New energy sending base long-time energy storage operation strategy optimization method and system

Abstract

This invention discloses a method and system for optimizing the long-term energy storage operation strategy of a new energy transmission base, belonging to the field of power system operation and new energy consumption technology. It predicts the output sequence for the first time period in the future; through a method for identifying continuous low-output extreme scenarios, the sequence is divided into two categories: those containing multiple consecutive low-output extreme scenarios in the third time period and those not containing such scenarios; for the sequences not containing extreme low-output scenarios, a clustering algorithm is used to generate multiple typical new energy output scenarios, replacing the original data with these typical scenarios to reconstruct the output sequence; based on the reconstructed sequence, overall production simulation of the new energy transmission base and its associated external power grid is conducted in a typical second time period to obtain optimization results, and a long-term energy storage operation strategy is formulated accordingly, ultimately completing the optimization of the long-term energy storage operation strategy for the new energy transmission base; wherein, the first time period includes the second and third time periods, and the second time period includes the third time period, this invention can balance extreme scenario preservation with model solution efficiency.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and new energy consumption technology, specifically involving a method and system for optimizing long-term energy storage operation strategy of new energy transmission bases. Background Technology

[0002] With the continuous development and construction of large-scale wind power, photovoltaic, and other new energy bases, and their transmission to load centers via ultra-high voltage (UHV) transmission lines and inter-regional interconnection lines, the operational characteristics of new energy transmission bases differ significantly from those of traditional conventional power bases. On the one hand, new energy output exhibits significant volatility, randomness, and seasonality. On the other hand, during periods of widespread and prolonged overcast skies, continuous weak winds, sandstorms, rain, snow, freezing temperatures, or other extreme weather conditions, new energy transmission bases may experience extreme operating conditions characterized by "consecutive days of low overall output." Such scenarios significantly reduce transmission capacity and may lead to problems such as power supply shortages in the receiving system, insufficient reserves, or imbalances in the utilization of interconnection lines.

[0003] Existing research on energy storage configuration in new energy bases mostly focuses on smoothing short-term fluctuations, improving ramp-up performance, reducing power curtailment, or participating in intraday peak shaving. Modeling is typically based on typical daily or routine statistical scenarios. While these methods are effective in handling general fluctuations, they do not adequately consider scenarios of "continuous days of overall low output" caused by extreme weather. If conventional typical scenario methods are still used to compress annual data, the persistence of extreme low-output segments may be weakened or even lost during scenario selection. This leads to long-term energy storage operation strategies biased towards optimizing routine operating conditions, failing to demonstrate their supply guarantee value in truly critical extreme scenarios.

[0004] Furthermore, while some existing medium- and long-term operation simulation methods incorporate typical scenarios, typical weeks, or clustering techniques to reduce the computational scale of 8760 hours of annual simulation or 52 weeks of hourly simulation, most assume that all weeks can be uniformly compressed. In reality, for renewable energy transmission bases, the operational mechanisms of regular weeks and extreme continuous low-output weeks differ significantly. The former is suitable for extracting representative features through clustering; the latter requires more complete preservation of its original continuous low-output time series to maintain the continuity of information in extreme scenarios. If all weeks are uniformly compressed, long-term energy storage strategies are prone to distortion in the scenarios that "most need optimization."

[0005] Meanwhile, long-term energy storage differs from short-term energy storage in its operational mechanism. Long-term energy storage is not merely used for hourly charge-discharge balancing; more importantly, it can improve the power transmission stability and external support capabilities of power transmission bases by transferring energy across days within a week, even when renewable energy levels are consistently low for several days. Therefore, how to retain information from extreme scenarios while maintaining computational efficiency on an annual scale, and further develop optimized operation strategies for long-term energy storage for renewable energy transmission bases, has become a pressing technical challenge. Summary of the Invention

[0006] The purpose of this invention is to overcome the problem that existing operation strategy optimization methods cannot simultaneously preserve extreme scenarios and improve model solution efficiency. It proposes an optimization method and system for long-term energy storage operation strategies of new energy transmission bases.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for optimizing the long-term energy storage operation strategy of new energy transmission bases, comprising the following steps: Based on the historical power output data of the new energy transmission base, the power output of new energy in the first time period of the future is predicted, and the new energy power output prediction sequence for the first time period of the future is obtained. Using a method for identifying and extracting continuous low-output extreme scenarios, multiple consecutive low-output extreme scenarios in the third time period are identified and extracted from the future first time period new energy output prediction sequence, resulting in a future first time period new energy output prediction sequence that includes multiple consecutive low-output extreme scenarios in the third time period and a future first time period new energy output prediction sequence that does not include multiple consecutive low-output extreme scenarios in the third time period. Clustering algorithm is used to generate typical new energy output scenarios for the predicted new energy output of the first time period in the future, which does not contain multiple consecutive third time period low output extreme scenarios. Multiple typical new energy output scenarios are obtained, and the original data is replaced by the typical new energy output scenarios to obtain the reconstructed new energy transmission base output sequence. Based on the reconstructed power output sequence of the new energy transmission base, production simulation is carried out on the new energy transmission base and its connected external power grid in the typical second time period to obtain the production simulation optimization results. Based on the production simulation optimization results, an operation strategy is formulated for the long-term energy storage built in the transmission base to complete the optimization of the long-term energy storage operation strategy of the new energy transmission base. The first time period is divided into several second time periods, and each second time period includes several third time periods.

[0008] Furthermore, based on historical power output data from renewable energy transmission bases, the power output of renewable energy in the first time period is predicted, resulting in a predicted sequence for renewable energy output in the first time period, including: The historical power output data of the new energy transmission base is cleaned, including interpolation and outlier processing, to obtain the cleaned historical power output data. Based on the actual operational capacity of the new energy transmission base in each first time period, the cleaned historical output data is normalized to obtain the normalized historical output data. A new energy output prediction sequence for the first time period in the future is generated based on historical output data after standardization using differentiated weights. The calculation of the future first time period renewable energy output forecast sequence is shown in the following formula:

[0009] in, express Real-time forecast of new energy power output Indicates the first Historical data on new energy power output for the first time period. Indicates the first Weighting factors for historical renewable energy output data in the first time period; the weighting factors satisfy the following conditions: .

[0010] Furthermore, the first time period is one year, the second time period is one week, and the third time period is one hour.

[0011] Furthermore, the method for identifying and extracting continuous low-output extreme scenarios is the time-segment merging threshold method, which includes: based on the normal output level of new energy, a low-output judgment threshold for new energy is preset according to a percentage, and the data below the low-output judgment threshold of new energy in the future new energy output prediction sequence is used as the initial selection segment of multiple consecutive third time-segment low-output extreme scenarios. The mutation values ​​in the initial selection segments of multiple consecutive third time period low-output extreme scenarios are removed, and the point values ​​of the mutation values ​​are replaced by the average value before and after. Then, the segments are spliced ​​with the subsequent sequences to obtain multiple consecutive third time period low-output extreme scenarios in the future new energy output prediction sequence.

[0012] Furthermore, the clustering algorithm clusters the number of new energy output data points in the third time period contained in each second time period into several typical values ​​according to their numerical values; based on the correspondence between the clusters and the original values, the typical scenarios are used to replace the original data to obtain the reconstructed new energy output data for the third time period.

[0013] Furthermore, the selection of a typical second time period follows the strategy below: Select all second time periods that contain multiple consecutive third time periods of low output extreme scenarios to obtain typical extreme second time periods; Based on the seasonal correspondence, two normal second time periods of new energy output are randomly selected for each season to obtain typical normal second time periods.

[0014] Furthermore, the objective function of the production simulation model is constructed by comprehensively considering the operating costs of conventional units, the penalty for curtailment of renewable energy, and the penalty for system load shedding; The model constraints for production simulation include the operational constraints of each conventional unit in the system, the operational constraints of long-term energy storage in the new energy transmission base, the power transmission constraints of tie lines, the system balance constraints, and the reserve constraints. The production simulation model runs through all typical second time periods sequentially in a third time period.

[0015] Secondly, the present invention provides a long-term energy storage operation strategy optimization system for new energy transmission bases, comprising: The future output forecasting module is used to forecast the new energy output for the first time period in the future based on the historical output data of the new energy transmission base, and to obtain the new energy output forecasting sequence for the first time period in the future. The extreme scene identification module is used to identify and extract multiple consecutive low-output extreme scenes in the third time period in the future new energy power output prediction sequence using the continuous low-output extreme scene identification and extraction method, so as to obtain the future new energy power output prediction sequence containing multiple consecutive low-output extreme scenes in the third time period and the future new energy power output prediction sequence not containing multiple consecutive low-output extreme scenes in the third time period. The output sequence reconstruction module is used to generate typical new energy output scenarios for future new energy output prediction sequences that do not contain multiple consecutive third time period low output extreme scenarios using clustering algorithms. Multiple typical new energy output scenarios are obtained, and the original data is replaced by the typical new energy output scenarios to obtain the reconstructed new energy transmission base output sequence. The operation strategy optimization module is used to perform production simulation on the new energy transmission base and its connected external power grid in a typical second time period based on the reconstructed power output sequence of the new energy transmission base, obtain the production simulation optimization results, formulate the operation strategy for the long-term energy storage built in the transmission base based on the production simulation optimization results, and complete the operation strategy optimization of the long-term energy storage of the new energy transmission base. The first time period is divided into several second time periods, and each second time period includes several third time periods.

[0016] Thirdly, the present invention provides an electronic device, including 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 a method for optimizing the long-term energy storage operation strategy of a new energy transmission base.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for optimizing the long-term energy storage operation strategy of a new energy transmission base.

[0018] Compared with the prior art, the present invention has the following beneficial technical effects: The proposed optimization method for long-term energy storage operation strategy of new energy transmission bases first forms a future new energy output prediction sequence based on historical new energy output data over many years. Then, it identifies and extracts the scenario of "overall continuous low output for many days" caused by extreme weather. For non-extreme time periods, clustering compression can be used to improve solution efficiency, while for extreme time periods, the original time series information is retained. Finally, a production simulation model is constructed on typical time periods, comprehensively considering the operating costs of conventional units, new energy curtailment, load shedding, and long-term energy storage operation constraints, thereby forming a long-term energy storage operation strategy suitable for new energy transmission bases. This method can retain the key features of extreme scenarios while avoiding the high computational complexity brought about by year-round, full-time-series simulation. The present invention can balance the preservation of extreme scenarios and the efficiency of model solution. Attached Figure Description

[0019] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of the invention in any way. Furthermore, the shapes and proportions of the components in the drawings are merely schematic to aid in understanding the invention and do not specifically limit the shapes and proportions of the components. In the drawings: Figure 1 This is a flowchart of the method for optimizing the long-term energy storage operation strategy of the new energy transmission base according to the present invention.

[0020] Figure 2 This is a simplified structural diagram of the long-term energy storage operation strategy optimization system for new energy transmission bases according to the present invention.

[0021] Figure 3 This is an electronic device diagram of the method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to the present invention.

[0022] Figure 4 This is an overall flowchart of the optimization method for long-term energy storage operation strategy of new energy transmission base in the embodiment.

[0023] Figure 5 This diagram illustrates the threshold processing for identifying continuous low-output extreme scenes and merging time periods.

[0024] Figure 6 This is a schematic diagram illustrating the working principle of long-term energy storage (seasonal hydrogen storage device).

[0025] Figure 7 This is a typical weekday long-term energy storage operation strategy diagram that does not include extreme scenarios.

[0026] Figure 8 This is a diagram illustrating the long-term energy storage operation strategy for a typical week, including extreme scenarios. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] Example 1 Existing research on long-term energy storage operation at new energy transmission bases does not adequately consider scenarios of continuous low output over multiple days due to extreme weather. This leads to the loss of continuity information in extreme scenarios during the compression of typical scenarios, making it difficult for long-term energy storage operation strategies to effectively reflect their supporting role in ensuring supply, stabilizing power transmission, and responding to extreme situations. Furthermore, directly simulating full production using hourly data from all 52 weeks of the year presents the problem of excessively large model size and high solution complexity. Therefore, this invention proposes an optimization method for long-term energy storage operation strategies at new energy transmission bases, which can balance the preservation of extreme scenarios with model solution efficiency.

[0029] See Figure 1 The optimization method for long-term energy storage operation strategy of new energy transmission bases includes the following steps: Based on historical power output data from renewable energy transmission bases, the power output of renewable energy in the first time period of the future is predicted, resulting in a predicted power output sequence for the first time period. This prediction process includes: cleaning the historical power output data of renewable energy transmission bases (including interpolation and outlier handling) to obtain cleaned historical power output data; normalizing the cleaned historical power output data based on the actual operational capacity of the renewable energy transmission bases in each first time period to obtain normalized historical power output data; and generating the predicted power output sequence for the first time period of the future based on the normalized historical power output data using differentiated weights. The calculation of the predicted power output sequence for the first time period of the future is shown in the following formula:

[0030] in, express Real-time forecast of new energy power output Indicates the first Historical data on new energy power output for the first time period. Indicates the first Weighting factors for historical renewable energy output data in the first time period; the weighting factors satisfy the following conditions: .

[0031] A method for identifying and extracting consecutive low-output extreme scenarios is used to identify and extract multiple consecutive low-output extreme scenarios in the third time period of the future renewable energy output prediction sequence. This results in future renewable energy output prediction sequences that include or do not contain multiple consecutive low-output extreme scenarios in the third time period. The method for identifying and extracting consecutive low-output extreme scenarios is a time period merging threshold method, which includes: based on the normal output level of renewable energy, a low-output judgment threshold for renewable energy is preset according to a percentage; data below the low-output judgment threshold in the future renewable energy output prediction sequence are used as initial segments of multiple consecutive low-output extreme scenarios in the third time period; abrupt values ​​in the initial segments of multiple consecutive low-output extreme scenarios in the third time period are removed, and the average value before and after the abrupt values ​​are replaced with the value of the abrupt value, and then concatenated with the subsequent sequence to obtain multiple consecutive low-output extreme scenarios in the third time period of the future renewable energy output prediction sequence.

[0032] A clustering algorithm is used to generate typical new energy output scenarios for future new energy output prediction sequences that do not contain multiple consecutive low-output extreme scenarios in the third time period. Multiple typical new energy output scenarios are obtained, and the original data is replaced by the typical new energy output scenarios to obtain the reconstructed output sequence of new energy transmission bases. The clustering algorithm clusters the number of new energy output data points in the third time period contained in each second time period into several typical values ​​according to their numerical values. According to the correspondence between the clusters and the original values, the original data is replaced by the typical scenarios to obtain the reconstructed new energy output data for the third time period.

[0033] Based on the reconstructed output sequence of the renewable energy transmission base, production simulations were conducted on the renewable energy transmission base and its connected external power grid during a typical second time period. The optimized production simulation results were obtained, and operational strategies were formulated for the long-term energy storage built within the transmission base based on these results, thus completing the optimization of the long-term energy storage operation strategy for the renewable energy transmission base. The selection of the typical second time period followed the following strategy: all second time periods containing multiple consecutive low-output extreme scenarios in the third time period were selected to obtain the typical extreme second time period; according to seasonal correspondence, two normal renewable energy output second time periods were randomly selected for each season to obtain the typical normal second time period. The objective function of the production simulation model comprehensively considered the operating costs of conventional units, renewable energy curtailment penalties, and system load shedding penalties. The constraints of the production simulation model included the operational constraints of each conventional unit in the system, the operational constraints of long-term energy storage in the renewable energy transmission base, tie-line transmission power constraints, system balance constraints, and reserve constraints. The production simulation model performed third-time period-by-third time period simulations on all typical second time periods.

[0034] Example 2 See Figure 2 The long-term energy storage operation strategy optimization system for new energy transmission bases includes: The future output forecasting module is used to forecast the new energy output for the first time period in the future based on the historical output data of the new energy transmission base, and to obtain the new energy output forecasting sequence for the first time period in the future. The extreme scene identification module is used to identify and extract multiple consecutive low-output extreme scenes in the third time period in the future new energy power output prediction sequence using the continuous low-output extreme scene identification and extraction method, so as to obtain the future new energy power output prediction sequence containing multiple consecutive low-output extreme scenes in the third time period and the future new energy power output prediction sequence not containing multiple consecutive low-output extreme scenes in the third time period. The output sequence reconstruction module is used to generate typical new energy output scenarios for future new energy output prediction sequences that do not contain multiple consecutive third time period low output extreme scenarios using clustering algorithms. Multiple typical new energy output scenarios are obtained, and the original data is replaced by the typical new energy output scenarios to obtain the reconstructed new energy transmission base output sequence. The operation strategy optimization module is used to perform production simulation on the new energy transmission base and its connected external power grid in a typical second time period based on the reconstructed power output sequence of the new energy transmission base, obtain the production simulation optimization results, formulate the operation strategy for the long-term energy storage built in the transmission base based on the production simulation optimization results, and complete the operation strategy optimization of the long-term energy storage of the new energy transmission base. The first time period is divided into several second time periods, and each second time period includes several third time periods.

[0035] Example 3 See Figure 3 An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements a method for optimizing the long-term energy storage operation strategy of a new energy transmission base.

[0036] Example 4 A computer-readable storage medium storing a computer program, which, when executed by a processor, implements a method for optimizing the long-term energy storage operation strategy of a new energy transmission base.

[0037] Example 5 This embodiment uses a 52-week forecast sequence of new energy output for the next year as an example to further explain the optimization method for long-term energy storage operation strategies in new energy transmission bases. Targeting new energy transmission bases, it considers extreme scenarios of continuous low output. Based on identifying overall continuous multi-day low output scenarios caused by extreme weather, it optimizes long-term energy storage operation strategies by combining typical weekly production simulations. (See also...) Figure 4 Specifically, it includes the following steps: Step 1: Based on historical data from new energy transmission bases, a new energy output forecast sequence for the next year is obtained for 52 weeks. The time accuracy of this sequence is on an hourly scale.

[0038] The forecast sequence for renewable energy output over the next 52 weeks was obtained using the following method: First, historical data from the past few years was cleaned using interpolation and outlier removal methods to ensure complete hourly data for each of the 52 weeks. Second, output data was normalized based on the actual annual operational capacity of the new energy transmission bases. Finally, a prediction sequence was generated based on historical data using a differentiated weighting method (higher weight for more recent years) according to the following formula: (1) In the formula, express Real-time forecast of new energy power output Indicates the first Historical data on new energy power output over the years Indicates the first Weighting factors for historical renewable energy output data for the year. The weighting factors satisfy the following conditions: (2) Step 2: For the generated new energy output prediction sequence, the extreme scenario of "overall continuous low output for multiple days" of new energy caused by extreme weather is identified and extracted using the continuous low output extreme scenario identification and extraction method.

[0039] See Figure 5This study employs a time-segment merging threshold method to identify continuous low-output extreme scenarios in the next year's renewable energy output forecast sequence. The method first sets a low-output judgment threshold and a duration threshold based on the normal renewable energy output level. The low-output judgment threshold can be given as a certain percentage of the rated capacity or normal output level, for example, 10%. Then, all hourly points below the low-output judgment threshold are identified. Next, continuous low-output segments are extracted according to the duration threshold. Considering that the sequence during extreme weather processes may have individual spikes due to local disturbances, to avoid affecting the judgment of scene continuity, abrupt values ​​within the segments are removed, and the average of the preceding and following time points is used for smoothing replacement before concatenation with subsequent low-output sequences. After the above processing, it is possible to extract extreme scenario segments of "overall continuous low output for multiple days" from renewable energy transmission bases.

[0040] Step 3: For weeks that do not contain the extreme scenario of "overall continuous low power output for multiple days" of new energy, a clustering algorithm is used to generate multiple typical power output scenarios of new energy. Then, the typical scenarios are used to replace the original data to obtain the reconstructed 168-hour new energy power output data. For weeks that do contain the extreme scenario of "overall continuous low power output for multiple days" of new energy, no compression processing is performed, and the original time series data is retained.

[0041] For weeks without extreme, continuous low-output scenarios, the K-means clustering algorithm is used to cluster the renewable energy output data for that week. Similar output levels are grouped into several typical values, and based on the "cluster-original value" correspondence, the typical values ​​replace the original values, resulting in reconstructed 168-hour renewable energy output data. This processing method effectively reduces time series complexity and improves the solution efficiency of subsequent production simulations. For weeks containing extreme scenarios, the original hourly data is fully preserved, ensuring that the continuity information of extreme scenarios is not destroyed.

[0042] Step 4: Based on the output sequence of the new energy transmission base obtained in Step 3, select a typical week and conduct production simulation on the new energy transmission base and its connected external power grid in the typical week. Based on the production simulation optimization results, formulate an operation strategy for the long-term energy storage built in the transmission base.

[0043] The selection of typical weeks follows these principles: First, all weeks containing the extreme scenario of "continuous low output of new energy sources for multiple days" are selected to ensure that extreme conditions are fully considered in subsequent production simulations. Second, according to seasonal correspondence, two weeks are randomly or regularly selected from the weeks with normal output of new energy sources in each of the four seasons (spring, summer, autumn, and winter) to form a regular set of typical weeks. This approach reflects seasonal differences while ensuring focused attention on extreme conditions.

[0044] The production simulation model is constructed according to the following approach: the objective function is constructed by comprehensively considering the operating costs of conventional units, the penalty for curtailment of renewable energy and the penalty for system load shedding; the constraints include the operating constraints of conventional units, the operating constraints of long-term energy storage, the power transmission constraints of tie lines, the system balance constraints and the reserve constraints; the model performs hourly operation simulations on selected typical weeks, and based on the optimization results, it forms the charging and discharging strategies, the state of charge evolution trajectory and the support methods for extreme scenarios of long-term energy storage in different typical weeks.

[0045] The specific forms of the objective function and constraints are as follows: Objective function: Sub-goal 1: Minimize system operating costs (3) Sub-objective 2: Minimize the risk of system failure. (4) Sub-goal 3: Minimize the amount of renewable energy wasted. (5) Considering the three sub-objectives above, the overall objective function is constructed as follows: (6) In the formula: For thermal (nuclear) power units The power generation cost function; The power plant's code name; and These represent the amount of electricity abandoned by renewable energy sources and the amount of electricity lost due to load shedding, respectively. for Time of the first The output of Taiwan's nuclear power units; , These are the penalty factors for load shedding and renewable energy curtailment, respectively.

[0046] Unit operating constraints: (7) (8) (9) (10) (11) (12) (13) (14) (15) In the formula: express Thermal power and nuclear power units The running state variables; Indicates thermal power and nuclear power units Minimum technical output; Indicates thermal power and nuclear power units Maximum unit output; express Thermal power and nuclear power units The power-on status variables; express Thermal power and nuclear power units The shutdown state variable; Indicates thermal power and nuclear power units Minimum continuous power-on time; Indicates thermal power and nuclear power units The minimum continuous downtime; Indicates thermal power and nuclear power units Maximum uphill power; Indicates thermal power and nuclear power units Maximum downhill climbing power; express Thermal power and nuclear power units For future use; express Thermal power and nuclear power units On standby; express Thermal power and nuclear power units Power output during a 60-minute downhill climb; express Thermal power and nuclear power units Power output during a 60-minute uphill climb; Long-term energy storage operation constraints: This invention considers a seasonal hydrogen storage device for long-term energy storage. A seasonal hydrogen storage device generally consists of three parts: an electrolysis unit, a hydrogen storage unit, and a fuel cell. The coordination between these three parts and their connection to the power grid are as follows: Figure 6 As shown. When the output of the new energy transmission base is excessive, power is supplied to the electrolysis unit to carry out the electrolysis reaction. The electrolysis produces hydrogen energy to supply the fuel cell, and the surplus is stored in the hydrogen storage device. When the output of the new energy transmission base is insufficient, the fuel cell uses the hydrogen energy provided by the electrolysis unit and the hydrogen storage device to generate electricity and send it to the grid to maintain the power balance of the system.

[0047] Electrolysis unit operating constraints: (16) (17) In the formula: This indicates the output power of the electrolysis unit at a given time. This indicates the electrical power input from the external power grid to the electrolysis unit at any given time; Indicates the energy conversion efficiency of the electrolysis device; This indicates the maximum input power of the electrolysis unit.

[0048] Fuel cell operating constraints: (18) (19) In the formula: Indicating fuel cells in Input power at any given time; Indicating fuel cells in The electrical power constantly supplied to the power grid; Indicates the energy conversion efficiency of a fuel cell; This indicates the maximum input power of the fuel cell.

[0049] Constraints related to hydrogen storage devices: (20) (twenty one) (twenty two) (twenty three) (twenty four) (25) In the formula: and Indicates in The power of the hydrogen storage device in storing and releasing hydrogen energy at any given time; express The operating status of the hydrogen storage device at all times; Indicates the maximum storage capacity of the hydrogen storage device; This indicates the maximum release power of the hydrogen storage device; Indicates hydrogen storage device Energy stored at all times; This represents the total duration of seasonal hydrogen storage operation in the entire simulation. This indicates the upper limit of the hydrogen storage capacity of the device; This indicates the unit time loss rate (self-discharge rate) of the hydrogen storage device. Indicates the length of a unit of time.

[0050] Tether transmission constraints: (26) In the formula, express Timeline Uploaded power; Indicates the contact line The upper limit of transmission power.

[0051] System balance and reserve constraints: (27) (28) In the formula, This matrix represents the connection relationships between various units, energy storage systems, and nodes in the system. The number of rows represents the number of nodes, and the number of columns represents the number of corresponding devices. If a device is located at a node, the corresponding matrix element is 1. The upper right subscript... , , and These respectively represent thermal power (nuclear power) units, transmission lines, new energy units, and seasonal hydrogen storage devices; This represents the line power transmission direction matrix, with the number of rows representing the number of nodes and the number of columns representing the number of transmission lines. If there is a transmission line between two nodes, the element at the starting node position is -1, and the element at the ending node position is 1. express The column vectors formed by these variables are similar to the column vectors formed by variables with the same name. Represents the load distribution coefficient matrix; express The value at time; express The column vector composed of the load loss of each node at each time step; express Total system load loss at any given time; This is a load fluctuation parameter, typically set to 0.1.

[0052] This embodiment first predicts the hourly renewable energy output sequence for 52 weeks of the coming year based on historical output data from renewable energy transmission bases over many years. Then, it uses a method to identify and extract continuous low-output extreme scenarios caused by extreme weather to identify scenarios of continuous low renewable energy output over multiple days. For weeks without extreme scenarios, a clustering algorithm is used to generate typical output scenarios and reconstruct the 168-hour output data for that week. For weeks containing extreme scenarios, the original time-series data is retained. Based on this, a typical week is selected to perform hourly production simulations on the renewable energy transmission base and its connected external power grid. Taking into account conventional unit operating costs, renewable energy curtailment penalties, load shedding penalties, and long-term energy storage operation constraints, an optimized long-term energy storage operation strategy is obtained. This invention can balance extreme scenario retention with model solution efficiency, improving the rationality and engineering applicability of long-term energy storage operation strategies in renewable energy transmission bases.

[0053] Example 6 The following example, taking the backup planning of a multi-zone power system that considers both wind power and photovoltaic power as new energy sources, further illustrates the implementation process of the method in Example 5.

[0054] This case study is based on a power system consisting of a renewable energy transmission base and a simplified external power grid. The components and parameters of this system are shown in the table below: Table 1. Case Study: Power System Components and Parameters

[0055] Step 1: Based on the historical data of the new energy transmission base of the test system, a new energy output prediction sequence for 52 weeks in the next year is obtained. The time accuracy of this sequence is on an hourly scale.

[0056] Step 2: For the generated new energy output prediction sequence, the continuous low output extreme scenario identification and extraction method is used to identify and extract the extreme scenarios of "overall continuous low output for multiple days" of new energy caused by extreme weather. The low output threshold is set to 10% of the maximum output, and the duration threshold is set to 72 hours. The identification results show that extreme scenarios will occur in the 17th and 22nd weeks of a year out of 52 weeks.

[0057] Step 3: For weeks that do not contain the extreme scenario of "overall continuous low power output for multiple days" of new energy, a clustering algorithm is used to generate multiple typical power output scenarios of new energy. Then, the typical scenarios are used to replace the original data to obtain the reconstructed 168-hour new energy power output data. For weeks that do contain the extreme scenario of "overall continuous low power output for multiple days" of new energy, no compression processing is performed, and the original time series data is retained.

[0058] Step 4: Based on the output sequence of the new energy transmission base obtained in Step 3, select a typical week and conduct production simulation on the new energy transmission base and its connected external power grid during the typical week. Based on the production simulation optimization results, examples of long-term energy storage operation strategies for weeks without extreme scenarios and weeks with extreme scenarios are as follows: Figure 7 and Figure 8 As shown.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, read-only optical discs, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. An optimization method for long-term energy storage operation strategy of new energy transmission bases, characterized in that, Includes the following steps: Based on the historical power output data of the new energy transmission base, the power output of new energy in the first time period of the future is predicted, and the new energy power output prediction sequence for the first time period of the future is obtained. Using a method for identifying and extracting continuous low-output extreme scenarios, multiple consecutive low-output extreme scenarios in the third time period are identified and extracted in the future first time period new energy output prediction sequence, resulting in a future first time period new energy output prediction sequence that includes multiple consecutive low-output extreme scenarios in the third time period and a future first time period new energy output prediction sequence that does not include multiple consecutive low-output extreme scenarios in the third time period. Using a clustering algorithm, the predicted output sequence of new energy in the first time period of the future, which does not contain multiple consecutive low-output extreme scenarios in the third time period, generates typical output scenarios of new energy. These typical output scenarios are then used to replace the original data to obtain a reconstructed output sequence of new energy transmission bases. Based on the reconstructed power output sequence of the new energy transmission base, a production simulation is performed on the new energy transmission base and its connected external power grid in a typical second time period to obtain the production simulation optimization results. Based on the production simulation optimization results, an operation strategy is formulated for the long-term energy storage built in the transmission base to complete the optimization of the long-term energy storage operation strategy of the new energy transmission base. The first time period is divided into several second time periods, and each second time period includes several third time periods.

2. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The method of predicting the renewable energy output for the first time period based on historical data of renewable energy transmission bases yields a renewable energy output prediction sequence for the first time period, including: The historical output data of the new energy transmission base is cleaned, including interpolation and outlier processing, to obtain cleaned historical output data. Based on the actual operational capacity of the new energy transmission base in each first time period, the cleaned historical output data is normalized to obtain the normalized historical output data. A new energy output prediction sequence for the first time period in the future is generated based on historical output data after standardization using differentiated weights. The calculation of the future first time period new energy output prediction sequence is shown in the following formula: in, express Forecast values ​​of new energy power output at all times Indicates the first Historical data on new energy power output for the first time period. Indicates the first Weighting factors for historical renewable energy output data in the first time period; the weighting factors satisfy the following conditions: .

3. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The first time period is one year, the second time period is one week, and the third time period is one hour.

4. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The method for identifying and extracting continuous low-output extreme scenarios is a time-period merging threshold method, which includes: based on the normal output level of new energy, a low-output judgment threshold for new energy is preset according to a percentage, and the data below the low-output judgment threshold of new energy in the new energy output prediction sequence of the first time period in the future are used as the initial selection segments of multiple consecutive low-output extreme scenarios in the third time period. The mutation values ​​in the initial selection segments of the multiple consecutive third time period low-output extreme scenarios are removed, and the point values ​​of the mutation values ​​are replaced by the average value before and after. Then, the segments are spliced ​​with the subsequent sequences to obtain the multiple consecutive third time period low-output extreme scenarios in the future new energy output prediction sequence.

5. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The clustering algorithm clusters the number of new energy output data points in the third time period contained in each second time period into several typical values ​​according to their numerical values; based on the correspondence between the clusters and the original values, the typical scenarios are used to replace the original data to obtain the reconstructed new energy output data for the third time period.

6. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The selection of the typical second time period follows the following strategy: Select all second time periods that contain multiple consecutive third time periods of low output extreme scenarios to obtain typical extreme second time periods; Based on the seasonal correspondence, two normal second time periods of new energy output are randomly selected for each season to obtain typical normal second time periods.

7. The method for optimizing the long-term energy storage operation strategy of new energy transmission bases according to claim 1, characterized in that, The objective function of the production simulation model is constructed by comprehensively considering the operating costs of conventional units, the penalty for curtailment of renewable energy, and the penalty for system load shedding. The model constraints of the production simulation include the operating constraints of each conventional unit in the system, the operating constraints of long-term energy storage in the new energy transmission base, the transmission power constraints of the tie line, the system balance constraints, and the reserve constraints. The production simulation model runs through all typical second time periods sequentially in a third time period.

8. A long-term energy storage operation strategy optimization system for new energy transmission bases, characterized in that: include: The future output forecasting module is used to forecast the new energy output for the first time period in the future based on the historical output data of the new energy transmission base, and to obtain the new energy output forecasting sequence for the first time period in the future. The extreme scene identification module is used to identify and extract multiple consecutive low-output extreme scenes in the third time period in the future first time period new energy output prediction sequence using the continuous low-output extreme scene identification and extraction method, so as to obtain the future first time period new energy output prediction sequence containing multiple consecutive low-output extreme scenes in the third time period and the future first time period new energy output prediction sequence not containing multiple consecutive low-output extreme scenes in the third time period. The output sequence reconstruction module is used to use a clustering algorithm to generate typical new energy output scenarios for the future first time period new energy output prediction sequence that does not contain multiple consecutive third time period low output extreme scenarios, and to obtain multiple typical new energy output scenarios. The original data is replaced by the typical new energy output scenarios to obtain the reconstructed new energy transmission base output sequence. The operation strategy optimization module is used to perform production simulation on the new energy transmission base and its connected external power grid as a whole in a typical second time period according to the reconstructed power output sequence of the new energy transmission base, obtain the production simulation optimization results, formulate the operation strategy for the long-term energy storage built in the transmission base according to the production simulation optimization results, and complete the optimization of the long-term energy storage operation strategy of the new energy transmission base. The first time period is divided into several second time periods, and each second time period includes several third time periods.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for optimizing the long-term energy storage operation strategy of the new energy transmission base as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for optimizing the long-term energy storage operation strategy of the new energy transmission base as described in any one of claims 1-7.

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