Electric power system capacity adequacy early warning method, system, equipment and medium

Abstract

The invention belongs to the technical field of power system planning and dispatching, and particularly relates to a power system capacity adequacy early warning method and system, electronic equipment and a storage medium. The method comprises the steps that meteorological data and operation data of a new energy unit are collected, the initial available capacity of the new energy unit is calculated, and the sufficient capacity of the new energy unit and the probability of the sufficient capacity are obtained in combination with fault factors of the new energy unit; power system capacity demand data and traditional energy sufficient capacity data are collected in real time, and the sufficient capacity supply quantity of the new energy unit is generated; matching the sufficient capacity supply quantity, the sufficient capacity and the probability to obtain a satisfaction probability of the sufficient capacity, and performing first early warning according to the size of the satisfaction probability; and constructing a power system abundance evaluation model according to the abundance capacity, generating an abundance evaluation coefficient, comparing the abundance evaluation coefficient with a standard threshold value, and performing second early warning according to a comparison result. According to the invention, optimal configuration of resources can be realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system planning and dispatching, and in particular relates to a power system capacity margin early warning method, system, electronic equipment and storage medium. Background Art

[0002] The power system adequacy refers to the ability of the power generation and transmission system to continuously provide users with power and electric energy demand within the allowable limits of the rated capacity of the power generation, transmission and transformation facilities, taking into account the planned and unplanned outages of components and operating constraints. There are generally two criteria for judging adequacy, one is the deterministic rule, and the other is to use probabilistic indicators to measure the reliability of the system. The marginal value of power generation capacity is decreasing. In order to control the cost of power supply and improve economic efficiency, it is necessary to ensure capacity adequacy and avoid redundant power generation capacity. Under the planned system, this goal is achieved through unified planning. Under the market environment, it is necessary to guide reasonable and optimal power generation capacity through certain market mechanisms. In the new power system dominated by low variable marginal cost power generation technology, it is becoming increasingly important to improve the accuracy of system power generation capacity margin assessment and accurately identify the risk of insufficient system capacity adequacy through market mechanism guidance and reasonable dispatch and control of power electronic equipment.

[0003] There are at least the following technical problems in the above-mentioned disclosed technical solutions: previous grid adequacy warnings often only carried out assessments and warnings on the power generation capacity adequacy of conventional units on the power generation side. Under the construction of new power systems, the configuration ratio of new energy units such as wind power and photovoltaics has increased, and new energy units have shown a certain capacity value. The original assessment and warning methods cannot comprehensively assess and warn of the capacity adequacy of new power systems. Summary of the invention

[0004] To solve the above problems, the present invention provides a power system capacity adequacy early warning method, system, electronic device and storage medium, which solve the problem of being unable to accurately and effectively evaluate the power system capacity adequacy and give early warning prompts from multiple directions.

[0005] In order to solve the above technical problems, the first aspect of the present invention provides a power system capacity margin early warning method, the method comprising:

[0006] Collect meteorological data and operation data of the new energy unit, calculate the initial available capacity of the new energy unit, and obtain the sufficient capacity and the probability of sufficient capacity of the new energy unit in combination with the failure factors of the new energy unit;

[0007] Collecting power system capacity demand data and traditional energy surplus capacity data in real time to generate the surplus capacity supply of the new energy unit;

[0008] Matching the surplus capacity supply, the surplus capacity and the probability to obtain the probability of satisfying the surplus capacity, and issuing a first warning according to the magnitude of the probability of satisfying the surplus capacity;

[0009] A power system margin assessment model is constructed based on the surplus capacity, a margin assessment coefficient is generated, and the coefficient is compared with a standard threshold value, and a second warning is issued based on the comparison result.

[0010] According to a preferred embodiment of the present invention, the collecting of meteorological data includes:

[0011] Collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can hold at the current air temperature and the saturated water vapor pressure at the current dew point temperature;

[0012] The relative humidity around the new energy unit is calculated according to the maximum water vapor amount and the saturated water vapor pressure to obtain the weather impact parameter.

[0013] According to a preferred embodiment of the present invention, the collecting of meteorological data and operation data of the new energy units and calculating the initial available capacity of the new energy units include:

[0014] Obtain the installed capacity of new energy units;

[0015] The initial available capacity of the new energy unit is determined according to the installed capacity and the operating data of the new energy unit.

[0016] According to a preferred embodiment of the present invention, the real-time collection of power system capacity demand data and traditional energy surplus capacity data to generate the surplus capacity supply of new energy units includes:

[0017] According to the real-time collection of power system capacity demand data and traditional energy surplus capacity data, the power generation capacity surplus is obtained;

[0018] The new energy penetration power coefficient is obtained, and combined with the power generation capacity margin, the sufficient capacity supply of the new energy unit is determined.

[0019] According to a preferred implementation of the present invention, performing a first warning according to the probability of satisfaction includes:

[0020] comparing the surplus capacity supply, the surplus capacity and the probability with a probability distribution table;

[0021] The probability of satisfying the sufficient capacity is obtained according to the comparison result, and a first warning is issued according to the satisfaction probability value.

[0022] According to a preferred embodiment of the present invention, generating a sufficiency assessment coefficient includes:

[0023] Constructing a weather influence coefficient prediction model according to the weather influence parameters to obtain the weather influence coefficient;

[0024] According to the weather impact coefficient, a power system capacity margin assessment coefficient is generated.

[0025] According to a preferred implementation manner of the present invention, the second warning according to the comparison result includes:

[0026] comparing the adequacy assessment coefficient with a standard threshold;

[0027] If the adequacy assessment coefficient is not less than the standard threshold, it means that the adequacy of the power system is good and no warning is required;

[0028] If the margin assessment coefficient is less than the standard threshold, it means that the power system margin is insufficient, a second warning is issued, and the number of routine maintenance of the new energy units is increased to ensure the stability and reliability of the power system.

[0029] In order to solve the above technical problems, the second aspect of the present invention proposes a power system capacity sufficiency early warning system, characterized in that the system comprises: a sufficiency capacity acquisition module, a supply quantity generation module, a first early warning module and a second early warning module;

[0030] The surplus capacity acquisition module is used to collect meteorological data and operation data of the new energy unit, calculate the initial available capacity of the new energy unit, and obtain the surplus capacity and the probability of the surplus capacity of the new energy unit in combination with the failure factors of the new energy unit;

[0031] The supply quantity generation module is used to collect power system capacity demand data and traditional energy surplus capacity data in real time to generate the surplus capacity supply quantity of the new energy unit;

[0032] The first warning module is used to match the sufficient capacity supply, the sufficient capacity and the probability, obtain the probability of satisfying the sufficient capacity, and issue a first warning according to the magnitude of the probability of satisfying the sufficient capacity;

[0033] The second warning module is used to construct a power system surplus capacity assessment model according to the surplus capacity, generate a surplus assessment coefficient, compare it with a standard threshold, and issue a second warning according to the comparison result.

[0034] According to a preferred embodiment of the present invention, it comprises:

[0035] The surplus capacity acquisition module is further used to collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can contain at the current air temperature and the saturated water vapor pressure at the current dew point temperature;

[0036] The surplus capacity acquisition module is also used to calculate the relative humidity around the new energy unit according to the maximum water vapor amount and the saturated water vapor pressure to obtain weather impact parameters.

[0037] According to a preferred embodiment of the present invention, it comprises:

[0038] The surplus capacity acquisition module is also used to obtain the installed capacity of the new energy unit;

[0039] The surplus capacity acquisition module is further used to determine the initial available capacity of the new energy unit according to the installed capacity and the operating data of the new energy unit.

[0040] According to a preferred embodiment of the present invention, it comprises:

[0041] The supply quantity generating module is further used to obtain the power generation capacity margin according to the real-time collected power system capacity demand data and the traditional energy surplus capacity data;

[0042] The supply quantity generation module is also used to obtain the new energy penetration power coefficient, and determine the sufficient capacity supply quantity of the new energy unit in combination with the power generation capacity margin.

[0043] According to a preferred embodiment of the present invention, it comprises:

[0044] The first warning module is further used to compare the surplus capacity supply, the surplus capacity and the probability with a probability distribution table;

[0045] The first warning module is further used to obtain the probability of satisfying the sufficient capacity according to the comparison result, and to issue a first warning according to the satisfaction probability value.

[0046] According to a preferred embodiment of the present invention, it comprises:

[0047] The second warning module is further used to construct a weather impact coefficient prediction model according to the weather impact parameters to obtain the weather impact coefficient;

[0048] The second warning module is also used to generate a power system capacity margin assessment coefficient based on the weather impact coefficient.

[0049] According to a preferred embodiment of the present invention, it comprises:

[0050] The second warning module is further used to compare the adequacy assessment coefficient with a standard threshold;

[0051] The second warning module is further configured to indicate that if the adequacy assessment coefficient is not less than the standard threshold, it indicates that the adequacy of the power system is good and no warning is required;

[0052] The second warning module is also used to issue a second warning if the margin assessment coefficient is less than the standard threshold, indicating that the system margin is insufficient, and increase the number of daily maintenance times of the new energy unit to ensure the stability and reliability of the power system.

[0053] In order to solve the above technical problem, the third aspect of the present invention provides an electronic device, comprising:

[0054] processor;

[0055] and a memory storing computer executable instructions, wherein the computer executable instructions, when executed, cause the processor to perform the method described in any one of the above embodiments.

[0056] In order to solve the above technical problems, the fourth aspect of the present invention proposes a computer storage medium, wherein the computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method described in any one of the above embodiments is implemented.

[0057] Compared with the prior art, the present invention has the following advantages: by considering the impact of various aspects of renewable energy generation on the capacity margin of the power system, the present invention can identify potential capacity shortages and risk points, thereby taking early warning measures to reduce the possibility of power outages and ensure the stability and reliability of the system during high-load periods. Through multi-faceted analysis, the present invention can identify situations where renewable energy generation resources need to be increased or adjusted, thereby achieving optimal resource allocation, improving the economy and operating efficiency of the overall system, and reducing operating costs.

[0058] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0060] Figure 1A schematic diagram of a method for early warning of power system capacity margin according to an embodiment of the present invention is shown;

[0061] Figure 2 A structural diagram of a power system capacity margin early warning system according to an embodiment of the present invention is shown;

[0062] Figure 3 A schematic diagram of the structure of an electronic device according to an embodiment of the present invention is shown;

[0063] Figure 4 A schematic diagram showing capacity margin is shown. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] The same reference numerals in the drawings represent the same or similar elements, components or parts, and thus the repeated description of the same or similar elements, components or parts may be omitted below. It should also be understood that although the first, second, third and other attributives representing the numbers may be used herein to describe various devices, elements, components or parts, these devices, elements, components or parts should not be limited by these attributives. In other words, these attributives are only used to distinguish one from another. For example, the first device may also be called the second device, but it does not deviate from the essential technical solution of the present invention. In addition, the terms "and / or" and "and / or" refer to all combinations including any one or more of the listed items.

[0066] like Figure 1 As shown, a power system capacity margin early warning method, the method comprising:

[0067] S1. Collect meteorological data and operating data of new energy units, calculate the initial available capacity of the new energy units, and combine the unit failure factors to obtain the sufficient capacity and the probability of sufficient capacity of the new energy units.

[0068] In this embodiment, meteorological data such as wind speed and photovoltaic irradiance are collected. The meteorological data include temperature echo data, relative humidity data, and wind speed. The initial available capacity of the new energy unit is calculated based on the operating data of the new energy unit. Combined with the unit failure factors, including failure rate, repair rate, and forced shutdown rate, the sufficient capacity and probability of sufficient capacity of the new energy unit are obtained.

[0069] In this embodiment, considering the two states of normal operation and failure shutdown of the new energy units, the failures of each unit in the new energy station can be considered independent of each other.

[0070] Specifically, MTTF i =8760 / λ i ;

[0071] MTTR i =8760 / μ i ;

[0072]

[0073] Among them, the failure rate of the i-th unit is λ i (times / year), repair rate is μ ii (times / year) under the condition of mean trouble-free operation time MTTF i MTTR i , Forced outage rate FOR i .

[0074] In this embodiment, the new energy station includes multiple types of identical units, and the new energy station can be equivalent to a multi-state unit.

[0075] Specifically,

[0076] Among them, n is the number of new energy units of the same type, r is the forced outage rate FOR of each unit i , P(K=m) is the probability that m (m≤n) stations operate normally at the same time.

[0077] Specifically, the cumulative probability distribution function is:

[0078] Specifically, if the new energy units in the power system are regarded as n multi-state units, the surplus capacity of the new energy units is expressed as follows:

[0079] Specifically, the corresponding relationship between the sufficient capacity of the new energy unit and its probability is further obtained by obtaining the probability corresponding to the sufficient capacity according to the following table.

[0080] An example calculation is performed with n=5 and the forced outage rate is 0.04, and Table 1 is obtained, which shows the correspondence table of the sufficient capacity and its probability under six states.

[0081]

[0082] Table 1

[0083] S11. Collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can contain at the current air temperature and the saturated water vapor pressure at the current dew point temperature.

[0084] In this embodiment, the current air temperature and dew point temperature are collected, and the maximum amount of water vapor that the air can contain at the current temperature and the saturated water vapor pressure at the current dew point temperature are calculated to represent the actual amount of water vapor in the air.

[0085] S12. Calculate the relative humidity around the new energy unit according to the maximum water vapor amount and the saturated water vapor pressure to obtain a weather impact parameter.

[0086] In this embodiment, the relative humidity around the power generation fan is calculated based on the acquired data.

[0087] Specifically, the specific calculation formula for relative humidity is:

[0088]

[0089] Where RH is the relative humidity, CG is the saturated water vapor pressure at the dew point temperature, and T is the saturated water vapor pressure at the current temperature.

[0090] S121. Construct a weather impact coefficient prediction model according to the weather impact parameters to obtain a weather impact coefficient.

[0091] In this embodiment, a weather influence coefficient prediction model is constructed according to the weather influence parameters to obtain the weather influence coefficient, that is, the weather influence coefficient is obtained according to the relative humidity.

[0092] Specifically, the specific calculation formula for the weather influence coefficient is:

[0093]

[0094] in, is the weather influence coefficient, RH is the relative humidity, hb is the temperature echo data, and f is the wind speed.

[0095] S122. Generate a power system capacity margin assessment coefficient based on the weather impact coefficient.

[0096] In this embodiment, the power system capacity margin assessment coefficient is generated according to the weather impact coefficient.

[0097] Specifically, the specific calculation formula of the adequacy assessment coefficient is:

[0098]

[0099] Among them, ψ is the adequacy assessment coefficient, ε is the power user load adjustment coefficient, is the weather influence coefficient, λ1 is the preset weight factor of the adequacy assessment coefficient, λ2 is the preset weight factor of the power generation capacity adequacy coefficient of the generator set, λ3 is the preset weight factor of the weather influence coefficient, N G is the number of generator sets.

[0100] S13. Obtain the installed capacity of the new energy units.

[0101] In this embodiment, the initial installed capacity of the wind turbine generator set and the photovoltaic generator set is obtained through historical data.

[0102] S14. Determine the initial available capacity of the new energy unit according to the installed capacity and the operating data of the new energy unit.

[0103] In this embodiment, the initial available capacities of the wind turbine generator set and the photovoltaic generator set are calculated by taking into account the annual utilization hours and initial available capacities of the wind turbine generator set and the photovoltaic generator set.

[0104] Specifically, the specific calculation formula for the initial available capacity is:

[0105]

[0106] Among them, C' new is the initial available capacity, n auh is the number of hours of use per year, C int is the initial installed capacity.

[0107] S2. Collect power system capacity demand data and traditional energy surplus capacity data in real time to generate surplus capacity supply for new energy units.

[0108] In this embodiment, power system capacity demand data and traditional energy surplus capacity data are collected in real time to evaluate the power system capacity value required by new energy, that is, the surplus capacity supply of new energy units.

[0109] S21. Obtain power generation capacity surplus according to the real-time collected power system capacity demand data and traditional energy surplus capacity data.

[0110] In this embodiment, the system power generation capacity margin is an indicator to measure the supply and demand balance ability of the system during normal operation, specifically referring to the ratio of the total surplus capacity provided by the system generator sets in a certain system or subsystem to the average load value in the highest load period in the system annual load curve.

[0111] Specifically, the specific calculation formula for the system power generation capacity margin is:

[0112]

[0113] Among them, θ represents the system power generation capacity margin, and its value range is (0, +∞), Sufficient capacity for traditional energy sources such as thermal power and hydropower, C new Sufficient capacity for new energy units, It is the average load value of the m highest load periods in the system annual load curve.

[0114] In this embodiment, if Figure 4 As shown in the figure, in the measurement of the system power generation capacity adequacy, when θ=1, it is the static critical adequacy point of the system power generation capacity. When θ∈(0,1), it represents the static insufficiency of the power system power generation capacity. When θ∈(1,+∞), it represents the static adequacy of the power system power generation capacity. At the same time, in the static adequacy interval, due to the volatility of renewable energy output and capacity disturbance, it is divided into dynamic risk adequacy interval and dynamic adequacy interval. In the static adequacy domain, if there is no capacity disturbance or under the specified capacity disturbance set, the system will not force power rationing.

[0115] S22. Obtain the new energy penetration power coefficient, and determine the sufficient capacity supply of the new energy unit in combination with the power generation capacity surplus.

[0116] In this embodiment, the supply of sufficient capacity of new energy units is defined as: when the capacity of new energy units such as wind power and photovoltaic units connected to the power system is low, the system is less affected by the volatility of new energy output, and sufficient sufficient capacity can be provided by traditional energy. In the context of a high proportion of new energy connected to the power system, the intermittency and volatility of new energy such as wind power and photovoltaic units will have a significant impact on the system load demand. In this regard, only traditional energy can provide sufficient capacity to meet the capacity demand during peak load periods, and new energy units need to bear a certain proportion of the system capacity from the machine, thereby tapping the capacity value of new energy units.

[0117] In this embodiment, the proportion of renewable energy that can be afforded is determined, and a renewable energy penetration power coefficient is constructed. The renewable energy penetration power coefficient represents the degree of limitation of the system on the proportion of renewable energy access. The penetration power coefficient δ is defined as the ratio of the output power of the renewable energy unit to the system load when the renewable energy penetration power reaches the limit. The renewable energy output must be less than or equal to the product of the penetration power coefficient and the load.

[0118] Specifically,

[0119] Among them, P rew,j Contribute to new energy, LD is the load power, P DF is the load shedding power, and δ is the new energy penetration power coefficient.

[0120] In this embodiment, in order to meet the static margin of the system, that is, θ=1, the new energy unit needs to make its surplus capacity meet the condition under its penetration power limit to obtain the surplus capacity supply of the new energy unit.

[0121] Specifically,

[0122] S3. Match the surplus capacity supply, the surplus capacity and the probability to obtain the probability of satisfying the surplus capacity, and issue a first warning according to the magnitude of the probability of satisfying the surplus capacity.

[0123] In this embodiment, the power system capacity supply borne by the new energy is obtained based on the determined system power generation margin. The power system capacity supply borne by the new energy is the surplus capacity supply of the new energy unit. The new energy unit can be a wind turbine unit and a photovoltaic unit.

[0124] S31. Compare the surplus capacity supply, the surplus capacity and the probability with a probability distribution table.

[0125] In this embodiment, the obtained surplus capacity supply, surplus capacity and probability are compared with Table 1, a correspondence table of surplus capacity and its probability under six states.

[0126] S32. Obtain the probability of satisfying the sufficient capacity according to the comparison result, and issue a first warning according to the satisfaction probability value.

[0127] In this embodiment, by setting the size of the system power generation capacity margin θ, the minimum boundary of the power system capacity supply undertaken by new energy can be obtained. By comparing this sufficient capacity boundary with Table 1, the probability of the capacity can be obtained, and the first warning can be issued based on the obtained probability.

[0128] S4. Construct a power system surplus capacity assessment model based on the surplus capacity, generate a surplus capacity assessment coefficient, compare it with a standard threshold, and issue a second warning based on the comparison result.

[0129] In this embodiment, a power system margin assessment model is constructed based on the surplus capacity of the wind turbine and photovoltaic units to generate a margin assessment coefficient, and the margin assessment coefficient is compared with a preset standard threshold to obtain a result, and an early warning is issued based on the comparison result.

[0130] S41, comparing the adequacy assessment coefficient with a standard threshold.

[0131] In this embodiment, the generated adequacy assessment coefficient is compared with a preset standard threshold to obtain a result, and an early warning is issued.

[0132] S42. If the adequacy assessment coefficient is not less than the standard threshold, it means that the system adequacy is good and no warning is required.

[0133] In this embodiment, if the adequacy assessment coefficient is greater than or equal to the standard threshold, it means that the system adequacy is good and no warning is required.

[0134] S43. If the margin assessment coefficient is less than the standard threshold, it indicates that the system margin is insufficient, a second warning is issued, and the number of daily equipment maintenance is increased to ensure the stability and reliability of the power system.

[0135] In this embodiment, the specific operations of the adjustment are to improve the operational stability of the equipment: increase the number of daily equipment maintenance: by increasing the number of daily inspections, timely discover and eliminate potential threats that may cause equipment downtime. Introduce backup generator sets: add backup power generation equipment to cope with high load demands. Optimize load management: Load forecasting: Improve the load forecasting model to ensure accurate forecasting of loads during high demand periods. Demand response measures: Implement demand response strategies to encourage users to reduce electricity consumption during high load periods. Improve grid reliability: Optimize grid dispatching and operation capabilities: Explore new grid dispatching and operation methods to improve grid reliability and transmission capabilities. Enhance the stability of transmission lines: Optimize transmission lines to ensure that electricity can be effectively distributed to areas where it is needed. Improve system flexibility: Energy storage system: Introduce energy storage systems (such as battery storage) to balance supply and demand fluctuations. Distributed generation: Promote distributed generation (such as solar energy and wind energy) to reduce reliance on centralized power generation.

[0136] In this embodiment, the power regulation coefficient is an important indicator in the fan system for describing the performance changes of the fan under different load requirements and external environmental conditions. It is mainly used to evaluate and adjust the working state of the fan to ensure that it can operate effectively under different working conditions. This coefficient can help the fan system to optimize and adjust according to the actual supply and demand conditions, thereby improving efficiency and stability.

[0137] Specifically, the specific calculation formula for the power regulation coefficient of the wind turbine is:

[0138]

[0139] The specific calculation formula of the power regulation coefficient is:

[0140]

[0141] Among them, ρ is the power fluctuation coefficient, ε is the power regulation coefficient, and p i is the real-time wind power at the i-th moment, n is the total number of moments collected within time t, and k is the preset correction factor.

[0142] In this embodiment, the comprehensive efficiency coefficient refers to the ratio of converting the solar radiation energy received by the photovoltaic panel into electrical energy. It combines the conversion efficiency, temperature influence, lighting conditions and other loss factors of the photovoltaic panel, and represents the ratio between the actual output power and the received solar power. The specific method for obtaining the comprehensive efficiency coefficient is as follows: obtain the size data of the solar photovoltaic panel and calculate the surface area; measure the total power output and light intensity of the solar photovoltaic panel within time t; combine the inverter efficiency and the average light intensity, and calculate the comprehensive efficiency coefficient based on the Logistic regression analysis method.

[0143] Specifically, the specific calculation formula for the comprehensive efficiency coefficient is as follows:

[0144]

[0145] Among them, ω is the comprehensive efficiency coefficient, S is the surface area of ​​the solar photovoltaic panel, QD is the light intensity, QD is the average light intensity, t is the illumination time, GL is the total power output, χ is the preset inverter efficiency, WD is the actual temperature of the solar photovoltaic panel, and T0 is the reference temperature under standard test conditions.

[0146] In this embodiment, the comprehensive efficiency coefficient reflects the ability of the photovoltaic panel to convert light energy into electrical energy under actual working conditions. By considering this coefficient, the actual power generation of the photovoltaic system can be predicted more accurately, thereby more accurately evaluating the capacity margin. This helps to avoid excess or insufficient capacity and ensure the stable operation of the power system.

[0147] In this embodiment, capacity margin is an important consideration in power system planning and operation. By comprehensively considering the comprehensive efficiency coefficient of photovoltaic panels, power resources can be more reasonably configured, including power generation equipment, transmission lines, and energy storage equipment. This helps to reduce system operating costs, improve energy utilization efficiency, and promote the sustainable development of the power system.

[0148] In this embodiment, the comprehensive efficiency coefficient not only reflects the performance of the photovoltaic panel, but also indirectly reflects the health of the entire photovoltaic system. When the comprehensive efficiency coefficient decreases, it may mean that the photovoltaic panel has problems such as aging, pollution or failure. By timely monitoring and analyzing the comprehensive efficiency coefficient, problems can be discovered and solved in a timely manner, thereby improving the reliability and stability of the photovoltaic system.

[0149] In this embodiment, with the development of smart grids, higher requirements are placed on the dispatching and management of power systems. The comprehensive efficiency coefficient can be used as an important input parameter of the intelligent dispatching system to optimize power distribution and dispatching strategies. By considering the comprehensive efficiency coefficient of photovoltaic panels, the power generation in different time periods can be predicted more accurately, thereby achieving more efficient power dispatching and management.

[0150] In this embodiment, the improvement of the comprehensive efficiency coefficient requires continuous technological innovation and industrial upgrading. By researching and developing more efficient photovoltaic materials, components, inverters and other technologies, the comprehensive efficiency coefficient of photovoltaic panels can be improved, thereby promoting the upgrading and development of the entire photovoltaic industry. This helps to reduce the cost of photovoltaic power generation, improve market competitiveness, and promote the widespread application of renewable energy.

[0151] In this embodiment, the comprehensive efficiency coefficient plays an important role in evaluating capacity adequacy, which helps to improve prediction accuracy, optimize resource allocation, enhance system reliability, support intelligent scheduling, and promote technological innovation and industrial upgrading.

[0152] In this embodiment, the weather impact coefficient refers to a parameter used to quantify and adjust the impact of weather conditions on the load and power generation capacity of the power system. These coefficients take into account the impact of weather changes on the power generation of renewable energy (such as wind and solar energy) and power demand. By introducing the weather impact coefficient, the system can more accurately evaluate the capacity requirements under different weather conditions, ensuring that the system can maintain stable and reliable power supply under various meteorological conditions.

[0153] like Figure 2 As shown, a power system capacity sufficiency early warning system includes: a sufficiency capacity acquisition module, a supply quantity generation module, a first early warning module and a second early warning module.

[0154] In this embodiment, the surplus capacity acquisition module is specifically used to collect meteorological data and operating data of the new energy unit, calculate the initial available capacity of the new energy unit, and obtain the surplus capacity and probability of surplus capacity of the new energy unit in combination with the failure factors of the new energy unit.

[0155] In this embodiment, the supply quantity generation module is specifically used to collect the power system capacity demand data and the traditional energy surplus capacity data in real time, and generate the surplus capacity supply quantity of the new energy unit.

[0156] In this embodiment, the first warning module is specifically used to match the surplus capacity supply, the surplus capacity and the probability, obtain the probability of satisfying the surplus capacity, and issue a first warning according to the magnitude of the satisfaction probability.

[0157] In this embodiment, the second warning module is specifically used to construct a power system surplus capacity assessment model according to the surplus capacity, generate a surplus assessment coefficient, compare it with the standard threshold, and issue a second warning according to the comparison result.

[0158] In this embodiment, the surplus capacity acquisition module is specifically used to collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can contain at the current air temperature and the saturated water vapor pressure at the current dew point temperature.

[0159] In this embodiment, the surplus capacity acquisition module is specifically used to calculate the relative humidity around the new energy unit according to the maximum water vapor amount and the saturated water vapor pressure to obtain the weather impact parameter.

[0160] In this embodiment, the surplus capacity acquisition module is specifically used to obtain the installed capacity of the new energy unit.

[0161] In this embodiment, the surplus capacity acquisition module is specifically used to determine the initial available capacity of the new energy unit according to the installed capacity and the operating data of the new energy unit.

[0162] In this embodiment, the supply quantity generating module is specifically used to obtain the power generation capacity margin according to the real-time collected power system capacity demand data and traditional energy surplus capacity data.

[0163] In this embodiment, the supply quantity generation module is specifically used to obtain the new energy penetration power coefficient, and determine the surplus capacity supply quantity of the new energy unit in combination with the power generation capacity surplus.

[0164] In this embodiment, the first warning module is specifically used to compare the surplus capacity supply, the surplus capacity and the probability with the probability distribution table.

[0165] In this embodiment, the first warning module is specifically used to obtain the probability of satisfaction of the sufficient capacity according to the comparison result, and to issue a first warning according to the probability value.

[0166] In this embodiment, the second warning module is specifically used to construct a weather impact coefficient prediction model according to the weather impact parameters to obtain the weather impact coefficient.

[0167] In this embodiment, the second warning module is specifically used to generate a power system capacity margin assessment coefficient according to the weather impact coefficient.

[0168] In this embodiment, the second warning module is specifically used to compare the adequacy assessment coefficient with a standard threshold.

[0169] In this embodiment, the second warning module is specifically used to indicate that if the adequacy assessment coefficient is not less than the standard threshold, it means that the adequacy of the power system is good and no warning is needed.

[0170] In this embodiment, the second warning module is specifically used to issue a secondary warning if the margin assessment coefficient is less than the standard threshold, indicating that the system margin is insufficient, and increase the number of daily maintenance times of the new energy unit to ensure the stability and reliability of the system.

[0171] like Figure 3 As shown, an embodiment of the present invention provides an electronic device, including a processor 1110, a communication interface 1120, a memory 1130 and a communication bus 1140, wherein the processor 1110, the communication interface 1120, and the memory 1130 communicate with each other through the communication bus 1140;

[0172] Memory 1130, used for storing computer programs;

[0173] The processor 1110 is configured to implement any of the above determination methods when executing a program stored in the memory 1130 .

[0174] In the electronic device provided by the embodiment of the present invention, the processor 1110 obtains the fluid flow rate of each branch in different switching states by executing the program stored in the memory 1130, and determines the initial volume flow rate of each branch; corrects the initial volume flow rate according to the pipeline parameters and fluid parameters of each branch when it is in working condition and standard condition, and obtains the standard condition volume flow rate of each branch; obtains multiple total standard condition volume flow rates according to the standard condition volume flow rates of each branch in different switching states, and determines the optimal switching state of each branch through the switching state of each branch when the total standard condition volume flow rate reaches a preset target.

[0175] The communication bus 1140 mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0176] The communication interface 1120 is used for communication between the above electronic device and other devices.

[0177] The memory 1130 may include a random access memory 1130 (RAM), or may include a non-volatile memory 1130 (non-volatile memory), such as at least one disk memory 1130. Optionally, the memory 1130 may also be at least one storage device located away from the processor 1110.

[0178] The above-mentioned processor 1110 can be a general-purpose processor 1110, including a central processing unit 1110 (CPU), a network processor 1110 (NP), etc.; it can also be a digital signal processor 1110 (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components.

[0179] An embodiment of the present invention provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors 1110 to implement the determination method of any of the above embodiments.

[0180] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk SolidStateDisk (SSD)), etc.

[0181] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A power system capacity margin early warning method, characterized in that: The method comprises: Collect meteorological data and operation data of the new energy unit, calculate the initial available capacity of the new energy unit, and obtain the sufficient capacity and the probability of sufficient capacity of the new energy unit in combination with the failure factors of the new energy unit; Collecting power system capacity demand data and traditional energy surplus capacity data in real time to generate the surplus capacity supply of the new energy unit; Matching the surplus capacity supply, the surplus capacity and the probability to obtain the probability of satisfying the surplus capacity, and issuing a first warning according to the magnitude of the probability of satisfying the surplus capacity; A power system margin assessment model is constructed based on the surplus capacity, a margin assessment coefficient is generated, and the coefficient is compared with a standard threshold value, and a second warning is issued based on the comparison result.

2. The early warning method according to claim 1, characterized in that: The collecting of meteorological data includes: Collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can hold at the current air temperature and the saturated water vapor pressure at the current dew point temperature; The relative humidity around the new energy unit is calculated according to the maximum water vapor amount and the saturated water vapor pressure to obtain the weather impact parameter.

3. The early warning method according to claim 1, characterized in that: The collecting of meteorological data and operation data of the new energy units and calculating the initial available capacity of the new energy units include: Obtain the installed capacity of new energy units; The initial available capacity of the new energy unit is determined according to the installed capacity and the operating data of the new energy unit.

4. The early warning method according to claim 1, characterized in that: The real-time collection of power system capacity demand data and traditional energy surplus capacity data to generate the surplus capacity supply of new energy units includes: According to the real-time collection of power system capacity demand data and traditional energy surplus capacity data, the power generation capacity surplus is obtained; The new energy penetration power coefficient is obtained, and combined with the power generation capacity margin, the sufficient capacity supply of the new energy unit is determined.

5. The early warning method according to claim 1, characterized in that: The first warning is issued according to the probability of satisfaction, including: comparing the surplus capacity supply, the surplus capacity and the probability with a probability distribution table; The probability of satisfying the sufficient capacity is obtained according to the comparison result, and a first warning is issued according to the satisfaction probability value.

6. The early warning method according to claim 2, characterized in that: The generation adequacy assessment coefficient includes: Constructing a weather influence coefficient prediction model according to the weather influence parameters to obtain the weather influence coefficient; According to the weather impact coefficient, a power system capacity margin assessment coefficient is generated.

7. The early warning method according to claim 1, characterized in that: The second warning according to the comparison result includes: comparing the adequacy assessment coefficient with a standard threshold; If the adequacy assessment coefficient is not less than the standard threshold, it means that the adequacy of the power system is good and no warning is required; If the margin assessment coefficient is less than the standard threshold, it means that the power system margin is insufficient, a second warning is issued, and the number of routine maintenance of the new energy units is increased to ensure the stability and reliability of the power system.

8. A power system capacity margin early warning system, characterized in that: The system comprises: a surplus capacity acquisition module, a supply quantity generation module, a first warning module and a second warning module; The surplus capacity acquisition module is used to collect meteorological data and operation data of the new energy unit, calculate the initial available capacity of the new energy unit, and obtain the surplus capacity and the probability of surplus capacity of the new energy unit in combination with the failure factors of the new energy unit; The supply quantity generation module is used to collect power system capacity demand data and traditional energy surplus capacity data in real time to generate the surplus capacity supply quantity of the new energy unit; The first warning module is used to match the sufficient capacity supply, the sufficient capacity and the probability, obtain the probability of satisfying the sufficient capacity, and issue a first warning according to the magnitude of the probability of satisfying the sufficient capacity; The second warning module is used to construct a power system surplus capacity assessment model according to the surplus capacity, generate a surplus assessment coefficient, compare it with a standard threshold, and issue a second warning according to the comparison result.

9. The early warning system according to claim 8, characterized in that: include: The surplus capacity acquisition module is further used to collect the current air temperature and dew point temperature, and calculate the maximum amount of water vapor that the air can contain at the current air temperature and the saturated water vapor pressure at the current dew point temperature; The surplus capacity acquisition module is also used to calculate the relative humidity around the new energy unit according to the maximum water vapor amount and the saturated water vapor pressure to obtain weather impact parameters.

10. The early warning system according to claim 8, characterized in that: include: The surplus capacity acquisition module is also used to obtain the installed capacity of the new energy unit; The surplus capacity acquisition module is further used to determine the initial available capacity of the new energy unit according to the installed capacity and the operating data of the new energy unit.

11. The early warning system according to claim 8, characterized in that: include: The supply quantity generating module is further used to obtain the power generation capacity margin according to the real-time collected power system capacity demand data and the traditional energy surplus capacity data; The supply quantity generation module is also used to obtain the new energy penetration power coefficient, and determine the sufficient capacity supply quantity of the new energy unit in combination with the power generation capacity margin.

12. The early warning system according to claim 8, characterized in that: include: The first warning module is further used to compare the surplus capacity supply, the surplus capacity and the probability with a probability distribution table; The first warning module is further used to obtain the probability of satisfying the sufficient capacity according to the comparison result, and to issue a first warning according to the satisfaction probability value.

13. The early warning system according to claim 9, characterized in that: include: The second warning module is further used to construct a weather impact coefficient prediction model according to the weather impact parameters to obtain the weather impact coefficient; The second warning module is also used to generate a power system capacity margin assessment coefficient based on the weather impact coefficient.

14. The early warning system according to claim 8, characterized in that: include: The second warning module is further used to compare the adequacy assessment coefficient with a standard threshold; The second warning module is further configured to indicate that if the adequacy assessment coefficient is not less than the standard threshold, it indicates that the adequacy of the power system is good and no warning is required; The second warning module is also used to issue a second warning if the margin assessment coefficient is less than the standard threshold, indicating that the system margin is insufficient, and increase the number of daily maintenance times of the new energy unit to ensure the stability and reliability of the power system.

15. An electronic device, characterized in that: include: processor; and a memory storing computer executable instructions which, when executed, cause the processor to perform the method according to any one of claims 1-7.

16. A computer storage medium, characterized in that: in, The computer storage medium stores one or more programs, and when the one or more programs are executed by a processor, the method of any one of claims 1 to 7 is implemented.

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