Adjusting power supply and new energy collaborative operation simulation method considering extreme weather scene

Abstract

The invention discloses an adjustment power supply and new energy collaborative operation simulation method, system and device considering an extreme weather scene, and a medium. The method comprises the following steps: constructing an extreme weather scene set; according to the extreme weather scene set, constructing a new energy output model driven by the extreme weather; classifying the adjusting power supplies, establishing an adjusting capability function for each type of resources, and outputting an operation boundary and a response model set of each type of adjusting power supplies; and constructing a scheduling simulation model for cooperative operation of the adjusting power supply and the new energy, and outputting an optimal power scheduling sequence and an executable scheduling strategy set of each adjusting resource. According to the method, a complete chain of scene construction, output modeling, power supply classification and scheduling optimization is formed, the self-adaptability and toughness of the power system in extreme weather can be remarkably improved, and safe, stable and efficient operation of the system under the condition of high-proportion access of new energy is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of collaborative operation technology, and in particular to a simulation method for the collaborative operation of regulating power sources and new energy sources considering extreme weather scenarios. Background Technology

[0002] In recent years, the penetration rate of new energy sources in the power system has been continuously increasing, and renewable energy sources such as photovoltaic and wind power have played an important role in ensuring energy security and promoting low-carbon transformation. However, the output of new energy sources is intermittent and fluctuates, especially under extreme weather conditions such as typhoons, blizzards, continuous rain, and heat waves. In these scenarios, the output of new energy sources deviates significantly from the predicted values, posing serious challenges to the power balance and safe operation of the power system. Existing technologies typically use scenario generation methods based on historical statistics or simple power correction models to simulate the output of new energy sources under extreme weather conditions, but these methods often lack refined modeling of meteorological driving factors and are unable to accurately reflect the impact of extreme weather events of different intensities and durations on the output of new energy sources.

[0003] On the other hand, existing power supply configuration and scheduling methods are mostly focused on normal operating conditions, relying primarily on conventional regulation methods such as pumped storage, batteries, and gas turbines. They lack a tiered response mechanism for extreme weather scenarios and cannot adequately distinguish the capability boundaries of fast-response and slow-response resources, leading to problems such as insufficient reserves, unreasonable start-up and shutdown, and line overload in actual operation. Furthermore, some existing studies only focus on cost minimization, neglecting system risk indicators under extreme weather conditions, making it difficult to guarantee the stability and resilience of the system under large disturbances.

[0004] Therefore, existing technologies urgently need a method that can construct a set of extreme weather scenarios based on meteorological data, establish a refined new energy output model, and combine the differentiated capability functions of fast and slow regulating power sources to construct a collaborative operation simulation method for extreme weather scenarios, so as to achieve comprehensive optimization of cost and risk scheduling and improve the safety and reliability of the power system under extreme conditions. Summary of the Invention

[0005] In view of the aforementioned existing problems, the present invention is proposed.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a simulation method for the coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios, including:

[0008] Construct a set of extreme weather scenarios;

[0009] Based on a set of extreme weather scenarios, construct a new energy output model driven by extreme weather.

[0010] The regulating power sources are classified, and a regulating capacity function is established for each type of resource. The operating boundary and response model set for each type of regulating power source are output.

[0011] A scheduling simulation model for the coordinated operation of regulating power sources and new energy sources is constructed, and the optimal power scheduling sequence and executable scheduling strategy set for each regulating resource are output.

[0012] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, wherein: the construction of the extreme weather scenario set includes,

[0013] Collect multi-source meteorological data for the target area;

[0014] Multi-source meteorological data are preprocessed, and a first-level algorithm is used to generate several representative extreme weather time series.

[0015] An extreme weather scenario set is constructed based on time series data, and each scenario is assigned an occurrence probability and severity index.

[0016] The beneficial effects of the preferred technical solution in the embodiments of this application are as follows: it can comprehensively cover extreme weather conditions of different types and intensities, providing accurate boundary conditions for modeling new energy output and regulating power sources; by introducing probability and severity, it not only improves the realism and representativeness of the scenario, but also provides a basis for risk quantification for subsequent scheduling optimization, thereby improving the robustness and adaptability of the power system under extreme weather conditions.

[0017] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, the step of constructing a new energy output model driven by extreme weather based on a set of extreme weather scenarios includes:

[0018] A photovoltaic power output model is used to map light intensity and temperature into a photovoltaic power curve.

[0019] The wind power output model is used to map wind speed and wind direction into a wind power curve;

[0020] By introducing a correction factor and combining the photovoltaic power curve and the wind power curve, the net load forecast is obtained.

[0021] The corresponding adjustment demand curve is calculated based on the net load forecast results.

[0022] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, the method involves classifying the regulating power sources, establishing a regulating capacity function for each type of resource, and outputting the operating boundary and response model set for each type of regulating power source, including:

[0023] Establish capability functions based on different response categories;

[0024] By introducing derating constraints for power lines under extreme weather conditions, the set of operating boundaries and response models for various types of regulating power sources is output, forming the feasible operating domain of the regulating power source.

[0025] The preferred technical solution in this application has the following advantages: it ensures the immediate adjustment capability of fast-response resources under sudden fluctuations, and also ensures the stability and economy of slow-response resources in long-term operation, thereby forming a complete feasible operating domain and providing a reliable foundation for collaborative scheduling under extreme weather conditions.

[0026] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, the step of constructing a scheduling simulation model for coordinated operation of regulating power sources and new energy sources, and outputting the optimal power scheduling sequence and executable scheduling strategy set for each regulating resource, includes:

[0027] Based on the output trajectory of new energy sources, the demand curve of regulation, and the operating boundary of regulation power sources, a collaborative operation scheduling simulation model is constructed.

[0028] The collaborative operation scheduling simulation model takes the joint minimization of system operating cost and risk indicators under extreme scenarios as the objective function. Under the constraints of power balance, reserve, network power flow and equipment capacity, it solves the optimal power scheduling sequence of each regulating power source under different scenarios.

[0029] Output an executable set of scheduling strategies to verify the synergistic operation effect of regulating power sources and new energy sources under extreme weather scenarios.

[0030] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, the first-level algorithm includes:

[0031] Preprocessing of meteorological time series data from the target area yields a continuous series X. t ;

[0032] Set an extreme threshold τ, when X t When the time exceeds τ, mark the current time as the extreme weather moment;

[0033] Aggregate consecutive time points exceeding extreme thresholds into extreme weather segments, and calculate the duration and intensity of these extreme weather segments;

[0034] All extreme segments are treated as a set of extreme weather scenarios, and probability weights are assigned based on the frequency of occurrence of the extreme segments.

[0035] As a preferred embodiment of the simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios described in this invention, wherein: the new energy output model driven by extreme weather includes:

[0036] Based on the time series of light intensity and ambient temperature in the extreme weather scenario set, and using the photovoltaic module output power model:

[0037]

[0038] Where, η pv Expressed as photoelectric conversion efficiency, G t Let α represent irradiance, A represent the module area, and α represent the irradiance. T Represented as a temperature correction factor, T t Represented as ambient temperature, T ref This is represented as a reference temperature;

[0039] Based on the wind speed and direction sequence in the extreme weather scenario set, and using the wind turbine power curve:

[0040]

[0041] Among them, v t Expressed as wind speed, v in v rated v out P represents the cut-in, rated, and cut-out velocities of the fan. rated This is expressed as rated power;

[0042] Under extreme weather conditions, correction factors are introduced for photovoltaic and wind power output:

[0043]

[0044] Where, γ pv (t) represents the capacity reduction factor that varies with the intensity of extreme weather;

[0045] Based on indicators such as temperature and humidity in extreme weather scenarios, a meteorological-driven power load model is constructed:

[0046]

[0047] in, Represented as the baseline load, β T β H Represented as the meteorological sensitivity coefficient, H t Represented as humidity;

[0048] By combining the output of new energy sources with the load trajectory, the net load forecast result R is obtained. t :

[0049]

[0050] In a second aspect, the present invention provides an electronic device, comprising:

[0051] Memory and processor;

[0052] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of a simulation method for the coordinated operation of power supply and new energy sources considering extreme weather scenarios.

[0053] Thirdly, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the simulation method for coordinated operation of regulating power supply and new energy considering extreme weather scenarios.

[0054] Compared with existing technologies, the beneficial effects of this invention are as follows: The proposed simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios effectively solves the problems of high uncertainty in new energy output, unclear boundaries of regulating power source capacity, and lack of robustness in scheduling strategies under extreme weather conditions, demonstrating significant advantages. By constructing an extreme weather scenario set through a first-level algorithm, the collected multi-source meteorological data is preprocessed and transformed into a continuous time series. Extreme segments are extracted to form a scenario set, which is then assigned probability and severity indices, thereby achieving a quantitative expression of the duration and intensity of extreme weather and providing accurate meteorological driving inputs for new energy output modeling. Based on this, by combining photovoltaic power models, wind power curves, and correction coefficients, meteorological variables such as sunlight, temperature, and wind speed are mapped to new energy output trajectories. Furthermore, a load model driven by temperature and humidity is introduced to obtain net load prediction results, effectively reflecting the combined impact of extreme weather on photovoltaic, wind power, and load, thus improving the accuracy and robustness of output prediction. Furthermore, this invention categorizes regulating power sources into fast-response and slow-response types, establishing capability functions for charging / discharging power constraints, start-up / shutdown times, ramp rates, and reserve regulation for each. Combined with derating constraints under extreme weather conditions, this forms a realistic and credible feasible operating domain, overcoming the "one-size-fits-all" problem of different power source types in existing methods. Finally, based on the output trajectory of new energy sources, the regulation demand curve, and the power source operating boundaries, a collaborative operation scheduling simulation model is constructed. With the goal of jointly minimizing system operating costs and risk indicators under extreme scenarios, the model is solved under power balance, reserve, network flow, and equipment constraints, yielding the optimal scheduling sequences and executable strategy sets for various power sources under different scenarios, achieving a balance between economy and safety. Overall, this invention forms a complete chain of "scenario construction—output modeling—power source classification—scheduling optimization," significantly improving the adaptability and resilience of the power system under extreme weather conditions and ensuring the safe, stable, and efficient operation of the system under conditions of high proportion of new energy access. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of the overall process of a simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios, as described in one embodiment of the present invention. Detailed Implementation

[0057] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0058] Example 1, referring to Figure 1 As an embodiment of the present invention, a simulation method for the coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios is provided, including:

[0059] S1: Construct a set of extreme weather scenarios;

[0060] S2: Based on a set of extreme weather scenarios, construct a new energy output model driven by extreme weather.

[0061] S3: Classify the regulating power sources, establish a regulating capacity function for each type of resource, and output the operating boundary and response model set for each type of regulating power source;

[0062] S4: Construct a scheduling simulation model for the coordinated operation of regulating power sources and new energy sources, and output the optimal power scheduling sequence and executable scheduling strategy set for each regulating resource.

[0063] It should be noted that this invention addresses the problems of insufficient modeling, large prediction bias, and unexecutable scheduling in the coordinated operation of new energy and regulating power sources under extreme weather conditions, and proposes a step-by-step solution. First, S1 constructs an extreme weather scenario set using a first-level algorithm, collects and preprocesses multi-source meteorological data, generates typical time series, and assigns probability and severity indices, improving the accuracy of extreme weather modeling from the source. Second, S2 establishes a new energy output model based on the scenario set, introducing photovoltaic and wind power curves and correction coefficients, and combining parameters such as temperature and humidity to obtain new energy and load trajectories that better match extreme conditions, solving the problem of large prediction errors. Then, S3 divides regulating power sources into fast-response and slow-response categories, constructing capability functions such as state evolution, charging and discharging constraints, start-up and shutdown times, and ramp rates for each, and combining line derating constraints to output the feasible operating domains for each type of power source, addressing the shortcomings of overly idealized regulating models. Finally, S4 establishes a scheduling simulation model based on this, with the goal of jointly minimizing operating costs and risk indicators, and outputs the optimal power scheduling sequence and strategy set for each regulation resource, so as to achieve efficient and robust coordinated operation of new energy sources and regulation power sources under extreme weather conditions.

[0064] Example 2, refer to Figure 1 As an embodiment of the present invention, based on the above embodiment, a simulation method for the coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios is provided.

[0065] In this embodiment of the application, the construction of the extreme weather scenario set in step S1 includes steps A1-A3:

[0066] A1: Collect multi-source meteorological data for the target area.

[0067] A2: Preprocess multi-source meteorological data and use a first-level algorithm to generate multiple representative extreme weather time series.

[0068] A3: Construct a set of extreme weather scenarios based on time series data, and assign probability and severity indicators to each scenario.

[0069] It should be noted that the multivariate meteorological data collected in step A1 includes:

[0070] Basic meteorological elements include temperature, humidity, wind speed, wind direction, air pressure, precipitation, solar irradiance, and cloud cover. Further, it includes meteorological disaster-related indicators such as the frequency of extreme high / low temperature events, rainstorm intensity, strong wind and typhoon path information, snowfall thickness and duration, etc. Historical meteorological anomaly data and real-time forecast data can also be incorporated to construct a comprehensive extreme weather input information set, providing data support for subsequent time series reconstruction and scenario aggregation.

[0071] In this embodiment of the application, the specific implementation of the first-level algorithm in step A2 is as follows:

[0072] A2-1: Preprocessing includes resampling to a uniform step size Δt, linear interpolation or Kalman smoothing for missing measurement points, and correction of obvious outliers using the median / MAD rule, resulting in a continuous, denoised meteorological sequence X. t .

[0073] A2-2: Set an extreme threshold τ, and determine it using the quantile method:

[0074]

[0075] When X t When the time is greater than or equal to τ, time t is marked as the extreme weather time;

[0076] Where T represents the total number of steps, and t represents the time index. Let it be the set of all sequences from t=1 to t=T. Represented as an empirical quantile function, it returns the q-quantile of a sequence, where q represents the quantile level.

[0077] A2-3: Continuously satisfying X t Time points ≥τ are aggregated into the k-th extreme weather segment. calculate:

[0078] Duration L k :

[0079]

[0080] Intensity S k :

[0081]

[0082] When L k <L min When that happens, remove that segment.

[0083] in, This represents the starting step of the k-th segment. L represents the end step of the k-th segment. min This represents the minimum retention time threshold, used to filter out short noise segments.

[0084] A2-4: Transform each segment ε k The corresponding original multi-element trajectories (such as temperature / wind speed / irradiance, etc., captured simultaneously) serve as an extreme weather time series. Several representative extreme weather time series are generated, represented as follows:

[0085]

[0086] Furthermore, the specific implementation of step A3 is as follows:

[0087] First construct the unnormalized weights Normalization is being performed:

[0088]

[0089] Where β represents the weighting index and γ represents the weighting index;

[0090] First, unify the dimensions to [0,1]:

[0091]

[0092] The severity index Severity(ω) is obtained using linear weighting. k ):

[0093]

[0094] When Severity(ω) k When θ1 ≥ θ2, it is judged as a severe scene; when θ1 ≤ Severity(ω) k If Severity(ω) < θ2, it is judged as a moderate scene; if Severity(ω) < θ2, it is judged as a moderate scene. kIf θ < 1, it is judged as a mild scene.

[0095] Where θ1 represents the first threshold, θ2 represents the second threshold, α1, α2, and α3 represent the linear weighting coefficients, and L k S represents the duration of the k-th segment. k Let P be the intensity of the k-th segment. k Let represent the peak value of the k-th segment, and j represent the traversal index when finding the maximum value (traversing all segments / scenes). Represented as the longest duration among all, This represents the strongest intensity among all. It represents the highest peak value among all values.

[0096] In this embodiment of the application, step S2, which involves constructing a new energy output model driven by extreme weather based on a set of extreme weather scenarios, includes steps B1-B4:

[0097] B1: Using a photovoltaic power output model, light intensity and temperature are mapped to a photovoltaic power curve;

[0098] B2: Using a wind power output model, wind speed and wind direction are mapped to a wind power curve;

[0099] B3: Introduce a correction factor and combine the photovoltaic power curve and the wind power curve to obtain the net load forecast;

[0100] B4: Calculate the corresponding adjustment demand curve based on the net load forecast results.

[0101] It should be noted that the specific implementation method of step B1 is as follows:

[0102] Based on the time series of light intensity and ambient temperature in the extreme weather scenario set, and using the photovoltaic module output power model:

[0103]

[0104] Where, η pv Expressed as photoelectric conversion efficiency, G t Let α represent irradiance, A represent the module area, and α represent the irradiance. T Represented as a temperature correction factor, T t Represented as ambient temperature, T ref This is represented as a reference temperature;

[0105] The specific implementation method of step B2 is as follows:

[0106] Based on the wind speed and direction sequence in the extreme weather scenario set, and using the wind turbine power curve:

[0107]

[0108] Among them, v t Expressed as wind speed, v in v rated v out P represents the cut-in, rated, and cut-out velocities of the fan. rated This is expressed as rated power.

[0109] Furthermore, the specific implementation of step B3 is as follows:

[0110] Under extreme weather conditions, correction factors are introduced for photovoltaic and wind power output:

[0111]

[0112] Where, γ pv (t) represents the capacity reduction factor that varies with the intensity of extreme weather;

[0113] Based on indicators such as temperature and humidity in extreme weather scenarios, a meteorological-driven power load model is constructed:

[0114]

[0115] in, Represented as the baseline load, β T β H Represented as the meteorological sensitivity coefficient, H t This is expressed as humidity.

[0116] By combining the output of new energy sources with the load trajectory, the net load forecast result R is obtained. t :

[0117]

[0118] It should be noted that the specific implementation method of step B4 is as follows:

[0119] When R t A value greater than 0 indicates the minimum power that the system needs to be supplied by the regulating power source at time t. The upward regulating power demand curve is represented as:

[0120] D t1 =(1+ρ)max(R) t ,0)

[0121] When R t <0 indicates the minimum power that the system needs to absorb at time t (e.g., for energy storage charging, load boosting, or power limiting), expressed as:

[0122] D t2 =(1+ρ)max(-R) t ,0)

[0123] Where ρ represents the uniform redundancy coefficient ρ=αSeverity(ω) k ), D t1 Represented as an upward adjustment of the power demand curve, D t2 This is represented as a downward adjustment in power demand curve;

[0124] It should be noted that what the system needs to adjust is the remaining power of "load - renewable energy output"; only by directly using net load can the comprehensive impact of renewable energy fluctuations and extreme weather on supply and demand be accurately reflected.

[0125] In this embodiment of the application, step S3, which involves classifying the regulated power sources, establishing a regulation capability function for each type of resource, and outputting the operating boundary and response model set for each type of regulated power source, includes steps C1-C2:

[0126] C1: Establish capability functions based on different response categories;

[0127] C2: Introduces derating constraints for lines under extreme weather conditions, outputs a set of operating boundaries and response models for various types of regulating power sources, forming the feasible operating domain of the regulating power source.

[0128] The specific implementation method of step C1 is as follows:

[0129] If the startup time is ≤60s, the incline rate is ≥20% of the rated rate per minute, and the function is bidirectionally adjustable, then it is classified as a fast response type.

[0130] Conversely, if the threshold is not met, it is considered a slow response class, and the threshold is considered satisfied; if any one of the criteria is not met, it is downgraded to a slow response class.

[0131] The capability function of a fast response class is represented as follows:

[0132]

[0133] The capability function for slow response classes is represented as follows:

[0134]

[0135] in, This represents the upscalability of the fast response class at time t. This represents the down-adjustment capability of the fast response class at time t. This represents the upscalability of a slow-response class at time t. This represents the ability of a slow-response class to be down-adjusted at time t. This represents the maximum discharge power of fast-discharge devices. E represents the maximum charging power of fast-charging devices. t E represents the current energy of fast-moving devices. min E is represented as the lower energy limit.max P represents the upper limit of energy. t P represents the current actual output of the slow-speed unit. min P represents the lower limit of the output of the slow-speed unit. max The upper limit of the slow-speed unit output is represented by RU, which represents the allowable uphill gradient for this step, and RD represents the allowable downhill gradient for this step.

[0136] It should be noted that the specific implementation method of step C2 is as follows:

[0137] The system receives the severity of the receiving scenario (0-1), the line's rated capacity, the equipment's (fast / slow) rated parameters and current status (energy storage capacity, current unit output), and two adjustment demand curves (upward / downward adjustment demand obtained from net load forecasts).

[0138] Based on the severity of the scenario, the rated transmission capacity of each line is discounted proportionally to obtain the up-and-down transmission margin that the power grid can bear under that scenario (which can be summarized by the entire network or by region).

[0139] Discounts are applied to key equipment parameters based on the severity of the same scenario:

[0140] For fast-response devices, the upper limits of charging and discharging power and energy are discounted; for slow-response devices, the upper limits of output and up / down ramping are discounted. Substituting the discounted parameters into the simplified capacity formula yields the up / down ramping capacity that each device can provide during the current time period.

[0141] For the fast-moving type, the range of power and energy values ​​is given; for the slow-moving type, the range of output and ramp values ​​is given; and the values ​​of "how much can be adjusted up / down at this moment" for all devices are collected to form a set of response models.

[0142] Add up / down the adjustment capabilities of all devices, and then take the minimum value of the up / down load margin of the power grid (after derating) to obtain the upper limit of the up / down adjustment of the entire system in this scenario.

[0143] The two demand curves can be covered by adjusting the upper / lower limits. If the upper limits are not less than the demand, then the time period is feasible in this scenario; otherwise, it is not feasible and additional resources or strategies need to be added.

[0144]

[0145] Along the reserve capacity, it is represented as:

[0146] k dev =1-as

[0147] The execution boundaries and responses of fast response classes:

[0148]

[0149] Slow-response class runtime boundaries and responses:

[0150] P max* =k dev P max ,RU * =k dev RU,RD * =k dev RD

[0151] P min ≤P t ≤P max* ,P t -P t-1 ≤RU * ,P t-1 -P t ≤RD *

[0152]

[0153] The available capacity of the entire system is represented as:

[0154]

[0155] The feasible operating domain is represented as:

[0156]

[0157] In this embodiment of the application, the specific implementation of step S4, which involves constructing a scheduling simulation model for the coordinated operation of regulating power sources and new energy sources, and outputting the optimal power scheduling sequence and executable scheduling strategy set for each regulating resource, is as follows:

[0158] If the severity metric indicates a severe scenario, a larger safety margin is applied; if the severity metric indicates a moderate scenario, a moderate safety metric is applied; if the severity metric indicates a mild scenario, the safety margin is 0. Simultaneously, C2's Jiang Rong is applied to obtain scenario-based capabilities.

[0159] like or At the same time, increase the output or absorption of energy storage / flexible loads (within the upper limit), and activate minimum generation / transferable loads, gradually increase the baseline and ramp of slow-speed units (meet the upper and lower limits and ramp limits), and control purchased power / emergency units / orderly loads, and calculate the high penalty coefficient.

[0160] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0161] Example 3

[0162] The third embodiment of the present invention differs from the first two embodiments in that:

[0163] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0164] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0165] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0166] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0167] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A simulation method for the coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios, characterized in that, include: Construct a set of extreme weather scenarios; Based on a set of extreme weather scenarios, construct a new energy output model driven by extreme weather. The regulating power sources are classified, and a regulating capacity function is established for each type of resource. The operating boundary and response model set for each type of regulating power source are output. A scheduling simulation model for the coordinated operation of regulating power sources and new energy sources is constructed, and the optimal power scheduling sequence and executable scheduling strategy set for each regulating resource are output.

2. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 1, characterized in that, The construction of an extreme weather scenario set include, Collect multi-source meteorological data for the target area; Multi-source meteorological data are preprocessed, and a first-level algorithm is used to generate several representative extreme weather time series. An extreme weather scenario set is constructed based on time series data, and each scenario is assigned an occurrence probability and severity index.

3. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 2, characterized in that, The construction of a new energy output model driven by extreme weather, based on a set of extreme weather scenarios, includes: A photovoltaic power output model is used to map light intensity and temperature into a photovoltaic power curve. The wind power output model is used to map wind speed and wind direction into a wind power curve; By introducing a correction factor and combining the photovoltaic power curve and the wind power curve, the net load forecast is obtained. The corresponding adjustment demand curve is calculated based on the net load forecast results.

4. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 3, characterized in that, The process involves classifying the regulating power sources, establishing a regulating capacity function for each type of resource, and outputting a set of operating boundaries and response models for each type of regulating power source, including... Establish capability functions based on different response categories; By introducing derating constraints for power lines under extreme weather conditions, the set of operating boundaries and response models for various types of regulating power sources is output, forming the feasible operating domain of the regulating power source.

5. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 4, characterized in that, The aforementioned construction of a scheduling simulation model for the coordinated operation of regulating power sources and new energy sources outputs the optimal power scheduling sequence for each regulating resource and a set of executable scheduling strategies, including: Based on the output trajectory of new energy sources, the demand curve of regulation, and the operating boundary of regulation power sources, a collaborative operation scheduling simulation model is constructed. The collaborative operation scheduling simulation model takes the joint minimization of system operating cost and risk indicators under extreme scenarios as the objective function. Under the constraints of power balance, reserve, network power flow and equipment capacity, it solves the optimal power scheduling sequence of each regulating power source under different scenarios. Output an executable set of scheduling strategies to verify the synergistic operation effect of regulating power sources and new energy sources under extreme weather scenarios.

6. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 5, characterized in that, The first-level algorithm includes: Preprocessing of meteorological time series data from the target area yields a continuous series X. t ; Set an extreme threshold τ, when X t When the time exceeds τ, mark the current time as the extreme weather moment; Aggregate consecutive time points exceeding extreme thresholds into extreme weather segments, and calculate the duration and intensity of these extreme weather segments; All extreme segments are treated as a set of extreme weather scenarios, and probability weights are assigned based on the frequency of occurrence of the extreme segments.

7. The simulation method for coordinated operation of regulating power sources and new energy sources considering extreme weather scenarios as described in claim 6, characterized in that, The new energy output model driven by extreme weather includes: Based on the time series of light intensity and ambient temperature in the extreme weather scenario set, and using the photovoltaic module output power model: Where, η pv Expressed as photoelectric conversion efficiency, G t Let α represent irradiance, A represent the module area, and α represent the irradiance. T Represented as a temperature correction factor, T t Represented as ambient temperature, T ref This is represented as a reference temperature; Based on the wind speed and direction sequence in the extreme weather scenario set, and using the wind turbine power curve: Among them, v t Expressed as wind speed, v in v rated v out P represents the cut-in, rated, and cut-out velocities of the fan. rated This is expressed as rated power; Under extreme weather conditions, correction factors are introduced for photovoltaic and wind power output: Where, γ pv (t) represents the capacity reduction factor that varies with the intensity of extreme weather; Based on indicators such as temperature and humidity in extreme weather scenarios, a meteorological-driven power load model is constructed: in, Represented as the baseline load, β T β H Represented as the meteorological sensitivity coefficient, H t Represented as humidity; By combining the output of new energy sources with the load trajectory, the net load forecast result R is obtained. t :

8. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, they implement the steps of the simulation method for coordinated operation of regulating power supply and new energy considering extreme weather scenarios as described in any one of claims 1 to 7.

9. A computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the simulation method for coordinated operation of regulating power supply and new energy sources considering extreme weather scenarios as described in any one of claims 1 to 7.

Images