Node electricity price determination method, device, equipment and storage medium

Abstract

The application provides a node electricity price determination method, device and equipment and a storage medium. The method comprises the following steps: constructing a first node electricity price clearing model based on power grid data information of a current scheduling time, wherein the first node electricity price clearing model comprises a plurality of slack variables; solving the first node electricity price clearing model to obtain a solution of the plurality of slack variables and an optimal base of the first node electricity price clearing model; updating the first node electricity price clearing model according to the solution of the plurality of slack variables to obtain a second node electricity price clearing model, and determining an optimal base of the second node electricity price clearing model according to the optimal base of the first node electricity price clearing model; and determining the electricity price of each node in the power grid according to the optimal base of the second node electricity price clearing model. The optimal base of the first node electricity price clearing model is used to assist in determining the optimal base of the second node electricity price clearing model, so that the solution time of the second node electricity price clearing model can be reduced, and the node electricity price calculation efficiency can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, apparatus, equipment and storage medium for determining nodal electricity prices. Background Technology

[0002] With the development of power systems and the improvement of their trading markets, smart grids have become a trend in modern power system development. In smart grid systems, a crucial computational task is the calculation of nodal tariffs.

[0003] Nodal pricing refers to the marginal cost of increasing unit load demand at a specific node, under the condition that the operating characteristics and constraints of various equipment and resources are met. In other words, it represents the additional cost required to consume "one more kilowatt-hour" of electricity at a certain time and place. Generally, the "equipment and resources" affecting nodal pricing mainly refer to generating units and transmission lines, while "operating characteristics and constraints" include power load balance, maximum and minimum output and ramping of generating units, and the transmission capacity of transmission lines and sections under normal and fault conditions. Typical definitions of nodal pricing include system energy price, congestion price, and network loss price.

[0004] Traditional methods for calculating nodal tariffs are computationally complex and require a long computation time, making it difficult to meet the real-time requirements of nodal tariff calculations. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and storage medium for determining nodal electricity prices, enabling efficient calculation of nodal electricity prices.

[0006] In a first aspect, embodiments of the present invention provide a method for determining nodal tariffs, the method comprising:

[0007] Based on the power grid data information at the current scheduling time, a first-node electricity price clearing model is constructed, which includes multiple slack variables.

[0008] Solve the first node electricity price clearing model to obtain the solution of the multiple slack variables and the optimal basis of the first node electricity price clearing model;

[0009] Based on the solutions of the multiple slack variables, update the first node electricity price clearing model to obtain the second node electricity price clearing model;

[0010] Based on the optimal basis of the first node electricity price clearing model, determine the optimal basis of the second node electricity price clearing model;

[0011] Based on the optimal basis of the second node price clearing model, the electricity price of each node in the power grid is determined.

[0012] Secondly, embodiments of the present invention provide a nodal pricing device, the device comprising:

[0013] The model building module is used to build a first-node electricity price clearing model based on the power grid data information of the current scheduling time. The first-node electricity price clearing model includes multiple slack variables.

[0014] The first solution module is used to solve the first node electricity price clearing model to obtain the solution of the multiple slack variables and the optimal basis of the first node electricity price clearing model;

[0015] The second solution module is used to update the first node electricity price clearing model to obtain the second node electricity price clearing model based on the solutions of the multiple slack variables, determine the optimal basis of the second node electricity price clearing model based on the optimal basis of the first node electricity price clearing model, and determine the electricity price of each node in the power grid based on the optimal basis of the second node electricity price clearing model.

[0016] Thirdly, embodiments of the present invention provide an electronic device, including: a memory, a processor, and a communication interface; wherein, the memory stores executable code, and when the executable code is executed by the processor, the processor can at least implement the node electricity price determination method as described in the first aspect.

[0017] Fourthly, embodiments of the present invention provide a non-transitory machine-readable storage medium storing executable code, which, when executed by a processor of an electronic device, enables the processor to at least implement the node electricity price determination method as described in the first aspect.

[0018] Fifthly, embodiments of the present invention provide a method for determining nodal tariffs, including:

[0019] Receive a request from a user equipment to call the node electricity price calculation service, the request including power grid data information for the current scheduling time;

[0020] The following steps are performed using the processing resources corresponding to the node electricity price calculation service:

[0021] A first-node electricity price clearing model is constructed based on the aforementioned power grid data information. The first-node electricity price clearing model includes multiple slack variables.

[0022] Solve the first node electricity price clearing model to obtain the solution of the multiple slack variables and the optimal basis of the first node electricity price clearing model;

[0023] Based on the solutions of the multiple slack variables, update the first node electricity price clearing model to obtain the second node electricity price clearing model;

[0024] Based on the optimal basis of the first node electricity price clearing model, determine the optimal basis of the second node electricity price clearing model;

[0025] Based on the optimal basis of the second node electricity price clearing model, determine the electricity price of each node in the power grid;

[0026] The electricity price of each node is sent to the user equipment.

[0027] In this embodiment of the invention, in the day-ahead electricity market, it is necessary to calculate nodal prices in real time at short intervals to guide market behavior. For a given scheduling time, when calculating the nodal price for that current scheduling time, firstly, a first nodal price clearing model is constructed based on the grid data information of the current scheduling time. This first nodal price clearing model includes multiple slack variables. The first nodal price clearing model is then solved to obtain the solutions for the multiple slack variables and the optimal basis of the first nodal price clearing model. Next, the first nodal price clearing model is updated based on the solutions for the multiple slack variables to obtain a second nodal price clearing model. The second nodal price clearing model has the same number of rows (constraints) and columns (variables) as the first nodal price clearing model. During the solution of the second nodal price clearing model, the optimal basis of the first nodal price clearing model is used to determine the optimal basis of the second nodal price clearing model. Finally, the electricity price of each node in the grid is determined based on the optimal basis of the second nodal price clearing model. Using the optimal basis of the solved first-node electricity price clearing model to help determine the optimal basis of the second-node electricity price clearing model can reduce the computational workload of solving the second-node electricity price clearing model, thereby improving the computational efficiency of node electricity prices. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram illustrating the principle of a traditional nodal pricing method.

[0030] Figure 2 A flowchart illustrating a method for determining nodal electricity prices provided in an embodiment of the present invention;

[0031] Figure 3 A schematic diagram illustrating the principle of a nodal pricing method provided in an embodiment of the present invention;

[0032] Figure 4This is a schematic diagram illustrating the application of a nodal pricing method provided in an embodiment of the present invention.

[0033] Figure 5 This is a schematic diagram of a nodal price determination device provided in an embodiment of the present invention;

[0034] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Where there is no conflict between the embodiments, the following embodiments and features can be combined with each other. Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0037] The node electricity price determination method provided in this embodiment of the invention can be executed by an electronic device, which can be a server or a user terminal. The server can be a physical server or a virtual server (virtual machine) in the cloud.

[0038] First, some concepts involved in the embodiments of the present invention will be explained.

[0039] The nodal price clearing model is a mathematical model used to calculate nodal prices. It can use variables such as the active power of generating units at the nodes as decision variables, and is a model with cybersecurity constraints, aiming to minimize total cost. In the design of the spot electricity market, the problem of calculating nodal prices is actually based on the security-constrained economic dispatch (SCED) problem.

[0040] Linear programming is a mathematical model where all decision variables are continuous real variables, and the objective function and constraints are linear. After solving the model using standard algorithms or solvers, the decision variables are divided into basic and non-basic variables, and a shadow price is assigned to each constraint. The nodal electricity price clearing model is essentially a linear programming model.

[0041] Slack variables: Additional decision variables artificially added in a linear programming model.

[0042] Optimal basis: refers to a set of basis information that enables a linear programming model to obtain an optimal basis, consisting of optimal basic variables and basis row constraints.

[0043] In traditional nodal pricing schemes, a corresponding linear programming model (such as a nodal pricing clearing model) can be established based on real-time grid status data. To obtain information for grid regulation, staff artificially add slack variables to this model and penalize them significantly in the objective function to ensure that the solution obtained by the model satisfies the constraints of the grid as much as possible. This model with added slack variables is called the first-stage model. The solution obtained from solving the first-stage model is usually only used to guide staff in adjusting grid parameters (from the model's perspective, i.e., using the solution of slack variables to adjust the constants on the right-hand side of the constraints) so that the grid constraints are not violated, but cannot be directly used to calculate nodal prices. To calculate nodal prices, it is usually necessary to solve the linear scale model corresponding to the regulated grid (called the second-stage model) to obtain the shadow price of each constraint for each node. Then, the nodal price of a node is finally formed by summing the shadow prices related to a node. Therefore, in this conventional method, only the solution of slack variables in the solution of the first-stage model is used, while other information in the solution is not utilized.

[0044] The following is combined with Figure 1 This paper provides an example of a traditional nodal pricing solution.

[0045] In the first stage, a nodal price clearing model (referred to as the first-stage model) consisting of an objective function and numerous constraints with slack variables is established. Based on experience, a large penalty coefficient is set for the slack variables in the objective function to ensure that the model does not exceed the limitations of the various constraints. The optimal solution of the model is obtained using a general solver.

[0046] For ease of understanding, the form of the first-stage model is illustrated below as an example:

[0047]

[0048] Where x1, ..., x n Let s1, ..., s be ordinary decision variables. n These are slack variables (also decision variables). a, b, c, and M are all known constants. Where c1, ..., c... n These are the objective function coefficients (also known as cost coefficients or costs) for the corresponding variables. Where M1, ..., M... mHere, b1, ..., b are the penalty coefficients for the corresponding variables, all of which are large positive real numbers. The product of the penalty coefficient and its corresponding slack variable, Ms, is called the penalty term. m A known constant is generally called an rvalue.

[0049] Based on the examples of the mathematical forms above, it can be seen that, as Figure 1 As shown, the first-stage model includes multiple slack variables and their corresponding penalty coefficients. Solving the first-stage model using a general solver (linear programming model solver) yields... Figure 1 Solution 1 is illustrated in the diagram, which includes solutions for each variable (including slack variables) and basis information.

[0050] The second stage, such as Figure 1 As shown, the slack variables in the objective function are removed (without penalty terms), and the solutions for the corresponding slack variables are subtracted from the constant terms on the right side of the constraint equations, forming the second-stage nodal price clearing model (referred to as the second-stage model). Then, a general solver is used to solve the nodal price clearing model generated in the second stage, yielding solution 2, which includes the shadow prices of each constraint corresponding to each node, thus obtaining the nodal price for each node.

[0051] Following the mathematical expression example of the first-stage model described above, the second-stage model takes the following form:

[0052] min c1x1+c2x2+…+c n x n

[0053]

[0054]

[0055] …

[0056]

[0057] x1, ..., x n ≥0

[0058] in, These are the solutions for each slack variable.

[0059] As can be seen from the examples of the two stages above, both stages belong to linear programming models. Here is a brief explanation of the solution process for linear programming models:

[0060] A linear programming model is a continuous optimization model where both the objective function and constraints are linear. The decision variables are also called variables or columns, and the constraints are also called constraints or rows. It can be solved using a general linear programming solver. After solving, the solver outputs the optimal values ​​of the variables, whether each variable is a basic variable, whether each constraint is a basic row constraint, and the shadow price corresponding to each constraint. Solving a linear programming model is equivalent to selecting appropriate rows and columns as basic rows and columns, because once the basic rows and columns are selected, the optimal solution for each variable and the shadow price for each constraint can be directly calculated using simple formulas.

[0061] In fact, almost all solvers implement a method called the simplex method. The simplex method involves first attempting to construct an initial set of basis rows and columns, then continuously selecting new rows and columns to replace them (i.e., basis replacement), until the optimal set of basis rows and columns is found to complete the solution.

[0062] (Default initial basis -- (basis change) --> new basis ... -- (basis change) --> optimal basis).

[0063] In other words, for each of the models established in the above stages, the solver actually uses a simplex method to solve the problem during the iterative search for the optimal solution. This involves first constructing an initial set of basis information (including basic variables and basic row constraints), then continuously selecting better variables and constraints to replace them as basic variables and basic row constraints. Finally, after a large number of iterations, the optimal set of basic variables and basic row constraints is found, and the nodal price is calculated based on this optimal basis. Therefore, it can be seen that the models in the above two stages use this general solution method, starting from a randomly initialized set of basis information and requiring a large number of iterations to determine the final optimal basis. This results in high computational complexity and makes it difficult to meet the real-time requirements of nodal price calculation. In actual industrial scenarios, the clearing model for safety constraints makes short-term power generation decisions, while actual needs are constantly changing. Therefore, the model needs to be frequently called for adjustment calculations, and the above solution method consumes a considerable amount of time.

[0064] Therefore, embodiments of the present invention provide an optimized nodal pricing calculation scheme.

[0065] Figure 2 A flowchart of a nodal pricing method provided in an embodiment of the present invention is shown below. Figure 2 As shown, the method includes the following steps:

[0066] 201. Construct a first-node electricity price clearing model based on the power grid data information at the current scheduling time. The first-node electricity price clearing model includes multiple slack variables.

[0067] 202. Solve the first-node electricity price clearing model to obtain solutions for multiple slack variables and the optimal basis of the first-node electricity price clearing model.

[0068] As mentioned above, the calculation of nodal prices in a power grid system requires "real-time" calculation. Therefore, nodal prices can be calculated every short period of time, such as 5 minutes. Each time nodal price calculation is required, power grid data information at the current scheduling time needs to be collected to construct the first nodal price clearing model for calculating nodal prices, which corresponds to the first-stage model mentioned above.

[0069] In practical applications, the collected power grid data includes relevant data for each generating unit node in the power grid system, load data for load nodes, and topology data of the power grid system, among others. The relevant data for each generating unit node may include, for example, the maximum ramp rate, maximum and minimum generating capacity, start-up cost, and operating cost of the generating unit node, etc. The topology data of the power grid system may include, for example, the active power flow limits of each transmission line, and the connection relationships between each node and the lines, etc.

[0070] In practical applications, some of the aforementioned power grid status data are observable, while others are predictable. For example, the load data of load nodes is predicted. For the current scheduling time, the load data of a load node describes its load demand over a given period (the duration of a scheduling cycle), and generally requires some prediction method. In some embodiments, the load data of the load node observed at the current scheduling time can be used directly as the prediction result, or the load data of the load node at the current scheduling time can be based on statistical analysis of the load data from previous or historical periods. The load data of a load node can include its active power and reactive power.

[0071] The first-node electricity price clearing model consists of an objective function and multiple constraints, and its mathematical form is shown in the first-stage model above. The objective function minimizes the total cost of grid generation. As shown in the first-stage model, the objective function of this first-node electricity price clearing model includes penalty terms corresponding to multiple slack variables. Each slack variable has a pre-set, relatively large penalty coefficient in the objective function. Additionally, the constraints also include slack variables.

[0072] In practical applications, optionally, during the construction of the first-node electricity price clearing model, a configuration input box corresponding to the slack variables can be provided on the interface of the computing device, where users can input each slack variable and its corresponding penalty coefficient.

[0073] Solving the first-node electricity price clearing model using a general solver yields Solution 1, mentioned above. This solution includes the solutions for each variable in the first-node electricity price clearing model and the optimal basis. The optimal basis comprises the final determined basis rows and basis columns, i.e., basic variables and basis row constraints. The optimal basis refers to the set of optimal bases determined by the solver through iterative calculations starting from an initialized set of basis information (initial basic variables, initial basis row constraints) and multiple basis replacement operations.

[0074] In practical applications, slack variables do not have a clear physical meaning, while non-slack variables often have a clear physical meaning, such as the active power, voltage amplitude, reactive power and voltage phase angle of the generator node, the voltage amplitude and voltage phase angle of the load node, and the active power and reactive power of the balancing node.

[0075] In addition, in practical applications, the constraints in the first-node electricity price clearing model may include, for example, energy balance constraints of each node, node voltage constraints, active power constraints of unit nodes, branch power flow constraints, variable capacitor constraints, and so on.

[0076] 203. Based on the solutions of multiple slack variables, update the first-node electricity price clearing model to obtain the second-node electricity price clearing model.

[0077] 204. Based on the optimal basis of the first node electricity price clearing model, determine the optimal basis of the second node electricity price clearing model, and based on the optimal basis of the second node electricity price clearing model, determine the electricity price of each node in the power grid.

[0078] After solving the first-node electricity price clearing model using traditional methods, the first-node electricity price clearing model can be updated based on the solutions of each slack variable to obtain the second-node electricity price clearing model.

[0079] Unlike traditional second-stage models, the second-node electricity price clearing model constructed in this embodiment can be considered an equivalent model of the traditional second-stage model. Mathematically, the second-node electricity price clearing model provided in this embodiment has the same number of rows and columns as the first-node electricity price clearing model; that is, the variables in the constraints remain unchanged, and the slack variables included in the constraints are not removed in form.

[0080] In summary, updating the first-node electricity price clearing model to obtain the second-node electricity price clearing model based on the solutions of the multiple slack variables can be achieved by: removing the penalty term contained in the objective function of the first-node electricity price clearing model, and setting the values ​​of the multiple slack variables in the second-node electricity price clearing model based on the solutions of the multiple slack variables, while keeping the constraints in the first-node electricity price clearing model unchanged, to obtain the second-node electricity price clearing model.

[0081] Following the example of the mathematical expression of the first-stage model, i.e., the first-node price clearing model, provided in this embodiment, the expression of the second-node price clearing model, i.e., the equivalent model, is as follows:

[0082]

[0083] It can be seen that the difference in expression between the second-node electricity price clearing model and the first-node electricity price clearing model lies in the removal of the penalty term in the objective function and the updating of the definition of the range of values ​​for the slack variables.

[0084] The optimal basis information obtained after solving the first-stage model (i.e., the first-node electricity price clearing model) cannot be directly applied to solving the traditional second-stage model because the optimal basis may contain some slack variables that are not present in the traditional second-stage model. Therefore, if a model is constructed that is equivalent to the traditional second-stage model and contains all the variables from the first-stage model, then the optimal basis from the solution of the first-node model can be used to search for a better basis, starting from the set of basis rows and columns corresponding to the optimal basis.

[0085] In summary, the second-node electricity price clearing model constructed in this embodiment is essentially equivalent to the traditional second-stage model, but differs in form. The reason for constructing this form is to utilize the basis information (i.e., the optimal basis) obtained from the first-stage model (i.e., the first-node electricity price clearing model). Because the basis information only involves whether a certain row / column is a basis, it can be used as long as the number of rows and columns is consistent with the first-stage model.

[0086] The application of this basis can be divided into two cases: First, the optimal basis of the first-node electricity price clearing model is exactly the same as the optimal basis of the second-node electricity price clearing model; second, the optimal basis of the first-node electricity price clearing model is not the same as the optimal basis of the second-node electricity price clearing model. In the first case, there is no need to calculate the optimal basis for the second-node electricity price clearing model again. In the second case, it is necessary to perform a "basis-changing" operation on the second-node electricity price clearing model to gradually find its true optimal basis before the solution is considered complete.

[0087] In other words, the optimal basis of the first-node electricity price clearing model obtained after solving the first-node electricity price clearing model can be used to assist in solving the second-node electricity price clearing model, thereby reducing the solution time of the second-node electricity price clearing model.

[0088] Specifically, if the optimal basis of the first-node electricity price clearing model does not include any of the multiple slack variables, then the optimal basis of the second-node electricity price clearing model is determined to be the same as the optimal basis of the first-node electricity price clearing model. If the optimal basis of the first-node electricity price clearing model includes at least one of the multiple slack variables, then the optimal basis of the second-node electricity price clearing model is iteratively solved using the optimal basis of the first-node electricity price clearing model as the initial basis.

[0089] In other words, if the optimal basis of the first-node electricity price clearing model does not contain slack variables (i.e., the basic variables in the optimal basis do not contain slack variables), then the optimal basis of the first-node electricity price clearing model and the optimal basis of the second-node electricity price clearing model must be the same. In this case, the solver does not need to start the solution process of "default initial basis -- (basis change) --> new basis ... -- (basis change) --> optimal basis". However, if the optimal basis of the first-node electricity price clearing model contains slack variables (i.e., the basic variables in the optimal basis contain slack variables), then the optimal basis of the first-node electricity price clearing model can be used as the initial basis. Compared to the solver starting from the default initial basis, using the optimal basis of the first-node electricity price clearing model as the initial basis allows for the solution of the second-node electricity price clearing model with only a few basis change operations.

[0090] After obtaining the optimal basis of the second-node electricity price clearing model based on the optimal basis of the first-node electricity price clearing model, the solutions for each variable in the second-node electricity price clearing model and the shadow price corresponding to each constraint can be completed based on the optimal basis of the second-node electricity price clearing model, thereby completing the calculation of the node electricity price. This solution process can be implemented with reference to existing technologies and will not be elaborated here. Only the calculation logic is briefly explained here:

[0091] Based on the basic variables and basic row constraints indicated in the optimal basis of the second-node electricity price clearing model, determine the solutions of the basic variables and the shadow prices corresponding to the basic row constraints in the second-node electricity price clearing model, and set the solutions of the non-basic variables and the shadow prices corresponding to the non-basic row constraints to zero.

[0092] Based on the shadow prices corresponding to each constraint of each node in the second-node electricity price clearing model, determine the node electricity price corresponding to each node in the second-node electricity price clearing model.

[0093] In the second-node electricity price clearing model, there are usually multiple constraints for the same node, such as energy balance constraints, voltage constraints, and so on. Based on solving the second-node electricity price clearing model, the shadow price corresponding to each constraint can be obtained. By summing the shadow prices of all constraints corresponding to the same node, the node price for that node can be obtained.

[0094] The above Figure 2The execution process of the illustrated embodiment can be referred to Figure 3 To understand.

[0095] In practical applications, optionally, after the node electricity price of each node is solved, the node electricity price of these nodes can be displayed on the interface of the computing device for the relevant operation and maintenance personnel of the power grid system to confirm whether to adopt it. If the operation and maintenance personnel edit and modify some of the node prices, the modified final node electricity price shall be adopted.

[0096] In summary, in solving the second-node price clearing model, the optimal basis of the first-node price clearing model is used to determine the optimal basis of the second-node price clearing model. Finally, the electricity price of each node in the power grid is determined based on the optimal basis of the second-node price clearing model. Using the optimal basis of the first-node price clearing model to assist in determining the optimal basis of the second-node price clearing model can reduce the computational load and solution time of solving the second-node price clearing model, thereby improving the computational efficiency of node prices.

[0097] The nodal pricing method provided by this invention can be executed in the cloud, where multiple computing nodes can be deployed, each with processing resources such as computing and storage. In the cloud, multiple computing nodes can be organized to provide a certain service; of course, a single computing node can also provide one or more services. The cloud can provide this service by providing an external service interface, which users can call to use the corresponding service. Service interfaces can take the form of Software Development Kits (SDKs), Application Programming Interfaces (APIs), etc.

[0098] According to the solution provided in this embodiment of the invention, the cloud can provide a service interface for node electricity price calculation. Users invoke this service interface through their user equipment to trigger a node electricity price calculation request to the cloud. This request includes grid data information for the current scheduling time. The cloud determines the computing node that responds to the request and utilizes the processing resources in that computing node to perform the following steps:

[0099] A first-node electricity price clearing model is constructed based on the aforementioned power grid data information. The first-node electricity price clearing model includes multiple slack variables.

[0100] Solve the first node electricity price clearing model to obtain the solution of the multiple slack variables and the optimal basis of the first node electricity price clearing model;

[0101] Based on the solutions of the multiple slack variables, update the first node electricity price clearing model to obtain the second node electricity price clearing model;

[0102] Based on the optimal basis of the first node electricity price clearing model, determine the optimal basis of the second node electricity price clearing model;

[0103] Based on the optimal basis of the second node electricity price clearing model, determine the electricity price of each node in the power grid;

[0104] The electricity price of each node is sent to the user equipment.

[0105] The above execution process can be referred to the relevant descriptions in the other embodiments mentioned above, and will not be repeated here.

[0106] For ease of understanding, combined with Figure 4 To illustrate this, users can... Figure 4 The diagram illustrates a user equipment (UE) E1 requesting a node electricity price calculation service to upload grid data information containing information for a specific scheduling time. In the cloud, as shown, in addition to several computing nodes, a management node E2 running management services is also deployed. After receiving the service request from UE E1, management node E2 determines the computing node E3 to respond. Upon receiving the request, computing node E3 executes the aforementioned calculation process to obtain the node electricity price for each node. Then, computing node E3 sends the node electricity prices of each node to UE E1, which displays these prices, allowing the user to perform further editing and other operations.

[0107] The nodal pricing determination apparatus of one or more embodiments of the present invention will be described in detail below. Those skilled in the art will understand that these apparatuses can be configured using commercially available hardware components through the steps taught in this solution.

[0108] Figure 5 This is a schematic diagram of a nodal price determination device provided in an embodiment of the present invention, as shown below. Figure 5 As shown, the device includes: a model building module 11, a first solution module 12, and a second solution module 13.

[0109] The model building module 11 is used to build a first node electricity price clearing model based on the power grid data information of the current scheduling time. The first node electricity price clearing model includes multiple slack variables.

[0110] The first solution module 12 is used to solve the first node electricity price clearing model to obtain the solution of the multiple slack variables and the optimal basis of the first node electricity price clearing model.

[0111] The second solution module 13 is used to update the first node electricity price clearing model to obtain the second node electricity price clearing model based on the solutions of the multiple slack variables, determine the optimal basis of the second node electricity price clearing model based on the optimal basis of the first node electricity price clearing model, and determine the electricity price of each node in the power grid based on the optimal basis of the second node electricity price clearing model.

[0112] The objective function of the first node electricity price clearing model includes penalty terms corresponding to the plurality of slack variables. Optionally, the second solution module 13 is specifically used to: remove the penalty terms contained in the objective function of the first node electricity price clearing model; and set the values ​​of the plurality of slack variables in the second node electricity price clearing model according to the solution of the plurality of slack variables, so as to obtain the second node electricity price clearing model.

[0113] Optionally, the second solution module 13 is specifically used to: if the optimal basis of the first node electricity price clearing model does not include any of the plurality of slack variables, then determine that the optimal basis of the second node electricity price clearing model is the same as the optimal basis of the first node electricity price clearing model.

[0114] Optionally, the second solution module 13 is specifically used to: if the optimal basis of the first node electricity price clearing model includes at least one of the plurality of slack variables, then using the optimal basis of the first node electricity price clearing model as the initial basis, iteratively solve for the optimal basis of the second node electricity price clearing model.

[0115] Wherein, the optimal basis of the second node electricity price clearing model indicates the basic row constraints and basic variables. Optionally, the second solution module 13 is specifically used to: determine the solutions of the basic variables and the shadow prices corresponding to the basic row constraints in the second node electricity price clearing model according to the optimal basis of the second node electricity price clearing model, and set the solutions of non-basic variables and the shadow prices corresponding to non-basic row constraints to zero; determine the node electricity price corresponding to each node in the second node electricity price clearing model according to the shadow prices corresponding to each constraint of each node in the second node electricity price clearing model.

[0116] Optionally, the device further includes: a display module, used to display the node electricity price corresponding to each node on the interface; and to receive the user's editing result of the node electricity price. Therefore, the second solving module 13 is specifically used to: determine the final node electricity price corresponding to each node based on the editing result.

[0117] In one possible design, the above Figure 5 The structure of the node electricity price determination device shown can be implemented as an electronic device. For example... Figure 6As shown, the electronic device may include: a processor 21, a memory 22, and a communication interface 23. The memory 22 stores executable code, which, when executed by the processor 21, enables the processor 21 to at least implement the node electricity price determination method provided in the foregoing embodiments.

[0118] In addition, embodiments of the present invention provide a non-transitory machine-readable storage medium storing executable code, which, when executed by a processor of an electronic device, enables the processor to at least implement the nodal price determination method provided in the foregoing embodiments.

[0119] The device embodiments described above are merely illustrative. The network elements described as separate components may or may not be physically separate. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0120] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of a necessary general-purpose hardware platform, or by a combination of hardware and software. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a computer product. The present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0121] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the marginal cost of a power grid node, characterized in that, include: Based on the grid data information at the current scheduling time, a marginal cost clearing model for the first grid node is constructed. The marginal cost clearing model for the first grid node includes multiple slack variables. The grid data information includes the maximum ramp rate of the unit node, the maximum and minimum generating capacity, the active power flow limit of each transmission line, and the connection relationship between each node and the line. Solve the marginal cost clearing model of the first power grid node to obtain the solution of the multiple slack variables and the optimal basis of the marginal cost clearing model of the first power grid node. The objective function of the marginal cost clearing model of the first power grid node includes the penalty terms corresponding to the multiple slack variables. Remove the penalty term contained in the objective function of the first power grid node marginal cost clearing model; and set the values ​​of the multiple slack variables in the second power grid node marginal cost clearing model according to the solution of the multiple slack variables, so as to obtain the second power grid node marginal cost clearing model. Based on the optimal basis of the marginal cost clearing model of the first power grid node, determine the optimal basis of the marginal cost clearing model of the second power grid node; Based on the shadow price corresponding to the base row constraint indicated by the optimal base in the marginal cost clearing model of the second power grid node, determine the marginal cost of each node in the power grid; Among them, the marginal cost clearing model of the first grid node and the marginal cost clearing model of the second grid node are linear programming models. The model constraints include energy balance constraints, node voltage constraints, active power constraints of unit nodes, branch power flow constraints, and variable capacitor constraints for each node.

2. The method according to claim 1, characterized in that, The step of determining the optimal basis of the marginal cost clearing model of the second power grid node based on the optimal basis of the first power grid node marginal cost clearing model includes: If the optimal basis of the marginal cost clearing model of the first power grid node does not include any of the plurality of slack variables, then the optimal basis of the marginal cost clearing model of the second power grid node is determined to be the same as the optimal basis of the marginal cost clearing model of the first power grid node.

3. The method according to claim 1, characterized in that, The step of determining the optimal basis of the marginal cost clearing model of the second power grid node based on the optimal basis of the first power grid node marginal cost clearing model includes: If the optimal basis of the first power grid node marginal cost clearing model includes at least one of the plurality of slack variables, then the optimal basis of the second power grid node marginal cost clearing model is iteratively solved using the optimal basis of the first power grid node marginal cost clearing model as the initial basis.

4. The method according to claim 1, characterized in that, The optimal basis of the marginal cost clearing model for the second power grid node indicates the basis row constraints and basic variables; The step of determining the electricity price at each node in the power grid based on the optimal basis of the marginal cost clearing model of the second power grid node includes: Based on the optimal basis of the marginal cost clearing model of the second power grid node, determine the solution of the basic variables and the shadow price corresponding to the basic row constraint in the marginal cost clearing model of the second power grid node, and set the solution of the non-basic variables and the shadow price corresponding to the non-basic row constraint to zero; Based on the shadow price corresponding to each constraint of each node in the second power grid node marginal cost clearing model, determine the power grid node marginal cost corresponding to each node in the second power grid node marginal cost clearing model.

5. The method according to claim 4, characterized in that, The method further includes: The interface displays the marginal cost of each power grid node corresponding to each node. Receive the user's edit results on the marginal cost of the power grid node; The final marginal cost of the power grid node corresponding to each node is determined based on the editing results.

6. A device for determining the marginal cost of a power grid node, characterized in that, include: The model building module is used to construct a marginal cost clearing model for the first power grid node based on the power grid data information at the current scheduling time. The marginal cost clearing model for the first power grid node includes multiple slack variables. The power grid data information includes the maximum ramp rate of the unit node, the maximum and minimum generating capacity, the active power flow limit of each transmission line, and the connection relationship between each node and the line. The first solution module is used to solve the marginal cost clearing model of the first power grid node to obtain the solution of the plurality of slack variables and the optimal basis of the marginal cost clearing model of the first power grid node. The objective function of the marginal cost clearing model of the first power grid node includes the penalty terms corresponding to the plurality of slack variables. The second solution module is used to remove the penalty terms contained in the objective function of the first power grid node marginal cost clearing model; Furthermore, based on the solution of the plurality of slack variables, the values ​​of the plurality of slack variables in the marginal cost clearing model of the second power grid node are set to obtain the marginal cost clearing model of the second power grid node. Based on the optimal basis of the marginal cost clearing model of the first power grid node, the optimal basis of the marginal cost clearing model of the second power grid node is determined. Based on the shadow price corresponding to the base row constraint indicated by the optimal basis in the marginal cost clearing model of the second power grid node, the marginal cost of each node in the power grid is determined. Among them, the marginal cost clearing model of the first grid node and the marginal cost clearing model of the second grid node are linear programming models. The model constraints include energy balance constraints, node voltage constraints, active power constraints of unit nodes, branch power flow constraints, and variable capacitor constraints for each node.

7. The apparatus according to claim 6, characterized in that, The second solution module is specifically used for: If the optimal basis of the marginal cost clearing model of the first power grid node does not include any of the plurality of slack variables, then the optimal basis of the marginal cost clearing model of the second power grid node is determined to be the same as the optimal basis of the marginal cost clearing model of the first power grid node. If the optimal basis of the first power grid node marginal cost clearing model includes at least one of the plurality of slack variables, then the optimal basis of the second power grid node marginal cost clearing model is iteratively solved using the optimal basis of the first power grid node marginal cost clearing model as the initial basis.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a communication interface; wherein the memory stores executable code, which, when executed by the processor, causes the processor to perform the method for determining the marginal cost of a power grid node as described in any one of claims 1 to 5.

9. A non-transitory machine-readable storage medium, characterized in that, The non-transitory machine-readable storage medium stores executable code that, when executed by a processor of an electronic device, causes the processor to perform the method for determining the marginal cost of a power grid node as described in any one of claims 1 to 5.

10. A method for determining the marginal cost of a power grid node, characterized in that, include: Receive a request from a user equipment to call the power grid node marginal cost calculation service, the request including power grid data information for the current scheduling time; The following steps are performed using the processing resources corresponding to the power grid node marginal cost calculation service: Based on the power grid data information, a first power grid node marginal cost clearing model is constructed. The first power grid node marginal cost clearing model includes multiple slack variables. The power grid data information includes the maximum ramp rate of the unit node, the maximum and minimum generating capacity, the active power flow limit of each transmission line, and the connection relationship between each node and the line. Solve the marginal cost clearing model of the first power grid node to obtain the solution of the multiple slack variables and the optimal basis of the marginal cost clearing model of the first power grid node. The objective function of the marginal cost clearing model of the first power grid node includes the penalty terms corresponding to the multiple slack variables. Remove the penalty term contained in the objective function of the first power grid node marginal cost clearing model; and set the values ​​of the multiple slack variables in the second power grid node marginal cost clearing model according to the solution of the multiple slack variables, so as to obtain the second power grid node marginal cost clearing model. Based on the optimal basis of the marginal cost clearing model of the first power grid node, determine the optimal basis of the marginal cost clearing model of the second power grid node; Based on the shadow price corresponding to the base row constraint indicated by the optimal base in the marginal cost clearing model of the second power grid node, determine the marginal cost of each node in the power grid; The electricity price of each node is sent to the user equipment; Among them, the marginal cost clearing model of the first grid node and the marginal cost clearing model of the second grid node are linear programming models. The model constraints include energy balance constraints, node voltage constraints, active power constraints of unit nodes, branch power flow constraints, and variable capacitor constraints for each node.

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