Methods, devices, storage media and electronic equipment for determining power system adequacy

Abstract

This invention discloses a method, apparatus, storage medium, and electronic device for determining the adequacy of a power system. The method includes: acquiring system parameters of the power system, including conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters; determining the target confidence probability corresponding to the power system; based on the system parameters, determining the effective generation capacity and maximum load demand of the power system under the target confidence probability; and based on the effective generation capacity and maximum load demand, determining the adequacy assessment result of the power system, wherein the adequacy assessment result indicates the power system's ability to continuously supply power to meet the needs of all users under normal operating conditions. This invention solves the technical problem in related technologies where incomplete consideration of factors leads to low accuracy in power system adequacy assessment under high uncertainty environments.

Description

Technical Field

[0001] This invention relates to the field of smart grids, and more specifically, to a method, apparatus, storage medium, and electronic device for determining the adequacy of a power system. Background Technology

[0002] In the power system sector, with the large-scale integration of renewable energy sources, such as fluctuating power sources like wind and solar, and the increasing diversification and unpredictability of user demand, power systems face unprecedented challenges of uncertainty. Current power system adequacy assessment methods often focus on deterministic analysis based on historical data, considering only a limited set of factors, such as the physical characteristics of the power grid, known installed power capacity, and planned maintenance information. They neglect the impact of numerous uncertainties, such as random power source disruptions, the uncertainty of fluctuating power output, fluctuations in the charging and discharging efficiency of energy storage devices, and load forecasting deviations. The existence of these uncertainties can lead to severe mismatches between power supply capacity and demand during actual operation, thereby threatening the stability and reliability of the power system.

[0003] Specifically, when assessing the functional adequacy of power systems, related technologies typically assume that power output and load demand are deterministic. While this simplified model facilitates calculations, the accuracy of the assessment results is significantly reduced under highly uncertain environments, failing to truly reflect the actual operating status and risks of the power system. In other words, related technologies do not comprehensively consider all factors when assessing the functional adequacy of power systems, leading to low accuracy in assessing the power supply adequacy of power systems under highly uncertain environments.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a method, apparatus, storage medium, and electronic device for determining power system adequacy, in order to at least solve the technical problem in the related art that the factors considered in assessing the functional adequacy of a power system are not comprehensive, resulting in low accuracy of power supply adequacy assessment under high uncertainty environments.

[0006] According to one aspect of the present invention, a method for determining the adequacy of a power system is provided, comprising: acquiring system parameters of the power system, wherein the system parameters include conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters, wherein the conventional power source parameters represent the device parameters corresponding to power generation equipment that provides a continuous and stable power supply, and the fluctuating power source parameters represent the device parameters corresponding to renewable energy power generation equipment; determining a target confidence probability corresponding to the power system, wherein the target confidence probability is used to reflect a predetermined risk preference of the power system; determining, based on the system parameters, the effective generation capacity and maximum load demand of the power system under the target confidence probability, wherein the effective generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainties; and determining an adequacy assessment result of the power system based on the effective generation capacity and maximum load demand, wherein the adequacy assessment result is used to indicate the ability of the power system to continuously supply power to meet the needs of all users under normal operating conditions.

[0007] According to another aspect of the present invention, a power system adequacy determination device is also provided, comprising: a system parameter acquisition module, configured to acquire system parameters of a power system, wherein the system parameters include conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters, wherein the conventional power source parameters represent the device parameters corresponding to power generation equipment providing continuous and stable power supply, and the fluctuating power source parameters represent the device parameters corresponding to renewable energy power generation equipment; a target confidence probability determination module, configured to determine the target confidence probability corresponding to the power system, wherein the target confidence probability is used to reflect the predetermined risk preference of the power system; a capacity demand determination module, configured to determine the effective power generation capacity and maximum load demand of the power system under the target confidence probability based on the system parameters, wherein the effective power generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainty factors; and an adequacy assessment module, configured to determine the adequacy assessment result of the power system based on the effective power generation capacity and maximum load demand, wherein the adequacy assessment result is used to indicate the ability of the power system to continuously supply power to meet the needs of all users under normal operating conditions.

[0008] According to another aspect of the present invention, a non-volatile storage medium is also provided, which stores a plurality of instructions adapted for a power system adequacy determination method, any one of which is loaded and executed by a processor.

[0009] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein 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 any one of the power system adequacy determination methods.

[0010] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any one of the power system adequacy determination methods.

[0011] In this embodiment of the invention, system parameters of the power system are obtained, including conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters. Conventional power source parameters represent the equipment parameters corresponding to power generation equipment providing a continuous and stable power supply, while fluctuating power source parameters represent the equipment parameters corresponding to renewable energy power generation equipment. A target confidence probability for the power system is determined, where the target confidence probability reflects the predetermined risk preference of the power system. Based on the system parameters, the effective generation capacity and maximum load demand of the power system under the target confidence probability are determined, where the effective generation capacity indicates the power supply capacity of the power system after considering various uncertainties. Based on the effective generation capacity and maximum load demand... Load demand is assessed to determine the adequacy of the power system. The adequacy assessment results indicate the power system's ability to continuously supply electricity to meet the needs of all users under normal operating conditions. This achieves the goal of quantifying the parameters of conventional and fluctuating power sources, combining the target confidence probability to determine the effective generation capacity and maximum load demand, and accurately assessing the adequacy of the power system under different risk preference levels. This improves the technical accuracy of the adequacy assessment of the power system under different risk preference levels and solves the technical problem in related technologies where the factors considered in the assessment of the functional adequacy of the power system are not comprehensive, resulting in low accuracy of the power supply adequacy assessment under high uncertainty environments. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0013] Figure 1 This is a flowchart of a method for determining the adequacy of a power system according to an embodiment of the present invention;

[0014] Figure 2 This is a flowchart of an optional method for determining power system adequacy according to an embodiment of the present invention;

[0015] Figure 3 This is a schematic diagram of a power system adequacy determination device according to an embodiment of the present invention. Detailed Implementation

[0016] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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 scope of protection of the present invention.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0018] According to an embodiment of the present invention, a method embodiment for determining the adequacy of a power system is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0019] Figure 1 This is a flowchart of a power system adequacy determination method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0020] Step S102: Obtain system parameters of the power system. The system parameters include conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters. Conventional power source parameters refer to the equipment parameters corresponding to the power generation equipment that provides a continuous and stable power supply, while fluctuating power source parameters refer to the equipment parameters corresponding to the renewable energy power generation equipment.

[0021] Optional, conventional power sources, such as coal-fired, gas-fired, and nuclear power plants that provide stable power output, have corresponding parameters including historical obstructed capacity data of the power system (i.e., power generation capacity loss due to equipment failure, maintenance, etc.), total installed capacity, and planned maintenance capacity. These parameters collectively reflect the available power generation capacity of conventional power sources at a specific point in time, providing necessary data for subsequent analysis. Fluctuating power sources mainly refer to renewable energy power generation equipment such as wind and solar power. The power generation capacity of these sources is severely affected by natural conditions such as weather and sunlight, exhibiting significant uncertainty. Corresponding parameters for fluctuating power sources include historical output data, i.e., the actual power generation records of these energy sources over a certain period in the past, and total installed capacity, the latter providing the theoretical maximum power generation capacity. Energy storage devices are playing an increasingly prominent role in the power system. Corresponding parameters for energy storage devices cover historical available power, rated capacity (i.e., the maximum capacity of the energy storage device to store and release electrical energy), and charge / discharge efficiency, etc. By analyzing these parameters, the role of energy storage devices in the balance of power supply and demand can be assessed. Load parameters refer to electricity demand data of consumers at different times, which can be analyzed based on historical data (such as historical load data). The uncertainty of these parameters is equally important, because users' electricity demand is affected by a variety of factors such as seasons, weather, and economic activities.

[0022] Step S104: Determine the target confidence probability corresponding to the power system, wherein the target confidence probability is used to reflect the predetermined risk preference of the power system.

[0023] Optionally, when assessing the adequacy of a power system, the target confidence probability is a crucial concept, reflecting the power system's attitude and preference towards risk. The target confidence probability can be determined based on the power system's predetermined risk preference, quantifying the probability thresholds of uncertainty on both the power generation and load sides. The target confidence probability ranges from (0,1) and indicates the confidence level at which the power system's effective generation capacity meets maximum load demand under a predetermined risk preference. Effective generation capacity indicates the power system's supply capacity after accounting for various uncertainties. The determination of the target confidence probability can be based on the power system's risk tolerance. For example, a higher confidence probability (such as 0.95 or higher) indicates that the power system tends to adopt a more cautious strategy, ensuring that power shortages do not occur in most cases. Conversely, a lower confidence probability may be used to reflect a higher tolerance for risk, such as in regions with cost priorities or fewer extreme weather events.

[0024] Optionally, there can be multiple target confidence probabilities, meaning multiple target confidence probabilities (within the range of (0,1]) can be set to suit different risk preferences. For example, a confidence probability set P={0.8,0.85,0.9,0.95} can be selected, where 0.95 corresponds to high-risk avoidance (such as ensuring power supply for critical loads or major events), and 0.8 corresponds to moderate risk tolerance (such as routine operation scheduling). The selection of confidence probabilities can be customized by the user according to actual needs (such as load importance or system operation scenarios).

[0025] Step S106: Based on system parameters, determine the effective generation capacity and maximum load demand of the power system under the target confidence probability. The effective generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainties, which may include, but are not limited to, contributions from conventional power sources, fluctuating power sources, and energy storage devices.

[0026] Optionally, by comprehensively analyzing the parameters of conventional power sources, fluctuating power sources, and energy storage devices, the effective generation capacity of the power system under a given confidence probability can be calculated. In this step, the effective output capacity of conventional power sources is calculated, taking into account the impact of disruptions and planned maintenance; the effective output capacity of fluctuating power sources can be based on their simultaneity rate probability distribution model and total installed capacity; the effective generation capacity of energy storage devices can be derived from the availability probability distribution model, rated capacity, and charge / discharge efficiency. Maximum load demand, i.e., the future maximum load, represents the load forecast value based on the target confidence probability. It can be determined, but is not limited to, based on load parameters and the corresponding probability distribution model, at a specified confidence probability. This forecast value reflects the expected peak load of the power system considering load uncertainty.

[0027] In one optional embodiment, determining the effective generation capacity and maximum load demand of the power system under a target confidence probability based on system parameters includes: determining the effective generation capacity based on conventional power source parameters, fluctuating power source parameters, and energy storage device parameters in the system parameters, by: determining the effective output capacity of conventional power sources in the power system based on conventional power source parameters and the target confidence probability, wherein the conventional power source parameters include historical blocked capacity data, installed capacity, and planned maintenance capacity of conventional power sources, and the effective output capacity of conventional power sources represents the actual generation capacity that conventional power sources can contribute to the power system under the target confidence probability; and determining the effective output capacity of conventional power sources based on fluctuating power source parameters. Based on power source parameters and target confidence probabilities, the effective output capacity of fluctuating power sources in the power system is determined. Fluctuating power source parameters include historical output data and installed capacity. The effective output capacity of a fluctuating power source represents its actual contribution to the power system's generation capacity under the target confidence probability. Based on energy storage device parameters and target confidence probabilities, the effective generation of energy storage devices in the power system is determined. Energy storage device parameters include historical available power, rated capacity, and charge / discharge efficiency. The effective generation capacity is obtained by summing the effective output capacity of conventional power sources, the effective output capacity of fluctuating power sources, and the effective generation of energy storage devices.

[0028] In this embodiment, the core step of the power system adequacy probability grading assessment method is to quantify and determine the effective generation capacity and maximum load demand of the power system under the target confidence probability. First, based on historical blocked capacity data, installed capacity, and planned maintenance capacity of conventional power sources, and combined with the target confidence probability, the effective output capacity of conventional power sources is determined through probability distribution fitting, representing the actual generation contribution of conventional power sources under a preset risk level. Second, using historical output data and installed capacity of fluctuating power sources, also based on the target confidence probability, the effective output capacity of fluctuating power sources is calculated, reflecting the generation potential of renewable energy sources such as wind and solar power under a specific risk tolerance. Third, considering the importance of energy storage devices, the effective generation capacity of energy storage devices is determined by analyzing historical available power, rated capacity, and charge / discharge efficiency, combined with the target confidence probability. Finally, the effective output of the above three types of power sources is summed to obtain the effective generation capacity of the entire power system under the target confidence probability. This series of calculations not only considers the reliability of conventional power sources, the intermittency of fluctuating power sources, and the volatility of energy storage devices, but also quantifies the demand uncertainty on the load side. Through the synergistic quantification of multi-source uncertainties, the accurate matching of effective power generation capacity and maximum load demand provides a scientific assessment basis based on risk preferences for the safe and stable operation of the power system.

[0029] In one optional embodiment, determining the effective output capacity of the conventional power source of the power system based on conventional power source parameters and a target confidence probability includes: constructing a probability distribution model of the blocked capacity of the conventional power source based on historical blocked capacity data in the conventional power source parameters, wherein the probability distribution model of the blocked capacity is used to indicate the probability that the blocked capacity of the conventional power source exceeds a predetermined value under normal operating conditions, and the expected blocked capacity of the conventional power source that cannot operate normally due to external or internal reasons at different confidence probability levels; using the probability distribution model of the blocked capacity, determining the predicted value of the blocked capacity of the conventional power source at the target confidence probability; and obtaining the effective output capacity of the conventional power source based on the installed capacity, planned maintenance capacity, and predicted blocked capacity of the conventional power source.

[0030] In this embodiment, the collection and preprocessing of basic power system parameters serve as the initial step. Following this, multiple target confidence probabilities are set to adapt to the risk assessment needs of different scenarios. Based on these target confidence probabilities, the specific process of obtaining the effective power generation capacity of the power system is further described, covering the effective output of conventional power sources, fluctuating power sources, and energy storage devices. Specifically, the derivation of the effective output capacity of conventional power sources begins by constructing a probability distribution model of the blocked capacity using historical blocked capacity data. This model accurately indicates the probability that the blocked capacity of conventional power sources will exceed a specific value under normal operating conditions, as well as the expected amount of blocked capacity. Subsequently, the predicted value of the blocked capacity is determined based on the target confidence probabilities, and combined with the installed capacity and planned maintenance capacity, the effective output capacity of conventional power sources is calculated. This technical solution, through the construction of a probability distribution model and the application of confidence probabilities, achieves a quantitative assessment of the blocked risk of conventional power sources, providing key data support for the overall analysis of power system adequacy, thereby improving the accuracy of the assessment results and the scientific nature of the decision-making. Through this technical path, the method of this embodiment can comprehensively consider the uncertainties of the power system, providing a probabilistic hierarchical assessment based on risk preferences, and providing a more refined decision-making basis for the planning, scheduling, and emergency response of the power system.

[0031] Optionally, the installed capacity, historical blocked capacity data, and planned maintenance capacity of conventional power sources can be obtained; based on the historical blocked capacity data, a probability distribution function of the blocked capacity (i.e., the blocked capacity probability distribution model) is fitted; according to the target confidence probability, the predicted value of the blocked capacity at the corresponding confidence level is determined from the probability distribution function of the blocked capacity; the effective output capacity of conventional power sources is calculated as follows: P_conv,e=P_conv,in-P_conv,out(P)-P_conv,main, where P_conv,e is the effective output of conventional power sources, P_conv,in is the installed capacity of conventional power sources, P_conv,out(P) is the predicted value of the blocked capacity at the target confidence probability P, and P_conv,main is the planned maintenance capacity. For example, based on historical blocked capacity data of conventional power sources, a probability distribution function of the blocked capacity is obtained by using kernel density estimation or normal distribution fitting. According to the target confidence probability P, the predicted blocked capacity value P_conv,out(P) at the corresponding confidence level is determined from the probability distribution function. A higher confidence probability results in a larger predicted blocked capacity value, reflecting a conservative estimate. Then, the effective output capacity of the conventional power source is calculated using the above formula. For example, if a conventional power source has an installed capacity of 1000MW, a planned maintenance capacity of 100MW, and a predicted blocked capacity of 50MW at a confidence probability of 0.95, then the corresponding effective output capacity of the conventional power source is P_conv,e = 1000 - 50 - 100 = 850MW.

[0032] In one optional embodiment, determining the effective output capacity of a fluctuating power source in a power system based on fluctuating power source parameters and a target confidence probability includes: constructing a simultaneity rate probability distribution model of the fluctuating power source output based on historical output data in the fluctuating power source parameters, wherein the simultaneity rate probability distribution model is used to indicate the simultaneity rate of the fluctuating power source under different confidence probabilities, and the simultaneity rate represents the proportion of the actual output of the fluctuating power source to the total installed capacity; using the simultaneity rate probability distribution model, determining the target simultaneity rate of the fluctuating power source under the target confidence probability; and obtaining the effective output capacity of the fluctuating power source based on the installed capacity of the fluctuating power source and the target simultaneity rate.

[0033] In this embodiment, based on the parameters of the fluctuating power source and the target confidence probability, a simultaneous rate probability distribution model for the output of the fluctuating power source is constructed, which can accurately reflect the distribution characteristics of the ratio between the actual output of the fluctuating power source and its total installed capacity. This model comprehensively considers the statistical characteristics of historical output data, providing a probabilistic basis for calculating the effective output capacity of the fluctuating power source. Using the simultaneous rate probability distribution model, the target simultaneous rate under the target confidence probability is determined. This target simultaneous rate reflects the output reliability of the fluctuating power source at a specific risk level. Combining the total installed capacity of the fluctuating power source with the target simultaneous rate, the effective output capacity of the fluctuating power source is obtained. This calculation result is closer to the actual operation of the power system and can effectively guide power system planning and dispatching decisions. By setting the confidence probability, the method in this embodiment can flexibly adapt to the needs of different scenarios, providing a more accurate quantitative assessment of uncertainty, thereby improving the accuracy and reliability of power system adequacy assessment. In other embodiments, the construction and parameter update mechanism of the simultaneous rate probability distribution model can be more dynamic. By periodically importing the latest historical data, the model fitting results can be optimized, further enhancing the adaptability of the assessment method to changes in the power system structure.

[0034] Optionally, obtain the installed capacity and historical output data of fluctuating power sources, which include at least hydropower, wind power, and photovoltaic power. Based on the historical output data, calculate the output simultaneity rate sequence of the fluctuating power sources, where the output simultaneity rate is the ratio of the sum of the actual output of each fluctuating power source at the same time to the total installed capacity. Fit the output simultaneity rate sequence to a probability distribution to obtain the simultaneity rate probability distribution function (i.e., the simultaneity rate probability distribution model). Based on the target confidence probability, determine the target simultaneity rate at the corresponding confidence level from the simultaneity rate probability distribution function. Calculate the effective output capacity of the fluctuating power sources as follows: P_fluc,e = P_fluc,in × _fluc(P), where P_fluc,e is the effective output capacity of the fluctuating power source, and P_fluc,in is the installed capacity of the fluctuating power source. _fluc(P) represents the target simultaneity rate under the target confidence probability P. For example, based on historical power output data of fluctuating power sources, a power output simultaneity rate sequence (the ratio of the sum of actual power output at the same time to the total installed capacity) can be calculated; a probability distribution (such as a Beta distribution or a Weibull distribution) can be fitted to the simultaneity rate sequence to obtain the simultaneity rate probability distribution function; and the target simultaneity rate can be determined according to the target confidence probability P. _fluc(P), where a higher confidence probability corresponds to a lower target simultaneity rate, reflecting a conservative estimate of the volatility of new energy sources; and further, the effective output capacity of the fluctuating power source is calculated according to the above formula. For example, if the total installed capacity of wind power is 800MW and the target simultaneity rate is 0.4 with a confidence probability of 0.9, then the corresponding effective output capacity of the fluctuating power source is P_fluc,e = 800 × 0.4 = 320MW.

[0035] In one optional embodiment, determining the effective power generation of the energy storage device in the power system based on the energy storage device parameters and the target confidence probability includes: constructing an availability probability distribution model of the energy storage device based on historical available power data in the energy storage device parameters, wherein the availability probability distribution model is used to indicate the expected probability that the energy storage device can discharge at rated efficiency under different confidence probabilities, and the confidence level that the energy storage device can provide effective power generation within a predetermined time window; using the availability probability distribution model, determining the target availability of the energy storage device under the target confidence probability; and obtaining the effective power generation of the energy storage device based on the rated capacity, charge and discharge efficiency, and target availability of the energy storage device.

[0036] In this embodiment, a series of steps are established to determine the effective power generation of energy storage devices in a power system based on energy storage device parameters and target confidence probabilities. First, an availability probability distribution model is constructed using historical available power data of the energy storage devices. This model explicitly indicates the expected probability that the energy storage devices can discharge at rated efficiency under different confidence probability levels, as well as the confidence level of the effective power generation provided within a predetermined time window. Next, based on the constructed model, the target availability rate is determined at a specific target confidence probability. Finally, by combining the rated capacity, charge / discharge efficiency, and target availability rate of the energy storage devices, the effective power generation of the energy storage devices under the current confidence probability is accurately calculated. This process not only quantifies the uncertainty contribution of energy storage devices in the power system but also ensures that the assessment results reflect the risk tolerance in actual operation, providing more accurate and flexible decision support for power system planning and scheduling.

[0037] Optionally, the rated capacity, charge / discharge efficiency, and historical availability data of the energy storage device can be obtained; based on the historical availability data, a probability distribution function of the energy storage availability (i.e., the availability probability distribution model) can be fitted; according to the target confidence probability, the target availability at the corresponding confidence level can be determined from the probability distribution function of the energy storage availability; the effective power generation capacity of the energy storage (i.e., the effective power generation of the energy storage device) can be calculated in the following way:

[0038] P_ess,e=P_ess,rat× _ess(P)× _ess, where P_ess,e is the effective power generation capacity of the energy storage, and P_ess,rat is the rated capacity of the energy storage device. _ess(P) represents the target availability rate under the target confidence probability P. _ess represents the charging and discharging efficiency of the energy storage device. For example, based on historical availability data of energy storage, an availability probability distribution function is fitted; the target availability is then determined based on the target confidence probability P. _ess(P); Further calculate the effective power generation of the energy storage device according to the above formula. For example, if the rated capacity of the energy storage is 200MW, the charge and discharge efficiency is 0.9, and the target availability rate is 0.9 with a confidence probability of 0.95, then the effective power generation of the corresponding energy storage device is P_ess,e=200×0.9×0.9=162MW.

[0039] In one optional embodiment, determining the effective generation capacity and maximum load demand of the power system under a target confidence probability based on system parameters includes: determining the maximum load demand based on load parameters in the system parameters by: constructing a load demand probability distribution model based on historical load data in the load parameters, wherein the load demand probability distribution model is used to indicate the confidence interval of the maximum load demand of the power system during the prediction period under different confidence probabilities, and the probability distribution of the load demand exceeding a predetermined value within a predetermined time window; using the load demand probability distribution model, determining the predicted load peak under the target confidence probability; and obtaining the maximum load demand based on the predicted load peak.

[0040] In this embodiment, the probabilistic grading assessment scheme for power system adequacy is further refined, focusing on how to determine the maximum load demand under a target confidence probability based on load parameters. Specifically, firstly, a load demand probability distribution model is constructed using historical load data. This model not only reflects the confidence interval of the maximum load demand under different confidence probabilities but also quantifies the probability distribution of load demand exceeding a specific value, providing a data foundation for assessing load uncertainty. Secondly, using this probability distribution model, the peak load for the forecast period, i.e., the expected value of the maximum load demand, is calculated for the set target confidence probability. This process comprehensively considers the randomness and long-term trend of load demand, enhancing the accuracy and reliability of the assessment. Finally, based on the obtained peak load, the maximum load demand of the power system under the target confidence level is determined, providing a key input for subsequent adequacy calculations. The method in this embodiment handles load uncertainty through a probabilistic approach, achieving flexibility and accuracy in the assessment process, and effectively supporting the power system in conducting probabilistic grading adequacy assessments when facing variable load demands.

[0041] Optionally, the historical load data is preprocessed to remove outliers and perform load growth trend analysis; based on the preprocessed historical load data, a load probability distribution function is fitted; according to the target confidence probability, the load forecast value at the corresponding confidence level is determined from the load probability distribution function, and the load forecast value is used as the future maximum load (i.e., maximum load demand) P_load,max(P).

[0042] Step S108: Based on the effective power generation capacity and maximum load demand, determine the adequacy assessment result of the power system, wherein the adequacy assessment result is used to indicate the power system's ability to continuously supply power to meet the needs of all users under normal operating conditions.

[0043] Optionally, the adequacy assessment result can be calculated by comparing effective generation capacity and maximum load demand. For example, if effective generation capacity exceeds maximum load demand, the power system is adequate under that confidence probability; conversely, there is a risk of power shortage. This result not only indicates whether the power system can meet user demand under normal operating conditions but also provides a quantitative indicator of power system adequacy under different risk preferences. This helps to make reasonable planning and scheduling arrangements based on specific circumstances. For example, the load dispatch strategy of the power system can be further determined based on the adequacy assessment result to improve the security and economy of the power system. The adequacy assessment result may include, but is not limited to, an adequacy index and a system adequacy level determined based on the index, to reflect the power system's ability to withstand potential supply and demand imbalances under different confidence probabilities.

[0044] In one optional embodiment, the adequacy assessment result of the power system is determined based on the effective generation capacity and maximum load demand, including: determining the adequacy index of the power system based on the effective generation capacity and maximum load demand; determining the target adequacy level to which the adequacy index belongs; and determining the adequacy assessment result based on the target adequacy level.

[0045] In this embodiment, the power system adequacy probability grading assessment method based on multi-source uncertainty quantification and confidence probability correlation is further deepened. By establishing the relationship between the total effective generating capacity of the power system and the future maximum load, the adequacy index is accurately calculated, thereby determining the adequacy level of the power system under a specific confidence probability. This embodiment first calculates the target effective generating capacity and load demand of the power system based on the probabilistic characteristics of conventional power source obstruction, fluctuating power source output fluctuations, changes in energy storage availability, and random load fluctuations. Then, the adequacy index S(P) is calculated and mapped to a specific adequacy level, such as ample, alert, strained, or power shortage, according to a preset grading standard. Through this combination of quantification mechanism and grading assessment, the goal of accurately characterizing the adequacy status of the power system under different risk preferences is achieved. Furthermore, the dynamic adjustment logic of periodically updating historical data and probability distribution parameters enables the assessment system to adapt to changes in the power system structure in a timely manner, ensuring the timeliness and adaptability of the assessment results, thereby effectively guiding power system planning, dispatching, and emergency response decisions. In other embodiments not shown, by increasing the range of confidence probabilities and refining the grading criteria, the flexibility and sophistication of the assessment system can be further enhanced to meet a wider range of risk management and decision-making needs.

[0046] Optionally, the adequacy index of the power system can be determined based on the effective generation capacity and maximum load demand. The adequacy index is obtained as follows: S(P) = (P_total,e - P_load,max(P)) / P_load,max(P) × 100%, where S(P) is the adequacy index under the target confidence probability P, P_total,e is the effective generation capacity of the energy storage system, and P_total,e = P_conv,e + P_fluc,e + P_ess,e. For example, in the above embodiment scenario, P_total,e = 850 + 320 + 162 = 1332MW. P_load,max(P) is the future maximum load (i.e., maximum load demand) under the target confidence probability P. When S(P) is positive, the effective generation capacity of the power system is greater than the load demand; the larger the positive value, the higher the adequacy. When S(P) is negative, there is a power supply gap in the power system; the smaller the negative value, the higher the risk of power shortage. For example, in the above embodiment scenario, the power system adequacy index S(0.9) = (1332-1500) / 1500×100% = -11.2%.

[0047] In one optional embodiment, determining the target sufficiency level to which the sufficiency index belongs includes: determining the target sufficiency range to which the sufficiency index belongs from multiple sufficiency ranges; and determining the target sufficiency level from multiple sufficiency levels based on the target sufficiency range, wherein the multiple sufficiency ranges correspond one-to-one with the multiple sufficiency levels.

[0048] In this embodiment, when determining the target adequacy range to which the adequacy index belongs from multiple adequacy ranges, and determining the target adequacy level from multiple adequacy levels based on the target adequacy range, this process achieves a refined classification assessment of the power system's adequacy status. By comparing the adequacy index calculated using the formula in the above embodiment with preset classification standards, which include four levels—adequate, alert, strained, and power shortage—each level corresponds to a different adequacy index range, the process first calculates the effective generating capacity and future maximum load of each power source type using a probability distribution model and confidence probability, and then calculates the adequacy index. Subsequently, the calculation results are matched with the classification standards to determine the specific adequacy level to which it belongs. This design ensures that the assessment results are no longer limited to a single "adequacy" or "insufficient" judgment, but can intuitively display the system's operating status under different confidence probabilities, providing power system planners and dispatchers with richer and more accurate information support, and helping to make more reasonable decisions based on actual needs. Furthermore, by regularly updating the probability distribution model parameters and grading thresholds based on historical data, the assessment system is ensured to reflect changes in the power system in a timely manner, improving the dynamic adaptability and accuracy of the assessment results. In other embodiments not detailed, the technical solution can also optimize the adequacy assessment process by adjusting the type of probability distribution model or the parameter update frequency, further enhancing the applicability and reliability of the assessment method.

[0049] Optionally, a sufficiency level grading standard can be preset. Different sufficiency levels correspond to different sufficiency ranges, and the grading standard can include at least: sufficiency level, alert level, stress level, and power shortage level; where the sufficiency range is S(P)≥ 1. If the sufficiency level is sufficiency, then the sufficiency level is determined to be sufficiency; if the sufficiency range is... 2≤S(P)< If 1, then the sufficiency level is determined to be the alert level; if the sufficiency range is... 3≤S(P)< 2. If the sufficiency level is determined to be the stress level, then if the sufficiency range is S(P) < 3. Therefore, the adequacy level is determined to be a power shortage level; among which, 1. 2. 3 represents the preset threshold for sufficiency grading, and 1> 2> 3≥0. According to the scenario described in the foregoing embodiments, if... 1=5%, 2=0% If 3 = -5%, then S(0.9) = -11.2% corresponds to the power shortage level.

[0050] Optionally, multiple different target confidence probability values ​​(such as 0.8, 0.85, 0.9, and 0.95) can be selected, and the adequacy index and adequacy level corresponding to each target confidence probability value can be calculated respectively. A confidence probability-adequacy level mapping table is generated, which is used to show the adequacy status of the power system under different confidence levels. An assessment report is generated based on the mapping table, which clarifies the system adequacy status and decision-making recommendations under different risk preferences (such as adding power generation capacity for power shortage levels under high confidence probability, and optimizing energy storage dispatch for alert levels under low confidence probability).

[0051] In an optional embodiment, before determining the target adequacy range to which the adequacy index belongs from multiple adequacy ranges, the method further includes: determining a predetermined risk preference of the power system; determining a target correction strategy based on the predetermined risk preference; and correcting multiple initial ranges corresponding to multiple adequacy levels based on the target correction strategy to obtain multiple adequacy ranges, wherein the multiple initial ranges correspond one-to-one with the multiple adequacy ranges.

[0052] In this embodiment, based on the predetermined risk preference of the power system, a target correction strategy is further proposed to optimize the initial ranges corresponding to multiple adequacy levels, forming adequacy ranges that better fit actual operational needs. Specifically, by analyzing historical data of power system operation, including power source obstruction, fluctuating power source output records, energy storage device utilization efficiency, and load demand changes, the risk preference of the power system is determined, such as the power system's tolerance for power shortages or the urgency of ensuring important loads. Based on this risk preference, this embodiment dynamically adjusts key parameters in the effective output calculation models and load forecasting models for various power sources, such as the probability distribution function of conventional power source obstruction capacity, the confidence interval of fluctuating power source output simultaneity rate, the confidence level of energy storage availability, and the uncertainty boundary of load forecasting. By correcting the model parameters, this embodiment can more accurately reflect the system adequacy under different confidence probabilities, thereby generating multiple adequacy ranges that match the risk preference based on multiple given initial ranges. This process ensures that the assessment system can flexibly adapt to the differentiated decision-making needs in power system planning and dispatch. For example, in high-risk avoidance scenarios, conservative parameter settings and a generous adequacy range can effectively avoid the risk of system power shortages; while in moderate risk tolerance scenarios, more reasonable parameter settings and a moderate adequacy range can be adopted to achieve a balance between the economy and safety of system operation. After implementing this embodiment, the accuracy of adequacy assessment and the pertinence of decision-making are significantly enhanced, providing a more scientific and reasonable basis for power system operation.

[0053] Through steps S102 to S108, the parameters of conventional and fluctuating power sources can be quantified, and the effective generation capacity and maximum load demand can be determined by combining the target confidence probability. This allows for an accurate assessment of the adequacy of the power system under different risk appetite levels, thereby improving the technical accuracy of power system adequacy assessment under different risk appetite levels. Furthermore, this addresses the technical problem in related technologies where incomplete consideration of factors leads to low accuracy in power system adequacy assessment under high uncertainty environments. Power system adequacy is a core indicator for ensuring the safe and stable operation of the power system, referring to the system's ability to continuously meet user power demands under specified operating conditions. With the integration of a high proportion of fluctuating power sources (hydropower, wind power, and photovoltaic) into new power systems and the widespread application of energy storage devices, the operating environment of power systems is becoming increasingly complex. For example, on the power source side, conventional power sources are subject to random disruption risks, and the output of fluctuating power sources is highly random due to natural conditions (the maximum daily output fluctuation can reach more than 80% of the rated capacity). The charging and discharging efficiency and available capacity of energy storage devices are also uncertain. On the load side, user electricity demand is affected by various factors such as economic activities, meteorological conditions, and electrification transition, making it difficult to accurately predict the maximum future load. The load forecasting deviation rate is significantly higher than that of traditional levels.

[0054] It can be seen that the core requirement for the current power system adequacy assessment has shifted from "single deterministic scenario analysis" to "probabilistic hierarchical assessment". It is necessary to break through the traditional framework and establish an assessment system that can quantify uncertainty and reflect risk preferences.

[0055] The methods in related technologies are mainly divided into two categories, and their specific implementation paths and limitations are as follows: 1) Deterministic scenario assessment method: The core idea is to calculate based on power output and load data under a single operating condition. Using fixed power parameters (such as rated installed capacity, fixed output coefficient) and load forecast values ​​(such as single peak forecast), the capacity balance method is used to determine system adequacy. This scheme has a simple process and low computational load, but it completely ignores uncertainties, resulting in rigid assessment results that cannot reflect random risks in actual operation. 2) Statistical probabilistic assessment method: This method uses historical data to fit the probability distribution of power output or load to calculate indicators such as the probability of power system shortages. Although this scheme considers some randomness, it does not establish a direct mapping between confidence probability and adequacy, cannot achieve hierarchical assessment, and does not comprehensively cover the synergistic effects of conventional power sources, fluctuating power sources, and energy storage devices, resulting in incomplete assessment dimensions.

[0056] The aforementioned technical solutions all have technical shortcomings and cannot meet the requirements of "probabilistic hierarchical assessment under high uncertainty." Specific limitations are as follows: 1) Incomplete uncertainty quantification: Deterministic scenario assessment methods completely fail to consider the probabilistic characteristics of power source disruptions, renewable energy fluctuations, changes in energy storage availability, and random load fluctuations, using only fixed parameters for calculation, leading to large deviations between assessment results and reality. Statistical probabilistic assessment methods often focus on single variables (e.g., only considering load fluctuations or renewable energy output fluctuations), failing to comprehensively quantify the synergistic effects of multi-source uncertainties. 2) Poor risk appetite adaptability: Neither type of solution establishes a quantitative correlation between confidence probability and adequacy, making it impossible to adjust assessment standards according to different risk tolerance levels (e.g., conservative / aggressive power dispatch strategies, critical load guarantee requirements), resulting in inflexible assessment results. 3) Lack of hierarchical assessment capabilities: Related technologies can only provide single-dimensional assessment results (e.g., "adequate" or "insufficient," a single power shortage probability value), failing to achieve multi-level classification of adequacy and struggling to support differentiated decisions in power system planning and dispatch (e.g., decision-making needs for different scenarios such as daily operation, emergency support, and long-term capacity expansion). 4) Insufficient parameter dynamism: Related technologies mostly use fixed parameter boundaries (such as fixed output coefficients and static load distribution), which cannot adapt to changes in system structure such as the increase in the penetration rate of new energy sources and the optimization of energy storage configuration, resulting in poor adaptability and timeliness of evaluation results.

[0057] To address the aforementioned problems, and in conjunction with the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a flowchart of an optional power system adequacy determination method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes:

[0058] Step S1: Receive an adequacy assessment request corresponding to the power system. The adequacy assessment request includes basic power system parameters, which include at least: installed capacity of conventional power sources, installed capacity of fluctuating power sources, energy storage equipment parameters, historical load data, historical obstruction data of conventional power sources, and historical output data of fluctuating power sources.

[0059] In step S1, the adequacy assessment request triggers the probabilistic grading assessment process for power system adequacy. The request initiator can be a power dispatch center, power planning department, etc. Power system basic parameters are the core input data for the assessment, covering key information from both the power source and load sides: conventional power sources include highly stable sources such as thermal power and nuclear power; their installed capacity, historical obstruction data (such as output reduction data due to equipment failure), and planned maintenance capacity are the basis for calculating effective output; fluctuating power sources include hydropower, wind power, and photovoltaic power; their installed capacity and historical output data are used to analyze the probabilistic characteristics of output simultaneity; energy storage equipment parameters include rated capacity, charge / discharge efficiency, and historical availability, used to quantify the effective power generation capacity of energy storage; historical load data is used to analyze load change patterns, providing a basis for predicting future maximum load.

[0060] Step S2: In response to the adequacy assessment request, determine the target confidence probability, wherein the target confidence probability is a probability threshold used to quantify the uncertainty on the power supply side and the load side, and the value range of the target confidence probability is (0,1].

[0061] In step S2, the target confidence probability is the core parameter connecting the uncertainty factor and the assessment result, reflecting the risk tolerance of the assessment. For example, a confidence probability of 0.95 indicates that the system adequacy is assessed at a 95% confidence level, corresponding to a higher risk aversion requirement; a confidence probability of 0.8 indicates that the assessment is conducted at an 80% confidence level, allowing for a certain degree of uncertainty risk. Users can select different target confidence probabilities according to their actual needs (such as the importance of power system operation, load type, etc.) to achieve differentiated assessments.

[0062] Step S3, based on the target confidence probability and the basic parameters of the power system, calculates the system's effective generation capacity and future maximum load, respectively. The effective generation capacity of the power system is the sum of the effective output of conventional power sources, the effective output of fluctuating power sources, and the effective generation capacity of energy storage. The future maximum load is the load forecast value based on the confidence probability. Step S3 is the core calculation step of the assessment, quantifying the impact of uncertainty from both the power source and load sides.

[0063] S31, Calculation of Effective Output of Conventional Power Source: The effective output of the conventional power source is calculated by fitting a probability distribution of the blocked capacity to historical blocked data. Based on the target confidence probability, the blocked capacity at the corresponding confidence level is determined (the higher the confidence probability, the larger the predicted blocked capacity, reflecting a conservative estimate of risk). The effective output of the conventional power source is then obtained by subtracting the planned maintenance capacity. For example, if a conventional power source has an installed capacity of 1000MW, and the historical blocked capacity follows a normal distribution, the blocked capacity at a confidence probability of 0.95 is 50MW, and the planned maintenance capacity is 100MW, then the effective output of the conventional power source is 1000 - 50 - 100 = 850MW.

[0064] S32, Calculation of Effective Output of Fluctuating Power Sources: The output simultaneity rate reflects the overall output level of fluctuating power sources. It is obtained by fitting a probability distribution of the simultaneity rate to historical output data, determining the target simultaneity rate based on the target confidence probability (the higher the confidence probability, the lower the target simultaneity rate, reflecting a conservative estimate of the fluctuating power source's output), and then multiplying it by the total installed capacity to obtain the effective output. For example, if the total installed wind power capacity is 800MW, and the target simultaneity rate is 0.4 at a confidence probability of 0.9, then the effective wind power output is 800 × 0.4 = 320MW.

[0065] S33, Calculation of Effective Power Generation Capacity of Energy Storage: Based on historical availability data, a probability distribution is fitted, the target availability rate is determined according to the target confidence probability, and the effective power generation capacity is calculated by combining the rated capacity and charge / discharge efficiency. For example, if the rated capacity of energy storage is 200MW, the charge / discharge efficiency is 0.9, and the target availability rate is 0.9 at a confidence probability of 0.95, then the effective power generation capacity of energy storage is 200 × 0.9 × 0.9 = 162MW.

[0066] S34, Calculation of Future Maximum Load: The load probability distribution is fitted using historical load data, and the future maximum load is determined based on the target confidence probability (the higher the confidence probability, the larger the predicted future maximum load, reflecting a conservative estimate of load growth). For example, after fitting historical load data, the future maximum load with a confidence probability of 0.9 is 1500MW. The effective generating capacity of the power system is the sum of the effective output of the three types of power sources mentioned above. For example, in the above example, the effective generating capacity of the system is 850 + 320 + 162 = 1332MW.

[0067] Step S4: Calculate the power system adequacy index based on the effective generating capacity and future maximum load of the power system. In step S4, the adequacy index quantifies the adequacy of the system using a relative value. When the effective generating capacity of the power system is greater than the future maximum load, the adequacy index is positive; the larger the positive value, the higher the adequacy. When the effective generating capacity of the power system is less than the future maximum load, the adequacy index is negative, indicating a risk of power shortage. For example, in the above example, the effective generating capacity of the power system is 1332MW, and the future maximum load is 1500MW, then the adequacy index is (1332-1500) / 1500×100%=-11.2%.

[0068] Step S5: Determine the adequacy level of the power system under the target confidence probability based on the adequacy index and the preset classification standard.

[0069] In step S5, the preset grading criteria can be adjusted according to the actual needs of power system operation, for example, by setting... 1 = 5%, 2=0%, 3 = -5%: When the adequacy index is ≥ 5%, it is considered an ample level, indicating sufficient power supply capacity of the power system; when 0% ≤ adequacy index < 5%, it is considered a warning level, requiring monitoring of the system's operating status; when -5% ≤ adequacy index < 0%, it is considered a strain level, indicating a potential power shortage; when the adequacy index < -5%, it is considered a power shortage level, indicating a serious risk of power shortage in the power system. In the example above, the adequacy index is -11.2%, corresponding to a power shortage level.

[0070] Optionally, in order to comprehensively display the system adequacy status under different confidence levels, multiple confidence probability values ​​(such as 0.8, 0.85, 0.9, 0.95, 0.99) can be selected to calculate the corresponding adequacy index and level, generate a mapping table, and provide a more comprehensive reference for decision-making.

[0071] Step S6: Select multiple target confidence probabilities (e.g., 0.8, 0.85, 0.9, 0.95), and repeat the calculation process from steps S1 to S5 above to obtain the adequacy index and grading results corresponding to each confidence probability, generating a "confidence probability-adequacy level" mapping table. Based on the mapping table, generate an assessment report to clarify the system adequacy status and decision-making recommendations under different risk preferences (e.g., under a high confidence probability, the power shortage level requires additional power generation capacity; under a low confidence probability, the alert level requires optimized energy storage scheduling).

[0072] Through steps S1 to S6, this embodiment incorporates confidence probability into the uncertainty quantification process on both the power supply and load sides, calculating the system's effective power generation capacity and future maximum load, respectively, to obtain an adequacy index and achieve tiered assessment. This method fully considers the randomness of power output and load demand, establishes a quantitative relationship between confidence probability and adequacy, improves the accuracy and practicality of the assessment results, and solves the technical problems of inaccurate assessment and inability to tier using traditional methods.

[0073] To address the issues of incomplete uncertainty quantification, poor risk appetite adaptability, lack of tiered assessment, and insufficient parameter dynamism in related technologies, the core objective of this embodiment is to construct a probabilistic tiered assessment technology system for power system adequacy, encompassing "multi-source uncertainty quantification - confidence probability correlation - tiered assessment output." This system follows a five-level technical path: "parameter input - confidence probability determination - multi-source capacity and load calculation - adequacy index solution - tiered assessment output," building a probabilistic tiered assessment system covering multi-source uncertainties on both the power generation and load sides. The core logic is as follows: First, basic power system parameters (including conventional power sources, fluctuating power sources, energy storage, and load-related data) are received; then, the target confidence probability (quantifying risk tolerance) is determined; based on the confidence probability and basic parameters, the effective output and future maximum load of conventional power sources, fluctuating power sources, and energy storage are calculated respectively; the adequacy index is calculated through capacity balance relationships; finally, the tiered results are determined according to preset standards, generating a probabilistic tiered assessment report, thus achieving a closed-loop process from uncertainty quantification to tiered decision-making. The specific objectives are as follows: 1) To achieve comprehensive quantification of multi-source uncertainties: Establish a comprehensive quantitative model covering conventional power source obstruction, fluctuating power source output, changes in energy storage availability, and random fluctuations in load demand. Through probability distribution fitting and confidence probability correlation, accurately characterize the impact of each uncertainty factor on system adequacy. 2) To establish a quantitative mapping between confidence probability and adequacy: Construct a direct correlation mechanism between target confidence probability and effective generation capacity, maximum load demand, and adequacy index, so that the assessment results can be adapted to different risk preferences (e.g., high confidence probability corresponds to conservative assessment, low confidence probability corresponds to moderate risk assessment). 3) To achieve graded assessment of adequacy: Formulate clear grading standards and classify different levels based on the adequacy index (e.g., ample, alert, strained, power shortage), providing an intuitive basis for differentiated decision-making. 4) To improve the dynamic adaptability of the assessment: By dynamically updating historical data and adaptively adjusting probability distribution parameters, the assessment model can adapt to changes in the power system structure (e.g., growth in new energy installed capacity, optimization of energy storage configuration), ensuring the timeliness and accuracy of the assessment results.

[0074] The method in this embodiment can achieve at least one of the following effects: 1) More comprehensive and accurate uncertainty quantification: It comprehensively covers the probabilistic characteristics of conventional power supply obstruction, fluctuating power supply fluctuation, changes in energy storage availability, and random load fluctuations. Through probability distribution fitting and confidence probability correlation, it achieves the coordinated quantification of multi-source uncertainties, significantly reducing the deviation between the assessment results and the actual operating conditions. 2) Strong adaptability to risk preferences: It establishes a direct mapping between confidence probability and adequacy, allowing users to select target confidence probabilities based on different risk tolerance levels (such as ensuring important loads and daily dispatching). The assessment results flexibly adapt to differentiated decision-making needs. 3) Intuitive and practical hierarchical assessment: Through clear hierarchical standards and a "confidence probability-adequacy level" mapping table, it transforms abstract probability assessment results into intuitive hierarchical conclusions, providing accurate decision-making basis for power system planning (such as power supply expansion), dispatching (such as load regulation and energy storage optimization), and emergency response (such as power shortage response). 4) Strong dynamic adaptability: By adopting a historical data fitting + dynamic update mechanism, the probability distribution parameters and classification thresholds can be optimized in real time according to changes in system structure (such as the increase in the penetration rate of new energy and the adjustment of energy storage configuration), ensuring the timeliness and adaptability of the evaluation results.

[0075] This embodiment also provides a power system adequacy determination device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0076] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described power system adequacy determination method is also provided. Figure 3 This is a schematic diagram of a power system adequacy determination device according to an embodiment of the present invention, as shown below. Figure 3 As shown, the aforementioned power system adequacy determination device includes: a system parameter acquisition module 300, a target confidence probability determination module 302, a capacity requirement determination module 304, and an adequacy assessment module 306, wherein:

[0077] The system parameter acquisition module 300 is used to acquire the system parameters of the power system. The system parameters include conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters. Conventional power source parameters represent the equipment parameters corresponding to the power generation equipment that provides a continuous and stable power supply, while fluctuating power source parameters represent the equipment parameters corresponding to the renewable energy power generation equipment.

[0078] The target confidence probability determination module 302 is connected to the system parameter acquisition module 300 and is used to determine the target confidence probability corresponding to the power system. The target confidence probability is used to reflect the predetermined risk preference of the power system.

[0079] The capacity requirement determination module 304 is connected to the target confidence probability determination module 302 and is used to determine the effective generation capacity and maximum load demand of the power system under the target confidence probability based on system parameters. The effective generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainty factors.

[0080] The adequacy assessment module 306, connected to the capacity demand determination module 304, is used to determine the adequacy assessment result of the power system based on the effective power generation capacity and the maximum load demand. The adequacy assessment result is used to indicate the power system's ability to continuously supply power to meet the needs of all users under normal operating conditions.

[0081] Optionally, the capacity requirement determination module includes: a conventional power source output determination module, used to determine the effective output capacity of conventional power sources in the power system based on conventional power source parameters and a target confidence probability. The conventional power source parameters include historical blocked capacity data, installed capacity, and planned maintenance capacity of the conventional power sources. The effective output capacity of the conventional power sources represents the actual power generation capacity that the conventional power sources can contribute to the power system under the target confidence probability. A fluctuating power source output determination module, used to determine the effective output capacity of fluctuating power sources in the power system based on fluctuating power source parameters and a target confidence probability. The parameters include historical power output data and installed capacity. The effective power output capacity of the fluctuating power source represents the actual power generation capacity that the fluctuating power source can contribute to the power system under the target confidence probability. The energy storage device output determination module is used to determine the effective power generation of the energy storage device in the power system based on the energy storage device parameters and the target confidence probability. The energy storage device parameters include historical available power, rated capacity, and charge / discharge efficiency. The effective power generation capacity determination module is used to sum the effective power output capacity of the conventional power source, the effective power output capacity of the fluctuating power source, and the effective power generation of the energy storage device to obtain the effective power generation capacity.

[0082] Optionally, the conventional power supply output determination module includes: a blocked capacity probability distribution model construction unit, used to construct a blocked capacity probability distribution model of the conventional power supply based on historical blocked capacity data in the conventional power supply parameters, wherein the blocked capacity probability distribution model is used to indicate the probability that the blocked capacity of the conventional power supply exceeds a predetermined value under normal operating conditions, and the expected blocked capacity of the conventional power supply due to external or internal reasons at different confidence probability levels; a blocked capacity probability distribution model construction unit, used to determine the predicted value of the blocked capacity of the conventional power supply at a target confidence probability using the blocked capacity probability distribution model; and a conventional power supply output determination unit, used to obtain the effective output capacity of the conventional power supply based on the installed capacity, planned maintenance capacity, and blocked capacity prediction value of the conventional power supply.

[0083] Optionally, the fluctuating power supply output determination module includes: a simultaneity rate probability distribution model construction unit, used to construct a simultaneity rate probability distribution model of the fluctuating power supply output based on historical output data in the fluctuating power supply parameters, wherein the simultaneity rate probability distribution model is used to indicate the simultaneity rate of the fluctuating power supply under different confidence probabilities, and the simultaneity rate represents the proportion distribution of the actual output of the fluctuating power supply to the total installed capacity; a target simultaneity rate probability determination unit, used to determine the target simultaneity rate of the fluctuating power supply under the target confidence probability using the simultaneity rate probability distribution model; and a fluctuating power supply output determination unit, used to obtain the effective output capacity of the fluctuating power supply based on the installed capacity of the fluctuating power supply and the target simultaneity rate.

[0084] Optionally, the energy storage device output determination module includes: an availability probability distribution model construction unit, used to construct an availability probability distribution model of the energy storage device based on historical available power data in the parameters of the energy storage device, wherein the availability probability distribution model is used to indicate the expected probability that the energy storage device can discharge at rated efficiency under different confidence probabilities, and the confidence level that the energy storage device can provide effective power generation within a predetermined time window; a target availability determination unit, used to determine the target availability of the energy storage device under the target confidence probability using the availability probability distribution model; and an energy storage device output determination unit, used to obtain the effective power generation of the energy storage device based on the rated capacity, charge and discharge efficiency, and target availability of the energy storage device.

[0085] Optionally, the capacity demand determination module further includes: a load demand probability distribution model construction unit, used to construct a load demand probability distribution model based on historical load data in the load parameters, wherein the load demand probability distribution model is used to indicate the confidence interval of the maximum load demand of the power system during the forecast period under different confidence probabilities, and the probability distribution of the load demand exceeding a predetermined value within a predetermined time window; a predicted load peak determination unit, used to determine the predicted load peak under the target confidence probability using the load demand probability distribution model; and a maximum load demand determination unit, used to obtain the maximum load demand based on the predicted load peak.

[0086] Optionally, the adequacy assessment module includes: an adequacy index determination module, used to determine the adequacy index of the power system based on effective power generation capacity and maximum load demand; an adequacy level determination module, used to determine the target adequacy level to which the adequacy index belongs; and a result determination module, used to determine the adequacy assessment result based on the target adequacy level.

[0087] Optionally, the sufficiency level determination module includes: an sufficiency range determination unit, used to determine the target sufficiency range to which the sufficiency index belongs from multiple sufficiency ranges; and an sufficiency level determination unit, used to determine the target sufficiency level from multiple sufficiency levels based on the target sufficiency range, wherein the multiple sufficiency ranges correspond one-to-one with the multiple sufficiency levels.

[0088] Optionally, the device further includes: a predetermined risk preference identification unit, used to determine the predetermined risk preference of the power system before determining the target adequacy range to which the adequacy index belongs from multiple adequacy ranges; a correction strategy determination unit, used to determine a target correction strategy based on the predetermined risk preference; and an adequacy range correction unit, used to correct multiple initial ranges corresponding to multiple adequacy levels based on the target correction strategy to obtain multiple adequacy ranges, wherein the multiple initial ranges correspond one-to-one with the multiple adequacy ranges.

[0089] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0090] It should be noted that the system parameter acquisition module 300, target confidence probability determination module 302, capacity requirement determination module 304, and sufficiency assessment module 306 mentioned above correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run on a computer terminal.

[0091] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0092] The aforementioned power system adequacy determination device may further include a processor and a memory. The aforementioned system parameter acquisition module 300, target confidence probability determination module 302, capacity requirement determination module 304, adequacy assessment module 306, etc., are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0093] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0094] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the aforementioned power system adequacy determination methods.

[0095] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0096] Optionally, a program may be used to control the device containing the non-volatile storage medium to execute any of the above-described steps of the power system adequacy determination method during program execution.

[0097] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described power system adequacy determination methods.

[0098] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the power system adequacy determination method steps described above.

[0099] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the power system adequacy determination method described above.

[0100] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0101] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0102] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0103] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0104] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0105] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile 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 of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0106] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining the adequacy of a power system, characterized in that, include: The system parameters of the power system are obtained, including conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters. The conventional power source parameters represent the equipment parameters corresponding to the power generation equipment that provides a continuous and stable power supply, and the fluctuating power source parameters represent the equipment parameters corresponding to the renewable energy power generation equipment. Determine the target confidence probability corresponding to the power system, wherein the target confidence probability is used to reflect the predetermined risk preference of the power system; Based on the system parameters, the effective generation capacity and maximum load demand of the power system under the target confidence probability are determined, wherein the effective generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainty factors; Based on the effective power generation capacity and the maximum load demand, the adequacy assessment result of the power system is determined, wherein the adequacy assessment result is used to indicate the power system's ability to continuously supply power to meet the needs of all users under normal operating conditions.

2. The method according to claim 1, characterized in that, Determining the effective power generation capacity and maximum load demand of the power system under the target confidence probability based on the system parameters includes: Based on the conventional power source parameters, the fluctuating power source parameters, and the energy storage device parameters in the system parameters, the effective power generation capacity is determined in the following manner: Based on the conventional power source parameters and the target confidence probability, the effective output capacity of the conventional power source in the power system is determined. The conventional power source parameters include the historical blocked capacity data, installed capacity, and planned maintenance capacity of the conventional power source. The effective output capacity of the conventional power source represents the actual power generation capacity that the conventional power source can contribute to the power system under the target confidence probability. Based on the fluctuating power source parameters and the target confidence probability, the effective output capacity of the fluctuating power source in the power system is determined. The fluctuating power source parameters include historical output data and installed capacity. The effective output capacity of the fluctuating power source represents the actual power generation capacity that the fluctuating power source can contribute to the power system under the target confidence probability. Based on the energy storage device parameters and the target confidence probability, the effective power generation of the energy storage device in the power system is determined, wherein the energy storage device parameters include historical available power, rated capacity, and charge / discharge efficiency; The effective power generation capacity is obtained by summing the effective output capacity of the conventional power source, the effective output capacity of the fluctuating power source, and the effective power generation of the energy storage device.

3. The method according to claim 2, characterized in that, Determining the effective output capacity of the conventional power source of the power system based on the conventional power source parameters and the target confidence probability includes: Based on the historical blocked capacity data in the parameters of the conventional power supply, a blocked capacity probability distribution model of the conventional power supply is constructed. The blocked capacity probability distribution model is used to indicate the probability that the blocked capacity of the conventional power supply exceeds a predetermined value under normal operating conditions, and the expected blocked capacity of the conventional power supply that cannot work normally due to external or internal reasons at different confidence probability levels. The blocked capacity probability distribution model is used to determine the predicted blocked capacity of the conventional power source under the target confidence probability. Based on the installed capacity, planned maintenance capacity, and predicted blocked capacity of the conventional power source, the effective output capacity of the conventional power source is obtained.

4. The method according to claim 2, characterized in that, Determining the effective output capacity of the fluctuating power source in the power system based on the fluctuating power source parameters and the target confidence probability includes: Based on the historical output data in the parameters of the fluctuating power source, a simultaneous rate probability distribution model of the output of the fluctuating power source is constructed. The simultaneous rate probability distribution model is used to indicate the simultaneous rate of the fluctuating power source under different confidence probabilities. The simultaneous rate represents the proportion of the actual output of the fluctuating power source to the total installed capacity. Using the aforementioned simultaneity rate probability distribution model, the target simultaneity rate of the fluctuating power source under the target confidence probability is determined; Based on the installed capacity of the fluctuating power source and the target simultaneity rate, the effective output capacity of the fluctuating power source is obtained.

5. The method according to claim 2, characterized in that, Determining the effective power generation of the energy storage device in the power system based on the energy storage device parameters and the target confidence probability includes: Based on the historical available power data in the parameters of the energy storage device, an availability probability distribution model of the energy storage device is constructed. The availability probability distribution model is used to indicate the expected probability that the energy storage device can discharge at rated efficiency under different confidence probabilities, and the confidence level that the energy storage device can provide effective power generation within a predetermined time window. The target availability of the energy storage device under the target confidence probability is determined using the aforementioned availability probability distribution model. Based on the rated capacity, charge / discharge efficiency, and target availability of the energy storage device, the effective power generation of the energy storage device is obtained.

6. The method according to claim 1, characterized in that, Determining the effective power generation capacity and maximum load demand of the power system under the target confidence probability based on the system parameters includes: Based on the load parameters in the system parameters, the maximum load demand is determined in the following manner: Based on the historical load data in the load parameters, a load demand probability distribution model is constructed. The load demand probability distribution model is used to indicate the confidence interval of the maximum load demand of the power system during the forecast period under different confidence probabilities, and the probability distribution of the load demand exceeding a predetermined value within a predetermined time window. Using the probability distribution model of the load demand, the predicted peak load under the target confidence probability is determined; The maximum load demand is obtained based on the predicted peak load.

7. The method according to any one of claims 1 to 6, characterized in that, The determination of the adequacy assessment result of the power system based on the effective power generation capacity and the maximum load demand includes: Based on the effective power generation capacity and the maximum load demand, the adequacy index of the power system is determined; Determine the target sufficiency level to which the sufficiency index belongs; Based on the target sufficiency level, the sufficiency assessment result is determined.

8. The method according to claim 7, characterized in that, Determining the target sufficiency level to which the sufficiency index belongs includes: From multiple sufficiency ranges, determine the target sufficiency range to which the sufficiency index belongs; Based on the target sufficiency range, the target sufficiency level is determined from multiple sufficiency levels, wherein the multiple sufficiency ranges correspond one-to-one with the multiple sufficiency levels.

9. The method according to claim 8, characterized in that, Before determining the target sufficiency range to which the sufficiency index belongs from multiple sufficiency ranges, the method further includes: Determine the predetermined risk preference of the power system; Based on the predetermined risk preference, a target adjustment strategy is determined; Based on the target correction strategy, the multiple initial ranges corresponding to the multiple sufficiency levels are corrected to obtain the multiple sufficiency ranges, wherein the multiple initial ranges correspond one-to-one with the multiple sufficiency ranges.

10. A device for determining the adequacy of a power system, characterized in that, include: The system parameter acquisition module is used to acquire system parameters of the power system. The system parameters include conventional power source parameters, fluctuating power source parameters, energy storage device parameters, and load parameters. The conventional power source parameters represent the equipment parameters corresponding to the power generation equipment that provides a continuous and stable power supply, and the fluctuating power source parameters represent the equipment parameters corresponding to the renewable energy power generation equipment. A target confidence probability determination module is used to determine the target confidence probability corresponding to the power system, wherein the target confidence probability is used to reflect the predetermined risk preference of the power system; A capacity requirement determination module is used to determine the effective generation capacity and maximum load demand of the power system under the target confidence probability based on the system parameters, wherein the effective generation capacity is used to indicate the power supply capacity of the power system after taking into account various uncertainty factors; An adequacy assessment module is used to determine the adequacy assessment result of the power system based on the effective power generation capacity and the maximum load demand, wherein the adequacy assessment result is used to indicate the power system's ability to continuously supply power to meet the needs of all users under normal operating conditions.

11. The apparatus according to claim 10, characterized in that, The capability requirement determination module includes: A conventional power source output determination module is used to determine the effective output capacity of conventional power sources in the power system based on the conventional power source parameters and the target confidence probability. The conventional power source parameters include the historical blocked capacity data, installed capacity, and planned maintenance capacity of the conventional power source. The effective output capacity of the conventional power source represents the actual power generation capacity that the conventional power source can contribute to the power system under the target confidence probability. A fluctuating power source output determination module is used to determine the effective output capacity of the fluctuating power source in the power system based on the fluctuating power source parameters and the target confidence probability. The fluctuating power source parameters include historical output data and installed capacity. The effective output capacity of the fluctuating power source represents the actual power generation capacity that the fluctuating power source can contribute to the power system under the target confidence probability. An energy storage device output determination module is used to determine the effective power generation of the energy storage device in the power system based on the energy storage device parameters and the target confidence probability, wherein the energy storage device parameters include historical available power, rated capacity, and charge / discharge efficiency; The effective power generation capacity determination module is used to sum the effective output capacity of the conventional power source, the effective output capacity of the fluctuating power source, and the effective power generation of the energy storage device to obtain the effective power generation capacity.

12. The apparatus according to claim 11, characterized in that, The conventional power output determination module includes: The obstructed capacity probability distribution model construction unit is used to construct an obstructed capacity probability distribution model of the conventional power supply based on historical obstructed capacity data in the parameters of the conventional power supply. The obstructed capacity probability distribution model is used to indicate the probability that the obstructed capacity of the conventional power supply exceeds a predetermined value under normal operating conditions, and the expected obstructed capacity of the conventional power supply that cannot work normally due to external or internal reasons at different confidence probability levels. The unit for constructing the probability distribution model of the blocked capacity is used to determine the predicted value of the blocked capacity of the conventional power supply under the target confidence probability by using the probability distribution model of the blocked capacity. A conventional power supply output determination unit is used to obtain the effective output capacity of the conventional power supply based on the installed capacity, planned maintenance capacity, and predicted value of the obstructed capacity.

13. The apparatus according to claim 11, characterized in that, The fluctuating power supply output determination module includes: Simultaneous rate probability distribution model construction unit is used to construct a simultaneous rate probability distribution model of the output of the fluctuating power source based on the historical output data in the parameters of the fluctuating power source. The simultaneous rate probability distribution model is used to indicate the simultaneous rate of the fluctuating power source under different confidence probabilities. The simultaneous rate represents the proportion distribution of the actual output of the fluctuating power source to the total installed capacity. The target simultaneity rate probability determination unit is used to determine the target simultaneity rate of the fluctuating power source under the target confidence probability using the simultaneity rate probability distribution model. A fluctuating power supply output determination unit is used to obtain the effective output capacity of the fluctuating power supply based on the installed capacity of the fluctuating power supply and the target simultaneity rate.

14. The apparatus according to claim 11, characterized in that, The energy storage device output determination module includes: The availability probability distribution model construction unit is used to construct the availability probability distribution model of the energy storage device based on the historical available power data in the parameters of the energy storage device. The availability probability distribution model is used to indicate the expected probability that the energy storage device can discharge at rated efficiency under different confidence probabilities, and the confidence level that the energy storage device can provide effective power generation within a predetermined time window. The target availability determination unit is used to determine the target availability of the energy storage device under the target confidence probability using the availability probability distribution model. An energy storage device output determination unit is used to obtain the effective power generation of the energy storage device based on the rated capacity, charge and discharge efficiency, and target availability of the energy storage device.

15. The apparatus according to claim 10, characterized in that, The capability requirement determination module also includes: The load demand probability distribution model construction unit is used to construct a load demand probability distribution model based on historical load data in the load parameters. The load demand probability distribution model is used to indicate the confidence interval of the maximum load demand of the power system during the forecast period under different confidence probabilities, and the probability distribution of the load demand exceeding a predetermined value within a predetermined time window. The peak load prediction unit is used to determine the peak load prediction under the target confidence probability by using the probability distribution model of the load demand. The maximum load demand determination unit is used to obtain the maximum load demand based on the predicted load peak.

16. The apparatus according to any one of claims 10 to 15, characterized in that, The adequacy assessment module includes: An adequacy index determination module is used to determine the adequacy index of the power system based on the effective power generation capacity and the maximum load demand. An adequacy level determination module is used to determine the target adequacy level to which the adequacy index belongs; The result determination module is used to determine the adequacy assessment result based on the target adequacy level.

17. The apparatus according to claim 16, characterized in that, The adequacy level determination module includes: The sufficiency range determination unit is used to determine the target sufficiency range to which the sufficiency index belongs from multiple sufficiency ranges; An adequacy level determination unit is used to determine the target adequacy level from multiple adequacy levels based on the target adequacy range, wherein the multiple adequacy ranges correspond one-to-one with the multiple adequacy levels.

18. The apparatus according to claim 17, characterized in that, The device further includes: A predetermined risk preference identification unit is used to determine the predetermined risk preference of the power system before determining the target adequacy range to which the adequacy index belongs from multiple adequacy ranges; The correction strategy determination unit is used to determine the target correction strategy based on the predetermined risk preference; The sufficiency range correction unit is used to correct multiple initial ranges corresponding to the multiple sufficiency levels based on the target correction strategy to obtain the multiple sufficiency ranges, wherein the multiple initial ranges correspond one-to-one with the multiple sufficiency ranges.

19. A non-volatile storage medium, characterized in that, The non-volatile storage medium stores multiple instructions, which are adapted to be loaded by a processor and executed by the power system adequacy determination method according to any one of claims 1 to 9.

20. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein 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 power system adequacy determination method according to any one of claims 1 to 9.

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