Data-driven urban power grid multi-stage operation scheduling method and system

Abstract

The invention discloses a data-driven urban power grid multi-stage operation scheduling method and system. The method comprises the following steps: establishing a power distribution system coordinated scheduling problem mathematical model containing various energy sources; based on the obtained mathematical model of the coordinated dispatching problem of the power distribution system, a linear budget constraint generation method based on historical data is adopted to construct a budget uncertainty set; based on the obtained budget uncertainty set, determining a coordinated scheduling scheme by adopting a multi-stage robust optimization method based on an implicit decision strategy; coordinated scheduling of a plurality of resources and devices is adaptively optimized based on a rolling time domain mode.

Description

Background Art

[0002] With the transformation of the global energy structure and the improvement of environmental awareness, as well as the requirements for safe, economical and low-carbon energy use in cities, renewable energy, especially wind and solar energy, has become an important part of the low-carbon and economic construction and operation of urban power grids. However, the intermittent and uncertain output of renewable energy such as wind and solar energy has brought new challenges to the safe and stable operation and economical and efficient dispatch of power systems. In order to meet this challenge and improve the reliability and stability of power systems, the research on active distribution networks and energy storage devices has gradually become the focus of solving the uncertainty problem of renewable energy.

[0003] In the power distribution system, the scheduling problem of multiple energy resources and energy storage and supply devices is usually regarded as a typical multi-stage stochastic optimization problem. In this optimization problem, there are two main difficulties: one is how to accurately describe the multidimensional uncertainty of multiple energy resources, and the other is how to design the optimal decision rule to deal with multidimensional uncertainty. There are many uncertainty set generation methods for describing multidimensional uncertainty, including scenario-based modeling, box uncertainty set, budget uncertainty set, etc.

[0004] Among the existing methods, the computational complexity of the scenario / scenario tree method increases sharply with the increase in the number of scenario sets, and it is unable to effectively deal with high-dimensional uncertainty problems. The quality of the selected scenarios also has a great impact on the results, and it is difficult to accurately reflect the uncertainty in the system; although the box uncertainty set is computationally simple, it ignores the correlation between different uncertainty dimensions and cannot fully and accurately characterize the uncertainty in the actual system, especially in the joint scheduling of multiple resources, it cannot fully consider the complementarity between the resources; the linear budget uncertainty set (an uncertainty set in the form of a convex polyhedron) takes into account the correlation between uncertainty variables, can effectively balance prediction accuracy and model convexity, and is an uncertainty set that is more suitable for optimal scheduling of power systems. However, the existing budget constraint generation methods often rely on experience, and it is difficult to obtain precise constraints with strong universality through objective data or algorithms, which may bring inevitable errors, thereby affecting the accuracy and effect of scheduling.

[0005] In summary, existing technologies have great limitations in describing the multidimensional uncertainty of multiple energy resources and decision-making for multi-resource scheduling problems. In order to solve these problems, a budget uncertainty set generation method that can avoid human interference and simultaneously handle spatiotemporal coupling relationships and a more adaptable decision rule are proposed, which is crucial to improving the safety, stability and economic operation of urban power grids. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a data-driven urban power grid multi-stage operation scheduling method and system in response to the above-mentioned deficiencies in the prior art, which is used to solve the technical problems existing in the existing budget constraint generation method that the spatiotemporal coupling relationship of various energy resources cannot be reflected and the effectiveness is greatly affected by the selection and setting of artificial parameters.

[0007] The present invention adopts the following technical solutions: A data-driven urban power grid multi-stage operation dispatching method comprises the following steps: Establish a mathematical model for the coordinated dispatching of distribution systems involving multiple energy sources; Based on the obtained mathematical model of the coordinated dispatch problem of the distribution system, the budget uncertainty set is constructed by adopting the linear budget constraint generation method based on historical data. Based on the obtained budget uncertainty set, a multi-stage robust optimization method based on implicit decision-making strategy is adopted to determine the coordinated scheduling scheme.

[0008] Preferably, the objective function of the mathematical model of the distribution system coordinated scheduling problem is the total power generation cost, the system operation constraints include flow constraints, various boundary constraints, thermal power unit operation constraints, budget constraints and specific boundary conditions together constitute the uncertain set of uncertain variables of wind power output power.

[0009] Preferably, the total power generation cost includes the cost of purchasing / selling electricity from the main grid and the fuel cost of the thermal power unit. The objective function is as follows:

[0010] in, For the period Active power transmission is The cost of purchasing / selling electricity from the main grid at that time, For the period bus The output power of the upper thermal power unit is The specific cost function can refer to existing research.

[0011] Preferably, the load balance constraint in the power flow constraint is:

[0012]

[0013]

[0014]

[0015]

[0016]

[0017] in, Indicates that the slave bus To the bus Branches , Respectively represent the bus to and from The branch set of The time periods Branches The active and reactive power on The time periods bus The net active and reactive power injected on The time periods bus Active and reactive power transmitted on The time periods bus Active and reactive load demands on The time periods bus thermal power, wind power, photovoltaic output power and renewable energy reduction, For the period End Bus The energy storage level on For the period bus The square value of the voltage on Voltage security constraints in power flow constraints:

[0018] in, It is the time period bus The voltage lower and upper bounds on ; Transmission capacity constraints in power flow constraints:

[0019] in, For branch The maximum apparent power capacity; Bound constraints on active and reactive transmission power:

[0020] in, are the lower and upper bounds of active transmission power, are the lower and upper bounds of reactive transmission power; Energy storage system energy storage level limit and charge and discharge level limit:

[0021]

[0022] Among them, are the lower and upper bounds of the energy storage level, For bus Maximum charge and discharge capacity; Constraints on the amount of renewable energy abandoned:

[0023] Reactive power generation limit constraints:

[0024] in, are the upper and lower bounds of reactive power generation; Generation capacity constraints, ramp constraints and minimum start / stop time constraints:

[0025]

[0026]

[0027] in, For the period bus The start and stop decision variables of the thermal power unit, are the upper and lower bounds of active power generation, For bus The upper and lower limits of the climbing rate of the upper thermal power unit, is a node set, For the time period collection, is the feasible state set for power on and off.

[0028] Preferably, the budget uncertainty set is constructed by using a linear budget constraint generation method based on historical data as follows: Initialize a box set based on historical data , used to wrap all historical data points and represent the initial uncertainty range; In the uncertain set Select a vertex as far away from all historical data points as possible ; By solving the following optimization problem, we can obtain the vertex with the largest distance from the historical data point; By introducing auxiliary variables , maximize the minimum distance between the vertex and each historical data, and reconstruct it into a single-layer structure; At the top and all historical data Find the cutting plane with the largest distance to the selected vertex and find the budget constraint closest to the historical data set; This process is repeated until the generated budget uncertainty set reaches the expected number of cutting planes, and the final generated budget set is used as the uncertainty constraint in subsequent system scheduling.

[0029] Preferably, the objective function of the optimization problem is to find a vertex , so that its distance from historical data is maximized, as follows:

[0030] in, are variables describing convex combinations, is the selected vertex variable, It is Historical data, ; The constraints of the optimization problem represent the convex combination of all historical data, the selected vertex should not be cut by the existing cutting plane, and the restriction Must be a vertex, as follows:

[0031]

[0032]

[0033] in, and are the coefficients of the existing budget constraint, and are the box uncertainty set and vertex set respectively, is a set of convex combination coefficients.

[0034] Preferably, finding the budget constraint that is closest to the historical dataset is described as an optimization problem as follows:

[0035] st

[0036]

[0037] in, and are the undetermined coefficients of the budget constraint currently being sought, is the farthest vertex selected, For the Historical data, The total number of historical data.

[0038] Preferably, a multi-stage robust optimization method based on an implicit decision strategy is used to determine the coordinated scheduling scheme: The mathematical model of the distribution system coordination dispatch problem is expressed in a compressed form; According to the convex optimization and robust optimization theories, the feasibility of decision making is ensured by optimizing the robust decision space; Before observing the uncertainty, pre-optimize the feasible decision space and use the constraint generation method to solve the following scheduling problem; After observing the uncertainty, the decision is optimized through the rolling horizon method to obtain the optimization model.

[0039] Preferably, the optimization model is as follows:

[0040] st

[0041]

[0042]

[0043] in, For the current stage, is the uncertainty observation value of the current stage, is the expected forecast value for the future, for The cost of the period, for t Time cost, and for t The time period coefficient matrix, for t Column vector of time periods, is the status of the current period, and for t The lower and upper bounds of the feasible region for the time period.

[0044] In a second aspect, an embodiment of the present invention provides a data-driven urban power grid multi-stage operation dispatching system, including: Building modules to establish a mathematical model for the coordinated dispatching problem of a distribution system involving multiple energy sources; The set module, based on the obtained mathematical model of the coordinated dispatch problem of the distribution system, constructs the budget uncertainty set by adopting the linear budget constraint generation method based on historical data; The scheduling module, based on the obtained budget uncertainty set, adopts a multi-stage robust optimization method based on implicit decision-making strategy to determine the coordinated scheduling scheme.

[0045] In a third aspect, a computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data-driven urban power grid multi-stage operation scheduling method when executing the computer program.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned data-driven urban power grid multi-stage operation scheduling method.

[0047] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned data-driven urban power grid multi-stage operation scheduling method when executing the computer program.

[0048] In a sixth aspect, an embodiment of the present invention provides an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-mentioned data-driven urban power grid multi-stage operation scheduling method.

[0049] Compared with the prior art, the present invention has at least the following beneficial effects: A data-driven multi-stage operation and dispatching method for urban power grids is characterized by comprising the following three steps: establishing a coordinated dispatching model for a multi-energy power grid, generating a budget uncertainty set (i.e., budget constraints) based on historical data, and determining a coordinated dispatching scheme using a multi-stage robust optimization method based on an implicit decision strategy. The first step is to establish a coordinated dispatching model to provide a comprehensive and accurate mathematical description of the current power grid distribution dispatching problem, so that the problem can be solved from a mathematical level; the second step is to generate a budget uncertainty set based on historical data, thereby achieving a more reasonable and accurate prediction of the uncertainty variables of renewable energy in the power grid, and ensuring the reliability of the optimized dispatching; the third step is to use a multi-stage robust optimization method based on an implicit decision strategy to obtain a dispatching scheme for the problem, and the decision scheme can be adaptively adjusted to avoid errors caused by preset decision strategies and improve optimization performance.

[0050] Furthermore, the mathematical model of the coordinated dispatching problem of the distribution system selects the total power generation cost as the objective function, which is based on the consideration of the economic and low-carbon operation of the power grid; the flow constraints including load balance constraints, voltage safety constraints and transmission capacity constraints are to ensure the safety of line transmission; various boundary constraints including transmission power, energy storage system, renewable energy abandonment, etc. are to ensure the safe operation of various equipment and the full utilization of renewable energy; the operation constraints of thermal power units including power generation capacity constraints, climbing constraints and minimum start / stop time constraints ensure the safety of thermal power unit operation; the uncertainty set composed of budget constraints and specific boundary conditions limits the possible range of uncertain variables such as wind power and photovoltaic power. The mathematical model composed of the above objective functions and constraints realizes the accurate description of the coordinated dispatching problem of the distribution system, which is the premise for obtaining a reliable dispatching plan.

[0051] Furthermore, the budget uncertainty set is constructed by using a linear budget constraint generation method based on historical data, which can avoid the problem that the traditional budget uncertainty set is usually obtained based on experience and is difficult to accurately describe the uncertainty of renewable energy output and the inherent spatiotemporal coupling relationship. In addition, the budget constraint iterative generation method based on historical data proposed in the present invention is computationally feasible. By setting the initial box set, selecting the farthest vertex, optimizing the cutting plane and other steps, the box set vertex is used as a breakthrough point. Each iteration constructs a cutting plane that cuts off as much volume as possible from the original box set. After multiple iterations to solve relatively simple optimization problems, a budget constraint that can effectively improve the accuracy of the uncertainty set can be obtained, which effectively avoids the problem of solving high-dimensional irregular convex hulls.

[0052] Furthermore, the budget constraint iterative generation method based on historical data proposed in the present invention greatly simplifies the solution of the budget convex polyhedron uncertainty set. However, due to the large number of vertices in the high-dimensional box set and the impossibility of complete traversal, it is necessary to find an optimal vertex that can cut off as much invalid volume as possible. Based on the idea that the minimum distance from a vertex to all data in the historical data set can represent the expectation of the volume that can be cut off by the vertex, the present invention sets the objective function of the vertex optimization problem as maximizing the minimum distance from the vertex to the historical data set.

[0053] Furthermore, the distance from the vertex to the cutting plane can be used to represent the size of the volume cut from the original box set. Therefore, after selecting the vertex, finding the budget constraint closest to the historical data set is equivalent to finding the cutting plane that cuts the largest volume of the original box set, which can be transformed into the following optimization problem: the objective function represents the maximum distance between the cutting plane and the selected vertex; the constraint condition indicates that the cutting plane should cut off the selected vertex, but not all historical data points.

[0054] Furthermore, the explicit decision strategy in the traditional method requires the pre-definition of the decision function, which is prone to errors caused by the inconsistency between the preset decision strategy and the optimal decision function; the present invention adopts a multi-stage robust optimization method based on an implicit decision strategy, which does not preset a decision strategy, but adaptively adjusts the decision through rolling optimization: before the uncertainty is observed, the feasible decision space is pre-optimized according to the generated uncertainty set; after the uncertainty is observed, the decision is further optimized through the rolling time domain method. The above method effectively improves the optimization performance of power system dispatching in uncertain scenarios and realizes the flexibility and robustness of multi-stage decision-making.

[0055] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0056] In summary, the present invention can more accurately express the spatiotemporal coupling relationship between different renewable energy sources, and reduce or avoid the setting of artificial parameters as much as possible; and on this basis, a multi-stage robust optimization method is designed based on the implicit decision-making strategy to realize the coordinated scheduling of multiple resources and equipment adaptively optimized based on the rolling time domain.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0059] Figure 1 Provide a technical flow chart for the multi-stage low-carbon economic operation of urban power grids driven by data; Figure 2 Generate algorithmic flowcharts for data-driven budget constraints; Figure 3 The structure diagram of the IEEE 33 bus system used for testing; Figure 4 It is the historical daily output curve of photovoltaic and wind power; Figure 5 is a schematic diagram of the budget constraint coefficient; Figure 6 A schematic diagram of a computer device provided by an embodiment of the present invention; Figure 7 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0062] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0063] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0064] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0065] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0066] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0067] The present invention provides a data-driven multi-stage operation and dispatching method for urban power grids, establishes a basic mathematical model for the coordinated dispatching problem of distribution systems, including objective functions, constraints and formulaic expressions of budget uncertainty sets; then, a data-driven budget constraint generation method proposed in the present invention is used to construct a budget uncertainty set based on historical data of renewable energy; finally, an optimal coordinated dispatching scheme for power grids is obtained according to a multi-stage robust optimization method based on an implicit decision-making strategy proposed in the present invention; the present invention can improve the prediction accuracy of renewable energy with uncertainty, such as wind power, and expand the feasible range of all-scenario power grid dispatching, thereby efficiently obtaining a low-carbon operation and dispatching scheme for a power system with high renewable energy utilization.

[0068] See also Figure 1 The present invention provides a data-driven urban power grid multi-stage operation scheduling method, comprising the following steps: S1. Establish a mathematical model of the system; The mathematical model characterization of the system mainly includes objective function, system operation constraints and budget constraints. The objective function is the total power generation cost. The system operation constraints include flow constraints, various boundary constraints, thermal power unit operation constraints, etc. The budget constraints usually constitute an uncertain set of uncertain variables such as wind power output power together with specific boundary conditions.

[0069] S101, objective function; For this type of power system scheduling problem, the objective function used in the present invention is to minimize the total power generation cost. Specifically, the total power generation cost includes the cost of purchasing / selling electricity from the main grid and the fuel cost of the thermal power unit. The objective function expression is as follows:

[0070] in, For the period Active power transmission is The cost of purchasing / selling electricity from the main grid at that time, For the period bus The output power of the upper thermal power unit is The specific cost function can refer to existing research.

[0071] S102, system operation constraints; System operation constraints include power flow constraints to ensure line transmission safety, various boundary constraints to ensure equipment operation safety and economy, and Operation constraints of thermal power units.

[0072] Load balance constraints in power flow constraints:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078] in, Indicates that the slave bus To the bus Branches , Respectively represent the bus to and from The branch set of The time periods Branches The active and reactive power on The time periods bus The net active and reactive power injected on The time periods bus Active and reactive power transmitted on The time periods bus Active and reactive load demands on The time periods bus thermal power, wind power, photovoltaic output power and renewable energy reduction, For the period End Bus The energy storage level, For the period bus It should be noted that since each bus has only one upstream node in the radial distribution network, There is only one element. The present invention denotes the balancing bus as bus number 0 and connects it to the main grid.

[0079] Voltage security constraints in power flow constraints:

[0080] in, It is the time period bus The voltage lower and upper bounds on .

[0081] Transmission capacity constraints in power flow constraints:

[0082] in, For branch The maximum apparent power capacity of

[0083] Bound constraints on active and reactive transmission power:

[0084] in, are the lower and upper bounds of active transmission power, are the lower and upper bounds of reactive transmission power.

[0085] Energy storage system energy storage level limit and charge and discharge level limit:

[0086]

[0087] Among them, are the lower and upper bounds of the energy storage level, For bus Maximum charge and discharge capacity.

[0088] The renewable energy curtailment constraint means that the sum of wind power and photovoltaic power output must at least meet the renewable energy reduction requirements, as follows:

[0089] Reactive power generation limit constraints:

[0090] in, are the upper and lower bounds of reactive power generation.

[0091] The operation constraints of thermal power units include power generation capacity constraints, ramp constraints and minimum start / stop time constraints, as follows:

[0092]

[0093]

[0094] in, For the period bus The start and stop decision variables of the thermal power unit, are the upper and lower bounds of active power generation, For bus The upper and lower limits of the climbing rate of the thermal power unit.

[0095] S103, budget constraints; The budget uncertainty set is used to describe The possible range of uncertain variables is equal to that of the budget uncertainty set in the form of a convex polyhedron, which is essentially equivalent to solving the linear budget constraint. For computational feasibility, budget constraints are usually predefined based on experience.

[0096] Taking load demand as an example, a widely used budget uncertainty set is expressed as follows:

[0097] in, is the budget value, For budget deviation.

[0098] Obviously, the above budget uncertainty set is generated based on experience and only describes part of the spatial correlation, and cannot reflect the spatiotemporal coupling relationship.

[0099] To this end, the present invention proposes a new data-driven budget constraint generation method to construct a more accurate budget uncertainty set to reflect the spatiotemporal coupling relationship between uncertainty variables, thereby improving the safety and economy of power system operation.

[0100] S2, data-driven budget constraint generation method; This paper proposes a data-driven budget constraint generation method to solve the problem that traditional budget uncertainty sets are usually obtained based on experience and are difficult to accurately describe the uncertainty of renewable energy output. This method analyzes the historical data of renewable energy and constructs a polyhedron set (i.e., budget uncertainty set) to encapsulate all historical data and cover potential uncertainty scenarios.

[0101] In order to cope with the computational difficulties caused by the increase in dimensionality, the present invention proposes an iterative generation method based on historical data, that is, in each iteration process, a cutting plane is generated to gradually reduce the range of the uncertainty set until the accuracy requirement is met. Figure 2 The two-dimensional example shown here describes the specific process: First, initialize a box set based on historical data , which is used to wrap all historical data points and represents the initial uncertainty range.

[0102] Then, in the uncertain set Select a vertex as far away from all historical data points as possible The vertex with the largest distance from the historical data point can be obtained by solving the following optimization problem.

[0103] The objective function of the optimization problem is to find a vertex , so that its distance from historical data is maximized, as follows:

[0104] in, are variables describing convex combinations, is the selected vertex variable, It is Historical data, .

[0105] The constraints of the optimization problem represent the convex combination of all historical data, the selected vertices should not be cut by the existing cutting planes, and the restrictions Must be a vertex, as follows:

[0106]

[0107]

[0108] in, and is the coefficient of the existing budget constraint, and represent the box uncertainty set and vertex set respectively.

[0109] However, the above optimization problem is a maximum-minimum structure and is difficult to solve directly. , so that the minimum distance between the vertex and each historical data is maximized, and it is reconstructed into a single-layer structure for easy solution; the single-layer structure is as follows:

[0110] st

[0111]

[0112]

[0113] Selected Vertices Finally, the cutting plane (i.e., budget constraint) is further optimized to remove the invalid areas that do not contain historical data as much as possible.

[0114] The main idea of ​​cutting plane optimization is to and all historical data Find a cutting plane with the largest distance to the selected vertex. In other words, find the budget constraint that is closest to the historical data set. The above idea can be described as the following optimization problem:

[0115] st

[0116]

[0117] Repeat the above process until the generated budget uncertainty set is accurate enough or the expected number of cutting planes is reached. At this point, the final generated budget set can more accurately describe the distribution of historical data and can be used as uncertainty constraints in subsequent system scheduling.

[0118] Compared with the traditional box set, the budget uncertainty set generated by the present invention has significant advantages in accuracy and computational efficiency. On the one hand, the data-driven approach can more comprehensively capture the uncertainty distribution characteristics; on the other hand, solving the optimization problem of vertices and cutting planes also avoids the computational difficulties of solving high-dimensional convex hulls, thereby improving computational feasibility.

[0119] S3, a multi-stage robust optimization method based on implicit decision-making strategy; After constructing the budget uncertainty set, the present invention proposes a multi-stage robust optimization method based on an implicit decision strategy to achieve coordinated scheduling. This method is different from the explicit decision strategy in the traditional method. The implicit decision strategy does not require the pre-definition of an explicit decision function, but adaptively adjusts the decision through rolling optimization to cope with uncertain scenarios. This method can avoid errors caused by the inconsistency between the preset decision strategy and the optimal decision function, thereby improving the optimization performance.

[0120] To simplify the expression, the system model in step S1 is uniformly expressed in the following compressed form:

[0121]

[0122] in, is the decision variable, is an uncertain variable. In order to ensure the computational efficiency of the model, all nonlinear functions can be linearized.

[0123] According to convex optimization and robust optimization theory, if it is possible to ensure that all vertices in the uncertainty set have feasible solutions, then any scenario in the uncertainty set has a feasible solution. Therefore, the feasibility of the decision can be guaranteed by optimizing the robust decision space. There is the following proposition: If the feasible decision space , satisfying the constraints

[0124]

[0125] There exists a set , a feasible solution can be constructed for any possible uncertainty realization according to the following formula.

[0126]

[0127] Based on the above propositions, the following implicit decision-making strategy can be proposed: First, the feasible decision space is pre-optimized before the uncertainty is observed. It should be noted that the scale of vertices is usually large. In order to simplify the calculation, the constraint generation method is usually used to solve the following scheduling problem.

[0128]

[0129] st

[0130]

[0131] Then, after observing the uncertainty, the decision can be optimized by the rolling horizon method. The specific optimization model is as follows:

[0132] st

[0133]

[0134]

[0135] in, Indicates the current stage. represents the uncertainty observation value at the current stage, Represents the expected forecast value for the future.

[0136] The multi-stage robust optimization method based on implicit decision-making strategy effectively improves the optimization performance of power system dispatching under uncertain scenarios and realizes the flexibility and robustness of multi-stage decision-making.

[0137] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0138] In another embodiment of the present invention, a data-driven urban power grid multi-stage operation and scheduling system is provided, which can be used to implement the above-mentioned data-driven urban power grid multi-stage operation and scheduling method. Specifically, the data-driven urban power grid multi-stage operation and scheduling system includes a construction module, a collection module and a scheduling module.

[0139] Among them, the construction module establishes a mathematical model for the coordinated scheduling problem of the distribution system containing multiple energy sources; The set module, based on the obtained mathematical model of the coordinated dispatch problem of the distribution system, constructs the budget uncertainty set by adopting the linear budget constraint generation method based on historical data; The scheduling module, based on the obtained budget uncertainty set, adopts a multi-stage robust optimization method based on implicit decision-making strategy to determine the coordinated scheduling scheme.

[0140] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, which are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions; the processor described in the embodiment of the present invention can be used for the operation of a data-driven urban power grid multi-stage operation scheduling method, including: A mathematical model for the coordinated scheduling problem of a distribution system involving multiple energy sources is established. Based on the obtained mathematical model for the coordinated scheduling problem of a distribution system, a linear budget constraint generation method based on historical data is used to construct a budget uncertainty set. Based on the obtained budget uncertainty set, a multi-stage robust optimization method based on an implicit decision-making strategy is used to determine the coordinated scheduling scheme.

[0141] See also Figure 6 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the data-driven urban power grid multi-stage operation dispatching method in the embodiment is implemented. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the data-driven urban power grid multi-stage operation dispatching system in the embodiment are implemented. To avoid repetition, it is not described one by one here.

[0142] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 6 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0143] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, graphics processing units (GPU), tensor processing units (TPU), digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0144] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0145] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0146] See also Figure 7 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0147] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0148] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0149] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0150] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0151] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0152] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0153] The processor may load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the data-driven urban power grid multi-stage operation scheduling method in the above embodiment; the processor may load and execute the following steps of one or more instructions in the computer-readable storage medium: A mathematical model for the coordinated scheduling problem of a distribution system involving multiple energy sources is established. Based on the obtained mathematical model for the coordinated scheduling problem of a distribution system, a linear budget constraint generation method based on historical data is used to construct a budget uncertainty set. Based on the obtained budget uncertainty set, a multi-stage robust optimization method based on an implicit decision-making strategy is used to determine the coordinated scheduling scheme.

[0154] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0155] Numerical tests are conducted on a modified IEEE 33 bus system, which includes thermal power, wind power, solar power and energy storage devices. Figure 3 shown.

[0156] The system consists of a balancing bus with a transmission power limit of [-3,3] MW and an average expected load demand of 2.96 MW (peak 3.75 MW, minimum 1.98 MW). The total installed capacity of wind power (WT) and photovoltaic (PV) is 2 MW each, the maximum generation capacity of thermal power units is 1 MW, the energy capacity of energy storage is 8 MWh, and the rated charging and discharging power is 4 MW. The voltage of the balancing bus is 1.0 pu, and the voltage limits of the remaining buses are [0.9, 1.1] pu. Figure 4 This is the historical daily output curve of photovoltaic and wind power (taking one month as an example).

[0157] Using the above parameters, the proposed implicit decision rule based on the budget uncertainty set is tested and compared with existing methods, including the LDR method and the scenario-based stochastic optimization method. In addition, the upper bound optimality gap approximation based on complete knowledge is also tested, assuming that all uncertainty observations are completely known. The solution based on complete knowledge can provide an upper bound approximation of the theoretical optimal solution, that is, the solution obtained by any method will not be better than (at most equal to) the solution based on complete knowledge.

[0158] First, the budget uncertainty set generated by the method proposed in the present invention is as follows: Figure 5 shown. Figure 5 is a 48-dimensional example (one photovoltaic power plant and one wind turbine, each with 24 time periods). The lighter the color, the larger the absolute value of the coefficient in the budget constraint, that is, the stronger the correlation. Two main conclusions can be drawn from this: (1) There is a strong correlation between consecutive time periods; (2) The spatiotemporal coupling relationship is not symmetric. This means that the existing empirically based budget constraints are not very applicable.

[0159] Table 1 Simulation results of different methods

[0160] In order to further test the performance of the method proposed in the present invention, 500 Monte Carlo simulations were performed to estimate the expected operating costs of different methods. The results are shown in Table 1. It can be seen that the average cost of the method proposed in the present invention is lower than that of the LDR method and the scenario-based stochastic optimization method. In addition, the optimality gap between the method proposed in the present invention and the solution based on complete knowledge is very small, indicating that the optimality gap of the proposed method is less than 0.46%, which verifies the effectiveness of the method proposed in the present invention.

[0161] In summary, the data-driven multi-stage operation and dispatching method and system of the urban power grid in the present invention effectively solves the problem that the budget constraint generation in the prior art cannot accurately reflect the spatiotemporal coupling relationship of multiple energy sources and the excessive influence of artificial parameter selection. Through the linear budget constraint generation method based on historical data and the multi-stage robust optimization method based on implicit decision-making strategy, the present invention improves the prediction accuracy of uncertain information such as wind power and photovoltaic output power, improves the optimization performance of the power grid dispatching scheme, and enhances the adaptability and robustness of the decision. The present invention first establishes a coordinated dispatching mathematical model containing multiple constraints to accurately describe the power grid dispatching problem; then, a budget uncertainty set is generated based on historical data to avoid the limitations of traditional methods; finally, an implicit decision-making strategy is adopted to adaptively adjust the decision through the rolling time domain to ensure the optimization performance under different uncertainty scenarios. The results of the application in the embodiment show that the method of the present invention is superior to the existing method in terms of average operating cost, and the optimality gap is very small, which verifies its effectiveness. The present invention improves the flexibility and reliability of power grid dispatching by accurately modeling the problem, especially greatly improving the generation of uncertainty sets, and designing dispatching scheme decisions for adaptive optimization, and has important application value.

[0162] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0163] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0164] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. 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.

[0165] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or 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.

[0166] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0167] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0168] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0169] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0170] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0171] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0172] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A data-driven multi-stage operation and dispatching method for urban power grid, characterized in that: The following steps are involved: Establish a mathematical model for the coordinated dispatching of distribution systems involving multiple energy sources; Based on the obtained mathematical model of the coordinated dispatch problem of the distribution system, the budget uncertainty set is constructed by adopting the linear budget constraint generation method based on historical data. Based on the obtained budget uncertainty set, a multi-stage robust optimization method based on implicit decision-making strategy is adopted to determine the coordinated scheduling scheme.

2. The data-driven urban power grid multi-stage operation dispatching method according to claim 1 is characterized in that: The objective function of the mathematical model of the coordinated dispatching problem of the distribution system is the total power generation cost. The system operation constraints include power flow constraints, various boundary constraints, thermal power unit operation constraints, budget constraints and specific boundary conditions, which together constitute the uncertain set of uncertain variables of wind power output power.

3. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 2 is characterized in that: The total power generation cost includes the cost of purchasing / selling electricity from the main grid and the fuel cost of the thermal power unit. The objective function is as follows: in, For the period Active power transmission is The cost of purchasing / selling electricity from the main grid at that time, For the period bus The output power of the upper thermal power unit is The specific cost function can refer to existing research.

4. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 2 is characterized in that: Load balance constraints in power flow constraints: in, Indicates that the slave bus To the bus Branches , Respectively represent the bus to and from The branch set of The time periods Branches The active and reactive power on The time periods bus The net active and reactive power injected on The time periods bus Active and reactive power transmitted on The time periods bus Active and reactive load demands on The time periods bus thermal power, wind power, photovoltaic output power and renewable energy reduction, For the period End Bus The energy storage level, For the period bus The square value of the voltage on Voltage security constraints in power flow constraints: in, It is the time period bus The voltage lower and upper bounds on ; Transmission capacity constraints in power flow constraints: in, For branch The maximum apparent power capacity; Bound constraints on active and reactive transmission power: in, are the lower and upper bounds of active transmission power, are the lower and upper bounds of reactive transmission power; Energy storage system energy storage level limit and charge and discharge level limit: Among them, are the lower and upper bounds of the energy storage level, For bus Maximum charge and discharge capacity; Constraints on the amount of renewable energy abandoned: Reactive power generation limit constraints: in, are the upper and lower bounds of reactive power generation; Generation capacity constraints, ramp constraints and minimum start / stop time constraints: in, For the period bus The start and stop decision variables of the thermal power unit, are the upper and lower bounds of active power generation, For bus The upper and lower limits of the climbing rate of the upper thermal power unit, is a node set, For the time period collection, is the feasible state set for power on and off.

5. The data-driven urban power grid multi-stage operation dispatching method according to claim 1 is characterized in that: The budget uncertainty set is constructed by using the linear budget constraint generation method based on historical data as follows: Initialize a box set based on historical data , used to wrap all historical data points and represent the initial uncertainty range; In the uncertain set Select a vertex as far away from all historical data points as possible ; By solving the following optimization problem, we can obtain the vertex with the largest distance from the historical data point; By introducing auxiliary variables , maximize the minimum distance between the vertex and each historical data, and reconstruct it into a single-layer structure; At the top and all historical data Find the cutting plane with the largest distance to the selected vertex and find the budget constraint closest to the historical data set; This process is repeated until the generated budget uncertainty set reaches the expected number of cutting planes, and the final generated budget set is used as the uncertainty constraint in subsequent system scheduling.

6. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 5 is characterized in that: The objective function of the optimization problem is to find a vertex , so that its distance from historical data is maximized, as follows: in, are variables describing convex combinations, is the selected vertex variable, It is Historical data, ; The constraints of the optimization problem represent the convex combination of all historical data, the selected vertices should not be cut by the existing cutting planes, and the restrictions Must be a vertex, as follows: in, and are the coefficients of the existing budget constraint, and are the box uncertainty set and vertex set respectively, is a set of convex combination coefficients.

7. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 5 is characterized in that: Finding the budget constraint that is closest to the historical dataset is described as the following optimization problem: s.t. in, and are the undetermined coefficients of the budget constraint currently being sought, is the farthest vertex selected, For the Historical data, The total number of historical data.

8. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 1 is characterized in that: The coordinated scheduling scheme is determined by using a multi-stage robust optimization method based on an implicit decision-making strategy: The mathematical model of the distribution system coordination dispatch problem is expressed in a compressed form; According to the convex optimization and robust optimization theories, the feasibility of decision making is ensured by optimizing the robust decision space; Before observing the uncertainty, pre-optimize the feasible decision space and use the constraint generation method to solve the following scheduling problem; After observing the uncertainty, the decision is optimized through the rolling horizon method to obtain the optimization model.

9. The data-driven multi-stage operation and dispatching method for urban power grid according to claim 1 is characterized in that: The optimization model is as follows: s.t. in, For the current stage, is the uncertainty observation value of the current stage, is the expected forecast value for the future, for The cost of the period, for t Time cost, and for t The time period coefficient matrix, for t Column vector of time periods, is the status of the current period, and for t The lower and upper bounds of the feasible region for the time period.

10. A data-driven urban power grid multi-stage operation dispatching system, characterized in that: include: Building modules to establish mathematical models for the coordinated dispatching of distribution systems involving multiple energy sources; The set module, based on the obtained mathematical model of the coordinated dispatch problem of the distribution system, constructs the budget uncertainty set by adopting the linear budget constraint generation method based on historical data; The scheduling module, based on the obtained budget uncertainty set, adopts a multi-stage robust optimization method based on implicit decision-making strategy to determine the coordinated scheduling scheme.

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