Water, wind, light, storage source network and load collaborative scheduling method and system

Abstract

The application provides a water, wind, light, storage, source, network, load, and coordination scheduling method and system. The initial state and boundary conditions of each device group are determined according to real-time working condition data and prediction data. The medium and long-term core balance device and the multi-time scale coordination scheduling strategy for coping with the minute-level fluctuation core are determined based on the initial state, boundary conditions, and working condition differences. The fluctuation of the instability of the power and load demand of the wind and light station is analyzed based on the prediction data and the preset safety threshold, and the maximum fluctuation range of the target power system is obtained. The safety adjustment of the coordination scheduling strategy is performed with the maximum fluctuation range as the constraint. The power adjustment of the energy storage device and the adjustable load is performed on the minute-level scale based on the adjusted coordination scheduling strategy, and the safe auxiliary power correction of the hydroelectric generating set is controlled. The scheme of the application can resist the uncertainty of the source and load and perform safe and rapid coordination scheduling of water, wind, light, and storage under multi-time scale.

Description

Technical Field

[0001] This application relates to the field of collaborative scheduling technology, and more specifically, to a method and system for collaborative scheduling of water, wind, solar, energy storage, power grid, and load. Background Technology

[0002] As the penetration rate of fluctuating renewable energy sources such as wind and solar power in the power system continues to increase, the randomness and intermittency of their output pose a severe challenge to the power balance of the power grid. In particular, the rapid fluctuations of renewable energy sources, which can occur on a minute-by-minute scale, can easily cause grid frequency deviations and line power exceeding limits, threatening the safe and stable operation of the system.

[0003] In existing technologies, the mainstream methods for dispatching power systems containing renewable energy sources mainly fall into two categories: one is the deterministic optimization-based dispatching method, which heavily relies on a single wind and solar load forecast scenario. Once the actual operation deviates from the forecast, the dispatching plan may fail, leading to wind and solar curtailment or power shortages. The other is the probabilistic model-based method, whose effectiveness depends on a precise description of the probability distribution of uncertainty. However, historical data is often insufficient in practice, making it difficult to establish accurate models. Furthermore, most existing methods fail to place the power source, grid, load, and energy storage components under a unified framework for deep coordination, thus failing to fully unleash the system's flexibility. Therefore, how to achieve safe and rapid coordinated dispatching of hydropower, wind power, solar power, and energy storage across multiple time scales under the condition of resisting uncertainties on both the source and load sides has become a challenge for the industry. Summary of the Invention

[0004] This application provides a method and system for coordinated scheduling of water, wind, solar, and energy storage, which can safely and quickly coordinate scheduling of water, wind, solar, and energy storage across multiple time scales under the condition of resisting uncertainty on both the source and load sides.

[0005] Firstly, this application provides a method for coordinated scheduling of water, wind, solar, energy storage, power grid, and load, including the following steps: Collect real-time operating data of hydropower units, wind and solar power plants, adjustable loads and energy storage devices in the target power system, and obtain predicted data of power generation and load of each equipment group within a minute time scale; The initial state and boundary conditions of each equipment group are determined based on the real-time operating data and the predicted data. Determine the differences in operating conditions among the various equipment groups, and based on the initial state, the boundary conditions, and the differences in operating conditions, determine a multi-timescale collaborative scheduling strategy with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core for dealing with minute-level fluctuations. Based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power stations, thereby obtaining the maximum fluctuation range of source load power that the target power system can withstand. The coordinated scheduling strategy is safely adjusted based on the maximum fluctuation range. The adjusted coordinated scheduling strategy performs power regulation on energy storage devices and adjustable loads on a minute-level scale, and controls hydropower units to perform safe auxiliary power correction.

[0006] In some embodiments, determining the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data specifically includes: The real-time output of the hydropower unit, the current reservoir capacity, the real-time state of charge of the energy storage device, the real-time power consumption of the adjustable load, and the real-time output of the wind and solar power station together constitute the initial state of each equipment group. The power output limit, safe ramp rate, and future reservoir capacity constraints of the hydropower unit are determined based on the real-time operating data and the predicted data, serving as the operating boundary for the medium- and long-term support of the hydropower unit. The minute-level predicted output curve and its confidence interval of the wind and solar power station are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the wind and solar power station. The maximum interruption transfer capacity, the shortest response recovery time, and the response compensation cost of the adjustable load are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the adjustable load. The maximum charging and discharging power, safe upper and lower limits of state of charge, and unit adjustment cost of the energy storage device are determined based on the real-time operating data and the predicted data, which serve as the operating boundary of the adjustable load. The set of all operating condition boundaries serves as the boundary conditions for each equipment group.

[0007] In some embodiments, determining the differences in operating conditions between equipment groups specifically includes: Determine the differences between hydropower units and energy storage devices; Determine the cost difference between energy storage devices and adjustable loads; Determine the regulation differences among wind and solar power plants, hydropower units, and energy storage devices; The set of the device differences, cost differences, and adjustment differences is taken as the operating condition differences between each equipment group.

[0008] In some embodiments, determining a multi-timescale coordinated scheduling strategy based on the initial state, the boundary conditions, and the operating condition differences, with hydropower units as the core balancing equipment in the medium and long term and energy storage devices and adjustable loads as the core for coping with minute-level fluctuations, specifically includes: Based on the differences in operating conditions, the adjustment tasks are divided into medium- and long-term adjustments and minute-level adjustments, resulting in multiple time scales. Based on the aforementioned boundary conditions, the hydropower unit is designated as the core balancing equipment for medium- and long-term operations, while the energy storage device and adjustable load are designated as the core components to address minute-level fluctuations. Based on the initial state and the boundary conditions, cooperative rules are formed, thereby obtaining a cooperative scheduling strategy with multiple time scales.

[0009] In some embodiments, based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power plants to obtain the maximum fluctuation range of source load power that the target power system can withstand. Specifically, this includes: The deviation coefficient combination for the power load of the wind and solar power stations is determined based on the predicted data; The preset safety threshold is used as a rigid constraint; The rigid constraints are solved by using an optimization algorithm to maximize the fluctuation of the power load deviation coefficient combination of the wind and solar power stations, thereby obtaining the maximum fluctuation range of the source load power that the target power system can withstand.

[0010] In some embodiments, adjusting the collaborative scheduling strategy with the maximum fluctuation range as a constraint specifically includes: Establish an optimization model for the aforementioned collaborative scheduling strategy; The maximum fluctuation range is used as a constraint in the optimization model to verify and adjust the output plan curve of the hydropower unit, thereby optimizing the coordinated scheduling strategy of energy storage device and adjustable load for safety.

[0011] In some embodiments, the adjusted coordinated scheduling strategy performs power regulation on energy storage devices and adjustable loads on a minute-by-minute scale, and controls hydropower units to perform safe auxiliary power correction, specifically including: The adjusted coordinated dispatch strategy is used to calculate the net load power deviation of the target power system in real time, on a rolling basis, in the next few minutes. Initiate minute-level real-time optimization with the goal of minimizing total adjustment costs, prioritizing the allocation of energy storage charging and discharging power and the amount of adjustable load to quickly smooth out most fluctuations; If, after the above rapid adjustment, the target power system still has a residual load power deviation exceeding the preset threshold, then within the safe ramp-up rate limit of the hydropower unit, its minute-level auxiliary correction power is calculated and issued for execution, ultimately achieving a coordinated balance of all resources.

[0012] Secondly, this application provides a hydropower-wind-solar-storage-grid-load coordinated scheduling system, comprising: The acquisition module is used to collect real-time operating data of hydropower units, wind and solar power plants, adjustable loads and energy storage devices in the target power system, and to obtain predicted data of power generation and load of each equipment group within a minute time scale. The processing module is used to determine the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data. The processing module is also used to determine the differences in operating conditions between each equipment group, and to determine a multi-timescale collaborative scheduling strategy based on the initial state, the boundary conditions and the differences in operating conditions, with hydropower units as the core balancing equipment in the medium and long term and energy storage devices and adjustable loads as the core to cope with minute-level fluctuations. The processing module is also used to perform fluctuation analysis on the instability of power and load demand during minute-level operation of wind and solar power stations based on the predicted data and preset safety thresholds, so as to obtain the maximum fluctuation range of source load power that the target power system can withstand. The execution module is used to safely adjust the coordinated scheduling strategy with the maximum fluctuation range as a constraint. The adjusted coordinated scheduling strategy performs power regulation on the energy storage device and adjustable load on a minute-level scale, and controls the hydropower unit to perform safe auxiliary power correction.

[0013] Thirdly, this application provides a computer device, the computer device including a memory and a processor, the memory storing code, and the processor being configured to acquire the code and execute the above-described hydro-wind-solar-storage-grid-load coordinated scheduling method.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described water, wind, solar, energy storage, grid, and load coordinated scheduling method.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The hydropower, wind power, solar power, energy storage, grid, and load coordinated dispatching method and system provided in this application first collects real-time operating data of hydropower units, wind and solar power plants, adjustable loads, and energy storage devices in the target power system, and obtains predicted data of power generation and load of each equipment group within a minute-level time scale. Based on the real-time operating data and the predicted data, the initial state and boundary conditions of each equipment group are determined. The differences in operating conditions between equipment groups are determined, and based on the initial state, the boundary conditions, and the differences in operating conditions, a multi-time-scale coordinated dispatching strategy is determined, with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core for coping with minute-level fluctuations. Based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power plants, thereby obtaining the maximum fluctuation range of source and load power that the target power system can withstand. The coordinated dispatching strategy is then safely adjusted using the maximum fluctuation range as a constraint. The adjusted coordinated dispatching strategy performs power regulation on energy storage devices and adjustable loads at the minute-level scale, and controls hydropower units to perform safe auxiliary power correction.

[0016] Therefore, in the process of the hydropower, wind power, solar power, energy storage, grid-load coordinated scheduling method of this application, firstly, real-time operating condition data of hydropower units, wind and solar power plants, adjustable loads, and energy storage devices in the target power system are collected, and predicted data of power generation and load of each equipment group within a minute-level time scale are obtained; the initial state and boundary conditions of each equipment group are determined based on the real-time operating condition data and the predicted data; the operating condition differences between each equipment group are determined, and based on the initial state, the boundary conditions, and the operating condition differences, a multi-time-scale coordinated scheduling strategy is determined, with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core for coping with minute-level fluctuations; wherein, the coordinated scheduling strategy is This scheme defines a set of operational principles for how hydropower units, energy storage devices, and adjustable loads should coordinate and cooperate based on their current state and capacity constraints in a multi-timescale coordinated dispatch strategy. Secondly, based on the predicted data and preset safety thresholds, it performs fluctuation analysis on the instability of power and load demand during minute-level operation of wind and solar power plants, thereby obtaining the maximum fluctuation range of source-load power that the target power system can withstand. Using this maximum fluctuation range as a constraint, the coordinated dispatch strategy is safely adjusted. The adjusted coordinated dispatch strategy then performs power regulation on energy storage devices and adjustable loads at the minute-level and controls hydropower units to perform safe auxiliary power correction. This scheme can achieve safe and rapid coordinated dispatch of hydropower, wind, solar, and storage systems across multiple timescales, even under conditions of uncertainty on both the source and load sides. Attached Figure Description

[0017] Figure 1 This is an exemplary flowchart of a water, wind, solar, energy storage, power grid, and load coordinated scheduling method according to some embodiments of this application; Figure 2 This is an exemplary flowchart illustrating the determination of operating condition differences according to some embodiments of this application; Figure 3 This is an exemplary flowchart illustrating the determination of the maximum fluctuation range according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a water-wind-solar-storage-grid-load coordinated scheduling method system according to some embodiments of this application; Figure 5 This is a schematic diagram of the structure of a computer device for implementing a coordinated scheduling method for water, wind, solar, energy storage, power grid, and load according to some embodiments of this application. Detailed Implementation

[0018] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0019] refer to Figure 1The figure is an exemplary flowchart of a water-wind-solar-storage-grid-load coordinated scheduling method according to some embodiments of this application. The water-wind-solar-storage-grid-load coordinated scheduling method mainly includes the following steps: In step 101, real-time operating data of hydropower units, wind and solar power plants, adjustable loads and energy storage devices in the target power system are collected, and predicted data of power generation and load of each equipment group within a minute time scale are obtained.

[0020] In practice, collecting real-time operating data of hydropower units, wind and solar power plants, adjustable loads, and energy storage devices in the target power system, and obtaining predicted power generation and load data for each equipment group within a minute-level timescale, can be achieved in the following way: Through a data acquisition and monitoring system deployed in the power system dispatch center, real-time uploaded data is simultaneously received from the hydropower station monitoring system, the renewable energy plant power prediction system, the load aggregator platform, and the energy storage management system. Specifically, real-time active power output, reservoir water level, and turbine opening of hydropower units are obtained from the hydropower station monitoring system; data with a period of 15 minutes and a timescale of 1 minute are obtained from the renewable energy plant power prediction system. The system generates time-resolution wind and solar power generation power prediction sequences, thereby obtaining predicted data on the power generation and load of each equipment group within a minute-level time scale; it obtains the real-time total power consumption and adjustable potential status of contracted adjustable loads from the load aggregator platform; it obtains the real-time charging and discharging power, voltage, and current of energy storage devices from the energy storage management system, thereby obtaining real-time operating data of hydropower units, wind and solar power plants, adjustable loads, and energy storage devices in the target power system; all the above real-time data and minute-level prediction data are standardized and then integrated and stored in the real-time data table of the scheduling database; other embodiments may also use other methods, which are not limited here.

[0021] It should be noted that the real-time operating data in this application is a set of measured values ​​or state variables that reflect the actual operating status of hydropower units, wind and solar power plants, adjustable loads, and energy storage devices at the current dispatch time; the minute-level time scale is a time resolution range with 1 minute as the basic unit for describing ultra-short-term power balance calculations and rapid control decisions; the power generation and load forecast data is a set of estimated data with time series characteristics that describes the power output of new energy sources such as wind power and photovoltaics in the next few minutes to tens of minutes, as well as the power demand of the total system load.

[0022] In step 102, the initial state and boundary conditions of each equipment group are determined based on the real-time operating data and the predicted data.

[0023] In some embodiments, determining the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data can be achieved by the following steps: The real-time output of the hydropower unit, the current reservoir capacity, the real-time state of charge of the energy storage device, the real-time power consumption of the adjustable load, and the real-time output of the wind and solar power station together constitute the initial state of each equipment group. The power output limit, safe ramp rate, and future reservoir capacity constraints of the hydropower unit are determined based on the real-time operating data and the predicted data, serving as the operating boundary for the medium- and long-term support of the hydropower unit. The minute-level predicted output curve and its confidence interval of the wind and solar power station are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the wind and solar power station. The maximum interruption transfer capacity, the shortest response recovery time, and the response compensation cost of the adjustable load are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the adjustable load. The maximum charging and discharging power, safe upper and lower limits of state of charge, and unit adjustment cost of the energy storage device are determined based on the real-time operating data and the predicted data, which serve as the operating boundary of the adjustable load. The set of all operating condition boundaries serves as the boundary conditions for each equipment group.

[0024] In specific implementation, the initial state of each equipment group can be determined by acquiring the real-time output of the hydropower unit, the current reservoir capacity, the real-time state of charge of the energy storage device, the real-time power consumption of the adjustable load, and the real-time output of the wind and solar power stations. This can be achieved by calling the state initialization submodule in the real-time data and prediction data generation process of the scheduling database. This submodule reads the hydropower unit output, reservoir capacity calculated from the water level, energy storage state of charge, adjustable load power, and actual wind and solar power generation from the latest timestamp in the data table, and directly uses these as the initial state of each equipment at the starting point of this scheduling calculation. The hydropower unit output limit, safe ramp rate, and future reservoir capacity constraints are determined based on the real-time operating data and the prediction data. This serves as the operating boundary for the long-term support of the hydropower unit. This can be achieved by using the real-time data in conjunction with the physical nameplate parameters of the equipment and the reservoir scheduling data. The regulations, battery technical specifications, and demand response contract terms are used to calculate and set the operating boundaries of various types of equipment. For example, based on the current reservoir capacity, inflow forecast, and downstream ecological flow requirements, the maximum and minimum technical output limits of the hydropower unit and the maximum allowable power change per minute (i.e., the safe ramp rate) are calculated within the future scheduling cycle. Similarly, based on the current reservoir capacity, inflow forecast, and downstream ecological flow requirements, and based on the water balance principle, the upper and lower limits of the reservoir capacity are simulated and calculated for each time period within the future scheduling cycle under constraints such as ecological discharge and flood control safety, forming a reservoir capacity constraint curve. Based on the current state of charge, health status, and parameters provided by the manufacturer, the maximum allowable charging and discharging power, as well as the safe upper and lower limits of the state of charge, are determined for the battery in the next time period. Other embodiments may also use other methods to achieve this, which are not limited here.

[0025] It should be noted that the initial state in this application is the instantaneous set of actual values ​​of power output or energy storage of hydropower units, energy storage devices, adjustable loads, and wind and solar power plants at the start of the collaborative scheduling optimization calculation; the boundary conditions refer to the comprehensive conditions of physical limits, technical parameters, and business rules that constrain the allowable range of variation of the operating variables (such as output and charging / discharging power) of hydropower units, wind and solar power plants, energy storage devices, and adjustable loads in the collaborative scheduling optimization model; the operating condition boundary describes the limit boundary that a certain type of equipment (such as hydropower units) must comply with, determined by the physical characteristics and operating procedures of the equipment itself.

[0026] In step 103, the differences in operating conditions between each equipment group are determined, and a multi-timescale collaborative scheduling strategy is determined based on the initial state, the boundary conditions, and the differences in operating conditions, with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core to cope with minute-level fluctuations.

[0027] In some embodiments, reference Figure 3As shown, this figure is an exemplary flowchart for determining operating condition differences in some embodiments of this application. In this embodiment, determining the operating condition differences between equipment groups can be achieved by the following steps: First, in step 1031, the device differences between the hydropower unit and the energy storage device are determined; Secondly, in step 1032, the cost difference between the energy storage device and the adjustable load is determined; Then, in step 1033, the adjustment differences between wind and solar power plants, hydropower units, and energy storage devices are determined; Finally, in step 1034, the set of the device differences, the cost differences, and the adjustment differences is taken as the operating condition differences between each equipment group.

[0028] In practice, the differences between hydropower units and energy storage devices can be determined by comparing their technical parameter databases. The primary comparison item is the power change rate: energy storage devices (such as lithium-ion batteries) have extremely fast power change rates, switching from maximum charging to maximum discharging within seconds or even milliseconds; while hydropower units are limited by the turbine guide vane opening speed and the inertia of water flow in pressure pipelines, their power change rate (ramp rate) is typically a few percent of their rated capacity per minute, exhibiting a significant minute-level delay. Therefore, the numerical comparison of power change rates differing by two orders of magnitude and response times differing by one to two orders of magnitude represents the differences between hydropower units and energy storage devices. The cost differences between energy storage devices and adjustable loads can be determined by analyzing the boundary conditions of energy storage devices and adjustable loads. For energy storage devices, the adjustment cost mainly comes from capacity decay caused by battery charge-discharge cycles, which can be converted into a unit adjustment cost (yuan / MWh); while for adjustable loads, the cost is a direct payment. The response compensation cost to the user (RMB / MWh) is calculated; then the calculation basis and numerical value of the two costs are compared: for example, the energy storage cost is the internal equipment loss, while the load cost is the external economic compensation. The two are different in nature and their values ​​are usually significantly different. Finally, the numerical difference in cost composition is taken as the cost difference between the energy storage device and the adjustable load. The adjustment difference between wind and solar power plants, hydropower units and energy storage devices can be determined by the following method: by comprehensively analyzing the operating characteristics of wind and solar power plants, hydropower units and energy storage devices. Among them, the output of wind and solar power plants depends on natural conditions, has strong randomness and uncontrollability, can only be predicted and is difficult to directly command and control, while the output of hydropower units can be actively controlled by adjusting the opening degree, but the adjustment speed is slow (minute level) and is constrained by inflow and reservoir capacity; the output of energy storage devices can be controlled quickly and accurately in both directions (second level) and is mainly constrained by its own state of charge. The order of controllability from weak to strong and adjustment speed from slow to fast is taken as the adjustment difference between wind and solar power plants, hydropower units and energy storage devices; other methods can also be used in other embodiments, which are not limited here.

[0029] It should be noted that the regulation differences in this application specifically refer to the fundamental differences in the controllability, predictability, and ability to follow dispatch instructions of wind and solar power plants, hydropower units, and energy storage devices; the operating condition differences comprehensively describe the differences between hydropower units, wind and solar power plants, energy storage devices, and adjustable loads in multiple dimensions such as physical response characteristics, economic characteristics, and dispatch characteristics; and the device differences describe the essential differences in the dynamic characteristics of power regulation between hydropower units and energy storage devices due to their different energy conversion media (hydropower and electrical / chemical energy) and mechanical structures.

[0030] In some embodiments, determining a multi-timescale coordinated scheduling strategy based on the initial state, the boundary conditions, and the differences in operating conditions, with hydropower units as the core balancing equipment in the medium and long term and energy storage devices and adjustable loads as the core for coping with minute-level fluctuations, can be achieved through the following steps: Based on the differences in operating conditions, the adjustment tasks are divided into medium- and long-term adjustments and minute-level adjustments, resulting in multiple time scales. Based on the aforementioned boundary conditions, the hydropower unit is designated as the core balancing equipment for medium- and long-term operations, while the energy storage device and adjustable load are designated as the core components to address minute-level fluctuations. Based on the initial state and the boundary conditions, cooperative rules are formed, thereby obtaining a cooperative scheduling strategy with multiple time scales.

[0031] In specific implementation, the adjustment tasks are divided into medium- and long-term adjustments and minute-level adjustments based on the differences in operating conditions. This can be achieved by the following method: Logical judgment is made based on the differences in adjustment and equipment within the differences in operating conditions. Tasks that require advance planning, have gradual changes, and are suitable for resources with slow adjustment speeds but long durations (such as hydropower units) are classified as medium- and long-term adjustments, and assigned hourly or longer decision cycles (e.g., every 15 minutes for the next 24 hours). Tasks that require rapid response, handle instantaneous fluctuations, and are suitable for rapid resources (such as energy storage and load) are classified as minute-level adjustments, and assigned minute-level decision cycles (e.g., rolling the next 5 minutes, every 1 minute). Thus, through this classification, adjustment tasks are divided into medium- and long-term adjustments and minute-level adjustments, resulting in multiple time scales. Here, a time scale refers to the time period division with different decision cycles and execution frequencies set for different types of adjustment tasks in the collaborative scheduling strategy. Other implementation methods can also be used in other embodiments, which are not limited here.

[0032] It should be noted that by identifying the differences in operating conditions among various equipment groups, a systematic understanding of the regulation dynamics, economic attributes, and controllability of the five types of resources—water, storage, load, wind, and solar—can be obtained. The core function of this process is to transform the static parameter tables of the equipment into a dynamic logical basis for scheduling decisions. Its effect is that it ensures that subsequent strategy formulation is no longer a simple parallelization or empirical combination of resources, but is based on profound physical and economic principles. Specifically, by quantitatively comparing the minute-level ramp-up rate of hydropower units with the second-level power response of energy storage devices, the strategy can fundamentally identify the natural division of labor between the two on a time scale. By analyzing the differences in the cost composition of internal circulation losses in energy storage and external load compensation, the strategy provides a precise quantitative benchmark for economic priority scheduling. By clarifying the regulation spectrum of wind and solar power being uncontrollable, hydropower being slowly controllable, and energy storage being rapidly controllable, the strategy clearly defines the responsibility chain of prediction-planning-response. This step essentially builds a cognitive model for complex multi-resource systems, enabling scheduling strategies to be targeted and avoiding strategic mismatches such as using fast resources to deal with slow-changing trends or using high-cost resources to complete low-cost tasks.

[0033] In specific implementation, based on the aforementioned boundary conditions, the hydropower unit is designated as the core balancing device for medium- and long-term operations, while the energy storage device and adjustable load are designated as the core for handling minute-level fluctuations. This can be achieved in the following way: First, check the boundary conditions of the hydropower unit to confirm that it has a remaining output range and reservoir capacity. The hydropower unit has the ability to perform hourly energy transfer, but its safe ramp-up rate limits its minute-level rapid adjustment capability. Therefore, the hydropower unit is designated as the core balancing device for medium- and long-term operations, responsible for tracking the daily planning curve and balancing power gaps on larger time scales. Second, check the boundary conditions of the energy storage and adjustable load to confirm that they have rapid power change capability and minute-level response characteristics. Therefore, the energy storage and adjustable load are jointly designated as the core for handling minute-level fluctuations, responsible for quickly smoothing minute-level random fluctuations in wind, solar, and load. Other embodiments may also employ other methods, which are not limited here.

[0034] In specific implementation, the collaborative rules formed based on the initial state and the boundary conditions, thereby obtaining a multi-time-scale collaborative scheduling strategy, can be implemented in the following way: integrating the initial state and boundary conditions to generate rules; for example, rule 1: the hydropower unit outputs based on the hourly plan, and the change in its actual output per minute must not exceed the safe ramp rate set in the boundary conditions; rule 2: minute-level net load fluctuations are preferentially compensated by energy storage and adjustable loads, according to the available capacity and cost parameters in their boundary conditions, in an economic order; rule 3: only when minute-level fluctuations exceed the rapid adjustment capabilities of energy storage and loads can hydropower be called upon for auxiliary correction within its ramp limit; the specific rules of slow hydropower base, rapid load storage main scheduling, and safe hydropower auxiliary are textualized, thereby using the collaborative rules formed based on the initial state and boundary conditions as a multi-time-scale collaborative scheduling strategy; other embodiments may also be implemented in other ways, which are not limited here.

[0035] It should be noted that the collaborative scheduling strategy in this application is a set of operational strategies that define how hydropower units, energy storage devices, and adjustable loads should cooperate and divide labor based on their current state and capacity constraints within a multi-timescale collaborative scheduling strategy. Specifically, by determining the multi-timescale collaborative scheduling strategy based on initial state, boundary conditions, and differences in operating conditions, a set of specific and executable operational paradigms and rules guiding the cooperation of water, storage, and load resources in the temporal and spatial dimensions can be obtained. The direct contribution of this process is that it integrates the aforementioned static understanding and dynamic constraints of resources into a clear action path. Its effect is that it establishes a scheduling architecture with the medium- and long-term hydropower plan as the stable base load and providing a flexible boundary, and energy storage and adjustable loads as the core execution units for smoothing minute-level fluctuations. This helps to achieve precise functional positioning and division of labor among adjustable resources at different timescales, thereby improving the overall flexibility of the system in dealing with multiple uncertainties and the scientific nature of scheduling decisions.

[0036] In step 104, based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power stations, thereby obtaining the maximum fluctuation range of source load power that the target power system can withstand.

[0037] In some embodiments, reference Figure 3 As shown, this figure is an exemplary flowchart for determining the maximum fluctuation range in some embodiments of this application. In this embodiment, based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power plants to obtain the maximum fluctuation range of source load power that the target power system can withstand. This can be achieved by the following steps: First, in step 1041, the deviation coefficient combination of the power load of the wind and solar power stations is determined based on the predicted data; Secondly, in step 1042, the preset safety threshold is used as a rigid constraint; Finally, in step 1043, the rigid constraints are solved by an optimization algorithm to maximize the fluctuation of the power load deviation coefficient combination of the wind and solar power stations, so as to obtain the maximum fluctuation range of the source load power that the target power system can withstand.

[0038] In specific implementation, the deviation coefficient combination of the power load of the wind and solar power station can be determined by the following method based on the predicted data: A deviation coefficient to be determined is set for each of the three uncertain variables: wind power, photovoltaic power, and load. The wind power deviation coefficient is 'a', where the actual wind power can be as low as (1-a) times its predicted value; the load deviation coefficient is 'b', where the actual load can be as high as (1+b) times its predicted value; and the photovoltaic deviation coefficient is 'c'. 'a', 'b', and 'c' are initialized to 0. First, the baseline predicted value sequence of wind power, photovoltaic power, and load is extracted from the predicted data, and a solution set containing the three variables 'a', 'b', and 'c' is constructed. Thus, the set of unknowns containing the deviation coefficients of wind power, photovoltaic power, and load is used as the deviation coefficient combination of the power load of the wind and solar power station. The deviation coefficient combination is a set of values ​​that quantitatively describes the maximum allowed negative (for wind and solar) or positive (for load) relative deviation of the predicted wind power, predicted photovoltaic power, and predicted load value when calculating the maximum fluctuation range. Other implementation methods can also be used in other embodiments, which are not limited here.

[0039] In specific implementation, the rigid constraints are solved by using an optimization algorithm to maximize the fluctuation of the power load deviation coefficient combination of the wind and solar power plants. The maximum fluctuation range of the source load power that the target power system can withstand can be achieved in the following way: Construct an optimization problem, the optimization objective of which is to maximize an index composed of the weighted sum of wind power, solar power, and load deviation coefficients; the decision variables are the aforementioned deviation coefficient combination (a, b, c); the constraints include all rigid constraints and the non-negativity of the deviation coefficients themselves, and the expression of the constraints (power flow equation) includes the worst-case scenario wind power calculated from the predicted values ​​and deviation coefficients. The model is then used to solve for photovoltaic and load power. A linear programming solver is invoked to find a specific set of deviation coefficients such that, in the extreme scenario defined by these coefficients—where wind power output is minimized and load demand is maximized—the power flow equations just touch but do not break any rigid constraint boundaries. Finally, the deviation coefficients output by the solver, along with the corresponding lower limit curve for wind and solar power output and upper limit curve for load, are used together to determine the maximum fluctuation range of source load power that the target power system can withstand. Other implementation methods can also be used in other embodiments, which are not limited here.

[0040] It should be noted that the maximum fluctuation range of source load power that the target power system can withstand in this application represents a numerical description of the maximum possible increase in net load power caused by a decrease in wind power output and an increase in load, which the power system can safely accept without violating any rigid safety constraints. Determining the maximum fluctuation range of source load power that the target power system can withstand yields a robust safety boundary characterizing the system's ability to resist interference under the current network structure and resource conditions. The direct effect of this process is to integrate the dual uncertainties of wind and solar power output and load demand into a clear and quantifiable safe operating space for the collaborative dispatch strategy. Its effect is that it transforms the strategy from a traditional mode of following a single forecast curve to a robust optimization mode that defends against the worst fluctuation scenarios. After the collaborative dispatch strategy is adjusted with this maximum fluctuation range as a constraint, the generated plan (especially the output curve of hydropower units and the reserve arrangement of energy storage) can guarantee that even if the actual output of wind and solar power falls to the lower limit of this fluctuation range and the load rises to the upper limit in the future, the system can still maintain safety and stability by calling on the adjustment resources in the plan, without having to urgently activate high-cost contingency plans or trigger load shedding. This significantly reduces the sensitivity of the scheduling plan to short-term forecast errors, and incorporates uncertainty risks into the optimization model in advance with minimal economic cost, thereby significantly improving the robustness of system operation and the reliability of decision-making.

[0041] In step 105, the coordinated scheduling strategy is safely adjusted with the maximum fluctuation range as a constraint. The adjusted coordinated scheduling strategy performs power regulation on the energy storage device and adjustable load on a minute-level scale, and controls the hydropower unit to perform safe auxiliary power correction.

[0042] In some embodiments, the following steps can be used to safely adjust the cooperative scheduling strategy with the maximum fluctuation range as a constraint: Establish an optimization model for the aforementioned collaborative scheduling strategy; The maximum fluctuation range is used as a constraint in the optimization model to verify and adjust the output plan curve of the hydropower unit, thereby optimizing the coordinated scheduling strategy of energy storage device and adjustable load for safety.

[0043] In specific implementation, establishing an optimization model for the aforementioned coordinated scheduling strategy can be achieved in the following way: An optimization model is constructed based on the coordinated scheduling strategy. This optimization model takes minimizing the total scheduling cost as its main objective function. The total cost includes the operating cost of the hydropower units, the regulation loss cost of the energy storage devices, and the response compensation cost of the adjustable loads. The decision variables of the optimization model are the planned output of the hydropower units, the planned charging and discharging power of the energy storage devices, and the planned call amount of the adjustable loads at each time point (e.g., every 15 minutes) within a future scheduling cycle (e.g., the next few hours). The constraint equations of the optimization model are the boundary conditions of each device, such as the ramp-up constraint for hydropower, the state of charge constraint for energy storage, and the equality constraint that the system power must be balanced at all times. The objective function, decision variables, and constraint equations are integrated to form a mathematical framework containing the objective function, decision variables, and constraint equations, which serves as the optimization model for the coordinated scheduling strategy. Other implementation methods can also be used in other embodiments, which are not limited here.

[0044] In specific implementation, the maximum fluctuation range is used as a constraint in the optimization model to verify and adjust the output plan curve of the hydropower unit. This allows for safe optimization of the coordinated scheduling strategy of energy storage devices and adjustable loads. This can be achieved by adding the maximum fluctuation range as a new constraint to the optimization model. Specifically, this constraint requires that the optimization model's plan, when facing severe scenarios such as wind power output dropping to the lower limit of its fluctuation range and load rising to the upper limit of its fluctuation range, still have sufficient backup resources. This means that the system can adjust the fast-moving hydropower, energy storage, and load to maintain power balance without exceeding limits. The optimization model with the new constraints mentioned above is re-solved. In the new solution, the power output plan curve of the hydropower unit will be adjusted, for example, reserving more upward adjustment reserve space during periods of low wind and solar forecasts. At the same time, the energy storage and adjustable load dispatch strategies will also be re-optimized to ensure sufficient and economical rapid adjustment capabilities even in the worst case. Thus, the hydropower unit power output plan curve, energy storage device dispatch strategy, and adjustable load dispatch strategy, which have been re-formulated after safety verification in severe scenarios, will be used together as a safety-optimized collaborative scheduling strategy. Other embodiments may also use other methods to achieve this, which are not limited here.

[0045] In some embodiments, the adjusted coordinated scheduling strategy performs power regulation on energy storage devices and adjustable loads on a minute-by-minute scale, and controls hydropower units to perform safe auxiliary power correction, which can be achieved by the following steps: The adjusted coordinated dispatch strategy is used to calculate the net load power deviation of the target power system in real time, on a rolling basis, in the next few minutes. Initiate minute-level real-time optimization with the goal of minimizing total adjustment costs, prioritizing the allocation of energy storage charging and discharging power and the amount of adjustable load to quickly smooth out most fluctuations; If, after the above rapid adjustment, the target power system still has a residual load power deviation exceeding the preset threshold, then within the safe ramp-up rate limit of the hydropower unit, its minute-level auxiliary correction power is calculated and issued for execution, ultimately achieving a coordinated balance of all resources.

[0046] In practice, the real-time rolling calculation of the net load power deviation of the target power system in the next few minutes, based on the adjusted collaborative dispatch strategy, can be achieved as follows: A rolling calculation task is initiated, automatically executed once per minute. At the beginning of each minute, this task first reads the latest ultra-short-term (5-10 minutes) wind and solar power forecasts and load forecasts, and calculates the predicted net load of the system for the next minute. Simultaneously, from the safety-optimized collaborative dispatch strategy, the corresponding planned total output value of the system for the next minute is retrieved, where the planned total output value = planned hydropower output + planned energy storage output + already dispatched power. The planned load value is used; then, the difference between the predicted net load and the total output value is calculated, which is the expected power imbalance in the next minute. The difference between the predicted net load and the planned total output value in the next minute is used as the net load power deviation of the target power system in the next minute. The net load power deviation is a value that measures the estimated difference between the sum of the actual total load and the actual total output of wind and solar power in the next minute of the power system in the minute-level real-time control link and the planned value of the corresponding minute in the safety-optimized coordinated dispatch strategy. Other embodiments may also use other methods to achieve this, which are not limited here.

[0047] Furthermore, in another aspect of this application, in some embodiments, this application provides a coordinated scheduling system for water, wind, solar, energy storage, power grid, and load, with reference to... Figure 4 The figure is a schematic diagram of the structure of a water-wind-solar-storage-grid-load coordinated scheduling system according to some embodiments of this application. The water-wind-solar-storage-grid-load coordinated scheduling system includes: an acquisition module 401, a processing module 402, and an execution module 403, which are described below: The acquisition module 401 in this application is mainly used to collect real-time operating data of hydropower units, wind and solar power stations, adjustable loads and energy storage devices in the target power system, and to obtain predicted data of power generation and load of each equipment group within a minute time scale. Processing module 402, in this application, is used to determine the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data; It should be noted that the processing module 402 in this application is also used to determine the differences in operating conditions between each equipment group, and to determine a multi-timescale collaborative scheduling strategy based on the initial state, the boundary conditions and the differences in operating conditions, with hydropower units as the core balancing equipment in the medium and long term and energy storage devices and adjustable loads as the core to cope with minute-level fluctuations. In addition, it should be noted that the processing module 402 in this application is also used to perform fluctuation analysis on the instability of power and load demand during minute-level operation of wind and solar power stations based on the predicted data and preset safety thresholds, so as to obtain the maximum fluctuation range of source load power that the target power system can withstand. The execution module 403 in this application is mainly used to make safe adjustments to the coordinated scheduling strategy with the maximum fluctuation range as a constraint. The adjusted coordinated scheduling strategy performs power regulation on the energy storage device and the adjustable load on a minute-level scale, and controls the hydropower unit to perform safe auxiliary power correction.

[0048] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the above-described hydro-wind-solar-storage-grid-load coordinated scheduling method.

[0049] In some embodiments, reference Figure 5 The figure is a schematic diagram of the structure of a computer device for implementing a water-wind-solar-storage-grid-load coordinated scheduling method according to some embodiments of this application. The water-wind-solar-storage-grid-load coordinated scheduling method in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device includes at least one processor 501, a communication bus 502, a memory 503, and at least one communication interface 504.

[0050] Processor 501 can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0051] The communication bus 502 can be used to transmit information between the aforementioned components.

[0052] Memory 503 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 503 may exist independently and be connected to processor 501 via communication bus 502. Memory 503 may also be integrated with processor 501.

[0053] The memory 503 stores program code for executing the scheme of this application, and its execution is controlled by the processor 501. The processor 501 executes the program code stored in the memory 503. The program code may include one or more software modules. The method used in the above embodiments can be implemented by the processor 501 and one or more software modules in the program code in the memory 503.

[0054] Communication interface 504 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0055] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single CPU) processor or a multi-core (multi CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0056] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0057] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described water, wind, solar, energy storage, grid, and load coordinated scheduling method.

[0058] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0059] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for coordinated scheduling of water, wind, solar, energy storage, power grid, and load, characterized in that, Includes the following steps: Collect real-time operating data of hydropower units, wind and solar power plants, adjustable loads and energy storage devices in the target power system, and obtain predicted data of power generation and load of each equipment group within a minute time scale; The initial state and boundary conditions of each equipment group are determined based on the real-time operating data and the predicted data. Determine the differences in operating conditions among the various equipment groups, and based on the initial state, the boundary conditions, and the differences in operating conditions, determine a multi-timescale collaborative scheduling strategy with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core for dealing with minute-level fluctuations. Based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the instability of power and load demand during minute-level operation of wind and solar power stations, thereby obtaining the maximum fluctuation range of source load power that the target power system can withstand. The coordinated scheduling strategy is safely adjusted based on the maximum fluctuation range. The adjusted coordinated scheduling strategy performs power regulation on energy storage devices and adjustable loads on a minute-level scale, and controls hydropower units to perform safe auxiliary power correction.

2. The method as described in claim 1, characterized in that, Determining the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data specifically includes: The real-time output of the hydropower unit, the current reservoir capacity, the real-time state of charge of the energy storage device, the real-time power consumption of the adjustable load, and the real-time output of the wind and solar power station together constitute the initial state of each equipment group. The power output limit, safe ramp rate, and future reservoir capacity constraints of the hydropower unit are determined based on the real-time operating data and the predicted data, serving as the operating boundary for the medium- and long-term support of the hydropower unit. The minute-level predicted output curve and its confidence interval of the wind and solar power station are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the wind and solar power station. The maximum interruption transfer capacity, the shortest response recovery time, and the response compensation cost of the adjustable load are determined based on the real-time operating data and the predicted data, and are used as the operating boundary of the adjustable load. The maximum charging and discharging power, safe upper and lower limits of state of charge, and unit adjustment cost of the energy storage device are determined based on the real-time operating data and the predicted data, which serve as the operating boundary of the adjustable load. The set of all operating condition boundaries serves as the boundary conditions for each equipment group.

3. The method as described in claim 1, characterized in that, Determining the differences in operating conditions between different equipment groups specifically includes: Determine the differences between hydropower units and energy storage devices; Determine the cost difference between energy storage devices and adjustable loads; Determine the regulation differences among wind and solar power plants, hydropower units, and energy storage devices; The set of the device differences, cost differences, and adjustment differences is taken as the operating condition differences between each equipment group.

4. The method as described in claim 1, characterized in that, Based on the initial state, the boundary conditions, and the differences in operating conditions, a multi-timescale coordinated scheduling strategy is determined, with hydropower units as the core balancing equipment in the medium and long term, and energy storage devices and adjustable loads as the core for coping with minute-level fluctuations. This strategy specifically includes: Based on the differences in operating conditions, the adjustment tasks are divided into medium- and long-term adjustments and minute-level adjustments, resulting in multiple time scales. Based on the aforementioned boundary conditions, the hydropower unit is designated as the core balancing equipment for medium- and long-term operations, while the energy storage device and adjustable load are designated as the core components to address minute-level fluctuations. Based on the initial state and the boundary conditions, cooperative rules are formed, thereby obtaining a cooperative scheduling strategy with multiple time scales.

5. The method as described in claim 1, characterized in that, Based on the predicted data and preset safety thresholds, fluctuation analysis is performed on the power and load demand instability during minute-level operation of wind and solar power plants to obtain the maximum fluctuation range of source load power that the target power system can withstand, specifically including: The deviation coefficient combination for the power load of the wind and solar power stations is determined based on the predicted data; The preset safety threshold is used as a rigid constraint; The rigid constraints are solved by using an optimization algorithm to maximize the fluctuation of the power load deviation coefficient combination of the wind and solar power stations, thereby obtaining the maximum fluctuation range of the source load power that the target power system can withstand.

6. The method as described in claim 1, characterized in that, The specific steps for making safety adjustments to the collaborative scheduling strategy based on the maximum fluctuation range include: Establish an optimization model for the aforementioned collaborative scheduling strategy; The maximum fluctuation range is used as a constraint in the optimization model to verify and adjust the output plan curve of the hydropower unit, thereby optimizing the coordinated scheduling strategy of energy storage device and adjustable load for safety.

7. The method as described in claim 1, characterized in that, The adjusted coordinated dispatch strategy performs power regulation on energy storage devices and adjustable loads on a minute-by-minute scale, and controls hydropower units to perform safe auxiliary power correction. Specifically, this includes: The adjusted coordinated dispatch strategy is used to calculate the net load power deviation of the target power system in real time, on a rolling basis, in the next few minutes. Initiate minute-level real-time optimization with the goal of minimizing total adjustment costs, prioritizing the allocation of energy storage charging and discharging power and the amount of adjustable load to quickly smooth out most fluctuations; If, after the above rapid adjustment, the target power system still has a residual load power deviation exceeding the preset threshold, then within the safe ramp-up rate limit of the hydropower unit, its minute-level auxiliary correction power is calculated and issued for execution, ultimately achieving a coordinated balance of all resources.

8. A hydropower-wind-solar-storage-grid-load coordinated scheduling system, characterized in that, include: The acquisition module is used to collect real-time operating data of hydropower units, wind and solar power plants, adjustable loads and energy storage devices in the target power system, and to obtain predicted data of power generation and load of each equipment group within a minute time scale. The processing module is used to determine the initial state and boundary conditions of each equipment group based on the real-time operating data and the predicted data. The processing module is also used to determine the differences in operating conditions between each equipment group, and to determine a multi-timescale collaborative scheduling strategy based on the initial state, the boundary conditions and the differences in operating conditions, with hydropower units as the core balancing equipment in the medium and long term and energy storage devices and adjustable loads as the core to cope with minute-level fluctuations. The processing module is also used to perform fluctuation analysis on the instability of power and load demand during minute-level operation of wind and solar power stations based on the predicted data and preset safety thresholds, so as to obtain the maximum fluctuation range of source load power that the target power system can withstand. The execution module is used to safely adjust the coordinated scheduling strategy with the maximum fluctuation range as a constraint. The adjusted coordinated scheduling strategy performs power regulation on the energy storage device and adjustable load on a minute-level scale, and controls the hydropower unit to perform safe auxiliary power correction.

9. A computer device, characterized in that, The computer device includes a memory and a processor, the memory storing code, and the processor being configured to retrieve the code and execute the hydro-wind-solar-storage-grid-load coordinated scheduling method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the water, wind, solar, energy storage, power grid, and load coordinated scheduling method as described in any one of claims 1 to 7.

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