New energy power generation system configuration method, device, storage medium and program product

Abstract

Embodiments of the present application provide a new energy power generation system configuration method, device, storage medium and program product, relating to the technical field of new energy power generation, comprising: determining the first wind power output sequence and the first photovoltaic output sequence corresponding to each optional region under the condition of unit installed capacity according to the historical meteorological data of each optional region in the new energy power generation system; clustering each optional region according to the first wind power output sequence and the first photovoltaic output sequence, and grouping each optional region according to the clustering result to obtain a plurality of region groups; generating a configuration strategy set according to the total installed capacity of the new energy power generation system and the plurality of region groups; iteratively updating the configuration strategy set based on the complementary evaluation index, and selecting one configuration strategy from the configuration strategy set at the end of iteration as the target strategy. The present application can improve the overall rationality of new energy power generation system configuration and system operation stability.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of new energy power generation technology, and in particular to a configuration method, equipment, storage medium and program product for a new energy power generation system. Background Technology

[0002] In the process of constructing new energy power generation systems, the output level of wind and solar energy resources is greatly affected by meteorological conditions due to their intermittent, fluctuating and random characteristics, resulting in strong uncertainty in the overall output of new energy power generation systems.

[0003] Current technologies for configuring new energy power generation systems typically focus on a single power station, which has a relatively small spatial scope and relatively consistent meteorological conditions across different locations, exhibiting strong local uniformity. However, in large-scale, multi-regional new energy power generation systems, meteorological conditions vary significantly between different regions, and the output characteristics of wind and solar power exhibit spatial inconsistencies. Therefore, existing analysis methods based on single power stations are difficult to apply to large-scale, multi-regional new energy power generation systems. Summary of the Invention

[0004] This invention provides a configuration method, equipment, storage medium, and program product for a new energy power generation system. It can perform collaborative analysis on the output characteristics of wind power and photovoltaic power generation in a large-scale, multi-regional new energy power generation system, reduce the fluctuation of new energy output, and improve the overall rationality of the configuration and the stability of the system operation.

[0005] In a first aspect, the configuration method for a new energy power generation system provided by the embodiments of the present invention includes: determining a first wind power output sequence and a first photovoltaic power output sequence corresponding to each selectable region under unit installed capacity conditions based on historical meteorological data of each selectable region in the new energy power generation system; clustering each selectable region according to the first wind power output sequence and the first photovoltaic power output sequence, and grouping each selectable region according to the clustering results to obtain multiple region groups; generating a configuration strategy set according to the total installed capacity of the new energy power generation system and the multiple region groups, wherein each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each region group; iteratively updating the configuration strategy set based on a complementarity evaluation index, and selecting a configuration strategy from the configuration strategy set at the end of the iteration as the target strategy.

[0006] Secondly, the configuration device for a new energy power generation system provided in this embodiment of the invention includes: a first sequence determination module, used to determine a first wind power output sequence and a first photovoltaic power output sequence corresponding to each selectable region under unit installed capacity conditions based on historical meteorological data of each selectable region in the new energy power generation system; a clustering module, used to cluster each selectable region according to the first wind power output sequence and the first photovoltaic power output sequence, and group each selectable region according to the clustering results to obtain multiple region groups; a configuration strategy generation module, used to generate a configuration strategy set according to the total installed capacity of the new energy power generation system and the multiple region groups, wherein each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each region group; and a target strategy determination module, used to iteratively update the configuration strategy set based on a complementarity evaluation index, and select a configuration strategy from the configuration strategy set at the end of the iteration as the target strategy.

[0007] Thirdly, the electronic device provided in the embodiments of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the configuration method of the new energy power generation system as in any embodiment of the present invention.

[0008] Fourthly, the computer-readable storage medium provided in the embodiments of the present invention stores a computer program thereon, which, when executed by a processor, implements the configuration method of the new energy power generation system as described in any embodiment of the present invention.

[0009] Fifthly, the computer program product provided in the embodiments of the present invention includes a computer program that, when executed by a processor, implements the configuration method of the new energy power generation system as described in any embodiment of the present invention.

[0010] In this embodiment of the invention, historical meteorological data of each selectable region is processed to determine the wind power output sequence and photovoltaic output sequence under unit installed capacity conditions. This allows the wind and solar power output characteristics of each selectable region to be characterized by a unified standard. Based on this, the selectable regions are clustered, merging regions with similar wind and solar power output characteristics into the same group, thereby reducing the scale of decision variables in the optimization problem and reducing redundant calculations. Furthermore, based on the total installed capacity constraint, a configuration strategy set containing multiple regional grouping wind and solar power capacity allocation schemes is constructed, enabling the system to perform a global search under various feasible capacity configuration combinations, improving the diversity and coverage of configuration schemes. Simultaneously, by introducing a complementarity evaluation index to iteratively update the configuration strategy set, different configuration strategies can be optimized and adjusted based on the complementary characteristics of wind and solar power output, thereby enhancing the smoothness and fluctuation suppression capability of the overall system output. Finally, a configuration strategy is selected as the target strategy from the configuration strategy set after iteration, realizing the optimized allocation of wind and solar resources of the new energy power generation system under the condition of meeting the installed capacity constraint, reducing the fluctuation of new energy output, and improving the stability of system operation. Attached Figure Description

[0011] To more clearly illustrate the technical solution of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a configuration method for a new energy power generation system provided in an embodiment of the present invention; Figure 2 This is another schematic flowchart of the configuration method for a new energy power generation system provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the configuration device of the new energy power generation system provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Figure 1 This is a flowchart illustrating a configuration method for a new energy power generation system provided in an embodiment of the present invention. This configuration method is applicable to scenarios involving the configuration of large-scale, multi-regional new energy power generation systems. The configuration method can be executed by a configuration device for the new energy power generation system provided in this embodiment, which can be implemented using software and / or hardware. In one specific embodiment, this device can be integrated into an electronic device, such as a computer or server. The following embodiment uses the integration of the configuration device for the new energy power generation system into an electronic device as an example. (See also...) Figure 1 The configuration method of the new energy power generation system in this embodiment may include the following steps: Step 101: Based on the historical meteorological data of each selectable region in the new energy power generation system, determine the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region under the unit installed capacity condition.

[0016] The new energy power generation system refers to a system that generates electricity using renewable energy sources such as wind energy and solar energy, which may include wind power systems, photovoltaic power generation systems, and integrated wind-solar power generation systems, and may also include energy storage systems, power transmission and distribution systems, energy management systems and the like that are arranged in support of the wind power systems and photovoltaic power generation systems. The optional areas refer to preset candidate construction areas during the planning process of the new energy power generation system, which are used to arrange wind turbines, photovoltaic modules or other new energy power generation equipment. The historical meteorological data refers to data characterizing the historical meteorological conditions of each optional area, such as data of wind speed, wind direction, solar irradiance, ambient temperature, air density, air pressure, cloud cover and the like. The unit installed capacity condition refers to taking the unit capacity (i.e., 1MW) as the calculation benchmark for the installed capacity of wind power or photovoltaic power, so that the output sequence of each optional area corresponds to the output result under the installed capacity of 1MW, which is used to eliminate the difference in installed scale between different areas and make the output sequences of different areas comparable. The first wind power output sequence refers to a time series formed by the time-varying wind power output power calculated under the unit installed capacity condition according to the historical meteorological data of the corresponding optional area. The first photovoltaic output sequence refers to a time series formed by the time-varying photovoltaic output power calculated under the unit installed capacity condition according to the historical meteorological data of the corresponding optional area.

[0017] Specifically, the historical meteorological data corresponding to each optional area in the new energy power generation system is acquired. Under the unit installed capacity condition, the wind power output power and the photovoltaic output power at the corresponding moment are respectively calculated according to the historical meteorological data at each moment based on the preset wind power output calculation model and photovoltaic output calculation model, and are arranged in chronological order to obtain the first wind power output sequence and the first photovoltaic output sequence corresponding to each optional area, so as to characterize the wind resource characteristics and solar resource characteristics of each optional area under the unit installed capacity condition, and provide basic data for subsequent optional area clustering and capacity configuration optimization. The preset wind power output calculation model is used to establish a mapping relationship between the wind power output power and meteorological data such as wind speed, wind direction and air density, and is configured to directly output the wind power output power at the corresponding moment under the unit installed capacity condition according to the input meteorological data. The preset photovoltaic output calculation model is used to establish a mapping relationship between the photovoltaic output power and meteorological data such as solar irradiance and ambient temperature, and is configured to directly output the photovoltaic output power at the corresponding moment under the unit installed capacity condition according to the input meteorological data.

[0018] Step 102: Cluster each optional area according to the first wind power output sequence and the first photovoltaic output sequence, and group the optional areas according to the clustering result to obtain a plurality of area groups.

[0019] Clustering is a processing method that divides objects into several categories based on the similarity characteristics between different objects. The clustering result refers to the classification output obtained after classifying each selectable region according to the first wind power output sequence and the first photovoltaic power output sequence using a clustering algorithm. It is used to characterize the similarity relationship of power output characteristics among the selectable regions. Region grouping refers to the set of selectable regions with similar power output characteristics obtained after clustering.

[0020] Specifically, based on the first wind power output sequence and the first photovoltaic output sequence corresponding to each optional region, each optional region is taken as the object to be analyzed. The time-series output characteristics represented by the wind power output sequence and the photovoltaic output sequence are used as input features. By calculating the similarity of the output sequences between different optional regions, clustering is performed on each optional region, so that optional regions with similar output characteristics are grouped into the same category. Subsequently, based on the clustering results, all optional regions are divided into multiple regional groups. Each regional group consists of optional regions with similar time-series wind power and photovoltaic output characteristics, thereby achieving a structured division of resource characteristics in different spatial locations. Through the above processing, the originally scattered multiple optional regions are transformed into several regional groups with consistent output characteristics. This reduces the scale of decision variables in the subsequent installed capacity configuration process based on regional groups, reduces redundant calculations, improves the convergence speed and computational efficiency of the configuration optimization process based on swarm intelligence algorithms, and enhances the stability of the optimization results.

[0021] Step 103: Based on the total installed capacity of the new energy power generation system and multiple regional groups, generate a configuration strategy set. Each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each regional group.

[0022] Total installed capacity refers to the permitted installed capacity of wind power, photovoltaic (PV), and other regulatory energy sources in a new energy power generation system during the planning or configuration process. A configuration strategy set is a collection of multiple different configuration strategies, each corresponding to a configuration relationship between a regional group and wind power and PV installed capacity, used to form a set of candidate solutions in the subsequent optimization process. A configuration strategy, under the constraint of total installed capacity, refers to the configuration scheme of wind power and PV installed capacity determined for each regional group, used to characterize a capacity combination method of the new energy power generation system. Wind power installed capacity refers to the total wind power installed capacity allocated to each regional group under the constraint of total installed capacity, used to characterize the wind power installed capacity scale corresponding to each regional group. PV installed capacity refers to the total PV installed capacity allocated to each regional group under the constraint of total installed capacity, used to characterize the PV installed capacity scale corresponding to each regional group.

[0023] Specifically, each regional group is used as the capacity configuration object. Under the condition of meeting the total installed capacity constraint, the wind power installed capacity and photovoltaic installed capacity corresponding to each regional group are combined and set to obtain multiple different capacity configuration strategies. Each configuration strategy corresponds to a specific capacity configuration method, which is used to characterize the wind power installed capacity and photovoltaic installed capacity corresponding to each regional group respectively, and provides candidate schemes for subsequent evaluation and iterative optimization of different configuration strategies based on complementary evaluation indicators.

[0024] For example, assuming the total installed capacity of the new energy power generation system is 1000MW, and after clustering, it is divided into three regional groups: Regional Group A, Regional Group B, and Regional Group C. Under the constraint that the sum of the installed capacities of each regional group equals 1000MW, multiple capacity configuration strategies can be generated. For instance, in configuration strategy 1, Regional Group A is configured with 200MW of wind power and 100MW of photovoltaic (PV) capacity; Regional Group B is configured with 150MW of wind power and 250MW of PV capacity; and Regional Group C is configured with 200MW of wind power and 100MW of PV capacity. In configuration strategy 2, Regional Group A is configured with 300MW of wind power and 50MW of PV capacity; Regional Group B is configured with 100MW of wind power and 300MW of PV capacity; and Regional Group C is configured with 150MW of wind power and 100MW of PV capacity. These different configuration strategies correspond to different combinations of wind and PV capacity. Subsequently, based on the combined wind and solar power output characteristics of each configuration strategy, corresponding complementarity evaluation indicators can be calculated, and configuration strategies that meet the target requirements can be selected through an iterative optimization process.

[0025] Step 104: Iteratively update the configuration strategy set based on the complementarity evaluation index, and select a configuration strategy from the configuration strategy set at the end of the iteration as the target strategy.

[0026] Complementarity evaluation indicators are used to evaluate the ability to offset fluctuations and the synergistic characteristics between wind power output sequences and photovoltaic output sequences. They are used to measure the degree of complementarity of the output of new energy power generation systems under different wind and solar capacity configuration schemes. Target strategy refers to the final configuration scheme determined from the configuration strategy set after iteration according to preset selection rules. It is used to determine the wind power installed capacity and photovoltaic installed capacity corresponding to each regional group in the new energy power generation system, thereby guiding the planning and construction of the new energy power generation system.

[0027] Specifically, during an iteration, for each configuration strategy in the configuration strategy set corresponding to wind power and photovoltaic (PV) installed capacity, the total wind power output sequence and total PV output sequence for each configuration strategy are calculated. Based on these sequences, a complementarity evaluation index is calculated for each configuration strategy. This complementarity evaluation index serves as a unified criterion for evaluating the merits of configuration strategies. Multiple configuration strategies in the set are compared and analyzed, and based on the evaluation results, strategies are selected, adjusted, or regenerated to form an updated configuration strategy set. After completing one iteration, it is determined whether a preset iteration termination condition is met. If not, the updated configuration strategy set is used as the new current configuration strategy set, and the process returns to the previous step to continue the next iteration. If the preset iteration termination condition is met, the iteration stops, and a configuration strategy is selected from the current configuration strategy set as the target strategy based on requirements.

[0028] In this embodiment, historical meteorological data from each selectable region is processed to determine wind power output sequences and photovoltaic output sequences under unit installed capacity conditions. This allows the wind and solar power output characteristics of each selectable region to be characterized by a unified standard. Based on this, the selectable regions are clustered, grouping regions with similar wind and solar power output characteristics into the same group, thereby reducing the scale of decision variables in the optimization problem and minimizing redundant computation. Furthermore, a configuration strategy set containing multiple regional grouping wind and solar power capacity allocation schemes is constructed based on the total installed capacity constraint. This enables the system to perform a global search under various feasible capacity configuration combinations, improving the diversity and coverage of configuration schemes. Simultaneously, by introducing a complementarity evaluation index to iteratively update the configuration strategy set, different configuration strategies can be optimized and adjusted based on the complementary characteristics of wind and solar power output, thereby enhancing the smoothness and fluctuation suppression capability of the overall system output. Finally, a configuration strategy is selected as the target strategy from the configuration strategy set after iteration, achieving optimized allocation of wind and solar resources in the new energy power generation system under the installed capacity constraint conditions, reducing new energy output fluctuations, and improving the stability of system operation.

[0029] Figure 2 This is another schematic flowchart of the configuration method for a new energy power generation system provided in an embodiment of the present invention, such as... Figure 2 As shown, the configuration method of the new energy power generation system in this embodiment may include: Step 201: Based on the historical meteorological data of each selectable region in the new energy power generation system, determine the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region under the unit installed capacity condition.

[0030] Optionally, based on historical meteorological data of each selectable region in the new energy power generation system, determine the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region under the unit installed capacity condition. This includes: determining whether the historical meteorological data of each selectable region is complete; for selectable regions with complete historical meteorological data, determining the first wind power output sequence and the first photovoltaic power output sequence corresponding to the selectable region under the unit installed capacity condition based on the historical meteorological data of the selectable region; for selectable regions with incomplete historical meteorological data, determining a partial wind power output sequence and a partial photovoltaic power output sequence corresponding to the selectable region under the unit installed capacity condition based on the historical meteorological data of the selectable region, and determining a wind power probability distribution model matching the partial wind power output sequence and a photovoltaic probability distribution model matching the partial photovoltaic power output sequence; generating the first wind power output sequence of the selectable region under the unit installed capacity condition based on the wind power probability distribution model, and generating the first photovoltaic power output sequence of the selectable region under the unit installed capacity condition based on the photovoltaic probability distribution model.

[0031] A partial wind power output sequence refers to a time series segment of wind power output calculated based only on existing data due to incomplete historical meteorological data. A partial solar power output sequence refers to a time series segment of solar power output calculated based only on existing data due to incomplete historical meteorological data. A wind power probability distribution model is a probabilistic statistical model fitted to a partial wind power output sequence to describe the random distribution characteristics of wind power output. A solar power probability distribution model is a probabilistic statistical model fitted to a partial solar power output sequence to describe the random distribution characteristics of solar power output.

[0032] Specifically, the completeness of historical meteorological data for each selectable region is assessed to determine whether the data meets the conditions for directly calculating wind and solar power output sequences. For selectable regions with complete historical meteorological data, the first wind power output sequence and the first solar power output sequence for each unit installed capacity are determined directly based on the historical meteorological data of that region. For selectable regions with incomplete historical meteorological data, partial wind power output sequences and partial solar power output sequences are first calculated based on the existing historical meteorological data. The parameters of the partial wind power output sequences are then fitted under different probability distribution models, and the optimal probability distribution model is selected as the wind power probability distribution model based on the goodness-of-fit evaluation results. Similarly, the optimal probability distribution model is selected as the solar power probability distribution model for the partial solar power output sequences. Finally, based on the determined wind power probability distribution model and photovoltaic probability distribution model, the wind power output and photovoltaic output for the missing time period are probabilistically inferred or randomly generated to obtain the first wind power output sequence and the first photovoltaic output sequence of the candidate area under the unit installed capacity condition. This ensures that the output sequences of different candidate areas under the unit installed capacity condition are consistent and comparable, providing basic data support for subsequent cluster analysis and capacity configuration optimization.

[0033] The probability distribution models include, but are not limited to, extreme value distribution, exponential distribution, gamma distribution, generalized extreme value distribution, log-normal distribution, normal distribution, Weibull distribution, generalized Pareto distribution, and Pearson-III type distribution. Parameters are fitted to a subset of wind power output sequences under different probability distribution models, and the optimal probability distribution model is selected as the wind power probability distribution model based on the goodness-of-fit evaluation results. Specifically, for a subset of wind power output sequences, multiple probability distribution models are selected for parameter fitting to ensure that the probability density function corresponding to each probability distribution model matches the empirical distribution of the subset of wind power output sequences, thereby obtaining the probability density function of each model. The parameterized expression of the distribution model is further used to calculate the corresponding theoretical probability distribution based on the parameterized expression of each probability distribution model. The theoretical probability distribution is then compared with the empirical distribution to calculate the goodness-of-fit evaluation index. The empirical distribution refers to the actual data distribution characteristics obtained by statistically analyzing the frequency of occurrence of each output value interval based on the observation data of a portion of the wind power output sequence. It is used to characterize the true distribution of a portion of the output sequence in a statistical sense. The theoretical probability distribution refers to the distribution result obtained based on the parameterized expression of the probability distribution model. It is used to characterize the random variation law of output under the assumed distribution model conditions. Goodness-of-fit evaluation metrics include Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC). RMSE characterizes the degree of deviation between the empirical distribution and the theoretical distribution; a smaller RMSE value indicates a better fit. AIC characterizes the balance between model fitting accuracy and model complexity; a smaller AIC value indicates better overall performance. In one implementation, a weighted fusion or average of RMSE and AIC is performed to obtain a comprehensive evaluation value, and the probability distribution model corresponding to the minimum comprehensive evaluation value is determined as the wind power probability distribution model.

[0034] Step 202: Cluster each selectable region according to the first wind power output sequence and the first photovoltaic power output sequence, and group each selectable region according to the clustering results to obtain multiple region groups.

[0035] Step 203: Based on the total installed capacity of the new energy power generation system and multiple regional groups, generate a configuration strategy set. Each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each regional group.

[0036] Step 204: Based on the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region, determine the second wind power output sequence and the second photovoltaic power output sequence corresponding to each region group under the unit installed capacity condition.

[0037] The second wind power output sequence refers to the group-level wind power output sequence under unit installed capacity conditions, used to characterize the temporal variation characteristics of wind resources for each regional group as a whole. The second photovoltaic output sequence refers to the group-level photovoltaic output sequence under unit installed capacity conditions, used to characterize the temporal variation characteristics of solar resources for each regional group as a whole.

[0038] In one specific implementation, the first wind power output sequence and the first photovoltaic power output sequence corresponding to any selectable region within each regional group can be selected as the second photovoltaic power output sequence and the second photovoltaic power output sequence corresponding to that regional group, so as to characterize the temporal variation characteristics of the wind and solar resources of the entire regional group through the power output sequence of the representative region.

[0039] Optionally, based on the first wind power output sequence and the first photovoltaic power output sequence corresponding to each optional region, determine the second wind power output sequence and the second photovoltaic power output sequence corresponding to each regional group under the unit installed capacity condition, including: for any regional group, weightedly fuse the first wind power output sequences corresponding to each optional region within the regional group to obtain the second wind power output sequence corresponding to the regional group; weightedly fuse the first photovoltaic power output sequences corresponding to each optional region within the regional group to obtain the second photovoltaic power output sequence corresponding to the corresponding regional group.

[0040] Specifically, for any given regional group, the first wind power output sequence corresponding to each selectable region within that regional group is weighted and fused according to its weights to obtain the second wind power output sequence corresponding to that regional group; the first photovoltaic output sequence corresponding to each selectable region within that regional group is weighted and fused according to its weights to obtain the second photovoltaic output sequence corresponding to that regional group. The weights can be determined based on the completeness of the historical meteorological data for each selectable region; the higher the completeness of the historical meteorological data for the selectable region, the greater the corresponding weight.

[0041] Step 205: Based on the second wind power output sequence and the second photovoltaic power output sequence, determine the third wind power output sequence and the third photovoltaic power output sequence corresponding to each regional group under each configuration strategy.

[0042] The third wind power output sequence refers to the wind power output time series obtained by adjusting the second wind power output sequence according to the configuration strategy and applying a capacity scale adjustment. It is used to characterize the wind power output sequence of each regional group under the corresponding configuration strategy. The third photovoltaic output sequence refers to the photovoltaic output time series obtained by adjusting the second photovoltaic output sequence according to the configuration strategy and applying a capacity scale adjustment. It is used to characterize the photovoltaic output sequence of each regional group under the corresponding configuration strategy.

[0043] Specifically, for different configuration strategies, the wind power installed capacity and photovoltaic installed capacity corresponding to each regional group under the configuration strategy are used as scaling coefficients to scale up the second wind power output sequence and the second photovoltaic output sequence, respectively, so as to obtain the third wind power output sequence and the third photovoltaic output sequence corresponding to each regional group under each configuration strategy, providing basic data support for subsequent complementarity evaluation and optimization based on configuration strategies.

[0044] For example, suppose the configuration strategy is as follows: Region A is configured with 200MW of wind power capacity and 100MW of photovoltaic capacity; Region B is configured with 150MW of wind power capacity and 250MW of photovoltaic capacity; and Region C is configured with 200MW of wind power capacity and 100MW of photovoltaic capacity. Correspondingly, the second wind power output sequence of Region A is scaled up to 200MW to obtain the third wind power output sequence corresponding to Region A, and the second photovoltaic output sequence is scaled up to 100MW to obtain the third photovoltaic output sequence corresponding to Region A. Similarly, the second wind power output sequence of Region B is scaled up to 150MW to obtain the third wind power output sequence corresponding to Region B, and the second photovoltaic output sequence is scaled up to 250MW to obtain the third photovoltaic output sequence corresponding to Region B. Likewise, the second wind power output sequence of Region C is scaled up to 200MW to obtain the third wind power output sequence corresponding to Region C, and the second photovoltaic output sequence is scaled up to 100MW to obtain the third photovoltaic output sequence corresponding to Region C.

[0045] Step 206: Determine the complementarity evaluation index corresponding to each configuration strategy based on the third wind power output sequence and the third photovoltaic power output sequence.

[0046] Complementarity evaluation index refers to a quantitative indicator that characterizes the degree of complementarity between wind power output and photovoltaic power output. It is used to measure the coordination and complementary matching degree of wind and solar power output fluctuations at different time scales.

[0047] Specifically, based on the third wind power output sequence and the third photovoltaic power output sequence of each region under each configuration strategy, the relationship between wind power output and photovoltaic power output is analyzed from the time series dimension to evaluate the degree of complementarity and matching between the two at different time scales, thereby determining the complementarity evaluation index corresponding to each configuration strategy, which is used to characterize the synergistic characteristics and stability of the new energy output structure under the configuration strategy.

[0048] Optionally, based on the third wind power output sequence and the third photovoltaic power output sequence, the complementarity evaluation index corresponding to each configuration strategy is determined, including: based on the third wind power output sequence and the third photovoltaic power output sequence, determining the total wind power output sequence and the total photovoltaic power output sequence corresponding to the new energy power generation system under each configuration strategy; and based on the total wind power output sequence and the total photovoltaic power output sequence, determining the complementarity evaluation index corresponding to each configuration strategy.

[0049] The total wind power output sequence refers to the system-level wind power output time series obtained by superimposing the third-level wind power output sequences of all regional groups in the new energy power generation system. It is used to characterize the wind power output variation characteristics of the entire new energy power generation system. The total photovoltaic power output sequence refers to the system-level photovoltaic power output time series obtained by superimposing the third-level photovoltaic power output sequences of all regional groups in the new energy power generation system. It is used to characterize the photovoltaic power output variation characteristics of the entire new energy power generation system.

[0050] Specifically, under each configuration strategy, the third wind power output sequence corresponding to each regional group in the new energy power generation system is summed time-by-time to obtain the total wind power output sequence corresponding to that configuration strategy. Similarly, the total photovoltaic power output sequence corresponding to that configuration strategy can be obtained. Furthermore, based on the total wind power output sequence and the total photovoltaic power output sequence under that configuration strategy, the output change relationship and complementary matching degree of wind power and photovoltaic at each time point are analyzed from a time dimension. This determines the complementarity evaluation index corresponding to each configuration strategy, which is used to characterize the overall wind-solar synergy characteristics and output smoothing effect of the new energy power generation system under different configuration strategies.

[0051] Optionally, the complementarity evaluation index includes at least three evaluation sub-indicators, and the time scales of any two evaluation sub-indicators are different; wherein, the evaluation sub-indicators include at least one of the following: standard deviation complementarity rate, range complementarity rate, slope complementarity rate, Richard-Beck flicker index complementarity rate, first-order difference complementarity rate, and process line length complementarity rate.

[0052] Evaluation sub-indicators refer to single evaluation indicators used to describe wind power and photovoltaic output sequences from different time scales or different statistical characteristic dimensions. Standard deviation is a statistical measure characterizing the dispersion of the output sequence around its mean. Range is the difference between the maximum and minimum values ​​in the output sequence, used to characterize the range of fluctuation. Slope is the rate of change of the output sequence over time, used to characterize the strength of the upward or downward trend in output change. The Richard-Baker flicker index is an indicator characterizing the severity of time series fluctuations, used to reflect the flicker characteristics and fluctuation frequency of the sequence. First difference is the difference between output values ​​at adjacent time points, used to characterize the short-term variation amplitude. Process line length is the total length of the broken line formed by connecting all adjacent points in the time series, used to characterize the overall complexity of the sequence's fluctuations.

[0053] Specifically, for the total wind power output sequence and total photovoltaic power output sequence corresponding to each configuration strategy, a complementarity evaluation index is constructed. This index consists of at least three evaluation sub-indicators, with different time scales for each sub-indicator to avoid the influence of a single time scale on the evaluation results and improve the comprehensiveness and robustness of the evaluation results. The evaluation sub-indicators include, but are not limited to, at least one of the following: standard deviation complementarity rate, range complementarity rate, slope complementarity rate, Richard-Beck flicker index complementarity rate, first-order difference complementarity rate, and process line length complementarity rate. These sub-indicators are used to quantitatively evaluate the complementary characteristics of wind power and photovoltaic power output from multiple perspectives, such as fluctuation amplitude, trend, and degree of change, thereby providing a unified evaluation basis for the optimal selection of different configuration strategies.

[0054] For example, the total wind power output sequence and the total photovoltaic power output sequence are hourly output sequences on a continuous time scale. When conducting complementarity evaluation, the total wind power output sequence and the total photovoltaic power output sequence are transformed according to the time scale requirements of different evaluation sub-indicators. Assume the total wind power output sequence is represented as {x1, x2, ..., x...} n}, where x t Let y1 represent the wind power output value corresponding to hour t, and let the total photovoltaic output sequence be represented as {y1, y2, ..., y3}. n}, where y t Let represent the wind power output value corresponding to hour t. Then, the combined wind and solar power output sequence is represented as {x1+y1, x2+y2, ..., x}. n +y n}, where x t +y t This represents the combined wind and solar power output value for the t-th hour.

[0055] For the daily time scale evaluation sub-indicators, the total wind power output sequence and the total photovoltaic power output sequence are divided into hours. Within a certain daily time window, the total wind power output sequence of that day is represented as X, and the total photovoltaic power output sequence of that day is represented as Y. Let T represent the length of the time series within the daily time window. T takes a value of 24. Then: (1) The standard deviation index of the total wind power output sequence corresponding to that day can be calculated according to the following formula: ,in, This represents the average value of the daily power output sequence; similarly, the standard deviation of the total photovoltaic power output sequence for that day can be calculated. and the standard deviation of the combined wind and solar power output sequence on that day. Then the standard deviation complementarity rate on that day It can be calculated using the following formula. (2) The range index of the total wind power output sequence on that day can be calculated according to the following formula. : Similarly, the range index corresponding to the total photovoltaic output sequence on that day can be calculated. and the range index corresponding to the combined wind and solar power output sequence on that day. Then the range complementarity rate on that day It can be calculated using the following formula. (3) The slope index corresponding to the total wind power output sequence on that day can be calculated according to the following formula. : ,in, Let be the slope of wind power output at adjacent time points, and h be the time difference between adjacent outputs; similarly, the slope index corresponding to the total photovoltaic power output sequence on that day can be calculated. and the slope index corresponding to the combined wind and solar power output sequence on that day. Then the slope complementarity rate on that day It can be calculated using the following formula. (4) The Richard-Beck flicker index corresponding to the total wind power output sequence on that day can be calculated according to the following formula. : Similarly, the Richard-Beck flicker index corresponding to the total photovoltaic power output sequence on that day can be calculated. And the Richard-Beck flicker index corresponding to the combined wind and solar power output sequence on that day. The Richard-Baker flicker index complementarity ratio for that day It can be calculated using the following formula. (5) The first-order difference index of the total wind power output sequence on that day can be calculated according to the following formula. : Similarly, the first-order difference index of the total photovoltaic power output sequence for that day can be calculated. and the first-order difference index of the combined wind and solar power output sequence on that day. Then the first-order difference complementarity rate on that day It can be calculated using the following formula. (6) The process line length index corresponding to the total wind power output sequence on that day can be calculated according to the following formula. : Similarly, the process line length index corresponding to the total photovoltaic power output sequence on that day can be calculated. And the process line length index corresponding to the combined wind and solar power output sequence on that day. Then the first-order difference complementarity rate on that day It can be calculated using the following formula. .

[0056] For the monthly time-scale evaluation sub-indicator, the total wind power output sequence and the total photovoltaic power output sequence are divided into days. Within a certain monthly time window, T represents the length of the time series within that month, with T taking a value of 30 or 31. At the monthly time scale, the original hourly output sequence undergoes time-scale transformation, and the hourly output data for each day is aggregated to obtain the corresponding daily-scale output value, which characterizes the wind or photovoltaic power generation for that day. Furthermore, following the same calculation method as the daily time-scale evaluation sub-indicator, the time index x... t The output value corresponding to day t is redefined and used as input to calculate evaluation sub-indicators such as standard deviation complementarity, range complementarity, slope complementarity, Richard-Beck flicker index complementarity, first-order difference complementarity, and process line length complementarity, thereby obtaining evaluation indicators on a monthly time scale.

[0057] For the annual time-scale evaluation sub-indicators, the total wind power output sequence and the total photovoltaic power output sequence are divided into ten-day periods. Within a given annual time window, T represents the length of the time series for that year, with T set to 36 (36 ten-day periods in a year). At the annual time scale, the original hourly output sequence undergoes time-scale transformation, and the hourly output data for each day is aggregated to obtain the corresponding ten-day scale output value. This ten-day scale output value characterizes the wind or photovoltaic power generation for that ten-day period. Furthermore, following the same calculation method as the daily time-scale evaluation sub-indicators, the time index x... t The output value corresponding to the t-th ten-day period is redefined and used as input to calculate evaluation sub-indicators such as standard deviation complementarity, range complementarity, slope complementarity, Richard-Beck flicker index complementarity, first-order difference complementarity, and process line length complementarity, thereby obtaining evaluation indicators on a monthly time scale.

[0058] Step 207: Iteratively update the configuration strategy set according to the complementarity evaluation index to obtain the updated configuration strategy set, and return to execute the step of "determining the third wind power output sequence and the third photovoltaic power output sequence corresponding to each regional group under each configuration strategy according to the second wind power output sequence and the second photovoltaic power output sequence" until the number of iterations of the configuration strategy set reaches the preset number.

[0059] Specifically, in one iteration, the complementarity evaluation index is used as the optimization target to update multiple configuration strategies in the configuration strategy set, resulting in an updated configuration strategy set. Step 205 is then executed until the number of iterations of the configuration strategy set reaches the preset number.

[0060] Step 208: Select a configuration strategy from the configuration strategy set at the end of the iteration as the target strategy.

[0061] Optionally, a configuration strategy can be selected as the target strategy from the configuration strategy set at the end of the iteration, including: selecting the configuration strategy with the highest complementarity evaluation index from the configuration strategy set at the end of the iteration as the target strategy; or, selecting the configuration strategy with the lowest power shortage rate from the configuration strategy set at the end of the iteration as the target strategy.

[0062] The power shortage rate refers to the percentage of time or energy during which the total output of a new energy power generation system is lower than the load demand, and is used to characterize the degree of power supply insufficiency of the system under this configuration strategy.

[0063] Specifically, the configuration strategy with the highest complementarity evaluation index value in the configuration strategy set at the end of the iteration is selected as the target strategy; or, based on the hourly comparison between the total output sequence of the new energy power generation system and the load demand curve, when the total output is less than the load demand at a certain moment, it is considered that there is a power shortage at that moment. By statistically analyzing the proportion of power shortage periods or the proportion of power shortage, the power shortage rate under the corresponding configuration strategy is obtained, and the configuration strategy with the lowest power shortage rate in the configuration strategy set at the end of the iteration is selected as the target strategy.

[0064] In one specific implementation, the complementarity evaluation index includes at least three evaluation sub-indicators. Each evaluation sub-indicator includes: standard deviation complementarity rate, range complementarity rate, slope complementarity rate, Richard-Beck flicker index complementarity rate, first-order difference complementarity rate, and process line length complementarity rate. The values ​​of each evaluation sub-indicator can be averaged to obtain the comprehensive complementarity evaluation index value corresponding to each configuration strategy. The configuration strategy with the largest comprehensive complementarity evaluation index value is taken as the target strategy.

[0065] In one specific implementation, the complementarity evaluation index includes at least three evaluation sub-indicators. Each evaluation sub-indicator includes: standard deviation complementarity rate, range complementarity rate, slope complementarity rate, Richard-Beck flicker index complementarity rate, first-order difference complementarity rate, and process line length complementarity rate. Then, for each complementarity rate index, the complementarity rate index corresponding to all configuration strategies can be ranked, and the configuration strategy ranked first can be determined as the dominant configuration strategy corresponding to that complementarity rate index. The number of times each configuration strategy becomes the dominant configuration strategy is counted, and the configuration strategy with the most times is taken as the target strategy.

[0066] In this embodiment, historical meteorological data from each selectable region is processed to determine wind power output sequences and photovoltaic output sequences under unit installed capacity conditions. This allows the wind and solar power output characteristics of each selectable region to be characterized by a unified standard. Based on this, the selectable regions are clustered, grouping regions with similar wind and solar power output characteristics into the same group, thereby reducing the scale of decision variables in the optimization problem and minimizing redundant computation. Furthermore, a configuration strategy set containing multiple regional grouping wind and solar power capacity allocation schemes is constructed based on the total installed capacity constraint. This enables the system to perform a global search under various feasible capacity configuration combinations, improving the diversity and coverage of configuration schemes. Simultaneously, by introducing a complementarity evaluation index to iteratively update the configuration strategy set, different configuration strategies can be optimized and adjusted based on the complementary characteristics of wind and solar power output, thereby enhancing the smoothness and fluctuation suppression capability of the overall system output. Finally, the configuration strategy with the best complementarity or the best power supply reliability is selected from the configuration strategy set after iteration as the target strategy. This achieves optimized allocation of wind and solar resources in the new energy power generation system under the installed capacity constraint conditions, reducing new energy output fluctuations and improving the stability and economy of system operation.

[0067] Figure 3 This is a schematic diagram of the configuration device of the new energy power generation system provided in an embodiment of the present invention, as shown below. Figure 3 As shown, the device includes: The first sequence determination module 301 is used to determine the first wind power output sequence and the first photovoltaic power output sequence corresponding to each optional region under the unit installed capacity condition, based on the historical meteorological data of each optional region in the new energy power generation system. Clustering module 302 is used to cluster each selectable region according to the first wind power output sequence and the first photovoltaic power output sequence, and to group each selectable region according to the clustering results to obtain multiple region groups; The configuration strategy generation module 303 is used to generate a configuration strategy set based on the total installed capacity of the new energy power generation system and multiple regional groups. Each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each regional group. The target strategy determination module 304 iteratively updates the configuration strategy set based on the complementarity evaluation index, and selects a configuration strategy from the configuration strategy set at the end of the iteration as the target strategy.

[0068] In one embodiment, the first sequence determination module 301 is specifically used for: Determine whether the historical meteorological data for each selectable area is complete; For selectable areas with complete historical meteorological data, the first wind power output sequence and the first photovoltaic power output sequence corresponding to the selectable area under the condition of unit installed capacity are determined based on the historical meteorological data of the selectable area. For optional areas with incomplete historical meteorological data, based on the historical meteorological data of the optional area, determine the partial wind power output sequence and partial photovoltaic power output sequence corresponding to the optional area under the condition of unit installed capacity, and determine the wind power probability distribution model matching the partial wind power output sequence and the photovoltaic probability distribution model matching the partial photovoltaic power output sequence; The first wind power output sequence of the selectable area under the condition of unit installed capacity is generated based on the wind power probability distribution model, and the first photovoltaic output sequence of the selectable area under the condition of unit installed capacity is generated based on the photovoltaic probability distribution model.

[0069] In one embodiment, the target strategy determination module 304 iteratively updates the configuration strategy set based on a complementarity evaluation index, including: Based on the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region, determine the second wind power output sequence and the second photovoltaic power output sequence corresponding to each region group under the unit installed capacity condition; Based on the second wind power output sequence and the second photovoltaic power output sequence, determine the third wind power output sequence and the third photovoltaic power output sequence corresponding to each regional group under each configuration strategy; Based on the third wind power output sequence and the third photovoltaic power output sequence, determine the complementary evaluation index corresponding to each configuration strategy; The configuration strategy set is iteratively updated based on the complementarity evaluation index to obtain the updated configuration strategy set. Then, the process of "determining the third wind power output sequence and the third photovoltaic output sequence corresponding to each regional group under each configuration strategy" is returned to execute until the number of iterations of the configuration strategy set reaches the preset number.

[0070] In one embodiment, based on the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region, a second wind power output sequence and a second photovoltaic power output sequence corresponding to each region group under the unit installed capacity condition are determined, including: For any given regional group, the first wind power output sequence corresponding to each selectable region within the regional group is weighted and fused to obtain the second wind power output sequence corresponding to the regional group. The first photovoltaic power output sequence corresponding to each selectable region within the regional group is weighted and fused to obtain the second photovoltaic power output sequence corresponding to the corresponding regional group.

[0071] In one embodiment, based on the third wind power output sequence and the third photovoltaic power output sequence, a complementarity evaluation index corresponding to each configuration strategy is determined, including: Based on the third wind power output sequence and the third photovoltaic power output sequence, determine the total wind power output sequence and total photovoltaic power output sequence of the new energy power generation system under each configuration strategy; Based on the total wind power output sequence and the total photovoltaic power output sequence, the complementarity evaluation index corresponding to each configuration strategy is determined.

[0072] In one embodiment, the complementarity evaluation index includes at least three evaluation sub-indicators, and the time scales of any two evaluation sub-indicators are different. The evaluation sub-indicators include at least one of the following: standard deviation complementarity, range complementarity, slope complementarity, Richard-Beck flicker index complementarity, first-order difference complementarity, and process line length complementarity.

[0073] In one embodiment, the target policy determination module 304 selects a configuration policy as the target policy from the configuration policy set at the end of the iteration, including: The configuration strategy with the highest complementarity evaluation index at the end of the iteration is taken as the target strategy. Alternatively, the configuration strategy with the lowest power shortage rate in the configuration strategy set at the end of the iteration can be taken as the target strategy.

[0074] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is merely an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the functional modules described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0075] The apparatus of this invention processes historical meteorological data from various selectable regions to determine wind power and photovoltaic (PV) output sequences under unit installed capacity conditions. This allows the wind and solar power output characteristics of each selectable region to be characterized by a unified standard. Based on this, the selectable regions are clustered, grouping regions with similar wind and solar power output characteristics into the same group, thereby reducing the scale of decision variables in the optimization problem and minimizing redundant computation. Furthermore, a configuration strategy set containing multiple regional groupings of wind and solar capacity allocation schemes is constructed based on the total installed capacity constraint. This enables the system to perform a global search under various feasible capacity configuration combinations, improving the diversity and coverage of configuration schemes. Simultaneously, by introducing a complementarity evaluation index to iteratively update the configuration strategy set, different configuration strategies can be optimized and adjusted based on the complementary characteristics of wind and solar power output, thereby enhancing the smoothness and fluctuation suppression capability of the overall system output. Finally, a configuration strategy is selected as the target strategy from the configuration strategy set after iteration, achieving optimized allocation of wind and solar resources in the new energy power generation system under the installed capacity constraint conditions, reducing new energy output fluctuations, and improving the stability of system operation.

[0076] The following is for reference. Figure 4 It shows a schematic diagram of the structure of a computer system 400 suitable for implementing an electronic device according to embodiments of the present invention. Figure 4 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0077] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 402 or programs loaded from storage section 408 into random access memory (RAM) 403. The RAM 403 also stores various programs and data required for the operation of the computer system 400. The CPU 401, ROM 402, and RAM 403 are interconnected via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0078] The following components are connected to I / O interface 405: input section 406 including keyboard, mouse, etc.; output section 407 including cathode ray tube, liquid crystal display, etc., and speakers, etc.; storage section 408 including hard disk, etc.; and communication section 409 including network interface card, such as modem, etc. Communication section 409 performs communication processing via a network such as the Internet. Drive 410 is also connected to I / O interface 405 as needed. Removable media 411, such as disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 410 as needed so that computer programs read from them can be installed into storage section 408 as needed.

[0079] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit (CPU) 401, it performs the functions defined above in the system of this invention.

[0080] It should be noted that the computer-readable medium shown in this invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical fiber, a portable compact disk read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, etc., or any suitable combination thereof.

[0081] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0082] The modules and / or units described in the embodiments of the present invention can be implemented in software or hardware. The described modules and / or units can also be housed in a processor; for example, a processor can be described as including a first sequence determination module, a clustering module, a configuration policy generation module, and a target policy determination module. The names of these modules do not necessarily limit the module itself.

[0083] In another aspect, the present invention also provides a computer-readable medium, which may be included in the device described in the above embodiments; or it may exist independently and not assembled into the device. The computer-readable medium carries one or more programs, which, when executed by the device, cause the device to include: Based on historical meteorological data of each selectable region in the new energy power generation system, the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region under the condition of unit installed capacity are determined. Each selectable region is clustered according to the first wind power output sequence and the first photovoltaic power output sequence, and then grouped according to the clustering results to obtain multiple regional groups. Based on the total installed capacity of the new energy power generation system and the multiple regional groups, a configuration strategy set is generated. Each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each regional group. The configuration strategy set is iteratively updated based on the complementarity evaluation index, and a configuration strategy is selected from the configuration strategy set at the end of the iteration as the target strategy.

[0084] The technical solution of this invention processes historical meteorological data from various selectable regions to determine wind power output sequences and photovoltaic output sequences under unit installed capacity conditions. This allows the wind and solar power output characteristics of each selectable region to be characterized by a unified standard. Based on this, the selectable regions are clustered, grouping regions with similar wind and solar power output characteristics into the same group, thereby reducing the scale of decision variables in the optimization problem and minimizing redundant computation. Furthermore, a configuration strategy set containing multiple regional groupings of wind and solar power capacity allocation schemes is constructed based on the total installed capacity constraint. This enables the system to perform a global search under various feasible capacity configuration combinations, improving the diversity and coverage of configuration schemes. Simultaneously, by introducing a complementarity evaluation index to iteratively update the configuration strategy set, different configuration strategies can be optimized and adjusted based on the complementary characteristics of wind and solar power output, thereby enhancing the smoothness and fluctuation suppression capability of the overall system output. Finally, a configuration strategy is selected as the target strategy from the configuration strategy set after iteration, achieving optimized allocation of wind and solar resources in the new energy power generation system under the installed capacity constraint conditions, reducing new energy output fluctuations, and improving the stability of system operation.

[0085] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the configuration method of a new energy power generation system as provided in any embodiment of this invention.

[0086] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0088] It should be noted that the collection, use, storage, sharing, and transfer of user personal information involved in the technical solution of this invention all comply with the provisions of relevant laws and regulations, and require notification to the user and obtaining the user's consent or authorization. Where applicable, user personal information has undergone de-identification and / or anonymization and / or encryption technical processing. In addition, a corresponding operation entry is provided for the user to choose to agree to or reject the automated decision result; if the user chooses to reject, the process proceeds to the expert decision-making process.

[0089] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can occur depending on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A configuration method for a new energy power generation system, characterized in that, include: Based on historical meteorological data of each selectable region in the new energy power generation system, the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region under the unit installed capacity condition are determined. Clustering is performed on each selectable region based on the first wind power output sequence and the first photovoltaic power output sequence, and the selectable regions are grouped according to the clustering results to obtain multiple region groups; Based on the total installed capacity of the new energy power generation system and the multiple regional groups, a configuration strategy set is generated. Each configuration strategy in the configuration strategy set is used to characterize the wind power installed capacity and photovoltaic installed capacity of each regional group. The configuration strategy set is iteratively updated based on the complementarity evaluation index, and a configuration strategy is selected from the configuration strategy set at the end of the iteration as the target strategy.

2. The method according to claim 1, characterized in that, The step of determining the first wind power output sequence and the first photovoltaic power output sequence for each selectable region under unit installed capacity conditions, based on historical meteorological data of each selectable region in the new energy power generation system, includes: Determine whether the historical meteorological data for each selectable area is complete; For selectable areas with complete historical meteorological data, the first wind power output sequence and the first photovoltaic power output sequence corresponding to the selectable area under the condition of unit installed capacity are determined based on the historical meteorological data of the selectable area. For optional areas with incomplete historical meteorological data, based on the historical meteorological data of the optional area, determine the partial wind power output sequence and partial photovoltaic power output sequence corresponding to the optional area under the condition of unit installed capacity, and determine the wind power probability distribution model matching the partial wind power output sequence and the photovoltaic probability distribution model matching the partial photovoltaic power output sequence; The first wind power output sequence of the selectable area under unit installed capacity conditions is generated based on the wind power probability distribution model, and the first photovoltaic output sequence of the selectable area under unit installed capacity conditions is generated based on the photovoltaic probability distribution model.

3. The method according to claim 1, characterized in that, The iterative update of the configuration strategy set based on the complementarity evaluation index includes: Based on the first wind power output sequence and the first photovoltaic power output sequence corresponding to each selectable region, determine the second wind power output sequence and the second photovoltaic power output sequence corresponding to each region group under the unit installed capacity condition; Based on the second wind power output sequence and the second photovoltaic power output sequence, determine the third wind power output sequence and the third photovoltaic power output sequence corresponding to each regional group under each configuration strategy; Based on the third wind power output sequence and the third photovoltaic power output sequence, determine the complementarity evaluation index corresponding to each configuration strategy; The configuration strategy set is iteratively updated according to the complementarity evaluation index to obtain the updated configuration strategy set. Then, the process of "determining the third wind power output sequence and the third photovoltaic power output sequence corresponding to each regional group under each configuration strategy" is returned to execute until the number of iterations of the configuration strategy set reaches the preset number.

4. The method according to claim 3, characterized in that, Based on the first wind power output sequence and the first photovoltaic output sequence corresponding to each selectable region, determine the second wind power output sequence and the second photovoltaic output sequence corresponding to each region group under the unit installed capacity condition, including: For any given regional group, the first wind power output sequence corresponding to each selectable region within the regional group is weighted and fused to obtain the second wind power output sequence corresponding to the regional group. The first photovoltaic power output sequence corresponding to each selectable region within the region group is weighted and fused to obtain the second photovoltaic power output sequence corresponding to the corresponding region group.

5. The method according to claim 3, characterized in that, The step of determining the complementarity evaluation index corresponding to each configuration strategy based on the third wind power output sequence and the third photovoltaic power output sequence includes: Based on the third wind power output sequence and the third photovoltaic power output sequence, determine the total wind power output sequence and the total photovoltaic power output sequence of the new energy power generation system under each configuration strategy; Based on the total wind power output sequence and the total photovoltaic power output sequence, the complementarity evaluation index corresponding to each configuration strategy is determined.

6. The method according to claim 1, characterized in that, The complementarity evaluation index includes at least three evaluation sub-indicators, and the time scales of any two evaluation sub-indicators are different. The evaluation sub-indicators include at least one of the following: standard deviation complementarity, range complementarity, slope complementarity, Richard-Beck flicker index complementarity, first-order difference complementarity, and process line length complementarity.

7. The method according to claim 1, characterized in that, The step of selecting a configuration strategy as the target strategy from the configuration strategy set at the end of the iteration includes: The configuration strategy with the highest complementarity evaluation index at the end of the iteration is taken as the target strategy. Alternatively, the configuration strategy with the lowest power shortage rate in the configuration strategy set at the end of the iteration can be taken as the target strategy.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the configuration method of the new energy power generation system as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the configuration method of the new energy power generation system as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the configuration method of the new energy power generation system as described in any one of claims 1 to 7.

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