Dual-high power system day-ahead operation risk assessment method and device considering inertia large-range fluctuation, equipment and medium

Abstract

The invention provides a day-ahead operation risk assessment method, device, equipment and medium for a double-high power system considering large-range inertia fluctuation, and relates to the field of power operation risk assessment, and the method comprises the steps: generating a plurality of wind and light output scenes and corresponding unit start-stop decisions through employing a scene generation model and a random unit combination optimization model; calculating probability distribution of N-1 faults of the equipment in different time periods based on the unit equipment parameters; respectively calculating a minimum inertia demand, a maximum inertia demand, a fixed inertia and an adjustable inertia based on a unit start-stop decision and probability distribution of N-1 faults of equipment in different time periods; and in combination with the fixed inertia, the adjustable inertia, the minimum inertia demand and the maximum inertia demand, an inertia supply shortage risk and an inertia supply excess risk are evaluated. Therefore, the fixed unit attributes and the uncertain fault time are combined, the potential risk of the power system is comprehensively captured, the minimum inertia and the maximum inertia required by the system are accurately calculated, and a scientific basis is provided for reasonable configuration of inertia resources.

Description

Technical Field

[0001] This invention relates to the field of power operation risk assessment technology, and more specifically, to a method, apparatus, equipment, and medium for day-ahead operation risk assessment of high-voltage and high-efficiency power systems that takes into account large-scale fluctuations in inertia. Background Technology

[0002] In the current trend of energy transition, renewable energy is being integrated into the grid on an unprecedented scale, profoundly changing the form and operating characteristics of electricity. Among them, typical renewable energy generating units such as wind and solar power achieve grid connection through power electronic devices and employ maximum power point tracking (MPPT) control strategies. However, this model typically prevents them from actively responding to frequency changes with inertia, unlike traditional power generation equipment. As the proportion of these new energy units in the grid continues to rise, inertia is gradually decreasing, making frequency stability issues increasingly severe and becoming a key hidden danger affecting the reliable operation of electricity. In recent years, large-scale blackouts caused by insufficient equivalent inertia have occurred frequently, such as the "9.28" blackout in Australia and the "8.9" blackout in the UK, sounding the alarm for the safe and stable operation of electricity. Meanwhile, the day-ahead phase occupies a crucial position in power operation and dispatch; however, most current analytical methods rely only on deterministic data in the present or short term, failing to adapt to the complex and ever-changing actual operating environment. Furthermore, existing research largely focuses on the hazards of low inertia and the assessment of minimum inertia requirements under high penetration rates of new energy sources.

[0003] To address the issue of inertia reduction, existing research has attempted to leverage additional control measures to encourage renewable energy units to participate in inertia response. However, because the inertia characteristics of renewable energy units differ from the inherent inertia of traditional generator rotors, and significant differences exist between various control strategies, this poses a significant challenge to frequency stability under disturbance conditions. It can not only interfere with the normal operation of protection devices based on the rate of change of frequency (RoCoF), but may also trigger unexpected power generation trips or load shedding.

[0004] In terms of inertia risk assessment, there is currently a lack of effective theories and methods for dealing with the multiple uncertainties of the day-ahead environment. Technically, basic inertia is mainly provided by synchronous generators such as thermal power plants, and its supply capacity is closely related to the unit's operating status. However, the start-up and shutdown process of thermal power plants is extremely time-consuming, taking several hours or even longer. This makes it difficult for dispatchers to mitigate risks by quickly adjusting the start-up methods of thermal power plants if inertia exceeds limits during the day-ahead real-time phase; they can only passively bear the risk. This contradiction between the lag in supply-side adjustment and the rapid changes in demand indicates that inertia risk management needs to be moved forward to the day-ahead dispatching stage. It requires a comprehensive consideration of various uncertainties such as load forecasting, renewable energy output forecasting, and unit maintenance plans, assessing the balance of inertia supply and demand under various possible operating scenarios, and planning dispatching strategies in advance.

[0005] Furthermore, with the large-scale investment in grid-connected equipment and the dramatic fluctuations in the output of high-proportion renewable energy sources, future inertia not only faces the risk of low inertia but may also face the risk of short-term excessive inertia. Excessive inertia will also impair dynamic stability and operational performance, and there is currently no effective solution to this problem.

[0006] Currently, the region faces multiple uncertainties, including errors in new energy output forecasting, deviations in load forecasting, and unplanned equipment outages. These uncertainties are coupled in time and space, jointly affecting the inertia supply and demand balance of the system. Specifically, the inertia risk of new power systems actually stems from two levels: first, the "slow" uncertainty arising from wind and solar power output and load forecasting errors, which determines the available inertia supply of the system at any given time; and second, the "fast" uncertainty arising from random line or unit failures, which determines the inertia demand required by the system under different operating modes. Therefore, how to accurately assess and effectively manage inertia fluctuation risks in the day-ahead phase has become a key technical issue in ensuring the safe and stable operation of high-proportion renewable energy power systems. Summary of the Invention

[0007] Given that existing technologies cannot effectively address the inertia risk assessment problem caused by dual-layer uncertainties, cannot achieve a unified assessment of the dual risks of insufficient and excessive inertia, and cannot provide scientific decision support for day-ahead scheduling, in conjunction with the first aspect of this invention, embodiments of this invention provide a day-ahead operation risk assessment method for high-inertia and high-voltage power systems considering large-scale inertia fluctuations, the method comprising:

[0008] By using a scenario generation model and a random unit combination optimization model, multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions are generated.

[0009] Based on the unit equipment parameters, the probability distribution of N-1 faults of the equipment in different time periods is calculated through time series decomposition and dynamic state space modeling.

[0010] Based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints, the minimum inertia requirement and the maximum inertia requirement are calculated respectively.

[0011] Based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods, the fixed inertia and the adjustable inertia are determined.

[0012] By combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, a progressive index system is used to assess the risks of insufficient inertia supply and excessive inertia supply.

[0013] As one possible implementation, the method further includes:

[0014] Combining the risks of insufficient inertia supply and excessive inertia supply, the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement are adjusted to output the safe operating range of inertia under different confidence levels.

[0015] As one possible implementation, the method further includes:

[0016] The Gurobi solver was used to verify and evaluate the risks of insufficient inertia supply and excessive inertia supply.

[0017] As one possible implementation, the method of generating multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions using a scenario generation model and a random unit combination optimization model includes:

[0018] Input the day-ahead forecast data of wind and solar power output into the scenario generation model to generate day-ahead forecast data that takes into account the error sequence, which can be used as multiple scenarios;

[0019] Using a stochastic unit combination optimization model, unit combination optimization analysis is performed for different scenarios to generate unit start-up and shutdown decisions. The stochastic unit combination optimization model is trained with the goal of minimizing the expected total operating cost under each scenario. The unit start-up and shutdown decisions include unit start-up and shutdown status, set of units in operation, continuous start-up / stop time, minimum start-up and stop time, unit active power output, and ramp rate.

[0020] As one possible implementation, the step of calculating the probability distribution of N-1 faults of the equipment in different time periods based on unit equipment parameters, through time-series decomposition and dynamic state-space modeling, includes:

[0021] System running state set Represented as:

[0022] ;

[0023] in, Indicates normal operating status. Indicates equipment The state of N-1 fault occurrence A collection of key equipment;

[0024] Calculate the probability of the normal operating state occurring. And the probability of N-1 failures occurring for:

[0025] ;

[0026] ;

[0027] in, For equipment Forced shutdown rate For equipment Forced shutdown rate;

[0028] Calculation Fault During the period Conditional probability of occurrence for:

[0029] ;

[0030] Where t is the time period. Total number of runtime segments;

[0031] Calculation Fault During the period Joint probability of occurrence for:

[0032] ;

[0033] in, Let N-1 be the probability of an N-1 fault occurring. For fault During the period The conditional probability of occurrence For equipment Forced shutdown rate For equipment Forced shutdown rate Total number of runtime segments;

[0034] Construct the system instantaneous state set for time period t for:

[0035] ;

[0036] in, This indicates that device i is in normal operating condition during time period t. This indicates that device i is in an N-1 fault state during time period t;

[0037] calculate Time is state probability for:

[0038] ;

[0039] in, For equipment When the time comes The cumulative probability of failures that have occurred so far. For equipment Forced shutdown rate For equipment The forced shutdown rate, where t is the time period. Total number of runtime segments;

[0040] calculate The probability of being in normal operating state S0 at any given time. for:

[0041] ;

[0042] in, This represents the probability of the normal operating state occurring. Let t represent the probability of an N-1 fault occurring, and t be the time interval. This represents the total number of runtime segments. For equipment Forced shutdown rate.

[0043] As one possible implementation, the calculation of minimum and maximum inertia requirements based on the unit start-up / shutdown decisions and the probability distribution of N-1 faults in the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints, includes:

[0044] The lower limit of inertia requirement is calculated based on the frequency change rate and the minimum frequency point constraint.

[0045] By equating new energy power plants and energy storage power stations at maximum load to thermal power units, the maximum inertia requirement is derived by calculating the inertia provided by existing thermal power and equivalent thermal power.

[0046] As one possible implementation, the combination of the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, through a progressive index system, assesses the risks of insufficient inertia supply and excessive inertia supply, including:

[0047] By combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, risk status is identified, including safe operation status, insufficient supply risk status, and oversupply risk status.

[0048] Based on the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, the inertia over-limit risk and extreme probability risk corresponding to the risk state are quantified.

[0049] Secondly, embodiments of the present invention provide a day-ahead operation risk assessment device for high-voltage power systems that considers large-scale fluctuations in inertia, the device comprising:

[0050] The start-stop decision module is used to generate multiple wind and solar power output scenarios and their corresponding unit start-stop decisions by using the scenario generation model and the random unit combination optimization model.

[0051] The probability calculation module is used to calculate the probability distribution of N-1 faults of the equipment in different time periods based on the unit equipment parameters, through time series decomposition and dynamic state space modeling.

[0052] The inertia extreme value calculation module is used to calculate the minimum inertia requirement and the maximum inertia requirement based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints.

[0053] The inertia adjustment calculation module is used to determine the fixed inertia and adjustable inertia based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods.

[0054] The risk assessment module is used to assess the risks of insufficient inertia supply and excessive inertia supply by combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement through a progressive indicator system.

[0055] Thirdly, embodiments of the present invention provide an electronic device, comprising:

[0056] One or more processors;

[0057] Storage device, on which one or more programs are stored,

[0058] When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in the first aspect.

[0059] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by one or more processors, implements the method described in the first aspect.

[0060] The embodiments of the present invention provide a method, apparatus, equipment, and medium for assessing the day-ahead operation risk of high-voltage power systems considering large-scale fluctuations in inertia. First, multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions are generated using a scenario generation model and a stochastic unit combination optimization model. Then, based on unit equipment parameters, the probability distribution of N-1 faults in different time periods is calculated through time-series decomposition and dynamic state-space modeling. Next, based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults in different time periods, combined with frequency safety constraints and dynamic stability constraints, the minimum inertia requirement and the maximum inertia requirement are calculated respectively. Furthermore, based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults in different time periods, fixed inertia and adjustable inertia are determined. Finally, combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, a progressive index system is used to assess the risk of insufficient inertia supply and the risk of excessive inertia supply.

[0061] This systematic assessment approach effectively addresses inertia risk under dual-layer uncertainty environments. It not only achieves a unified assessment of both insufficient and excessive inertia risks but also provides scientific decision support for day-ahead dispatching. This method fully considers various uncertainties such as load forecasting, renewable energy output forecasting, and unit maintenance plans, enabling accurate assessment of inertia supply and demand balance under various possible operating scenarios. Furthermore, the progressive indicator system design makes the risk assessment results more intuitive and reliable, helping dispatchers to formulate reasonable dispatching strategies in advance, thereby effectively improving the frequency stability and dynamic performance of the power system and ensuring the safe and stable operation of the power grid. Attached Figure Description

[0062] Figure 1 This is an exemplary architecture diagram in which an embodiment of the present invention can be applied;

[0063] Figure 2A This is a flowchart of an embodiment of the day-ahead operation risk assessment method for high-inertia power systems that considers large-scale fluctuations in inertia provided by the present invention;

[0064] Figure 2B This is a diagram showing the fluctuation and prediction error of new energy output in one embodiment of the risk assessment method for day-ahead operation of a high-energy-density power system considering large-scale fluctuations in inertia provided by the present invention.

[0065] Figure 2C This is a schematic diagram illustrating the inertia supply-demand imbalance risk of one embodiment of the day-ahead operation risk assessment method for high-inertia power systems considering large-scale fluctuations in inertia provided by the present invention.

[0066] Figure 2D This is a risk value diagram of an embodiment of the day-ahead operation risk assessment method for high-inertia power systems considering large-scale fluctuations in inertia provided by the present invention.

[0067] Figure 2E This is a flowchart of a risk assessment method for day-ahead operation risk assessment of a high-inertia power system considering large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0068] Figure 3 This is a schematic diagram of the equivalent model of the actual DC receiving end system of an embodiment of the risk assessment method for day-ahead operation of a high-voltage power system considering large-scale fluctuations in inertia provided in this invention.

[0069] Figure 4 This is a DC transmission plan diagram of the day-ahead operation risk assessment method for high-voltage and high-efficiency power systems that considers large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0070] Figure 5 This is a diagram showing the day-ahead load forecast results of the day-ahead operation risk assessment method for high-inertia and high-voltage power systems, which takes into account large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0071] Figure 6 This is a clustering scenario diagram of wind and solar power reduction for a day-ahead operation risk assessment method for high-voltage and high-efficiency power systems that considers large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0072] Figure 7 This is a heatmap of the first-level risk indicators of the day-ahead operation risk assessment method for high-inertia and high-voltage power systems that takes into account large-scale fluctuations in inertia, provided in this embodiment of the invention.

[0073] Figure 8 This is a time-series diagram showing the minimum inertia requirement under different system operating states of the daytime operation risk assessment method for high-voltage and high-efficiency power systems that considers large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0074] Figure 9 This is a diagram showing the minimum inertia requirement and the lower limit of the final safe range after different scenarios for the day-ahead operation risk assessment method of the dual-high power system considering large-scale fluctuations in inertia, provided in the embodiments of the present invention.

[0075] Figure 10 This is a comparison chart of the number of generating units in operation and the fixed inertia supply in the day-ahead operation risk assessment method for high-voltage power systems that considers large-scale fluctuations in inertia provided in this embodiment of the invention.

[0076] Figure 11 This is an adjustable inertia influence diagram of the day-ahead operation risk assessment method for high-voltage power systems that considers large-scale fluctuations in inertia, provided in an embodiment of the present invention.

[0077] Figure 12 This is a comparison chart of the minimum inertia supply of a power system with different adjustable equipment ratios, based on an embodiment of the risk assessment method for day-ahead operation of a high-voltage power system considering large-scale fluctuations in inertia provided by this invention.

[0078] Figure 13 This is a schematic diagram of a structure of an embodiment of the day-ahead operation risk assessment device for high-inertia power systems that considers large-scale fluctuations in inertia provided in this invention.

[0079] Figure 14 This is a schematic diagram of the structure of a computer suitable for implementing embodiments of the present disclosure. Detailed Implementation

[0080] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0081] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0082] Figure 1 An exemplary architecture 100 is shown, illustrating an embodiment of the present invention’s method, apparatus, electronic equipment, and storage medium for assessing day-ahead operation risk of high-inertia power systems considering a wide range of inertia fluctuations.

[0083] like Figure 1 As shown, architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0084] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as risk assessment applications, voice recognition applications, short video social applications, audio and video conferencing applications, live video streaming applications, document editing applications, input method applications, web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0085] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc. When terminal devices 101, 102, and 103 are software, they can be installed on the terminal devices listed above. They can be implemented as multiple software programs or software modules (e.g., used to provide risk assessment services) or as a single software program or software module. No specific limitations are imposed here.

[0086] In some cases, the day-ahead operation risk assessment method for high-voltage power systems considering large-scale inertia fluctuations provided by this invention can be executed by terminal devices 101, 102, and 103. Correspondingly, the day-ahead operation risk assessment device for high-voltage power systems considering large-scale inertia fluctuations can be installed in terminal devices 101, 102, and 103. In this case, architecture 100 may not include server 105.

[0087] In some cases, the day-ahead operation risk assessment method for high-voltage power systems considering large-scale inertia fluctuations provided by this invention can be jointly executed by terminal devices 101, 102, and 103 and server 105. For example, the step of "generating multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions using a scenario generation model and a random unit combination optimization model" can be executed by terminal devices 101, 102, and 103, and the step of "assessing the risk of insufficient inertia supply and the risk of excessive inertia supply by combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement through a progressive index system" can be executed by server 105. This invention does not limit this. Correspondingly, the day-ahead operation risk assessment device for high-voltage power systems considering large-scale inertia fluctuations can also be respectively set in terminal devices 101, 102, 103, and server 105.

[0088] In some cases, the day-ahead operation risk assessment method for high-voltage power systems considering large-scale fluctuations in inertia provided by the present invention can be executed by server 105. Correspondingly, the day-ahead operation risk assessment device for high-voltage power systems considering large-scale fluctuations in inertia can also be set in server 105. In this case, architecture 100 may not include terminal devices 101, 102, and 103.

[0089] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (for example, used to provide distributed services), or as a single software program or software module. No specific limitations are made here.

[0090] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0091] Continue to refer to Figure 2A The document illustrates a flowchart 200 of an embodiment of a day-ahead operation risk assessment method for high-voltage power systems considering large-scale inertia fluctuations according to the present invention. This method includes the following steps:

[0092] Step S101: Using the scenario generation model and the random unit combination optimization model, generate multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions.

[0093] To address the inherent operational uncertainties in high-voltage and high-efficiency power systems, this embodiment constructs a multi-scenario stochastic unit commitment (UC) model. A scenario generation and reduction technique combining time series models and clustering algorithms is employed to describe the stochastic fluctuation characteristics of wind and solar power output and their day-ahead prediction errors.

[0094] First, an Auto-Regressive Moving Average (ARMA) model is used to model the day-ahead forecast error series of wind and solar power output. The ARMA model effectively captures the inherent autocorrelation and dynamic characteristics of time-series data, thereby generating a large number (e.g., 1000 sets) of random scenarios that retain the statistical properties of the original data. Each generated scenario represents a possible all-day wind or solar power output curve.

[0095] However, directly introducing massive amounts of scenarios into the unit combination optimization model can lead to the "curse of dimensionality," making problem-solving extremely difficult or even infeasible. Therefore, the generated scenario set must be reduced to preserve the uncertainty information inherent in the original scenario set to the greatest extent possible while ensuring computational feasibility. Optionally, this embodiment can use the k-means clustering algorithm to implement scenario reduction, dividing a large number of scenarios into a specified number of categories by minimizing the sum of squared errors within clusters.

[0096] Specifically, firstly, the day-ahead forecast data of wind and solar power output is input into the scenario generation model to generate day-ahead forecast data that takes into account the error sequence, serving as multiple scenarios.

[0097] In clustering algorithms, the corresponding probability can be calculated based on the number of original scenes contained in each cluster:

[0098]

[0099] in, For scenario probabilities, For the first A cluster, This represents the total number of original scenes.

[0100] Next, using a stochastic unit combination optimization model, unit combination optimization analysis is performed for different scenarios to generate unit start-up and shutdown decisions.

[0101] The stochastic unit combination optimization model is a multi-scenario stochastic programming model. Its training objective is to minimize the expected total operating cost under each scenario. Inputs include nine combined wind and solar power output scenarios, thermal power unit parameters (such as output limits and ramp rates), energy storage parameters (such as rated capacity and charge / discharge efficiency), and load forecast data. The output is a unified unit start-up and shutdown decision across all scenarios, which not only determines the fixed inertia supply but also provides an operational basis for lower-level risk assessment. A constraint system is set for the stochastic unit combination optimization model, defining insurmountable boundaries for the model from multiple dimensions to ensure that the model output (such as unit start-up and shutdown plans and inertia ranges) meets the actual power operation requirements.

[0102] The unit start-up and shutdown decision includes data such as unit start-up and shutdown status, set of units in operation, continuous start-up / shutdown time, minimum start-up and shutdown time, unit active power output, and ramp rate. Specific outputs can be set as needed and are not limited here.

[0103] The constraint system that is strictly satisfied at each time section and scenario is shown in the following formula. The constraint system of this model directly determines the "fixed inertia supply" - the start-up and shutdown state of the thermal power unit (determined by the minimum start-up and shutdown time, output constraints, etc.) is the core source of fixed inertia. If the constraints are relaxed (such as ignoring the ramp rate), it may lead to unreasonable start-up and shutdown plans of the unit, which in turn will cause the subsequent inertia supply calculation to deviate from reality.

[0104] (1) Safety operation constraints of thermal power units are used to avoid over-generation / under-generation, power surges (damage to equipment), and frequent start-stop (increase losses) of thermal power units, and to ensure the safe and stable operation of thermal power equipment.

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111]

[0112] in, , , , , , , These are identifiers for time period, thermal power unit, wind farm, photovoltaic farm, nodal load, energy storage, and real-time scenario, respectively. , , , , , , These are collections of time periods, thermal power units across the entire system, wind farms, photovoltaic power plants, nodal loads, energy storage, and scenarios. , These are binary variables representing the start-up and shutdown of unit g during time period t. When they are equal to 1, it indicates that unit g is started up or shut down during time period t, respectively. This refers to the active power output of thermal power unit g during the daytime period t. , These refer to the upward and downward adjustment of reserve capacity for unit g during the daytime period t. The active power output of thermal power unit g during time period t under a specified scenario s.

[0113] in, , These are the upper and lower limits of the active power output of thermal power unit g; , These are the upper and lower limits for adjusting the reserve capacity of unit g, respectively. , These represent the maximum upward and downward ramp rates of thermal power unit g, respectively. , These represent the minimum continuous start-up and shutdown times for thermal power unit g, respectively.

[0114] (2) Power balance constraint is used to ensure that the power generation is matched with the load demand at any time and under any wind and solar scenario, so as to avoid power deficit (leading to voltage / frequency fluctuations) or surplus (wasting electricity).

[0115]

[0116] (3) Energy storage operation constraints are used to avoid overcharging (damaging the battery), over-discharging (shortening the lifespan), and simultaneous charging and discharging (logical contradiction) of energy storage, and to ensure that energy storage participates in scheduling according to engineering characteristics.

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] in, , These are the charge of energy storage k at time t and the rated charge of energy storage k, respectively. , These represent the maximum charging and discharging power of energy storage k; , These are the charging and discharging indicators of energy storage k at time t, with 1 indicating that energy storage k is in a charging or discharging state at time t; Energy storage k at time t ; , They are respectively The upper and lower limits.

[0125] The above steps output a unified unit start-up and shutdown decision across all scenarios, which not only determines the system's fixed inertia supply but also provides an operational basis for lower-level risk assessment, constructing a multi-scenario stochastic unit commitment (UC) model. A scenario generation and reduction technique combining time series models and clustering algorithms is employed to describe the stochastic fluctuation characteristics of wind and solar power output and their day-ahead prediction errors, generating a day-ahead scheduling scheme that balances economic efficiency and robustness.

[0126] The reduced scenarios were used in subsequent unit combination optimization analysis. The stochastic unit combination optimization model aims to minimize the expected total operating cost under each scenario at each time segment. and scene The strictly satisfied constraint system is shown in Appendix A. This optimization layer outputs a unified unit start-up and shutdown decision across all scenarios. This not only determined the fixed inertia supply of the system, but also provided an operational basis for lower-level risk assessment.

[0127] Step S102: Based on the unit equipment parameters, calculate the probability distribution of N-1 faults of the equipment in different time periods through time-series decomposition and dynamic state-space modeling.

[0128] Based on the operating scheme determined by the unit combination, a refined operating state model is constructed. The system's inertia risk under operational uncertainty is assessed by deterministically enumerating all possible operating states through a priori probabilistic modeling methods.

[0129] First, consider the N-1 failure scenarios for all critical devices in the system, and the system operating state set. Represented as:

[0130]

[0131] in, Indicates normal operating status. Indicates equipment The state of N-1 fault occurrence It is a collection of key equipment, such as wind turbines, energy storage, and power transmission lines.

[0132] Then, the probabilities of the normal operating state and the state with N-1 faults are calculated as follows:

[0133]

[0134]

[0135] in, For equipment The forced outage rate is an inherent parameter reflecting the probability of equipment failure, obtained from statistics from equipment manufacturers or power grid operation and maintenance data.

[0136] Then, for each N-1 fault, a time-segment probabilistic model is performed based on a preset number of runtime segments (e.g., 24). Assuming the probability of a fault occurring within each time segment follows a uniform distribution, then the fault... During the period The conditional probability of occurrence is:

[0137]

[0138] in, This represents the total number of runtime segments.

[0139] Fault During the period The joint probability of occurrence is:

[0140]

[0141] Finally, taking into account the coupling relationship between equipment type and fault period, a set of instantaneous system states for period t is constructed:

[0142]

[0143] in, This indicates that device i experiences an N-1 fault during time period t.

[0144] Assuming the fault is permanent and its condition persists until the end of the cycle, the equipment is taken into account. When the time comes The cumulative probability of failures that have occurred so far. Time is state The probability is:

[0145]

[0146] The probability of being in normal operating condition at any given time includes two parts: 1. the probability of no failure throughout the entire cycle; 2. the probability of a failure that has not yet occurred. Therefore, the probability of normal operating condition S0 is... for:

[0147]

[0148] For each given system operating state in the state space Based on the established computational framework, the corresponding inertia demand curve and inertia supply curve can be obtained respectively. By comparing and analyzing the relative positions of the two, it can be determined whether there is a risk of inertia exceeding the limit under each system state, thus providing a quantitative basis for the system inertia safety assessment.

[0149] By introducing a fault timing awareness mechanism, the N-1 fault of equipment is subdivided into 24-hour time periods, clearly distinguishing the system operating characteristics before and after the fault, thereby assessing the dynamic impact of random faults on system inertia demand. Through scenario analysis and unit combination, the probability distribution characteristics of available system inertia supply caused by source-load uncertainty are accurately captured. Using the unit combination scheme determined at the upper layer as input, the focus is on assessing the inertia risk of the system when facing operational uncertainty. A priori probabilistic modeling method is used to deterministically enumerate all possible operating states of the system, calculate the inertia demand boundary under each state, and combine it with the supply interval to ultimately achieve a probabilistic and quantitative assessment of system inertia risk.

[0150] Step S103: Based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints, calculate the minimum inertia requirement and the maximum inertia requirement respectively.

[0151] The system's maximum inertia supply consideration can provide the system with the operating mode of maximum inertia, namely: all wind turbines with grid-connected switching capability are operating in grid-connected mode; all energy storage power stations with grid-connected switching capability are operating in grid-connected mode and provide the system with their maximum inertia.

[0152] The minimum inertia supply for the system is considered in the operating mode that provides the minimum inertia for the system. That is, all wind turbines with grid-connected switching capability operate in grid-connected mode. All energy storage power stations with grid-connected switching capability operate in grid-connected mode, and only grid-connected energy storage provides the system with its maximum inertia.

[0153] Step S1031: Calculate the lower limit of inertia requirement based on the frequency change rate and the minimum frequency point constraint.

[0154] The rate of change of frequency and the constraint of the minimum frequency point are both specific parameters of the start-up and shutdown status of thermal power units in the unit start-up and shutdown decision-making process.

[0155] First, the system frequency characteristics can be represented by the equivalent generator rotor motion equation. The frequency response process described by the equivalent rotor motion equation is as follows:

[0156]

[0157] in, The system inertia is in the form of an inertial constant. Let be the frequency at time t. The system's rated frequency, This represents the per-unit value of the disturbance power.

[0158] Then, at any specific operating time t, the minimum inertia requirement of the system is determined by the frequency safety constraints, and the minimum inertia is calculated based on the rate of change of frequency (RoCoF) constraint and the minimum frequency point constraint respectively:

[0159] 1) The minimum inertia based on RoCoF constraints is:

[0160]

[0161] in, Let t be the minimum inertia requirement of the system based on RoCoF constraints. Let be the maximum power deficit of the system at time t, and be a per-unit value; This is the per-unit value of the maximum allowable rate of change of frequency in the system.

[0162] 2) The minimum inertia based on frequency deviation constraints is:

[0163]

[0164] in, The minimum inertia requirement of the system is expressed in the form of the inertia constant based on the minimum frequency constraint. The time of one frequency modulation operation is expressed in seconds (s). This represents the per-unit value of the primary frequency modulation rate of the system, in units of 1 / s; The system's equivalent damping per unit value is given by the system's damping per unit value. and the per-unit value of the primary frequency regulation coefficient of the load composition; The slope of the load power frequency response curve; This is the per-unit value of the system load power based on the system's rated generating capacity; This is the per-unit value of the minimum frequency limit for the system.

[0165] Finally, for those constrained by both RoCoF and the minimum frequency point, the maximum value of the two is taken as the lower limit of the inertia requirement, and its minimum inertia requirement is:

[0166]

[0167] From a day-ahead scheduling perspective, the system's minimum inertia requirement exhibits significant temporal fluctuations. Within a 24-hour operating cycle, temporal changes in load levels, unit operating modes, and potential failure risks directly impact the lower limit of inertia requirement at each moment.

[0168] In addition, the minimum inertia requirement of the system also exhibits random uncertainty. The minimum inertia requirement of the system varies significantly under different operating modes (such as various N-1 modes) that may exist the next day. Therefore, the inertia requirement assessment based on dynamic frequency constraints needs to incorporate failure probability analysis to balance safety and economy.

[0169] Step S1032: Equivalently convert the new energy power plants and energy storage power plants at maximum load into thermal power units, and calculate the maximum inertia requirement by calculating the inertia provided by existing thermal power and equivalent thermal power.

[0170] First, if the system is equivalent to a second-order system, the formula for calculating the system damping ratio is:

[0171]

[0172] in, The system damping ratio; For system damping; Synchronous angular velocity; This refers to the grid voltage. The equivalent inertia of the system; This is the internal potential of the equivalent power source; This refers to the line reactance.

[0173] As shown in the formula above, excessive system inertia will reduce the damping ratio and increase the risk of oscillations after disturbances. Furthermore, excessive system inertia will slow down the response speed of power adjustment, leading to a longer frequency recovery time, which is detrimental to the system's frequency stability. Moreover, maintaining an unnecessarily high level of inertia will result in significant economic losses. To maintain high inertia, the system requires more traditional generating units to operate online or forces renewable energy equipment to operate in suboptimal conditions, directly leading to increased curtailment of renewable energy. Simultaneously, switching to grid-connected energy storage devices will be forced into a continuous charging and discharging state for extended periods, reducing the lifespan of the energy storage devices. Therefore, it is necessary to set an upper limit for the required system inertia during operation.

[0174] Since there is currently no clear operational standard for the maximum inertia requirement of the system, this study considers calculating the maximum inertia requirement by equating energy storage and new energy power plants with thermal power units. The number of equivalent thermal power units for energy storage and new energy power plants is:

[0175]

[0176] in, The number of equivalent thermal power units at time t; Let be the load of node l at time t; Let g be the power generated by thermal power unit g at time t; , These are sets of thermal power units and nodes, respectively. The most commonly used thermal power units in the system capacity, This is the floor symbol.

[0177] Then, the inertia provided by existing and equivalent thermal power units is calculated to obtain the maximum inertia requirement:

[0178]

[0179] in, Let t be the maximum inertia requirement of the system at time t. , Let g be the inertial constant and capacity of the thermal power unit, respectively. , These are the inertial constant and capacity of the most commonly used thermal power unit m in the system, respectively. The value represents the operating state of thermal power unit g at time t, where 1 indicates operation and 0 indicates non-operation.

[0180] Corresponding to the minimum inertia requirement, the system's maximum inertia requirement also manifests as a dynamically changing upper boundary curve over the operating cycle. This curve is influenced by a combination of factors, including the system's operating state, equipment configuration, and stability margin requirements.

[0181] In this way, by constructing a system inertia demand boundary calculation model, the minimum inertia demand of the system is calculated based on frequency safety constraints, and the maximum inertia demand of the system is calculated based on dynamic stability constraints and the principle of equivalent substitution.

[0182] Step S104: Based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods, determine the fixed inertia and the adjustable inertia.

[0183] In traditional power systems, the physical inertia provided by synchronous generators is relatively stable, and the total system inertia is mainly adjusted slowly by the start-up and shutdown schedules of generators, making its variation pattern relatively predictable and controllable. However, the output of new energy sources exhibits strong randomness and intermittency, such as... Figure 2B As shown, its large-scale integration fundamentally alters the deterministic nature of system inertia supply. Renewable energy output forecasting errors not only directly affect unit combination decisions but also indirectly impact system inertia supply capacity by influencing the operating capacity of conventional units. When actual renewable energy output is lower than the forecast, more conventional unit output is needed to compensate, potentially leading to a higher-than-expected inertia supply capacity; conversely, insufficient inertia supply may occur.

[0184] The total system inertia supply is divided into fixed inertia and adjustable inertia. Fixed inertia is mainly provided by thermal power units that have been scheduled to start operation. Its value is basically fixed after the day-ahead dispatch is determined and cannot be adjusted in real time.

[0185] Based on the characteristics of the equipment, the inertia contribution of the asynchronous motor at the moment of disturbance is extremely small and negligible. Therefore, the inertia supply resources considered in this embodiment include thermal power units, grid-connected energy storage, and grid-connected wind power units. Photovoltaic units, since they do not contain rotating components and have zero equivalent inertia, are not considered for their direct contribution. The system inertia supply at any given time is expressed as:

[0186]

[0187] in, and They are time points A collection of online traditional and new energy generating units; The inertia constant of a traditional unit, It is the equivalent inertia constant of new energy. and Traditional units New energy units exist Contributing effort at all times; and Traditional units New energy units Rated capacity; and The system is divided into fixed inertia and adjustable inertia at time t under scenario s and operating mode k.

[0188] 1) Fixed inertia ( Fixed inertia is primarily provided by the thermal power units already scheduled to start operation. Its value is essentially fixed after the day-ahead dispatch is determined and cannot be adjusted in real time. The calculation formula is:

[0189]

[0190] in, The maximum inertial constant that k can provide for grid-type energy storage; Let k be the capacity of the energy storage. For the scene Unit under operating mode k exist The power-on status at any given time (0 or 1); , These are the predicted power outputs of wind farm w and photovoltaic farm v at time t under scenario s, respectively. , , , , These are respectively a collection of energy storage systems that can be networked and operated, a collection of energy storage systems, thermal power units, wind farms, and photovoltaic power stations.

[0191] 2) Adjustable inertia ( The adjustable inertia is primarily provided by grid-connected energy storage and grid-connected wind turbines, featuring real-time adjustment. Based on the configuration ratio of grid-connected equipment in the system, the inertia supply is divided into boundary scenarios: the maximum adjustable inertia supply considers all wind turbines and energy storage devices with grid-connected / grid-connected switching capabilities operating in grid-connected mode, providing the maximum inertia contribution; the minimum adjustable inertia supply considers all switching-capable devices operating in grid-connected mode, with inertia support provided only by fixed grid-connected equipment. Its adjustment range is... :

[0192]

[0193]

[0194] in, and These represent the upper and lower limits of the adjustable inertia of the system at time t under scenario s and operating mode k. Let w be the equivalent inertial constant of the wind farm station; The proportion of grid-connected turbines in wind farms; This refers to the proportion of wind turbines in a wind farm that have the capability to switch to the grid.

[0195] Corresponding to the inertia demand, the system's inertia supply also exhibits significant uncertainty. The uncertainty in the system's inertia supply mainly stems from the randomness of new energy output, the diversity of equipment operating modes, and the adjustability of the inertia constant of grid-connected energy storage.

[0196] The interleaved characteristics of intervalization and temporal volatility mean that the inertia supply of a high-proportion renewable energy system is neither a deterministic time-series curve in the traditional sense nor a simple static interval, but a dynamic interval that evolves continuously in the time domain.

[0197]

[0198] in, and These represent the upper and lower limits of system inertia supply under scenario s and operating mode k, respectively.

[0199] Thus, an analytical model for the system's inertia supply range is established, dividing the inertia supply into fixed inertia and adjustable inertia, and analyzing the impact of the uncertainty of new energy output on the inertia supply capacity.

[0200] Step S105: Combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, the risk of insufficient inertia supply and the risk of excessive inertia supply are assessed through a progressive index system.

[0201] Based on the above analysis, the current-day system inertia supply and demand balance is facing a coupled challenge of two layers of uncertainty. On the supply side, the strong randomness of new energy output and the limited adjustment capabilities of traditional units combine to make it difficult for inertia supply to respond quickly and accurately to system demand; while on the demand side, load fluctuations and increasingly complex system operation modes jointly drive the dynamic changes in inertia demand.

[0202] When the temporal fluctuations of supply and demand become unfavorably coupled, complex supply-demand mismatch patterns emerge, such as... Figure 2C As shown, since both supply and demand are time-series curves, their crossovers often occur within specific time windows, exhibiting "instantaneous" and "local" characteristics. The system may maintain safe operation for most of the time, but faces the risk of supply and demand imbalance at a specific moment.

[0203] The intermittent fluctuations in renewable energy output and the random failures of system equipment intertwine to constitute the core risk source affecting the safe and stable operation of the system. Accurately identifying, modeling, and quantifying these uncertainties are prerequisites for building an effective risk assessment system.

[0204] The progressive indicator system includes a first-level assessment indicator (certain scenario risk) and a second-level assessment indicator (extreme probability risk).

[0205] First, the system's operating status is divided based on the inertia supply and demand relationship, and risk status is identified.

[0206] 1) Safe operating state: When the adjustment range of the system's inertia supply capacity overlaps with the inertia demand range, the system is in a safe operating state.

[0207]

[0208] 2) Supply Shortage Risk State: When the sum of fixed inertia and maximum adjustable inertia is still insufficient to meet the system's minimum inertia requirement, the system faces the risk of supply shortage.

[0209]

[0210] 3) Oversupply Risk: When the sum of fixed inertia and minimum adjustable inertia still exceeds the system's maximum inertia requirement, the system faces the risk of oversupply.

[0211]

[0212] To accurately quantify the inertia safety risks faced by high-proportion renewable energy power systems, this embodiment constructs a two-layer progressive inertia safety risk assessment framework for the traditional economic-oriented dispatch mode, and provides the inertia supply fluctuation range under different confidence levels, thereby achieving accurate quantification of the inertia risk of high-proportion renewable energy power systems.

[0213] This framework employs a hierarchical and progressive evaluation strategy: the first layer of indicators specifically addresses the uncertainty of system operation status, quantifying the impact of changes in operating modes such as equipment failures on inertial safety; the second layer of indicators further addresses the randomness of renewable energy output, capturing tail risk characteristics under extreme scenarios based on conditional value at risk theory. Through this hierarchical design, orderly decoupling and precise quantification of multiple uncertainties are achieved.

[0214] Based on the first-level evaluation indicators, the risk of inertia exceeding limits in deterministic scenarios is quantified. The first-level evaluation indicators correspond to the uncertainty of the operation mode of the lower-level model, quantifying the risk of inertia exceeding limits in deterministic output scenarios.

[0215] 1. Risk indicators of insufficient inertia supply ( )for:

[0216] This indicator specifically quantifies the expected risk of insufficient inertia supply faced by the system, reflecting the degree of risk that even with maximum inertia supply capacity, the minimum inertia requirement cannot be met:

[0217]

[0218] in, This represents the number of system running states; This represents the probability of the system's operating state. This is due to insufficient inertia under the current operating mode.

[0219] 2. Inertia-based oversupply risk indicators ( )for:

[0220] This indicator specifically quantifies the expected risk of excess inertia supply faced by the system, reflecting the degree of excess that still exceeds the maximum inertia demand even under minimum inertia supply capacity:

[0221]

[0222] in, This represents the excess inertia under the current operating mode.

[0223] 3. The inertia requirement range for deterministic scenarios is as follows:

[0224]

[0225] in, and These represent the upper and lower limits of the inertia requirement at time t under scenario s.

[0226] 4. Based on the first-level risk indicators, the inertia demand correction range that takes into account the uncertainty of the operating mode can be obtained as follows:

[0227]

[0228] in, and These are the upper and lower limits of the inertia requirement at time t under scenario s after correction by the first-level risk indicators.

[0229] The second-level indicators correspond to the uncertainty of renewable energy output, further considering the impact of random fluctuations in renewable energy output based on the first-level indicators. This level of indicators deepens risk assessment from a probability distribution perspective, and uses the Conditional Value at Risk (CVaR) method to focus on capturing extreme events and tail risk characteristics, thereby achieving precise quantification of the systemic risks caused by deviations in renewable energy output.

[0230] In power system operation risk assessment, Value at Risk (VaR) reflects the maximum potential loss the system faces at a certain confidence level. A VaR diagram is shown below. Figure 2D As shown in the figure, As a system risk indicator; Confidence level; Value at risk.

[0231] Conditional Value at Risk (CVaR) reflects the expected extreme loss a system faces at a certain confidence level. The formula for calculating CVaR in discrete scenarios is:

[0232]

[0233] Among them, the optimal solution The objective function value is equal to .

[0234] It should be noted that the optimization process here is not an optimization of the scheduling scheme, but rather a method for calculating risk statistics. Decision variables are introduced. It is a clever transformation proposed by Rockafellar-Uryasev that unifies the original two-step calculation of CVaR (first calculate VaR and then calculate conditional expectation) into a single convex optimization problem.

[0235] Therefore, the second-level risk assessment indicator is defined as:

[0236]

[0237] In the formula, Number of scenarios contributing to new energy; This represents the probability of new energy scenarios.

[0238] In this way, a two-layer uncertainty inertia risk scenario is constructed. The upper layer adopts multi-scenario unit combination optimization to generate day-ahead scheduling scheme, and the lower layer evaluates the inertia adequacy under N-1 random faults based on the fault timing perception mechanism.

[0239] Step S106: Combining the risks of insufficient inertia supply and excessive inertia supply, adjust the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, and output the safe operating range of inertia under different confidence levels.

[0240] Based on the results of the two-layer progressive risk assessment, the system inertia safety fluctuation range The output is based on a calculation mechanism of "critical scenario determination + bidirectional risk superposition", which is implemented in two steps:

[0241] 1. Determining the critical scenario

[0242] By solving the CVaR optimization model in the CVaR calculation formula under discrete scenarios, the α quantile values ​​are obtained. This value divides all scenarios into "tolerable risk zones" (…). "Extreme risk zone" In a discrete scene set, there exists a critical scene. This makes its first-level risk value closest to ,Right now: This scenario represents the worst operating condition boundary that the system can withstand under a given confidence level α. Therefore, its correction requirement range is selected as the benchmark for the safe fluctuation range.

[0243] 2. The dual superposition of extreme risks

[0244] CVaR index and The conditional expected loss for inertia exceeding limits in all super-VaR scenarios was quantified, and these extreme risks were superimposed on the lower and upper bounds of the baseline range. Specifically, the lower bound subtracts the supply shortage risk to cover the additional increase in inertia demand in extreme scenarios, while the upper bound adds the supply surplus risk to accommodate demand reductions under extremely low load conditions. The final output is a safe inertia operating range that can accommodate multiple risk deviations, thus more realistically reflecting the acceptable inertia operating range of the system under uncertain environments.

[0245]

[0246] In the formula, and These are the upper and lower limits of the system inertia safety fluctuation range at time t, respectively. and Critical scenarios The upper and lower limits of the demand range are adjusted at time t.

[0247] In this way, the mechanism enables the final output range to simultaneously reflect both "high-probability conventional risks" (represented through VaR scenarios) and "low-probability extreme risks" (represented through CVaR overlay), providing schedulers with a decision-making basis that balances economy and safety.

[0248] In summary, the inertial operation risk assessment process begins with inputting basic system parameters and assessment boundary conditions. After modeling and reducing the uncertainty of new energy output to obtain multi-scenario unit combination schemes, it enters a three-layer nested loop structure. At each time t, two layers of risk assessment are executed sequentially: the first layer is a traversal... Each operating mode calculates the severity of inertia exceeding limits under specific scenarios and obtains basic risk indicators through probability weighting. and Second level traversal In a new energy power generation scenario, the CVaR method is used to quantify extreme risks based on the first-level interval correction results, and the tail risk index is obtained. and Ultimately, the safe inertia fluctuation range is output through a mechanism of "critical scenario anchoring + two-way risk superposition". The entire process proceeds in stages to complete the risk assessment of inertial operation for the following day, achieving a complete closed loop from uncertainty modeling to risk quantification and safety margin determination. The risk assessment process is as follows: Figure 2E As shown.

[0249] To verify the effectiveness of the above embodiments, the Gurobi solver was used to solve the two-layer uncertainty risk assessment model, and the effectiveness and superiority of the method in this embodiment were verified through multi-dimensional comparative analysis.

[0250] The example uses an equivalent model of a real DC receiving-end system. The method used is to simplify the network to its equivalent form. The simplified network is as follows: Figure 3 As shown. The DC tie line is connected to node 40, with a rated capacity of 8000MW. The DC transmission plan is formulated using the traditional two-stage method. The DC transmission plan and day-ahead load forecast are shown below. Figure 4 , Figure 5 .

[0251] Considering the relatively small proportion of renewable energy generation at the receiving end, some thermal power units and equivalent turbines will be replaced with wind farms of equal capacity. At nodes with large renewable energy installed capacity, 600MW / 600MWh energy storage will be configured, with a charge / discharge efficiency of 0.95, totaling 14 units. Of these, 5 are grid-connected energy storage units, 1 has grid-connected / connected switching capability, and 8 are grid-connected energy storage units. The maximum system load is 70GW. In each wind farm, 30% of the units are grid-connected wind turbines, 20% have grid-connected / connected switching capability, and 50% are grid-connected wind turbines. It is 0.3. The equivalent inertial constant of the wind farm is 0.2. The maximum inertia constant of the grid-type energy storage is taken as 6.2s. The value is taken as 10s. The improved power supply structure is shown in Table 1 below. Maximum frequency change rate of the system. The frequency modulation action time is set to 0.75 Hz / s. The value is set to 0.25s; the system's primary frequency modulation rate. The value is set at 3GW / s; the minimum limit for system frequency. The frequency was set to 49.25Hz. The day was divided into hours, T=24. All calculations were performed on a computer with an AMD Ryzen 9 7945HX processor (2.5GHz) and 16GB of RAM, using Python to call the Gurobi solver.

[0252] Table 1 Power Supply Structure

[0253] Power type Total installed capacity (GW) thermal power 68.21 wind power 31.78 Photovoltaics 2.6 Energy storage 8.4

[0254] In this embodiment of the invention, wind power and photovoltaic scenarios are each clustered and reduced into three representative scenarios. The output probabilities of each wind and photovoltaic scenario are shown in Table 2, and are formed by combining them through Cartesian product. An independent joint operation scenario, Figure 6The day-ahead forecast curves for wind power and photovoltaic power, as well as the typical day-ahead scenario curves after scenario reduction, are presented.

[0255] Table 2 Probability of Wind and Solar Power Output Scenarios

[0256] Scene 1 Scene 2 Scene 3 wind power 0.254 0.444 0.302 Photovoltaics 0.28 0.453 0.267

[0257] Figure 7 A heatmap of the first-level risk indicators is presented. The analysis results show that all nine risk scenarios exhibit significant time-varying characteristics and a hierarchical structure within the 24-hour operating cycle. They are low-risk during the nighttime period (1-6 hours), while the risk impact is significantly enhanced during the daytime operating period (7-24 hours), reflecting the cumulative effect of gradual risks such as operating modes and failures.

[0258] Figure 8 This study demonstrates the temporal variation characteristics of the minimum inertia requirement of the system under normal operation and when wind farm WG4 experiences an N-1 fault at different times in scenario 1. When an N-1 fault occurs at wind farm WG4, the system inertia requirement is higher than under normal operation, showing a slight decreasing trend as the fault occurrence time shifts later. This phenomenon stems from the fault timing characteristics: before the fault, the system operates in normal mode; after the fault, it switches to N-1 operation mode. Therefore, the later the fault occurs, the larger the proportion of normal operation time, and the closer the overall system performance is to the normal operation state. In contrast, in the early fault state, N-1 operation mode dominates all-day operation, increasing system vulnerability and highlighting the risk of insufficient inertia, reflecting the temporal dependence of inertia risk assessment under the N-1 safety criterion.

[0259] Based on the calculation results of the second-level CVaR risk index at different confidence levels at some time points extracted in Table 3, and Figure 9 The demonstration of minimum inertia requirements and the lower limit of the final safe interval after corrections for different scenarios shows that the risk adjustment amount varies under different confidence levels, resulting in corresponding changes in the width and position of the final output interval. Higher confidence levels correspond to larger risk adjustment amounts and wider requirement intervals, providing the system with a more sufficient safety margin; lower confidence levels correspond to smaller risk adjustment amounts and relatively compact requirement intervals, improving economy while ensuring basic safety. This confidence-driven interval adjustment mechanism allows the inertia configuration strategy to be flexibly adjusted according to the system's risk tolerance.

[0260] The CVaR algorithm quantifies the tail risk of uncertainty in renewable energy output. Compared with the first layer of expected risk based on probability weighting, it captures the extreme cases of risk distribution in various output scenarios. The high consistency of the three sets of values ​​in Table 2 verifies the stability and convergence of the CVaR algorithm, providing a reliable quantitative basis for establishing differentiated inertia risk management strategies and optimizing scheduling operations in the power system.

[0261] Table 3. Second-level risk indicators at different confidence levels

[0262] α=0.9 α=0.95 α=0.99 1 0.454792975 0.454803826 0.454909978 5 0.55296616 0.552977784 0.55311301 9 0.003232212 0.003243916 0.003324044 13 0.001371327 0.001374524 0.00141328 17 0.905572122 0.905576157 0.905612508 21 0 0 0

[0263] On the one hand, different new energy output scenarios and different thermal power unit start-up conditions will result in different fixed inertia timing supply intervals. The unit combination results for each scenario and the corresponding fixed inertia timing supply intervals under normal operating conditions are as follows: Figure 10 As shown.

[0264] On the other hand, such as Figure 11 The adjustable inertia provided by new energy sources and energy storage also affects the inertia supply capacity of new energy sources. In the above example, the proportion of grid-type equipment is relatively low, so the system does not have the risk of excessive minimum inertia supply. If all energy storage is set as grid-type energy storage, considering the minimum inertia supply capacity of the system when the proportion of grid-type units in wind farms is 30%, 40%, and 50%, respectively, as follows... Figure 12 As shown, with the increase in the proportion of grid-connected wind turbines, the system's inertia supply capacity continuously improves. When the proportion of grid-connected wind turbines reaches 50%, the system experiences a risk of excessive minimum inertia supply between 2:00-3:00 and after 16:00. If a fault occurs during this period, it may lead to a longer system frequency recovery time and unsatisfactory power quality.

[0265] In summary, this embodiment effectively quantifies the safe operating range of power system inertia under uncertain renewable energy output by constructing a two-layer uncertain inertia risk scenario and introducing an inertia supply fluctuation range assessment method. This method not only considers the dual risks of insufficient and excessive inertia supply but also accurately captures extreme tail risks through Conditional Value at Risk (CVaR), providing a more flexible safety margin for system operation. Practical examples demonstrate that the proposed method accurately reflects the time-varying characteristics and risk accumulation effect of system inertia under different confidence levels, providing a reliable basis for formulating differentiated inertia configuration strategies. Furthermore, by comparing the inertia supply capacity under different grid configuration equipment ratios, the mechanism of system inertia excess risk is revealed, further enhancing the practicality and foresight of risk assessment.

[0266] Further reference Figure 13 As an implementation of the methods shown in the above figures, the present invention provides an embodiment of a day-ahead operation risk assessment device for high-inertia power systems that considers large-scale fluctuations in inertia. This device embodiment corresponds to the method embodiment shown in Figure 2, and the device can be specifically applied to various electronic devices.

[0267] like Figure 13 As shown, the day-ahead operation risk assessment device 1300 for high-inertia power systems considering large-range fluctuations in inertia in this embodiment includes:

[0268] The start-stop decision module 1301 is used to generate multiple wind and solar power output scenarios and their corresponding unit start-stop decisions by utilizing the scenario generation model and the random unit combination optimization model.

[0269] The probability calculation module 1302 is used to calculate the probability distribution of N-1 faults of the equipment in different time periods based on the unit equipment parameters, through time series decomposition and dynamic state space modeling.

[0270] The inertia extreme value calculation module 1303 is used to calculate the minimum inertia requirement and the maximum inertia requirement based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints.

[0271] The inertia adjustment calculation module 1304 is used to determine the fixed inertia and the adjustable inertia based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods.

[0272] The risk assessment module 1305 is used to combine the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, and to assess the risk of insufficient inertia supply and the risk of excessive inertia supply through a progressive index system.

[0273] In this embodiment, the specific processing of the day-ahead operation risk assessment device 1300 for high-inertia power systems with large-scale fluctuations in inertia and the resulting technical effects can be referred to the relevant descriptions of the steps in the corresponding embodiment of Figure 2, and will not be repeated here.

[0274] As one possible implementation, the device 1300 further includes:

[0275] The adjustment module is used to combine the risks of insufficient inertia supply and excessive inertia supply, adjust the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, and output the safe operating range of inertia under different confidence levels.

[0276] As one possible implementation, the device 1300 further includes:

[0277] The verification module is used to verify and evaluate the risks of insufficient inertia supply and excessive inertia supply using the Gurobi solver.

[0278] As one possible implementation, the start / stop decision module 1301 includes:

[0279] The scenario building unit is used to input the day-ahead forecast data of wind and solar power output into the scenario generation model to generate day-ahead forecast data that takes into account the error sequence, which serves as multiple scenarios;

[0280] The start-stop unit is used to perform unit combination optimization analysis for different scenarios using a stochastic unit combination optimization model, and generate unit start-stop decisions. The stochastic unit combination optimization model is trained with the goal of minimizing the expected total operating cost under each scenario. The unit start-stop decisions include unit start-stop status, set of operating units, continuous start / stop time, minimum start-stop time, unit active power output, and ramp rate.

[0281] As one possible implementation, the probability calculation module 1302 includes:

[0282] Operating state unit, used for system operating state set Represented as:

[0283] ;

[0284] in, Indicates normal operating status. Indicates equipment The state of N-1 fault occurrence A collection of key equipment;

[0285] The normal fault calculation unit is used to calculate the probability of the occurrence of normal operating conditions. And the probability of N-1 failures occurring for:

[0286] ;

[0287] ;

[0288] in, For equipment Forced shutdown rate For equipment Forced shutdown rate;

[0289] Conditional probability unit, used to calculate faults During the period Conditional probability of occurrence for:

[0290] ;

[0291] Where t is the time period. Total number of runtime segments;

[0292] Joint probabilistic unit, used to calculate faults During the period Joint probability of occurrence for:

[0293] ;

[0294] in, Let N-1 be the probability of an N-1 fault occurring. For fault During the period The conditional probability of occurrence For equipment Forced shutdown rate For equipment Forced shutdown rate Total number of runtime segments;

[0295] Instantaneous state set unit, used to construct the system instantaneous state set for time period t. for:

[0296] ;

[0297] in, This indicates that device i is in normal operating condition during time period t. This indicates that device i is in an N-1 fault state during time period t;

[0298] The fault probability unit at time t is used to calculate... Time is state probability for:

[0299] ;

[0300] in, For equipment When the time comes The cumulative probability of failures that have occurred so far. For equipment Forced shutdown rate For equipment The forced shutdown rate, where t is the time period. Total number of runtime segments;

[0301] The normal probability unit at time t is used to calculate... The probability of being in normal operating state S0 at any given time. for:

[0302] ;

[0303] in, This represents the probability of the normal operating state occurring. Let t represent the probability of an N-1 fault occurring, and t be the time interval. This represents the total number of runtime segments. For equipment Forced shutdown rate.

[0304] As one possible implementation, the inertia extremum calculation module 1303 includes:

[0305] The lower bound unit is used to calculate the lower bound of inertia requirement based on the frequency change rate and the minimum frequency point constraint.

[0306] The upper limit unit is used to equate new energy power plants and energy storage power stations at maximum load to thermal power units, and to derive the maximum inertia requirement by calculating the inertia provided by existing thermal power and equivalent thermal power.

[0307] As one possible implementation, the risk assessment module 1305 includes:

[0308] The status identification unit is used to identify risk status by combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement. The risk status includes safe operation status, insufficient supply risk status, and oversupply risk status.

[0309] The inertia limit exceedance and extreme probability risk identification unit is used to quantify the inertia limit exceedance risk and extreme probability risk corresponding to the risk state based on the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement.

[0310] It should be noted that the implementation details and technical effects of each module and unit in the device provided in the embodiments of the present invention can be referred to the description of other embodiments of the present invention, and will not be repeated here.

[0311] The following is for reference. Figure 14 It shows a schematic diagram of the structure of a computer 1400 suitable for implementing the electronic device of the present invention. Figure 14 The computer 1400 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0312] like Figure 14 As shown, computer 1400 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 1401, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 1402 or a program loaded from storage device 1408 into random access memory (RAM) 1403. RAM 1403 also stores various programs and data required for the operation of computer 1400. Processing device 1401, ROM 1402, and RAM 1403 are interconnected via bus 1404. Input / output (I / O) interface 1405 is also connected to bus 1404.

[0313] Typically, the following devices can be connected to I / O interface 1405: input devices 1406 including, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, etc.; output devices 1407 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1408 including, for example, magnetic tape, hard disk, etc.; and communication devices 1409. Communication device 1409 allows computer 1400 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 A computer 1400 with various electronic devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0314] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1409, or installed from a storage device 1408, or installed from a ROM 1402. When the computer program is executed by the processing device 1401, it performs the functions defined in the methods of the embodiments of the present invention.

[0315] It should be noted that the computer-readable medium described above in this invention can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device or apparatus, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be executed by instructions, used by a device or apparatus, or used in conjunction with it. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with instructions, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0316] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0317] The aforementioned computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the methods shown in the embodiments and optional embodiments of FIG2.

[0318] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0319] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using dedicated hardware-based implementations that perform the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0320] The units or modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the units or modules do not necessarily limit the specific unit itself.

[0321] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A method for assessing the day-ahead operation risk of a high-voltage, high-efficiency power system considering large-scale fluctuations in inertia, characterized in that, The method includes: By using a scenario generation model and a random unit combination optimization model, multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions are generated. Based on the unit equipment parameters, the probability distribution of N-1 faults of the equipment in different time periods is calculated through time series decomposition and dynamic state space modeling. Based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints, the minimum inertia requirement and the maximum inertia requirement are calculated respectively. Based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods, the fixed inertia and the adjustable inertia are determined. By combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, a progressive index system is used to assess the risks of insufficient inertia supply and excessive inertia supply.

2. The method according to claim 1, characterized in that, The method further includes: Combining the risks of insufficient inertia supply and excessive inertia supply, the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement are adjusted to output the safe operating range of inertia under different confidence levels.

3. The method according to claim 1, characterized in that, The method further includes: The Gurobi solver was used to verify and evaluate the risks of insufficient inertia supply and excessive inertia supply.

4. The method according to claim 1, characterized in that, The scenario generation model and the random unit combination optimization model are used to generate multiple wind and solar power output scenarios and their corresponding unit start-up and shutdown decisions, including: Input the day-ahead forecast data of wind and solar power output into the scenario generation model to generate day-ahead forecast data that takes into account the error sequence, which can be used as multiple scenarios; Using a stochastic unit combination optimization model, unit combination optimization analysis is performed for different scenarios to generate unit start-up and shutdown decisions. The stochastic unit combination optimization model is trained with the goal of minimizing the expected total operating cost under each scenario. The unit start-up and shutdown decisions include unit start-up and shutdown status, set of units in operation, continuous start-up / stop time, minimum start-up and stop time, unit active power output, and ramp rate.

5. The method according to claim 1, characterized in that, The method of calculating the probability distribution of N-1 faults of equipment in different time periods based on unit equipment parameters, through time-series decomposition and dynamic state-space modeling, includes: System running state set Represented as: ； in, Indicates normal operating status. Indicates device The state of N-1 fault occurrence A collection of key equipment; Calculate the probability of the normal operating state occurring. And the probability of N-1 failures occurring for: ； ； in, For equipment Forced shutdown rate For equipment Forced shutdown rate; Calculation Fault During the period Conditional probability of occurrence for: ； Where t is the time period. Total number of runtime segments; Calculation Fault During the period Joint probability of occurrence for: ； in, Let N-1 be the probability of an N-1 fault occurring. For fault During the period The conditional probability of occurrence For equipment Forced shutdown rate For equipment Forced shutdown rate Total number of runtime segments; Construct the system instantaneous state set for time period t for: ； in, This indicates that device i is in normal operating condition during time period t. This indicates that device i is in an N-1 fault state during time period t; calculate Time is state probability for: ； in, For equipment When the time comes The cumulative probability of failures that have occurred so far. For equipment Forced shutdown rate For equipment The forced shutdown rate, where t is the time period. Total number of runtime segments; calculate The probability of being in normal operating state S0 at any given time. for: ； in, This represents the probability of the normal operating state occurring. Let t represent the probability of an N-1 fault occurring, and t be the time interval. This represents the total number of runtime segments. For equipment Forced shutdown rate.

6. The method according to claim 1, characterized in that, Based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints, the minimum inertia requirement and the maximum inertia requirement are calculated respectively, including: The lower limit of inertia requirement is calculated based on the frequency change rate and the minimum frequency point constraint. By equating new energy power plants and energy storage power stations at maximum load to thermal power units, the maximum inertia requirement is derived by calculating the inertia provided by existing thermal power and equivalent thermal power.

7. The method according to claim 4, characterized in that, The combination of the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, through a progressive indicator system, assesses the risks of insufficient inertia supply and excessive inertia supply, including: By combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, risk status is identified, including safe operation status, insufficient supply risk status, and oversupply risk status. Based on the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement, the inertia over-limit risk and extreme probability risk corresponding to the risk state are quantified.

8. A risk assessment device for day-ahead operation of a high-voltage power system considering large-scale fluctuations in inertia, characterized in that, The device includes: The start-stop decision module is used to generate multiple wind and solar power output scenarios and their corresponding unit start-stop decisions by using the scenario generation model and the random unit combination optimization model. The probability calculation module is used to calculate the probability distribution of N-1 faults of the equipment in different time periods based on the unit equipment parameters, through time series decomposition and dynamic state space modeling. The inertia extreme value calculation module is used to calculate the minimum inertia requirement and the maximum inertia requirement based on the unit start-up and shutdown decision and the probability distribution of N-1 faults of the equipment at different time periods, combined with frequency safety constraints and dynamic stability constraints. The inertia adjustment calculation module is used to determine the fixed inertia and adjustable inertia based on the unit start-up and shutdown decisions and the probability distribution of N-1 faults of the equipment at different time periods. The risk assessment module is used to assess the risks of insufficient inertia supply and excessive inertia supply by combining the fixed inertia, the adjustable inertia, the minimum inertia requirement, and the maximum inertia requirement through a progressive indicator system.

9. An electronic device, characterized in that, include: One or more processors; Storage device, on which one or more programs are stored, When the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, It stores a computer program thereon, wherein the computer program, when executed by one or more processors, implements the method as described in any one of claims 1-7.

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