Energy storage configuration method considering robustness of wind and light storage system

Abstract

The invention discloses an energy storage configuration method considering the robustness of a wind and light storage system, and the method comprises the steps: presetting a basic structure frame of the wind and light storage system under the grid-connected operation; integrating the basic structure framework, the initial uncertain operation interval, and the operation constraint and the operation state variable of the energy storage system to obtain an energy storage configuration model of the wind and light storage system; a space-time budget constraint and a space cluster constraint are utilized to obtain an intermediate uncertain operation interval; adjusting the operation constraint of the energy storage system by adopting the middle uncertain operation interval to obtain the uncertain constraint of the wind and light storage system; under the constraint of uncertainty, the energy storage configuration model of the wind-solar storage system is adjusted on the basis of taking total cost minimization as a target function, and an energy storage robust configuration model of the wind-solar storage system is obtained; and solving the energy storage robust configuration model by adopting an improved sparrow algorithm to obtain energy storage configuration parameters of the wind and light storage system, and the energy storage capacity can be configured scientifically and economically.

Description

Technical Field

[0001] This application relates to the field of power system planning and operation technology, and relates to, but is not limited to, an energy storage configuration method that considers the robustness of wind-solar-storage systems. Background Technology

[0002] Enhancing system flexibility while ensuring stable grid operation and promoting the grid connection and consumption of new energy power generation is one of the important research directions at present. Centralized and large-scale wind and solar power plants constitute new energy power generation systems, utilizing nearby thermal power plants for stability support and accepting unified grid dispatch, which is currently the main consumption method. However, due to the intermittent, fluctuating, and uncertain output of wind and solar power (collectively referred to as "uncontrollability"), when their penetration rate is low, the new energy power generation system can balance its fluctuations by adjusting the output of traditional thermal power units. But as their proportion continues to increase, the lack of flexibility resources, difficulties in peak shaving, and huge pressure on real-time supply and demand balance of new energy power generation systems have become bottlenecks restricting the consumption of new energy, resulting in a serious "wind and solar curtailment" phenomenon.

[0003] In existing technologies, deterministic models (ignoring uncertainty) are usually used when planning energy storage, or only the fluctuations in wind power or photovoltaic output are considered, ignoring the superposition effect of load forecasting errors and wind and solar fluctuations, and lacking quantitative means to measure the coupling effect of uncertainties on both the "source-load" side. This leads to a serious disconnect between the configuration results and actual operating needs, either resulting in over-configuration of capacity and wasted investment, or under-configuration that fails to meet the requirements for safe operation of the system. Summary of the Invention

[0004] In view of this, the embodiments of this application provide an energy storage configuration method that considers the robustness of wind-solar-storage systems. The aim is to scientifically and economically configure energy storage capacity under the premise of fully considering the multiple uncertainties of wind and solar loads, so that it can ensure the safety and stability of the system in actual operation and maximize the economic benefits of carbon electricity.

[0005] The technical solution of this application embodiment is implemented as follows: This application provides an energy storage configuration method considering the robustness of wind-solar-storage systems. The method includes: pre-setting a basic structural framework for a wind-solar-storage system integrating wind power generation, photovoltaic power generation, and energy storage under grid-connected operation; integrating the basic structural framework, the initial uncertain operating range of the wind-solar-storage system, and the operating constraints and operating state variables of the energy storage system at each moment to obtain an energy storage configuration model for the wind-solar-storage system; compressing the initial uncertain operating range using spatiotemporal budget constraints and spatial cluster constraints to obtain an intermediate uncertain operating range; adjusting the operating constraints of the energy storage system using the intermediate uncertain operating range to obtain the uncertainty constraints of the wind-solar-storage system; adjusting the energy storage configuration model of the wind-solar-storage system under the uncertainty constraints, based on minimizing the total cost as the objective function, to obtain a robust energy storage configuration model for the wind-solar-storage system; and solving the robust energy storage configuration model using an improved sparrow algorithm to obtain the energy storage configuration parameters of the wind-solar-storage system.

[0006] The beneficial effects of the technical solutions provided in this application include at least the following: This application discloses an energy storage configuration method that considers the robustness of wind-solar-storage systems. On the one hand, by jointly modeling the uncertainties of wind, solar, and load, and introducing spatiotemporal budget constraints and spatial cluster constraints to tighten the initial uncertain operating range of the wind-solar-storage system, a precise and compact uncertainty description framework is constructed. This effectively improves the problem of over-conservatism caused by the reliance on extreme scenarios in traditional robust models. Furthermore, by combining uncertainty budget driven by confidence probability, objective statistical laws are used to replace human experience settings, significantly reducing the subjective defects of model construction. On the other hand, at the optimization solution level, an improved sparrow algorithm is used to efficiently handle high-dimensional nonlinear problems, greatly improving the solution efficiency. Through cost minimization analysis, the energy storage configuration and system operation are driven to achieve cost minimization within the safety boundary, thereby systematically completing the entire process enhancement from modeling, optimization to decision-making. In other words, this application improves upon the problems of overly conservative, subjective, and inefficient solutions in traditional robust models by jointly modeling uncertainties on the source and load sides, tightening the uncertainty set by spatiotemporal cluster constraints, and using confidence probability-driven uncertainty budgeting. It solves the problem using an improved sparrow algorithm and through total cost minimization analysis, as well as energy storage configuration and system operation. Thus, it scientifically and economically configures energy storage capacity while fully considering the multiple uncertainties of wind, solar, and loads. In actual operation, it can ensure system safety and stability while minimizing costs, i.e. maximizing the economic benefits of carbon electricity. Attached Figure Description

[0007] Figure 1 This application provides a schematic flowchart of an energy storage configuration method that considers the robustness of a wind-solar-storage system. Figure 2A schematic diagram of the grid-connected operation of a wind-solar-storage system provided in this application embodiment; Figure 3 A schematic diagram illustrating a sparrow algorithm solution provided in an embodiment of this application; Figure 4 To generate relevant output values ​​and electricity price prediction diagrams for typical days in four seasons using the robust energy storage configuration model of the wind-solar-storage system provided in the embodiments of this application; Figure 5 This is a schematic diagram of the uncertainty set corresponding to wind power, photovoltaic power, and load provided in the embodiments of this application; Figure 6 A comparative diagram showing the configuration schemes for this application considering time constraints and those not considering time constraints; Figure 7 This is a schematic diagram of the typical day configuration results for the four seasons under the spatial cluster constraints of this application. Figure 8 The number of uncertainties in this application and E pvsr Relationship diagram; Figure 9 This is a comparative diagram showing the difference between considering and not considering spatiotemporal effects in this application; Figure 10 This diagram illustrates the relationship between the uncertainty confidence probability and robustness of this application. Figure 11 This is a schematic diagram illustrating the impact of the confidence probability of this application on energy storage configuration; Figure 12 This is a schematic diagram of the ultra-short-term wind and solar power prediction curves of this application; Figure 13 This is a schematic diagram showing the fitting of wind speed distribution, wind speed probability density, light frequency distribution, and the values ​​of Beta distribution parameters for each time period in this application. Figure 14 This is a schematic diagram of the intraday optimized scheduling results of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0009] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0010] It should be noted that the terms "first, second, and third" used in the embodiments of this application are merely to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0011] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments of this application pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have a meaning consistent with their meaning in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0012] In related technologies, to reduce the dependence of new energy power generation systems on thermal power and to build a new type of power system in which renewable energy sources such as wind power and photovoltaics replace thermal power as the main power source, it is urgent to find a clean and efficient resource to meet the flexibility required for high-proportion grid connection of wind and solar power. Planning energy storage facilities and proposing optimized operation strategies for wind and solar power generation systems can more efficiently coordinate photovoltaic, wind turbine, generator set, energy storage equipment and other devices to achieve power matching in time and space, thereby peak shaving and valley filling, greatly increasing the amount of renewable power that can be connected, which is beneficial to building a green and smart grid.

[0013] Energy storage, as a highly efficient and convenient flexible resource, has been widely applied in new power systems. For systems primarily powered by wind and solar power, energy storage serves to: mitigate the reduction in spinning reserve capacity caused by flexibility upgrades; smooth out fluctuations in wind and solar power output, thereby improving their reliability; compensate for power forecasting errors, enhancing grid-friendliness of wind and solar power, and reducing curtailment rates. However, a mature market mechanism is currently lacking, and the high cost of energy storage configuration is the biggest obstacle to its development. Even with strong government support for the energy storage industry, continuous improvement of energy storage technology and security, as well as continuous optimization of energy storage application models, are necessary to fully realize its value and ensure its comprehensive development.

[0014] Based on this, embodiments of this application provide an energy storage configuration method that considers the robustness of wind-solar-storage systems, referring to... Figure 1 As shown, the method includes at least the following steps: Step 110: Preset the basic structural framework of the wind-solar-storage system that integrates wind power generation, photovoltaic power generation and energy storage system under grid-connected operation.

[0015] In some embodiments, grid-connected operation refers to the state in which power plants (units) or power users' electrical equipment are connected to the power grid. Here, in grid-connected operation mode, the basic structural framework of the wind-solar-storage system is based on DC and AC buses as the core of power collection. Specifically, the photovoltaic power generation outputs power through photovoltaic inverters, the wind power generation outputs power through generator-side and grid-side converters, and the energy storage system is connected to the system through bidirectional converters. These power conversion devices (photovoltaic power generation, wind power generation, and energy storage system) are all ultimately connected to the AC bus and connected to the public power grid through a main step-up transformer. The entire wind-solar-storage system is uniformly dispatched by a central control system, which intelligently controls the charging and discharging of the energy storage system based on grid commands and wind and solar power predictions, thereby smoothly transitioning the originally highly fluctuating combined wind and solar power output into a stable and controllable power source for grid transmission.

[0016] This can be used as a reference. Figure 2 As shown, the wind-solar-storage system is the power generation side, including: wind turbines (corresponding to wind power generation in the text), photovoltaic arrays (corresponding to photovoltaic power generation in the text), and an energy storage system. These are connected to a central common AC bus via their respective power electronic converters, such as DC-to-AC or AC-to-DC converters. Figure 2 The other side, as shown relative to the power generation side, also involves: conventional units (such as thermal power units) providing stable output support and spinning reserve, the upstream power grid, and local loads. Here, Figure 2 In the grid connection mode of the wind-solar-storage combined power generation system shown, conventional units provide stable power, renewable energy provides clean power, and the energy storage system smooths out the fluctuations of renewable energy and fills the valleys through flexible charging and discharging, ultimately ensuring that the entire system efficiently and stably delivers high-quality power to the local area and the grid.

[0017] In some embodiments, the infrastructure framework includes at least: a wind farm, a photovoltaic power station, a load side, conventional generating units, an energy storage system, and a tie line.

[0018] In some embodiments, the topological connections of the components within this infrastructure framework are clearly defined, including the interface configurations of key equipment such as wind farms, photovoltaic power generation (PV power plants), load sides, conventional units, and energy storage systems. Simultaneously, the basic operating parameter ranges for each unit are defined, such as the rated power of wind and photovoltaic power generation, the capacity of the energy storage system, and charging / discharging power limits. This provides an initial model basis for subsequent robustness analysis and energy storage configuration calculations. Within this infrastructure framework, the interaction between the wind-solar-storage system and the external power grid must also be considered, setting transmission power constraints for grid connection lines and the system's reference voltage level to ensure that the framework reflects the equipment characteristics in actual engineering scenarios while also meeting the needs of mathematical modeling and simulation calculations.

[0019] Step 120: Integrate the basic structural framework, the initial uncertain operating range of the wind, solar and energy storage system, and the operating constraints and operating state variables of the energy storage system at each moment to obtain the energy storage configuration model of the wind, solar and energy storage system.

[0020] In some embodiments, due to the inherent uncertainty and volatility of wind power generation, photovoltaic power generation, and load, they cannot be simply represented by deterministic values. A probabilistic model must be used to describe the uncertainty range of their predicted values; that is, a probabilistic model is needed to describe the initial uncertain operating range of the predicted values ​​of the wind-solar-storage system. For example, based on the predicted values ​​of wind power generation, photovoltaic power generation, and load from the probabilistic model, the three are analyzed using a probabilistic model to obtain the initial uncertain operating range of the predicted values ​​of the wind-solar-storage system.

[0021] The operational constraints of an energy storage system can include: power constraints, energy constraints, energy state evolution equations, and mutual exclusion constraints. Among them, power constraints mean that the charging and discharging power of the energy storage system cannot exceed the rated power; energy constraints mean that the state of charge (SOC) of the energy storage system must be within a safe range (e.g., 20%~90%) to prevent overcharging and over-discharging; energy state evolution equations are used to calculate the energy state of the energy storage system; mutual exclusion constraints mean that the energy storage system generally cannot charge and discharge at the same time.

[0022] It should be noted that the operating state variables of an energy storage system at any given moment may include the charging power and discharging power at that moment, as well as the state of charge (SOC) at that moment.

[0023] Thus, establishing an energy storage configuration model for a wind-solar-storage system means transforming the actual physical system into a mathematical problem framework that includes deterministic parameters, uncertainties, and operational constraints, laying a solid and reliable foundation for subsequent optimization modeling.

[0024] Step 130: Using spatiotemporal budget constraints and spatial cluster constraints, compress the initial uncertain operating interval to obtain the intermediate uncertain operating interval.

[0025] In some embodiments, spatiotemporal budget constraints and spatial cluster constraints can refer to the spatiotemporal budget constraints and spatial cluster constraints of wind, solar, and energy storage systems. The spatiotemporal budget constraint refers to the rigid limitations on power generation capacity imposed by both time (time budget) and geographical location (spatial budget). In the time dimension, wind and solar energy exhibit significant intermittency and volatility; wind does not blow constantly, and sunlight follows day-night and seasonal cycles. This constitutes an inherent, uncontrollable time budget for the system, preventing it from outputting power on demand like conventional power sources. In the spatial dimension, areas rich in wind and solar resources are often far from power load centers, such as large wind and solar bases in the western and northern parts of the country and urban clusters in the southeastern coastal areas. Electricity must be transmitted through transmission lines, i.e., across space, and the capacity and construction cost of these transmission channels constitute a strict spatial budget. The essence of this constraint is the mismatch between the spatiotemporal distribution of energy endowment and energy demand. Furthermore, the spatial cluster constraint of wind, solar, and energy storage systems refers to the technical and management challenges arising from the concentrated deployment of a large number of wind turbines, photovoltaic panels, and energy storage power stations in specific geographical areas with abundant resources for large-scale and economical development. This constraint stems from both the natural clustering of resources and the planning requirements for intensive development. However, this spatial clustering brings significant clustering effects: wind and solar resources across a wide area remain correlated, causing the output power of the entire cluster to experience simultaneous sharp fluctuations or sustained troughs, rather than independent random fluctuations in individual units. This makes system-level power forecasting and scheduling exceptionally complex, placing far higher demands on grid regulation capabilities and energy storage capacity than on decentralized development. Therefore, the core contradiction of spatial clustering constraints lies in the trade-off between economies of scale and the concentration of technological risks after clustering.

[0026] In one possible implementation, step 130 above can be achieved by following steps 1301 to 1304. Figure 1 (not shown in the image) Step 1301: Subtract the actual output value and the predicted output value of wind power generation in the initial uncertain operating range to obtain the wind power deviation.

[0027] Step 1302: Subtract the actual output value and the predicted output value of photovoltaic power generation in the initial uncertain operating range to obtain the photovoltaic deviation.

[0028] Step 1303: Subtract the actual output value and the predicted output value corresponding to the load in the initial uncertain operating range to obtain the load deviation.

[0029] Step 1304: Using spatiotemporal budget constraints and spatial cluster constraints, perform synergistic optimization on wind power deviation, photovoltaic deviation and load deviation to obtain the intermediate uncertain operating range.

[0030] In some embodiments, the wind force deviation can be calculated using the following formula (1): Formula (1); in, , as well as These represent the wind power generation units within the wind power generation system. The actual output (power), predicted output (power), and wind force deviation at time t; and These represent the wind power generation units within the wind power generation system. The minimum and maximum values ​​of the wind force deviation at time t. Correspondingly, the photovoltaic deviation can be calculated using the following formula (2): Formula (2); in, , as well as These represent the photovoltaic power generation units within the photovoltaic power generation system. The actual output value, predicted output value, and photovoltaic deviation at time t; and Representing photovoltaic power generation units The minimum and maximum values ​​of the photovoltaic deviation at time t. Correspondingly, the load deviation (the sum of all energy-consuming devices, users, or links in the wind-solar-storage system) can be calculated using the following formula (3): Formula (3); in, Indicates load The actual power consumed at time t; Indicates load The predicted power consumption at time t; Indicates load The load deviation at time t; and Representing loads respectively The minimum and maximum values ​​of the load deviation at time t.

[0031] Here, the wind power deviation, photovoltaic deviation, and load deviation can be normalized to obtain the deviation coefficient, thus unifying their deviation ranges. Secondly, spatial cluster constraints and spatiotemporal budget constraints are used to impose restrictions on the sum of the absolute values ​​of all deviations, forcing the deviations of wind power generation, photovoltaic power generation, and load to constrain each other spatially. For example, if the wind power deviation is large at a certain moment, then the photovoltaic deviation and load deviation at that moment will be relatively small to satisfy the balance of the total deviation. That is, the intermediate uncertain operating range can correspond to the intermediate uncertain range of wind power generation, the intermediate uncertain range of photovoltaic power generation, and the intermediate uncertain range of load output. Correspondingly, the intermediate uncertain range can be determined by referring to the following formula (4): Formula (4); in, This represents the intermediate uncertainty range of wind power generation. Indicates the intermediate uncertainty interval of photovoltaic power generation, This represents the intermediate uncertain range of load output; , , The introduced deviation coefficient characterizes the relative degree of deviation of the uncertainty. , , This represents the budget set for the sum of all deviation coefficients for the uncertainties in time period t; , , This indicates setting a budget for the sum of all deviation coefficients of the uncertain quantity within the period; These represent the total number of wind power generation devices, the total number of photovoltaic power generation devices, and the total load on the load side within the wind-solar-storage system, respectively.

[0032] This section uses wind power generation as an example to explain the intermediate uncertainty interval. Assume the predicted power output of wind farm i within the wind power generation area at time t is... Within the range, the interval of output of wind farm i at that moment is redescribed as follows: ;in ∈[0,1], representing the deviation coefficient, whose value reflects the tolerance of the decision scheme to the degree of uncertainty. When When the value is 0, it means that the actual output value of wind farm i is equal to the predicted output value, that is, the scheme does not allow any unpredictable fluctuations in wind farm i; when When the value is 1, it means that the actual output value has reached the boundary, that is, the worst output situation of wind farm i has occurred, and the scheme can allow this situation.

[0033] For wind farm i, the upper limit of the sum of the deviation coefficients for all time periods within its period T is given by an uncertain budget. Constraints are applied, and it can be known that... ∈[0,T]. In fact, this constraint reflects the time smoothing effect: for wind-solar-storage systems, the predicted values ​​at different time periods cannot simultaneously reach the boundary. When When the value is 0, it means that the error of the predicted power output value is not considered in the decision-making of wind farm i; when When T is taken, it means that the decision made can satisfy the condition that the predicted output value of wind farm i reaches the worst boundary in each time period.

[0034] When the wind, solar and energy storage system contains N W For each wind farm, the upper limit of the sum of the deviation coefficients of all wind farms at time t is given by an uncertain budget. Constraints are applied, and it can be known that... ∈[0, N W In fact, this constraint reflects the spatial clustering effect: at time t, the predicted power output values ​​of different wind, solar, and energy storage systems are unlikely to reach the boundary simultaneously. The value of determines the robustness of the decision: when When the value is 0, the energy storage configuration model of the wind-solar-storage system degenerates into a deterministic model, meaning that the decision-making does not consider the occurrence of prediction bias, and the system robustness is the worst; when Take N W When all wind farms experience the worst output conditions simultaneously, the energy storage configuration model tends to allocate more energy storage to meet the system's power balance requirements. In this case, the wind-solar-storage system has the highest robustness but the worst economic performance.

[0035] Therefore, in order to mitigate the conservatism of energy storage configuration models, an uncertainty budget is generally agreed upon. The confidence level is used to filter out events with extremely low probabilities from the uncertainty set. However, setting the uncertainty budget too subjectively can lead to results that lack objectivity. Therefore, the budget values ​​for the spatial and temporal uncertainties of wind, solar, and energy storage systems are constrained by the confidence probability of the uncertainty, in order to scientifically balance the robustness and economy of the system.

[0036] Taking wind power generation as an example, the formulas for time-uncertain budgets and space-uncertain budgets are (5): Formula (5); in, , These represent the confidence probabilities of spatial and temporal uncertainty budgets, respectively. The normalized standard deviation of wind speed used in wind power generation; It is the inverse function of the cumulative probability density function of the normal distribution.

[0037] Step 140: Using the intermediate uncertain operating range, adjust the operating constraints of the energy storage system to obtain the uncertainty constraints of the wind-solar-storage system.

[0038] Here, the intermediate uncertain operating range is integrated into the operating constraints of the energy storage system, deeply coupling uncertainty with system operation to form a new form of constraint that can withstand risks.

[0039] In some embodiments, uncertainty constraints may include: power balance constraints, conventional unit constraints, energy storage system constraints, spinning reserve constraints, grid-connected power constraints of wind, solar and energy storage systems, and carbon emission constraints. It should be noted that this is only an example and does not limit the specific content of uncertainty constraints.

[0040] Here, the power balance constraint ensures that, under any possible wind-solar-storage load fluctuation scenario, the real-time power generation (including uncontrollable wind power generation, photovoltaic power generation, and controllable conventional units and energy storage power generation) and power consumption (load and energy storage charging) of the wind-solar-storage system always maintain a dynamic balance. This power balance constraint can be expressed by the following formula (6): Formula (6); in, The total number of wind power generation (wind farm) devices representing a wind-solar-storage system. The total number of photovoltaic power generation devices representing a wind-solar-storage system. The total number of conventional units in a wind, solar, and energy storage system. Let be the power (maximum available output) of the i-th device in the conventional unit of the wind-solar-storage system at time t. The power of the load-side energy storage system; This refers to the power exchanged between the wind, solar, and energy storage system and the upstream power grid.

[0041] Conventional unit constraints ensure that conventional units such as thermal power plants remain within safe and technically permissible operating ranges when dealing with uncertain fluctuations. The corresponding characterization formula is shown in (7): Formula (7); in, , This represents the upper and lower limits of the output of the i-th unit in a conventional wind-solar-storage system, in MW. , This represents the maximum uphill / downhill ramp power of the i-th device in a conventional unit, in MW; Let be the power of the i-th device in the conventional unit of the wind-solar-storage system at time t-1.

[0042] Energy storage system constraints ensure that the energy storage operation strategy (charge and discharge power, SOC state) operates safely throughout the process of smoothing fluctuations, and that its operation strategy is sustainable (e.g., avoiding depletion of power at the beginning of dispatch, which would prevent the system from coping with fluctuations later). The corresponding constraints can be represented by formula (8): Formula (8); in, and These respectively characterize the upper and lower limits of safe operation of the energy storage system under its state of charge; This refers to the instantaneous state of charge (i.e., the current remaining power and rated capacity) of the energy storage system at time t. This refers to the charging and discharging power of the energy storage system at time t, i.e., the power of the load-side energy storage system. This refers to the rated power of the energy storage system. For the energy storage system to instantaneously store energy at time t; This refers to the rated capacity of the energy storage system. The self-discharge rate of the energy storage system within a scheduling period; For the energy storage system to store energy instantaneously at time t-1; and The charging efficiency and discharging efficiency of the energy storage system are characterized respectively.

[0043] The spinning reserve constraint ensures that the system maintains sufficient and readily available reserve resources during real-time operation to cope with unforeseen fluctuations (i.e., instantaneous deviations from the predicted values). The corresponding constraint characterizes that at any given time t, the total capacity of all dispatchable power generation resources (including those already activated and those in reserve) must be at least L% higher than the system's total predicted load. This excess is the spinning reserve capacity to cope with unexpected situations (such as sudden load increases or wind turbine shutdowns). This spinning reserve constraint can be represented by formula (9): Formula (9); in, The system's custom rotational reserve rate.

[0044] The grid-connected power constraint of the wind-solar-storage system ensures that the power exchange at the connection point between the entire wind-solar-storage system and the main grid fluctuates smoothly and does not exceed the transmission limit of the tie line. The corresponding formula is shown in formula (10) below: Formula (10); in, , This indicates the maximum and minimum power values ​​of the interconnecting lines within the wind-solar-storage system. , This represents the maximum and minimum ramp power of the interconnecting lines within the wind-solar-storage system, both in MW. This refers to the planned power exchange between the wind, solar, and energy storage system and the upstream power grid via a tie line at time t-1.

[0045] Carbon emission constraints incorporate environmental protection requirements into the optimization model in the form of hard constraints, ensuring that the total carbon emissions of the system are controlled within the preset target while coping with uncertainties. The corresponding constraint formula is shown in the following formula (11): Formula (11); in, It refers to the total carbon dioxide emissions generated by conventional units (such as coal-fired and gas-fired power plants) in a wind, solar and energy storage system within a specific accounting period. This refers to the maximum allowable carbon emissions or carbon emission quotas for wind, solar, and energy storage systems.

[0046] In some embodiments, step 140 above can be implemented by steps 1401 and 1402. Figure 1 (not shown in the image) Step 1401: Using the intermediate uncertain operating range, define the upper and lower limits corresponding to the operating constraints of the energy storage system.

[0047] Step 1402: Using the upper and lower limits, adjust the operating constraints of the energy storage system to obtain the uncertainty constraints of the wind-solar-storage system.

[0048] In some embodiments, firstly, the upper and lower boundaries of the intermediate uncertain operating range can be directly used to dynamically define the safe operating upper and lower limits of the energy storage system's charging and discharging power and energy storage capacity in each scheduling period, so that the energy storage's regulation capability is sufficient to smooth out power fluctuations within this range. For example, the power deficit corresponding to the upper boundary of the range is covered by the energy storage discharge capacity, and the power surplus corresponding to the lower boundary is absorbed by the energy storage charging capacity. Then, the energy storage operating limits (upper and lower boundaries) that are bound to the range and reflect the dynamic balance of the system are combined with the operating constraints of the energy storage system, such as balance constraints and line power flow constraints, to jointly construct a set of robust or chance constraint mathematical expressions, thereby ultimately forming an uncertainty constraint of the wind-solar-storage system that can be used for optimization solutions. This uncertainty constraint of the wind-solar-storage system can include multiple constraints as exemplified above, which will not be elaborated here.

[0049] Step 150: Under uncertainty constraints, based on maximizing the economic benefits of carbon electricity as the objective function, the energy storage configuration model of the wind-solar-storage system is adjusted to obtain the robust energy storage configuration model of the wind-solar-storage system.

[0050] In some embodiments, based on the original deterministic configuration model, namely the energy storage configuration model of the wind-solar-storage system, a robust energy storage configuration model for the wind-solar-storage system is constructed by introducing an uncertain constraint describing the uncertainty of wind and solar power output and load, and by introducing a robust optimization or partial robust optimization framework, with the objective function being the maximization of carbon electricity economic benefits including carbon trading revenue and electricity market revenue.

[0051] Here, acknowledging the uncertainty of the output of wind power, photovoltaic power, and energy storage, we design an optimal energy storage configuration scheme. In other words, we establish a robust energy storage configuration model for a wind-solar-storage system. This scheme must not only be economical but also be able to withstand all possible deviations between the actual output values ​​of wind power, photovoltaic power, and energy storage and the predicted values, ensuring the safe and stable operation of the system.

[0052] In some embodiments, step 150 above can be implemented by steps 1501 to 1503. Figure 1 (not shown in the image) Step 1501: Add the total life cycle cost of the energy storage system, the cost of conventional units, the cost of tie lines, the cost of tie line power fluctuation penalties, the cost of robustness penalties, and the cost of carbon emissions to obtain the total cost of the wind-solar-storage system.

[0053] Step 1502: Based on the total cost of the wind, solar and energy storage system, construct a function with the objective of minimizing the total cost.

[0054] Step 1503: Under uncertainty constraints, based on the objective function, adjust the operating parameters and configuration parameters of the energy storage configuration model of the wind-solar-storage system to obtain the robust energy storage configuration model of the wind-solar-storage system.

[0055] In some embodiments of this application, it is assumed that the wind-solar-storage system includes multiple wind farms and photovoltaic power stations, and an energy storage power station is built at the convergence point to receive unified grid dispatch. The objective function of the robust energy storage configuration model of the wind-solar-storage system is then constructed. For reference, see formula (12): Formula (12); in, This represents the total cost of a wind, solar, and energy storage system. This represents the total life-cycle cost of an energy storage system; This indicates the operating cost of a conventional generating unit; The term "tethering cost" refers to the cost or benefit incurred by the wind, solar, and energy storage system in exchanging power (purchasing or selling electricity) with the upstream power grid through the tie line. This represents the cost of penalizing power fluctuations in tie lines. In order to maintain grid stability, it penalizes severe fluctuations in tie line power to promote smooth power output within the system. For the cost of robust penalty or the cost of penalty for curtailing wind and solar power; Cost of carbon emissions; Indicates an uncertain operating range in the middle; This represents the set of human decision-making quantities. Each cost can be specifically represented by the following formula (13): Formula (13); in, , , This indicates the unit power investment cost, capacity investment cost, and unit operation and maintenance cost of the energy storage system. , , This indicates the inflation rate, discount rate, and energy storage lifespan of the energy storage system. , This indicates the power and capacity of the energy storage system; , , Indicates the fuel cost coefficient; , Indicates the threshold effect coefficient; This represents the connection line transaction coefficient, which numerically reflects the higher-level demand management. This represents the robustness penalty coefficient, which corresponds to the penalty cost of increasing energy storage capacity. This indicates the agreed transmission power of the tie line, which is usually taken as the average value over a certain period. This represents the penalty coefficient for fluctuations in the connecting line; This indicates the base price for carbon emission trading; Indicates the price increase rate; This indicates the range of carbon emission allowances.

[0056] Correspondingly, the objective function for the intraday operation optimization of the wind-solar-storage system It can be expressed using formula (14): Formula (14); in, The penalty cost for output deviation can be represented by formula (15): Formula (15); in, , , , , , These represent the deviation penalty cost coefficients for wind power generation, photovoltaic power generation, and load, respectively. ; , , These represent the actual output or consumption values ​​of wind power generation, photovoltaic power generation, and load at time t, respectively, in MW; , , This represents the predicted output or consumption value of wind power generation, photovoltaic power generation, and load at time t, in MW.

[0057] Here, the output deviation penalty cost aims to quantify and penalize the economic risks arising from the deviation between the actual output and the predicted values ​​of wind, solar, and load. This cost item uses asymmetric pricing for the power deviations of wind power, solar power, and load: when the actual value is higher than the predicted value, it may lead to power surplus, wind and solar curtailment, or the need to reduce the output of conventional units, requiring a specific penalty unit price (e.g., ...). , , ) Calculate the cost of the excess; conversely, when the actual value is lower than the predicted value, it may lead to power shortages, require the emergency use of expensive backup resources, or result in a load shedding penalty, necessitating the use of a different unit price (e.g., : , , ) Calculate the cost of the shortfall. This asymmetric penalty mechanism forces the optimization model to carefully assess forecast uncertainty while seeking economic benefits, in order to develop a more robust operating strategy that is insensitive to deviations.

[0058] In some embodiments, an energy storage configuration and operation strategy X can be sought to maximize the net carbon electricity economic benefit of the entire wind-solar-storage system even under the most unfavorable wind and solar load fluctuation scenario. The robust energy storage configuration model of the wind-solar-storage system perfectly unifies the three major objectives of robustness, economy, and environmental protection, providing a scientific basis for investment decisions.

[0059] Step 160: Use the improved sparrow algorithm to solve the robust energy storage configuration model and obtain the energy storage configuration parameters of the wind-solar-storage system.

[0060] Here, traditional swarm intelligence algorithms are prone to premature convergence and slow convergence due to the difficulty and low computational efficiency in solving large-scale mixed-integer nonconvex nonlinear models. Therefore, this application proposes an improved sparrowsearch algorithm (ISSA), which intelligently searches for the optimal energy storage configuration parameters under the objective function within a solution space that satisfies all robust constraints by simulating the foraging and anti-predation behaviors of sparrow populations. At the same time, it integrates Levy flight and adaptive differential evolution mechanisms to improve global optimization ability and convergence speed, thereby achieving fast and stable solutions to high-dimensional complex energy storage configuration models.

[0061] In one possible implementation, before performing step 160 above, the following step A1 may be performed: Step A1: Using the Lagrange multiplier method, based on the number of renewable energy power plants in the wind-solar-storage system, the predicted output value of the renewable energy power plants, and the output deviation coefficient of the renewable energy power plants, the uncertainty constraints in the robust energy storage configuration model of the wind-solar-storage system are transformed into deterministic constraints.

[0062] Here, because some constraints (especially the spinning reserve constraint) in the robust energy storage configuration model of the wind, solar and energy storage system obtained in step 150 contain uncertain variables and cannot be solved directly, it is necessary to transform those constraints containing unknown fluctuations (uncertainties) in the robust energy storage configuration model of the wind, solar and energy storage system into completely deterministic and computable constraints.

[0063] For example, the rotational spare constraint containing uncertain variables is transformed into a deterministic constraint formula (16): Formula (16); in, The sum represents the number of renewable energy power plants; This represents the maximum deviation between the actual output value and the predicted output value of the i-th renewable energy power station at time t. This represents the maximum deviation between the actual load value and the predicted load value of the i-th loadable element at time t; This represents the predicted output value of the i-th renewable energy power station within the renewable energy power station at time t; This represents the predicted load value. This represents the output deviation coefficient of the i-th renewable energy power plant at time t. The deviation coefficient represents the load.

[0064] By processing formula (16) according to linear duality theory and the Lagrange multiplier method, formula (17) is obtained. Therefore, the rotational spare constraint is finally transformed into formula (17): Formula (17).

[0065] in, and All are robust adjustment parameters, and Let be the conservatism parameter at time t (downward uncertainty budget parameter). Let be the optimism parameter at time t (upward uncertainty budget parameter).

[0066] After converting the uncertain constraints into deterministic constraints, the improved sparrow algorithm is used to solve the robust energy storage configuration model under deterministic constraints, and the energy storage configuration parameters of the wind-solar-storage system are obtained.

[0067] Before solving the robust energy storage configuration model using the improved sparrow algorithm, to ensure model accuracy, one possible implementation uses the probability of violating safety reserve constraints as a robustness quantification index to evaluate the robust energy storage configuration model of the wind-solar-storage system, obtaining the evaluation results. Based on the evaluation results, the model parameters of the robust energy storage configuration model of the wind-solar-storage system are adjusted to obtain the adjusted robust energy storage configuration model of the wind-solar-storage system. In this way, the robustness of the energy storage configuration model is transformed from a qualitative design goal into a quantifiable and optimizable specific index. By calculating the probability of the model configuration scheme violating safety reserve constraints in actual operation, an accurate assessment of the actual risk resistance capability of the configuration scheme can be achieved. Based on the evaluation results, key parameters in the model (such as robustness conservatism, reserve rate requirements, or cost weights) can be further adjusted in a targeted manner, thereby guiding the optimization model to make a more scientific trade-off between economy and safety, ultimately generating an energy storage configuration scheme that is neither overly conservative nor overly aggressive, and whose robustness level is more in line with engineering practice.

[0068] For example, considering only one uncertainty factor, the robustness quantification index of energy storage configuration For formula (18): Formula (18); Robustness Quantitative Indicators Considering Two Uncertain Factors For formula (19): Formula (19); Robustness Quantitative Indicators Considering Three Uncertain Factors For formula (20): Formula (20); Based on the established robustness index, the model proposed in this application is verified in both robustness and economy to demonstrate its superiority. At the same time, the energy storage configuration results under different confidence probability conditions are analyzed to verify the impact of confidence probability on the model's economy and robustness, providing decision-makers with a scientific and reasonable basis for decision-making.

[0069] Correspondingly, after performing step A1 above, step 160 can be achieved through the following step A2: Step A2: Under deterministic constraints, the improved sparrow algorithm is used to solve the robust energy storage configuration model to obtain the energy storage configuration parameters of the wind-solar-storage system.

[0070] In some embodiments, the improved sparrow algorithm is an algorithm that integrates the Levy flight strategy and the adaptive differential evolution mechanism, and step A2 above can be implemented through the following process: First, under deterministic constraints, the configuration space of multiple configuration parameters of the robust configuration model for energy storage is determined.

[0071] In some embodiments, an improved sparrow algorithm (an algorithm that integrates the Levy flight strategy and the adaptive differential evolution mechanism) can be first used to initialize the population of the energy storage robust configuration model to obtain multiple configuration parameters. Then, under deterministic constraints, the configuration space of the multiple configuration parameters is determined.

[0072] Secondly, an improved Levy flight strategy based on the sparrow algorithm and an adaptive differential evolution mechanism are adopted. Based on the fitness of multiple configuration parameters, multiple configuration parameters are iteratively optimized in the configuration space until the maximum number of iterations is reached.

[0073] The fitness of the configuration parameters is the value obtained by evaluating the robust energy storage configuration model with configuration values ​​substituted with the configuration parameters according to the preset evaluation index.

[0074] In some embodiments, discoverer parameters, follower parameters, and vigilant parameters can be distinguished from multiple configuration parameters based on fitness. Then, the Levy flight strategy is used to iteratively update the configuration values ​​corresponding to the discoverer parameters, and an adaptive differential evolution mechanism is used to perform mutation, crossover, and selection operations on the configuration values ​​corresponding to the follower parameters and vigilant parameters to iteratively update the configuration values ​​corresponding to the follower parameters and vigilant parameters until the maximum number of iterations is reached.

[0075] Then, the configuration values ​​corresponding to multiple configuration parameters under the maximum number of iterations will be used as the energy storage configuration parameters of the wind-solar-storage system.

[0076] For example, at the start of the algorithm, a population initialization is performed on the robust energy storage configuration model within a predefined configuration space, resulting in multiple configuration parameters (individual sparrows). The position vector of each individual sparrow represents the configuration value corresponding to a complete configuration parameter. The configuration space is determined by the upper and lower bounds of the decision variables. This configuration space is pre-set based on factors such as engineering practice experience, equipment manufacturing level, and project budget, ensuring the scientific validity and feasibility of the initial solution. After determining the configuration space for multiple configuration parameters, as... Figure 3 As shown, an improved sparrow algorithm is used to distinguish discoverer parameters, follower parameters, and vigilant parameters from multiple configuration parameters based on fitness. The Levy flight strategy is used to iteratively update the configuration values ​​corresponding to the discoverer parameters, and an adaptive differential evolution mechanism is used simultaneously to perform mutation, crossover, and selection operations on the configuration values ​​corresponding to the follower parameters and vigilant parameters to iteratively update the configuration values ​​corresponding to the follower parameters and vigilant parameters until the maximum number of iterations is reached.

[0077] For example, in the transformed deterministic objective function, the total cost of the wind-solar-storage system is the sole criterion for evaluating the merits of the scheme. For each individual (i.e., the configuration values ​​corresponding to a set of configuration parameters), its variable values ​​are substituted into the deterministic model to calculate its total cost under the worst-case scenario. Simultaneously, it is checked whether all deterministic constraints are satisfied; if violated, a significant penalty is imposed. This process yields the fitness of each individual. From the population, the top n individuals with the highest fitness are selected as discoverer parameters, representing the currently found optimal solution; most of the remaining individuals become follower parameters. They search around the discoverer parameters, especially near the current optimal solution, for local development. m individuals are randomly selected from the population as watchdog parameters. The configuration values ​​of the discoverer parameters are updated using the Levy flight strategy, and the configuration values ​​of the follower and watchdog parameters are updated using an adaptive differential evolution mechanism. The algorithm stops when it reaches the maximum number of iterations, and the configuration values ​​corresponding to multiple configuration parameters at the maximum number of iterations are selected as the energy storage configuration parameters of the wind-solar-storage system.

[0078] The study compares the results obtained under scenarios that do not consider spatiotemporal effects, only consider time smoothing effects, only consider spatial effects, and consider spatiotemporal coupling effects. Based on the established robustness index, the robust energy storage configuration model of the wind-solar-storage system proposed in this application is verified in both robustness and economy to demonstrate its superiority. Simultaneously, the energy storage configuration results under different confidence probability conditions are analyzed to verify the impact of confidence probability on the economy and robustness of the robust energy storage configuration model of the wind-solar-storage system, providing decision-makers with a scientific and reasonable basis for decision-making. Furthermore, by comparing the energy storage configuration model considering both source and load uncertainties with the model considering only source-side uncertainties, the study demonstrates that the model considering both uncertainties has higher rationality. The carbon-electricity economy (i.e., total cost) of the system is analyzed to provide a reference for energy storage configuration of systems with different thermal power ratios. Finally, based on the aforementioned energy storage configuration results, the energy storage charging and discharging strategy, conventional unit output plan, and tie-line power plan are refined and optimized to verify the effectiveness of the model proposed in this application.

[0079] Here, a specific embodiment can be used to illustrate the robust energy storage configuration model of the wind-solar-storage system provided in the above-described embodiments of this application. However, it is worth noting that this specific embodiment is only for better illustration of this application and does not constitute an improper limitation of this application. That is, the robust energy storage configuration model of the wind-solar-storage system provided in the embodiments of this application can be applied to... Figure 2 Under the basic structural framework of the wind-solar-storage system shown, the energy storage configuration parameters of the wind-solar-storage system are predicted, and the corresponding results are as follows: Figure 4The figure shows the power output and electricity price predictions for typical days in all four seasons using the robust energy storage configuration model of the wind-solar-storage system provided in this application embodiment. For visual clarity, the four prediction curves are displayed on a single graph. Since there is no temporal continuity between typical days in the four seasons, this allows for a direct comparison of the time-of-use electricity prices (in yuan·MW) corresponding to the curve shapes of wind power predicted output (expressed in terms of the power involved), photovoltaic predicted output, and load predicted output over different time periods. -1 (differences)

[0080] At the same time, refer to Figure 5 As shown, Figure 5 'a' represents the predicted power output curve for wind power generation. Figure 5 b is the predicted power output curve corresponding to photovoltaic power generation, and Figure 5 c is the predicted output curve corresponding to the load output, from Figure 5 a to Figure 5 From c, we can conclude that time constraints can shrink the uncertainty set of wind and solar loads. Correspondingly, please refer to... Figure 6 The diagram shows a comparison of configuration parameters for robust energy storage configuration models of wind-solar-storage systems under and without time constraints, as provided in this application embodiment. Model 1 is the robust energy storage configuration model of a wind-solar-storage system considering time constraints, and Model 2 is the robust energy storage configuration model of a wind-solar-storage system without time constraints. Figure 6 From the perspectives of energy storage power and energy storage capacity, it can be seen that the total cost under the time smoothing effect (Model 1) is lower than the total cost without considering the time smoothing effect (Model 2).

[0081] like Figure 7 As shown, the configuration results for typical days of the four seasons under spatial clustering in this application are presented. It can be seen that as the number of uncertainties increases, the spatial clustering effect exerts a stronger constraint on the smoothing of uncertainties, the spatial uncertainty budget is larger, the volatility and randomness of each uncertainty can be better represented, and the total scheduling cost is reduced accordingly. Correspondingly, Figure 8 Give the number of uncertainties in this application and E pvsr A diagram illustrating the relationship between the two entities is provided for reference. Figure 8 As shown, as the number of uncertainties increases, the spatial uncertainty budget also increases. With the increase in the number of uncertainties, E... pvsr As the size decreases, the robustness of the system increases. Meanwhile, reference can be made to... Figure 9As shown, under the spatiotemporal effect, the tie line can transmit more power, and the charging and discharging depth of energy storage is less than that when only the spatial effect is considered. This is because the coupling effect of time smoothing and spatial clustering further reduces the fluctuation range of uncertain factors, thus making the system's demand for energy storage more realistic. It is worth noting that under the influence of time-of-use pricing, the tie line transmits more power during peak electricity consumption periods accompanied by energy storage discharge, and less power during off-peak electricity consumption periods accompanied by energy storage charging, which can effectively respond to demand-side management. Table 1 shows... Figure 9 The optimization results of the four models are shown.

[0082] Table 1 Optimization results of the four models Figure 10 It can be seen that when decision-makers adopt a conservative risk-taking attitude and select a larger confidence probability for the uncertainty set, E pvsr As the confidence probability decreases, the robustness of the system increases accordingly; among them, the decrease in violation probability is greatest when the confidence probability is in the range of 0 to 0.25. Figure 11 To increase the confidence probability of uncertainty based on results obtained on a typical day, the energy storage configuration cost increases accordingly, leading to a rise in the total system cost. This reflects the effect of E... pvsr It has a negative correlation with economic efficiency; decision-makers can use this to select an appropriate level of uncertainty confidence to achieve a reasonable balance between the economic efficiency and robustness of the solution.

[0083] Figure 12 The 6-hour power output curve for a typical day is used for ultra-short-term forecasting. Figure 13 This represents the wind speed probability distribution, wind speed probability density, light intensity, light intensity frequency, and Beta distribution parameters for a specific location in a given year. The example calculation follows a Weibull distribution, with a wind speed shape parameter of 2 and a scale parameter of 8. Figure 13 Figure b shows the scheduling results of intraday rolling optimization. The planned power of both conventional standby units is close to their lower operating limit, indicating that energy storage provides more spinning capacity within the flexibility resources, capable of meeting the fluctuating demands of wind and solar load uncertainties. (See also...) Figure 14 As shown, a schematic diagram of the intraday optimized scheduling results of this application is presented, with conventional unit 1, conventional unit 2 and energy storage charging / discharging power as examples.

[0084] In summary, the purpose of this application is to address the energy storage configuration deviation caused by the uncertain coupling of wind, solar, and loads by introducing a spatiotemporal clustering effect to constrain the uncertainty set and accurately characterize the fluctuations; through E pvsrThe system correlates indicators with confidence probabilities to quantify the balance between robustness and economic efficiency; incorporates uncertainties on both the source and load sides to improve modeling; integrates carbon-electricity economics to achieve "low-carbon-economic" synergy; and improves algorithms to enhance solution efficiency, ultimately providing an energy storage configuration method that is robust, economical, and environmentally friendly. Specifically, it achieves the following: 1. Addressing the difficulty in quantifying the coupled uncertainties between the source and load sides, leading to significant deviations between energy storage configuration results and actual operation. Existing technologies often only consider power fluctuations on the wind and solar sides, neglecting the superposition effect of load forecasting errors and source-side fluctuations, resulting in either overestimation or underestimation of energy storage capacity. This application addresses this issue by using joint probabilistic modeling on both the source and load sides, unifying the uncertainties of wind power, solar power, and load into a single robust optimization framework, significantly reducing configuration errors caused by one-sided approximations.

[0085] 2. Addressing the issues of overly conservative and poor economic efficiency in robust optimization models: Traditional robust optimization uses box-type uncertainty sets, resulting in excessively large uncertainty intervals and high solution conservatism. This application introduces a spatiotemporal clustering effect, using temporal smoothing constraints and spatial complementary constraints to adaptively compress the uncertainty set boundary, significantly reducing energy storage capacity and total system cost while ensuring safety margins.

[0086] 3. Addressing the issues of strong subjectivity and lack of scientific basis in determining uncertain budget values: Existing technologies typically rely on experience to set robust budgets, lacking quantitative basis. This application proposes using the probability of violating safety reserve constraints (Epvsr) as a robustness quantification indicator and provides a budget analytical formula based on confidence probability, enabling decision-makers to objectively determine the budget according to acceptable risk levels and avoid subjective assumptions.

[0087] 4. Addressing the challenges of solving large-scale mixed-integer nonconvex nonlinear models and their low computational efficiency: Traditional swarm intelligence algorithms are prone to premature convergence and slow convergence. This application proposes ISSA, which integrates Levy flight and adaptive differential evolution mechanisms to improve global optimization capabilities and convergence speed, enabling fast and stable solutions to high-dimensional complex energy storage configuration models.

[0088] 5. Addressing the challenge of balancing carbon electricity economic benefits and robustness in energy storage configuration schemes: Existing technologies often consider energy storage investment costs or renewable energy absorption rates in isolation, neglecting the impact of tiered carbon trading and time-of-use pricing on system economics. This application introduces tiered carbon trading costs and tie-line fluctuation penalties into the objective function to achieve synergistic optimization of carbon electricity economic benefits and robustness.

[0089] 6. Addressing the issue of insufficient rolling verification of configuration results and difficulty in guiding actual operation: Traditional methods only provide capacity / power at the planning level, without closed-loop verification of intraday operation strategies. This application constructs a configuration-operation phase model: the first phase outputs robust energy storage capacity; the second phase uses ultra-short-term forecasts to continuously optimize intraday energy storage, conventional units, and tie-line output, ensuring the effectiveness and economy of the scheme in actual operation.

[0090] It should be noted that, in the embodiments of this application, if the above-mentioned energy storage configuration method considering the robustness of wind-solar-storage systems is implemented in the form of software functional modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memory, magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0091] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0092] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0093] In the several embodiments provided in this application, it should be understood that the disclosed methods can be implemented in other ways. Furthermore, the methods disclosed in the several method embodiments provided in this application can be arbitrarily combined to obtain new method embodiments without conflict. Moreover, the features disclosed in the several methods provided in this application can be arbitrarily combined to obtain new method embodiments without conflict.

[0094] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy storage configuration method considering the robustness of wind-solar-storage systems, characterized in that, include: The basic structural framework of a wind-solar-storage system that integrates wind power generation, photovoltaic power generation and energy storage systems is pre-designed for grid-connected operation. By integrating the basic structural framework, the initial uncertain operating range of the wind, solar and energy storage system, and the operating constraints and operating state variables of the energy storage system at each moment, the energy storage configuration model of the wind, solar and energy storage system is obtained. By utilizing spatiotemporal budget constraints and spatial cluster constraints, the initial uncertain operating range is compressed to obtain the intermediate uncertain operating range; By adopting an intermediate uncertain operating range, the operating constraints of the energy storage system are adjusted to obtain the uncertainty constraints of the wind-solar-storage system; Under uncertainty constraints, the energy storage configuration model of the wind-solar-storage system is adjusted based on minimizing the total cost as the objective function, resulting in a robust energy storage configuration model for the wind-solar-storage system. An improved sparrow algorithm is used to solve the robust energy storage configuration model to obtain the energy storage configuration parameters of the wind-solar-storage system.

2. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, The method of compressing the initial uncertain operating interval using spatiotemporal budget constraints and spatial cluster constraints to obtain the intermediate uncertain operating interval includes: The wind power deviation is obtained by subtracting the actual output value and the predicted output value of wind power generation in the initial uncertain operating range. The photovoltaic deviation is obtained by subtracting the actual output value and the predicted output value of photovoltaic power generation in the initial uncertain operating range. The load deviation is obtained by subtracting the actual output value and the predicted output value corresponding to the load in the initial uncertain operating range. By utilizing spatiotemporal budget constraints and spatial cluster constraints, wind power deviation, photovoltaic deviation, and load deviation are synergistically optimized to obtain the intermediate uncertain operating range.

3. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, The method of adjusting the operational constraints of the energy storage system using an intermediate uncertain operating range yields the uncertainty constraints of the wind-solar-storage system, including: By utilizing the intermediate uncertain operating range, the upper and lower limits corresponding to the operating constraints of the energy storage system are defined; By using upper and lower limits, the operational constraints of the energy storage system are adjusted to obtain the uncertainty constraints of the wind-solar-storage system.

4. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, Under uncertainty constraints, the energy storage configuration model of the wind-solar-storage system is adjusted based on minimizing the total cost as the objective function to obtain a robust energy storage configuration model for the wind-solar-storage system, including: The total cost of a wind-solar-storage system is obtained by adding the life-cycle cost of the energy storage system, the cost of conventional units, the cost of tie lines, the cost of tie line power fluctuation penalties, the cost of robustness penalties, and the cost of carbon emissions. Based on the total cost of a wind-solar-storage system, a function is constructed with the objective function of minimizing the total cost. Under uncertainty constraints, based on the objective function, the operating parameters and configuration parameters of the energy storage configuration model of the wind-solar-storage system are adjusted to obtain the robust energy storage configuration model of the wind-solar-storage system.

5. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, Before using the improved sparrow algorithm to solve the robust energy storage configuration model and obtain the energy storage configuration parameters of the wind-solar-storage system, the method further includes: Using the Lagrange multiplier method, based on the number of renewable energy power plants in the wind-solar-storage system, the predicted output of the renewable energy power plants, and the output deviation coefficient of the renewable energy power plants, the uncertainty constraints in the robust energy storage configuration model of the wind-solar-storage system are transformed into deterministic constraints. The improved sparrow algorithm is used to solve the robust energy storage configuration model to obtain the energy storage configuration parameters of the wind-solar-storage system, including: Under deterministic constraints, an improved sparrow algorithm is used to solve the robust energy storage configuration model to obtain the energy storage configuration parameters of the wind-solar-storage system.

6. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 5, characterized in that, The improved sparrow algorithm is an algorithm that integrates the Levy flight strategy and the adaptive differential evolution mechanism. Under deterministic constraints, the improved sparrow algorithm is used to solve the robust energy storage configuration model to obtain the energy storage configuration parameters of the wind-solar-storage system, including: Under deterministic constraints, determine the configuration space of multiple configuration parameters for the robust configuration model of energy storage; The Levy flight strategy and adaptive differential evolution mechanism of the improved sparrow algorithm are adopted. Based on the fitness of multiple configuration parameters, multiple configuration parameters are iteratively optimized in the configuration space until the maximum number of iterations is reached. The fitness of the configuration parameters is the value obtained by evaluating the energy storage robust configuration model with configuration values ​​of configuration parameters according to the preset evaluation index. The configuration values ​​corresponding to multiple configuration parameters under the maximum number of iterations will be used as the energy storage configuration parameters of the wind, solar and energy storage system.

7. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 6, characterized in that, Under the deterministic constraints, the configuration space of multiple configuration parameters of the robust energy storage configuration model is determined, including: An improved sparrow algorithm is used to perform population initialization on the energy storage robust configuration model to obtain multiple configuration parameters; Under deterministic constraints, the configuration space for multiple configuration parameters is determined.

8. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 6 or 7, characterized in that, The Levy flight strategy employing the improved sparrow algorithm and the adaptive differential evolution mechanism, based on the fitness of multiple configuration parameters, iterates and optimizes multiple configuration parameters in the configuration space multiple times until the maximum number of iterations is reached, including: Based on fitness, discoverer parameters, follower parameters, and vigilant parameters are distinguished from multiple configuration parameters; Using the Levy flight strategy, the configuration values ​​corresponding to the discoverer parameters are iteratively updated, and an adaptive differential evolution mechanism is simultaneously used to perform mutation, crossover, and selection operations on the configuration values ​​corresponding to the follower parameters and the watchdog parameters, so as to iteratively update the configuration values ​​corresponding to the follower parameters and the watchdog parameters until the maximum number of iterations is reached.

9. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, The method further includes: The probability of violating safety reserve constraints is used as a robustness quantification index to evaluate the energy storage robustness configuration model of wind, solar and energy storage systems, and the evaluation results are obtained. Based on the evaluation results, the model parameters of the robust energy storage configuration model of the wind-solar-storage system were adjusted to obtain the adjusted robust energy storage configuration model of the wind-solar-storage system.

10. The energy storage configuration method considering the robustness of wind-solar-storage systems according to claim 1, characterized in that, The basic structural framework includes at least: wind farm, photovoltaic power station, load side, conventional units, energy storage system and interconnection line.

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