A power system main coordination operation scheduling method, system, device and medium

Abstract

The application discloses a power system main coordination operation scheduling method, system, equipment and medium, relates to the technical field of power system planning, and comprises the following steps: collecting power grid parameters, power parameters and input cost parameters; constructing a target of minimum operation cost and a constraint function; constructing a matrix form of the operation scheduling model, splitting the matrix into one upper model UP, a plurality of middle problems MP and a plurality of lower models LP, and obtaining the feasible region projection of each LP and the feasible region projection of each MP by using a time sequence operation domain equivalent projection fast generation algorithm; and obtaining the optimal operation scheduling mode by obtaining the optimal value when the upper model UP, the middle problem MP and the lower model LP are used for operation scheduling. The large-scale operation optimization problem is split into a plurality of small-scale problems for solving, and the disadvantage that a large amount of repeated calculation is needed when a traditional method is used for processing the time sequence coupling operation optimization problem is avoided.

Description

Technical Field

[0001] This invention relates to the field of power system planning technology, and in particular to a method, system, equipment and medium for coordinated operation and scheduling of power system main and distribution systems. Background Technology

[0002] With the large-scale grid connection of renewable energy and the intelligent development of power systems, grid operation requires coordinated optimization of the transmission network (main grid) and distribution network. Traditional centralized dispatching methods face bottlenecks such as low computational efficiency and difficulty in meeting real-time decision-making needs due to the large system scale, high variable dimensionality, and complex temporal coupling constraints. Especially in systems with a high proportion of renewable energy, it is necessary to respond quickly to power fluctuations and coordinate time-series equipment such as energy storage and unit start-up and shutdown, which places higher demands on the solution efficiency of the dispatching model.

[0003] Existing master-slave collaborative scheduling methods mainly rely on the following two types of technologies: Centralized optimization: This method models the power transmission and distribution network as a single spatiotemporal optimization problem and directly solves the mixed integer linear programming (MILP) or nonlinear programming model. While this method guarantees theoretical optimality, the size of the variables grows exponentially with the number of nodes and time periods, making it difficult to meet the practical engineering requirements in terms of solution time.

[0004] Distributed / Decomposition Coordination Optimization: This approach reduces computational complexity by decomposing the problem. These methods can be further divided into model simplification and model decomposition. For model simplification, in transmission network operation optimization, the distribution network is often considered as a load node, ignoring its internal structure. Similarly, in distribution network operation optimization, the transmission network is considered as a power source node. Model decomposition, on the other hand, starts from the perspective of solving the optimization problem, using specific mathematical methods to decompose the baseline model into several smaller sub-problems, and then solving the original large-scale optimization problem through iterative convergence. Common decomposition methods can be divided into primal decomposition and dual decomposition. Primitive decomposition includes Benders decomposition, while dual decomposition includes methods such as Lagrange relaxation. These methods can be further combined with methods such as Alternating Direction Multipliers (ADMM) and cutting plane methods.

[0005] In recent years, the equivalent projection method has been proposed for solving the optimal operation of the main and distribution coordinated system. This method is a model decomposition approach. It has significant advantages such as fast computation speed, no need for iterative calculations, and low communication overhead. Specifically, this method decomposes the optimization operation model into an upper-level problem (UP) and a lower-level problem (LP). The upper-level problem optimizes the operation of the transmission network, while the lower-level problem optimizes the operation of the distribution network. The upper and lower levels are coupled through "connection variables," typically selected as the active and reactive power of transformers between the transmission and distribution networks, as well as the operating cost of the distribution network. In the solution process, this method first uses algorithms such as progressive vertex enumeration (PVE) to calculate the equivalent projection of the distribution network operating domain of the lower-level problem onto the subspace containing the connection variables. Then, it solves the upper-level problem to determine the optimal operation mode of the transmission network and the active and reactive power of each transformer. Finally, based on the known power of each transformer, it calculates the optimal operation mode of each distribution network.

[0006] In existing technologies, centralized optimization suffers from the following drawbacks: the scale of variables grows exponentially with the number of nodes and time periods, making it difficult to meet the actual engineering requirements when solving large-scale main and distribution systems. Distributed / decomposition coordination optimization, for model simplification methods, cannot accurately characterize the characteristics of adjacent networks and cannot achieve the optimal operating mode. For model decomposition methods, multiple iterations are required to exchange boundary variable information, resulting in high communication overhead; multiple iterations are also required, leading to difficulties in convergence and slow convergence speed; and it is difficult to efficiently handle bidirectional power coupling between transmission networks and multiple distribution networks, as well as cross-time period constraints such as generators and energy storage. The equivalent projection method has the following defects: 1) This method cannot handle optimization scheduling problems containing 0-1 variables. In optimization scheduling problems, generator start-stop constraints, energy storage charging and discharging constraints, etc., often require the introduction of 0-1 variables for accurate characterization. 2) This method cannot handle optimization scheduling problems with temporally coupled constraints. Temporally coupled constraints refer to constraints that contain variables from both the current time and other time periods. In optimization scheduling models, generator ramping constraints, energy storage state of charge constraints, etc., are all temporally coupled constraints.

[0007] In summary, the equivalent projection method requires extensive simplification of the generator and energy storage models, sacrificing the accuracy of the results, and still cannot meet the requirements. The asymptotic vertex enumeration method, which is used in conjunction with the equivalent projection method, requires a lot of repetitive calculations when dealing with time-coupled operation optimization problems, and is inefficient in collaborative operation scheduling. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of the prior art by providing a method, system, equipment, and medium for coordinated operation and scheduling of power systems, thereby solving the problems in the prior art.

[0009] The present invention specifically provides the following technical solution: A method for coordinated operation and dispatching of primary and secondary power systems includes: Collect grid parameters, power parameters, and input cost parameters; Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, an objective function for the operation scheduling model is constructed to minimize the operating cost. Based on the various outputs, power and voltages in the transmission and distribution networks, the objective function is constructed in matrix form. The matrix is ​​then split into one upper-level model UP, several middle-level problems MP and several lower-level models LP. The feasible domain projection of each LP and the feasible domain projection of each MP are obtained by using the temporal running domain equivalent projection fast generation algorithm. The upper-level model UP, the middle-level problem MP, and the lower-level model LP are regenerated by projecting the feasible region of LP and the feasible region of MP. The optimal running and scheduling method is determined by obtaining the optimal value of each regenerated model during the running and scheduling process.

[0010] Preferably, the acquisition of power grid parameters, power parameters, and input cost parameters specifically includes: The power grid parameters include: the number of each node, its rated voltage, and the operating voltage range; the number of each line, the nodes it connects to, the type and length of the conductors, and the maximum current flowing through it. Power parameters include: maximum output of each thermal / gas turbine unit, predicted output of each new energy unit at each time, predicted power of each load at each time, power capacity, energy capacity, and charge / discharge efficiency of each energy storage unit; specifically, those installed in... Subsystem The maximum output of the thermal / gas turbine unit at the node is Maximum climbing power is Minimum boot time is Minimum shutdown time is Installed Subsystem New energy units at the node The predicted output at time is Installed Subsystem The load on the node The predicted active power at time is Reactive power is Installed Subsystem The power capacity of the energy storage unit at the node is Energy capacity is The charge / discharge efficiency is ; Input cost parameters include: unit energy load shedding penalty cost Penalty cost per unit of energy curtailment of wind and solar power and unit energy generation cost .

[0011] Preferably, the objective function of the operation scheduling model, which uses grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, and constructs the objective function of the operation scheduling model when the operating cost is minimized, is as follows: Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, and minimizing operating cost as the objective of the operation scheduling model, the output of each generator, the curtailment of wind and solar power of each renewable energy unit, and the charging and discharging power of each energy storage unit at each time point are determined. The specific expression of the objective function is as follows: ; ; In the formula, To represent the subsystem's subscripts, To characterize the subscripts of power system nodes, The index is used to represent the execution time. , and Subsystems, The set of nodes and their running times in a subsystem; represent The operating cost of the subsystem; represent Subsystem Node load at The load shedding power at any given time represent Subsystem New energy units at node loads The amount of wind and solar power curtailed at any given time. represent Subsystem Node thermal / gas turbine units at Efforts made at all times; It represents the time interval between two adjacent moments.

[0012] Preferably, the matrix form of the objective function constructed based on various outputs, power, and voltages in the transmission and distribution networks is specifically expressed as follows: ; ; ; ; In the formula, represent Distribution network subsystem The column vector consisting of the discharge power and charging power of all energy storage units at any given time. represent Distribution network subsystem The vector consisting of the active power and reactive power of all transformers at any given moment; represent In the power transmission subsystem A vector consisting of the active power output of all generator units, the reactive power output of generator units, the discharge power of energy storage units, and the charging power of energy storage units at any given moment; represent Electronic distribution system A vector consisting of the remaining variables at each time step; It is a power transmission network subsystem A vector consisting of the remaining variables at each time step; and The way vectors are constructed and , Similarly, Represents other variables in thermal / gas turbine units and energy storage units. Represents a value vector. and Represents the vector on the right-hand side of the constraint. , , as well as Represents the coefficient matrix.

[0013] Preferably, the matrix is ​​split into one upper-level model UP, several middle-level problems MP, and several lower-level models LP, specifically as follows: The decomposed upper-level model UP optimizes the operation of thermal / gas turbine units and energy storage units with time-series coupling constraints. The specific expression is as follows: ; ; ; In the formula, It is an auxiliary variable representing the relationship between the transmission network and all distribution networks. The sum of operating costs at each moment; The projection representing the feasible region of the mid-level problem; The decomposed intermediate-level problem, MP optimization, addresses the operation of a transmission network. The operation problem of the transmission network at any given time is represented as an MP problem, which has a total of... MP, of which The expression for time MP is: ; ; ; ; In the formula, It is an auxiliary variable, representing Distribution network in The operating cost at any given moment; Representing the power transmission network The operating cost at any given time represent The upper limit estimate is a preset value derived from historical operating data; The projection of the feasible region representing the lower-level problem; The decomposed lower-level model LP optimizes distribution network operation. The operation problem of any distribution network at any time is represented as an LP, therefore there are a total of One LP; Distribution network in The specific LP expression for time step is: In the formula, yes The upper limit estimate is a preset value derived from historical operating data.

[0014] Preferably, the step of using the temporal runtime domain equivalent projection fast generation algorithm to obtain the feasible region projection of each LP and the feasible region projection of each MP specifically involves: In each LP and each MP, the current solution time is recorded. Temporal low-dimensional space basis vectors and temporal high-dimensional feasible region As input data; where LP , For the distribution network in The constraint representation at time, in MP , For the power transmission network in The constraints at any given time are represented; The algorithm for fast generation of equivalent projections in temporal running domains is used to filter the vertices of equivalent projections at adjacent time steps in LP and MP, and obtain the candidate vertex set of equivalent projections at that time step. The output is the projection of the feasible domain of the temporal high-dimensional space to the low-dimensional space spanned by the basis vectors of the temporal low-dimensional space, which serves as the feasible domain projection of LP and MP.

[0015] Preferably, the step of obtaining the optimal value during runtime scheduling through the regenerated models and determining the optimal runtime scheduling method specifically involves: For the upper-level model UP, optimize the operation of thermal power / gas turbine units and energy storage units with time-series coupling constraints, and obtain the optimal values ​​of active power, reactive power, charging power, and discharging power of thermal power / gas turbine units at each time step. The optimal values ​​at each time point are used as the given values ​​of the corresponding variables in the intermediate-level problem MP. The operation problem of the transmission network at any time point is calculated to obtain the optimal values ​​of the active power of all transformers, the reactive power of transformers, the power of wind and solar power curtailment in the transmission network, and the power of load shedding in the transmission network. The optimal values ​​of the active power of all transformers, the reactive power of transformers, the power of wind and solar power curtailment in the transmission network, and the power of load shedding in the transmission network are taken as the given values ​​of the corresponding variables in LP. The LP shown by each subsystem at each time point is solved to obtain the optimal values ​​of the power of wind and solar power curtailment and the power of load shedding in each distribution network.

[0016] This invention provides a power system main and distribution coordinated operation and dispatching system, comprising: The data acquisition module is used to collect power grid parameters, power parameters, and input cost parameters; The model objective construction module is used to construct the objective function of the operation and scheduling model when the operating cost is minimized, using grid parameters, power parameters, and input cost parameters as inputs to the operation and scheduling model. The decomposition module is used to construct the matrix form of the objective function based on various outputs, power and voltages in the transmission and distribution networks. The matrix is ​​decomposed into one upper-level model UP, several middle-level problems MP and several lower-level models LP. The feasible domain projection of each LP and the feasible domain projection of each MP are obtained by using the temporal running domain equivalent projection fast generation algorithm. The scheduling module is used to regenerate the upper-level model UP, the middle-level problem MP, and the lower-level model LP by projecting the feasible region of the LP and the feasible region of the MP, and to obtain the optimal value when scheduling the regenerated models to determine the optimal scheduling method.

[0017] The present invention provides a computer device, including a memory and a processor. The memory stores a program, and when the program is executed by the processor, the processor performs the steps of the above-described power system main and distribution coordinated operation scheduling method.

[0018] The present invention provides a storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, implements the steps of the above-described power system main and distribution coordinated operation scheduling method.

[0019] Compared with the prior art, the present invention has the following significant advantages: This invention collects grid parameters, power parameters, and input cost parameters, constructs an objective function for the operation scheduling model with the goal of minimizing operating costs, determines relevant parameters and constraints, constructs the matrix form of the operation scheduling model, and performs matrix decomposition. This breaks down a large-scale operation optimization problem into several smaller problems for separate solving. Variables containing 0-1 variables and temporally coupled constraints are removed to exclude them. The concept of equivalent projection is used to obtain the feasible region projection. The upper-level model UP, the middle-level problem MP, and the lower-level model LP are regenerated, and optimal values ​​are obtained to determine the optimal operation scheduling method. The reconstruction of the solution results for the several smaller problems avoids the problem of extensive repetitive calculations required by traditional methods when dealing with temporally coupled operation optimization problems, greatly improving the calculation speed of temporally equivalent projection and significantly increasing the efficiency of collaborative operation scheduling. Attached Figure Description

[0020] Figure 1 This is a flowchart from the present invention; Figure 2 This is a flowchart of the fast algorithm for generating equivalent projections of the temporal running domain in this invention; Figure 3 This is a flowchart of the progressive vertex enumeration algorithm mentioned in the embodiments of the present invention. Detailed Implementation

[0021] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] like Figure 1 As shown in the figure, a power system primary and secondary coordinated operation and scheduling method in this embodiment includes the following steps: Step S1: Initialization: Collect grid parameters, power parameters and input cost parameters.

[0023] Input power grid parameters include: the number of each node, rated voltage, and operating voltage range; the number of each line, the nodes connected to it, the type and length of the conductor, and the maximum current flowing through it.

[0024] Input power parameters include: the maximum output of each thermal / gas turbine unit, the predicted output of each new energy unit at each time, the predicted power of each load at each time, and the power capacity, energy capacity, and charge / discharge efficiency of each energy storage unit. Specifically, it is installed on... Subsystem The maximum output of the thermal / gas turbine unit at the node is Maximum climbing power is Minimum boot time is Minimum shutdown time is Installed Subsystem New energy units at the node The predicted output at time is Installed Subsystem The load on the node The predicted active power at time is Reactive power is Installed Subsystem The power capacity of the energy storage unit at the node is Energy capacity is The charge / discharge efficiency is .

[0025] Input cost parameters include: unit energy load shedding penalty cost Penalty cost per unit of energy curtailment of wind and solar power and unit energy generation cost Generally, the following can be selected: The price is 45 yuan / kWh. For 4 yuan / kWh and It costs 0.25 yuan / kWh.

[0026] Step S2: Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, construct the objective function of the operation scheduling model when the operating cost is minimized.

[0027] With the goal of minimizing operating costs, the system determines the output of each generator at each time, the curtailment of wind and solar power of each new energy unit at each time, and the charging and discharging power of each energy storage unit at each time. It also obtains the operating constraints of the transmission network, distribution network, thermal / gas turbine units, and energy storage units.

[0028] Establish a power system primary and secondary coordinated operation and scheduling model: Establish the operation scheduling objective function. Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, and taking the minimum operation cost as the objective of the operation scheduling model, determine the output of each generator in the system at each time, the wind and solar power curtailment of each new energy unit at each time, and the charging and discharging power of each energy storage unit at each time. The specific expression of the objective function is shown in Equation (1):

[0029] In the formula, To represent the subsystem's subscript, To characterize the subscripts of power system nodes, The index is used to represent the execution time. , and Subsystems, The set of nodes and their running times in a subsystem; represent The operating cost of the subsystem; represent Subsystem Node load at The load shedding power at any given time represent Subsystem New energy units at node loads The amount of wind and solar power curtailed at any given time. represent Subsystem Node thermal / gas turbine units at Efforts made at all times; It represents the time interval between two adjacent moments, usually 1 hour.

[0030] Establish transmission network operation constraints. Transmission network operation constraints mainly include transmission network power flow constraints and system power balance constraints, as shown in equations (2) and (3), respectively.

[0031] The power flow constraints of the transmission network are as follows: ; ; In the formula, , and Represent The set consisting of nodes, relaxation nodes, and generator nodes in the subsystem. represent The set of lines in a subsystem; and Represent Subsystem Node at The actual voltage amplitude and the target voltage amplitude at any given time. and Represent Subsystem Node at The actual voltage phase angle and the target voltage phase angle at time t, where, and These are preset values, typically given based on the system's historical operating data; and Represent The starting point in the subsystem is a node The endpoint is a node The line ( (Line) in Active and reactive power at time (node) Flow to Node (in the positive direction of power). It is an auxiliary variable, representing Subsystem The line is Reactive power deviation at any given time; and Represent Subsystem Node thermal / gas turbine units at The effort exerted and the effort exerted in a given moment; and Represent Subsystem Node energy storage units in Discharge power and charging power at any given time; and Represent Subsystem node transformers at The active and reactive power outputs at any given time are defined with the power flow from the transmission network to the distribution network as the positive direction of the transformer power. represent Subsystem Node load at Reactive power at any given moment; and Represent Subsystem The electrical conductance and susceptance of the circuit; It is an auxiliary variable, representing Approximate value; , and These are preset values, representing the number of hyperplanes in the cosine polyhedron, the spacing between hyperplanes, and the maximum permissible deviation between the actual voltage phase angle and the target phase angle, respectively. These three values ​​must satisfy the following conditions: Requirements.

[0032] The power balance constraints of the transmission network are as follows: Establish distribution network operation constraints. Distribution network operation constraints include power flow constraints, as shown in equation (4):

[0033] ; In the formula, and Represent Lines in the subsystem Resistance and reactance; yes Subsystem Node at The square of the actual voltage amplitude at time; and Represent Subsystem The lower and upper limits of the square of the node voltage are typically taken as 0.81 and 1.21, respectively.

[0034] Establish operating constraints for thermal power / gas turbine units. The operating constraints for thermal power / gas turbine units are shown in equation (5):

[0035] In the formula, It is a representation Subsystem 0-1 variables representing the on / off status of node thermal / gas turbine units.

[0036] Establish operational constraints for energy storage units.

[0037] In the formula, and These are the characteristics Subsystem Node energy storage units in A 0-1 variable representing the charging and discharging states at any given time; represent Subsystem Node energy storage units in The state of energy (SOE) at any given moment. and These represent the lower and upper limits of the state of charge (SOC) of the energy storage unit, respectively. These are preset values, typically 0.2 and 0.8. Representative set The number of all elements in the array; This represents the number of time slots within a scheduling day. This is a preset value, typically set to 24.

[0038] Step S3: Construct the matrix form of the objective function based on various outputs, power and voltages in the transmission and distribution networks. Decompose the matrix into one upper-level model UP, several middle-level problems MP and several lower-level models LP. Use the temporal running domain equivalent projection fast generation algorithm to obtain the feasible domain projection of each LP and the feasible domain projection of each MP.

[0039] Establish the matrix form of the operation scheduling model. Note that the above models are all linear models, therefore the operation scheduling model can be equivalently written in matrix form as shown in (7). Note that in this invention, unless otherwise specified, vectors are column vectors; bold mathematical symbols represent vectors or matrices, and non-bold mathematical symbols represent scalars. In particular, in this invention, the subsystem where the transmission network is located is always numbered as ,and The subsystem is the distribution network, and its specific expression is:

[0040] In the formula, represent Distribution network subsystem The column vector consisting of the discharge power and charging power of all energy storage units at any given time, i.e. ;but , . represent Distribution network subsystem The vector formed by the active power and reactive power of all transformers at any given moment, i.e. .

[0041] represent In the power transmission subsystem The vector consisting of the active power output of all generator units, the reactive power output of generator units, the discharge power of energy storage units, and the charging power of energy storage units at any given moment. ;but .

[0042] represent Electronic distribution system The vector consisting of the remaining variables at time step, i.e., all of them. , , , , , The vector formed; It is a power transmission network subsystem The vector consisting of the remaining variables at time step, i.e., all , , , , , , , , , The vector formed; and The way vectors are constructed and , Similarly. Represents other variables in thermal / gas turbine units and energy storage units, i.e. , and The vector formed by the vector, and the way the vector is constructed are... Similarly.

[0043] Represents a value vector. and Represents the vector on the right-hand side of the constraint. , , as well as Represents the coefficient matrix.

[0044] The runtime scheduling model can be split into two layers, including an upper layer model UP, several middle layer problems MP, and several lower layer models LP.

[0045] The split upper-level model UP optimizes the operation of thermal / gas turbine units and energy storage units with time-series coupling constraints. Its expression is shown in (8): In the formula, It is an auxiliary variable representing the relationship between the transmission network and all distribution networks. The sum of operating costs at each moment; The projection of the feasible region of the mid-level problem.

[0046] The decomposed intermediate-level problem, MP optimization, addresses the operation of a transmission network. The operation of the transmission network at any given time is represented as an MP problem, thus involving a total of... MP, The expression for time MP is shown in (9): In the formula, It is an auxiliary variable, representing Distribution network in The operating cost at any given moment; Representing the power transmission network The operating cost at any given time represent The upper limit estimate is a preset value derived from historical operating data; The projection of the feasible region represents the lower-level problem.

[0047] The decomposed lower-level model LP optimizes distribution network operation. The operation problem of any distribution network at any time is represented as an LP, therefore there are a total of One LP; Distribution network in The LP at time (10) is shown below: In the formula, yes The upper limit estimate is a preset value derived from historical operating data.

[0048] like Figure 2 As shown, the feasible region projections of each LP and each MP are obtained using a fast generation algorithm based on the equivalent projection of the temporal running domain. Specifically: The current solution time, the basis vectors in the low-dimensional temporal space, and the feasible region in the high-dimensional temporal space are taken as inputs.

[0049] The algorithm for fast generation of equivalent projections in the temporal running domain filters the vertices of the equivalent projections at adjacent time steps, thus forming a candidate vertex set for the equivalent projection at that time step. The output is the projection of the feasible domain of the temporal high-dimensional space onto the low-dimensional space spanned by the basis vectors of the temporal low-dimensional space.

[0050] This step is specifically represented as follows: S301: The feasible region projection of each LP is calculated using the fast generation algorithm for temporal running domain equivalent projection described in S303. Among them, the input in S303 is The feasible region characterized by equations (10b) and (10c) is the distribution network in... The constraints at any given time are represented. .

[0051] S302: The feasible region projection of each MP is calculated using the fast generation algorithm for equivalent projection of the time-series running domain described in S303. Among them, the input in S303 is The feasible region characterized by equations (9b), (9c), and (9d) is the region of the transmission network in... The constraints at any given time are represented. .

[0052] S303: A fast algorithm for generating equivalent projections of the temporal feasible domain is used to calculate the temporal feasible domain projection. The specific steps are as follows:

[0053] Algorithm input: Temporal low-dimensional space basis vectors Temporal high-dimensional feasible region That is, in each LP and each MP, the current solution time is recorded. Temporal low-dimensional space basis vectors and temporal high-dimensional feasible region As input data.

[0054] Algorithm output: Feasible region in high-dimensional temporal space Towards Zhang Cheng's projection in low-dimensional space .

[0055] Step 1: Initialization. Set the current solution time... , , Reference vertex set .

[0056] Step2: Judgment If the condition is met, proceed to Step 14; otherwise, check if the condition is met. Is it true? If it is true, then let Then proceed to Step 11; if the condition is not met, continue to Step 3.

[0057] Step 3: Set the current iteration number Current vertex set Current error ;make For dimension identity matrix For dimension The zero vector, where equal The dimension of, i.e. , Given the maximum number of iterations Maximum error threshold Generally speaking, You can take 10. A value of 0.01 to 0.1 is acceptable.

[0058] Step4: Judgment Is it true? If it is true, then let Continue with Step 5; otherwise proceed to Step 10.

[0059] Step 5: Judgment Check if the condition is met. If it is met, continue to Step 6; otherwise, proceed to Step 9.

[0060] Step 6: Solve the optimization problem shown in equation (11) to obtain the optimal solution to the optimization problem. with the optimal value ; and then calculate ,in, represent The Middle One element, Representative matrix The List, represent The length of the module.

[0061] In the formula, It is a vector composed of auxiliary variables.

[0062] Step 7: Judgment Is it true? If it is true, then... , .

[0063] Step 8: Proceed to Step 5.

[0064] Step 9: ; Calculated convex hull ,Right now ,in To find the convex hull function; based on the convex hull solution, update... and , ;make equal The row number. Jump to Step 4.

[0065] Step 10: , , , , , , , Proceed to Step 2.

[0066] Step 11: Solve the optimization problem as shown in equation (12), where , represent The Middle 1 element, and let If the optimization problem has a feasible solution, and let... .

[0067] Step12: Judgment If the condition is met, proceed to Step 11; otherwise... Then continue with Step 13.

[0068] Step 13: Calculation convex hull ,Right now Update based on the convex hull solution results. and , ;make equal The row number. Jump to Step 4.

[0069] Step 14: Output ,stop.

[0070] Step S4: Regenerate the upper-level model UP, the middle-level problem MP, and the lower-level model LP using the feasible region projection of LP and the feasible region projection of MP. Obtain the optimal values ​​for runtime scheduling using the regenerated models and determine the optimal runtime scheduling method.

[0071] For the upper-level model UP, solve UP as shown in equation (8) to obtain... The optimal values ​​are obtained to optimize the operation of thermal power / gas turbine units and energy storage units with time-series coupling constraints, and to obtain the optimal values ​​of active power, reactive power, charging power, and discharging power of thermal power / gas turbine units at each time point.

[0072] The solution obtained , , The optimal value is used as the given value of the corresponding variable of the intermediate-level problem MP. The operation problem of the transmission network at any time (MP shown in equation (9) at each time) is calculated to obtain the optimal value. , and The optimal value is obtained, that is, the optimal value of the active power of all transformers, the reactive power of transformers, the power of wind and solar power curtailment in the transmission network, and the power of load shedding in the transmission network.

[0073] Solving for the results The optimal value is obtained by solving. and The optimal value is taken as the given value of the corresponding variable in LP, that is, the optimal value of the active power of all transformers, the reactive power of transformers, the wind and solar power curtailment power of the transmission network, and the load shedding power of the transmission network are taken as the given value of the corresponding variable in LP. Solve the LP of each subsystem at each time as shown in equation (10) to obtain The optimal values ​​are obtained for the wind and solar power curtailment and load shedding power of each distribution network.

[0074] One of the methods for optimizing the operation sequence scheduling of power system main and distribution coordinated operation can effectively avoid iterative solutions and accelerate the generation of operation scheduling schemes.

[0075] For example, the fast generation algorithm for equivalent projection of time-series running domain is characterized by filtering the vertices of equivalent projections at adjacent time steps through Step 7 to Step 13, thereby forming a vertex set of equivalent projections at the current time step containing more initial vertices, thus avoiding repeated calculation of initial vertices and greatly improving the calculation speed of time-series equivalent projection.

[0076] For example, the decomposition method of the main-distribution coordinated operation model of a power system. Its main feature is that it divides the main-distribution coordinated operation model into three parts: an upper-level problem, a middle-level problem, and a lower-level problem. The upper-level problem consists of only one element: finding the optimal operating mode for time-coupled components (such as generators and energy storage). The middle-level problem includes... There are several intermediate-level problems, each solving for the optimal operation mode of the transmission network at the corresponding time; the lower-level problems include... Each lower-level problem is solved to determine the optimal operating mode of the distribution network at the corresponding time.

[0077] Compared with existing technologies, it has the following advantages: 1. Compared with centralized optimization methods, the method proposed in this invention decomposes a large-scale operational optimization problem into several smaller problems that are solved separately, thereby significantly accelerating the solution speed.

[0078] 2. Compared with distributed / decomposition coordination optimization methods, the method proposed in this invention considers the internal characteristics of the transmission network and distribution network, ensuring the optimality of the solution results; the method proposed in this invention does not require iterative solution, avoiding the convergence problem and slow computation problem caused by iterative solution in traditional methods.

[0079] 3. Compared to the original equivalent projection method, the method proposed in this invention can effectively handle runtime scheduling problems containing 0-1 variables and temporal coupling constraints. Specifically, the method proposed in this invention decomposes the temporal variables containing 0-1 variables and temporal coupling constraints to the top level. The middle and lower level problems therefore do not contain 0-1 variables or temporal coupling constraints, and can be handled using the idea of ​​equivalent projection.

[0080] 4. Compared with the asymptotic vertex enumeration method, the fast generation algorithm for temporal equivalent projection proposed in this invention filters the vertices of the equivalent projections at adjacent time steps to form a vertex set of the current time equivalent projection containing more initial vertices, thereby avoiding repeated calculation of initial vertices and greatly improving the calculation speed of temporal equivalent projection.

[0081] There are several other algorithms for calculating equivalent projection. In addition to the progressive vertex enumeration algorithm used in this patent, other methods such as Fourier-Motzkin Elimination (FME) and progressive vertex enumeration (PVE) can also be used.

[0082] Fourier-Motzkin elimination is a very mature algorithm. Fourier-Motzkin elimination requires all decision variables in the capacity optimization problem to be considered. , as well as The calculation is performed within the solution space, while the fast generation algorithm for equivalent projection of the temporal running domain used in this patent only needs to be performed within the solution space. as well as The calculations are performed within the solution space, which greatly reduces the dimension of the solution space, thus enabling faster calculations in large-scale power systems.

[0083] like Figure 3 As shown, the specific steps of the asymptotic vertex enumeration algorithm are as follows: Algorithm input: Low-dimensional space basis vectors High-dimensional feasible domain .

[0084] Algorithm output: Feasible region in high-dimensional space Towards Zhang Cheng's projection in low-dimensional space .

[0085] Step 1: Initialization, let the current iteration number be... Current vertex set Current error ;make For dimension identity matrix For dimension The zero vector, where equal The dimension of, i.e. , Given the maximum number of iterations Maximum error threshold Generally speaking, You can take 10. A value of 0.01 to 0.1 is acceptable.

[0086] Step2: Judgment If the condition is met, proceed to Step 3 and set the condition to be met. Otherwise, proceed to Step 8.

[0087] Step3: Judgment Check if the condition is met. If it is met, proceed to Step 4; otherwise, proceed to Step 7.

[0088] Step 4: Solve the optimization problem shown in equation (13) to obtain the optimal solution to the optimization problem. with the optimal value ; and then calculate ,in, represent The Middle One element, Representative matrix The List, represent The length of the module.

[0089] In the formula, It is a vector composed of auxiliary variables.

[0090] Step 5: Judgment Is it true? If it is true, then... , .

[0091] Step 6: Proceed to Step 3.

[0092] Step 7: ; Calculated convex hull ,Right now ,in To find the convex hull function; based on the convex hull solution, update... and , ;make equal The row number. Jump to Step 2.

[0093] Step 8: Output equivalent projection ,stop.

[0094] This invention proposes a power system main and distribution coordinated operation and scheduling system, including: a data acquisition module, a model target construction module, a splitting module and a scheduling module.

[0095] The system comprises the following modules: a data acquisition module for collecting grid parameters, power parameters, and input cost parameters; a model objective construction module for constructing an objective function for the operation scheduling model that minimizes operating costs, using grid parameters, power parameters, and input cost parameters as inputs; a decomposition module for constructing a matrix form of the objective function based on various outputs, power, and voltages in the transmission and distribution networks, decomposing the matrix into one upper-level model (UP), several middle-level problems (MP), and several lower-level models (LP), and using a fast generation algorithm based on equivalent projection of the time-series operating domain to obtain the feasible domain projections of each LP and each MP; and a scheduling module for regenerating the upper-level model (UP), middle-level problems (MP), and lower-level models (LP) using the feasible domain projections of LP and MP, and obtaining the optimal values ​​for operation scheduling based on the regenerated models to determine the optimal operation scheduling method.

[0096] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a program, and when the program is executed by the processor, the processor performs the steps of a power system main and distribution coordinated operation scheduling method.

[0097] According to the disclosed embodiments, the computer device can communicate with one or more external devices (e.g., keyboard, pointing device, Bluetooth communication, etc.) or with any device that enables the computing device to communicate with one or more other computing devices (e.g., router, demodulator, etc.).

[0098] The present invention provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described power system main and distribution coordinated operation scheduling method.

[0099] According to the disclosed embodiments, the storage medium can be a non-volatile computer-readable storage medium, such as, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, the storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0100] The above description, in conjunction with specific preferred embodiments, provides a more detailed explanation of the present invention. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such deductions or substitutions should be considered to fall within the scope of protection of the present invention.

Claims

1. A method for coordinated operation and dispatching of primary and secondary power systems, characterized in that, include: Collect grid parameters, power parameters, and input cost parameters; Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, an objective function for the operation scheduling model is constructed to minimize the operating cost. Based on the various outputs, power and voltages in the transmission and distribution networks, the objective function is constructed in matrix form. The matrix is ​​then split into one upper-level model UP, several middle-level problems MP and several lower-level models LP. The feasible domain projection of each LP and the feasible domain projection of each MP are obtained by using the temporal running domain equivalent projection fast generation algorithm. The upper-level model UP, the middle-level problem MP, and the lower-level model LP are regenerated by projecting the feasible region of LP and the feasible region of MP. The optimal running and scheduling method is determined by obtaining the optimal value of each regenerated model during the running and scheduling process. The objective function of the operation and scheduling model, which uses grid parameters, power parameters, and input cost parameters as inputs, is constructed to minimize the operating cost. Specifically: Using grid parameters, power parameters, and input cost parameters as inputs to the operation scheduling model, and minimizing operating cost as the objective of the operation scheduling model, the output of each generator, the curtailment of wind and solar power of each renewable energy unit, and the charging and discharging power of each energy storage unit at each time point are determined. The specific expression of the objective function is as follows: ； ； In the formula, To represent the subsystem's subscript, To characterize the subscripts of power system nodes, The index is used to represent the execution time. , and Subsystems, The set of nodes and their running times in a subsystem; represent The operating cost of the subsystem; represent Subsystem Node load at The load shedding power at any given time represent Subsystem New energy units at node loads The amount of wind and solar power curtailed at any given time. represent Subsystem Node thermal / gas turbine units at Efforts made at all times; This represents the time interval between two adjacent moments; The cost of load shedding per unit of energy; The penalty cost per unit of energy curtailment of wind and solar power; Cost of generating electricity per unit of energy.

2. The power system main and distribution coordinated operation and dispatching method as described in claim 1, characterized in that, The collection of power grid parameters, power parameters, and input cost parameters specifically includes: The power grid parameters include: the number of each node, its rated voltage, and the operating voltage range; the number of each line, the nodes it connects to, the type and length of the conductors, and the maximum current flowing through it. Power parameters include: maximum output of each thermal / gas turbine unit, predicted output of each new energy unit at each time, predicted power of each load at each time, power capacity, energy capacity, and charge / discharge efficiency of each energy storage unit; specifically, those installed in... Subsystem The maximum output of the thermal / gas turbine unit at the node is Maximum climbing power is Minimum boot time is Minimum shutdown time is Installed Subsystem New energy units at the node The predicted output at time is Installed Subsystem The load on the node The predicted active power at time is Reactive power is Installed Subsystem The power capacity of the energy storage unit at the node is Energy capacity is The charge / discharge efficiency is ; Input cost parameters include: unit energy load shedding penalty cost Penalty cost per unit of energy curtailment of wind and solar power and unit energy generation cost .

3. The power system main and distribution coordinated operation and dispatching method as described in claim 2, characterized in that, The matrix form of the objective function, constructed based on various outputs, power, and voltages in the transmission and distribution networks, is specifically expressed as follows: ； ； ； ； In the formula, represent Distribution network subsystem The column vector consisting of the discharge power and charging power of all energy storage units at any given time. represent Distribution network subsystem The vector consisting of the active power and reactive power of all transformers at any given moment; represent In the power transmission subsystem A vector consisting of the active power output of all generator units, the reactive power output of generator units, the discharge power of energy storage units, and the charging power of energy storage units at any given moment; represent Electronic distribution system A vector consisting of the remaining variables at each time step; It is a power transmission network subsystem A vector consisting of the remaining variables at each time step; and The way vectors are constructed and , Similarly, Represents other variables in thermal / gas turbine units and energy storage units. Represents a value vector. and Represents the vector on the right-hand side of the constraint. , , as well as Represents the coefficient matrix.

4. The power system primary and secondary coordinated operation and dispatching method as described in claim 1, characterized in that, The matrix is ​​split into one upper-level model UP, several middle-level problems MP, and several lower-level models LP, specifically: The decomposed upper-level model UP optimizes the operation of thermal / gas turbine units and energy storage units with time-series coupling constraints. The specific expression is as follows: ； ； ； In the formula, It is an auxiliary variable representing the relationship between the transmission network and all distribution networks. The sum of operating costs at each moment; The projection representing the feasible region of the mid-level problem; The decomposed intermediate-level problem, MP optimization, addresses the operation of a transmission network. The operation problem of the transmission network at any given time is represented as an MP problem, which has a total of... MP, of which The expression for time MP is: ； ； ； ； In the formula, It is an auxiliary variable, representing Distribution network in The operating cost at any given moment; Representing the power transmission network The operating cost at any given time represent The upper limit estimate is a preset value derived from historical operating data; The projection of the feasible region representing the lower-level problem; The decomposed lower-level model LP optimizes distribution network operation. The operation problem of any distribution network at any time is represented as an LP, therefore there are a total of One LP; Distribution network in The specific LP expression for time step is: In the formula, yes The upper limit estimate is a preset value derived from historical operating data.

5. The power system main and distribution coordinated operation and scheduling method as described in claim 4, characterized in that, The method of using the temporal running domain equivalent projection fast generation algorithm to obtain the feasible domain projection of each LP and the feasible domain projection of each MP is as follows: In each LP and each MP, the current solution time is recorded. Temporal low-dimensional space basis vectors and temporal high-dimensional feasible region As input data; where LP , For the distribution network in The constraint representation at time, in MP , For the power transmission network in The constraints at any given time are represented; The algorithm for fast generation of equivalent projections in temporal running domains is used to filter the vertices of equivalent projections at adjacent time steps in LP and MP, and obtain the candidate vertex set of equivalent projections at that time step. The output is the projection of the feasible domain of the temporal high-dimensional space to the low-dimensional space spanned by the basis vectors of the temporal low-dimensional space, which serves as the feasible domain projection of LP and MP.

6. The power system primary and secondary coordinated operation and dispatching method as described in claim 1, characterized in that, The process of obtaining the optimal value during runtime scheduling by regenerating each model and determining the optimal runtime scheduling method specifically involves: For the upper-level model UP, optimize the operation of thermal power / gas turbine units and energy storage units with time-series coupling constraints, and obtain the optimal values ​​of active power, reactive power, charging power, and discharging power of thermal power / gas turbine units at each time step. The optimal values ​​at each time point are used as the given values ​​of the corresponding variables in the intermediate-level problem MP. The operation problem of the transmission network at any time point is calculated to obtain the optimal values ​​of the active power of all transformers, the reactive power of transformers, the power of wind and solar power curtailment in the transmission network, and the power of load shedding in the transmission network. The optimal values ​​of the active power of all transformers, the reactive power of transformers, the power of wind and solar power curtailment in the transmission network, and the power of load shedding in the transmission network are taken as the given values ​​of the corresponding variables in LP. The LP shown by each subsystem at each time point is solved to obtain the optimal values ​​of the power of wind and solar power curtailment and the power of load shedding in each distribution network.

7. A system for a power system primary and secondary coordinated operation and dispatching method as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to collect power grid parameters, power parameters, and input cost parameters; The model objective construction module is used to construct the objective function of the operation and scheduling model when the operating cost is minimized, using grid parameters, power parameters, and input cost parameters as inputs to the operation and scheduling model. The decomposition module is used to construct the matrix form of the objective function based on various outputs, power and voltages in the transmission and distribution networks. The matrix is ​​decomposed into one upper-level model UP, several middle-level problems MP and several lower-level models LP. The feasible domain projection of each LP and the feasible domain projection of each MP are obtained by using the temporal running domain equivalent projection fast generation algorithm. The scheduling module is used to regenerate the upper-level model UP, the middle-level problem MP, and the lower-level model LP by projecting the feasible region of the LP and the feasible region of the MP, and to obtain the optimal value when scheduling the regenerated models to determine the optimal scheduling method.

8. A computer device, characterized in that, The system includes a memory and a processor. The memory stores a program, which, when executed by the processor, causes the processor to perform the steps of a power system primary and secondary coordinated operation scheduling method as described in any one of claims 1 to 6.

9. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power system main and distribution coordinated operation scheduling method according to any one of claims 1 to 6.

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