Power distribution system safe operation optimization method and system based on low inertia

Abstract

The invention relates to the technical field of power system safety, and provides a power distribution system safety operation optimization method and system based on low inertia. The invention discloses a power distribution system safe operation optimization method based on low inertia. The method comprises the following steps: constructing an evaluation model based on a minimum inertia demand by taking minimization of the total operation cost of a system as an optimization target; under a set constraint condition, solving the evaluation model based on the minimum inertia demand by using a grey wolf algorithm to obtain a minimum inertia demand value at each moment; in the process of searching the minimum inertia demand value at each moment, the grey wolf algorithm adopts a convergence factor to determine a search area; the convergence factor is represented as the fractional order power of the ratio of the difference value of the maximum number of iterations and the current number of iterations to the maximum number of iterations. Response reliability of the system to sudden disturbance can be improved.

Description

Technical Field

[0001] This invention relates to the field of power system safety technology, and in particular to a method and system for optimizing the safe operation of a power distribution system based on low inertia. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Under traditional control methods, power electronic interface resources have poor fault response capabilities and grid integration performance, failing to provide rotational inertia support to the grid after active power disturbances. With the increasing digitalization of power systems, the operating capacity of synchronous power sources has significantly decreased, leading to a substantial drop in system rotational inertia. Traditional high-inertia power systems are gradually evolving into low-inertia power systems dominated by power electronic interface resources. The level of inertia directly affects the power system's ability to withstand active power disturbances. The weak inertia, underdamped characteristics, and uncertainties of low-inertia power systems result in increasingly prominent frequency stability issues. Under active power disturbances, the frequency changes at a faster rate and fluctuates more significantly, increasing the risk of triggering "third-line" protection devices such as low-frequency load shedding / high-frequency generator tripping. Consequently, the control rate and emergency control quantities required to respond to severe faults also increase.

[0004] Existing technologies lack a quantitative model for the "minimum inertia requirement" that takes frequency security as its core and comprehensively considers factors such as power disturbance amplitude, disturbance duration, and frequency allowable deviation. This makes it difficult to accurately determine the inertia required by the system and each node. At the same time, there is a lack of a reasonable inertia allocation mechanism based on the actual output ratio among the virtual inertia controllers of multi-source heterogeneous power electronic devices, which makes it impossible to efficiently utilize the inertia support capacity of the equipment.

[0005] Traditional optimization algorithms suffer from problems such as linear changes in convergence factors and poor adaptability to actual search processes when dealing with nonlinear constraint models of low-inertia systems. This leads to low accuracy and inefficiency in solving optimal inertia configuration and operation schemes. In addition, existing scheduling does not take into account the comprehensive costs of renewable energy curtailment, energy storage operation, network losses, and virtual inertia control, making it difficult to minimize operating costs while ensuring system safety. In low-inertia systems, multiple constraints such as power balance, frequency safety, voltage stability, upper limits for energy storage charging and discharging, and virtual inertia adjustment capabilities must be met simultaneously. Existing technologies lack a unified control framework that can coordinate multiple constraints and integrate the response characteristics of multiple devices, failing to fully leverage the millisecond-level response advantages of power electronic equipment to reduce the inertia support pressure of synchronous units. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides a method and system for optimizing the safe operation of a power distribution system based on low inertia, which can improve the system's response reliability to sudden disturbances.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: The first aspect of the present invention provides a method for optimizing the safe operation of a power distribution system based on low inertia.

[0008] In one or more embodiments, a method for optimizing the safe operation of a power distribution system based on low inertia is provided, comprising: With minimizing the total system operating cost as the optimization objective, an evaluation model based on minimum inertia requirement is constructed; where the minimum total system operating cost includes the cost of renewable energy curtailment, energy storage operation cost, grid loss cost, and virtual inertia control cost; Under the given constraints, the Grey Wolf algorithm is used to solve the evaluation model based on the minimum inertia requirement to obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

[0009] As one implementation method, the constraints include power balance constraints, frequency safety constraints, voltage safety constraints, energy storage operation constraints, and virtual inertia control constraints.

[0010] As one implementation method, the convergence factor is expressed as: ; In the formula, The convergence factor; This is a non-linear adjustment coefficient, with a value range of [0,1]. This represents the maximum number of iterations. This represents the current iteration number.

[0011] As one implementation method, the expression for the cost of virtual inertia control is: ; In the formula, Cost of virtual inertia control; This represents the number of virtual inertia controllers. For the first Adjustment cost coefficient of a virtual inertia controller; For the first A virtual inertia controller in The actual virtual inertia value at any given moment; For the first A virtual inertia controller in The inertia requirement at any given moment.

[0012] As one implementation method, the first A virtual inertia controller in Inertia requirement at time The expression is: ; ; In the formula, For the first Virtual Inertia Controller Actual output at any given moment; for Minimum inertia requirement of the system at any given time; The frequency response time constant; This is the stable frequency value after the disturbance; This is the disturbance correction factor; Frequency safety threshold; The rated frequency; for Maximum power disturbance amplitude at any given moment; The power disturbance change rate is denoted as .

[0013] As one implementation method, the sliding window method is used for statistics. Time before The power disturbance characteristics over a given time period, including the maximum disturbance amplitude and the rate of change of disturbance, are expressed by the following formula: ; ; In the formula, They are respectively Real-time system input and output power; The maximum disturbance amplitude at time t; This represents the maximum disturbance amplitude at time t-1.

[0014] As one implementation method, the expressions for the cost of renewable energy curtailment, energy storage operation cost, and system network loss cost are as follows: ; ; ; in, Costs of curtailing renewable energy; For energy storage operating costs; For system network loss costs; To optimize the total cycle time; The number of new energy nodes; For the first The curtailment penalty coefficient for each new energy node; For the first A new energy node Maximum transmittable power at any given time; For the first A new energy node Actual output at any given moment; For time step; This refers to the number of energy storage nodes; The first The charging and discharging cost coefficient of each energy storage node; The first One energy storage node in The charging and discharging power at any given moment; For the number of lines, The first The line is in Active power and reactive power at any given time This is the voltage at the beginning of the line. This represents the line resistance.

[0015] A second aspect of the present invention provides a power distribution system safety operation optimization system based on low inertia.

[0016] In one or more embodiments, a power distribution system safety operation optimization system based on low inertia includes: The evaluation model construction module is used to build an evaluation model based on the minimum inertia requirement with the optimization objective of minimizing the total system operating cost; where minimizing the total system operating cost includes the cost of curtailment of renewable energy, the operating cost of energy storage, the cost of grid loss, and the cost of virtual inertia control; The evaluation model solving module is used to solve the evaluation model based on the minimum inertia requirement under set constraints using the Grey Wolf algorithm to obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

[0017] A third aspect of the present invention provides a computer-readable storage medium.

[0018] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described optimization method for safe operation of a low-inertia power distribution system.

[0019] A fourth aspect of the present invention provides an electronic device.

[0020] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the above-described method for optimizing the safe operation of a power distribution system based on low inertia.

[0021] Compared with the prior art, the beneficial effects of the present invention are: This invention integrates power disturbance characteristics and frequency safety constraints through an inertia demand assessment model, quantifies and calculates the minimum inertia demand, and allocates it to each virtual inertia controller. This avoids the problem of "rapid fluctuation and large deviation" in frequency under active power disturbances from the source, and reduces the probability of malfunction of protection devices such as low-frequency load shedding / high-frequency tripping. The inertia demand assessment model introduces the power disturbance change rate and disturbance correction coefficient, which can dynamically adjust the inertia demand calculation results according to the actual disturbance magnitude. Compared with the traditional fixed inertia configuration scheme, it is more suitable for the characteristics of low-inertia systems, such as "weak inertia, underdamping, and high uncertainty", and improves the system's response reliability to sudden disturbances.

[0022] The evaluation model based on minimum inertia requirement of this invention aims to minimize total operating cost, taking into account the cost of renewable energy curtailment, energy storage operation cost, network loss cost, and virtual inertia control cost. By precisely configuring inertia, it reduces renewable energy curtailment (lower curtailment penalties), optimizes energy storage charging and discharging strategies, and reduces system network losses. Compared with the traditional scheduling scheme that prioritizes safety over cost, it significantly reduces the total operating expenditure throughout the entire lifecycle. In solving the evaluation model based on minimum inertia requirement, the Grey Wolf algorithm changes the traditional linear convergence factor to a nonlinear form, improving the accuracy and efficiency of solving the nonlinear constraint model. It can quickly find the "lowest cost operation scheme that meets safety constraints," avoiding the additional cost waste caused by slow algorithm convergence and suboptimal solutions. Attached Figure Description

[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0024] Figure 1 This is a flowchart of the method for optimizing the safe operation of a power distribution system based on low inertia according to an embodiment of the present invention; Figure 2 This is the gray wolf hierarchy system according to an embodiment of the present invention; Figure 3 This is a flowchart illustrating how the Grey Wolf algorithm is used to solve an evaluation model based on minimum inertia requirements, according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the optimized system structure for safe operation of a power distribution system based on low inertia, according to an embodiment of the present invention. Figure 5 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0027] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0028] Figure 1 A schematic diagram of the principle of the power distribution system safety operation optimization method based on low inertia according to an embodiment of the present invention is provided. Figure 1 The method for optimizing the safe operation of a power distribution system based on low inertia in this embodiment may include the following steps S101~S102.

[0029] The specific implementation process of steps S101 to S102 is as follows: Step S101: With minimizing the total system operating cost as the optimization objective, construct an evaluation model based on the minimum inertia requirement; wherein, minimizing the total system operating cost includes the cost of curtailment of new energy, the operating cost of energy storage, the cost of grid loss, and the cost of virtual inertia control.

[0030] The inertia requirement assessment model is primarily constructed with frequency security as its core objective. In its calculations, it comprehensively considers multiple key factors, including the power disturbance amplitude (a crucial indicator of power fluctuation), the disturbance duration (the length of time the power disturbance lasts), the permissible frequency deviation (the range of frequency deviation the system can tolerate), and the frequency change rate threshold (a boundary value measuring the rate of frequency change). By comprehensively considering these factors, the model can achieve accurate quantitative calculations of the minimum inertia requirement.

[0031] Statistics using the sliding window method Time before Time period ( The power disturbance characteristics of ( ), including the maximum disturbance amplitude and the rate of change of disturbance, are expressed by the following formula: (1) (2) In the formula, , for , Maximum power disturbance amplitude at any given time, in kW; The power disturbance rate is expressed in kW / s. They are respectively The system's input and output power at all times.

[0032] In the process of in-depth research on the operating characteristics of power systems, based on two important theoretical foundations—the rotor motion equation and frequency safety constraints—a minimum inertia requirement formula was derived through rigorous mathematical derivation. This formula is crucial for accurately assessing the minimum inertia required by the system under different operating conditions. Simultaneously, to further optimize system performance and better adapt to dynamically changing operating environments, a disturbance rate of change correction factor was introduced. This correction factor allows for flexible adjustment of relevant parameters based on the actual disturbance rate of change in the system, thereby significantly improving the system's dynamic adaptability. (3) In the formula, for Minimum inertia requirement of the system at any given time, in kg·m²; The frequency response time constant is 0.2s. The frequency stability value after the disturbance (determined by frequency safety constraints, taken as 49.7~53.3Hz); The disturbance correction factor is taken as 0.001 to 0.005. ·s, the larger the perturbation, the larger the value). The frequency safety threshold is 50.5Hz. The rated frequency is 50Hz.

[0033] To effectively achieve a reasonable distribution of inertia, after in-depth research and analysis, it was decided to precisely allocate the minimum inertia requirement to each virtual inertia controller based on the power ratio of the new energy nodes. This allocation method is based on a full consideration of the characteristics of the new energy system, ensuring a balanced distribution of inertia within the system and improving system stability and reliability. Specifically, the allocation formula is as follows: (4) In the formula, For the first Virtual Inertia Controller Inertia requirement at any given moment. For the first Virtual Inertia Controller Actual output at any given moment, in kW; The total number of virtual inertia controllers (the total number of all controllers involved in inertia allocation). ).

[0034] The optimization objective is to minimize the total system operating cost. The objective function consists of multiple costs, including the cost of renewable energy curtailment, energy storage operation cost, grid loss cost, and virtual inertia control cost, as expressed below: (5) in, The total operating cost of the system is expressed in yuan. Cost of curtailment of renewable energy, unit: yuan; Energy storage operating costs, unit: yuan; System network loss cost, unit: yuan; Cost of virtual inertia control, unit: yuan.

[0035] Cost of curtailment of renewable energy: (6) In the formula, To optimize the total cycle time, the unit is hours (h). The number of new energy nodes; For the first The curtailment penalty coefficient for each new energy node, in yuan / (kW·h); For the first A new energy node Maximum power output at any given time, in kW; For the first A new energy node Actual output at any given moment, in kW; The time step is expressed in hours (h).

[0036] Energy storage operating costs: (7) In the formula, This refers to the number of energy storage nodes; The first The charging and discharging cost coefficient of each energy storage node, in yuan / (kW·h); The first One energy storage node in The charging and discharging power at any given time, in kW.

[0037] System network loss cost: (8) In the formula, This is the network loss cost coefficient, in yuan / (kW·h); To optimize the total cycle time, For the system in The total network loss power at any given time, expressed in kW, is calculated using the following formula: (9) in, For the number of lines, The first The line is in Active power and reactive power at any given time This is the voltage at the beginning of the line. This represents the line resistance.

[0038] Virtual inertia control cost (10) In the formula, This represents the number of virtual inertia controllers. For the first Adjustment cost coefficient of a virtual inertia controller, unit: yuan / (kg·m²); For the first A virtual inertia controller in The actual virtual inertia value at time t, in kg·m²; For the first A virtual inertia controller in The inertia requirement at any given moment.

[0039] Step S102: Under the given constraints, the Grey Wolf algorithm is used to solve the evaluation model based on the minimum inertia requirement to obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as: the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

[0040] The constraints include the following: Power balance constraint is a crucial condition for the operation of a power system. Specifically, the active and reactive power of the system must remain balanced at any given time to ensure the stable and reliable operation of the entire power system. Its specific expression is as follows: (11) (12) In the formula, The first The active and reactive power regulation of a virtual inertia controller; The first The active and reactive power of each load node; These are the reactive power outputs from new energy sources and energy storage, respectively.

[0041] Regarding frequency safety constraints, in low-inertia systems, frequency fluctuations must be strictly controlled within permissible ranges. Simultaneously, constraints on the rate of frequency change must also be fully considered. (13) In the formula, for The system frequency at any given time, These are the minimum and maximum allowable frequencies (usually 49.5Hz and 50.5Hz). This is the maximum permissible rate of frequency change (typically 0.5 Hz / s for low inertia systems). The relationship between system frequency and equivalent inertia is described by the rotor motion equations: (14) In the formula, for Equivalent inertia of the system at any given moment These are the system input and output active power, respectively. The damping coefficient; The rated frequency is 50Hz. For a moment System frequency (real-time operating frequency of the system).

[0042] Voltage safety constraints are a crucial limiting condition in the operation of power systems. Specifically, the voltage at each node must always be maintained within the allowable deviation range specified by the rated voltage to ensure the stable and reliable operation of the entire power system. This is a key requirement for ensuring the normal operation of power equipment and the quality of power supply. (15) In the formula, For the first Each node Voltage at time, These are the minimum and maximum allowable node voltages, respectively (typically taken as 0.95U and 1.05U, where U is the rated voltage).

[0043] Energy storage operation constraints: Charge and discharge power constraints: (16) In the formula, These are the maximum charging and discharging power of the energy storage, respectively. The first One energy storage node in The charging and discharging power at any given time, measured in kW.

[0044] Regarding virtual inertia control constraints, it is necessary to consider the detailed results of the inertia demand assessment. Specifically, virtual inertia must satisfy two constraints: demand matching constraints and adjustability constraints. (17) (18) In the formula, This represents the maximum output inertia of the virtual inertia controller. The maximum adjustment step size for virtual inertia; For the first A virtual inertia controller in The inertia requirement at any given moment; For the first A virtual inertia controller in The actual virtual inertia value at time t, in kg·m².

[0045] The Grey Wolf Optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 by Mirjalili et al., based on the group behavior of grey wolves in nature. Its main advantages are its simple structure, few required parameters, and ease of implementation in a program. In GWO, candidate solutions are represented as individuals within the grey wolf pack, including... , and The leader wolf of the rank, and other ordinary gray wolves. The gray wolves follow a strict hierarchy, such as Figure 2 As shown.

[0046] The entire hunting process of gray wolves can be divided into three stages: encirclement, hunting, and killing. , and Higher-ranking gray wolves have higher fitness values, allowing them to get closer to their prey. Once the prey's location is pinpointed, these higher-ranking wolves will work together to guide other lower-ranking gray wolves. They approach their prey. Due to the prey's escape response, their positions constantly change, so the alpha gray wolves also need to continuously update their positions. Ultimately, the gray wolf pack successfully surrounds the prey and achieves predation. The mathematical model of the gray wolf hunting process is shown below: (19) (20) (twenty one) (twenty two) (twenty three) In the formula, Indicates the distance between the wolf and its prey; A random value between [0.2]; This represents the number of iterations. Iteration The location of the prey at that time; and These are the positions of the individual wolves at iteration t and iteration t+1, respectively. It is an adaptive vector; and It is a random number between [0,1]. This represents the maximum number of iterations. is the convergence factor.

[0047] Grey Wolf , and Together they determined the ordinary gray wolf Location: (twenty four) (25) (26) (27) (28) (29) (30) In the formula, , , They are Wolf, wolves and wolves and The distance between the wolves; , ,and They are Wolf, wolves and The wolf's current location; , ,and They represent Wolf Wolf, wolves and The direction and distance the wolf moved; This is the current location of the gray wolves; It's the updated version. The wolf's final location.

[0048] The size is closely related to GWO's search capability, when At times, gray wolf packs expand their search area when hunting prey, achieving a global search to quickly converge on the target area; when At this stage, after locking onto prey, the gray wolf pack narrows its search area before launching an attack, a process known as local search, which results in a relatively slow convergence rate. As the number of iterations increases, the convergence factor of the GWO... It decreases linearly from 2 to 0, but Since the algorithm does not maintain a linear change throughout the convergence process, it may not effectively reflect the actual optimization search process. Therefore, an improved Grey Wolf Optimizer (IGWO) is proposed, which adopts a non-linear form. Improve the search process: (31) In the formula, This is a non-linear adjustment coefficient, with a value range of [0,1].

[0049] The process of solving the evaluation model based on minimum inertia requirement using the Grey Wolf algorithm, such as... Figure 3 As shown: First, input the parameters required for each time period in the model and initialize the gray wolf population. Then, calculate the fitness value of each individual gray wolf in the pack, and assign the fitness values ​​of individuals with higher fitness values ​​to specific individuals. Wolf, wolves and Wolves, and update the gray wolf. The system uses the position and related parameters to transmit population information and determine whether the optimal solution has been reached. If an individual that meets the termination condition has reached the optimal solution, the minimum inertia value at that moment can be output; if the optimal solution has not been reached, the fitness value of the individual needs to be adjusted and the iteration continues until the optimal solution is reached. The fitness value here is calculated based on the "system comprehensive operating cost," with lower costs indicating better fitness.

[0050] The method for optimizing the safe operation of a power distribution system based on low inertia provided in this invention has the following advantages: (1) Significantly improves the frequency safety of low inertia systems and reduces operational risks; Precisely ensure frequency stability: By using the "inertia demand assessment model", the minimum inertia demand is quantitatively calculated and allocated to each virtual inertia controller, taking into account the power disturbance characteristics and frequency safety constraints. This avoids the problem of "rapid fluctuation and large deviation" of frequency under active power disturbance from the source, and reduces the probability of malfunction of protection devices such as low-frequency load shedding / high-frequency tripping. Enhanced dynamic adaptability: The model introduces a "disturbance change rate correction factor", which can dynamically adjust the inertia requirement calculation results according to the actual disturbance magnitude. Compared with the traditional fixed inertia configuration scheme, it is more adaptable to the characteristics of low inertia systems such as "weak inertia, underdamped and high uncertainty", and improves the system's response reliability to sudden disturbances.

[0051] (2) Fully explore the potential of power electronic equipment and optimize resource utilization efficiency; Activating virtual inertia support capability: By optimizing the control parameters of multi-source heterogeneous power electronic equipment such as wind power, photovoltaic, energy storage, and DC inverters, their millisecond-level response characteristics are transformed into inertia support capability, making up for the inertia gap caused by the reduction of synchronous power generation capacity, reducing the inertia support and reserve pressure of synchronous units, and releasing the power generation potential of synchronous power sources; Achieve reasonable inertia allocation: Allocate minimum inertia requirements according to the "power ratio of new energy nodes" to avoid overloading or idleness of some virtual inertia controllers, ensure that the inertia adjustment capabilities of each device are used efficiently, and improve the overall system's inertia support efficiency.

[0052] (3) Reduce the overall operating cost of the system and achieve a win-win situation of "safety and economy". Multi-dimensional cost control: The "assessment model based on minimum inertia requirement" aims to "minimize total operating costs" by comprehensively considering the costs of renewable energy curtailment, energy storage operation, network loss, and virtual inertia control. Through precise inertia configuration, it reduces renewable energy curtailment (lower curtailment penalties), optimizes energy storage charging and discharging strategies, and reduces system network losses. Compared to traditional "safety-first, cost-light" scheduling schemes, it significantly reduces total lifecycle operating expenses. Optimize the algorithm to reduce costs and increase efficiency: The improved Grey Wolf (IGWO) algorithm changes the traditional linear convergence factor to a nonlinear form, which improves the solution accuracy and efficiency of nonlinear constraint models. It can quickly find the "lowest cost operation scheme that satisfies the safety constraints" and avoid the extra cost waste caused by slow algorithm convergence and suboptimal solution.

[0053] (4) Adapt to high proportion of new energy and power electronic equipment access, and help achieve the "dual carbon" target. Breaking through technical compatibility bottlenecks: This solution is designed for the trend of "new energy installed capacity exceeding 50% and the scale of power electronic interface resources continuing to expand". It can effectively solve the problem of insufficient inertia under high proportion of new energy access, provide technical support for the safe operation of the power grid, and break through the "inertia constraint" bottleneck of new energy consumption; Promote innovation in dispatching modes: Incorporate the coupling constraint of "inertia level - frequency security" into operation and dispatching decisions, replace the traditional dispatching scheme that "does not consider inertia adequacy", form a new dispatching logic adapted to low inertia systems, and provide key technical support for the power system to transform from "high inertia synchronous dominance" to "low inertia power electronics dominance".

[0054] like Figure 4 As shown, the power distribution system safety operation optimization system based on low inertia provided in this embodiment of the invention can be implemented in software. The power distribution system safety operation optimization system based on low inertia includes the following software modules: evaluation model construction module 401 and evaluation model solving module 402.

[0055] The functions of each software module in the low-inertia power distribution system safety operation optimization system are described below: The evaluation model construction module 401 is used to construct an evaluation model based on the minimum inertia requirement with the optimization objective of minimizing the total system operating cost; wherein, minimizing the total system operating cost includes the cost of curtailment of new energy, the operating cost of energy storage, the cost of grid loss and the cost of virtual inertia control.

[0056] The inertia requirement assessment model is primarily constructed with frequency security as its core objective. In its calculations, it comprehensively considers multiple key factors, including the power disturbance amplitude (a crucial indicator of power fluctuation), the disturbance duration (the length of time the power disturbance lasts), the permissible frequency deviation (the range of frequency deviation the system can tolerate), and the frequency change rate threshold (a boundary value measuring the rate of frequency change). By comprehensively considering these factors, the model can achieve accurate quantitative calculations of the minimum inertia requirement.

[0057] Statistics using the sliding window method Time before Time period ( The power disturbance characteristics of ( ), including the maximum disturbance amplitude and the rate of change of disturbance, are expressed by the following formula:

[0058]

[0059] In the formula, , for , Maximum power disturbance amplitude at any given time, in kW; The power disturbance rate is expressed in kW / s. They are respectively The system's input and output power at all times.

[0060] In the process of in-depth research on the operating characteristics of power systems, based on two important theoretical foundations—the rotor motion equation and frequency safety constraints—a minimum inertia requirement formula was derived through rigorous mathematical derivation. This formula is crucial for accurately assessing the minimum inertia required by the system under different operating conditions. Simultaneously, to further optimize system performance and better adapt to dynamically changing operating environments, a disturbance rate of change correction factor was introduced. This correction factor allows for flexible adjustment of relevant parameters based on the actual disturbance rate of change in the system, thereby significantly improving the system's dynamic adaptability.

[0061] In the formula, for Minimum inertia requirement of the system at any given time, in kg·m²; The frequency response time constant is 0.2s. The frequency stability value after the disturbance (determined by frequency safety constraints, taken as 49.7~53.3Hz); The disturbance correction factor is taken as 0.001 to 0.005. ·s, the larger the perturbation, the larger the value). The frequency safety threshold is 50.5Hz. The rated frequency is 50Hz.

[0062] To effectively achieve a reasonable distribution of inertia, after in-depth research and analysis, it was decided to precisely allocate the minimum inertia requirement to each virtual inertia controller based on the power ratio of the new energy nodes. This allocation method is based on a full consideration of the characteristics of the new energy system, ensuring a balanced distribution of inertia within the system and improving system stability and reliability. Specifically, the allocation formula is as follows:

[0063] In the formula, For the first Virtual Inertia Controller Inertia requirement at any given moment. For the first Virtual Inertia Controller Actual output at any given moment, in kW; The total number of virtual inertia controllers (the total number of all controllers involved in inertia allocation). ).

[0064] The optimization objective is to minimize the total system operating cost. The objective function consists of multiple costs, including the cost of renewable energy curtailment, energy storage operation cost, grid loss cost, and virtual inertia control cost, as expressed below:

[0065] in, The total operating cost of the system is expressed in yuan. Cost of curtailment of renewable energy, unit: yuan; Energy storage operating costs, unit: yuan; System network loss cost, unit: yuan; Cost of virtual inertia control, unit: yuan.

[0066] Cost of curtailment of renewable energy:

[0067] In the formula, To optimize the total cycle time, the unit is hours (h). The number of new energy nodes; For the first The curtailment penalty coefficient for each new energy node, in yuan / (kW·h); For the first A new energy node Maximum power output at any given time, in kW; For the first A new energy node Actual output at any given moment, in kW; The time step is expressed in hours (h).

[0068] Energy storage operating costs:

[0069] In the formula, This refers to the number of energy storage nodes; The first The charging and discharging cost coefficient of each energy storage node, in yuan / (kW·h); The first One energy storage node in The charging and discharging power at any given time, in kW.

[0070] System network loss cost:

[0071] In the formula, This is the network loss cost coefficient, in yuan / (kW·h); To optimize the total cycle time, For the system in The total network loss power at any given time, expressed in kW, is calculated using the following formula:

[0072] in, For the number of lines, The first The line is in Active power and reactive power at any given time This is the voltage at the beginning of the line. This represents the line resistance.

[0073] Virtual inertia control cost

[0074] In the formula, This represents the number of virtual inertia controllers. For the first Adjustment cost coefficient of a virtual inertia controller, unit: yuan / (kg·m²); For the first A virtual inertia controller in The actual virtual inertia value at time t, in kg·m²; For the first A virtual inertia controller in The inertia requirement at any given moment.

[0075] The evaluation model solving module 402 is used to solve the evaluation model based on the minimum inertia requirement using the Grey Wolf algorithm under set constraints, and obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

[0076] The constraints include the following: Power balance constraint is a crucial condition for the operation of a power system. Specifically, the active and reactive power of the system must remain balanced at any given time to ensure the stable and reliable operation of the entire power system. Its specific expression is as follows:

[0077]

[0078] In the formula, The first The active and reactive power regulation of a virtual inertia controller; The first The active and reactive power of each load node; These are the reactive power outputs from new energy sources and energy storage, respectively.

[0079] Regarding frequency safety constraints, in low-inertia systems, frequency fluctuations must be strictly controlled within permissible ranges. Simultaneously, constraints on the rate of frequency change must also be fully considered.

[0080] In the formula, for The system frequency at any given time, These are the minimum and maximum allowable frequencies (usually 49.5Hz and 50.5Hz). This is the maximum permissible rate of frequency change (typically 0.5 Hz / s for low inertia systems). The relationship between system frequency and equivalent inertia is described by the rotor motion equations:

[0081] In the formula, for Equivalent inertia of the system at any given moment These are the system input and output active power, respectively. The damping coefficient; The rated frequency is 50Hz. For a moment System frequency (real-time operating frequency of the system).

[0082] Voltage safety constraints are a crucial limiting condition in the operation of power systems. Specifically, the voltage at each node must always be maintained within the allowable deviation range specified by the rated voltage to ensure the stable and reliable operation of the entire power system. This is a key requirement for ensuring the normal operation of power equipment and the quality of power supply.

[0083] In the formula, For the first Each node Voltage at time, These are the minimum and maximum allowable node voltages, respectively (typically taken as 0.95U and 1.05U, where U is the rated voltage).

[0084] Energy storage operation constraints: Charge and discharge power constraints:

[0085] In the formula, These are the maximum charging and discharging power of the energy storage, respectively. The first One energy storage node in The charging and discharging power at any given time, measured in kW.

[0086] Regarding virtual inertia control constraints, it is necessary to consider the detailed results of the inertia demand assessment. Specifically, virtual inertia must satisfy two constraints: demand matching constraints and adjustability constraints.

[0087]

[0088] In the formula, This represents the maximum output inertia of the virtual inertia controller. The maximum adjustment step size for virtual inertia; For the first A virtual inertia controller in The inertia requirement at any given moment; For the first A virtual inertia controller in The actual virtual inertia value at time t, in kg·m².

[0089] The Grey Wolf Optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 by Mirjalili et al., based on the group behavior of grey wolves in nature. Its main advantages are its simple structure, few required parameters, and ease of implementation in a program. In GWO, candidate solutions are represented as individuals within the grey wolf pack, including... , and The leader wolf of the rank, and other ordinary gray wolves. The gray wolves follow a strict hierarchy, such as Figure 2 As shown.

[0090] The entire hunting process of gray wolves can be divided into three stages: encirclement, hunting, and killing. , and Higher-ranking gray wolves have higher fitness values, allowing them to get closer to their prey. Once the prey's location is pinpointed, these higher-ranking wolves will work together to guide other lower-ranking gray wolves. They approach their prey. Due to the prey's escape response, their positions constantly change, so the alpha gray wolves also need to continuously update their positions. Ultimately, the gray wolf pack successfully surrounds the prey and achieves predation. The mathematical model of the gray wolf hunting process is shown below:

[0091]

[0092]

[0093]

[0094]

[0095] In the formula, Indicates the distance between the wolf and its prey; A random value between [0.2]; This represents the number of iterations. Iteration The location of the prey at that time; and These are the positions of the individual wolves at iteration t and iteration t+1, respectively. It is an adaptive vector; and It is a random number between [0,1]. This represents the maximum number of iterations. is the convergence factor.

[0096] Grey Wolf , and Together they determined the ordinary gray wolf Location:

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, , , They are Wolf, wolves and wolves and The distance between the wolves; , ,and They are Wolf, wolves and The wolf's current location; , ,and They represent Wolf Wolf, wolves and The direction and distance the wolf moved; This is the current location of the gray wolves; It's the updated version. The wolf's final location.

[0104] The size is closely related to GWO's search capability, when At times, gray wolf packs expand their search area when hunting prey, achieving a global search to quickly converge on the target area; when At this stage, after locking onto prey, the gray wolf pack narrows its search area before launching an attack, a process known as local search, which results in a relatively slow convergence rate. As the number of iterations increases, the convergence factor of the GWO... It decreases linearly from 2 to 0, but Since the algorithm does not maintain a linear change throughout the convergence process, it may not effectively reflect the actual optimization search process. Therefore, an improved Grey Wolf Optimizer (IGWO) is proposed, which adopts a non-linear form. Improve the search process:

[0105] In the formula, This is a non-linear adjustment coefficient, with a value range of [0,1].

[0106] The process of solving the evaluation model based on minimum inertia requirement using the Grey Wolf algorithm, such as... Figure 3 As shown: First, input the parameters required for each time period in the model and initialize the gray wolf population. Then, calculate the fitness value of each individual gray wolf in the pack, and assign the fitness values ​​of individuals with higher fitness values ​​to specific individuals. Wolf, wolves and Wolves, and update the gray wolf. The system uses the position and related parameters to transmit population information and determine whether the optimal solution has been reached. If an individual that meets the termination condition has reached the optimal solution, the minimum inertia value at that moment can be output; if the optimal solution has not been reached, the fitness value of the individual needs to be adjusted and the iteration continues until the optimal solution is reached. The fitness value here is calculated based on the "system comprehensive operating cost," with lower costs indicating better fitness.

[0107] It should be noted that each module in the low-inertia power distribution system safety operation optimization system of this invention corresponds one-to-one with each step in the low-inertia power distribution system safety operation optimization method in the above embodiments, and their specific implementation processes are the same, so they will not be repeated here.

[0108] This embodiment integrates power disturbance characteristics and frequency safety constraints through an inertia demand assessment model, quantifies and calculates the minimum inertia demand, and allocates it to each virtual inertia controller. This avoids the problem of "rapid fluctuation and large deviation" of frequency under active power disturbances from the source, and reduces the probability of malfunction of protection devices such as low-frequency load shedding / high-frequency tripping. The inertia demand assessment model introduces the power disturbance change rate and disturbance correction coefficient, which can dynamically adjust the inertia demand calculation results according to the actual disturbance magnitude. Compared with the traditional fixed inertia configuration scheme, it is more suitable for the characteristics of low-inertia systems, such as "weak inertia, underdamping, and high uncertainty", and improves the system's response reliability to sudden disturbances.

[0109] The evaluation model based on minimum inertia requirement in this embodiment aims to minimize the total operating cost, taking into account the cost of renewable energy curtailment, energy storage operation, network loss, and virtual inertia control. By precisely configuring inertia, it reduces renewable energy curtailment (lowering curtailment penalties), optimizes energy storage charging and discharging strategies, and reduces system network losses. Compared with the traditional scheduling scheme that prioritizes safety over cost, it significantly reduces the total lifecycle operating expenditure. In solving the evaluation model based on minimum inertia requirement, the Grey Wolf algorithm changes the traditional linear convergence factor to a nonlinear form, improving the accuracy and efficiency of solving the nonlinear constraint model. It can quickly find the "lowest cost operation scheme that meets safety constraints," avoiding the additional cost waste caused by slow algorithm convergence or suboptimal solutions.

[0110] The structure of the electronic device according to an embodiment of the present invention will be described in detail below. Figure 5 This is a schematic diagram of the composition structure of an electronic device provided in an embodiment of the present invention. It can be understood that... Figure 5 The diagram shows only an exemplary structure of the electronic device, not the entire structure. Some or all of the structures shown may be implemented as needed.

[0111] The electronic device provided in this embodiment of the invention includes: at least one processor 501, a memory 502, a user interface 503, and at least one network interface 504. The various components in the low-inertia power distribution system safety operation optimization system are coupled together via a bus system 505. It can be understood that the bus system 505 is used to realize the connection and communication between these components. In addition to a data bus, the bus system 505 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general designated all buses as Bus System 505.

[0112] The user interface 503 may include a monitor, keyboard, mouse, trackball, click wheel, buttons, touchpad, or touch screen.

[0113] It is understood that memory 502 can be volatile memory or non-volatile memory, or both. In this embodiment of the invention, memory 502 is capable of storing data to support the operation of the terminal. Examples of this data include any computer programs used to operate on the terminal, such as operating systems and applications. The operating system includes various system programs, such as framework layers, core library layers, driver layers, etc., used to implement various basic services and handle hardware-based tasks. Applications can include various applications.

[0114] In some embodiments, the power distribution system safety operation optimization system based on low inertia provided in this invention can be implemented using a combination of hardware and software. For example, the power distribution system safety operation optimization system based on low inertia provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the power distribution system safety operation optimization method based on low inertia provided in this invention. For instance, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0115] As an example, processor 501 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., wherein the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0116] As an example of the hardware implementation of the low-inertia-based power distribution system safety operation optimization system provided in this embodiment of the invention, the device provided in this embodiment of the invention can be directly executed by a processor 501 in the form of a hardware decoding processor. For example, it can be executed by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components to implement the low-inertia-based power distribution system safety operation optimization method provided in this embodiment of the invention.

[0117] The memory 502 in this embodiment of the invention is used to store various types of data to support the operation of the low-inertia power distribution system safety operation optimization system, or to store data for execution. Figure 1 The program code for the method shown. Examples of this data include: any executable instructions for operation on a low-inertia-based power distribution system safety operation optimization system, such as executable instructions that can be included in the executable instructions, implementing the low-inertia-based power distribution system safety operation optimization method of the embodiments of the present invention.

[0118] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including functions for executing... Figure 1 The program code for the method shown. In such an embodiment, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by the central processing unit, it performs the various functions defined in the apparatus of this application.

[0119] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0120] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the safe operation of a power distribution system based on low inertia, characterized in that, include: With the goal of minimizing the total system operating cost, an evaluation model based on minimum inertia requirements is constructed. The minimum total system operating cost includes the cost of curtailment of renewable energy, the operating cost of energy storage, the cost of grid loss, and the cost of virtual inertia control. Under the given constraints, the Grey Wolf algorithm is used to solve the evaluation model based on the minimum inertia requirement to obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

2. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 1, characterized in that, The constraints include power balance constraints, frequency safety constraints, voltage safety constraints, energy storage operation constraints, and virtual inertia control constraints.

3. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 1, characterized in that, The expression for the convergence factor is: ； In the formula, The convergence factor; This is a non-linear adjustment coefficient, with a value range of [0,1]. This represents the maximum number of iterations. This represents the current iteration number.

4. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 1, characterized in that, The expression for the cost of virtual inertia control is: ； In the formula, To optimize the total cycle time; Cost of virtual inertia control; This represents the number of virtual inertia controllers. For the first Adjustment cost coefficient of a virtual inertia controller; For the first A virtual inertia controller in The actual virtual inertia value at any given moment; For the first A virtual inertia controller in The inertia requirement at any given moment.

5. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 4, characterized in that, No. A virtual inertia controller in Inertia requirement at time The expression is: ； ； In the formula, For the first Virtual Inertia Controller Actual output at any given moment; for Minimum inertia requirement of the system at any given time; The frequency response time constant; This is the stable frequency value after the disturbance; This is the disturbance correction factor; Frequency safety threshold; The rated frequency; for Maximum power disturbance amplitude at any given moment; The power disturbance change rate is denoted as .

6. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 5, characterized in that, Statistics using the sliding window method Time before The power disturbance characteristics over a given time period, including the maximum disturbance amplitude and the rate of change of disturbance, are expressed by the following formula: ； ； In the formula, They are respectively Real-time system input and output power; The maximum disturbance amplitude at time t; This represents the maximum disturbance amplitude at time t-1.

7. The method for optimizing the safe operation of a power distribution system based on low inertia as described in claim 1, characterized in that, The expressions for the costs of renewable energy curtailment, energy storage operation, and system network losses are as follows: ； ； ； in, Costs of curtailing renewable energy; For energy storage operating costs; For system network loss costs; To optimize the total cycle time; The number of new energy nodes; For the first The curtailment penalty coefficient for each new energy node; For the first A new energy node Maximum transmittable power at any given time; For the first A new energy node Actual output at any given moment; For time step; This refers to the number of energy storage nodes; The first The charging and discharging cost coefficient of each energy storage node; The first One energy storage node in The charging and discharging power at any given moment; For the number of lines, The first The line is in Active power and reactive power at any given time This is the voltage at the beginning of the line. This represents the line resistance.

8. A power distribution system safety operation optimization system based on low inertia, characterized in that, The method for optimizing the safe operation of a power distribution system based on low inertia, as described in any one of claims 1-7, includes: The evaluation model construction module is used to construct an evaluation model based on the minimum inertia requirement with the optimization objective of minimizing the total system operating cost; where minimizing the total system operating cost includes the cost of curtailment of renewable energy, the operating cost of energy storage, the cost of grid loss, and the cost of virtual inertia control; The evaluation model solving module is used to solve the evaluation model based on the minimum inertia requirement under set constraints using the Grey Wolf algorithm to obtain the minimum inertia requirement value at each time step. During the search for the minimum inertia requirement value at each time step, the Grey Wolf algorithm uses a convergence factor to determine the search area. The convergence factor is represented as the fractional power of the ratio of the difference between the maximum number of iterations and the current number of iterations to the maximum number of iterations.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for optimizing the safe operation of a power distribution system based on low inertia as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for optimizing the safe operation of a power distribution system based on low inertia as described in any one of claims 1-7.

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