A method and device for evaluating new energy carrying capacity based on scenario focusing

Abstract

The present invention discloses a method and device for evaluating new energy carrying capacity based on scenario focusing. The present invention determines the A, B, and C operating index values ​​of each curve group obtained in the current cycle according to predefined A, B, and C scenario focusing strategies, selects several curve groups with the highest A, B, and C operating index values ​​from all curve groups as A, B, and C curve groups, obtains a screening curve set for the constraint scenario, constructs a power generation operation simulation model based on the screening curve set, solves and obtains the combined output result of the generator set, and evaluates the new energy carrying capacity based on the combined output result of the generator set based on the predefined new energy carrying capacity evaluation index system to obtain the new energy carrying capacity evaluation result, thereby being able to screen key constraint scenarios from massive scenarios and efficiently and comprehensively evaluate the new energy carrying capacity for the constraint scenarios.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a scenario-focused new energy carrying capacity assessment method and device. Background Art

[0002] The randomness, volatility, and uncertainty of renewable energy generator output place enormous pressure on grid operation and scheduling. This not only complicates peak and frequency regulation but also easily leads to frequent adjustments in the power angles of conventional generators. This exacerbates power angle oscillations after a major grid disturbance, making power angle instability more likely. Existing techniques often use Monte Carlo sampling to perform a massive enumeration of possible output scenarios for renewable energy generators based on the probabilistic distribution of renewable energy output. Analyzing grid operation requires simultaneous analysis and calculation of all sampled scenarios to ensure safe and stable grid operation within the sampled scenarios. While sampling scenarios may not fully reflect actual conditions, the more sampling scenarios, the more accurately they reflect reality.

[0003] As the number of sampled scenarios increases, the amount of computation increases dramatically. Simulating power generation scheduling and evaluating indicators one by one for the massive amount of renewable energy output and net load curves obtained through random sampling is computationally prohibitive and hinders problem discovery. Furthermore, when it comes to evaluating renewable energy carrying capacity, existing technologies mostly focus on the grid's ability to accommodate renewable energy (including electricity and power). These evaluation indicators are relatively simple and difficult to expand to include indicators such as renewable energy absorption, grid operational flexibility, grid source-load characteristics, and power and power sufficiency. Consequently, they are unable to efficiently and comprehensively assess renewable energy carrying capacity. Summary of the Invention

[0004] In order to overcome the shortcomings of the existing technology, the present invention provides a scenario-focused new energy carrying capacity assessment method and device, which can screen key constraint scenarios from massive scenarios and efficiently and comprehensively assess the new energy carrying capacity for the constraint scenarios.

[0005] In order to solve the above technical problems, in a first aspect, an embodiment of the present invention provides a method for evaluating new energy carrying capacity based on scenario focusing, comprising:

[0006] According to a predefined Class A scenario focus strategy, the Class A operating index value of each curve group obtained in the current period is determined, and several curve groups with the highest Class A operating index values ​​are selected from all the curve groups as Class A curve groups; wherein each curve group includes multiple new energy output curves and multiple net load curves;

[0007] According to a predefined Class B scenario focusing strategy, a Class B operation index value of each of the curve groups is determined respectively, and several curve groups with the highest Class B operation index values ​​are selected from all the curve groups as Class B curve groups;

[0008] According to a predefined Class C scene focusing strategy, respectively determine the Class C operation index value of each of the curve groups, and select several curve groups with the highest Class C operation index values ​​from all the curve groups as Class C curve groups;

[0009] Combining all of the Class A curve groups, all of the Class B curve groups, and all of the Class C curve groups to obtain a screening curve set for the constraint scenario, extracting operating data from the screening curve set, constructing an objective function with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost, and building a power generation operation simulation model in combination with the constructed constraint conditions;

[0010] Solving the power generation operation simulation model to obtain the combined output result of the generator sets, and based on a predefined new energy carrying capacity evaluation index system, performing a new energy carrying capacity evaluation according to the combined output result of the generator sets to obtain a new energy carrying capacity evaluation result.

[0011] Furthermore, according to the predefined Class A scene focus strategy, the Class A operation index value of each curve group obtained in the current period is determined respectively, and several curve groups with the highest Class A operation index values ​​are selected from all the curve groups as Class A curve groups, specifically:

[0012] According to the first criterion in the Class A scenario focus strategy, the daily peak value of the hourly electricity penetration rate of new energy for each of the curve groups is determined respectively, and the first preset number of curve groups with the highest daily peak value of the hourly electricity penetration rate of new energy are selected from all the curve groups as the Class A curve groups;

[0013] Determine, according to the second criterion in the Class A scenario focus strategy, the daily peak value of the regional new energy power generation of each of the curve groups, and select, from all the curve groups, a second preset number of curve groups with the highest daily peak values ​​of the regional new energy power generation as the Class A curve groups;

[0014] According to the third criterion in the Class A scenario focus strategy, determining the peak load time difference between each of the curve groups and the corresponding load curve, and selecting a third preset number of curve groups with the highest peak load time differences from all the curve groups as the Class A curve groups;

[0015] According to the fourth criterion in the Class A scene focusing strategy, the net load peak value of each curve group is determined respectively, and the fourth preset number of curve groups with the highest net load peak value are selected from all the curve groups as the Class A curve groups.

[0016] Furthermore, according to the predefined Class B scene focus strategy, the Class B operation index value of each curve group is determined respectively, and several curve groups with the highest Class B operation index values ​​are selected from all the curve groups as Class B curve groups, specifically:

[0017] Determine the net load valley value of each curve group according to the first criterion in the Class B scenario focus strategy, and select the fifth preset number of curve groups with the highest net load valley value from all the curve groups as the Class B curve groups;

[0018] Determine the net load peak-to-valley difference of each curve group according to the second criterion in the Class B scenario focus strategy, and select a sixth preset number of curve groups with the highest net load peak-to-valley differences from all the curve groups as the Class B curve groups;

[0019] determining, according to the third criterion in the Class B scenario focus strategy, a valley-load time difference between each of the curve groups and the corresponding load curve, and selecting, from all the curve groups, a preset number of curve groups having the highest valley-load time differences as the Class B curve groups;

[0020] According to the fourth criterion in the Class B scenario focus strategy, the net load peak-to-valley difference change rate of each curve group is determined respectively, and the eighth preset number of curve groups with the highest net load peak-to-valley difference change rate are selected from all the curve groups as the Class B curve groups.

[0021] Furthermore, according to the predefined Class C scene focus strategy, the Class C operation index value of each curve group is determined respectively, and several curve groups with the highest Class C operation index values ​​are selected from all the curve groups as Class C curve groups, specifically:

[0022] Determine, according to the first criterion in the Class C scenario focus strategy, a net load positive ramp rate for each of the curve groups, and select, from all the curve groups, a preset number of nine curve groups with the highest net load positive ramp rates as the Class C curve groups;

[0023] Determine the net load negative climbing rate of each curve group according to the second criterion in the Class C scenario focus strategy, and select the tenth preset number of curve groups with the highest net load negative climbing rate from all the curve groups as the Class C curve groups;

[0024] According to the third criterion in the Class C scenario focus strategy, the new energy power generation power disturbance amount of each of the curve groups is determined respectively, and the eleventh preset number of curve groups with the highest new energy power generation power disturbance amount are selected from all the curve groups as the Class C curve groups;

[0025] According to the fourth criterion in the Class C scenario focusing strategy, the new energy power generation disturbance rate of each curve group is determined respectively, and the twelfth preset number of curve groups with the highest new energy power generation disturbance rate are selected from all the curve groups as the Class C curve group.

[0026] Furthermore, the scenario-focused new energy carrying capacity assessment method further includes:

[0027] A curve group selected according to the first criterion in the Class A scenario focus strategy is used as a first curve group. Based on the combined output result of the generator sets, whether an operation risk occurs according to each of the first curve groups is determined. When it is determined that all of the first curve groups have an operation risk, the first preset number is adjusted according to a preset adjustment formula. When it is determined that none of the first curve groups have an operation risk, the first criterion is deleted from the Class A scenario focus strategy.

[0028] The curve group selected according to the second criterion in the Class A scenario focus strategy is used as the second curve group. According to the combined output result of the generator sets, whether an operation risk occurs according to each of the second curve groups is determined. If it is determined that all the second curve groups have an operation risk, the second preset number is adjusted according to the preset adjustment formula. If it is determined that none of the second curve groups have an operation risk, the second criterion is deleted from the Class A scenario focus strategy.

[0029] The curve group selected according to the third criterion in the Class A scenario focus strategy is used as the third curve group. According to the combined output result of the generator sets, whether an operation risk occurs according to each of the third curve groups is determined. When it is determined that all the third curve groups have an operation risk, the third preset number is adjusted according to the preset adjustment formula. When it is determined that none of the third curve groups have an operation risk, the third criterion is deleted from the Class A scenario focus strategy.

[0030] The curve group selected according to the fourth criterion in the Class A scenario focus strategy is used as the fourth curve group. According to the combined output result of the generator set, it is judged whether an operation risk occurs according to each of the fourth curve groups. When it is determined that all the fourth curve groups have an operation risk, the fourth preset quantity is adjusted according to the preset adjustment formula. When it is determined that all the fourth curve groups do not have an operation risk, the fourth criterion is deleted from the Class A scenario focus strategy.

[0031] Furthermore, the preset adjustment formula is:

[0032] K′ i =2K i ;

[0033] Among them, i=1, 2, 3, 4; K1 is the first preset quantity, K′1 is the quantity after the first preset quantity is adjusted, K2 is the second preset quantity, K′2 is the quantity after the second preset quantity is adjusted; K3 is the third preset quantity, K′3 is the quantity after the third preset quantity is adjusted; K4 is the fourth preset quantity, K′4 is the quantity after the fourth preset quantity is adjusted.

[0034] Furthermore, the objective function is:

[0035]

[0036] Among them, F1 is the power generation cost of the generator set; Cost of starting and stopping the generator set; is the output ramp cost of the generator set, F4 is the energy storage power generation cost; F5 is the penalty cost of the constraint scenario; F β is the reward cost with respect to the spinning reserve factor β.

[0037] Furthermore, the constraints include generator set operation constraints and energy storage operation constraints.

[0038] Furthermore, the new energy carrying capacity evaluation index system includes grid source-load characteristic index, power supply sufficiency index, new energy consumption index, and grid operation flexibility index.

[0039] In a second aspect, an embodiment of the present invention provides a new energy carrying capacity assessment device based on scenario focusing, comprising:

[0040] A Class A scenario focusing module is configured to determine the Class A operating index value of each curve group obtained in the current period according to a predefined Class A scenario focusing strategy, and select several curve groups with the highest Class A operating index values ​​from all the curve groups as Class A curve groups; wherein each curve group includes multiple new energy output curves and multiple net load curves;

[0041] A Class B scene focusing module is used to determine the Class B operation index value of each of the curve groups according to a predefined Class B scene focusing strategy, and select several curve groups with the highest Class B operation index values ​​from all the curve groups as the Class B curve groups;

[0042] A Class C scene focusing module is used to determine the Class C operation index value of each of the curve groups according to a predefined Class C scene focusing strategy, and select several curve groups with the highest Class C operation index values ​​from all the curve groups as Class C curve groups;

[0043] a power generation operation simulation model construction module, configured to combine all of the Class A curve groups, all of the Class B curve groups, and all of the Class C curve groups to obtain a screening curve set for the constraint scenario, extract operating data from the screening curve set, construct an objective function with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost, and construct a power generation operation simulation model in combination with the constructed constraint conditions;

[0044] The new energy carrying capacity evaluation module is used to solve the power generation operation simulation model to obtain the combined output result of the generator set, and based on a pre-defined new energy carrying capacity evaluation index system, perform new energy carrying capacity evaluation according to the combined output result of the generator set to obtain a new energy carrying capacity evaluation result.

[0045] The embodiments of the present invention have the following beneficial effects:

[0046] According to the predefined Class A scenario focusing strategy, the Class A operation index value of each curve group obtained in the current period is determined respectively, and several curve groups with the highest Class A operation index value are selected from all curve groups as Class A curve groups; wherein each curve group includes a new energy output curve and a net load curve; according to the predefined Class B scenario focusing strategy, the Class B operation index value of each curve group is determined respectively, and several curve groups with the highest Class B operation index value are selected from all curve groups as Class B curve groups; according to the predefined Class C scenario focusing strategy, the Class C operation index value of each curve group is determined respectively, and several curve groups with the highest Class C operation index value are selected from all curve groups. Several curve groups are taken as Class C curve groups; all Class A curve groups, all Class B curve groups, and all Class C curve groups are combined to obtain a screening curve set for the constraint scenario, and operation data is extracted from the screening curve set. An objective function is constructed with the sum of the operating cost of the generator set and the penalty cost of the constraint scenario as the minimum. Combined with the constructed constraint conditions, a power generation operation simulation model is constructed; the power generation operation simulation model is solved to obtain the combined output result of the generator set, and based on a pre-defined new energy carrying capacity evaluation index system, the new energy carrying capacity is evaluated according to the combined output result of the generator set to obtain the new energy carrying capacity evaluation result, thereby realizing the new energy carrying capacity evaluation under the constraint scenario. Compared with the prior art, the embodiments of the present invention determine the A, B, and C operating index values ​​of each curve group obtained in the current period according to predefined A, B, and C scenario focusing strategies, select several curve groups with the highest A, B, and C operating index values ​​from all curve groups as A, B, and C curve groups, obtain a screening curve set for the constraint scenario, construct a power generation operation simulation model based on the screening curve set, and solve to obtain the combined output result of the generator set. Based on the predefined new energy carrying capacity evaluation index system, the new energy carrying capacity is evaluated according to the combined output result of the generator set to obtain the new energy carrying capacity evaluation result, thereby being able to screen key constraint scenarios from massive scenarios and efficiently and comprehensively evaluate the new energy carrying capacity for the constraint scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Schematic diagram of a flow chart of a method for evaluating new energy carrying capacity based on scenario focusing in the first embodiment of the present invention;

[0048] Figure 2 This is a data flow diagram of adjusting the focus strategy for scenes of types A, B, and C as exemplified in the first embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the linearization of the quadratic function of the fuel cost of a thermal power unit according to the first embodiment of the present invention;

[0050] Figure 4This is a data flow diagram of a new energy carrying capacity assessment method based on scenario focus as exemplified in the first embodiment of the present invention;

[0051] Figure 5 This is a structural diagram of a new energy carrying capacity assessment device based on scene focusing in the second embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be noted that the step numbers in this article are only for the convenience of explaining the specific embodiment and do not limit the order in which the steps are executed. The method provided in this embodiment can be executed by a related terminal device, and the following description will be based on the processor as the execution subject.

[0054] like Figure 1 As shown, the first embodiment provides a method for evaluating new energy carrying capacity based on scenario focusing, including steps S1 to S5:

[0055] S1. Determine the Class A operating index value for each curve group obtained in the current period according to a predefined Class A scenario focus strategy, and select several curve groups with the highest Class A operating index values ​​from all curve groups as Class A curve groups; each curve group includes multiple new energy output curves and multiple net load curves;

[0056] S2. Determine the Class B operation index value of each curve group according to the predefined Class B scenario focus strategy, and select several curve groups with the highest Class B operation index values ​​from all curve groups as Class B curve groups;

[0057] S3. Determine the Class C operation index value of each curve group according to the predefined Class C scenario focus strategy, and select several curve groups with the highest Class C operation index values ​​from all curve groups as Class C curve groups;

[0058] S4. Combine all Class A curve groups, all Class B curve groups, and all Class C curve groups to obtain a screening curve set for the constraint scenario, extract operating data from the screening curve set, construct an objective function with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost, and build a power generation operation simulation model based on the constructed constraint conditions;

[0059] S5. Solve the power generation operation simulation model to obtain the combined output results of the generator sets, and based on the pre-defined new energy carrying capacity evaluation index system, perform a new energy carrying capacity evaluation based on the combined output results of the generator sets to obtain a new energy carrying capacity evaluation result.

[0060] It is understandable that by using different probability models or random process models, a large number of new energy output curves and load curves can be obtained. The algebraic sum of the new energy output curve and the load curve forms the net load curve of the high-proportion new energy power system, which is the basis for power generation scheduling.

[0061] This embodiment primarily considers the challenge of defining constraint scenarios for daily regulation capacity and conventional power backup, focusing on strategies. These scenarios, where bottlenecks in daily regulation capacity and conventional power backup occur, can be categorized into the following three categories: Category A: During peak net load periods, the conventional power source may experience insufficient peak-shaving capacity / reserve, leading to load shedding; Category B: During valley net load periods, the conventional power source may experience insufficient downward regulation capacity / reserve, leading to wind and solar power curtailment; and Category C: During periods when the net load curve changes too quickly (either upward or downward), the conventional power source may adjust too slowly, leading to wind and solar power curtailment or load shedding.

[0062] As an example, in step S1, all curve groups obtained in the current period are sorted out, and according to the pre-defined Class A scene focus strategy, the Class A operation index value of each curve group obtained in the current period is determined, and all curve groups are arranged in descending order according to the Class A operation index value, and several curve groups with the highest Class A operation index values ​​are selected from all curve groups as Class A curve groups.

[0063] In step S2, according to the predefined Class B scene focus strategy, the Class B operation index value of each curve group obtained in the current period is determined, all curve groups are arranged in order from high to low according to the Class B operation index value, and several curve groups with the highest Class B operation index value are selected from all curve groups as Class B curve groups.

[0064] In step S3, according to the predefined Class C scene focus strategy, the Class C operating index values ​​of each curve group obtained in the current period are determined, all curve groups are arranged in descending order according to the Class C operating index values, and several curve groups with the highest Class C operating index values ​​are selected from all curve groups as Class C curve groups.

[0065] In step S4, all Class A curve groups, all Class B curve groups, and all Class C curve groups are combined to obtain a screening curve set for the constraint scenario, and operating data such as generator output and load power are extracted from the screening curve set. An objective function is constructed with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost. Combined with the constructed generator set operating constraints and other constraint conditions, a power generation operation simulation model is constructed.

[0066] In step S5, the power generation operation simulation model is solved to obtain the simulated output of each generator set under the constraint scenario, that is, the combined output result of the generator sets, and based on a predefined new energy carrying capacity evaluation index system including grid source-load characteristic indicators, power and electricity sufficiency indicators, new energy absorption indicators, and grid operation flexibility indicators, the new energy carrying capacity is evaluated according to the combined output results of the generator sets to obtain a new energy carrying capacity evaluation result.

[0067] This embodiment determines the A, B, and C operating index values ​​of each curve group obtained in the current period according to predefined A, B, and C scenario focus strategies, selects several curve groups with the highest A, B, and C operating index values ​​from all curve groups as A, B, and C curve groups, obtains a screening curve set for the constraint scenario, constructs a power generation operation simulation model based on the screening curve set, solves and obtains the combined output results of the generator sets, and evaluates the new energy carrying capacity based on the combined output results of the generator sets based on the predefined new energy carrying capacity evaluation indicator system to obtain the new energy carrying capacity evaluation results. This makes it possible to screen key constraint scenarios from massive scenarios and efficiently and comprehensively evaluate the new energy carrying capacity for the constraint scenarios.

[0068] In a preferred embodiment, the method of determining the Class A operation index value of each curve group obtained in the current period according to the predefined Class A scenario focusing strategy, and selecting several curve groups with the highest Class A operation index values ​​from all curve groups as Class A curve groups, specifically: according to the first criterion in the Class A scenario focusing strategy, determining the daily peak value of the hourly electricity penetration rate of new energy for each curve group, and selecting a first preset number of curve groups with the highest daily peak value of the hourly electricity penetration rate of new energy from all curve groups as Class A curve groups; according to the second criterion in the Class A scenario focusing strategy, determining the daily peak value of the regional new energy power generation power for each curve group, and selecting a second preset number of curve groups with the highest daily peak value of the regional new energy power generation power from all curve groups as Class A curve groups; according to the third criterion in the Class A scenario focusing strategy, determining the peak load time difference between each curve group and the corresponding load curve, and selecting a third preset number of curve groups with the highest peak load time difference from all curve groups as Class A curve groups; according to the fourth criterion in the Class A scenario focusing strategy, determining the net load peak value of each curve group, and selecting a fourth preset number of curve groups with the highest net load peak value from all curve groups as Class A curve groups.

[0069] In a preferred embodiment, the Class B operation index value of each curve group is determined respectively according to the predefined Class B scenario focusing strategy, and several curve groups with the highest Class B operation index values ​​are selected from all curve groups as Class B curve groups. Specifically, according to the first criterion in the Class B scenario focusing strategy, the net load valley value of each curve group is determined respectively, and the fifth preset number of curve groups with the highest net load valley value are selected from all curve groups as Class B curve groups; according to the second criterion in the Class B scenario focusing strategy, the net load peak-to-valley difference of each curve group is determined respectively, and the sixth preset number of curve groups with the highest net load peak-to-valley difference are selected from all curve groups as Class B curve groups; according to the third criterion in the Class B scenario focusing strategy, the valley load time difference between each curve group and the corresponding load curve is determined respectively, and the seventh preset number of curve groups with the highest valley load time difference are selected from all curve groups as Class B curve groups; according to the fourth criterion in the Class B scenario focusing strategy, the net load peak-to-valley difference change rate of each curve group is determined respectively, and the eighth preset number of curve groups with the highest net load peak-to-valley difference change rate are selected from all curve groups as Class B curve groups.

[0070] In a preferred embodiment, the C-class operating index value of each curve group is determined according to a predefined C-class scenario focusing strategy, and several curve groups with the highest C-class operating index values ​​are selected from all curve groups as C-class curve groups. Specifically, according to the first criterion in the C-class scenario focusing strategy, the net load positive climbing rate of each curve group is determined, and the ninth preset number of curve groups with the highest net load positive climbing rate are selected from all curve groups as C-class curve groups; according to the second criterion in the C-class scenario focusing strategy, the net load negative climbing rate of each curve group is determined, and the tenth preset number of curve groups with the highest net load negative climbing rate are selected from all curve groups as C-class curve groups; according to the third criterion in the C-class scenario focusing strategy, the new energy power generation power disturbance amount of each curve group is determined, and the eleventh preset number of curve groups with the highest new energy power generation power disturbance amount are selected from all curve groups as C-class curve groups; according to the fourth criterion in the C-class scenario focusing strategy, the new energy power generation power disturbance rate of each curve group is determined, and the twelfth preset number of curve groups with the highest new energy power generation power disturbance rate are selected from all curve groups as C-class curve groups.

[0071] As an example, the three constraint scenarios of A, B, and C are obviously related to the shape of the net load curve and the conventional power supply startup mode generated by power generation scheduling. To this end, the pre-defined focus strategies for scenarios A, B, and C are as follows:

[0072] Focus strategy for Class A scenarios:

[0073] First criterion: the curve group with the top K1 ranking in terms of daily peak value (from high to low) of hourly electricity penetration rate of new energy;

[0074] The second criterion: the curve group with the top K2 ranking in terms of daily peak value of renewable energy power generation in each region (from high to low);

[0075] The third criterion: the peak load time difference between the net load curve and the load curve (from high to low) ranks top K3 curve groups;

[0076] The fourth criterion: the curve group with the top K4 ranking in terms of net load peak value (from high to low);

[0077] Focus strategy for Category B scenarios:

[0078] First criterion: the curve group with the top K5 ranking in terms of net load valley value (from low to high);

[0079] The second criterion: the curve group with the top K6 ranking in terms of net load peak-to-valley difference (from high to low);

[0080] The third criterion: the curve group with the top K7 ranking in valley load time difference (from high to low) between the net load curve and the load curve;

[0081] The fourth criterion: the curve group with the top K8 ranking in the net load peak-to-valley difference change rate (from high to low);

[0082] Focus strategy for Category C scenarios:

[0083] First criterion: the curve group with the top K9 ranking in terms of net load positive ramp rate (from high to low);

[0084] Second criterion: Net load negative ramp rate (from high to low) ranked top K 10 The curve group of

[0085] The third criterion: the top K in terms of new energy power generation disturbance (from high to low) 11 The curve group of

[0086] The fourth criterion: the top K in terms of new energy power generation disturbance rate (from high to low) 12 The curve group.

[0087] The daily peak value of renewable energy hourly electricity penetration is obtained by integrating the renewable energy output curve group time. Other regional renewable energy power generation daily peak values, peak-load time difference, net load peak-load value, net load valley load value, net load peak-valley difference, valley-load time difference, net load peak-valley difference change rate, renewable energy power generation power disturbance, and renewable energy power generation power disturbance rate can be directly obtained from the renewable energy output curve and load curve. The net load positive ramp rate and net load negative ramp rate are calculated using the power change within 1 hour. Net load refers to the difference between load and renewable energy output. A large peak-load time difference indicates a significant impact from renewable energy output, potentially leading to peak shaving and reserve constraints.

[0088] It is understandable that K1, K2, ..., K 12 All are set to an initial value of 3. Then, when the curve groups are initially screened, 36 curve groups under constraint scenarios can be screened out for power generation scheduling and calculation of the abundance and flexibility related indicator sets.

[0089] In a preferred embodiment, the new energy carrying capacity assessment method based on scenario focusing also includes: using the curve group selected according to the first criterion in the Class A scenario focusing strategy as the first curve group, judging whether an operation risk occurs according to each first curve group according to the combined output result of the generator set, and when it is judged that all first curve groups have an operation risk, adjusting the first preset quantity according to the preset adjustment formula, and when it is judged that all first curve groups do not have an operation risk, deleting the first criterion from the Class A scenario focusing strategy; using the curve group selected according to the second criterion in the Class A scenario focusing strategy as the second curve group, judging whether an operation risk occurs according to each second curve group according to the combined output result of the generator set, and when it is judged that all second curve groups have an operation risk, adjusting the second preset quantity according to the preset adjustment formula, and when it is judged that all second curve groups do not have an operation risk. , delete the second criterion from the Class A scenario focus strategy; use the curve group selected according to the third criterion in the Class A scenario focus strategy as the third curve group, and judge whether an operation risk occurs according to the combined output result of the generator set according to each third curve group respectively, and when it is judged that all third curve groups have an operation risk, adjust the third preset quantity according to the preset adjustment formula; when it is judged that all third curve groups do not have an operation risk, delete the third criterion from the Class A scenario focus strategy; use the curve group selected according to the fourth criterion in the Class A scenario focus strategy as the fourth curve group, and according to the combined output result of the generator set, judge whether an operation risk occurs according to each fourth curve group respectively, and when it is judged that all fourth curve groups have an operation risk, adjust the fourth preset quantity according to the preset adjustment formula; when it is judged that all fourth curve groups do not have an operation risk, delete the fourth criterion from the Class A scenario focus strategy.

[0090] In a preferred embodiment, the new energy carrying capacity assessment method based on scenario focusing also includes: using the curve group selected according to the first criterion in the Class B scenario focusing strategy as the fifth curve group, judging whether an operation risk occurs according to each fifth curve group based on the combined output results of the generator sets, and when it is judged that all fifth curve groups have an operation risk, adjusting the first preset quantity according to the preset adjustment formula, and when it is judged that all fifth curve groups do not have an operation risk, deleting the first criterion from the Class B scenario focusing strategy; using the curve group selected according to the second criterion in the Class B scenario focusing strategy as the sixth curve group, judging whether an operation risk occurs according to each sixth curve group based on the combined output results of the generator sets, and when it is judged that all sixth curve groups have an operation risk, adjusting the second preset quantity according to the preset adjustment formula, and when it is judged that all sixth curve groups do not have an operation risk. , delete the second criterion from the Class B scenario focus strategy; use the curve group selected according to the third criterion in the Class B scenario focus strategy as the seventh curve group, and judge whether an operation risk occurs according to the combined output result of the generator set according to each seventh curve group respectively, and when it is judged that all seventh curve groups have an operation risk, adjust the third preset quantity according to the preset adjustment formula; when it is judged that all seventh curve groups do not have an operation risk, delete the third criterion from the Class B scenario focus strategy; use the curve group selected according to the fourth criterion in the Class B scenario focus strategy as the eighth curve group, and according to the combined output result of the generator set, judge whether an operation risk occurs according to each eighth curve group respectively, and when it is judged that all eighth curve groups have an operation risk, adjust the fourth preset quantity according to the preset adjustment formula; when it is judged that all eighth curve groups do not have an operation risk, delete the fourth criterion from the Class B scenario focus strategy.

[0091] In a preferred embodiment, the new energy carrying capacity assessment method based on scenario focusing also includes: using the curve group selected according to the first criterion in the C-type scenario focusing strategy as the ninth curve group, judging whether an operation risk occurs according to each ninth curve group according to the combined output result of the generator set, and when it is judged that all ninth curve groups have an operation risk, adjusting the first preset quantity according to the preset adjustment formula, and when it is judged that all ninth curve groups do not have an operation risk, deleting the first criterion from the C-type scenario focusing strategy; using the curve group selected according to the second criterion in the C-type scenario focusing strategy as the tenth curve group, judging whether an operation risk occurs according to each tenth curve group according to the combined output result of the generator set, and when it is judged that all tenth curve groups have an operation risk, adjusting the second preset quantity according to the preset adjustment formula, and when it is judged that all tenth curve groups do not have an operation risk, deleting the first criterion from the C-type scenario focusing strategy. The second criterion is deleted from the scenario focusing strategy; the curve group selected according to the third criterion in the scenario focusing strategy of Class C is used as the eleventh curve group, and according to the combined output results of the generator sets, whether an operation risk occurs according to each eleventh curve group is judged respectively, and when it is judged that all eleventh curve groups have an operation risk, the third preset quantity is adjusted according to the preset adjustment formula; when it is judged that all eleventh curve groups do not have an operation risk, the third criterion is deleted from the scenario focusing strategy of Class C; the curve group selected according to the fourth criterion in the scenario focusing strategy of Class C is used as the twelfth curve group, and according to the combined output results of the generator sets, whether an operation risk occurs according to each twelfth curve group is judged respectively, and when it is judged that all twelfth curve groups have an operation risk, the fourth preset quantity is adjusted according to the preset adjustment formula; when it is judged that all twelfth curve groups do not have an operation risk, the fourth criterion is deleted from the scenario focusing strategy of Class C.

[0092] In a preferred embodiment, the preset adjustment formula is:

[0093] K′ i =2K i (1);

[0094] Wherein, i=1, 2, 3, 4; K1 is the first preset quantity, K′1 is the quantity after the first preset quantity is adjusted, K2 is the second preset quantity, K′2 is the quantity after the second preset quantity is adjusted; K3 is the third preset quantity, K′3 is the quantity after the third preset quantity is adjusted; K4 is the fourth preset quantity, K′4 is the quantity after the fourth preset quantity is adjusted.

[0095] As an example, Figure 2As shown, according to the combined output results of the generator sets, it is judged whether all the curve groups obtained by each criterion in the A, B, and C scenario focusing strategies have the operation risk of load shedding or abandoning solar power, wind power, and hydropower. If all the curve groups obtained by a criterion have the operation risk, the number of curve group screenings corresponding to the criterion is adjusted, and the curve groups with the adjusted number of curve group screenings are selected from all the curve groups obtained in the next period according to the criterion again; if all the curve groups obtained by a criterion do not have the operation risk, that is, only some of the curve groups have the operation risk, the number of curve group screenings corresponding to the criterion is kept unchanged, and the curve groups with the number of curve group screenings are selected from all the curve groups obtained in the next period according to the criterion again; if all the curve groups obtained by a criterion do not have the operation risk, the criterion is deleted from the A / B / C scenario focusing strategy, and the curve groups are no longer selected from all the curve groups obtained in the next period according to the criterion.

[0096] This embodiment adjusts the A, B, and C scenario focusing strategies by adjusting the operational impact of all curve groups obtained from each criterion in the A, B, and C scenario focusing strategies based on the combined output results of the generator sets, which is conducive to the subsequent rapid screening of key constraint scenarios from massive scenarios.

[0097] In a preferred embodiment, the objective function is:

[0098]

[0099] Among them, F1 is the power generation cost of the generator set; Cost of starting and stopping the generator set; is the output ramp cost of the generator set, F4 is the energy storage power generation cost; F5 is the constraint scenario penalty cost; F β is the reward cost with respect to the spinning reserve factor β.

[0100] As an example, considering the economic efficiency of the operating costs of thermal power units (including coal-fired, peak-shaving gas-fired, and thermal power gas-fired) and pumped storage units and the priority of consuming clean energy, the objective function is set to minimize the operating cost of the generator unit. At the same time, four power curtailment penalty factors are introduced to penalize the load shedding and curtailment of solar power, wind power, and hydropower. The comprehensive cost includes the power generation cost of the thermal power unit, the start-up and shutdown cost of the thermal power unit, the output ramping cost of the thermal power unit, the energy storage power generation cost of pumped storage units, and the penalty cost of the constraint scenario. That is, the objective function is:

[0101]

[0102] In formula (2), F1 is the power generation cost of the generator set (thermal power unit); Startup and shutdown costs of generator sets (thermal power units); is the output ramping cost of the generator set (thermal power unit), F4 is the power generation cost of energy storage (pumped storage unit, nuclear power); F5 is the penalty cost of the constraint scenario; F β is the reward cost with respect to the spinning reserve factor β.

[0103] The cost of generating electricity from a thermal power unit is:

[0104] F1(P g,t )=a g (P g,t ) 2 +b g P g,t +c g (3);

[0105] In formula (3), a g 、b g 、c g are the coefficients of the quadratic term, linear term and constant term of the quadratic function relationship between the fuel cost of thermal power unit g and the generated power; P g,t is the output of thermal power unit g at time t.

[0106] The quadratic function representing the fuel cost of thermal power units can be processed by piecewise linearization method. Assuming that the output range of thermal power units is [P min , P max ], using n+1 points x0≤x1≤…≤x k ≤…≤x n (where x0=P min , x n =P max ), divide the interval into n small intervals, such as Figure 3 shown.

[0107] The quadratic function value in each small interval can be approximately linearized using a secant line:

[0108]

[0109] In formula (4), x k ≤x≤x k+1 .

[0110] The startup and shutdown costs of thermal power units are:

[0111]

[0112] In formula (5), is the total start-up and shutdown cost of thermal power unit g; are the startup cost and shutdown cost of thermal power unit g at time t, t is time, and T is period.

[0113] The output ramp cost of a thermal power unit is:

[0114]

[0115] In formula (6), is the total ramp cost of thermal power unit g, P g,t is the total operating output of thermal power unit g at time t.

[0116] The cost of pumped storage and nuclear power generation is:

[0117] F4=∑c ps ·P ps,t +∑c nc ·P nc,t (7);

[0118] In formula (7), c ps 、c nc are the unit power generation costs of pumped storage units and nuclear power units, respectively, in yuan / MW; P ps,t 、P nc,t are the total operating outputs of the pumped storage unit and the nuclear power unit at time t, respectively.

[0119] The penalty cost for the constraint scenario is:

[0120]

[0121] In formula (8), c pl,cut 、c i,cut are the penalty costs for load shedding and power curtailment of flexible resources (i is wind power, photovoltaic power, and small hydropower respectively); are the load shedding power and the curtailment power of flexibility resources at time t, respectively.

[0122] In a preferred embodiment, the constraints include generator set operation constraints and energy storage operation constraints.

[0123] For example, given the large number of units within a region, simulating the operational status of each unit at every moment would require high computer configuration requirements, and the operating speed would decrease exponentially over time. Based on this, this embodiment utilizes the concept of unit equivalent aggregation when processing unit output, clustering thermal power units and pumped-storage units based on unit type, capacity, and operating characteristics to simplify the unit operation simulation process. The following constraints are imposed on the operational status of aggregated thermal power units and aggregated pumped-storage units.

[0124] For generator set operation constraints:

[0125] In this embodiment, thermal power units are classified into coal-fired units, peak-shaving gas-fired units, and thermal power gas-fired units according to their types to establish the operation constraints of aggregated thermal power units:

[0126] 1. Upper and lower limits of aggregate unit output:

[0127] For coal-fired units and peak-shaving gas-fired units, the output upper and lower limits are as follows:

[0128]

[0129] In formula (9), t is time; g is the type number of the aggregated unit, which can represent coal-fired units, peak-shaving gas-fired units, and thermal power gas-fired units; P g,t is the total operating output of the aggregated gas-fired units or coal-fired units at time t; S g,t is the total number of aggregated thermal power units started at time t; are the maximum power generation and minimum power generation of a single unit of the aggregated thermal power unit at time t;

[0130] For thermal power gas generator sets, in addition to satisfying the constraint of formula (5), they should also meet the basic requirement curve of heating load, namely:

[0131] P g,t ≥P hl,t (10);

[0132] In formula (10), P hl,t is the heating load value at time t.

[0133] 2. Aggregate unit climbing constraints:

[0134] For all types of units, the ramp constraint needs to be met, that is, the power change cannot exceed the ramp rate in normal operation, but the ramp rate limit can be exceeded during startup and shutdown, that is:

[0135]

[0136]

[0137] In formulas (11) and (12), U g,max 、D g,max are the maximum climbing power and the maximum sliding power of the aggregated thermal power units in adjacent periods; S g,t 、S g,t-1 are the number of thermal power units in operation at time t and time t-1 respectively.

[0138] 3. Aggregate unit start and stop constraints:

[0139]

[0140] In formula (13), are the minimum and maximum number of operating units of the aggregated thermal power units at time t.

[0141] 4. Constraints on the startup time of aggregate units:

[0142] Since only one-day power generation scheduling is performed, there is no need to consider the start / stop time constraints of coal-fired power (the start / stop interval must be at least 3 days). Therefore, the start / stop status of coal-fired power remains unchanged within a day as a constraint. For coal-fired units, the thermal power generation is restricted from being shut down after the first start-up time in a day, that is:

[0143] S g,t -S g,t-1 =0 (14);

[0144] For peak-shaving gas-fired units and thermal power gas-fired units, there are no restrictions on the startup time due to the convenience of starting and stopping.

[0145] 5. Cost constraints for starting and shutting down thermal power units:

[0146] Startup cost constraints:

[0147]

[0148]

[0149]

[0150] Downtime cost constraint:

[0151]

[0152] C off,1 =0 (19);

[0153]

[0154] In formulas (10)-(15), are the startup cost and shutdown cost of the aggregated thermal power unit at time t, respectively; It is the unit start-up and shutdown cost of thermal power units, in Yuan / MW.

[0155] Cost constraints for starting and shutting down thermal power units:

[0156]

[0157] In formula (21), j is the type of aggregated thermal power unit, N is the number of clusters of coal-fired / gas-fired units, P install is the installed capacity of the corresponding thermal power unit type.

[0158] For energy storage operation constraints:

[0159] Pumped storage units have two operating states: pumping state and power generation state. Since pumped storage units have almost no upper or lower output limits, all pumped storage units in the area are directly equivalent to a single large-capacity pumped storage unit. The specific operating conditions are as follows:

[0160] 1. Upper and lower limits of pumped storage unit output:

[0161] -P up ≤P ps,t ≤P up (twenty two);

[0162] In formula (22), P up It is the upper limit of the output of the equivalent pumped storage unit, and its value is equal to the installed capacity of the pumped storage unit.

[0163] 2. Reservoir capacity constraints for pumped storage units:

[0164] Set a coefficient α and let:

[0165]

[0166] α·0.25T·(-P up )≤W ps,sum ≤α·0.25T·P up (twenty four);

[0167] In formulas (23) and (24), W ps,sum is the equivalent power generation / pumping volume of the pumped storage unit in one day; the coefficient α is actually a limit on the total amount of water / power generation that the pumped storage unit can continuously pump / generate.

[0168] For other runtime constraints:

[0169] 1. Positive and negative spinning reserve constraints:

[0170]

[0171]

[0172] In formulas (25) and (26), P g,max is the output upper limit of each type of unit, P g,min is the lower limit of output of each type of unit, P l,t is the load value at time t, 0≤β≤0.05.

[0173] 2. Flexibility resource constraints and load constraints:

[0174]

[0175] In formula (27), i is wind power, photovoltaic power, small hydropower or load, P is the power abandonment power or load removal power of a certain type of power supply at time t; i t is the output or load value of the corresponding power source or load at time t.

[0176] 3. Power balance constraints:

[0177]

[0178] In formula (28), is the actual output of wind power, photovoltaic power, and small hydropower at time t; is the power value of the West-to-East Power Transmission Curve at time t.

[0179] In a preferred embodiment, the new energy carrying capacity evaluation index system includes a grid source-load characteristic index, an electric power sufficiency index, a new energy consumption index, and a grid operation flexibility index.

[0180] As an example, a new energy carrying capacity evaluation index system is established from four aspects to evaluate the impact of a high proportion of new energy access on the power grid's power supply capacity, reliability, and operational safety. The new energy carrying capacity evaluation index system is designed as follows:

[0181] 1. Grid source-load characteristic indicators (can be statistically analyzed monthly from January to December): mean and variance of daily renewable energy power generation; mean and variance of daily renewable energy power generation curve (in hourly units); mean curve of renewable energy hourly power penetration rate; renewable energy power penetration rate; renewable energy power generation power disturbance and disturbance rate; renewable energy-load time series correlation coefficient; net load peak-to-valley difference and peak-to-valley difference change rate; net load positive / negative ramp rate change rate;

[0182] 2. Power adequacy indicators (can be calculated monthly from January to December): power shortage probability (loss of load probability, LOLP); expected power shortage value; expected power not supplied (EPNS); expected power shortage caused by power shortage; power shortage frequency; average power shortage duration;

[0183] 3. New energy consumption indicators (can be calculated on an annual or monthly basis): new energy power consumption rate / abandoned power / power abandonment rate; new energy power consumption; probability of wind power / photovoltaic power abandonment; expected wind power / photovoltaic power abandonment power; expected wind power / photovoltaic power abandonment; average wind power / photovoltaic power abandonment duration; wind power / photovoltaic power abandonment frequency;

[0184] 4. Grid operation flexibility indicators (can be calculated on an annual or monthly basis): proportion of flexible power sources; (upward / downward) probability of insufficient flexibility; (upward / downward) expectation of insufficient flexibility; average (upward / downward) duration of insufficient regulation capacity; (upward / downward) frequency of insufficient regulation capacity; upward adjustment of spinning reserve ratio; downward adjustment of spinning reserve ratio.

[0185] This embodiment establishes a new energy carrying capacity evaluation index system from four aspects: grid source-load characteristic index, power and electricity sufficiency index, new energy consumption index, and grid operation flexibility index. It can efficiently and comprehensively evaluate the new energy carrying capacity for constraint scenarios.

[0186] In order to verify the effectiveness of the scenario-focused new energy carrying capacity assessment method provided by the first embodiment, and to analyze the new energy carrying capacity indicators of the power grid based on actual data, the idea of ​​massive new energy-load scenario generation → constraint daily scenario focus → daily operation mode simulation → new energy carrying capacity indicator analysis is formed based on the idea of ​​massive new energy-load scenario generation → constraint daily scenario focus → daily operation mode simulation → new energy carrying capacity indicator analysis, forming a carrying capacity indicator analysis example of the Guangdong power grid in 2020 considering the daily regulation capacity constraint. The specific process idea is as follows: Figure 4 shown.

[0187] It has been verified that the scenario-focused new energy carrying capacity assessment method provided in the first embodiment has the following advantages:

[0188] 1. The optimization of daily power generation scheduling and operation mode simulation methods are used to simulate and analyze the operation of the power grid, which can more accurately calculate the operation status of the power grid and evaluate the new energy carrying capacity;

[0189] 2. It can summarize and generalize massive operating scenarios, identify and quantify the key characteristics of peak load shaving and reserve constraints;

[0190] 3. It can filter and focus on peak-shaving and backup-constrained scenarios from a large number of operating scenarios. While reducing the number of scenarios that need to be analyzed, it also retains the key features of peak-shaving and backup-constrained scenarios and reduces the computational complexity of the analysis.

[0191] 4. Massive scenarios are screened using a sorting method, which has high screening accuracy and focuses primarily on the peak-to-valley difference of the operating curve, requiring relatively little computation.

[0192] 5. Several quantitative indicators for evaluating the carrying capacity of new energy have been proposed from four aspects: new energy consumption, grid operation flexibility, grid source-load characteristics, and power supply sufficiency, so that the evaluation of new energy carrying capacity is no longer limited to the consumption capacity of new energy.

[0193] Based on the same inventive concept as the first embodiment, the second embodiment provides Figure 5The device for evaluating the carrying capacity of a new energy source based on scenario focusing is shown, comprising: a class A scenario focusing module 21, for determining the class A operating index value of each curve group obtained in the current period according to a predefined class A scenario focusing strategy, and selecting several curve groups with the highest class A operating index value from all curve groups as class A curve groups; wherein each curve group includes multiple new energy output curves and multiple net load curves; a class B scenario focusing module 22, for determining the class B operating index value of each curve group according to a predefined class B scenario focusing strategy, and selecting several curve groups with the highest class B operating index value from all curve groups as class B curve groups; a class C scenario focusing module 23, for determining the class C operating index value of each curve group according to a predefined class C scenario focusing strategy. Operation index value, select several curve groups with the highest Class C operation index value from all curve groups as Class C curve groups; power generation operation simulation model construction module 24, used to combine all Class A curve groups, all Class B curve groups, and all Class C curve groups to obtain a screening curve set of constraint scenarios, and extract operation data from the screening curve set, construct an objective function with the goal of minimizing the sum of the generator set operation cost and the constraint scenario penalty cost, and construct a power generation operation simulation model in combination with the constructed constraint conditions; new energy carrying capacity evaluation module 25, used to solve the power generation operation simulation model to obtain the combined output result of the generator set, and based on the pre-defined new energy carrying capacity evaluation index system, perform new energy carrying capacity evaluation according to the combined output result of the generator set to obtain the new energy carrying capacity evaluation result.

[0194] In a preferred embodiment, the Class A scenario focusing module 21 is specifically used to: determine the daily peak value of the hourly electricity penetration rate of new energy for each curve group according to the first criterion in the Class A scenario focusing strategy, and select the first preset number of curve groups with the highest daily peak value of the hourly electricity penetration rate of new energy from all curve groups as the Class A curve group; determine the daily peak value of the regional new energy power generation power for each curve group according to the second criterion in the Class A scenario focusing strategy, and select the second preset number of curve groups with the highest daily peak value of the regional new energy power generation power from all curve groups as the Class A curve group; determine the peak load time difference between each curve group and the corresponding load curve according to the third criterion in the Class A scenario focusing strategy, and select the third preset number of curve groups with the highest peak load time difference from all curve groups as the Class A curve group; determine the net load peak value of each curve group according to the fourth criterion in the Class A scenario focusing strategy, and select the fourth preset number of curve groups with the highest net load peak value from all curve groups as the Class A curve group.

[0195] In a preferred embodiment, the Class B scenario focusing module 22 is specifically used to: determine the net load valley load value of each curve group according to the first criterion in the Class B scenario focusing strategy, and select the fifth preset number of curve groups with the highest net load valley load value from all curve groups as the Class B curve group; determine the net load peak-to-valley difference of each curve group according to the second criterion in the Class B scenario focusing strategy, and select the sixth preset number of curve groups with the highest net load peak-to-valley difference from all curve groups as the Class B curve group; determine the valley load time difference between each curve group and the corresponding load curve according to the third criterion in the Class B scenario focusing strategy, and select the seventh preset number of curve groups with the highest valley load time difference from all curve groups as the Class B curve group; determine the net load peak-to-valley difference change rate of each curve group according to the fourth criterion in the Class B scenario focusing strategy, and select the eighth preset number of curve groups with the highest net load peak-to-valley difference change rate from all curve groups as the Class B curve group.

[0196] In a preferred embodiment, the Class C scenario focusing module 23 is specifically used to: determine the net load positive climbing rate of each curve group according to the first criterion in the Class C scenario focusing strategy, and select the ninth preset number of curve groups with the highest net load positive climbing rate from all curve groups as the Class C curve group; determine the net load negative climbing rate of each curve group according to the second criterion in the Class C scenario focusing strategy, and select the tenth preset number of curve groups with the highest net load negative climbing rate from all curve groups as the Class C curve group; determine the new energy power generation power disturbance of each curve group according to the third criterion in the Class C scenario focusing strategy, and select the eleventh preset number of curve groups with the highest new energy power generation power disturbance from all curve groups as the Class C curve group; determine the new energy power generation power disturbance rate of each curve group according to the fourth criterion in the Class C scenario focusing strategy, and select the twelfth preset number of curve groups with the highest new energy power generation power disturbance rate from all curve groups as the Class C curve group.

[0197] In a preferred embodiment, the new energy carrying capacity assessment device based on scenario focus also includes a scenario focus strategy adjustment module, which is used to: use the curve group selected according to the first criterion in the Class A scenario focus strategy as the first curve group, and judge whether an operation risk occurs according to each first curve group based on the combined output result of the generator set, and when it is judged that all first curve groups have an operation risk, adjust the first preset quantity according to the preset adjustment formula, and when it is judged that all first curve groups do not have an operation risk, delete the first criterion from the Class A scenario focus strategy; use the curve group selected according to the second criterion in the Class A scenario focus strategy as the second curve group, and when it is judged that all second curve groups have an operation risk, adjust the second preset quantity according to the preset adjustment formula, and when it is judged that all second curve groups do not have an operation risk. When an operation risk occurs, the second criterion is deleted from the Class A scenario focus strategy; the curve group selected according to the third criterion in the Class A scenario focus strategy is used as the third curve group, and according to the combined output result of the generator set, it is judged whether an operation risk occurs according to each third curve group, and when it is judged that all third curve groups have an operation risk, the third preset quantity is adjusted according to the preset adjustment formula; when it is judged that all third curve groups have no operation risk, the third criterion is deleted from the Class A scenario focus strategy; the curve group selected according to the fourth criterion in the Class A scenario focus strategy is used as the fourth curve group, and according to the combined output result of the generator set, it is judged whether an operation risk occurs according to each fourth curve group, and when it is judged that all fourth curve groups have an operation risk, the fourth preset quantity is adjusted according to the preset adjustment formula; when it is judged that all fourth curve groups have no operation risk, the fourth criterion is deleted from the Class A scenario focus strategy.

[0198] In a preferred embodiment, the preset adjustment formula is:

[0199] K′ i =2K i (29);

[0200] Wherein, i=1, 2, 3, 4; K1 is the first preset quantity, K′1 is the quantity after the first preset quantity is adjusted, K2 is the second preset quantity, K′2 is the quantity after the second preset quantity is adjusted; K3 is the third preset quantity, K′3 is the quantity after the third preset quantity is adjusted; K4 is the fourth preset quantity, K′4 is the quantity after the fourth preset quantity is adjusted.

[0201] In a preferred embodiment, the objective function is:

[0202]

[0203] Among them, F1 is the power generation cost of the generator set; Cost of starting and stopping the generator set; is the output ramp cost of the generator set, F4 is the energy storage power generation cost; F5 is the constraint scenario penalty cost; F β is the reward cost with respect to the spinning reserve factor β.

[0204] In a preferred embodiment, the constraints include generator set operation constraints and energy storage operation constraints.

[0205] In a preferred embodiment, the new energy carrying capacity evaluation index system includes a grid source-load characteristic index, an electric power sufficiency index, a new energy consumption index, and a grid operation flexibility index.

[0206] In summary, the implementation of the embodiments of the present invention has the following beneficial effects:

[0207] By determining the Class A operation index value of each curve group obtained in the current period according to a predefined Class A scenario focus strategy, several curve groups with the highest Class A operation index values ​​are selected from all curve groups as Class A curve groups; determining the Class B operation index value of each curve group according to a predefined Class B scenario focus strategy, several curve groups with the highest Class B operation index values ​​are selected from all curve groups as Class B curve groups; determining the Class C operation index value of each curve group according to a predefined Class C scenario focus strategy, several curve groups with the highest Class C operation index values ​​are selected from all curve groups as Class C curve groups; Combine all Class A curve groups, all Class B curve groups, and all Class C curve groups to obtain a screening curve set for the constraint scenario, and extract operating data from the screening curve set. Construct an objective function with the goal of minimizing the sum of the operating cost of the generator set and the penalty cost of the constraint scenario. Combined with the constructed constraints, build a power generation operation simulation model; solve the power generation operation simulation model to obtain the combined output results of the generator sets, and based on the pre-defined new energy carrying capacity evaluation index system, perform new energy carrying capacity evaluation according to the combined output results of the generator sets to obtain the new energy carrying capacity evaluation results, thereby realizing the new energy carrying capacity evaluation under the constraint scenario. The embodiments of the present invention determine the A, B, and C operating index values ​​of each curve group obtained in the current period according to predefined A, B, and C scenario focusing strategies, select several curve groups with the highest A, B, and C operating index values ​​from all curve groups as A, B, and C curve groups, obtain a screening curve set for the constraint scenario, construct a power generation operation simulation model based on the screening curve set, solve and obtain the combined output result of the generator set, and based on the predefined new energy carrying capacity evaluation index system, perform new energy carrying capacity evaluation according to the combined output result of the generator set to obtain a new energy carrying capacity evaluation result, thereby being able to screen key constraint scenarios from massive scenarios and efficiently and comprehensively evaluate the new energy carrying capacity for the constraint scenarios.

[0208] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

[0209] Those skilled in the art will appreciate that all or part of the processes in the above embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

Claims

1. A new energy carrying capacity assessment method based on scenario focus, characterized in that: include: According to the predefined Class A scenario focusing strategy, the Class A operation index value of each curve group obtained in the current period is determined respectively, and several curve groups with the highest Class A operation index value are selected from all the curve groups as Class A curve groups; wherein each of the curve groups includes multiple new energy output curves and multiple net load curves; the Class A scenario focusing strategy according to the predefined Class A scenario is determined respectively. The Class A operation index value of each curve group obtained in the current period is determined respectively, and several curve groups with the highest Class A operation index value are selected from all the curve groups as Class A curve groups, specifically: according to the first criterion in the Class A scenario focusing strategy, the Class A operation index value of each curve group obtained in the current period is determined respectively, and several curve groups with the highest Class A operation index value are selected from all the curve groups as Class A curve groups. Determine the daily peak value of the hourly electricity penetration rate of new energy for each of the curve groups, and select the first preset number of curve groups with the highest daily peak value of the hourly electricity penetration rate of new energy from all the curve groups as the Class A curve group; determine the daily peak value of the regional new energy power generation power for each of the curve groups according to the second criterion in the Class A scenario focusing strategy, and select the second preset number of curve groups with the highest daily peak value of the regional new energy power generation power from all the curve groups as the Class A curve group; determine the peak load time difference between each of the curve groups and the corresponding load curve according to the third criterion in the Class A scenario focusing strategy, and select the second preset number of curve groups with the highest daily peak value of the regional new energy power generation power from all the curve groups as the Class A curve group. Selecting a third preset number of curve groups with the highest peak load time differences from the line group as the Class A curve group; determining the net load peak value of each curve group according to the fourth criterion in the Class A scenario focusing strategy, and selecting a fourth preset number of curve groups with the highest net load peak value from all the curve groups as the Class A curve group; using the curve groups selected according to the first criterion in the Class A scenario focusing strategy as the first curve group, judging whether an operation risk occurs according to each of the first curve groups according to the combined output results of the generator sets, and adjusting the first preset number according to a preset adjustment formula when it is determined that all of the first curve groups have an operation risk, and deleting the first criterion from the Class A scenario focusing strategy when it is determined that none of the first curve groups have an operation risk; using the curve groups selected according to the second criterion in the Class A scenario focusing strategy as the second curve group, judging whether an operation risk occurs according to each of the second curve groups according to the combined output results of the generator sets, and adjusting the second preset number according to the preset adjustment formula when it is determined that all of the second curve groups have an operation risk, and deleting the second criterion from the Class A scenario focusing strategy when it is determined that none of the second curve groups have an operation risk;The curve group selected according to the third criterion in the Class A scenario focus strategy is used as the third curve group. According to the combined output result of the generator sets, whether an operation risk occurs according to each of the third curve groups is judged respectively. When it is judged that all the third curve groups have an operation risk, the third preset number is adjusted according to the preset adjustment formula. When it is judged that none of the third curve groups have an operation risk, the third criterion is deleted from the Class A scenario focus strategy. The curve group selected according to the fourth criterion in the Class A scenario focus strategy is used as the fourth curve group. According to the combined output result of the generator sets, whether an operation risk occurs according to each of the fourth curve groups is judged respectively. When it is judged that all the fourth curve groups have an operation risk, the fourth preset number is adjusted according to the preset adjustment formula. When it is judged that none of the fourth curve groups have an operation risk, the fourth criterion is deleted from the Class A scenario focus strategy. According to a predefined Class B scenario focusing strategy, a Class B operation index value of each of the curve groups is determined respectively, and several curve groups with the highest Class B operation index values ​​are selected from all the curve groups as Class B curve groups; According to a predefined Class C scene focusing strategy, respectively determine the Class C operation index value of each of the curve groups, and select several curve groups with the highest Class C operation index values ​​from all the curve groups as Class C curve groups; Combining all of the Class A curve groups, all of the Class B curve groups, and all of the Class C curve groups to obtain a screening curve set for the constraint scenario, extracting operating data from the screening curve set, constructing an objective function with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost, and building a power generation operation simulation model in combination with the constructed constraint conditions; Solving the power generation operation simulation model to obtain the combined output result of the generator sets, and based on a predefined new energy carrying capacity evaluation index system, performing a new energy carrying capacity evaluation according to the combined output result of the generator sets to obtain a new energy carrying capacity evaluation result.

2. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The Class B operation index value of each curve group is determined according to the predefined Class B scene focus strategy, and several curve groups with the highest Class B operation index values ​​are selected from all the curve groups as Class B curve groups, specifically: Determine the net load valley value of each curve group according to the first criterion in the Class B scenario focus strategy, and select the fifth preset number of curve groups with the highest net load valley value from all the curve groups as the Class B curve groups; Determine the net load peak-to-valley difference of each curve group according to the second criterion in the Class B scenario focus strategy, and select a sixth preset number of curve groups with the highest net load peak-to-valley differences from all the curve groups as the Class B curve groups; determining, according to the third criterion in the Class B scenario focus strategy, a valley-load time difference between each of the curve groups and the corresponding load curve, and selecting, from all the curve groups, a preset number of curve groups having the highest valley-load time differences as the Class B curve groups; According to the fourth criterion in the Class B scenario focus strategy, the net load peak-to-valley difference change rate of each curve group is determined respectively, and the eighth preset number of curve groups with the highest net load peak-to-valley difference change rate are selected from all the curve groups as the Class B curve groups.

3. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The C-class operating index value of each curve group is determined according to the predefined C-class scene focusing strategy, and several curve groups with the highest C-class operating index values ​​are selected from all the curve groups as the C-class curve groups, specifically: Determine, according to the first criterion in the Class C scenario focus strategy, a net load positive ramp rate for each of the curve groups, and select, from all the curve groups, a preset number of nine curve groups with the highest net load positive ramp rates as the Class C curve groups; Determine the net load negative climbing rate of each curve group according to the second criterion in the Class C scenario focus strategy, and select the tenth preset number of curve groups with the highest net load negative climbing rate from all the curve groups as the Class C curve groups; According to the third criterion in the Class C scenario focus strategy, the new energy power generation power disturbance amount of each of the curve groups is determined respectively, and the eleventh preset number of curve groups with the highest new energy power generation power disturbance amount are selected from all the curve groups as the Class C curve groups; According to the fourth criterion in the Class C scenario focusing strategy, the new energy power generation disturbance rate of each curve group is determined respectively, and the twelfth preset number of curve groups with the highest new energy power generation disturbance rate are selected from all the curve groups as the Class C curve group.

4. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The preset adjustment formula is: K′ i =2K i ; Among them, i=1, 2, 3, 4; K1 is the first preset quantity, K′1 is the quantity after the first preset quantity is adjusted, K2 is the second preset quantity, K′2 is the quantity after the second preset quantity is adjusted; K3 is the third preset quantity, K′3 is the quantity after the third preset quantity is adjusted; K4 is the fourth preset quantity, K′4 is the quantity after the fourth preset quantity is adjusted.

5. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The objective function is: Among them, F1 is the power generation cost of the generator set; Cost of starting and stopping the generator set; is the output ramp cost of the generator set, F4 is the energy storage power generation cost; F5 is the penalty cost of the constraint scenario; F β is the reward cost with respect to the spinning reserve factor β.

6. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The constraints include generator set operation constraints and energy storage operation constraints.

7. The scenario-focused new energy carrying capacity assessment method according to claim 1, characterized in that: The new energy carrying capacity evaluation index system includes grid source-load characteristic index, power supply sufficiency index, new energy consumption index, and grid operation flexibility index.

8. A new energy carrying capacity assessment device based on scene focusing, characterized in that: include: A class A scenario focusing module is used to determine the class A operation index value of each curve group obtained in the current period according to a predefined class A scenario focusing strategy, and select several curve groups with the highest class A operation index value from all the curve groups as class A curve groups; wherein each of the curve groups includes multiple new energy output curves and multiple net load curves; specifically used to: determine the daily peak value of the hourly electricity penetration rate of new energy for each curve group according to the first criterion in the class A scenario focusing strategy, and select the first preset number of curve groups with the highest daily peak value of the hourly electricity penetration rate of new energy from all the curve groups as the class A curve groups; according to the second criterion in the class A scenario focusing strategy, Determine the daily peak value of the regional new energy power generation power of each of the curve groups respectively, and select the second preset number of curve groups with the highest daily peak value of the regional new energy power generation power from all the curve groups as the Class A curve group; determine the peak load time difference between each of the curve groups and the corresponding load curve according to the third criterion in the Class A scene focusing strategy respectively, and select the third preset number of curve groups with the highest peak load time difference from all the curve groups as the Class A curve group; determine the net load peak value of each curve group according to the fourth criterion in the Class A scene focusing strategy respectively, and select the fourth preset number of curve groups with the highest net load peak value from all the curve groups as the Class A curve group; The curve group selected by the first criterion in the Class A scenario focusing strategy is used as the first curve group. According to the combined output result of the generator set, it is judged whether an operation risk occurs according to each of the first curve groups. When it is judged that all the first curve groups have an operation risk, the first preset quantity is adjusted according to the preset adjustment formula. When it is judged that all the first curve groups do not have an operation risk, the first criterion is deleted from the Class A scenario focusing strategy. The curve group selected by the second criterion in the Class A scenario focusing strategy is used as the second curve group. According to the combined output result of the generator set, it is judged whether an operation risk occurs according to each of the second curve groups. When it is judged that all the second curve groups do not have an operation risk, the first criterion is deleted from the Class A scenario focusing strategy. When all curve groups have operational risks, the second preset number is adjusted according to the preset adjustment formula. When it is determined that all the second curve groups do not have operational risks, the second criterion is deleted from the Class A scenario focus strategy. The curve group selected according to the third criterion in the Class A scenario focus strategy is used as the third curve group. According to the combined output result of the generator set, it is determined whether each of the third curve groups has operational risks. When it is determined that all the third curve groups have operational risks, the third preset number is adjusted according to the preset adjustment formula. When it is determined that all the third curve groups do not have operational risks, the third criterion is deleted from the Class A scenario focus strategy.The curve group selected according to the fourth criterion in the Class A scenario focus strategy is used as the fourth curve group. According to the combined output result of the generator sets, whether an operation risk occurs according to each of the fourth curve groups is determined. When it is determined that all the fourth curve groups have an operation risk, the fourth preset number is adjusted according to the preset adjustment formula. When it is determined that none of the fourth curve groups have an operation risk, the fourth criterion is deleted from the Class A scenario focus strategy. A Class B scene focusing module is used to determine the Class B operation index value of each of the curve groups according to a predefined Class B scene focusing strategy, and select several curve groups with the highest Class B operation index values ​​from all the curve groups as the Class B curve groups; A Class C scene focusing module is used to determine the Class C operation index value of each of the curve groups according to a predefined Class C scene focusing strategy, and select several curve groups with the highest Class C operation index values ​​from all the curve groups as Class C curve groups; a power generation operation simulation model construction module, configured to combine all of the Class A curve groups, all of the Class B curve groups, and all of the Class C curve groups to obtain a screening curve set for the constraint scenario, extract operating data from the screening curve set, construct an objective function with the goal of minimizing the sum of the generator set operating cost and the constraint scenario penalty cost, and construct a power generation operation simulation model in combination with the constructed constraint conditions; The new energy carrying capacity evaluation module is used to solve the power generation operation simulation model to obtain the combined output result of the generator set, and based on a pre-defined new energy carrying capacity evaluation index system, perform new energy carrying capacity evaluation according to the combined output result of the generator set to obtain a new energy carrying capacity evaluation result.

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