New energy consumption space optimization method and system

Abstract

The invention discloses a new energy consumption space optimization method and system, relates to the technical field of power system planning and operation, and solves the problem of large calculation and evaluation deviation of a new energy consumption space. On the basis of power grid hardware constraint, the environment temperature and the starting number of the thermal power generating units are introduced to generate the dynamic limit matrix, and the method can adapt to changes of the power adjusting range and the safe operation boundary of the direct-current power transmission system caused by starting and stopping of the thermal power generating units; and then a constraint equation set capable of representing AC / DC coupling characteristics is constructed, and a mixed integer programming model is established and solved by taking maximization of a new energy consumption space of the power grid as an objective function, so that errors caused by calculation after an AC / DC system of the power grid is decoupled in a traditional method can be reduced. Through dynamic constraint and refined modeling, the problem of evaluation deviation caused by static hypothesis in a traditional method is solved, and a high-credibility new energy consumption decision basis can be provided for power grid dispatching.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning and operation, and in particular to a method and system for optimizing new energy consumption space. Background Art

[0002] Driven by the "dual carbon" goals, large-scale integration of renewable energy into the power grid has become an inevitable trend in global energy transformation. Accurately assessing renewable energy absorption capacity is key to ensuring the safe and stable operation of power systems and improving the utilization rate of clean energy. Currently, most existing renewable energy absorption space calculation technologies use static power limit constraint strategies, which set a power transmission upper limit based on fixed transmission line capacity, equipment parameters, and other factors. However, this calculation model has significant limitations and is difficult to adapt to the dynamic operating characteristics of modern complex AC / DC hybrid power grids.

[0003] On the one hand, the static limit assumption ignores the dynamic impact of thermal power generation startup and shutdown on the DC transmission power limit. In actual grid operation, the startup and shutdown of thermal power units changes the system inertia and voltage support capacity, which in turn causes changes in the power regulation range and safe operating margins of the DC transmission system. For example, after a thermal power unit is shut down, the system's spinning reserve capacity decreases, and DC transmission lines must reduce transmission power to maintain system stability. At this time, if the static limit is still used to calculate the renewable energy absorption capacity, the actual connectable capacity will be seriously overestimated, which may lead to the risk of power limit violations and system instability.

[0004] On the other hand, this method fails to fully consider the coupling characteristics of the AC and DC systems. Different thermal power generation startup methods can significantly alter the power flow distribution in the AC channel, thereby affecting the operating conditions of the DC converter station. For example, when thermal power units are started up en masse, the AC system voltage rises, potentially causing DC commutation failures. Conversely, when large numbers of thermal power units are shut down, the AC system's short-circuit capacity decreases, weakening its ability to support the DC system. Because static calculations fail to capture this dynamic coupling, the results of the new energy absorption capacity assessment deviate significantly from actual operating conditions, failing to provide a reliable basis for grid planning and operation scheduling.

[0005] Overall, the current scheme's calculation results for new energy consumption space deviate significantly from actual operating conditions. Therefore, a new energy consumption space optimization method and system is needed. Summary of the Invention

[0006] To address the problem that the calculation results of new energy consumption space in the existing technology deviate significantly from the actual operating conditions, the present invention provides a new energy consumption space optimization method and system that can more accurately calculate the new energy consumption space. The specific technical solution is as follows:

[0007] In a first aspect, an embodiment of the present application provides a method for optimizing new energy consumption space, comprising:

[0008] Obtain grid parameters, the number of thermal power units in operation in the grid, and the ambient temperature; the grid parameters include a reference power capacity of a DC converter station and a maximum reactive capacity of an AC channel; generate a DC transmission power dynamic limit matrix based on the reference power capacity of the DC converter station, the maximum reactive capacity of the AC channel, the number of thermal power units in operation, and the ambient temperature; construct a set of constraint equations based on the grid parameters, and use the set of constraint equations and the dynamic limit matrix of DC transmission power as constraint conditions, and construct a mixed integer programming model with maximizing the renewable energy consumption space of the grid as an objective function, wherein the set of constraint equations is used to characterize the AC / DC coupling characteristics of the grid; solve the mixed integer programming model using an improved Benders decomposition algorithm to obtain the maximum renewable energy consumption space of the grid.

[0009] Preferably, the generating of the DC transmission power dynamic limit matrix based on the DC converter station benchmark power capacity, the AC channel maximum reactive capacity, the number of thermal power units in operation, and the ambient temperature includes: inputting the number of units in operation and the ambient temperature into a preset neural network model to obtain a DC power limit adjustment coefficient output by the neural network model; generating the DC transmission power dynamic limit matrix based on the DC power limit adjustment coefficient, the DC converter station benchmark power capacity, and the AC channel maximum reactive capacity; the expression of the DC transmission power dynamic limit matrix includes:

[0010]

[0011] Where D(t) represents the dynamic limit matrix of DC transmission power, Indicates the DC power limit of the DC converter station, Indicates the maximum reactive capacity of the AC channel, represents the benchmark power capacity of the DC converter station, and α(t) represents the DC power limit adjustment coefficient.

[0012] Preferably, the constraint equation group includes an AC system power flow equation, a DC converter station power transmission equation, and a converter station discrete constraint; the expression of the AC system power flow equation includes:

[0013]

[0014] Among them, P ij , Q ij are the active power and reactive power from node i to node j, P gen,i , Q gen,i are the active and reactive outputs of the generator at node i, respectively, P load,i , Q load,i are the active power and reactive power in the load power of node i, Ω iis the set of nodes connected to node i; the expression of the power transmission equation of the DC converter station includes:

[0015] P dc =k·V ac ·V dc ·cosθ;

[0016] Among them, P dc is the DC power of the DC converter station, k is the transformation ratio of the converter transformer in the DC converter station, V dc 、V ac are the voltages on the DC side and AC side of the DC converter station, respectively, and θ is the commutation angle. The expressions for the discrete constraints of the converter station include:

[0017]

[0018] Where z is the switching state variable of the DC converter station, 0 means disconnected operation, and 1 means switched on operation; is the rated minimum DC power of the DC converter station, is the DC power limit of the DC converter station.

[0019] Preferably, the mixed integer programming model is solved by an improved Benders decomposition algorithm to obtain the maximum new energy consumption space of the power grid, including: generating the main problem and sub-problems of the mixed integer programming model based on the objective function; wherein the main problem is to solve the startup state combination scheme and the maximum new energy consumption space of the thermal power unit, and the sub-problem is to verify whether the violation amount of the constraint condition is greater than a preset threshold, and the violation amount refers to the amount by which the actual value of the variable corresponding to the constraint condition exceeds the constraint value; solving the main problem to obtain the startup state combination scheme and the maximum new energy consumption space; fixing the startup state combination scheme, obtaining the violation amount, and verifying whether the violation amount is greater than the preset threshold; when the violation amount is greater than the preset threshold, based on the violation amount and the constraint condition corresponding to the violation amount, adding a cutting plane constraint to the set corresponding to the constraint condition, and returning to the step of solving the main problem; when the violation amount is less than or equal to the preset threshold, outputting the maximum new energy consumption space.

[0020] Preferably, the expression of the main problem may include:

[0021] max(∑P new -β·ΔN coal );

[0022] Where, ΔN coal is the change in the number of thermal power plants in operation, β is the sensitivity factor, P new Indicates the output of new energy stations.

[0023] Preferably, the calculation formula of the sensitivity factor β includes:

[0024]

[0025] in, Indicates the DC power limit of the DC converter station, N coal Indicates the number of thermal power units in operation.

[0026] Preferably, after obtaining the maximum new energy consumption space of the power grid, the method also includes: identifying the key constraints of the new energy consumption space based on the violation amount and the constraint conditions corresponding to the violation amount, and the key constraints are constraints that limit the new energy consumption space; generating a power grid weak link report based on the key constraints.

[0027] In a second aspect, an embodiment of the present application provides a new energy consumption space optimization system, which is applied to the method described in the first aspect, and the system includes:

[0028] An acquisition module is used to obtain grid parameters, the number of thermal power units in operation in the grid, and the ambient temperature; the grid parameters include the DC converter station reference power capacity and the AC channel maximum reactive capacity;

[0029] A generation module, configured to generate a dynamic limit matrix of DC transmission power based on the benchmark power capacity of the DC converter station, the maximum reactive capacity of the AC channel, the number of started thermal power units, and the ambient temperature;

[0030] a construction module for constructing a set of constraint equations based on the grid parameters, and constructing a mixed integer programming model using the set of constraint equations and the DC transmission power dynamic limit matrix as constraint conditions and maximizing the grid's renewable energy consumption space as an objective function, wherein the set of constraint equations is used to characterize the AC / DC coupling characteristics of the grid;

[0031] The solution module is used to solve the mixed integer programming model by improving the Benders decomposition algorithm to obtain the maximum renewable energy consumption space of the power grid.

[0032] In a third aspect, an embodiment of the present application provides a computing device, comprising: a memory for storing a program; and a processor for loading the program to execute the method described in the first aspect.

[0033] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method as described in the first aspect.

[0034] Compared with the existing technology, the beneficial effects of the present invention are as follows: by introducing the ambient temperature and the number of thermal power units started on the basis of the original hardware constraints of the power grid to generate a dynamic limit matrix for DC transmission power, it can adapt to the changes in the power regulation range and safe operation boundary of the DC transmission system caused by the start and stop of thermal power units; then construct a set of constraint equations that can characterize the AC / DC coupling characteristics, and use the constraint equations and the dynamic limit matrix as constraints to establish and solve a mixed integer programming model, which can eliminate the errors caused by the traditional method of decoupling the AC / DC system of the power grid and then performing calculations. The embodiment of the present application solves the evaluation deviation problem caused by static assumptions in traditional methods through dynamic constraint updates and refined modeling, and at the same time combines the Benders decomposition algorithm to improve computing efficiency, which can provide the power grid dispatching department with a highly reliable decision-making basis for new energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0036] Figure 1 A schematic diagram of a process for optimizing a new energy consumption space provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of the structure of a new energy consumption space optimization system provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0040] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0041] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0042] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0043] In order to solve the problem that the calculation results of the new energy consumption space in traditional methods deviate greatly from the actual operating conditions, the present invention provides a new energy consumption space optimization method and system, which can more accurately calculate the new energy consumption space.

[0044] See also Figure 1 , Figure 1 A schematic flow chart of a method for optimizing a new energy consumption space is provided for an embodiment of the present application, and the method is applied to a computing device; Figure 1 As shown, the method includes:

[0045] Step 101: A computing device obtains grid parameters, the number of thermal power generating units in operation in the grid, and the ambient temperature of a DC converter station.

[0046] Among them, the computing device can be a computing node in the power grid, a computing device in the main control room of the power grid, or a computing device that is communicatively connected to the control module and data acquisition module of the power grid; the computing device can specifically be a server, or a smart terminal such as a personal computer or tablet directly operated by power grid management personnel or maintenance personnel.

[0047] Among them, the power grid in the embodiment of the present application is a distribution network system connected to new energy, and the power grid includes thermal power units, DC converter stations and new energy stations.

[0048] Among them, the computing device can collect the start and stop status of the thermal power units in real time through the supervisory control and data acquisition system (SCADA). The start and stop status can be represented by a Boolean variable, 0 for shutdown and 1 for startup; then the number of thermal power units in operation can be counted based on the Boolean variable.

[0049] The computing device can obtain the ambient temperature of the DC converter station through temperature sensors placed near or on the exterior of the DC converter station. A DC converter station is a key facility in a DC transmission system, primarily used to convert alternating current (AC) to direct current (DC) and to control and protect the DC transmission system.

[0050] The grid parameters include fixed parameters of the equipment in the grid, such as the benchmark power capacity of the DC converter station and the maximum reactive capacity of the AC channel; they also include dynamic parameters of the equipment in the grid during operation, such as the active and reactive output of the generator sets.

[0051] Specifically, the computing device may obtain the rated capacity of the DC converter station as the reference power capacity of the DC converter station; and obtain the maximum reactive capacity of the AC channel from the power grid topology parameter library.

[0052] For some dynamic parameters, such as current and voltage, the computing device can obtain them directly through sensors; for dynamic parameters that cannot be obtained directly, such as power, the computing device can obtain them indirectly through calculation.

[0053] Preferably, the computing device can also obtain the output prediction curve of the new energy station.

[0054] The output forecast curves for renewable energy stations are used to estimate the output of renewable energy generation equipment such as wind farms and photovoltaic power plants in the future. When calculating the power consumption capacity, computing equipment must use these forecast curves to determine the upper limit of renewable energy that the grid can accommodate, to avoid overloads or curtailment of wind and solar power due to output fluctuations.

[0055] Step 102: The computing device generates a dynamic limit matrix of DC transmission power based on the benchmark power capacity of the DC converter station, the maximum reactive capacity of the AC channel, the number of started thermal power units, and the ambient temperature.

[0056] Among them, the maximum reactive capacity of the AC channel is an attribute that is jointly affected by multiple equipment in the power system, such as transmission lines, transformers, and reactive compensation equipment, and reflects the ability of relevant equipment in the power system to transmit and regulate reactive power.

[0057] Among them, the benchmark power capacity of the DC converter station is an important indicator to measure the transmission power capability of the DC converter station. It usually refers to the rated active power that the converter station can continuously transmit under the specified standard conditions, reflecting the scale of the transmission task it can undertake.

[0058] Among them, considering the nonlinear relationship between the cooling system capacity and power transmission capacity of the DC converter station when the number of thermal power units in operation changes, the computing equipment can fit this nonlinear relationship through a neural network model.

[0059] Specifically, the ambient temperature of a DC converter station directly affects the heat dissipation efficiency of the converter station's cooling system. High temperatures reduce the converter station's cooling capacity, requiring a reduction in maximum transmission power to prevent overheating. Low temperatures, however, allow for a modest increase in the power limit. The ambient temperature and the number of operating thermal power units jointly determine the dynamic DC power limit. Therefore, a neural network model can be used to determine the coefficient of influence on the DC power limit based on the ambient temperature and the number of operating thermal power units.

[0060] Preferably, the computing device can coal and the ambient temperature T amb Input a preset neural network model to obtain a DC power limit adjustment coefficient α(t) output by the neural network model; the calculation formula may include:

[0061] α(t)=NeuralNet(N coal ,T amb );

[0062] Among them, NeuralNet() represents the operation process of the neural network model.

[0063] The neural network model has been pre-trained, and the pre-training process can be completed by a computing device or by another device before being deployed in the computing device. Specifically, the input layer of the neural network model includes 2 nodes, the hidden layer includes 8 nodes, and the output layer includes 1 node.

[0064] It is understandable that the power grid includes one or more DC converter stations, and the neural network model can calculate the global DC power limit adjustment coefficient α(t) based on the ambient temperature of the one or more DC converter stations.

[0065] By dynamically correcting the DC power limit adjustment coefficient through a neural network model, the error caused by the static limit assumption can be eliminated; the dynamic limit coefficient can quantify the impact of thermal power startup on the DC limit.

[0066] Then, the computing device may generate the DC transmission power dynamic limit matrix based on the DC power limit adjustment coefficient, the DC converter station reference power capacity, and the AC channel maximum reactive capacity; the expression of the DC transmission power dynamic limit matrix includes:

[0067]

[0068] Where D(t) represents the dynamic limit matrix of DC transmission power, Indicates the DC power limit of the DC converter station, Indicates the maximum reactive capacity of the AC channel, represents the benchmark power capacity of the DC converter station, and α(t) represents the DC power limit adjustment coefficient.

[0069] It is understandable that although the above expression of the DC transmission power dynamic limit matrix only shows two rows, in the case where the power grid includes multiple DC converter stations, the power of each converter station is As the number of thermal power units starts up, the constraints change dynamically and need to be updated separately.

[0070] By constructing a dynamic matrix, the real-time constraint boundary of new energy consumption can be obtained.

[0071] Preferably, the computing device can update the dynamic matrix every 5 minutes to adapt to the impact of thermal power start-up and shutdown on the DC power limit.

[0072] Step 103: The computing device constructs a set of constraint equations based on the grid parameters, and constructs a mixed integer programming model using the set of constraint equations and the DC transmission power dynamic limit matrix as constraint conditions and maximizing the new energy consumption space of the grid as the objective function.

[0073] The constraint equations are used to characterize the AC-DC coupling characteristics of the power grid.

[0074] Preferably, the constraint equation group includes an AC system power flow equation, a DC converter station power transmission equation, and a converter station discrete constraint; the expression of the AC system power flow equation includes:

[0075]

[0076] Among them, P ij , Q ij are the active power and reactive power from node i to node j, P gen,i , Q gen,i are the active and reactive outputs of the generator at node i, respectively, P load,i , Q load,i are the active power and reactive power in the load power of node i, Ω i is the set of nodes connected to node i.

[0077] The AC system power flow equation can ensure that the power generation, load and line transmission power of each node in the power grid are physically feasible, avoiding overload or voltage limit.

[0078] Preferably, the constraint equations also include the power transmission equation P of the line ij =V i V j Y ij cos(θ i -θ j). Where, represents the active power transmitted between node i and node j, represents the voltage amplitude of node i and node j, represents the admittance between node i and node j, and represents the voltage phase angle between node i and node j.

[0079] Based on the power transmission equation of the line, the mixed integer programming model can determine the maximum transmission capacity of the line and prevent the line from being overloaded due to excessive output of renewable energy in the solution.

[0080] The power transmission equation of the DC converter station includes:

[0081] P dc =k·V ac ·V dc ·cosθ;

[0082] Among them, P dc is the DC power of the DC converter station, k is the transformation ratio of the converter transformer in the DC converter station, V dc 、V ac are the voltages on the DC side and AC side of the DC converter station respectively, and θ is the commutation angle.

[0083] The expressions of the discrete constraints of the converter station include:

[0084]

[0085] Where z is the switching state variable of the DC converter station, 0 means disconnected operation, and 1 means switched on operation; is the rated minimum DC power of the DC converter station; The DC power limit of the DC converter station can be determined based on the DC transmission power dynamic limit matrix in step 101.

[0086] Specifically, the DC converter station power transmission equation and the converter station discrete constraints are both applicable to a single DC converter station. When the power grid includes multiple DC converter stations, it is necessary to establish multiple corresponding equations and a set of parallel equations.

[0087] The DC converter station equations incorporate the coupled characteristics of the AC / DC system into the model, ensuring accurate modeling of the converter station's actual operating conditions and dynamic limits. By introducing a 0-1 variable z to represent the converter station's switching state, the model can reflect the impact of the DC converter station's actual start-up and shutdown decisions on renewable energy consumption.

[0088] The AC power flow equation is used to constrain the power balance basis of the power grid, the DC converter station equation is used to cross-characterize the DC coupling characteristics, and the converter station discrete constraints are used to characterize the switching state of the DC converter station. By combining the above equations to obtain a group of constraint equations, the AC / DC coupling characteristics can be accurately characterized, eliminating the errors of the traditional decoupling model.

[0089] Among them, the objective function of the mixed integer programming model can be expressed as: max∑P new Among them, P new Indicates the output of new energy stations.

[0090] Step 104: The computing device solves the mixed integer programming model by using an improved Benders decomposition algorithm to obtain the maximum new energy consumption space of the power grid.

[0091] Preferably, the computing device can generate the main problem and sub-problems of the mixed integer programming model based on the objective function; wherein the main problem is to solve the startup state combination scheme of the thermal power unit and the maximum new energy consumption space, and the sub-problem is to verify whether the violation amount of the constraint condition is greater than a preset threshold, and the violation amount refers to the amount by which the actual value of the variable corresponding to the constraint condition exceeds the constraint value.

[0092] The improved Benders decomposition algorithm is an optimization and improvement of the traditional Benders decomposition algorithm, designed to more efficiently solve complex problems such as mixed-integer programming. It decomposes programming problems with complex variables into a master problem and multiple subproblems. The master problem primarily deals with integer variables, while the subproblems deal with continuous variables. By iteratively solving the master and subproblems, and utilizing cutting plane techniques to continuously update the feasible region of the master problem, the optimal solution to the original problem is ultimately obtained.

[0093] The computing device may decompose a mixed-integer nonlinear programming (MINLP) problem of a mixed-integer programming model into a main problem and sub-problems by improving the Benders decomposition algorithm.

[0094] Specifically, the main problem deals with integer decision variables (such as the number of thermal power plants in operation); the sub-problems deal with continuous decision variables (such as renewable energy output and line power).

[0095] The constraint value can be obtained based on the constraint conditions of the mixed integer programming model.

[0096] Then, the computing device can solve the main problem, obtain the power-on state combination scheme and the maximum new energy consumption space; fix the power-on state combination scheme, obtain the violation amount, and verify whether the violation amount is greater than the preset threshold; when the violation amount is greater than the preset threshold, based on the violation amount and the constraint condition corresponding to the violation amount, add a cutting plane constraint to the set corresponding to the constraint condition, and return to the step of solving the main problem; when the violation amount is less than or equal to the preset threshold, output the maximum new energy consumption space.

[0097] Preferably, the expression of the main problem may include:

[0098] max(∑P new -β·ΔN coal );

[0099] Where, ΔN coal is the change in the number of thermal power plants in operation, β is the sensitivity factor, P new Indicates the output of new energy stations.

[0100] The sensitivity factor β is used to characterize the impact of the change in the number of thermal power units on the DC power limit. For example, for each additional thermal power unit, Increase by 10MW, then it is equal to 10MW / unit; by subtracting β·ΔN coal , which can give priority to adjusting the thermal power units that significantly improve the DC limit during the iterative calculation process, guide the optimization direction, and accelerate convergence.

[0101] Preferably, the calculation formula of the sensitivity factor β includes:

[0102]

[0103] in, Indicates the DC power limit of the DC converter station, N coal Indicates the number of thermal power units in operation.

[0104] During the subproblem verification phase of the improved Benders decomposition algorithm, the computing device checks the relationship between the actual and limit values of all constraints one by one. For each constraint, the difference between the actual and constraint values is calculated to obtain the set of violated constraints and the corresponding violation amount.

[0105] The violation amount δ is the sum of the actual values corresponding to all constraints exceeding the constraint values, and the calculation formula is:

[0106]

[0107] Among them, C i (x) represents the actual value corresponding to the i-th constraint condition, C lim,i Represents the constraint value of the i-th constraint condition.

[0108] Exemplarily, the constraints include a DC power limit constraint, an AC power flow balance constraint, and a voltage safety constraint.

[0109] Specifically, when the total violation amount δ is greater than a preset threshold, the computing device can add cutting plane constraints to the constraint condition set based on the violation results of the sub-problems to exclude infeasible solutions and gradually narrow the search space to a feasible area.

[0110] Preferably, after obtaining the maximum new energy consumption space of the power grid, the computing device can identify the key constraints of the new energy consumption space based on the violation amount and the constraint conditions corresponding to the violation amount, and the key constraints are constraints that limit the new energy consumption space; and generate a power grid weak link report based on the key constraints.

[0111] The computing device may identify the key constraint based on the violation constraint set and the corresponding violation amount.

[0112] Specifically, the computing device may calculate the violation strength of each sample in the violation constraint combination, using a calculation formula including:

[0113] Violation Strength = δ i / C lim,i *100%;

[0114] Among them, δ i represents the violation amount of the i-th violation sample, C lim,i Represents the constraint value of the constraint condition for the i-th violation sample.

[0115] Then, the computing device may count the violation frequency of each constraint condition based on the violation constraint set. The higher the frequency of the same constraint condition being triggered in multiple rounds of iterations, the more critical it is.

[0116] The computing device can then identify key constraints based on the violation intensity and frequency, and then map the mathematical constraints to physical devices or grid nodes to identify specific bottlenecks.

[0117] For example, when the violation intensity is greater than 5%, the corresponding constraint is marked as a critical constraint; when the violation intensity is greater than 2% and the number of violations is greater than 3, the corresponding constraint is marked as a critical constraint.

[0118] For example, for a DC power constraint, the computing device may identify a violation sample (P dc Greater than ) The associated converter stations and their associated lines are easily overloaded converter stations and easily overloaded DC lines; for voltage constraints, the computing equipment can identify violation samples The associated node is a voltage-exceeding-limit node.

[0119] For reactive capacity constraints, the computing device can identify violation samples (Q ac / greater than 0.9) and determines the node location as the recommended installation location for the reactive power compensation device.

[0120] After identifying the above key constraints, the computing device can generate corresponding power grid weak link reports based on these key constraints.

[0121] In a specific example, a regional power grid contains 3 new energy sites, 2 thermal power plants and 1 DC converter station; the predicted output of new energy is 200MW of wind power and 150MW of photovoltaic power; the number of thermal power units in operation is 2, and the ambient temperature of the DC converter station is 30°C.

[0122] The computing device can calculate: the neural network model output α(t) = 1.05, the reference DC power Dynamic Limits

[0123] The optimization result is: the maximum new energy consumption space P new Including 190MW of wind power and 140MW of photovoltaic power (total 330MW). Due to the limitation of DC converter stations, the output of some renewable energy has been reduced.

[0124] The key constraints identified by the computing equipment include: the DC converter station power reaches 1048MW (close to the 1050MW limit).

[0125] Based on this data example, we can know that the AC power flow equation and the DC converter station constraints jointly limit the maximum absorption capacity of new energy. The maximum absorption space for new energy P new It is the actual total output of new energy stations under safety constraints.

[0126] In the embodiment of the present application, by introducing the ambient temperature and the number of thermal power units on-line to generate a dynamic limit matrix for DC transmission power based on the original hardware constraints of the power grid, it is possible to adapt to changes in the power regulation range and safe operation boundary of the DC transmission system caused by the start and stop of thermal power units; then, a set of constraint equations that can characterize the AC / DC coupling characteristics is constructed, and the set of constraint equations and the dynamic limit matrix are used as constraints to establish and solve a mixed integer programming model, which can eliminate the errors caused by the traditional method of decoupling the AC / DC system of the power grid and performing calculations. The embodiment of the present application solves the evaluation deviation problem caused by static assumptions in traditional methods through dynamic constraint updates and refined modeling, and at the same time combines the Benders decomposition algorithm to improve computing efficiency, which can provide the power grid dispatching department with a highly reliable decision-making basis for new energy consumption.

[0127] The above describes the method part provided by the embodiment of the present application. The following describes the system part provided by the embodiment of the present application.

[0128] See also Figure 2 , Figure 2 A schematic diagram of a new energy consumption space optimization system provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the system 20 includes:

[0129] An acquisition module 201 is configured to acquire grid parameters, the number of thermal power units in operation in the grid, and the ambient temperature of the DC converter station; the grid parameters include the DC converter station's reference power capacity and the AC channel's maximum reactive capacity;

[0130] A generating module 202 is configured to generate a dynamic limit matrix for DC transmission power based on the benchmark power capacity of the DC converter station, the maximum reactive capacity of the AC channel, the number of operating thermal power units, and the ambient temperature;

[0131] A construction module 203 is configured to construct a set of constraint equations based on the grid parameters, and to construct a mixed integer programming model using the set of constraint equations and the DC transmission power dynamic limit matrix as constraint conditions and maximizing the grid's renewable energy consumption space as an objective function, wherein the set of constraint equations is used to characterize the AC / DC coupling characteristics of the grid;

[0132] The solving module 204 is configured to solve the mixed integer programming model by using an improved Benders decomposition algorithm to obtain the maximum renewable energy consumption space of the power grid.

[0133] Preferably, the generating module 202 is specifically configured to input the number of powered-on units and the ambient temperature into a preset neural network model to obtain a DC power limit adjustment coefficient output by the neural network model; and generate the DC transmission power dynamic limit matrix based on the DC power limit adjustment coefficient, the DC converter station reference power capacity, and the AC channel maximum reactive capacity; the expression of the DC transmission power dynamic limit matrix includes:

[0134]

[0135] Where D(t) represents the dynamic limit matrix of DC transmission power, Indicates the DC power limit of the DC converter station, Indicates the maximum reactive capacity of the AC channel, represents the benchmark power capacity of the DC converter station, and α(t) represents the DC power limit adjustment coefficient.

[0136] Preferably, the constraint equation group includes an AC system power flow equation, a DC converter station power transmission equation, and a converter station discrete constraint; the expression of the AC system power flow equation includes:

[0137]

[0138] Among them, P ij , Q ij are the active power and reactive power from node i to node j, P gen,i , Q gen,i are the active and reactive outputs of the generator at node i, respectively, P load,i , Qload,i are the active power and reactive power in the load power of node i, Ω i is the set of nodes connected to node i; the expression of the power transmission equation of the DC converter station includes:

[0139] P dc =k·V ac ·V dc ·cosθ;

[0140] Among them, P dc is the DC power of the DC converter station, k is the transformation ratio of the converter transformer in the DC converter station, V dc 、V ac are the voltages on the DC side and AC side of the DC converter station, respectively, and θ is the commutation angle. The expressions for the discrete constraints of the converter station include:

[0141]

[0142] Where z is the switching state variable of the DC converter station, 0 means disconnected operation, and 1 means switched on operation; is the rated minimum DC power of the DC converter station, is the DC power limit of the DC converter station.

[0143] Preferably, the solving module 204 is specifically used to generate the main problem and sub-problems of the mixed integer programming model based on the objective function; wherein the main problem is to solve the startup state combination scheme of the thermal power unit and the maximum new energy consumption space, and the sub-problem is to verify whether the violation amount of the constraint condition is greater than a preset threshold, and the violation amount refers to the amount by which the actual value of the variable corresponding to the constraint condition exceeds the constraint value; solve the main problem to obtain the startup state combination scheme and the maximum new energy consumption space; fix the startup state combination scheme, obtain the violation amount, and verify whether the violation amount is greater than the preset threshold; when the violation amount is greater than the preset threshold, based on the violation amount and the constraint condition corresponding to the violation amount, add a cutting plane constraint to the set corresponding to the constraint condition, and return to the step of solving the main problem; when the violation amount is less than or equal to the preset threshold, output the maximum new energy consumption space.

[0144] Preferably, the expression of the main problem may include:

[0145] max(∑P new -β·ΔN coal );

[0146] Where, ΔN coal is the change in the number of thermal power plants in operation, β is the sensitivity factor, P new Indicates the output of new energy stations.

[0147] Preferably, the calculation formula of the sensitivity factor β includes:

[0148]

[0149] in, Indicates the DC power limit of the DC converter station, N coal Indicates the number of thermal power units in operation.

[0150] Preferably, the system 20 also includes an identification module 205, which is used to identify the key constraints of the new energy consumption space based on the violation amount and the constraint conditions corresponding to the violation amount, and the key constraints are constraints that limit the new energy consumption space; and generate a power grid weak link report based on the key constraints.

[0151] The new energy consumption space optimization system provided in the embodiment of the present application can be understood by referring to the corresponding content of the aforementioned method embodiment part, and will not be repeated here.

[0152] like Figure 3 As shown, Figure 3 A possible logical structure diagram of a computing device provided in an embodiment of the present application. The computing device 30 includes: a processor 301, a communication interface 302, a memory 303, and a bus 304. The processor 301, the communication interface 302, and the memory 303 are interconnected via the bus 304. In the embodiment of the present application, the processor 301 is used to control and manage the actions of the computing device 30. For example, the processor 301 is used to execute Figure 1 The steps in the embodiments and / or other processes for the technology described herein. The communication interface 302 is used to support the computing device 30 to communicate. The memory 303 is used to store program codes and data of the computing device 30.

[0153] Among them, the processor 301 can be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component or any combination thereof. It can implement or execute the various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a digital signal processor and a microprocessor, and so on. The bus 304 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0154] In another embodiment of the present application, a computer-readable storage medium is further provided, wherein the computer-readable storage medium includes instructions, which, when executed on a computer, causes the computer to execute the above-mentioned Figure 1 The method described in the embodiment.

[0155] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0156] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0158] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0160] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, magnetic disk or optical disk, and other media that can store program codes.

[0161] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for optimizing new energy consumption space, characterized in that: include: Obtain grid parameters, the number of thermal power units in operation in the grid, and the ambient temperature of the DC converter station; The grid parameters include the DC converter station reference power capacity and the AC channel maximum reactive capacity; Generate a DC transmission power dynamic limit matrix based on the DC converter station benchmark power capacity, the AC channel maximum reactive capacity, the number of startup units, and the ambient temperature; A set of constraint equations is constructed based on the grid parameters, and a mixed integer programming model is constructed using the set of constraint equations and the dynamic limit matrix of direct current transmission power as constraint conditions and maximizing the new energy consumption space of the grid as the objective function, wherein the set of constraint equations is used to characterize the AC / DC coupling characteristics of the grid; The mixed integer programming model is solved by improving the Benders decomposition algorithm to obtain the maximum new energy consumption space of the power grid.

2. The method according to claim 1, characterized in that The generating of the DC transmission power dynamic limit matrix based on the DC converter station benchmark power capacity, the AC channel maximum reactive capacity, the number of startup units, and the ambient temperature includes: Inputting the number of powered-on devices and the ambient temperature into a preset neural network model to obtain a DC power limit adjustment coefficient output by the neural network model; The DC power limit adjustment coefficient, the DC converter station reference power capacity, and the AC channel maximum reactive capacity are used to generate the DC power dynamic limit matrix. The expression of the DC power dynamic limit matrix includes: Where D(t) represents the dynamic limit matrix of DC transmission power, Indicates the DC power limit of the DC converter station, Indicates the maximum reactive capacity of the AC channel, represents the benchmark power capacity of the DC converter station, and α(t) represents the DC power limit adjustment coefficient.

3. The method according to claim 1, characterized in that The constraint equation group includes the AC system power flow equation, the DC converter station power transmission equation, and the converter station discrete constraints; The expression of the AC system power flow equation includes: Among them, P ij , Q ij are the active power and reactive power from node i to node j, P gen,i , Q gen,i are the active and reactive outputs of the generator at node i, respectively, P load,i , Q load,i are the active power and reactive power in the load power of node i, Ω i is the set of nodes connected to node i; The expression of the power transmission equation of the DC converter station includes: P dc =k·V ac ·V dc ·cosθ; Among them, P dc is the DC power of the DC converter station, k is the transformation ratio of the converter transformer in the DC converter station, V dc 、V ac are the voltages on the DC side and AC side of the DC converter station respectively, and θ is the commutation angle; The expressions of the discrete constraints of the converter station include: Where z is the switching state variable of the DC converter station, 0 means disconnected operation, and 1 means switched on operation; is the rated minimum DC power of the DC converter station, is the DC power limit of the DC converter station.

4. The method according to any one of claims 1 to 3, characterized in that The method of solving the mixed integer programming model by improving the Benders decomposition algorithm to obtain the maximum renewable energy consumption space of the power grid includes: generating a main problem and a sub-problem of the mixed integer programming model based on the objective function; wherein the main problem is to solve the startup state combination scheme of the thermal power unit and the maximum new energy consumption space, and the sub-problem is to verify whether the violation amount of the constraint condition is greater than a preset threshold, and the violation amount refers to the amount by which the actual value of the variable corresponding to the constraint condition exceeds the constraint value; Solve the main problem to obtain the startup state combination scheme and the maximum new energy consumption space; Fixing the power-on state combination scheme, obtaining the violation amount, and verifying whether the violation amount is greater than the preset threshold; When the violation amount is greater than the preset threshold, based on the violation amount and the constraint condition corresponding to the violation amount, adding a cutting plane constraint to the set corresponding to the constraint condition, and returning to the step of solving the main problem; When the violation amount is less than or equal to the preset threshold, the maximum new energy consumption space is output.

5. The method according to claim 4, characterized in that The expression of the main problem includes: max(∑P new -β·ΔN coal ); Where, ΔN coal is the change in the number of thermal power plants in operation, β is the sensitivity factor, P new Indicates the output of new energy stations.

6. The method according to claim 5, characterized in that The calculation formula of the sensitivity factor β includes: in, Indicates the DC power limit of the DC converter station, N coal Indicates the number of thermal power units in operation.

7. The method according to claim 4, characterized in that After obtaining the maximum new energy consumption capacity of the power grid, the method further includes: Based on the violation amount and the constraint condition corresponding to the violation amount, identifying a key constraint of the new energy consumption space, where the key constraint is a constraint that limits the new energy consumption space; A power grid weak link report is generated based on the key constraints.

8. A new energy consumption space optimization system, characterized in that: The method according to any one of claims 1 to 7, wherein the system comprises: An acquisition module is used to obtain grid parameters, the number of thermal power units in operation in the grid, and the ambient temperature of the DC converter station; the grid parameters include the DC converter station reference power capacity and the AC channel maximum reactive capacity; A generating module, configured to generate a dynamic limit matrix of DC transmission power based on the DC converter station benchmark power capacity, the AC channel maximum reactive capacity, the number of startup units, and the ambient temperature; a construction module, configured to construct a set of constraint equations based on the grid parameters, and to construct a mixed integer programming model using the set of constraint equations and the dynamic limit matrix of direct current transmission power as constraint conditions and maximizing the new energy consumption space of the grid as an objective function, wherein the set of constraint equations is used to characterize the AC / DC coupling characteristics of the grid; The solution module is used to solve the mixed integer programming model by improving the Benders decomposition algorithm to obtain the maximum new energy consumption space of the power grid.

9. A computing device, characterized in that include: Memory, used to store programs; A processor, configured to load the program to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 7.

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