of nodes.

[0027] Furthermore, in the step of integrating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, the specific steps for iteratively updating the incentive unit price to obtain the optimal incentive unit price are as follows: Fix the incentive unit price of the remaining partitions, set the incentive unit price update step size for the current target partition; adjust the incentive unit price of the current target partition within the incentive unit price constraint interval, and substitute the adjusted incentive unit price into the two-level Steinberg game model to calculate the total cost of the power transmission system operator; If the total cost of the power transmission system operator decreases, the current incentive unit price is retained; otherwise, the update step size is reduced and the calculation is repeated until the current target partition incentive unit price reaches the optimal level. After traversing all partitions and updating the incentive unit price, the optimal incentive unit price of each partition is mapped to each power grid node through the projection function to obtain the final optimal incentive unit price of each node.

[0028] Furthermore, the specific formula for iteratively updating the incentive unit price for each node is as follows:

[0029] In the formula, The optimal incentive unit price; , These are the lower limit and upper limit of the incentive price for the i-th node, respectively; The sensitivity of the node incentive price to the average incentive unit price of the region; The regional average nodal marginal electricity price for region M; The average incentive electricity price for the transmission system operators in region M; The projection function is as follows:

[0030] , These represent the minimum and maximum values ​​of the projection range, respectively. This represents the input value to be projected.

[0031] Furthermore, in the interaction process between the transmission system operator and the distribution system operator, the exponential nonlinear term and the product nonlinear term in the demand response incentive service quantity of the distribution system operator are linearized to obtain the linearized demand response incentive service quantity, including: Define the electricity consumption shape variable of the distribution system operator, and transform the square of the Euclidean distance between the actual electricity consumption curve of the distribution system operator and the load guideline of the transmission system operator into a quadratic term of the electricity consumption shape variable, thus completing the variable substitution of the exponential nonlinear term; The exponential nonlinear term after variable substitution is linearly approximated using a piecewise linearization method, and a piecewise incremental variable is introduced to construct a linear expression for the exponential term; Define the node marginal electricity price weights, and integrate the node marginal electricity prices into a linear expression of an exponential term determined by the optimal incentive unit price as a parameter, to obtain a bilinear product term that includes the node marginal electricity prices; The McCormick linearization method is used to construct convex envelope constraints for the bilinear product terms, thereby completing the linearization of the nonlinear product terms and obtaining the linearized demand response incentive service quantity.

[0032] Furthermore, in the interaction process between the transmission system operator and the distribution system operator, the exponential nonlinear term and the product nonlinear term in the demand response incentive service quantity of the distribution system operator are linearized to obtain the linearized demand response incentive service quantity, as shown in the following formula: According to the law of conservation of energy, the total power output of the system's generating units is equal to the total power consumption of the load:

[0033] Demand response incentive service volume:

[0034] Then we have:

[0035] Define the shape variables of electricity consumption for power distribution system operators. :

[0036] in, ; Then we have:

[0037] The squared Euclidean distance between the actual electricity consumption curve of the distribution system operator and the load baseline of the transmission system operator's nodes is transformed into a quadratic term of the electricity consumption shape variable, thus completing the variable substitution of the exponential nonlinear term. A piecewise linearization method is then used to approximate the transformed exponential nonlinear term linearly, introducing piecewise incremental variables to construct a linear expression for the exponential term. The specific formula is as follows:

[0038] Select the number of segments Introducing incremental variables And the length of each segment Then we have:

[0039] The derivative of an exponential function for:

[0040] Midpoint of each segment for:

[0041] Then, the curve of the exponential function approximated by the broken line with the slope of each segment is:

[0042] Define the weight of the nodal marginal electricity price, and incorporate the nodal marginal electricity price into a linear expression of an exponential term determined by the optimal incentive unit price as a parameter, to obtain a bilinear product term including the nodal marginal electricity price, as shown in the following formula: The weights of the marginal electricity price at each node are:

[0043] The bilinear product term is:

[0044] In the formula, This represents the actual electricity consumption of the power distribution system operator's nodes at time t. This represents the total electricity demand of the power distribution system operator nodes. Incentives for demand response service volume for power distribution system operators; It is the Euclidean distance between the actual electricity consumption curve of the nodes of the distribution system operator and the guideline of the transmission system operator; This indicates the similarity between the actual electricity consumption curves of the nodes of the distribution system operator and the baselines of the transmission system operator. It is the similarity penalty coefficient; Represents a node; The normalized target load shape of the transmission system operator at time t is the standardization of the transmission system operator's directive at node t of the distribution system operator. Represents the set of scheduling periods; Assuming the load is The corresponding shape variable of electricity consumption; For the squared Euclidean distance variable of the operator nodes in the power distribution system; for The upper limit of the possible values; For the exponential similarity weights of the nodes of the power distribution system operator; It is an exponential antiderivative. q represents the value of the exponential function at x=0; q is the number of segments. The marginal electricity price weight of the node at time t for the power distribution system operator; Let be the marginal electricity price at node t of the power distribution system operator. , These are the minimum and maximum marginal electricity prices at node t of the power distribution system operator, respectively. This represents the demand response incentive service volume integrated into the marginal electricity price of the node.

[0045] Furthermore, the step of solving the game equilibrium state of the two-level Steinberg game model based on the linearized demand response incentive service quantity and the optimal incentive unit price to generate the node guideline load response under transmission and distribution coordination includes: Transmission system operators will send the optimal incentive unit price and transmission-side node load baseline to distribution system operators; The power distribution system operator generates the distribution-side incentive unit price and the distribution-side node load baseline based on the optimal incentive unit price and distributes them to users; Users adjust their own power consumption curves based on the distribution-side incentive unit price and the distribution-side node load baseline, and feed back the power consumption response results to the distribution system operator. Based on user response results and the optimal incentive unit price, the distribution system operator optimizes its own operating costs and feeds back the demand response incentive service volume and power consumption of the distribution system operator to the transmission system operator; Based on feedback from distribution system operators and the optimal incentive unit price, transmission system operators optimize their own operating costs, iteratively calculate until a game equilibrium is reached, and output the node quasi-line load response under transmission and distribution coordination. Among them, the game equilibrium state of the two-level Steinberg game model is solved based on the Gurobi optimizer.

[0046] A system for generating nodal baseline load response under transmission and distribution coordination includes: The incentive unit price update module is used to integrate the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, and obtain the optimal incentive unit price; wherein, the two-layer Steinberg game model includes three-layer decision entities: transmission system operator, distribution system operator and user, and is used to characterize the hierarchical master-slave decision interaction relationship among the three-layer decision entities; The linearization module is used to linearize the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator during the interaction between the transmission system operator and the distribution system operator, so as to obtain the linearized demand response incentive service quantity. The linearization process incorporates the marginal electricity price of the nodes; the demand response incentive service volume of the power distribution system operator is calculated based on the optimal incentive unit price. The solution module is used to solve the game equilibrium state of the two-level Steinberg game model based on the linearized demand response incentive service volume and the optimal incentive unit price, and generate the node guideline load response under the transmission and distribution coordination.

[0047] Compared with the prior art, the present invention has the following beneficial effects: This invention provides a method for generating nodal baseline load response under transmission and distribution coordination. It integrates the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, iteratively updates the incentive unit price to obtain the optimal value, and linearizes the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator. Finally, based on the linearization result and the optimal unit price, it solves the game equilibrium to generate the nodal baseline load response. In this method, the nodal marginal electricity price can characterize the differences in the geographical distribution of nodes, thus transforming a single incentive signal into a differentiated and precise incentive signal. Simultaneously, the linearization process eliminates the limitations of nonlinear terms on commonly used optimizers through mathematical transformation. This method enables the demand response mechanism to adapt to the actual operating needs of the power grid under high renewable energy penetration, improves the accuracy of incentives and the feasibility of optimization, effectively enhances the overall effect of demand response, and promotes its practical application. Attached Figure Description

[0048] Figure 1 A schematic diagram of the two-layer Staberg model framework provided in an embodiment of the present invention; Figure 2 A flowchart of the optimized two-layer Staberg game process provided in this embodiment of the invention; Figure 3 The core flowchart of a method for generating node guideline load response under transmission and distribution coordination provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of a node guideline load response generation system under transmission and distribution coordination, provided in an embodiment of the present invention. Detailed Implementation

[0049] To further understand the content of this invention, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments are merely illustrative and not limiting of the invention.

[0050] The following explanations are provided for the relevant technical terms involved in this invention: DR stands for Demand Response, which refers to an important means of guiding users to actively change their electricity consumption behavior (consumption time, power consumption, or consumption mode) through price signals or incentive mechanisms during the operation of the power system, in order to achieve a balance between power supply and demand and improve the efficiency of system operation.

[0051] TS: The full name is Transmission System, which refers to the power network layer that undertakes the transmission of large-capacity, long-distance electrical energy.

[0052] DS: Short for Distribution System, which refers to the distribution network level that distributes electrical energy from the transmission system to end users.

[0053] LCDL stands for Location-based Expected Demand Load curve, which refers to the target load curve constructed according to different node locations.

[0054] T-LCDL: Full name is Transmission-side Load Curve Directive Line, which is the load curve directive line of the operator layer node of the power transmission system.

[0055] D-LCDL: The full name is Distribution-side Load Curve Directive Line, which is the load curve directive line for the node of the power distribution system operator.

[0056] TSO stands for Transmission System Operator, which refers to the entity responsible for the scheduling, operation, and safety management of the power transmission network.

[0057] DSO: Short for Distribution System Operator, which refers to the entity responsible for the operation of the distribution network, load management, and user-side coordination.

[0058] LMP stands for Locational Marginal Price, which refers to the marginal cost of electricity that reflects the supply and demand of electricity at different nodes in a power system and network constraints. Specifically, it refers to the marginal impact of supplying one more unit of electricity at a certain node on the optimal scheduling objective of the system (usually the total cost of the system or social welfare).

[0059] SG: Stochastic Game, a game model used to characterize the interactive decision-making behavior between two entities, transmission system operators (TSO) and distribution system operators (DSO).

[0060] Gurobi optimizer: A high-performance mathematical programming solver used to solve large-scale linear programming, mixed-integer linear programming, quadratic programming, mixed-integer quadratic programming and more complex nonlinear optimization problems.

[0061] SE: The full name is Stackelberg Equilibrium, which is a core equilibrium concept in game theory used to describe sequential games.

[0062] Load profile: refers to the ideal load curve profile that can smooth out fluctuations in non-adjustable resources (such as renewable energy generation and rigid loads) within the system. It eliminates the influence of load magnitude through mathematical methods (such as per-unit scaling), retaining only the shape characteristics, and reflects the system's real-time demand for flexibly adjustable resources.

[0063] McCormick linearization method, also known as McCormick envelope, is a convex relaxation technique used to handle the nonlinear relationship between the product terms of two continuous variables. It transforms a nonlinear problem into a linear problem by constructing a linear inequality.

[0064] As mentioned in the background section, current demand response technologies for large-scale deployment still have the following shortcomings: First, although the load curve of users has been proven to have a certain relationship with the power balance of the power grid system, the relationship between the demand curve of users and the marginal electricity price of nodes has not been studied in depth. If the current DR mechanism ignores the differences in nodes due to their different locations, it will lead to the incentive signals issued by the upper layer of the DR mechanism being too simplistic, thus deviating from the actual distribution of the power grid.

[0065] Second, the response quantity of the DR issued by DSO to the stimulus in response to TSO. In, there exists Such nonlinear terms cannot be solved directly in optimizers such as Gurobi.

[0066] To address the aforementioned issues, this embodiment provides a method for generating nodal guideline load responses under transmission and distribution coordination. This method optimizes a novel demand response scheme based on the expected load curve (LCDL) constructed from entities at different locations, and considers the incentive price published by the TSO. and DSO response to stimulus Integrating the nodal marginal price (LMP) into the system makes the incentive unit price of nodes at different locations more realistic; and for The nonlinear part in the formula is linearized to facilitate the subsequent solution of the SG model in the Gurobi optimizer.

[0067] For example, such as Figure 3 As shown, this embodiment provides a method for generating nodal baseline load response under transmission and distribution coordination, including: The marginal electricity price at each node is incorporated into a pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, thereby obtaining the optimal incentive unit price. The two-layer Steinberg game model includes three decision-making entities: the transmission system operator, the distribution system operator, and the user, which are used to characterize the hierarchical master-slave decision-making interaction relationship among the three decision-making entities. In the interaction between transmission system operators and distribution system operators, the exponential and product nonlinear terms in the demand response incentive service quantity of distribution system operators are linearized to obtain a linearized demand response incentive service quantity; wherein, the linearization process incorporates the nodal marginal electricity price; the demand response incentive service quantity of distribution system operators is calculated based on the optimal incentive unit price; Based on the linearized demand response incentive service volume and the optimal incentive unit price, the game equilibrium state of the two-level Steinberg game model is solved to generate the nodal guideline load response under transmission and distribution coordination.

[0068] The method for generating nodal baseline load response under transmission and distribution coordination provided in this embodiment will be further explained below with reference to the accompanying drawings: For example, this embodiment provides a method for generating nodal load responsiveness under transmission and distribution coordination. The optimization process of this method is based on the location-based nodal load responsiveness (DR) scheme and the incentive price published by the TSO. The concept of nodal marginal pricing is incorporated to ensure fairness in the DR incentives received by nodes at different locations from the upper layer; this is to address the incentive response of DSOs. The algorithm incorporates LMP, linearizes the exponent using a piecewise method, and linearizes the product using the McCormick method. The specific implementation process is as follows: Step 1: Construct a two-layer Steinberg game model: In the location-based LCDL (LCDL) DR (Diversion and Reception) scheme, interactions between three entities are involved: transmission system operators, distribution system operators, and customers. To describe these interactions, a two-layer Steinberg game model is introduced. For example... Figure 1 As shown, the bilevel Stackelberg game is a type of game model with a hierarchical decision-making structure, often used to characterize strategic interaction problems in a "leader-follower" relationship: typically, the upper-level leader makes the decision first, and the lower-level followers observe the leader's behavior and then make the optimal response. Usually, the leader has already anticipated the lower-level response when making the decision.

[0069] For example, the two-layer Steinberg game model provided in this embodiment is specifically constructed as follows: (1) TSO: In this embodiment, the role of the TSO is to achieve power balance of the TS by scheduling conventional units and purchasing DR services from the DSO. The objective function formula of the TSO is as follows:

[0070] in, It is the minimum total cost at the TSO level. This is the power generation cost of a conventional generating unit; the specific formula is... , This is the output of the gth conventional unit; This refers to the curtailment cost of renewable energy at the TSO level, and the specific formula is... , This is the output of the rth new energy unit; It is the incentive unit price that TSO gives to DSO. This refers to the DR service volume responded to by the DSO, and the specific formula is: , This represents the actual power consumption of each node at the DSO level at time t. It is the Euclidean distance between the actual power consumption curve of the DSO node and the T-LCDL plane guideline of the TSO. This reflects the similarity between the two; It is the similarity penalty coefficient; Represents a node.

[0071] The constraints on power transmission system operators include: a. Adjustable balance but not adjustable constraint:

[0072] b. Output constraints of conventional units:

[0073] c. Renewable energy output constraints:

[0074] d. Unit ramp-up constraints:

[0075] e. Power flow constraints of transmission system lines:

[0076] In the formula, This is the output of the gth conventional unit. It is a collection of generator sets; This is the output of the rth new energy unit. It is a set of adjustable resources; It is the power consumption of inelastic loads. It is a collection of inelastic loads; Represents the set of scheduling periods; Represents a set of transmission lines; This is the minimum output of the g-th conventional unit; This is the maximum output value of the g-th conventional unit; It is the upper limit of the downward ramp rate of the g-th conventional generator unit; It is the active power output of the g-th conventional generator unit at time t; It is the gth conventional generator unit at t One moment of effort; It is the upper limit of the upward ramp rate of the g-th conventional generator unit; It is the minimum allowable transmission power of the l-th transmission line; This is the maximum allowable transmission power of the l-th transmission line; It is the power transfer distribution factor from the r-th renewable energy resource to the l-th line; It is the power transfer distribution factor from the g-th conventional generator unit to the l-th line; It is the power transfer distribution factor from the i-th distributed generation device to the l-th line; It is the power transfer distribution factor from the j-th inelastic load to the l-th line.

[0077] Among them, constraint a stipulates the power conservation of the system throughout the entire time period, where the unit output always equals the load power consumption; the output of conventional units and renewable units are constrained by constraints b and c, respectively; constraint d allows for more flexible adjustment of the generation side when RE resources change drastically; and constraint e describes the flow of power transmitted through different lines in the grid system, which can reduce the occurrence of line overload. Here, l is the index of the line with the corresponding set L.

[0078] (2) DSO: For a DSO, on the one hand, its goal is to maximize revenue by improving the DR services provided by the TSO, which depends on the similarity between the total electricity consumption curve of the DS and T-LCDL; on the other hand, the DSO also aims to minimize the incentive fees paid to D users. Furthermore, the cost of renewable energy reduction must be considered, reflecting the goal of maximizing the inclusion of distributed renewable energy within the DS. Therefore, the DSO objective function is:

[0079] in It represents the minimum total cost at the DSO level; This refers to the curtailment cost of renewable energy at the DSO level; This is the service volume incentivized by responding to user demand; the specific formula is: ; It represents the actual electricity consumption level of each user at time t; k represents the user.

[0080] Relevant constraints: Adjustable balance, non-adjustable constraint:

[0081] Output constraints of conventional units:

[0082] Renewable energy output constraints:

[0083] Power flow constraints in power distribution system:

[0084] In the formula, It refers to the active power output of renewable energy within the distribution network; It refers to the active power of inelastic loads within the distribution network; It is the active power consumption of users within the distribution network; It is the active power of the tie line of the i-th node of the power distribution system operator; It is the maximum active power of the tie line of the i-th node of the power distribution system operator; It is the distribution-side power transfer distribution factor from the r-th renewable energy source to the l-th distribution line; It is the distribution-side power transfer distribution factor from the i0th distribution system operator tie-line node to the lth distribution line; It is the power transfer distribution factor on the distribution side from the k-th user to the l-th distribution line; It is the distribution-side power transfer distribution factor from the j-th inelastic load to the l-th distribution line.

[0085] (3) User: Users aim to maximize the benefits of the DR services provided by the DSO by adjusting their electricity consumption curves to align with the DSO's D-LCDL benchmark. Their objective function is:

[0086] In the formula, It represents the user's comfort level when using electricity, which takes the form of a piecewise function: when the electricity consumption is less than a critical value, the user's comfort level increases with the increase in electricity consumption; when it is greater than the critical value, the user's comfort level no longer changes with the increase in electricity consumption. It is the sum of the user's electricity comfort benefits throughout the entire time period.

[0087] User constraints include: Constraint 1:

[0088] Constraint 2:

[0089] In the formula, It is the maximum allowable power consumption of users in the distribution network at time t; This is the user's total electricity consumption.

[0090] Among them, constraint one is used to limit the user's daily power consumption to no more than the maximum power of the equipment, and constraint two is used to represent the power balance between the total system load and the total power consumption.

[0091] Step 2: The specific game process of the two-layer Steinberg game model is as follows: The two-level Steinberg framework consists of two loops: TSO-DSO and DSO-User. Each loop comprises a leader and multiple followers. The TSO is the leader in the upper loop, the Users are the followers in the lower loop, and the DSO is both a follower in the upper loop and a leader in the lower loop. The Steinberg equilibrium (SE) can be used to describe the ideal outcome of two SG loops, where none of the entities change their game strategies to disrupt the equilibrium; for this invention, this means that once the system reaches the SE, there is no better outcome than the current state. , , and This will reduce the cost of TSO and DSO, and increase the benefits for users.

[0092] For example, such as Figure 2 As shown, the specific optimized game (solution) process is as follows: (1) TSO first sets the incentive unit price Initialization: Partition the DSO and convert the marginal electricity price of the zone node into an equivalent incentive unit price. Then send it along with T-LCDL to each DSO; (2) DSO received Then, a genetic algorithm is used to randomly generate... Adjust its own power consumption curve according to T-LCDL and formulate D-LCDL, and finally... And D-LCDL were sent to each user; (3) The user receives Then, to maximize its own efficiency, it adjusts its power consumption curve according to D-LCDL and converts the response (power vector) and response quantity Return to DSO; (4) The DSO calculates the cost based on the response and obtains the optimal cost (as small as possible). (No longer changing), the response quantity Electricity consumption of DSO Return to TSO; (5) The TSO calculates the cost based on the response and obtains the optimal cost (as small as possible), and records the current cost. Continuously updated until Maximum. Iterate through each region sequentially and use... The function is mapped to a specific node, thus completing a game.

[0093] The specific formula for calculating the nodal marginal electricity price is as follows: (4) (5) (6) (7) Formula (4) is the objective of the LMP model, namely, minimizing the total cost of nodes, and its specific expression is: ; It is the optimal operating cost. This is the power generation capacity of the node; the power balance of the power grid is ensured through constraint (5). It is the load power. It is the line susceptance. It is the node phase angle; the generator's active power output is limited by constraint (6). and These represent the minimum and maximum active power output of the generator, respectively; the line power flow constraints (leading to congestion prices) are shown in (7). This represents the maximum allowable power flow on line ij.

[0094] It is evident that there are two methods for calculating the nodal marginal electricity price, both of which involve constructing a Lagrangian function based on the objective function and constraints of the LMP model described above. One method involves taking the partial derivative with respect to the nodal load to obtain... That is, LMP = electricity price + congestion price; another is to take the partial derivative of the unit's output to obtain ;in, It's the generator unit that's producing power. This is the quote for unit i. It is the shadow price with upper and lower backup constraints. The price is a shadow price constrained by the upper and lower limits of unit power. The price difference is due to the price increase constraint between the preceding and following periods. It is the shadow price difference of landslide constraints in the preceding and following periods. The first calculation method aims to quickly obtain the LMP, while the second calculation method analyzes the impact of each constraint on it through the LMP.

[0095] Step 3: Integrate LMP into the SG game process, that is, substitute it into the two-level Steinberg game model: LMP is the marginal cost of electricity supply at different locations in a power network. It takes into account generation costs and transmission constraints. In this embodiment, the node marginal electricity price is integrated into the update of the incentive price in the two-layer SG model game process, ensuring that the incentive unit price provided by the TSO to each DSO in the lower layer is affected by the node location. This has the following advantages: First, the system can more accurately reflect the attributes of nodes at different locations (whether they are generating electricity or loads) and the actual situation of surrounding transmission lines, thereby promoting the flexibility of TSO in issuing excitation signals.

[0096] Second, incentive unit price is determined based on LMP. This helps guide the system to integrate renewable energy in different locations and reduce wind and solar curtailment.

[0097] Third, LMP reflects network congestion problems. Integrating it into the game process can help predict different power flow patterns in the lines, which helps TSO and DSO better cope with network constraints.

[0098] Fourth, adding LMP to the DSO at the lower level of TSO helps to make the cooperation and interaction between transmission and distribution networks closer.

[0099] In this embodiment, LMP is first used for... Initialization: Assuming M DSO layers connect N nodes, and each DSO corresponds to a region of nodes, all nodes will be divided into regions 1...M, and the initial incentive unit price of each node will be mapped to its marginal electricity price.

[0100] Therefore, the initial incentive price for the node in region M is:

[0101] In this embodiment, LMP is used again for the outer layer. Multidimensional update (region-level coordinate search): Temporarily fix other regions M-1, and unify the step size within region M. Limit the incentive price at each point to Within the specified range, the cost of TSO is then calculated using Gurobi at the next level. If the cost is reduced after the improvement, it is accepted; otherwise, it is reduced. , Until the total cost of TSO If the minimum value is found, then the Mth zone is temporarily optimal; using the same method, update the incentive prices of all zones in the DSO sequentially.

[0102] Finally, the i-th node in each region is processed in detail. Taking region M as an example:

[0103] It is the sensitivity of the node incentive price to the area average LMP. This approach achieves the optimization of costs or benefits for each entity through SG game theory, while also ensuring fairness by adjusting the game strategy based on real-time incentive prices.

[0104] In the formula, It is the initial incentive unit price of the node; It is the marginal electricity price of node i; The optimal incentive unit price; , These are the lower limit and upper limit of the incentive price for the i-th node, respectively; The sensitivity of the node incentive price to the average incentive unit price of the region; The regional average nodal marginal electricity price for region M; The average incentive electricity price for the transmission system operators in region M; For projection functions; , These represent the minimum and maximum values ​​of the projection range, respectively. This represents the input value to be projected.

[0105] Step 4: Linearize the lower-level incentive price. This involves linearizing the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator during the interaction between the transmission system operator and the distribution system operator, resulting in a linearized demand response incentive service quantity. During the interaction between TSO and DSO, the stimulus response quantity ; in, The similarity between the actual power consumption curve of DSO and T-LCDL. It is the actual electricity consumption of DSO and the benchmark published by TSO. The Euclidean distance.

[0106] For optimizers like Gurobi, this exponential + quadratic expression cannot be directly substituted into the solution. Therefore, in this embodiment, linearization is considered and LMP is incorporated into the linearization process.

[0107] First, according to the law of conservation of energy, the total power output of the system's generating units is equal to the total power consumption of the load, that is:

[0108] Therefore, in the original formula

[0109] Secondly, define a shape variable. And regulations Therefore, we can obtain:

[0110] Similarly, if the DSO does not follow the guidelines issued by the TSO and uses electricity arbitrarily, assuming the load is... Then the shape variable at this time .

[0111] Next, regarding the index part Using a piecewise linearization method, let... , Select Upper and lower limits of the region: The lower limit is the minimum distance, set to 0, and the upper limit is the distance without considering DR, i.e.:

[0112] Then, select the number of segments. Introducing incremental variables And the length of each segment ,but Let the derivative of the exponential function be... Take the midpoint of each segment. Then, for each segment, the curve of the exponential function can be approximated by a broken line with a slope. .

[0113] Next, incorporate LMP into the linearization process: define an LMP weight. Therefore, the DSO response service volume after combining LMP .

[0114] In the formula, This represents the actual electricity consumption of the power distribution system operator's nodes at time t. This represents the total electricity demand of the power distribution system operator nodes. Incentives for demand response service volume for power distribution system operators; It is the Euclidean distance between the actual electricity consumption curve of the nodes of the distribution system operator and the guideline of the transmission system operator; This indicates the similarity between the actual electricity consumption curves of the nodes of the distribution system operator and the baselines of the transmission system operator. It is the similarity penalty coefficient; Represents a node; The normalized target load shape of the transmission system operator at time t is the standardization of the transmission system operator's directive at node t of the distribution system operator. Represents the set of scheduling periods; Assuming the load is The corresponding shape variable of electricity consumption; For the squared Euclidean distance variable of the operator nodes in the power distribution system; for The upper limit of the possible values; For the exponential similarity weights of the nodes of the power distribution system operator; It is an exponential antiderivative. q represents the value of the exponential function at x=0; q is the number of segments. The marginal electricity price weight of the node at time t for the power distribution system operator; Let be the marginal electricity price at node t of the power distribution system operator. , These are the minimum and maximum marginal electricity prices at node t of the power distribution system operator, respectively. This represents the demand response incentive service volume integrated into the marginal electricity price of the node.

[0115] Finally, for the product term Linearization is performed; in this embodiment, the McCormick method is used: [Definition] Then on and Find upper and lower bound constraints to enclose the boundary. The upper bound can be obtained from the range. The lower realm ;because Then the upper boundary The lower realm And because McCormick's four constraints are as follows:

[0116]

[0117]

[0118]

[0119] Therefore, for the objective function have:

[0120]

[0121]

[0122]

[0123] In the formula, Linearization variables for the product term; , General variables The lower bound and the upper bound of the value.

[0124] By applying the above four constraints to the envelope of the objective function in product form, a convex approximation is used to replace the non-convex bilinear one, thus determining the final DSO response service volume. The nonlinear part is no longer present, which facilitates the subsequent optimization. After the above linearization process, the linearized demand response incentive service quantity is obtained.

[0125] Step 5: Based on the linearized demand response incentive service volume and the optimal incentive unit price, solve the game equilibrium state of the two-level Steinberg game model to generate the nodal guideline load response under transmission and distribution coordination.

[0126] Therefore, this embodiment provides a method for generating nodal baseline load response under transmission and distribution coordination, which has the following advantages compared with existing methods: First, node differences are considered: The price differences of nodes at different positions are considered in the SG two-level game model, thus combining LMP to ensure the fairness of incentive prices for nodes at each position while optimizing the income and expenditure of each subject in the system.

[0127] Second, improve the renewable energy absorption capacity of the power grid system: LMP-based incentive prices can better guide the system's DSO to integrate renewable energy in different locations, optimize the transmission and distribution network's capabilities, thereby reducing the curtailment of renewable energy, especially during periods of high renewable energy generation.

[0128] Third, reduce line congestion caused by network constraints: LMP can reveal network congestion problems. After incorporating it into the game theory model, TSO and DSO can more effectively deal with network constraints and optimize the flow of electricity throughout the system.

[0129] In summary, this invention improves the DR scheme for location-based nodal load profiles by utilizing the nodal marginal price (LMP) to optimize the update process in a two-level SG game model and employing linearization to handle the incentive service quantity of the DSO response. Increasing consideration of node location is beneficial for the absorption of renewable energy and for addressing network constraints.

[0130] like Figure 4 As shown, this embodiment also provides a system for generating node baseline load response under transmission and distribution coordination, including: The incentive unit price update module is used to integrate the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, and obtain the optimal incentive unit price; wherein, the two-layer Steinberg game model includes three-layer decision entities: transmission system operator, distribution system operator and user, and is used to characterize the hierarchical master-slave decision interaction relationship among the three-layer decision entities; The linearization module is used to linearize the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator during the interaction between the transmission system operator and the distribution system operator, to obtain a linearized demand response incentive service quantity; wherein the linearization process incorporates the nodal marginal electricity price; the demand response incentive service quantity of the distribution system operator is calculated based on the optimal incentive unit price; The solution module is used to solve the game equilibrium state of the two-level Steinberg game model based on the linearized demand response incentive service volume and the optimal incentive unit price, and generate the node guideline load response under the transmission and distribution coordination.

[0131] The present invention also provides a device for generating node guideline load response under transmission and distribution coordination, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the method for generating node guideline load response under transmission and distribution coordination.

[0132] The present invention also provides a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the method for generating the node guideline load response under the transmission and distribution coordination.

[0133] When the processor executes the computer program, it implements the steps for generating the node baseline load response under transmission and distribution coordination, such as integrating the node marginal electricity price into a pre-constructed two-layer Steinberg game model, initializing and iteratively updating the incentive unit price issued by the transmission system operator to the distribution system operator to obtain the optimal incentive unit price; wherein, the two-layer Steinberg game model includes three layers of decision entities: transmission system operator, distribution system operator, and user, used to characterize the hierarchical master-slave decision interaction relationship among the three layers of decision entities; in the interaction process between the transmission system operator and the distribution system operator, the exponential nonlinear term and the product nonlinear term in the demand response incentive service quantity of the distribution system operator are linearized to obtain the linearized demand response incentive service quantity; wherein, the node marginal electricity price is integrated into the linearization process; the demand response incentive service quantity of the distribution system operator is calculated based on the optimal incentive unit price; based on the linearized demand response incentive service quantity and the optimal incentive unit price, the game equilibrium state of the two-layer Steinberg game model is solved to generate the node baseline load response under transmission and distribution coordination.

[0134] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units can be a series of computer program instruction segments capable of performing preset functions. These instruction segments describe the execution process of the computer program in the node quasi-line load response generation device under the transmission and distribution coordination. For example, the computer program can be divided into an incentive unit price update module, a linearization processing module, and a solution module, with the following specific functions: The incentive unit price update module is used to integrate the node marginal electricity price into a pre-constructed two-layer Steinberg game model, initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, and obtain the optimal incentive unit price; wherein, the two-layer Steinberg game model includes three layers of decision-making entities: the transmission system operator, the distribution system operator, and the user, used to characterize the hierarchical master-slave decision-making interaction relationship among the three layers of decision-making entities; the linearization processing module is used to... In the interaction between transmission system operators and distribution system operators, the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator are linearized to obtain a linearized demand response incentive service quantity. The linearization process incorporates the nodal marginal electricity price. The demand response incentive service quantity of the distribution system operator is calculated based on the optimal incentive unit price. The solution module is used to solve the game equilibrium state of the two-level Steinberg game model based on the linearized demand response incentive service quantity and the optimal incentive unit price, generating the nodal baseline load response under transmission and distribution coordination.

[0135] The device for generating the node baseline load response under transmission and distribution coordination can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. This device may include, but is not limited to, processors and memory. Those skilled in the art will understand that the above are examples of devices for generating the node baseline load response under transmission and distribution coordination and do not constitute a limitation on such devices. The device may include more components than described above, or combine certain components, or use different components. For example, the device for generating the node baseline load response under transmission and distribution coordination may also include input / output devices, network access devices, buses, etc.

[0136] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor, or any conventional processor. The processor is the control center for generating the node guideline load response under the transmission and distribution coordination, and connects various parts of the node guideline load response generation equipment under the entire transmission and distribution coordination system using various interfaces and lines.

[0137] The memory can be used to store the computer program and / or modules. The processor, by running or executing the computer program and / or modules stored in the memory and calling the data stored in the memory, realizes various functions of the node guideline load response generation device under the transmission and distribution coordination.

[0138] The memory may primarily include a program storage area and a data storage area. The program storage area may store the operating system and at least one application program required for a function (such as sound playback, image playback, etc.). The data storage area may store data created based on the use of the mobile phone (such as audio data, phonebook, etc.). Furthermore, the memory may include high-speed random access memory and non-volatile memory, such as hard disks, RAM, plug-in hard disks, smart media cards (SMC), secure digital cards (SD cards), flash cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0139] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method for generating a node baseline load response under transmission and distribution coordination.

[0140] If the module / unit of the node guideline load response generation system under the transmission and distribution coordination is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium.

[0141] Based on this understanding, the present invention can implement all or part of the processes in the above-described method for generating nodal baseline load response under transmission and distribution coordination. This can also be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described method for generating nodal baseline load response under transmission and distribution coordination. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or a preset intermediate form, etc.

[0142] The computer-readable storage medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0143] It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0144] The above embodiments are merely one of the implementation methods for achieving the technical solution of the present invention. The scope of protection claimed by the present invention is not limited to this embodiment, but also includes any variations, substitutions and other implementation methods that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention.

[0145] 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, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for generating nodal baseline load response under transmission and distribution coordination, characterized in that, include: The marginal electricity price at each node is incorporated into a pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, thereby obtaining the optimal incentive unit price. The two-layer Steinberg game model includes three decision-making entities: the transmission system operator, the distribution system operator, and the user, which are used to characterize the hierarchical master-slave decision-making interaction relationship among the three decision-making entities. In the interaction between transmission system operators and distribution system operators, the exponential and product nonlinear terms in the demand response incentive service quantity of distribution system operators are linearized to obtain a linearized demand response incentive service quantity; wherein, the linearization process incorporates the nodal marginal electricity price; the demand response incentive service quantity of distribution system operators is calculated based on the optimal incentive unit price; Based on the linearized demand response incentive service volume and the optimal incentive unit price, the game equilibrium state of the two-level Steinberg game model is solved to generate the nodal guideline load response under transmission and distribution coordination.

2. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 1, characterized in that, Before incorporating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model, and initializing and iteratively updating the incentive unit price issued by the transmission system operator to the distribution system operator, the process also includes: Constructing a two-level Steinberg game model and obtaining the marginal electricity price at each node; The specific steps for constructing a two-layer Steinberg game model are as follows: With the goal of minimizing the total cost of transmission system operators as the upper-level game objective, and the goal of minimizing the total cost of distribution system operators and maximizing the electricity consumption benefits of users as the lower-level game objectives, a two-level Steinberg game model is established, consisting of a master-slave game between transmission system operators and distribution system operators, and a master-slave game between distribution system operators and users. Power balance constraints, equipment output constraints, and line flow constraints are set for transmission system operators, distribution system operators, and users, respectively, with the incentive unit price serving as the core interaction parameter of the two-level Steinberg game model. The calculation method for the marginal electricity price at the node is as follows: The first method: Calculate the marginal electricity price at each node by taking the partial derivative with respect to the node load; The second method involves calculating the marginal electricity price at the node by taking the partial derivative of the unit's output.

3. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 2, characterized in that, The specific expression of the two-layer Steinberg game model is as follows: The objective function of the power transmission system operator is: In the formula, It is the minimum total cost for power transmission system operators; This refers to the cost of generating electricity from conventional generating units; It is the cost of renewable energy curtailment for power transmission system operators; It is the incentive unit price issued by the power transmission system operator to the power distribution system operator; It is the demand response incentive service volume of the power distribution system operator; The constraints on transmission system operators include adjustable balance non-adjustable constraints, conventional unit output constraints, renewable energy output constraints, unit ramping constraints, and transmission system line power flow constraints. The objective function of the power distribution system operator is: In the formula, It is the minimum total cost for power distribution system operators; It is the cost of renewable energy curtailment for power distribution system operators; It is the service volume that incentivizes responding to user demand; It is the incentive unit price issued by the power distribution system operator to the user; k represents the user; The constraints on power distribution system operators include adjustable balance non-adjustable constraints, conventional unit output constraints, renewable energy output constraints, and power flow constraints in the power distribution system. The user's objective function is: In the formula, It refers to the user's comfort when using electricity; It is the sum of the user's electricity comfort benefits throughout the entire time period; User constraints include maximum allowable power consumption constraints and total power consumption constraints.

4. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 1, characterized in that, In the step of incorporating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, the specific steps for initializing the incentive unit price are as follows: The power distribution area corresponding to the power distribution system operator is divided into several zones according to the power grid nodes; The marginal electricity price of each power grid node is used as the initial incentive unit price for the transmission system operator in the corresponding partition, thus completing the initialization of the incentive unit price. Specifically, the initial incentive unit price of a node is mapped to its marginal electricity price, as shown in the following formula: In the formula, It is the initial incentive unit price of the node; It is the marginal electricity price of node i; Therefore, the initial incentive price for the corresponding block node in region M is: In the formula, k represents the number of nodes.

5. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 4, characterized in that, In the step of incorporating the nodal marginal electricity price into the pre-constructed two-layer Steinberg game model to initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, the specific steps for iteratively updating the incentive unit price to obtain the optimal incentive unit price are as follows: Fix the incentive unit price of the remaining partitions, set the incentive unit price update step size for the current target partition; adjust the incentive unit price of the current target partition within the incentive unit price constraint interval, and substitute the adjusted incentive unit price into the two-level Steinberg game model to calculate the total cost of the power transmission system operator; If the total cost of the power transmission system operator decreases, the current incentive unit price is retained; otherwise, the update step size is reduced and the calculation is repeated until the current target partition incentive unit price reaches the optimal level. After traversing all partitions and updating the incentive unit price, the optimal incentive unit price of each partition is mapped to each power grid node through the projection function to obtain the final optimal incentive unit price of each node.

6. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 5, characterized in that, The specific formula for iteratively updating the incentive unit price for each node is as follows: In the formula, The optimal incentive unit price; , These are the lower limit and upper limit of the incentive price for the i-th node, respectively; The sensitivity of the node incentive price to the average incentive unit price of the region; The regional average nodal marginal electricity price for region M; The average incentive electricity price for the transmission system operators in region M; The projection function is as follows: , These represent the minimum and maximum values ​​of the projection range, respectively. This represents the input value to be projected.

7. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 1, characterized in that, In the interaction process between the transmission system operator and the distribution system operator, the exponential nonlinear term and the product nonlinear term in the demand response incentive service quantity of the distribution system operator are linearized to obtain the linearized demand response incentive service quantity, including: Define the electricity consumption shape variable of the distribution system operator, and transform the square of the Euclidean distance between the actual electricity consumption curve of the distribution system operator and the load guideline of the transmission system operator into a quadratic term of the electricity consumption shape variable, thus completing the variable substitution of the exponential nonlinear term; The exponential nonlinear term after variable substitution is linearly approximated using a piecewise linearization method, and a piecewise incremental variable is introduced to construct a linear expression for the exponential term; Define the node marginal electricity price weights, and integrate the node marginal electricity prices into a linear expression of an exponential term determined by the optimal incentive unit price as a parameter, to obtain a bilinear product term that includes the node marginal electricity prices; The McCormick linearization method is used to construct convex envelope constraints for the bilinear product terms, thereby completing the linearization of the nonlinear product terms and obtaining the linearized demand response incentive service quantity.

8. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 7, characterized in that, The Euclidean distance between the actual electricity consumption curve of the power distribution system operator and the node load baseline of the power transmission system operator is calculated based on the actual electricity consumption curve, the node load baseline, and the total electricity demand. The node marginal electricity price weight is calculated based on the node marginal electricity price and the maximum / minimum value of the node marginal electricity price.

9. The method for generating nodal baseline load response under transmission and distribution coordination according to claim 1, characterized in that, The process of solving the game equilibrium state of a two-level Steinberg game model based on linearized demand response incentive service volume and optimal incentive unit price to generate the nodal baseline load response under transmission and distribution coordination includes: Transmission system operators will send the optimal incentive unit price and transmission-side node load baseline to distribution system operators; The power distribution system operator generates the distribution-side incentive unit price and the distribution-side node load baseline based on the optimal incentive unit price and distributes them to users; Users adjust their own power consumption curves based on the distribution-side incentive unit price and the distribution-side node load baseline, and feed back the power consumption response results to the distribution system operator. Based on user response results and the optimal incentive unit price, the distribution system operator optimizes its own operating costs and feeds back the demand response incentive service volume and power consumption of the distribution system operator to the transmission system operator; Based on feedback from distribution system operators and the optimal incentive unit price, transmission system operators optimize their own operating costs, iteratively calculate until a game equilibrium is reached, and output the node quasi-line load response under transmission and distribution coordination. Among them, the game equilibrium state of the two-level Steinberg game model is solved based on the Gurobi optimizer.

10. A system for generating nodal baseline load response under transmission and distribution coordination, characterized in that, include: The incentive unit price update module is used to integrate the nodal marginal electricity price into a pre-constructed two-layer Steinberg game model, initialize and iteratively update the incentive unit price issued by the transmission system operator to the distribution system operator, and obtain the optimal incentive unit price; wherein, the two-layer Steinberg game model includes three-layer decision entities: transmission system operator, distribution system operator and user, and is used to characterize the hierarchical master-slave decision interaction relationship among the three-layer decision entities; The linearization module is used to linearize the exponential and product nonlinear terms in the demand response incentive service quantity of the distribution system operator during the interaction between the transmission system operator and the distribution system operator, to obtain a linearized demand response incentive service quantity; wherein the linearization process incorporates the nodal marginal electricity price; the demand response incentive service quantity of the distribution system operator is calculated based on the optimal incentive unit price; The solution module is used to solve the game equilibrium state of the two-level Steinberg game model based on the linearized demand response incentive service volume and the optimal incentive unit price, and generate the node guideline load response under the transmission and distribution coordination.

Images