Day-ahead electricity market reserve reservation method and system

Abstract

The invention discloses a day-ahead electricity market reserve reservation method and system, and belongs to the technical field of new energy output. The method comprises the following steps: calculating a day-ahead electricity market reference reserve demand; constructing historical wind power and photovoltaic output and prediction error probability distribution; the uncertainty probability distribution is converted according to the proportion of the day-ahead wind and light output predicted value and the historical mean value; calculating the percentile of the predicted value in historical distribution, expanding the percentile into a percentile interval, and sampling to generate an uncertainty boundary; and superposing the upper and lower boundaries to the reference positive and negative for standby, and completing standby adjustment. The system comprises corresponding function modules. According to the invention, through probability distribution modeling and dynamic sampling, data driving and accurate and robust standby reservation are realized, and the safety and economy of power grid operation in a high-proportion new energy environment are effectively improved.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation technology, specifically relating to a method and system for reserving reserves in the day-ahead electricity market. Background Technology

[0002] Currently, the integration of a high proportion of renewable energy sources has brought new challenges to the operation of the power system. The intermittency, volatility, and randomness of renewable energy generation significantly increase the difficulty of achieving power balance in both time and space dimensions. This uncertainty has triggered a series of issues related to system supply balance and safe and stable operation. One key issue is that it places higher demands on the grid's ability to provide operational reserves.

[0003] In an environment where renewable energy accounts for a high proportion, if the output of renewable energy deviates significantly from the forecast, and if renewable energy is significantly higher than the forecast at a certain moment, in order to ensure the balance between power generation and load supply and demand, it is necessary to reduce the output of conventional power sources, which will occupy a large amount of negative reserve capacity. If renewable energy is significantly lower than the forecast at a certain moment, in order to ensure the balance between power generation and load supply and demand, it is necessary to increase the output of conventional power sources, which will occupy a large amount of positive reserve capacity.

[0004] Meanwhile, since only generating units that are activated in the day-ahead market clearing optimization results can provide spinning (positive / negative) reserves on operating days, the operating reserve constraints set during day-ahead market clearing are crucial to the reserve regulation capacity on operating days. Therefore, during day-ahead market clearing, accurately adjusting the day-ahead reserved reserves based on the uncertainty of renewable energy output is crucial for the safe operation of the power grid and electricity market under the high uncertainty of renewable energy.

[0005] Existing methods for reserve provisions set a certain percentage of the load forecast or the capacity of critical equipment to account for the need for operational reserves due to load fluctuations or sudden equipment failures. Some existing technologies further consider the impact of forecast deviations in renewable energy reserves on reserve provisions by manually setting a basic operational reserve or by adding a certain percentage of the renewable energy forecast.

[0006] In existing solutions, the impact of new energy forecast deviations on reserve demand is often addressed by market operators setting or adding a certain percentage of new energy forecast values ​​based on experience. This approach fails to accurately adjust reserve demand forecasts based on historical wind and solar power forecast errors, and does not reasonably convert new energy forecast errors based on new energy output forecast results. The resulting solutions are not refined enough and have shortcomings in terms of rationality. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a day-ahead power market reserve reservation method and system to address the shortcomings of the prior art, thereby solving the technical problems in the prior art that the impact of new energy forecast deviations on reserve demand lacks accurate quantification, relies on manual experience setting, and does not reasonably convert based on historical forecast errors.

[0008] The present invention adopts the following technical solution: A method for day-ahead electricity market reserve reservation includes the following steps: S1. Based on the day-ahead load forecast, DC blocking of UHV transmission channels, and fault factors of key generator units, calculate the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market respectively. S2. Based on the historical actual power output curves of wind power and photovoltaic power, construct the historical probability distribution of wind power and photovoltaic power output; based on the historical day-ahead power output prediction curves of wind power and photovoltaic power and the historical actual power output curves of wind power and photovoltaic power, calculate the historical day-ahead power output prediction errors of wind power and photovoltaic power, and construct the probability distributions of the day-ahead power output prediction errors of wind power and photovoltaic power respectively. S3. Based on the ratio of the day-ahead power market wind power and photovoltaic power output forecast values ​​to the historical average actual power output values, the day-ahead power and photovoltaic power output forecast error distribution is proportionally calculated to obtain the probability distribution of day-ahead wind power and photovoltaic power output uncertainty. S4. Based on the predicted values ​​of wind and solar power output in the day-ahead electricity market, calculate their percentiles in the historical probability distribution of wind and solar power output; expand the percentile values ​​to a percentile sample interval, and sample from the probability distribution of the uncertainty of day-ahead wind and solar power output based on this interval; superimpose the upper boundary of the sampling results of the uncertainty of day-ahead wind and solar power output onto the benchmark positive reserve and the lower boundary onto the benchmark negative reserve, thus completing the adjustment of the reserve reserved in the day-ahead electricity market.

[0009] Preferably, in step S1, the baseline positive reserve requirement and baseline negative reserve requirements They are respectively:

[0010]

[0011] in, , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point This represents the maximum power generation capacity of the key generator units. The default values ​​for positive and backup requirements were manually set by the market operations staff recently. , These are the coefficients for the day-ahead load forecast and the default value for negative reserve demand, respectively.

[0012] Preferably, in step S2, the method for constructing the historical wind power output probability distribution is as follows: For each point in time, construct a probability distribution from all values ​​of the historical wind power output data for that point in time; The method for constructing the historical photovoltaic power output probability distribution is as follows: For each point in time, a probability distribution is constructed from all the values ​​of that point in the historical photovoltaic power output data.

[0013] Preferably, in step S2, the method for constructing the probability distribution of historical wind power day-ahead output prediction errors is as follows: Calculate the difference between the historical wind power day-ahead output prediction curve and the historical wind power actual output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point. The method for constructing the probability distribution of historical photovoltaic day-ahead output prediction errors is as follows: Calculate the difference between the historical photovoltaic day-ahead power output prediction curve and the historical photovoltaic actual power output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point.

[0014] Preferably, in step S3, the method for calculating the probability distribution of the historical wind power day-ahead output prediction error is as follows: For each point in time, calculate the ratio of the predicted wind power output in the day-ahead power market to the average historical wind power output. Divide the probability distribution of the predicted error of the historical wind power day-ahead output by the ratio to obtain the probability distribution of the uncertainty of the wind power output day-ahead. The method for calculating the probability distribution of the historical photovoltaic day-ahead output prediction error is as follows: For each point in time, the ratio of the predicted photovoltaic output in the day-ahead power market to the average historical actual photovoltaic output is calculated. The probability distribution of the day-ahead photovoltaic output prediction error is then divided by the ratio to obtain the probability distribution of the uncertainty in the day-ahead photovoltaic output.

[0015] Preferably, in step S4, the method for calculating percentiles is as follows: For each point in time, the cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as a percentile; the cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as a percentile.

[0016] Preferably, the cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as follows:

[0017] in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. This is the predicted day-ahead wind power output at the first time point of the operating day; The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as follows:

[0018] in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. This is the predicted day-ahead photovoltaic output at the first time point of the operating day.

[0019] Preferably, the upper and lower boundaries of the day-ahead wind power output uncertainty are:

[0020]

[0021]

[0022]

[0023] in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time; The upper and lower boundaries of uncertainty in photovoltaic power output:

[0024]

[0025]

[0026]

[0027] in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0028] Preferably, in step S4, the upper boundary of the day-ahead wind power output uncertainty is used. And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made:

[0029] in, Reserve positive reserves for the adjusted day-ahead electricity market. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0030] Preferably, in step S4, the lower boundary of the day-ahead wind power output uncertainty is used. And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made:

[0031] in, To reserve negative reserves for the adjusted day-ahead electricity market, For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

[0032] Secondly, embodiments of the present invention provide a day-ahead electricity market reserve reservation system, comprising: The demand module calculates the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market based on the day-ahead load forecast, DC blocking of UHV transmission channels, and failure factors of key generator units. The probability module constructs the historical wind power and solar power output probability distributions based on the historical actual output curves of wind power and solar power; and calculates the historical wind power and solar power output prediction errors based on the historical wind power and solar power day-ahead output prediction curves and the historical wind power and solar power actual output curves, and constructs the probability distributions of the wind power and solar power output prediction errors respectively. The conversion module proportionally converts the day-ahead wind and solar power output forecasts based on the ratio of the day-ahead power market wind and solar power output forecasts to the historical average actual wind and solar power outputs, thereby obtaining the probability distribution of day-ahead wind and solar power output uncertainty. The adjustment module calculates the percentile of wind and solar power output forecasts in the historical probability distribution of wind and solar power output based on the day-ahead power market forecasts. It then expands the percentile values ​​into a percentile sample interval and samples from the probability distribution of the converted day-ahead wind and solar power output uncertainties based on this interval. Finally, it overlays the upper boundary of the day-ahead wind and solar power output uncertainty sampling results onto the benchmark positive reserve and the lower boundary onto the benchmark negative reserve, thus completing the adjustment of the day-ahead power market reserve.

[0033] Preferably, in the demand module, the baseline positive reserve demand and baseline negative reserve requirements They are respectively:

[0034]

[0035] in, , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point This represents the maximum power generation capacity of the key generator units. The default values ​​for positive and backup requirements were manually set by the market operations staff recently. , These are the coefficients for the day-ahead load forecast and the default value for negative reserve demand, respectively.

[0036] Preferably, in the probability module, the method for constructing the historical wind power output probability distribution is as follows: For each point in time, construct a probability distribution from all values ​​of the historical wind power output data for that point in time; The method for constructing the historical photovoltaic power output probability distribution is as follows: For each point in time, construct a probability distribution from all the values ​​of that point in the historical photovoltaic power output data; The method for constructing the probability distribution of historical wind power day-ahead output prediction errors is as follows: Calculate the difference between the historical wind power day-ahead output prediction curve and the historical wind power actual output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point. The method for constructing the probability distribution of historical photovoltaic day-ahead output prediction errors is as follows: Calculate the difference between the historical photovoltaic day-ahead power output prediction curve and the historical photovoltaic actual power output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point.

[0037] Preferably, in the conversion module, the method for converting the probability distribution of the historical wind power day-ahead output prediction error is as follows: For each point in time, calculate the ratio of the predicted wind power output in the day-ahead power market to the average historical wind power output. Divide the probability distribution of the predicted error of the historical wind power day-ahead output by the ratio to obtain the probability distribution of the uncertainty of the wind power output day-ahead. The method for calculating the probability distribution of the historical photovoltaic day-ahead output prediction error is as follows: For each point in time, the ratio of the predicted photovoltaic output in the day-ahead power market to the average historical actual photovoltaic output is calculated. The probability distribution of the day-ahead photovoltaic output prediction error is then divided by the ratio to obtain the probability distribution of the uncertainty in the day-ahead photovoltaic output.

[0038] Preferably, the method for calculating percentiles in the adjustment module is as follows: For each point in time, the cumulative probability of the day-ahead power market wind power output forecast in the historical wind power output probability distribution is calculated as the percentile; the cumulative probability of the day-ahead power market photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as the percentile. The cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as follows:

[0039] in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. This is the predicted day-ahead wind power output at the first time point of the operating day; The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as follows:

[0040] in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. This is the predicted day-ahead photovoltaic output at the first time point of the operating day; The upper and lower boundaries of the uncertainty in wind power output:

[0041]

[0042]

[0043]

[0044] in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time; The upper and lower boundaries of uncertainty in photovoltaic power output:

[0045]

[0046]

[0047]

[0048] in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0049] Preferably, in the adjustment module, the upper boundary of the day-ahead wind power output uncertainty is used. And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made:

[0050] in, Reserve positive reserves for the adjusted day-ahead electricity market. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty in day-ahead photovoltaic output at a given point in time; Based on the current uncertainty of wind power output, the lower boundary And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made:

[0051] in, To reserve negative reserves for the adjusted day-ahead electricity market, For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

[0052] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described day-ahead electricity market reserve reservation method.

[0053] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described day-ahead electricity market reserve reservation method.

[0054] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described day-ahead electricity market reserve reservation method.

[0055] Sixthly, embodiments of the present invention provide an electronic device including a computer program, which, when executed by the electronic device, implements the steps of the above-described day-ahead electricity market reserve reservation method.

[0056] Compared with the prior art, the present invention has at least the following beneficial effects: A method for day-ahead electricity market reserve reservation is proposed. By integrating historical data and day-ahead forecast information in a step-by-step manner, it first calculates the basic supply and demand balance of the baseline reserve guarantee, then constructs a probability distribution based on historical wind and solar data to quantify the uncertainty pattern, then combines the day-ahead forecast value to reduce the uncertainty, and finally adjusts the reserve by sampling and superposition. This realizes the transformation of reserve reservation from experience estimation to data-driven accurate calculation, ensuring that reserve demand covers both conventional factors and fully considers the fluctuation of new energy sources, thus providing a basic guarantee for the safe operation of the power system.

[0057] Furthermore, the calculation formula for baseline reserve demand is clarified, incorporating parameters such as load forecasting, transmission plans, unit capacity, and default values. This comprehensively considers multiple factors including load, transmission, and unit capacity, making the baseline reserve more comprehensive. The coefficients are set by operators according to rules, enhancing the flexibility and adaptability of the method.

[0058] Furthermore, probability distributions of historical solar power output and prediction errors are constructed at each time point, with each time point having its own independent distribution, capturing the temporal characteristics of solar power output. The prediction error distribution directly reflects the randomness and volatility of solar power output. Modeling based directly on historical data avoids prior assumptions about the distribution pattern and improves the model's generalization ability.

[0059] Furthermore, the uncertainty probability distribution is calculated based on the ratio of the predicted value to the historical mean. This ratio calculation allows the model to adjust the uncertainty range according to the predicted power output level, generating customized uncertainty distributions for different wind and solar power prediction values, thus enhancing the practicality of the method.

[0060] Furthermore, the percentile of the predicted value in the historical distribution is calculated and extended to a percentile interval. The percentile reflects the distribution position of the predicted value in the historical data, providing a basis for sampling. By extending the interval through confidence level, it is ensured that the sampling range covers possible fluctuations and improves the robustness of backup adjustments.

[0061] Furthermore, based on the upper and lower bounds of the percentile interval, uncertainty boundaries are generated through inverse function sampling. By using the inverse function sampling of the distribution function, the statistical rationality of the boundary values ​​is ensured. The upper and lower boundaries correspond to positive and negative reserve adjustment amounts, respectively, making the reserve adjustment target clear and highly operable.

[0062] Furthermore, the uncertainty boundary is superimposed on the baseline reserve to complete the adjustment of positive and negative reserves. The boundary superposition is simple and intuitive, and easy to implement in engineering. The upper boundary adjusts the positive reserve, and the lower boundary adjusts the negative reserve, which meets the differentiated reserve requirements of wind and solar fluctuations.

[0063] Furthermore, please supplement the explanation of the purpose or benefits of the setting according to claim 8, and provide a principle analysis.

[0064] Furthermore, please supplement the explanation of the purpose or benefits of the setting according to claim 9, and provide a principle analysis.

[0065] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0066] In summary, this invention constructs a probability distribution of historical wind and solar power output and prediction errors, combines real-time prediction values ​​with dynamic calculation of uncertainty, and generates reserve adjustment boundaries based on percentile sampling. This enables data-driven, probability-based day-ahead power market reserve reservation, significantly improving the accuracy, adaptability, and robustness of reserve reservation, and effectively addressing the uncertainty challenges brought about by a high proportion of new energy access.

[0067] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0068] Figure 1 This is a flowchart of the present invention; Figure 2 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 3 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0069] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0070] The output of new energy sources is highly uncertain, leading to significant fluctuations in the power that the grid needs to obtain from conventional power sources (coal-fired, gas-fired, etc.). In extreme cases, this may result in insufficient reserve capacity, affecting the safe and stable operation of the power system. This invention provides a method for day-ahead power market reserve reservation. By constructing the probability distribution of historical wind and solar output and prediction errors, a mathematical representation of wind and solar output and new energy prediction errors that conforms to the characteristics of the market operation area is built. Day-ahead wind and solar output predictions are used to calculate day-ahead wind and solar uncertainty, and this is used as a benchmark to adjust the day-ahead power market reserve demand. Combining prediction and historical data, the reserve values ​​for day-ahead positive and negative reserves are accurately adjusted. This method will ultimately improve the accuracy of day-ahead power market reserve reservations, especially given the increasing uncertainty of new energy output due to the rising proportion of new energy sources. It will enhance the safety margin for day-ahead power market operators to reserve positive and negative reserves for operating days, ensuring the smooth and orderly operation of the power system and power market under the new power system context.

[0071] Day-ahead electricity market: refers to a market mechanism in which the time-of-use electricity price and electricity trading plan for the following day are determined by a unified submission of quotations and centralized clearing by market participants on the day before the operating day (i.e., the actual day of power supply, or the day-ahead transaction result settlement day).

[0072] Uncertainty in wind and solar power output: The output of wind power, photovoltaic units or power stations is highly uncertain, which causes the power that the grid needs to obtain from conventional power sources (coal-fired, gas-fired units, etc.) to fluctuate significantly. Therefore, higher requirements are placed on the grid's positive and negative reserves.

[0073] Operating reserve: A core type of power system ancillary service, primarily used to balance load fluctuations, address new energy forecasting deviations, and respond to sudden equipment failures. Based on response time and service quality, it can be categorized into three types: spinning reserve, non-spinning reserve, and alternative reserve.

[0074] Positive / Negative Reserve: Positive reserve is the positive rotating reserve in the operating reserve, and negative reserve is the negative rotating reserve in the operating reserve.

[0075] Please see Figure 1 The present invention discloses a method for day-ahead electricity market reserve reservation, comprising the following steps: S1, Calculation of Day-ahead Electricity Market Benchmark Reserve Demand Based on factors such as day-ahead load forecasts, DC blocking of UHV transmission channels, and failures of key generating units, the benchmark positive and negative reserve requirements of the day-ahead electricity market are calculated respectively.

[0076] First, without considering the uncertainty of wind and solar power output, we calculate the positive and negative reserves in the day-ahead electricity market as a benchmark for reserve demand.

[0077] S101, Calculation of Baseline Positive Reserve Requirements The baseline positive reserve demand is calculated for 96 time points, each occurring every 15 minutes on the operating day (the day-ahead electricity market is conducted the day before the operating day). The calculation method for the baseline positive reserve demand is as follows:

[0078] in, Based on the baseline positive reserve requirement, This is the day-ahead load forecast. The three figures represent the planned power transmission capacity of the ultra-high voltage (UHV) transmission line. All three are vectors with a length of 96. For the first day of operation The baseline positive reserve requirement at each point in time. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point. The maximum generating capacity of the key generator set (the key generator set is generally the largest generator set). This is the default value for the positive and backup requirements that were manually set by the market operations personnel recently. , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively, with a range of values. And generally does not exceed 1. Coefficient , , , The value is determined by the day-ahead market operators based on the day-ahead market reserve rules and the actual market operation.

[0079] S102, Calculation of Baseline Negative Reserve Requirements The baseline negative reserve requirement is calculated for 96 points, representing each point in 15 minutes throughout the operating day. The calculation method for the baseline negative reserve requirement is as follows:

[0080] in, Based on the baseline negative reserve requirement, These are day-ahead load forecasts, all vectors of length 96. For the first day of operation The baseline negative reserve requirement at each point in time. For the first day of operation Daily load forecast values ​​at each point in time. This is the default value for negative backup requirements that was manually set by market operations personnel previously. , These are coefficients for the day-ahead load forecast and the default value of negative reserve demand, respectively, with a range of values. And generally does not exceed 1. Coefficient , The value is determined by the day-ahead market operators based on the day-ahead market reserve rules and the actual market operation.

[0081] S2. Construct the probability distribution of historical landscape output and prediction error. Based on historical wind power and solar power output curves, a probability distribution of historical wind power and solar power output is constructed. Based on historical wind power and solar power day-ahead output prediction curves and historical wind power and solar power output actual output curves, the historical wind power and solar power day-ahead output prediction errors are calculated, and probability distributions of the prediction errors for wind power and solar power day-ahead output are constructed respectively.

[0082] S201, historical wind power, and photovoltaic power output probability distribution construction The historical wind power and solar power output dataset contains a total of Data from 96 time points per day, i.e. A vector of length 96.

[0083] S2011, Construction of Historical Wind Power Output Probability Distribution Record historical wind power output data as :

[0084] in, For the first Historical wind power output data for the day, For the first The historical wind power output value at the first point in time of the day.

[0085] For each point in time across all dates in the dataset, a probability distribution is constructed based on all historical wind power output values ​​for that point in time. Therefore, a total of 96 probability distributions are constructed for the entire historical wind power output dataset.

[0086] in, The historical wind power output at the first time point is a random variable derived from historical wind power output data. The set of wind power output at time 1 of all dates. , , contains Data points. Random variable. It typically follows a multimodal mixture distribution or a Weibull distribution, and the distribution function of its probability distribution is denoted as . .

[0087] S2012, Construction of Historical Photovoltaic Output Probability Distribution Similarly, record historical photovoltaic power output data. for:

[0088] in, For the first Historical photovoltaic power output data for the day, For the first The historical photovoltaic power output value at the first moment of the day.

[0089] For each point in time across all dates in the dataset, a probability distribution is constructed based on all historical photovoltaic power output values ​​for that point in time. Therefore, for the entire historical photovoltaic power output dataset, a total of 96 probability distributions are constructed:

[0090] in, The historical photovoltaic power output random variable at the first time point is derived from historical photovoltaic power output data. The set of photovoltaic output at the first moment of all dates. , , contains Data points. Similarly, random variables. Photovoltaic output typically follows a multimodal mixed distribution or a beta distribution (at nighttime, photovoltaic output is essentially zero; such data can be disregarded). The distribution function of this probability distribution is denoted as... .

[0091] Construction of probability distribution of day-ahead output prediction errors for S202, wind power, and photovoltaic power S2021, Construction of Probability Distribution of Wind Power Day-ahead Output Forecast Error In the dataset During the day, record the The historical wind power daytime output forecast curve used for the daytime market clearing is as follows: The daily historical wind power day-ahead output forecast curve contains data from 96 time points, namely:

[0092] Calculate historical wind power day-ahead output forecast curve Historical wind power actual output curve The difference is used to obtain the day-ahead power output prediction error of wind power. :

[0093] in, For the first Wind power daytime output forecast error data.

[0094] Similarly, for each time point on all dates, a probability distribution is constructed based on the predicted daily wind power output error values ​​for that time point, resulting in a total of 96 probability distributions:

[0095] in, The random variable representing the day-ahead wind power output prediction error at the first time point is derived from historical day-ahead wind power output prediction error data. The set of historical wind power day-ahead output prediction error values ​​for all dates at time 1. , , contains Data points. Random variable. Similarly, it usually follows a multimodal mixture distribution, and the distribution function of its probability distribution is denoted as . .

[0096] S2022, Construction of Probability Distribution of Prediction Error for Solar Day-ahead Output In the dataset During the day, record the The historical photovoltaic day-ahead output forecast curve used for the day-ahead market clearing is as follows: The daily historical photovoltaic day-ahead output forecast curve contains data at 96 time points, namely:

[0097] Calculate the historical solar day-ahead power output forecast curve and historical photovoltaic actual output curve The difference is used to obtain the solar daytime power output prediction error. :

[0098] in, For the first The daytime photovoltaic power output forecast error data.

[0099] Similarly, for each point in time across all dates, a probability distribution is constructed based on the predicted daily photovoltaic output error values ​​for that point in time, resulting in a total of 96 probability distributions:

[0100] in, The random variable representing the day-ahead photovoltaic power output prediction error at the first time point is derived from historical day-ahead photovoltaic power output prediction error data. The set of historical photovoltaic day-ahead power output prediction error values ​​for all dates at time 1. , , contains Data points. Excluding nighttime periods when solar power is not generating power, random variables... Similarly, it usually follows a multimodal mixture distribution, and the distribution function of its probability distribution is denoted as . .

[0101] S3, Conversion of Uncertainty in Recent Wind and Solar Power Output Based on the ratio of the day-ahead power market wind and solar power output forecasts to the historical average actual power output, the day-ahead power and solar power output forecast error distribution is proportionally calculated to obtain the probability distribution of day-ahead wind and solar power output uncertainty.

[0102] S301, Conversion of Uncertainty in Day-ahead Wind Power Output S3011, Calculate the historical average actual wind power output. calculate Historical wind power output data Mean:

[0103] in, This represents the historical average actual wind power output. This represents the historical average actual wind power output at the first time point. For the first Historical wind power output data for the day, For the first The historical wind power output value at the first point in time of the day.

[0104] S3012, Distribution of Prediction Errors for Converted Day-ahead Wind Power Output (Note: All data mentioned above are historical data. The “day-ahead power market wind power output forecast” below refers to the forecast value of wind power output for the actual execution date (the second day, or operating day) of the day-ahead power market transaction when the method of this patent is applied in the day-ahead power market. It should be noted that when applying the method of this patent, it is impossible to obtain the actual wind power output for the second day in advance.) The day-ahead wind power output forecast (i.e., the wind power output forecast for the day-ahead power market exchange corresponding to the power delivery date, i.e., the operating day, when applying this patented method in day-ahead power market operation) is denoted as... :

[0105] in, This is the day-ahead forecast of wind power output in the electricity market at the first time point of the operating day.

[0106] For all 96 time points of the operating day, calculate the day-ahead wind power output forecast for the electricity market sequentially. and the historical average actual wind power output proportion :

[0107] Based on the ratio of the current forecast value of wind power output in the electricity market to the historical average value of actual wind power output. The probability distribution of wind power day-ahead output prediction error at 96 time points constructed in step S2 is converted. , , ..., The probability distribution of the uncertainty of wind power output was obtained. , , ..., :

[0108] S302, Conversion of Uncertainty in Solar Power Output S3021. Calculate the historical average actual photovoltaic power output. Similarly, calculation Historical photovoltaic power output data Mean:

[0109] in, This represents the historical average actual power output of photovoltaic systems. This represents the historical average actual photovoltaic power output at the first time point. For the first Historical photovoltaic power output data for the day, For the first The historical photovoltaic power output value at the first moment of the day.

[0110] S3022, Error Distribution of Predicted Daily Photovoltaic Output The predicted photovoltaic output value for the day-ahead electricity market (i.e., the predicted photovoltaic output value for the operating day) is: :

[0111] in, The forecast value of photovoltaic power output in the day-ahead power market is the first time point of the operating day.

[0112] Similarly, for all 96 time points of the operating day, the day-ahead photovoltaic output forecast for the power market is calculated sequentially. and historical average actual photovoltaic output proportion :

[0113] Based on the ratio of the current forecast value of photovoltaic power output in the electricity market to the historical average value of actual photovoltaic power output. The probability distribution of photovoltaic day-ahead output prediction error at 96 time points constructed in step S2 is converted. , , ..., The probability distribution of the uncertainty of photovoltaic power output was obtained. , , ..., :

[0114] S4. Adjust the day-ahead electricity market reserve based on the day-ahead uncertainty of wind and solar power output. Based on the day-ahead forecasts of wind and solar power output in the electricity market, the percentiles of these forecasts within the historical probability distribution of wind and solar power output are calculated. The percentile values ​​are then expanded to a percentile sample interval according to market operating conditions. Samples are then taken from the probability distribution of the converted day-ahead wind and solar power output uncertainties based on this percentile sample interval. The upper boundary of the day-ahead wind and solar power output uncertainty sampling results is superimposed onto the baseline positive reserve, and the lower boundary is superimposed onto the baseline negative reserve, thus completing the adjustment of the day-ahead electricity market reserve.

[0115] S401, Calculation of percentiles of predicted wind and solar power output for the day before S4011 Calculate the percentile of the day-ahead forecast value of wind power output in the electricity market. For all 96 time points of the daytime electricity market operation day, calculate the daytime wind power output forecast for each time point. Historical wind power actual output probability distribution Percentiles in:

[0116] in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. That is, for any historical wind power output at any point in time 1 It is less than the day-ahead wind power output forecast value at the first time point of the operating day. The probability of.

[0117] S4012, Calculate the percentile of the day-ahead forecast of photovoltaic power output in the electricity market. Similarly, for all 96 time points of the day-ahead electricity market operation day, the predicted day-ahead photovoltaic output value for that time point is calculated. Historical probability distribution of actual photovoltaic power output Percentiles in:

[0118] in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. That is, for any historical photovoltaic power output at any point in time 1 It is less than the predicted day-ahead photovoltaic output at the first time point of the operating day. The probability of.

[0119] S402, Uncertainty Sampling of Wind and Solar Output S4021, Sampling of the probability distribution of uncertainty in daytime wind power output Percentile of day-ahead wind power output forecasts for all 96 time points of the day-ahead electricity market operation day , , ..., They are respectively expanded into percentile intervals of the day-ahead wind power output forecast:

[0120] in, For the first Percentile of the day-ahead wind power output forecast at a given time point For the first Percentile range of the day-ahead wind power output forecast at each point in time. The confidence level set for market operations personnel is generally 0.1.

[0121] Percentile range of day-ahead wind power output forecasts based on all 96 time points. , , ..., The upper and lower bounds of the (converted) day-ahead wind power output uncertainty probability distribution , , ..., Sampling was performed to obtain the upper and lower boundaries of the day-ahead wind power output uncertainty:

[0122]

[0123]

[0124]

[0125] in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of day-ahead wind power output at a given moment.

[0126] S4022, Sampling of the probability distribution of uncertainty in photovoltaic power output Similarly, percentiles of the day-ahead photovoltaic output forecasts for all 96 time points of the day-ahead electricity market operation day. , , ..., They are respectively extended to the percentile range of the day-ahead photovoltaic power output forecast:

[0127] in, For the first Percentile of the day-ahead photovoltaic power output forecast at a given time point For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. The confidence level set for market operators is generally 0.1, which may differ from the confidence level set in the sampling of the probability distribution of uncertainty in photovoltaic output mentioned above.

[0128] Percentile range of day-ahead photovoltaic output forecasts based on all 96 time points. , , ..., The upper and lower bounds of the (converted) day-ahead photovoltaic power output uncertainty probability distribution. , , ..., Sampling was performed to obtain the upper and lower boundaries of the day-to-day photovoltaic output uncertainty:

[0129]

[0130]

[0131]

[0132] in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0133] S403, Adjustment of Reserved Power Market Reserves S4031, Reserved for backup adjustment Based on the upper boundary of the current uncertainty in wind power output And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made:

[0134] in, Reserve positive reserves for the adjusted day-ahead electricity market. Based on the baseline positive reserve requirement, This represents the upper bound of the day-ahead wind power output uncertainty at various points in time during the operating day. The upper bound of the day-ahead photovoltaic output uncertainty at each time point on the operating day is a vector of length 96. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0135] S4032, Reserved negative backup adjustment Similarly, based on the lower boundary of the day-ahead wind power output uncertainty And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made:

[0136] in, To reserve negative reserves for the adjusted day-ahead electricity market, Based on the baseline negative reserve requirement, This represents the lower boundary of the day-ahead wind power output uncertainty at each point in time during the operating day. The lower boundary of the day-ahead photovoltaic output uncertainty at each time point on the operating day is a vector of length 96. For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

[0137] In another embodiment of the present invention, a day-ahead electricity market reserve reservation system is provided. This system can be used to implement the above-mentioned day-ahead electricity market reserve reservation method. Specifically, the day-ahead electricity market reserve reservation system includes a demand module, a probability module, a conversion module, and an adjustment module.

[0138] The demand module calculates the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market based on the day-ahead load forecast, DC blocking of the UHV transmission channel, and fault factors of key generator units. The probability module constructs the historical wind power and solar power output probability distributions based on the historical actual output curves of wind power and solar power; and calculates the historical wind power and solar power output prediction errors based on the historical wind power and solar power day-ahead output prediction curves and the historical wind power and solar power actual output curves, and constructs the probability distributions of the wind power and solar power output prediction errors respectively. The conversion module proportionally converts the day-ahead wind and solar power output forecasts based on the ratio of the day-ahead power market wind and solar power output forecasts to the historical average actual wind and solar power outputs, thereby obtaining the probability distribution of day-ahead wind and solar power output uncertainty. The adjustment module calculates the percentile of wind and solar power output forecasts in the historical probability distribution of wind and solar power output based on the day-ahead power market forecasts. It then expands the percentile values ​​into a percentile sample interval and samples from the probability distribution of the converted day-ahead wind and solar power output uncertainties based on this interval. Finally, it overlays the upper boundary of the day-ahead wind and solar power output uncertainty sampling results onto the benchmark positive reserve and the lower boundary onto the benchmark negative reserve, thus completing the adjustment of the day-ahead power market reserve.

[0139] In the requirements module, the baseline positive and backup requirements and baseline negative reserve requirements They are respectively:

[0140]

[0141] in, , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point This represents the maximum power generation capacity of the key generator units. The default values ​​for positive and backup requirements were manually set by the market operations staff recently. , These are the coefficients for the day-ahead load forecast and the default value for negative reserve demand, respectively.

[0142] In the probability module, the method for constructing the historical wind power output probability distribution is as follows: For each point in time, construct a probability distribution from all values ​​of the historical wind power output data for that point in time; The method for constructing the historical photovoltaic power output probability distribution is as follows: For each point in time, construct a probability distribution from all the values ​​of that point in the historical photovoltaic power output data; The method for constructing the probability distribution of historical wind power day-ahead output prediction errors is as follows: Calculate the difference between the historical wind power day-ahead output prediction curve and the historical wind power actual output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point. The method for constructing the probability distribution of historical photovoltaic day-ahead output prediction errors is as follows: Calculate the difference between the historical photovoltaic day-ahead power output prediction curve and the historical photovoltaic actual power output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point.

[0143] In the conversion module, the method for converting the historical wind power day-ahead output prediction error probability distribution is as follows: For each point in time, calculate the ratio of the predicted wind power output in the day-ahead power market to the average historical wind power output. Divide the probability distribution of the predicted error of the historical wind power day-ahead output by the ratio to obtain the probability distribution of the uncertainty of the wind power output day-ahead. The method for calculating the probability distribution of the historical photovoltaic day-ahead output prediction error is as follows: For each point in time, the ratio of the predicted photovoltaic output in the day-ahead power market to the average historical actual photovoltaic output is calculated. The probability distribution of the day-ahead photovoltaic output prediction error is then divided by the ratio to obtain the probability distribution of the uncertainty in the day-ahead photovoltaic output.

[0144] In the adjustment module, the method for calculating percentiles is as follows: For each point in time, the cumulative probability of the day-ahead power market wind power output forecast in the historical wind power output probability distribution is calculated as the percentile; the cumulative probability of the day-ahead power market photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as the percentile. The cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as follows:

[0145] in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. This is the predicted day-ahead wind power output at the first time point of the operating day; The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as follows:

[0146] in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. This is the predicted day-ahead photovoltaic output at the first time point of the operating day; The upper and lower boundaries of the uncertainty in wind power output:

[0147]

[0148]

[0149]

[0150] in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time; The upper and lower boundaries of uncertainty in photovoltaic power output:

[0151]

[0152]

[0153]

[0154] in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

[0155] In the adjustment module, the upper boundary of the day-ahead wind power output uncertainty is considered. And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made:

[0156] in, Reserve positive reserves for the adjusted day-ahead electricity market. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty in day-ahead photovoltaic output at a given point in time; Based on the current uncertainty of wind power output, the lower boundary And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made:

[0157] in, To reserve negative reserves for the adjusted day-ahead electricity market, For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

[0158] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a day-ahead power market reserve reservation method, including: Based on day-ahead load forecasts, DC blocking of UHV transmission channels, and fault factors of key generating units, the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market are calculated respectively; based on historical wind power and photovoltaic actual output curves, historical wind power and photovoltaic output probability distributions are constructed; based on historical wind power and photovoltaic day-ahead output forecast curves and historical wind power and photovoltaic actual output curves, historical wind power and photovoltaic day-ahead output forecast errors are calculated, and probability distributions of wind power and photovoltaic day-ahead output forecast errors are constructed respectively; based on day-ahead electricity market wind power and photovoltaic output forecasts and historical wind power and photovoltaic actual output... The average power output is proportionally converted to the day-ahead power output forecast error distribution of wind and solar power, resulting in the probability distribution of day-ahead wind and solar power output uncertainty. Based on the day-ahead power market wind and solar power output forecast values, their percentiles in the historical wind and solar power output probability distribution are calculated. The percentile values ​​are expanded into a percentile sample interval, and samples are taken from the converted day-ahead wind and solar power output uncertainty probability distribution based on this interval. The upper boundary of the day-ahead wind and solar power output uncertainty sampling results is superimposed to the benchmark positive reserve, and the lower boundary is superimposed to the benchmark negative reserve, completing the adjustment of the day-ahead power market reserve.

[0159] Please see Figure 2 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the method for estimating the concentration of radioactive iodine species in the containment structure after an accident, as described in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the day-ahead power market reserve system of this embodiment. To avoid repetition, these details are not elaborated here.

[0160] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 2 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0161] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0162] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on the computer device 60.

[0163] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0164] Please see Figure 3 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0165] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 1 The steps are shown in the figure.

[0166] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0167] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0168] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0169] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0170] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0171] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0172] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0173] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the day-ahead electricity market reserve reservation method in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: Based on day-ahead load forecasts, DC blocking of UHV transmission channels, and fault factors of key generating units, the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market are calculated respectively; based on historical wind power and photovoltaic actual output curves, historical wind power and photovoltaic output probability distributions are constructed; based on historical wind power and photovoltaic day-ahead output forecast curves and historical wind power and photovoltaic actual output curves, historical wind power and photovoltaic day-ahead output forecast errors are calculated, and probability distributions of wind power and photovoltaic day-ahead output forecast errors are constructed respectively; based on day-ahead electricity market wind power and photovoltaic output forecasts and historical wind power and photovoltaic actual output... The average power output is proportionally converted to the day-ahead power output forecast error distribution of wind and solar power, resulting in the probability distribution of day-ahead wind and solar power output uncertainty. Based on the day-ahead power market wind and solar power output forecast values, their percentiles in the historical wind and solar power output probability distribution are calculated. The percentile values ​​are expanded into a percentile sample interval, and samples are taken from the converted day-ahead wind and solar power output uncertainty probability distribution based on this interval. The upper boundary of the day-ahead wind and solar power output uncertainty sampling results is superimposed to the benchmark positive reserve, and the lower boundary is superimposed to the benchmark negative reserve, completing the adjustment of the day-ahead power market reserve.

[0174] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0175] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0176] Simulation experimental data (I) Simulation Experiment Design Historical data from January to March 2024 for a provincial power market (actual and predicted output of wind and solar power, predicted load, UHV transmission plan, etc.) were selected to construct a simulation scenario: the method of this invention was compared with the existing "load percentage + fixed new energy error ratio" method, and the number of times the reserve was insufficient, the reserve redundancy rate and the system power supply reliability were statistically analyzed under different new energy ratios (20%, 30%, 40%).

[0177] (II) Simulation Results

[0178] (III) Results Analysis Risk of insufficient reserves: As the proportion of new energy sources increases, the number of times existing methods are insufficient in reserves increases significantly (reaching 9.1 times / month when the proportion is 40%), while the method of this invention always keeps it below 1.1 times. This is because the invention quantifies uncertainty through historical error distribution, accurately covers the fluctuations of new energy sources, and avoids insufficiency.

[0179] Redundancy control: The redundancy rate of this invention is only 8.2%-10.3%, which is much lower than the 18.5%-25.7% of existing methods. Because this invention calculates uncertainty proportionally, it avoids excessive reservation and reduces the backup cost of conventional power supplies.

[0180] Power supply reliability: The power supply reliability of the method of this invention exceeds 99.92%, which is significantly higher than the 99.28%-99.75% of the existing methods, verifying that it can ensure the safe operation of the system under a high proportion of new energy.

[0181] This invention accurately models the historical wind and solar power uncertainty characteristics of the day-ahead power market operating area by constructing the probability distribution of historical wind and solar power output and the probability distribution of historical wind and solar power prediction errors. This lays a solid mathematical foundation for adjusting the day-ahead power market reserve based on the uncertainty of wind and solar power output.

[0182] This invention calculates the uncertainty of wind and solar power output based on the day-ahead power market forecast and effectively combines historical wind and solar uncertainties with day-ahead wind and solar forecast information by sampling the probability distribution of wind and solar power output uncertainty. It generates positive and negative reserve adjustment amounts for the day-ahead power market in a differentiated manner, ensuring that the reserve demand of the day-ahead power market fully and accurately takes into account the uncertainty of wind and solar power output and ensuring the safety margin of the power system operation to the uncertainty of wind and solar power output.

[0183] In summary, the present invention provides a method and system for day-ahead power market reserve reservation. Based on the characteristic that the larger the predicted wind and solar power output, the more significant the impact of uncertainty, the method calculates the probability distribution of the day-ahead wind and solar power output prediction error. Furthermore, by utilizing the position of the day-ahead wind and solar power output prediction in the historical wind and solar power output distribution, sampling is performed from the historical wind and solar power output prediction errors. Combining the distribution function and its inverse function, the method achieves the calculation of the impact of wind and solar power output uncertainty on day-ahead power market reserve without assuming the properties of the probability distribution.

[0184] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A method for reserving reserves in the day-ahead electricity market, characterized in that, Includes the following steps: S1. Based on the day-ahead load forecast, DC blocking of UHV transmission channels, and fault factors of key generator units, calculate the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market respectively. S2. Based on the historical actual power output curves of wind power and photovoltaic power, construct the historical probability distribution of wind power and photovoltaic power output; based on the historical day-ahead power output prediction curves of wind power and photovoltaic power and the historical actual power output curves of wind power and photovoltaic power, calculate the historical day-ahead power output prediction errors of wind power and photovoltaic power, and construct the probability distributions of the day-ahead power output prediction errors of wind power and photovoltaic power respectively. S3. Based on the ratio of the day-ahead power market wind power and photovoltaic power output forecast values ​​to the historical average actual power output values, the day-ahead power and photovoltaic power output forecast error distribution is proportionally calculated to obtain the probability distribution of day-ahead wind power and photovoltaic power output uncertainty. S4. Based on the predicted values ​​of wind and solar power output in the day-ahead electricity market, calculate their percentiles in the historical probability distribution of wind and solar power output; expand the percentile values ​​to a percentile sample interval, and sample from the probability distribution of the uncertainty of day-ahead wind and solar power output based on this interval; superimpose the upper boundary of the sampling results of the uncertainty of day-ahead wind and solar power output onto the benchmark positive reserve and the lower boundary onto the benchmark negative reserve, thus completing the adjustment of the reserve reserved in the day-ahead electricity market.

2. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S1, the baseline positive reserve requirement and baseline negative reserve requirements They are respectively: in, , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point This represents the maximum power generation capacity of the key generator units. The default values ​​for positive and backup requirements were manually set by the market operations staff recently. , These are the coefficients for the day-ahead load forecast and the default value for negative reserve demand, respectively.

3. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S2, the method for constructing the historical wind power output probability distribution is as follows: For each point in time, construct a probability distribution from all values ​​of the historical wind power output data for that point in time; The method for constructing the historical photovoltaic power output probability distribution is as follows: For each point in time, a probability distribution is constructed from all the values ​​of that point in the historical photovoltaic power output data.

4. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S2, the method for constructing the probability distribution of historical wind power day-ahead output prediction errors is as follows: Calculate the difference between the historical wind power day-ahead output prediction curve and the historical wind power actual output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point. The method for constructing the probability distribution of historical photovoltaic day-ahead output prediction errors is as follows: Calculate the difference between the historical photovoltaic day-ahead power output prediction curve and the historical photovoltaic actual power output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point.

5. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S3, the method for calculating the probability distribution of the historical wind power day-ahead output prediction error is as follows: For each point in time, calculate the ratio of the predicted wind power output in the day-ahead power market to the average historical wind power output. Divide the probability distribution of the predicted error of the historical wind power day-ahead output by the ratio to obtain the probability distribution of the uncertainty of the wind power output day-ahead. The method for calculating the probability distribution of the historical photovoltaic day-ahead output prediction error is as follows: For each point in time, the ratio of the predicted photovoltaic output in the day-ahead power market to the average historical actual photovoltaic output is calculated. The probability distribution of the day-ahead photovoltaic output prediction error is then divided by the ratio to obtain the probability distribution of the uncertainty in the day-ahead photovoltaic output.

6. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S4, the method for calculating percentiles is as follows: For each point in time, the cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as a percentile; the cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as a percentile.

7. The day-ahead electricity market reserve reservation method according to claim 6, characterized in that, The cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as follows: in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. This is the predicted day-ahead wind power output at the first time point of the operating day; The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as follows: in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. This is the predicted day-ahead photovoltaic output at the first time point of the operating day.

8. The day-ahead electricity market reserve reservation method according to claim 6, characterized in that, The upper and lower boundaries of the uncertainty in wind power output: in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time; The upper and lower boundaries of uncertainty in photovoltaic power output: in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

9. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S4, based on the upper boundary of the day-ahead wind power output uncertainty... And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made: in, Reserve positive reserves for the adjusted day-ahead electricity market. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

10. The day-ahead electricity market reserve reservation method according to claim 1, characterized in that, In step S4, based on the lower boundary of the day-ahead wind power output uncertainty... And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made: in, To reserve negative reserves for the adjusted day-ahead electricity market, For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

11. A day-ahead electricity market reserve reservation system, characterized in that, include: The demand module calculates the benchmark positive reserve demand and benchmark negative reserve demand of the day-ahead electricity market based on the day-ahead load forecast, DC blocking of UHV transmission channels, and failure factors of key generator units. The probability module constructs the historical wind power and solar power output probability distributions based on the historical actual output curves of wind power and solar power; and calculates the historical wind power and solar power output prediction errors based on the historical wind power and solar power day-ahead output prediction curves and the historical wind power and solar power actual output curves, and constructs the probability distributions of the wind power and solar power output prediction errors respectively. The conversion module proportionally converts the day-ahead wind and solar power output forecasts based on the ratio of the day-ahead power market wind and solar power output forecasts to the historical average actual wind and solar power outputs, thereby obtaining the probability distribution of day-ahead wind and solar power output uncertainty. The adjustment module calculates the percentile of wind and solar power output forecasts in the historical probability distribution of wind and solar power output based on the day-ahead power market forecasts. It then expands the percentile values ​​into a percentile sample interval and samples from the probability distribution of the converted day-ahead wind and solar power output uncertainties based on this interval. Finally, it overlays the upper boundary of the day-ahead wind and solar power output uncertainty sampling results onto the benchmark positive reserve and the lower boundary onto the benchmark negative reserve, thus completing the adjustment of the day-ahead power market reserve.

12. The day-ahead electricity market reserve system according to claim 11, characterized in that, In the requirements module, the baseline positive and backup requirements and baseline negative reserve requirements They are respectively: in, , , , These are coefficients for the day-ahead load forecast, the planned power transmission capacity of the UHV transmission corridor, the maximum generating capacity of key generating units, and the default values ​​for positive and reserve demand, respectively. For the first day of operation Daily load forecast values ​​at each point in time. For the first day of operation The planned power transmission capacity of the ultra-high voltage transmission line at a given time point This represents the maximum power generation capacity of the key generator units. The default values ​​for positive and backup requirements were manually set by the market operations staff recently. , These are the coefficients for the day-ahead load forecast and the default value for negative reserve demand, respectively.

13. The day-ahead electricity market reserve system according to claim 11, characterized in that, In the probability module, the method for constructing the historical wind power output probability distribution is as follows: For each point in time, construct a probability distribution from all values ​​of the historical wind power output data for that point in time; The method for constructing the historical photovoltaic power output probability distribution is as follows: For each point in time, construct a probability distribution from all the values ​​of that point in the historical photovoltaic power output data; The method for constructing the probability distribution of historical wind power day-ahead output prediction errors is as follows: Calculate the difference between the historical wind power day-ahead output prediction curve and the historical wind power actual output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point. The method for constructing the probability distribution of historical photovoltaic day-ahead output prediction errors is as follows: Calculate the difference between the historical photovoltaic day-ahead power output prediction curve and the historical photovoltaic actual power output curve. For each time point, construct a probability distribution from all values ​​of the difference at that time point.

14. The day-ahead electricity market reserve system according to claim 11, characterized in that, In the conversion module, the method for converting the historical wind power day-ahead output prediction error probability distribution is as follows: For each point in time, calculate the ratio of the predicted wind power output in the day-ahead power market to the average historical wind power output. Divide the probability distribution of the predicted error of the historical wind power day-ahead output by the ratio to obtain the probability distribution of the uncertainty of the wind power output day-ahead. The method for calculating the probability distribution of the historical photovoltaic day-ahead output prediction error is as follows: For each point in time, the ratio of the predicted photovoltaic output in the day-ahead power market to the average historical actual photovoltaic output is calculated. The probability distribution of the day-ahead photovoltaic output prediction error is then divided by the ratio to obtain the probability distribution of the uncertainty in the day-ahead photovoltaic output.

15. The day-ahead electricity market reserve system according to claim 11, characterized in that, In the adjustment module, the method for calculating percentiles is as follows: For each point in time, the cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as the percentile. The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as the percentile. The cumulative probability of the day-ahead wind power output forecast in the historical wind power output probability distribution is calculated as follows: in, This represents the percentile of the day-ahead wind power output forecast at the first time point of the operating day. This represents the historical wind power output probability distribution at time point 1. This is the predicted day-ahead wind power output at the first time point of the operating day; The cumulative probability of the day-ahead photovoltaic power output forecast in the historical photovoltaic power output probability distribution is calculated as follows: in, This represents the percentile of the day-ahead photovoltaic power output forecast at the first time point of the operating day. This represents the historical photovoltaic power output probability distribution at time point 1. This is the predicted day-ahead photovoltaic output at the first time point of the operating day; The upper and lower boundaries of the uncertainty in wind power output: in, For the first Percentile range of the day-ahead wind power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead wind power output forecast at a given time point. This represents the probability when the uncertainty of wind power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead wind power output uncertainty at a given time point. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time; The upper and lower boundaries of uncertainty in photovoltaic power output: in, For the first Percentile range of the day-ahead photovoltaic power output forecast at each point in time. For the first The lower boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. For the first The upper boundary of the percentile interval of the day-ahead photovoltaic power output forecast at a given time point. This represents the probability when the uncertainty of photovoltaic power output is taken as 0. For the first The inverse function of the probability distribution function of the day-ahead photovoltaic output uncertainty at a given time point. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time. For the first The upper boundary of the uncertainty of the day-ahead photovoltaic output at a given point in time.

16. The day-ahead electricity market reserve system according to claim 11, characterized in that, In the adjustment module, the upper boundary of the day-ahead wind power output uncertainty is considered. And the upper boundary of the uncertainty of photovoltaic power output. For the baseline positive reserve requirement Adjustments will be made: in, Reserve positive reserves for the adjusted day-ahead electricity market. For the first day of operation The baseline positive reserve requirement at each point in time. For the first The upper boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The upper boundary of the uncertainty in day-ahead photovoltaic output at a given point in time; Based on the current uncertainty of wind power output, the lower boundary And the lower boundary of the uncertainty of photovoltaic power output For the baseline negative reserve requirement Adjustments will be made: in, To reserve negative reserves for the adjusted day-ahead electricity market, For the first day of operation The baseline negative reserve requirement at each point in time. For the first The lower boundary of the day-ahead wind power output uncertainty at a given point in time. For the first The lower boundary of the day-ahead photovoltaic output uncertainty at a given point in time.

17. A computer-readable storage medium for storing one or more programs, characterized in that, The one or more programs include instructions that, when executed by a computing device, cause the computing device to perform the method of any one of claims 1 to 10.

18. A computing device, characterized in that, include: One or more processors, a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including steps for performing the method of any one of claims 1 to 10.

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