Method and device for drawing hill climbing auxiliary service demand price curve and storage medium

Abstract

The application discloses a kind of climbing auxiliary service demand price curve drawing method, device and storage medium, wherein method includes: obtaining the system net load value of preset scheduling interval, system net load value includes system net load forecast value and system net load measured value;System net load value is classified as the typical day data set of different time periods;Calculate the system net load error of each scheduling interval, obtain the system net load error set corresponding to different time periods;According to system net load error set, the frequency of system net load prediction error in different error intervals is counted, and the error frequency of frequency relative to the data quantity in the system net load error set of current period is calculated;According to error frequency, probability density distribution histogram is drawn, and the climbing auxiliary service demand price curve of each period is drawn.The present application can comprehensively reflect electric energy cost and climbing auxiliary service benefit, and provide effective data support for building climbing auxiliary service market.

Description

Technical Field

[0001] This invention relates to the field of power auxiliary services technology, and in particular to a method, apparatus and storage medium for plotting demand-price curves for ramp-up auxiliary services. Background Technology

[0002] The main purpose of ramp-up ancillary services is to cope with changes in the net load of the system caused by fluctuations in renewable energy generation in order to maintain power balance. Therefore, it is an important part of improving the flexibility, ramp-up capacity and power balance security of new power systems.

[0003] Currently, there is no relevant research on ramp-up assistance services in existing technologies. However, with the high proportion of new energy sources being connected to the grid, the net load fluctuation of the domestic power system will gradually increase, and the demand for flexible ramp-up capabilities will also grow accordingly. Existing technologies cannot provide effective data support for building the ramp-up assistance service market. Summary of the Invention

[0004] This invention provides a method, apparatus, and storage medium for plotting the demand-price curve of hill-climbing assistance services, which can comprehensively reflect the cost of electricity and the benefits of hill-climbing assistance services, providing effective data support for the development of the hill-climbing assistance service market.

[0005] One embodiment of the present invention provides a method for plotting a demand-price curve for hill-climbing assistance services, comprising:

[0006] Obtain the system net load value at a preset scheduling interval, the system net load value including the predicted system net load value and the measured system net load value;

[0007] Based on weekdays, Saturdays, Sundays, and holidays, the system net load value is classified into typical daily data sets for different time periods;

[0008] For each typical day's data set, calculate the system net load error for each scheduling interval to obtain the system net load error set corresponding to different time periods;

[0009] Based on the system net load error set, the frequency of the system net load prediction error in different error intervals is counted, and the error frequency of the frequency relative to the number of data in the system net load error set for the current time period is calculated.

[0010] Based on the error frequency, a probability density distribution histogram is plotted, and based on the probability density distribution histogram, a price curve for the demand for hill-climbing auxiliary services is plotted for each time period.

[0011] Furthermore, the predicted net load value of the system is:

[0012]

[0013] The measured net load of the system is:

[0014] ND t =|Load t |-|Generation t |

[0015] Wherein, the subscript t represents the time period, and the length of the time period is the scheduling interval; The system net load forecast value for time period t; The system load forecast value for time period t; The predicted output values ​​of new energy units such as wind power and photovoltaic power generation for time period t; ND t The measured net system load for time period t; Load t The measured system load for time period t; Generation t The measured output values ​​of new energy units such as wind power and photovoltaic power generation during time period t.

[0016] Furthermore, the system net load error for each scheduling interval is calculated, including:

[0017]

[0018] Among them, e t Let ND be the system net load forecast error for time period t. t The system net load forecast value for time period t. The measured net system load for time period t is given.

[0019] Furthermore, the horizontal axis of the probability density distribution histogram represents the error interval, and the vertical axis represents the probability density of the system net load prediction error within the interval.

[0020] Furthermore, based on the probability density distribution histogram, the demand-price curve for the hill-climbing auxiliary service is plotted for each time period, including:

[0021] The predicted net load fluctuation value for the next period is calculated based on the predicted net load value for the system, and the deterministic ramp-up demand and deterministic ramp-down demand are calculated based on the predicted net load fluctuation value for the next period.

[0022] The positive error of system net load prediction at the upper limit of the confidence interval and the negative error of system net load prediction at the lower limit of the confidence interval are determined based on the probability density distribution histogram.

[0023] Uncertainty ramping demand is calculated based on the positive error of the system net load forecast, and uncertainty ramping demand is calculated based on the service difference of the system net load forecast.

[0024] Based on the deterministic uphill demand and the uncertain uphill demand, plot the uphill ancillary service demand price curve; based on the deterministic downhill demand and the uncertain downhill demand, plot the downhill ancillary service demand price curve.

[0025] Furthermore, based on the system net load forecast, the system net load fluctuation forecast for the next period is calculated, and based on the system net load fluctuation forecast for the next period, deterministic ramp-up demand and deterministic ramp-down demand are calculated, including:

[0026]

[0027] FRUR NDt =max(0,ΔND) t )

[0028] FRDR NDt =min(0,ΔND) t )

[0029] Where, ΔND t FRUR represents the predicted net load fluctuation of the system in the next time period that needs to be considered in time period t. NDt For deterministic uphill climbing requirements; FRDR NDt For certainty, climbing requirements

[0030] Furthermore, based on the probability density distribution histogram, the system net load forecast positive error (upper limit of confidence interval) and the system net load forecast negative error (lower limit of confidence interval) are determined, including:

[0031]

[0032] EU t =max(0,PU) t )

[0033] FRUR Ut =max(0,EU) t +FRDR NDt )

[0034]

[0035] ED t =min(0,PD) t )

[0036] FRDR Ut =min(0,ED) t +FRUR NDt )

[0037] Where, p t (e tThe system net load forecast error e during time period t is... t Histogram of probability density distribution; PU t The system net load prediction error corresponds to the upper limit of the confidence interval; CLU is the probability density distribution histogram integral from negative infinity to PU. t The value of EU, i.e., the upper limit of the confidence interval; t The positive error in the system net load forecast corresponds to the upper limit of the confidence interval; PD t The system net load prediction error corresponds to the lower limit of the confidence interval; CLD is the integral of the probability density distribution histogram from negative infinity to PD. t The value of ED is the lower limit of the confidence interval. t The negative error in the system net load forecast corresponds to the lower limit of the confidence interval; FRUR Ut Due to uncertainty, there is a need for ramp-up demand; FRDR Ut This is to meet the demand for climbing under uncertainty.

[0038] Furthermore, based on the deterministic uphill demand and the uncertain uphill demand, an uphill ancillary service demand-price curve is plotted; based on the deterministic downhill demand and the uncertain downhill demand, a downhill ancillary service demand-price curve is plotted, including:

[0039]

[0040] Among them, CDU t Let be the demand-price curve for ramp-up auxiliary services during time period t. The demand price corresponding to any point on the curve represents the expected loss caused by power imbalance due to insufficient system ramp-up capacity, assuming a certain quantity of ramp-up auxiliary services has not been purchased. (CDD) t Let be the demand-price curve for downhill ancillary services during time period t. The demand price at any point on the curve represents the expected loss due to power imbalance caused by insufficient system ramping capacity, assuming a certain quantity of ramping ancillary services has not been procured. STU is the upper limit of the first segment of the demand-price curve for uphill ancillary services; STD is the upper limit of the first segment of the demand-price curve for downhill ancillary services; D is the length of each segment of the demand-price curve excluding the first segment; r is the number of segments in the demand-price curve for uphill or downhill ancillary services excluding the first segment; n is the segment number of the demand-price curve for uphill or downhill ancillary services; FRUS t The variable representing the number of hill-climbing assistance services that were not procured; FRDS t is the variable representing the quantity of downhill ancillary services not procured; PC is the upper limit of the bid price in the spot market for electricity; FRUP is the reference price when uphill ancillary services are insufficient; PF can be the lower limit of the negative price of the deep peak shaving ancillary service market or the negative value of its compensation price upper limit; FRDP is the reference price when downhill ancillary services are insufficient.

[0041] One embodiment of the present invention provides a device for plotting the demand-price curve of hill-climbing assistance services, comprising:

[0042] The system net load value acquisition module is used to acquire the system net load value at a preset scheduling interval. The system net load value includes the predicted system net load value and the measured system net load value.

[0043] The typical daily data set classification module is used to classify the system net load value into typical daily data sets for different time periods based on weekdays, Saturdays, Sundays, and holidays;

[0044] The system net load error calculation module is used to calculate the system net load error for each scheduling interval for each typical daily data set, and obtain the system net load error set corresponding to different time periods.

[0045] The error frequency calculation module is used to count the frequency of the system net load prediction error in different error intervals based on the system net load error set, and calculate the error frequency of the frequency relative to the number of data in the system net load error set for the current time period.

[0046] The demand-price curve plotting module is used to plot a probability density distribution histogram based on the error frequency, and plot the demand-price curve for the ramp-up auxiliary service for each time period based on the probability density distribution histogram.

[0047] One embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described method for plotting the demand-price curve for hill-climbing auxiliary services.

[0048] This invention calculates the frequency of the system net load prediction error within different error intervals based on the predicted and measured system net load values. It also calculates the error frequency relative to the number of data points in the current period's system net load error set. A probability density distribution histogram is plotted based on the error frequency, and a ramp-up ancillary service demand price curve is plotted using the expected loss as the price of the demand price curve. The expected loss is the expected loss caused by insufficient system ramp-up capacity leading to power imbalance when a certain quantity of ramp-up ancillary services has not been procured. This invention plots the demand price curve using the expected loss as the price of the demand price curve. The demand price curve can serve as the price multiplier for the ramp-up ancillary service benefit term (demand curve price * ramp-up ancillary service clearing quantity) in the joint clearing model of electricity and ramp-up ancillary services. This allows the joint clearing model to weigh the electricity cost and the ramp-up ancillary service benefit, thereby increasing the price elasticity of the ramp-up ancillary service clearing quantity. This enables the clearing results to more comprehensively reflect the electricity cost and the ramp-up ancillary service benefit, thus effectively promoting the development of the ramp-up ancillary service market. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating the method for drawing the demand-price curve of hill-climbing assistance services provided in an embodiment of the present invention.

[0050] Figure 2 This is a histogram of the probability density distribution of the system net load prediction error provided in this embodiment of the invention;

[0051] Figure 3 This is a schematic diagram of the demand-price curve for hill-climbing assistance services provided in an embodiment of the present invention;

[0052] Figure 4 This is a schematic diagram of the structure of the hill-climbing auxiliary service demand-price curve plotting device provided in an embodiment of the present invention. Detailed Implementation

[0053] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0054] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "multiple" means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0056] Please see Figure 1 An embodiment of the present invention provides a method for plotting a demand-price curve for hill-climbing assistance services, comprising:

[0057] S1. Obtain the system net load value of the preset scheduling interval. The system net load value includes the predicted system net load value and the measured system net load value.

[0058] In this embodiment of the invention, the net system load value within all 15-minute dispatch intervals within a line can be collected, which is the difference between the absolute value of the system load and the output of new energy units such as wind power and photovoltaic power generation.

[0059] S2. Based on weekdays, Saturdays, Sundays, and holidays, classify the system net load value into typical daily data sets for different time periods;

[0060] In this embodiment of the invention, the system net load value is classified into typical data sets for different time periods based on weekdays, Saturdays, Sundays and holidays, which enables the plotting of the ramp-up auxiliary service demand-price curve for each time period, making the plotting of the ramp-up auxiliary service demand-price curve more refined and accurate.

[0061] S3. For each typical day data set, calculate the system net load error for each scheduling interval to obtain the system net load error set corresponding to different time periods.

[0062] In this embodiment of the invention, the scheduling interval can be set and adjusted according to actual needs. For example, the scheduling interval can be set to 15 minutes, 18 minutes or 20 minutes.

[0063] S4. Based on the system net load error set, count the frequency of the system net load prediction error in different error intervals, and calculate the error frequency relative to the number of data in the system net load error set for the current time period.

[0064] S5. Draw a probability density distribution histogram based on the error frequency, and draw the demand price curve for ramp-up auxiliary services for each time period based on the probability density distribution histogram.

[0065] This invention calculates the frequency of the system net load prediction error within different error intervals based on the predicted and measured system net load values. It also calculates the error frequency relative to the number of data points in the current period's system net load error set. A probability density distribution histogram is plotted based on the error frequency, and a ramp-up ancillary service demand price curve is plotted using the expected loss as the price of the demand price curve. The expected loss is the expected loss caused by insufficient system ramp-up capacity leading to power imbalance when a certain quantity of ramp-up ancillary services has not been procured. This invention plots the demand price curve using the expected loss as the price of the demand price curve. The demand price curve can serve as the price multiplier for the ramp-up ancillary service benefit term (demand curve price * ramp-up ancillary service clearing quantity) in the joint clearing model of electricity and ramp-up ancillary services. This allows the joint clearing model to weigh the electricity cost and the ramp-up ancillary service benefit, thereby increasing the price elasticity of the ramp-up ancillary service clearing quantity. This enables the clearing results to more comprehensively reflect the electricity cost and the ramp-up ancillary service benefit, thus effectively promoting the development of the ramp-up ancillary service market.

[0066] In one embodiment, the system net load forecast value is:

[0067]

[0068] The measured net load of the system is:

[0069] ND t =|Load t |-|Generation t |

[0070] Wherein, the subscript t represents the time period, and the length of the time period is the scheduling interval. In this embodiment of the invention, the scheduling interval can be 15 minutes. The system net load forecast value for time period t; The system load forecast value for time period t; The predicted output values ​​of new energy units such as wind power and photovoltaic power generation for time period t; ND t The measured net system load for time period t; Load t The measured system load for time period t; Generation tThe measured output values ​​of new energy units such as wind power and photovoltaic power generation during time period t.

[0071] In one embodiment, calculating the system net load error for each scheduling interval includes:

[0072]

[0073] Among them, e t Let ND be the system net load forecast error for time period t. t The system net load forecast value for time period t. The measured net system load for time period t is given.

[0074] In one embodiment, the horizontal axis of the probability density distribution histogram represents the error interval, and the vertical axis represents the probability density of the system net load prediction error within the interval.

[0075] Please see Figure 2 This is a probability density distribution histogram provided in an embodiment of the present invention.

[0076] In one embodiment, step S5, which plots the demand-price curve for ramp-up auxiliary services for each time period based on the probability density distribution histogram, may further include the following sub-steps:

[0077] S51. Calculate the predicted net load fluctuation value of the system for the next period based on the predicted net load value of the system for the next period, and calculate the deterministic ramp-up demand and deterministic ramp-down demand based on the predicted net load fluctuation value of the system for the next period.

[0078] In this embodiment of the invention, the difference between the predicted system net load for the next time period and the predicted system net load for the current time period can be used as the predicted system net load fluctuation for the next time period.

[0079] S52. Determine the positive error of system net load prediction at the upper limit of the confidence interval and the negative error of system net load prediction at the lower limit of the confidence interval based on the probability density distribution histogram.

[0080] S53. The uncertainty ramp-up demand is calculated based on the positive error of the system net load forecast, and the uncertainty ramp-up demand is calculated based on the service difference of the system net load forecast.

[0081] S54. Based on deterministic uphill demand and uncertain uphill demand, draw the uphill ancillary service demand price curve; based on deterministic downhill demand and uncertain downhill demand, draw the downhill ancillary service demand price curve.

[0082] In one embodiment, the predicted system net load fluctuation for the next time period is calculated based on the predicted system net load value, and deterministic ramp-up demand and deterministic ramp-down demand are calculated based on the predicted system net load fluctuation for the next time period, including:

[0083]

[0084] FRUR NDt =max(0,ΔND) t )

[0085] FRDR NDt =min(0,ΔND) t )

[0086] Where, ΔND t FRUR represents the predicted net load fluctuation of the system in the next time period that needs to be considered in time period t. NDt For deterministic uphill climbing requirements; FRDR NDt To determine the climbing requirements.

[0087] In one embodiment, determining the positive error of the system net load forecast (upper limit) and the negative error of the system net load forecast (lower limit) based on the probability density distribution histogram includes:

[0088]

[0089] EU t =max(0,PU) t )

[0090] FRUR Ut =max(0,EU) t +FRDR NDt )

[0091]

[0092] ED t =min(0,PD) t )

[0093] FRDR Ut =min(0,ED) t +FRUR NDt )

[0094] Where, p t (e t ) represents the system net load forecast error e for time period t. t Histogram of probability density distribution; PU t The system net load prediction error corresponds to the upper limit of the confidence interval; CLU is the probability density distribution histogram integral from negative infinity to PU.t The value of , i.e., the upper limit of the confidence interval, can be taken as 97.5%; EU t The positive error in the system net load forecast corresponding to the upper limit of the confidence interval; PD t The system net load prediction error corresponds to the lower limit of the confidence interval; CLD is the integral of the probability density distribution histogram from negative infinity to PD. t The value of , i.e., the lower limit of the confidence interval, can be taken as 2.5%; ED t (Negative value) represents the negative error in the system net load forecast corresponding to the lower limit of the confidence interval; FRUR Ut Uncertainty regarding ramp-up demand, caused by system net load forecasting errors within a specific confidence interval; FRDR Ut (Negative value) represents ramp-up demand under uncertainty, which is caused by the system net load prediction error within a specific confidence interval.

[0095] In one embodiment, an uphill ancillary service demand-price curve is plotted based on deterministic uphill demand and uncertain uphill demand; a downhill ancillary service demand-price curve is plotted based on deterministic downhill demand and uncertain downhill demand, including:

[0096]

[0097]

[0098] Among them, CDU t Let be the demand-price curve for ramp-up auxiliary services during time period t. The demand price corresponding to any point on the curve represents the expected loss caused by power imbalance due to insufficient system ramp-up capacity, assuming a certain quantity of ramp-up auxiliary services has not been purchased. (CDD) t Let t be the downhill ancillary service demand-price curve. The demand price at any point on the curve represents the expected loss due to power imbalance caused by insufficient system ramping capacity, assuming a certain quantity of ramping ancillary services has not been procured. STU is the upper limit of the first segment of the uphill ancillary service demand-price curve; STD (negative value) is the upper limit of the first segment of the downhill ancillary service demand-price curve; D is the length of each segment of the demand-price curve excluding the first segment; r is the number of segments of the uphill or downhill ancillary service demand-price curve excluding the first segment; n is the segment number of the uphill or downhill ancillary service demand-price curve; FRUS t The variable representing the number of hill-climbing assistance services that were not procured; FRDS tThe variable (negative value) represents the quantity of unprocured downhill ancillary services; PC represents the upper limit of the spot market bid price; FRUP represents the reference price when uphill ancillary services are insufficient, which can be set considering the importance and priority of uphill ancillary services compared to frequency regulation, reserve, and other ancillary services; PF (negative value) can be the lower limit of the negative price in the deep peak shaving ancillary service market or the negative value of its compensation price upper limit. After the electricity market matures, if the spot market allows the bidding of negative electricity prices, it can be the lower limit of the spot market bid price; FRDP represents the reference price when downhill ancillary services are insufficient. After the electricity market matures, if the spot market allows the bidding of negative electricity prices, it can be the lower limit of the spot market bid price.

[0099] Please refer to Figure 3, which is a schematic diagram of the demand-price curve for hill-climbing assistance services provided in an embodiment of the present invention.

[0100] Implementing the embodiments of the present invention has the following beneficial effects:

[0101] This invention calculates the frequency of the system net load prediction error within different error intervals based on the predicted and measured system net load values. It also calculates the error frequency relative to the number of data points in the current period's system net load error set. A probability density distribution histogram is plotted based on the error frequency, and a ramp-up ancillary service demand price curve is plotted using the expected loss as the price of the demand price curve. The expected loss is the expected loss caused by insufficient system ramp-up capacity leading to power imbalance when a certain quantity of ramp-up ancillary services has not been procured. This invention plots the demand price curve using the expected loss as the price of the demand price curve. The demand price curve can serve as the price multiplier for the ramp-up ancillary service benefit term (demand curve price * ramp-up ancillary service clearing quantity) in the joint clearing model of electricity and ramp-up ancillary services. This allows the joint clearing model to weigh the electricity cost and the ramp-up ancillary service benefit, thereby increasing the price elasticity of the ramp-up ancillary service clearing quantity. This enables the clearing results to more comprehensively reflect the electricity cost and the ramp-up ancillary service benefit, thus effectively promoting the development of the ramp-up ancillary service market.

[0102] Please see Figure 4 Based on the same inventive concept as the above embodiments, one embodiment of the present invention provides a device for plotting the demand-price curve of hill-climbing assistance services, comprising:

[0103] The system net load value acquisition module 10 is used to acquire the system net load value of a preset scheduling interval. The system net load value includes the predicted system net load value and the measured system net load value.

[0104] Typical daily data set classification module 20 is used to classify the system net load value into typical daily data sets for different time periods based on weekdays, Saturdays, Sundays and holidays;

[0105] The system net load error calculation module 30 is used to calculate the system net load error for each scheduling interval for each typical daily data set, and obtain the system net load error set corresponding to different time periods.

[0106] The error frequency calculation module 40 is used to count the frequency of the system net load prediction error in different error intervals based on the system net load error set, and to calculate the error frequency relative to the number of data in the system net load error set for the current time period.

[0107] The demand-price curve plotting module 50 is used to plot a probability density distribution histogram based on the error frequency, and to plot the demand-price curve for the ramp-up auxiliary service for each time period based on the probability density distribution histogram.

[0108] In one embodiment, the system net load forecast value is:

[0109]

[0110] The measured net load of the system is:

[0111] ND t =|Load t |-|Generation t |

[0112] Wherein, the subscript t represents the time period, and the length of the time period is the scheduling interval; The system net load forecast value for time period t; The system load forecast value for time period t; The predicted output values ​​of new energy units such as wind power and photovoltaic power generation for time period t; ND t The measured net system load for time period t; Load t The measured system load for time period t; Generation t The measured output values ​​of new energy units such as wind power and photovoltaic power generation during time period t.

[0113] In one embodiment, calculating the system net load error for each scheduling interval includes:

[0114]

[0115] Among them, e t Let ND be the system net load forecast error for time period t. t The system net load forecast value for time period t. The measured net system load for time period t is given.

[0116] In one embodiment, the horizontal axis of the probability density distribution histogram represents the error interval, and the vertical axis represents the probability density of the system net load prediction error within the interval.

[0117] In one embodiment, the demand-price curve for ramp-up auxiliary services is plotted for each time period based on a probability density distribution histogram, including:

[0118] The predicted net load fluctuation of the system in the next period is calculated based on the predicted net load of the system in the next period, and the deterministic ramp-up demand and deterministic ramp-down demand are calculated based on the predicted net load fluctuation of the system in the next period.

[0119] The positive error of system net load prediction at the upper limit of the confidence interval and the negative error of system net load prediction at the lower limit of the confidence interval are determined based on the probability density distribution histogram.

[0120] Uncertainty-based ramp-up demand is calculated based on the positive error of the system net load forecast, and uncertainty-based ramp-up demand is calculated based on the service difference of the system net load forecast.

[0121] Based on deterministic and uncertain uphill demand, plot the uphill ancillary service demand price curve; based on deterministic and uncertain downhill demand, plot the downhill ancillary service demand price curve.

[0122] In one embodiment, the predicted system net load fluctuation for the next time period is calculated based on the predicted system net load value, and deterministic ramp-up demand and deterministic ramp-down demand are calculated based on the predicted system net load fluctuation for the next time period, including:

[0123]

[0124] FRUR NDt =max(0,ΔND) t )

[0125] FRDR NDt =min(0,ΔND) t )

[0126] Where, ΔND t FRUR represents the predicted net load fluctuation of the system in the next time period that needs to be considered in time period t. NDt For deterministic uphill climbing requirements; FRDR NDt For certainty, climbing requirements

[0127] In one embodiment, determining the positive error of the system net load forecast (upper limit) and the negative error of the system net load forecast (lower limit) based on the probability density distribution histogram includes:

[0128]

[0129] EU t =max(0,PU) t )

[0130] FRUR Ut =max(0,EU) t +FRDR NDt )

[0131]

[0132] ED t =min(0,PD) t )

[0133] FRDR Ut =min(0,ED) t +FRUR NDt )

[0134] Where, p t (e t The system net load forecast error e during time period t is... t Histogram of probability density distribution; PU t The system net load prediction error corresponds to the upper limit of the confidence interval; CLU is the probability density distribution histogram integral from negative infinity to PU. t The value of EU, i.e., the upper limit of the confidence interval; t The positive error in the system net load forecast corresponds to the upper limit of the confidence interval; PD t The system net load prediction error corresponds to the lower limit of the confidence interval; CLD is the integral of the probability density distribution histogram from negative infinity to PD. t The value of ED is the lower limit of the confidence interval. t The negative error in the system net load forecast corresponds to the lower limit of the confidence interval; FRUR Ut Due to uncertainty, there is a need for ramp-up demand; FRDR Ut This is to meet the demand for climbing under uncertainty.

[0135] In one embodiment, an uphill ancillary service demand-price curve is plotted based on deterministic uphill demand and uncertain uphill demand; a downhill ancillary service demand-price curve is plotted based on deterministic downhill demand and uncertain downhill demand, including:

[0136]

[0137]

[0138] Among them, CDU tLet be the demand-price curve for ramp-up auxiliary services during time period t. The demand price corresponding to any point on the curve represents the expected loss caused by power imbalance due to insufficient system ramp-up capacity, assuming a certain quantity of ramp-up auxiliary services has not been purchased. (CDD) t Let be the demand-price curve for downhill ancillary services during time period t. The demand price at any point on the curve represents the expected loss due to power imbalance caused by insufficient system ramping capacity, assuming a certain quantity of ramping ancillary services has not been procured. STU is the upper limit of the first segment of the demand-price curve for uphill ancillary services; STD is the upper limit of the first segment of the demand-price curve for downhill ancillary services; D is the length of each segment of the demand-price curve excluding the first segment; r is the number of segments in the demand-price curve for uphill or downhill ancillary services excluding the first segment; n is the segment number of the demand-price curve for uphill or downhill ancillary services; FRUS t The variable representing the number of hill-climbing assistance services that were not procured; FRDS t is the variable representing the quantity of downhill ancillary services not procured; PC is the upper limit of the bid price in the spot market for electricity; FRUP is the reference price when uphill ancillary services are insufficient; PF can be the lower limit of the negative price of the deep peak shaving ancillary service market or the negative value of its compensation price upper limit; FRDP is the reference price when downhill ancillary services are insufficient.

[0139] One embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described method for plotting the demand-price curve of hill-climbing auxiliary services.

[0140] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for plotting a demand-price curve for hill-climbing assistance services, characterized in that, include: Obtain the system net load value at a preset scheduling interval, the system net load value including the predicted system net load value and the measured system net load value; Based on weekdays, Saturdays, Sundays, and holidays, the system net load value is classified into typical daily data sets for different time periods; For each typical day's data set, calculate the system net load error for each scheduling interval to obtain the system net load error set corresponding to different time periods; Based on the system net load error set, the frequency of the system net load prediction error in different error intervals is counted, and the error frequency of the frequency relative to the number of data in the system net load error set for the current time period is calculated. A probability density distribution histogram is plotted based on the error frequency, and a ramp-up auxiliary service demand-price curve is plotted for each time period based on the probability density distribution histogram; the plotting of the ramp-up auxiliary service demand-price curve for each time period based on the probability density distribution histogram includes: calculating the system net load fluctuation forecast value for the next time period based on the system net load forecast value, and calculating the deterministic ramp-up demand and deterministic ramp-down demand based on the system net load fluctuation forecast value for the next time period. The system net load forecast positive error (upper limit) and system net load forecast negative error (lower limit) are determined based on the probability density distribution histogram. Uncertainty-based ramp-up demand is calculated based on the system net load forecast positive error, and uncertainty-based ramp-up demand is calculated based on the system net load forecast service difference. A ramp-up ancillary service demand price curve is plotted based on the deterministic ramp-up demand and the uncertainty ramp-up demand. A ramp-up ancillary service demand price curve is plotted based on the deterministic ramp-up demand and the uncertainty ramp-up demand.

2. The method for drawing the demand-price curve for hill-climbing auxiliary services as described in claim 1, characterized in that, The predicted net load value for the system is: The measured net load of the system is: Among them, subscript This indicates a time period, the length of which is the scheduling interval; for Forecasted system net load for the specified time period; for System load forecast for the time period; for Forecast output values ​​of wind power and photovoltaic power generation units for different time periods; for Measured net system load for the specified time period; for Measured system load values ​​for the specified time period; for Measured output values ​​of wind power and photovoltaic power generation units during the specified time period.

3. The method for drawing the demand-price curve for hill-climbing auxiliary services as described in claim 1, characterized in that, Calculate the system net load error for each scheduling interval, including: in, for System net load forecast error for a given period for Forecasted system net load for the time period for The measured net system load for the specified time period.

4. The method for drawing the demand-price curve for hill-climbing auxiliary services as described in claim 1, characterized in that, The horizontal axis of the probability density distribution histogram represents the error interval, and the vertical axis represents the probability density of the system net load prediction error within the interval.

5. The method for plotting the demand-price curve for hill-climbing auxiliary services as described in claim 1, characterized in that, The predicted system net load fluctuation for the next time period is calculated based on the predicted system net load value. Then, based on this predicted system net load fluctuation, deterministic ramp-up demand and deterministic ramp-down demand are calculated, including: Among them, subscript This indicates a time period, the length of which is the scheduling interval. for The system net load fluctuation forecast for the next time period needs to be considered. To ensure the need for a certain uphill climb; For certainty, the climbing requirement; for Forecasted system net load for the time period for The predicted net system load for the +1 time period for The measured net system load for the specified time period.

6. The method for plotting the demand-price curve for hill-climbing auxiliary services as described in claim 5, characterized in that, The positive error in system net load forecasting at the upper limit of the confidence interval and the negative error in system net load forecasting at the lower limit of the confidence interval are determined based on the probability density distribution histogram, including: in, for System net load forecast error Histogram of probability density distribution; This represents the system net load prediction error corresponding to the upper limit of the confidence interval; Integrating the probability density distribution histogram from negative infinity to... The value of is the upper limit of the confidence interval; The positive error in the system net load forecast corresponds to the upper limit of the confidence interval; This represents the system net load prediction error corresponding to the lower limit of the confidence interval; Integrating the probability density distribution histogram from negative infinity to... The value of is the lower limit of the confidence interval; The negative error in the system net load forecast corresponds to the lower limit of the confidence interval; The need to climb uphill due to uncertainty; This is to meet the demand for climbing uphill under uncertainty.

7. The method for plotting the demand-price curve for hill-climbing auxiliary services as described in claim 6, characterized in that, Based on the deterministic uphill demand and the uncertain uphill demand, plot the uphill ancillary service demand price curve; based on the deterministic downhill demand and the uncertain downhill demand, plot the downhill ancillary service demand price curve, including: in, for The demand-price curve for ramp-up assistance services represents the expected loss caused by power imbalance due to insufficient system ramp-up capability if a certain amount of ramp-up assistance services are not purchased. for The demand-price curve for ramp-up assistance services is shown. The demand price corresponding to any point on the curve represents the expected loss caused by power imbalance due to insufficient system ramp-up capability if a certain amount of ramp-up assistance services are not purchased. This represents the upper limit of the first segment of the price curve for uphill auxiliary services. This represents the upper limit of the first segment of the price curve for downhill auxiliary services; This represents the length of each segment of the demand-price curve, excluding the first segment. This refers to the number of intervals in the price curve for uphill or downhill auxiliary services, excluding the first interval. The interval number of the price curve for auxiliary services during uphill or downhill climbing; The variable representing the number of uphill auxiliary services that were not procured; The variable representing the number of downhill auxiliary services that were not procured; This represents the upper limit of the bidding price in the spot market for electrical energy. This is a reference price when hill-climbing assistance services are insufficient; This refers to the negative lower limit of the market price for deep peak shaving ancillary services or the negative value of its compensation price upper limit. This is a reference price when downhill climbing assistance services are insufficient.

8. A device for plotting the demand-price curve of hill-climbing assistance services, characterized in that, include: The system net load value acquisition module is used to acquire the system net load value at a preset scheduling interval. The system net load value includes the predicted system net load value and the measured system net load value. The typical daily data set classification module is used to classify the system net load value into typical daily data sets for different time periods based on weekdays, Saturdays, Sundays, and holidays; The system net load error calculation module is used to calculate the system net load error for each scheduling interval for each typical daily data set, and obtain the system net load error set corresponding to different time periods. The error frequency calculation module is used to count the frequency of the system net load prediction error in different error intervals based on the system net load error set, and calculate the error frequency of the frequency relative to the number of data in the system net load error set for the current time period. The demand-price curve plotting module is used to plot a probability density distribution histogram based on the error frequency, and plot the ramp-up auxiliary service demand-price curve for each time period based on the probability density distribution histogram; the plotting of the ramp-up auxiliary service demand-price curve for each time period based on the probability density distribution histogram includes: calculating the system net load fluctuation forecast value for the next time period based on the system net load forecast value, and calculating the deterministic up-climb demand and deterministic down-climb demand based on the system net load fluctuation forecast value for the next time period; The system net load forecast positive error (upper limit) and system net load forecast negative error (lower limit) are determined based on the probability density distribution histogram. Uncertainty-based ramp-up demand is calculated based on the system net load forecast positive error, and uncertainty-based ramp-up demand is calculated based on the system net load forecast service difference. A ramp-up ancillary service demand price curve is plotted based on the deterministic ramp-up demand and the uncertainty ramp-up demand. A ramp-up ancillary service demand price curve is plotted based on the deterministic ramp-up demand and the uncertainty ramp-up demand.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device on which the computer-readable storage medium is located to perform the hill-climbing auxiliary service demand price curve plotting method as described in any one of claims 1 to 7.

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