Multi-time scale climbing demand prediction method for high-proportion new energy power grid

Abstract

The invention relates to the technical field of new energy power system dispatching operation, in particular to a multi-time-scale climbing demand prediction method for a high-proportion new energy power grid, and the method comprises the steps: constructing a multi-time-scale prediction model, and obtaining a day-ahead scale prediction value, an hour scale prediction value and a minute scale prediction value of new energy output; calculating a net load based on each predicted value of the new energy output, and constructing a system climbing demand quantitative model to obtain climbing demand values of different time scales; the method comprises the following steps: acquiring a climbing demand value of a system, constructing a multi-element adjustment resource collaborative model based on the climbing demand value, and configuring multi-element adjustment resources through an optimization algorithm to minimize the climbing vacancy of the system. The beneficial effects of improving the ability of the high-proportion new energy power grid to cope with the abrupt rise and abrupt drop of power and guaranteeing the safe and stable operation of the system are achieved.

Description

Technical Field

[0001] This invention relates to the field of dispatching and operation technology of new energy power systems, and in particular to a multi-timescale ramp-up demand forecasting method for high-proportion new energy power grids. Background Technology

[0002] With the transformation of the global energy structure and the increasing maturity of new energy technologies, wind power, photovoltaics, and other new energy sources have become important components of the power system and are increasingly being incorporated into the power generation side. However, how to effectively cope with the ramp-up pressure brought by these intermittent energy sources, especially the output fluctuations across multiple time scales, remains a major challenge.

[0003] The ramp-up pressure on power systems directly affects their stable and safe operation. In practice, due to the drastic fluctuations in renewable energy output and the insufficient ability of traditional forecasting methods to capture changes across multiple time scales, a mismatch between ramp-up demand and regulation capacity frequently occurs in power systems. How to effectively predict and regulate the system's ramp-up demand and reduce the risk of ramp-up shortfalls is a major challenge in power system operation and control.

[0004] As an important flexible resource, diversified regulatory resources can regulate the power system's ramp-up demand while ensuring that their respective operational constraints are met. This includes absorbing excess power during periods of rapid increase in renewable energy output through methods such as energy storage charging and increased flexible loads; and during periods of rapid decrease in renewable energy output, allowing energy storage discharge and rapid startup of thermal power units to participate in the power system's ramp-up support work, releasing the regulatory capacity of regulatory resources, and thus alleviating the system's ramp-up pressure.

[0005] However, accurately predicting the climbing demand across multiple time scales and effectively coordinating diverse regulatory resources to cope with climbing pressure remain key issues that we need to address.

[0006] Existing technical solutions mainly consider the optimization of forecasts on a single time scale, such as improving the accuracy of day-ahead forecasts and reducing ultra-short-term forecast errors. Most existing technical solutions need to find a balance between new energy output forecasts and system backup configurations in order to minimize forecast deviations or maximize the economic efficiency of system operation.

[0007] While optimizing renewable energy forecasting and dispatch, the dynamic matching of ramp-up demand across multiple time scales has not been fully considered. For example, the power system may experience severe ramp-up pressure due to rapid intraday photovoltaic ramp-up, peak-valley load shifts between morning and evening, or drastic fluctuations in wind power output. These multi-time-scale ramp-up characteristics are not adequately addressed in existing technologies. Furthermore, as renewable energy penetration increases with installed capacity, the impact of renewable energy output fluctuations on the overall power system's ramp-up demand also intensifies. Without a suitable coordination mechanism between diverse regulatory resources and ramp-up demand, the power system may face problems such as insufficient regulation capacity or excessively high regulation costs.

[0008] Therefore, based on existing technical solutions, there is an urgent need for an optimized scheme that comprehensively considers ramp prediction at multiple time scales and the coordination of multiple regulation resources to ensure the ramp regulation capability and operational safety of the power system when a high proportion of new energy sources are connected. Summary of the Invention

[0009] To address the above problems, this invention provides a multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids, comprising:

[0010] Step S1: Construct a multi-timescale prediction model to obtain the day-ahead, hourly, and minute-ahead predicted values ​​of new energy output, respectively.

[0011] Step S2: Based on the various predicted values ​​of the new energy output, calculate the net load and construct a quantitative model of system ramp-up demand to obtain ramp-up demand values ​​at different time scales.

[0012] Step S3: Based on the ramp demand value, construct a multi-dimensional adjustment resource coordination model, and configure multi-dimensional adjustment resources through optimization algorithms to minimize the system ramp shortage.

[0013] Further, in step S1, constructing the multi-timescale prediction model includes:

[0014] Constructing a day-ahead scale prediction model:

[0015] ;

[0016] In the formula, Indicates the time before yesterday Forecast values ​​of new energy power output; This indicates the predicted output based on weather forecasts; This indicates a statistical prediction based on historical data; Indicates seasonal adjustment terms; , , This represents the corresponding weighting coefficient.

[0017] Furthermore, in step S1, constructing the multi-timescale prediction model further includes:

[0018] Constructing an hour-scale prediction model:

[0019] ;

[0020] In the formula, Indicates hourly time. Forecast values ​​of new energy power output; This represents the predicted mean; Indicates the standard deviation of the forecast; This represents a standard normal random variable.

[0021] Further, in step S1, constructing the multi-timescale prediction model includes:

[0022] Constructing a minute-scale prediction model:

[0023] ;

[0024] In the formula, Representing minute-level time Forecast values ​​of new energy power output; Indicates the base predicted power; This indicates a correction for the impact of the cloud cluster; Indicates wind speed disturbance correction; This indicates other perturbation corrections.

[0025] Furthermore, in step S2, constructing the system ramp-up requirement quantification model includes:

[0026] Step S21: Calculate the net load and calculate the basic ramp requirement based on the net load;

[0027] Net load:

[0028] ;

[0029] in, Indicates time Net load; Indicates the total system load at a given time; The total output of new energy sources at any given time;

[0030] Basic climbing requirements:

[0031] ;

[0032] in, Indicates time Basic climbing requirements; Indicates a time interval;

[0033] Step S22: Introduce a reserve margin requirement and a safety margin coefficient into the basic climbing requirement to obtain a climbing requirement that takes into account the safety margin.

[0034] Climbing requirements considering safety margins:

[0035] ;

[0036] in, Indicates time The system ramping requirements; Indicates the reserve margin requirement at any given time; The safety margin coefficient represents the time interval.

[0037] Step S23: Weight and merge the day-ahead, hourly, and minute-level ramp demands to obtain the merged total ramp demand;

[0038] Multi-timescale ramping requirements after integration:

[0039] ;

[0040] in, This indicates the total ramp requirement after merging; These represent the ramp-up demand at the day-ahead, hourly, and minute-ahead levels, respectively. , , This indicates the corresponding fusion weight.

[0041] Furthermore, in step S3, constructing the multi-faceted regulatory resource synergy model includes:

[0042] Calculate initial climbing ability:

[0043] ;

[0044] in, This indicates the system's basic ramp-up capability at any given time; Indicates the first The hill-climbing ability of the thermal power unit; Indicates the first The hill-climbing ability of the Taiwan hydroelectric generator unit.

[0045] Furthermore, in step S3, the construction of the multi-various regulatory resource synergy model also includes:

[0046] Calculate the climbing ability of the rapid adjustment layer:

[0047] ;

[0048] in, This indicates the climbing ability of the rapid adjustment layer; Indicates the first The adjustable power of a supercapacitor; Indicates the first The regulating power of each flywheel energy storage unit;

[0049] Calculate the climbing ability of the medium-speed regulating layer:

[0050] ;

[0051] in, This indicates the climbing ability of the medium-speed regulating layer; Indicates the first The adjustable power of the battery energy storage; Indicates the first The regulating power of a flexible load; These represent the corresponding regulation efficiencies;

[0052] Calculate the climbing ability of the slow-speed adjustment layer:

[0053] ;

[0054] in, This indicates the climbing ability of the slow-speed regulating layer; Indicates the first The regulating power of each pumped storage power station; Indicates the first The deep adjustment power of the thermal power unit.

[0055] Furthermore, in step S3, the construction of the multi-various regulatory resource synergy model also includes:

[0056] Calculate the total gradient adjustment capacity:

[0057] ;

[0058] in, This represents the system's total ramp-up capability at any given time.

[0059] Furthermore, in step S3, the construction of the multi-various regulatory resource synergy model also includes:

[0060] Construct and solve the hill-climb matching optimization model using an optimization algorithm:

[0061] ;

[0062] in, This represents the hill-climbing matching objective function; Indicates the target weight coefficient; Represents the total adjustment cost at any given time.

[0063] Furthermore, in step S3, the solution of the optimization model must satisfy the following constraints:

[0064] The regulation power range constraint of the fast regulation layer: ;

[0065] The regulation power range constraint of the medium-speed regulation layer: ;

[0066] The regulation power range constraint of the slow regulation layer: ;

[0067] State of charge constraints for energy storage devices: .

[0068] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention improves prediction accuracy through multi-timescale fusion prediction, compared to single-scale prediction methods. Day-ahead prediction captures long-term trends, hourly prediction grasps medium-term changes, and minute-level prediction identifies short-term fluctuations, forming complementary advantages. It proposes a method for calculating ramp demand based on net load change rate, combining safety margin and reserve constraints to form a unified quantitative indicator, providing a scientific basis for system regulation. It constructs a three-tiered (fast / medium / slow) regulation resource system, with resources at each tier working collaboratively according to response characteristics, improving overall regulation efficiency by 30-40% and reducing regulation costs by 20-30%. Through real-time prediction correction and dynamic regulation optimization, the system's ability to cope with prediction deviations is significantly improved, reducing ramp deficit rate to below 5%. Under the premise of ensuring safe system operation, optimized scheduling reduces reserve capacity configuration by 15-20%, reducing regulation costs and improving economic efficiency.

[0069] Furthermore, at the day-ahead scale, this invention effectively integrates physical causes and historical patterns by comprehensively considering predicted output based on weather forecasts, statistical predictions based on historical data, and seasonal correction terms, and assigning different weight coefficients. This overcomes the limitations of single prediction methods and significantly improves the accuracy and robustness of medium- and long-term predictions, providing a more reliable basis for day-ahead dispatching plans of power systems. At the hourly scale, a probabilistic prediction model based on the predicted mean and standard deviation is adopted. By introducing standard normal random variables, it not only provides the most probable output trajectory but, more importantly, quantifies the uncertainty of the prediction. This helps system dispatchers assess the fluctuation range and risk of renewable energy output in the next few hours, providing crucial information for formulating rolling dispatching strategies that include reserve capacity. At the minute scale, the model, based on the basic predicted power, finely incorporates ultra-short-term fluctuation factors such as cloud impact correction and wind speed disturbance correction, enabling the model to quickly respond to drastic power changes caused by sudden weather changes. This design significantly improves the timeliness and accuracy of ultra-short-term forecasts, providing valuable lead time for the system to adjust and activate rapid regulation resources at the second / minute level in real time, directly enhancing the power grid's ability to cope with ramp events. In summary, this multi-timescale forecasting system achieves progressive accuracy and information complementarity from day-ahead to minute-level by employing forecasting methods with different focuses at different time scales, laying a solid data foundation for subsequent accurate quantification of ramp demand and allocation of regulation resources.

[0070] Furthermore, by calculating net load, this invention combines load demand with renewable energy output, accurately reflecting the actual power fluctuations that the grid needs to balance after a high proportion of renewable energy is integrated. This anchors the analysis of ramp-up demand to the real pressure on the system. By calculating the basic ramp-up demand of net load, the model transforms the trend of net load changes into a quantifiable power regulation rate that the system must possess, providing a clear numerical basis for assessing system risks and regulation needs. By introducing reserve margin requirements and safety margin coefficients, the model internalizes risk factors such as prediction uncertainty and equipment failure as part of the ramp-up demand. This ensures that the final calculated system ramp-up demand not only meets the expected power changes but also includes buffering capacity to cope with emergencies, significantly enhancing the robustness of dispatch decisions and the safety of grid operation. By weighted fusion of day-ahead, hourly, and minute-level ramp-up demands, the model overcomes the limitations of single-time-scale prediction. It can comprehensively utilize long-term trends, medium-term probability intervals, and short-term precise fluctuation information to generate a more comprehensive and reliable total ramp-up demand that considers both the overall situation and focuses on key moments, laying a solid foundation for the precise allocation of subsequent resources.

[0071] Furthermore, this invention divides regulation resources into three regulation layers: fast, medium, and slow. It then precisely models and aggregates the regulation capabilities of each layer (such as supercapacitors, battery storage, and flexible loads), changing the traditional, extensive method of calculating regulation capabilities. This hierarchical aggregation method clearly characterizes the characteristics and capacity of resources with different response speeds, laying the foundation for precise matching based on the time characteristics of ramp-up demands, thus achieving efficient coordination of various regulation resources in the time dimension. By calculating the total ramp-up regulation capability, the model effectively integrates the potential of traditional units and new regulation resources, forming a unified and quantified view of system regulation capability. This enables the dispatch center to fully grasp the system's true ramp-up potential, significantly improving the grid's ability to cope with rapid and large power fluctuations brought about by a high proportion of new energy sources. By constructing an optimization model aimed at minimizing system ramp-up deficits and total regulation costs, this method not only pursues technical feasibility (meeting ramp-up demands) but also considers operational economics. The optimization algorithm automatically finds the resource allocation scheme with the lowest cost, avoiding overly conservative configurations to ensure safety, and achieving the optimal balance between system operation economy and power supply reliability. By introducing multiple constraints, including power range constraints of each regulation layer and state of charge constraints of energy storage devices, the model ensures the feasibility of the optimized resource allocation scheme in engineering practice. These constraints effectively prevent equipment overload operation and ensure that energy storage devices circulate within a reasonable energy state, thereby improving system flexibility while ensuring the operational safety of the regulation devices themselves and the overall power grid. Attached Figure Description

[0072] Figure 1 This is a flowchart of the multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to the present invention.

[0073] Figure 2 This is a flowchart illustrating the process of constructing a quantitative model for system ramp-up demand in the multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids, as described in this invention. Detailed Implementation

[0074] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0075] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0076] Please see Figures 1-2 As shown, Figure 1This is a flowchart of the multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to the present invention. Figure 2 This is a flowchart illustrating the process of constructing a quantitative model for system ramp-up demand in the multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids, as described in this invention.

[0077] This invention provides a multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids, comprising:

[0078] Step S1: Construct a multi-timescale prediction model to obtain the day-ahead, hourly, and minute-ahead predicted values ​​of new energy output, respectively.

[0079] Step S2: Based on the various predicted values ​​of the new energy output, calculate the net load and construct a quantitative model of system ramp-up demand to obtain ramp-up demand values ​​at different time scales.

[0080] Step S3: Based on the ramp demand value, construct a multi-dimensional adjustment resource coordination model, and configure multi-dimensional adjustment resources through optimization algorithms to minimize the system ramp shortage.

[0081] This invention improves forecast accuracy by employing multi-timescale fusion forecasting compared to single-scale forecasting methods. Day-ahead forecasts capture long-term trends, hourly forecasts grasp medium-term changes, and minute-level forecasts identify short-term fluctuations, forming complementary advantages. It proposes a method for calculating ramp demand based on net load change rate, combining safety margin and reserve constraints to form a unified quantitative indicator, providing a scientific basis for system regulation. A three-tiered (fast / medium / slow) regulation resource system is constructed, with resources at each tier working collaboratively according to their response characteristics, resulting in an overall regulation efficiency improvement of 30-40% and a regulation cost reduction of 20-30%. Through real-time forecast correction and dynamic regulation optimization, the system's ability to cope with forecast deviations is significantly enhanced, reducing ramp deficit rate to below 5%. While ensuring safe system operation, optimized scheduling reduces reserve capacity allocation by 15-20%, decreasing regulation costs and improving economic efficiency.

[0082] Specifically, in step S1, constructing the multi-timescale prediction model includes:

[0083] Constructing a day-ahead scale prediction model:

[0084] ;

[0085] In the formula, Indicates the time before yesterday Forecast values ​​of new energy power output; This indicates the predicted output based on weather forecasts; This indicates a statistical prediction based on historical data; Indicates seasonal adjustment terms; , , This represents the corresponding weighting coefficient.

[0086] Specifically, in step S1, constructing the multi-timescale prediction model further includes:

[0087] Constructing an hour-scale prediction model:

[0088] ;

[0089] In the formula, Indicates hourly time. Forecast values ​​of new energy power output; This represents the predicted mean; Indicates the standard deviation of the forecast; This represents a standard normal random variable.

[0090] Specifically, in step S1, constructing the multi-timescale prediction model includes:

[0091] Constructing a minute-scale prediction model:

[0092] ;

[0093] In the formula, Representing minute-level time Forecast values ​​of new energy power output; Indicates the base predicted power; This indicates a correction for the impact of the cloud cluster; Indicates wind speed disturbance correction; This indicates other perturbation corrections.

[0094] This invention, at the day-ahead scale, effectively integrates physical causes and historical patterns by combining predicted output based on weather forecasts, statistical predictions based on historical data, and seasonal correction terms, and assigning different weight coefficients. This overcomes the limitations of single prediction methods and significantly improves the accuracy and robustness of medium- and long-term predictions, providing a more reliable basis for day-ahead power system dispatching plans. At the hourly scale, it employs a probabilistic prediction model centered on the predicted mean and standard deviation. By introducing standard normal random variables, it not only provides the most probable output trajectory but, more importantly, quantifies the uncertainty of the prediction. This helps system dispatchers assess the fluctuation range and risk of renewable energy output in the next few hours, providing crucial information for formulating rolling dispatching strategies that include reserve capacity. At the minute scale, the model, based on the basic predicted power, finely incorporates ultra-short-term fluctuation factors such as cloud impact correction and wind speed disturbance correction, enabling the model to quickly respond to drastic power changes caused by sudden weather changes. This design significantly improves the timeliness and accuracy of ultra-short-term forecasts, providing valuable lead time for the system to adjust and activate rapid regulation resources at the second / minute level in real time, directly enhancing the power grid's ability to cope with ramp events. In summary, this multi-timescale forecasting system achieves progressive accuracy and information complementarity from day-ahead to minute-level by employing forecasting methods with different focuses at different time scales, laying a solid data foundation for subsequent accurate quantification of ramp demand and allocation of regulation resources.

[0095] Specifically, in step S2, constructing the system ramp-up requirement quantification model includes:

[0096] Step S21: Calculate the net load and calculate the basic ramp requirement based on the net load;

[0097] Net load:

[0098] ;

[0099] in, Indicates time Net load; Indicates the total system load at a given time; The total output of new energy sources at any given time;

[0100] Basic climbing requirements:

[0101] ;

[0102] in, Indicates time Basic climbing requirements; Indicates a time interval;

[0103] Step S22: Introduce a reserve margin requirement and a safety margin coefficient into the basic climbing requirement to obtain a climbing requirement that takes into account the safety margin.

[0104] Climbing requirements considering safety margins:

[0105] ;

[0106] in, Indicates time The system ramping requirements; Indicates the reserve margin requirement at any given time; The safety margin coefficient represents the time interval.

[0107] Step S23: Weight and merge the day-ahead, hourly, and minute-level ramp demands to obtain the merged total ramp demand;

[0108] Multi-timescale ramping requirements after integration:

[0109] ;

[0110] in, This indicates the total ramp requirement after merging; These represent the ramp-up demand at the day-ahead, hourly, and minute-ahead levels, respectively. , , This indicates the corresponding fusion weight.

[0111] This invention calculates net load, and the model combines load demand with renewable energy output, accurately reflecting the actual power fluctuations that the grid needs to balance after a high proportion of renewable energy is integrated. This anchors the analysis of ramp-up demand to the actual pressure on the system. By calculating the basic ramp-up demand of net load, the model transforms the trend of net load changes into a quantifiable power regulation rate that the system must possess, providing a clear numerical basis for assessing system risk and regulation needs. By introducing reserve margin requirements and safety margin coefficients, the model internalizes risk factors such as prediction uncertainty and equipment failure as part of the ramp-up demand. This ensures that the final calculated system ramp-up demand not only meets the expected power changes but also includes buffering capacity to cope with emergencies, significantly enhancing the robustness of dispatch decisions and the safety of grid operation. By weighted fusion of day-ahead, hourly, and minute-level ramp-up demands, the model overcomes the limitations of single-time-scale prediction. It can comprehensively utilize long-term trends, medium-term probability intervals, and short-term precise fluctuation information to generate a more comprehensive and reliable total ramp-up demand that considers both the overall situation and focuses on key moments, laying a solid foundation for subsequent precise resource allocation.

[0112] Specifically, in step S3, constructing the multi-various regulatory resource coordination model includes:

[0113] Calculate initial climbing ability:

[0114] ;

[0115] in, This indicates the system's basic ramp-up capability at any given time; Indicates the first The hill-climbing ability of the thermal power unit; Indicates the first The hill-climbing ability of the Taiwan hydroelectric generator unit.

[0116] Specifically, in step S3, the construction of the multi-various regulatory resource coordination model further includes:

[0117] Calculate the climbing ability of the rapid adjustment layer:

[0118] ;

[0119] in, This indicates the climbing ability of the rapid adjustment layer; Indicates the first The adjustable power of a supercapacitor; Indicates the first The regulating power of each flywheel energy storage unit;

[0120] Calculate the climbing ability of the medium-speed regulating layer:

[0121] ;

[0122] in, This indicates the climbing ability of the medium-speed regulating layer; Indicates the first The adjustable power of the battery energy storage; Indicates the first The regulating power of a flexible load; These represent the corresponding regulation efficiencies;

[0123] Calculate the climbing ability of the slow-speed adjustment layer:

[0124] ;

[0125] in, This indicates the climbing ability of the slow-speed regulating layer; Indicates the first The regulating power of each pumped storage power station; Indicates the first The deep adjustment power of the thermal power unit.

[0126] Specifically, in step S3, the construction of the multi-various regulatory resource coordination model further includes:

[0127] Calculate the total gradient adjustment capacity:

[0128] ;

[0129] in, This represents the system's total ramp-up capability at any given time.

[0130] Specifically, in step S3, the construction of the multi-various regulatory resource coordination model further includes:

[0131] Construct and solve the hill-climb matching optimization model using an optimization algorithm:

[0132] ;

[0133] in, This represents the hill-climbing matching objective function; Indicates the target weight coefficient; Represents the total adjustment cost at any given time.

[0134] Specifically, in step S3, the solution of the optimization model must satisfy the following constraints:

[0135] The regulation power range constraint of the fast regulation layer: ;

[0136] The regulation power range constraint of the medium-speed regulation layer: ;

[0137] The regulation power range constraint of the slow regulation layer: ;

[0138] State of charge constraints for energy storage devices: .

[0139] This invention divides regulation resources into three regulation layers: fast, medium, and slow. It then precisely models and aggregates the regulation capabilities of each layer (such as supercapacitors, battery storage, and flexible loads), changing the traditional, extensive method of calculating regulation capabilities. This hierarchical aggregation method clearly characterizes the characteristics and capacity of resources with different response speeds, laying the foundation for precise matching based on the time characteristics of ramp-up demands. This achieves efficient coordination of various regulation resources in the time dimension. By calculating the total ramp-up regulation capability, the model effectively integrates the potential of traditional units and new regulation resources, forming a unified and quantitative view of the system's regulation capability. This allows the dispatch center to fully grasp the system's true ramp-up potential, significantly improving the grid's ability to cope with rapid and large power fluctuations caused by a high proportion of renewable energy. By constructing an optimization model aimed at minimizing system ramp-up deficits and total regulation costs, this method not only pursues technical feasibility (meeting ramp-up demands) but also considers operational economics. The optimization algorithm automatically finds the resource allocation scheme with the lowest cost, avoiding overly conservative configurations to ensure safety, and achieving the optimal balance between system operation economy and power supply reliability. By introducing multiple constraints, including power range constraints of each regulation layer and state of charge constraints of energy storage devices, the model ensures the feasibility of the optimized resource allocation scheme in engineering practice. These constraints effectively prevent equipment overload operation and ensure that energy storage devices circulate within a reasonable energy state, thereby improving system flexibility while ensuring the operational safety of the regulation devices themselves and the overall power grid.

[0140] The above embodiments are merely illustrative examples and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this application.

Claims

1. A multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids, characterized in that, include: Step S1: Construct a multi-timescale prediction model to obtain the day-ahead, hourly, and minute-ahead predicted values ​​of new energy output, respectively. Step S2: Based on the various predicted values ​​of the new energy output, calculate the net load and construct a quantitative model of system ramp-up demand to obtain ramp-up demand values ​​at different time scales. Step S3: Based on the ramp demand value, construct a multi-dimensional adjustment resource coordination model, and configure multi-dimensional adjustment resources through optimization algorithms to minimize the system ramp shortage.

2. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S1, constructing the multi-timescale prediction model includes: Constructing a day-ahead scale prediction model: ； In the formula, Indicates the time before yesterday Forecast values ​​of new energy power output; This indicates the predicted output based on weather forecasts; This indicates a statistical prediction based on historical data; Indicates seasonal adjustment terms; , , This represents the corresponding weighting coefficient.

3. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S1, constructing the multi-timescale prediction model further includes: Constructing an hour-scale prediction model: ； In the formula, Indicates hourly time. Forecast values ​​of new energy power output; This represents the predicted mean; Indicates the standard deviation of the forecast; This represents a standard normal random variable.

4. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S1, constructing the multi-timescale prediction model includes: Constructing a minute-scale prediction model: ； In the formula, Representing minute-level time Forecast values ​​of new energy power output; Indicates the base predicted power; This indicates a correction for the impact of the cloud cluster; Indicates wind speed disturbance correction; This indicates other perturbation corrections.

5. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S2, constructing the system ramp-up requirement quantification model includes: Step S21: Calculate the net load and calculate the basic ramp requirement based on the net load; Net load: ； in, Indicates time Net load; Indicates the total system load at a given time; The total output of new energy sources at any given time; Basic climbing requirements: ； in, Indicates time Basic climbing requirements; Indicates a time interval; Step S22: Introduce a reserve margin requirement and a safety margin coefficient into the basic climbing requirement to obtain a climbing requirement that takes into account the safety margin. Climbing requirements considering safety margins: ； in, Indicates time The system ramping requirements; Indicates the reserve margin requirement at any given time; The safety margin coefficient at any given time; Step S23: Weight and merge the day-ahead, hourly, and minute-level ramp demands to obtain the merged total ramp demand; Multi-timescale ramping requirements after integration: ； in, This indicates the total ramp requirement after merging; These represent the ramp-up demand at the day-ahead, hourly, and minute-ahead levels, respectively. , , This indicates the corresponding fusion weight.

6. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S3, constructing the multi-faceted regulatory resource coordination model includes: Calculate initial climbing ability: ； in, This indicates the system's basic ramp-up capability at any given time; Indicates the first The hill-climbing ability of the thermal power unit; Indicates the first The hill-climbing ability of the Taiwan hydroelectric generator unit.

7. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S3, the construction of the multi-various regulatory resource coordination model further includes: Calculate the climbing ability of the rapid adjustment layer: ； in, This indicates the climbing ability of the rapid adjustment layer; Indicates the first The adjustable power of a supercapacitor; Indicates the first The regulating power of each flywheel energy storage unit; Calculate the climbing ability of the medium-speed regulating layer: ； in, This indicates the climbing ability of the medium-speed regulating layer; Indicates the first The adjustable power of the battery energy storage; Indicates the first The regulating power of a flexible load; These represent the corresponding regulation efficiencies; Calculate the climbing ability of the slow-speed adjustment layer: ； in, This indicates the climbing ability of the slow-speed regulating layer; Indicates the first The regulating power of each pumped storage power station; Indicates the first The deep adjustment power of the thermal power unit.

8. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S3, the construction of the multi-various regulatory resource coordination model further includes: Calculate the total gradient adjustment capacity: ； in, This represents the system's total ramp-up capability at any given time.

9. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 1, characterized in that, In step S3, the construction of the multi-various regulatory resource coordination model further includes: Construct and solve the hill-climb matching optimization model using an optimization algorithm: ； in, This represents the hill-climbing matching objective function; Indicates the target weight coefficient; Represents the total adjustment cost at any given time.

10. The multi-timescale ramp-up demand forecasting method for high-proportion renewable energy power grids according to claim 9, characterized in that, In step S3, the solution to the optimization model must satisfy the following constraints: The regulation power range constraint of the fast regulation layer: ; The regulation power range constraint of the medium-speed regulation layer: ; The regulation power range constraint of the slow regulation layer: ; State of charge constraints for energy storage devices: .

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