Remote new energy system frequency safety and coordinated control method and collaborative decision platform

Abstract

The application discloses a kind of multi-scene frequency safety and coordination control method and collaborative decision-making platform, belong to power system technical field.The method includes: constructing multiple operation scenes covering different grid strengths;The frequency reserve requirement is decomposed into fast reserve requirement and sustained reserve requirement;A collaborative optimization model coupled with long-term planning of device capacity and multi-scene short-term scheduling is established;In the model, the fast reserve capacity of battery energy storage and photovoltaic is matched with the fast reserve requirement by matching constraints, the sustained reserve capacity of wind power and electrolytic cell is matched with the sustained reserve requirement, and the physical feasible region of each resource is considered;Finally, the model is solved to obtain a device capacity configuration scheme and a scenario-based scheduling strategy that takes into account safety and economy.The application can systematically ensure the frequency safety of remote new energy systems in weak grids, islanded grids and other operating scenarios during the planning stage through integrated collaborative decision-making, improving the engineering implementability and economy of the scheme.

Description

Technical Field

[0001] This application relates to the field of power systems, and in particular to a method and collaborative decision-making platform for frequency security and coordinated control of remote new energy systems. Background Technology

[0002] With the continued development of large-scale wind and solar power bases in desert areas such as the Gobi Desert, the number of remote renewable energy systems far from load centers is increasing. These systems typically employ a combination of resources, including wind power, solar power, energy storage, and hydrogen production loads, and transmit power over long distances via voltage source converter high-voltage direct current (VSC-HVDC) technology. Their operating environment differs significantly from that of conventional grid-connected renewable energy plants; the sending-end system often operates under weak AC grid conditions or even islanded operation, posing a severe challenge to the system's frequency stability.

[0003] Under different operating scenarios, such as changes in power grid structure, start-up, shutdown, or failure of synchronous generator units, the synchronous support capability, equivalent inertia level, available backup resources, and acceptable frequency security boundaries of the sending-end system will all change significantly. Therefore, frequency security is no longer just a matter of controller parameter tuning, but a systemic issue closely coupled with equipment capacity configuration, steady-state scheduling, backup arrangements, and disturbance levels.

[0004] While there is considerable research on the planning and scheduling of integrated wind, solar, and energy storage systems in existing technologies, most focuses on economic objectives such as energy balance, cost reduction, or increased profitability. These methods often assume a single, relatively ideal operating scenario or use only empirical fixed disturbances to approximate frequency security requirements. For remote renewable energy systems with variable operating conditions, this approach is clearly insufficient. If capacity configuration and scheduling schemes fail to explicitly consider scenario differences, insufficient backup capacity may occur in certain weak grid or isolated scenarios, leading to the risk of frequency instability.

[0005] Furthermore, existing technologies do not adequately consider the differences in physical characteristics and dynamic responses of heterogeneous resources such as wind power, photovoltaics, energy storage, and electrolyzers when conducting scheduling planning. This leads to a discrepancy between the backup capacity assessed during the planning phase and the actual support capacity that can be provided. Consequently, the resulting solutions may be difficult to directly apply to engineering implementation or may fail to achieve the expected control effects when actual disturbances occur. Summary of the Invention

[0006] The purpose of this application is to provide a method and collaborative decision-making platform for frequency security and coordinated control of remote new energy systems, which aims to solve the problems of frequency security risks and poor engineering feasibility caused by neglecting the differences in multiple operating scenarios and the physical constraints of heterogeneous resources in the planning and scheduling of remote new energy systems in the existing technology.

[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a method for frequency security and coordinated control of a remote new energy system, including: Construct multiple operating scenarios and configure different frequency security requirement parameters for different operating scenarios; Based on different stages of frequency dynamic response, the total frequency security reserve requirement is decomposed into two types of reserve requirements with different response time scales. Both types of reserve requirements with different response time scales are calculated based on frequency security requirement parameters. A collaborative optimization model is established that couples long-term planning decisions with short-term operational decisions; wherein the long-term planning decisions include determining equipment capacity, the short-term operational decisions include equipment scheduling strategies under multiple operational scenarios, and the equipment capacity serves as the boundary constraint of the equipment scheduling strategies; In the collaborative optimization model, a matching constraint is established to match the backup demand with two different response time scales with the backup capacity of heterogeneous resources. Solve the collaborative optimization model to obtain collaborative decision results, which include the equipment capacity configuration scheme and the equipment scheduling strategy for different operating scenarios.

[0008] Secondly, this application provides a collaborative decision-making platform, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the remote new energy system frequency security and coordinated control method described in the first aspect.

[0009] According to the specific embodiments provided in this application, the following technical effects are disclosed: This application provides a method and collaborative decision-making platform for frequency security and coordinated control of remote renewable energy systems. By constructing multiple operating scenarios and establishing scenario-related frequency security constraints, the security requirements under different operating scenarios are internalized into capacity configuration and scheduling decisions. This ensures that the system still has sufficient backup capacity under harsh scenarios such as weak networks and isolated environments, fundamentally avoiding the risk of frequency instability. Simultaneously, it unifies long-term capacity planning with short-term operation scheduling decisions and refines the physical feasible domains of heterogeneous resources such as wind, solar, energy storage, and hydrogen through detailed modeling. This makes the output capacity configuration and scheduling plans not only physically executable but also achieves a coordinated balance between security and economy by maximizing the net present value over the entire lifecycle. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 This is an application environment diagram of the frequency security and coordinated control method for a remote new energy system in one embodiment of this application; Figure 2 This is a schematic diagram of the remote new energy system in one embodiment of this application. Detailed Implementation

[0012] 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, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0013] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0014] The frequency security and coordination control method of the remote new energy system in this embodiment is executed by a collaborative decision-making platform, which is a computer device, which can be a terminal computing device or a server.

[0015] Reference Figure 1 and Figure 2 In one specific embodiment, the frequency security and coordination control method for remote new energy systems provided in this application specifically includes: Step S100: Construct multiple operating scenarios and configure different frequency security requirement parameters for different operating scenarios.

[0016] Specifically, different operating scenarios can include weak AC interconnection scenarios, planned islanded operation scenarios, and all-power electronic islanded scenarios caused by faults, while frequency safety requirements parameters can include equivalent inertia level, acceptable extreme disturbance default probability, frequency change rate limit, frequency minimum point constraint value, and maximum allowable frequency deviation, etc.

[0017] A collection of running scenarios It can be represented as: ; in, For the first One operating scenario, This represents the total number of running scenarios.

[0018] No. One operating scenario At least include scenario probabilities Equivalent inertia level Acceptable extreme disturbances in default probability Frequency change rate limit and the minimum frequency constraint value The set of multiple operating scenarios is determined by the power grid operation mode, sending-end support conditions, and frequency security requirements, and is used to characterize the differences in frequency security boundaries of remote renewable energy systems under different power grid structures and operating conditions.

[0019] By constructing a set of multiple operating scenarios to characterize the different operating conditions faced by remote renewable energy systems, the control calculations for these systems can be performed using frequency safety parameters that match their current operating scenarios. Compared to using uniform frequency safety parameters, this invention enables more refined control of remote renewable energy systems.

[0020] Furthermore, in addition to constructing a set of multiple operating scenarios to characterize the different operating conditions faced by remote renewable energy systems, this method also includes: extracting multiple typical operating days based on historical resource and load data to characterize the changes in resources and load over time, and coupling and mapping typical operating days with operating scenarios.

[0021] Specifically, this step aims to more precisely characterize the operational status of remote renewable energy systems. The collaborative decision-making platform extracts multiple typical operating days based on historical wind power, solar power, and load data throughout the year using algorithms such as clustering. These typical operating days are used to represent resource fluctuations and load changes in different seasons and time periods. Meanwhile, the aforementioned typical grid operation scenarios reflect the dynamic characteristics and frequency security requirements of the grid under different grid connection methods and support conditions in the structural dimension. Although independent of each other, they jointly determine the system's scheduling status at a specific moment. By establishing a coupling mapping between the set of typical operating days and the set of multiple operating scenarios, this structure allows the optimization model to simultaneously consider changes in both time and structural dimensions, greatly improving the comprehensiveness and accuracy of decision-making.

[0022] To further enhance the precision of decision-making, each typical operating day can be divided into multiple different scheduling periods, such as every 15 minutes or 1 hour, forming a three-dimensional input structure of "typical operating day - operating scenario - scheduling period". For example, during peak photovoltaic power generation periods in the daytime, the available backup of remote renewable energy systems is relatively abundant, and the requirements for disturbances can be appropriately relaxed; while during peak electricity consumption periods at night, the backup of remote renewable energy systems is strained, requiring the setting of stricter frequency safety parameters. Finally, the collaborative decision-making platform outputs all preprocessed and organized data, including resource data, load data, typical operating day data, and refined multi-operating scenario parameters, as a standardized multi-operating scenario input set for use in subsequent steps.

[0023] Step S200: Based on the different stages of frequency dynamic response, the total frequency security reserve requirement is decomposed into two types of reserve requirements with different response time scales. Both types of reserve requirements with different response time scales are calculated based on frequency security requirement parameters.

[0024] Specifically, the two types of backup requirements with different response time scales include rapid backup requirements and continuous backup requirements; rapid backup requirements are the rapid power support capabilities needed to suppress a rapid decline in frequency after a disturbance occurs; continuous backup requirements are the continuous power support capabilities needed to limit the drop in frequency to the lowest point after a rapid decline in frequency.

[0025] The calculation steps for rapid standby requirements include: constructing a probability distribution of net power disturbances based on the forecast errors of wind power, photovoltaics, and loads; extracting credible extreme disturbances from the probability distribution of net power disturbances based on the default probability of extreme disturbances; and calculating rapid standby requirements based on credible extreme disturbances, inertia levels, and acceptable frequency change rate limits. The calculation steps for continuous standby requirements include: calculating continuous standby requirements based on the minimum frequency point constraint and the maximum permissible frequency deviation.

[0026] For example, the specific process of this step is as follows: 1. Acquisition of Prediction Error Parameters. Based on wind power, solar power, and load data, the collaborative decision-making platform acquires statistical parameters for wind power output prediction errors, solar power output prediction errors, and load prediction errors under various typical operating days and scheduling periods. Among these, the... A typical operating day, the first The wind power forecasting error, photovoltaic forecasting error, and load forecasting error within each scheduling period are respectively denoted as follows: , and Their corresponding standard deviations are denoted as follows: , and Statistical parameters can be calculated from the deviation samples between historical predicted values ​​and measured values, and are used to characterize the uncertainty of source load fluctuations under different scheduling periods.

[0027] 2. Construction of Net Power Disturbance Distribution. The collaborative decision-making platform constructs the net power disturbance based on wind power, photovoltaic, and load forecasting errors. A typical operating day, the first Net power disturbance during each scheduling period Represented as: .

[0028] Assuming that the errors in wind power, solar power, and load forecasting are independent and have a mean of zero, the variance of the net power disturbance is: ; In the formula, For the first A typical operating day, the first The standard deviation of net power disturbance within each scheduling period. Using the above formula, the collaborative decision-making platform can obtain the net power disturbance distribution for each scheduling period based on the collected wind power, photovoltaic, and load data.

[0029] 3. Scene-related reliable extreme perturbation extraction. The collaborative decision-making platform extracts the scene-related reliable extreme perturbations based on the first... Acceptable extreme disturbance default probability for each operating scenario Determine the quantile coefficients on one side of the scene. And based on this, reliable extreme perturbations are extracted. The first operating scenario, the first A typical operating day, the first Reliable extreme disturbances within a scheduling period Represented as: ; In the formula, To cause the probability of default due to extreme disturbances Determined standard normal quantile coefficients.

[0030] Through this step, the collaborative decision-making platform combines prediction error statistics with the safety requirements of the operating scenario to obtain scenario-related credible extreme perturbations.

[0031] 4. Classification of Backup Requirements. Due to the differences in response speed and sustainable support capacity of various adjustable resources within the new energy base, and because frequency security constraints include both the frequency change rate constraint in the initial stage of disturbance and the frequency minimum point constraint in the subsequent stage, the collaborative decision-making platform classifies frequency security requirements into rapid backup requirements and continuous backup requirements.

[0032] Among them, rapid standby requirement is used to characterize the rapid power support capability required to suppress the rapid frequency decline in the initial stage after a disturbance occurs; continuous standby requirement is used to characterize the continuous power support capability required to limit the drop in frequency to the lowest point as the frequency continues to evolve to the lowest point.

[0033] The above classification enables different types of equipment to participate in reserve allocation according to their dynamic response characteristics. Energy storage systems take priority in rapid reserve, while electrolyzers and other resources with large regulation capacity but slow response take on continuous reserve, thus ensuring that the subsequent reserve demand calculation results are consistent with the actual physical characteristics of the equipment.

[0034] 5. Rapid backup demand calculation. The collaborative decision-making platform calculates backup demand based on the... Equivalent inertia level for each operating scenario Frequency change rate limit and the system rated frequency Calculate the rapid standby requirement needed to meet the initial frequency change rate constraint under this scenario. The first operating scenario, the first A typical operating day, the first Rapid backup requirements within a scheduling period Represented as: ; In the formula, This is the base value of the equivalent power of the remote new energy system.

[0035] Through this step, the collaborative decision-making platform determines the minimum rapid support capability that the remote new energy system needs to be configured in the initial stage after a disturbance occurs, in order to avoid the frequency change rate exceeding the safety boundary of the corresponding operating scenario.

[0036] 6. Continuous Backup Requirement Calculation. After completing the rapid backup requirement calculation, the collaborative decision-making platform further calculates the backup requirement based on the following... Minimum frequency constraint value for each running scenario Lowest frequency point time window Including the configured rapid standby capability, the continuous standby requirement needed to satisfy the minimum frequency constraint is calculated. The continuous standby requirement constraint is established using the integral form of the linearized oscillation equation as follows: ; In the formula, For the first The first operating scenario, the first A typical operating day, the first Continuous backup requirements within a scheduling period; For transient processes, time variables are used.

[0037] Through this step, the collaborative decision-making platform determines the sustained support capacity required to prevent the frequency from continuing to decline and breaking through the frequency minimum point constraint in subsequent stages, following the rapid support effect.

[0038] 7. Frequency security requirements output. The collaborative decision-making platform will collect the rapid backup requirements and continuous backup requirements for each typical operating day, operating scenario, and scheduling period, and organize them into a unified frequency security requirements set, which will be provided to subsequent steps.

[0039] In steps S300 and S400, a collaborative optimization model is established that couples long-term planning decisions with short-term operational decisions. In the collaborative optimization model, a matching constraint is established to match the backup demand with two different response time scales with the backup capacity of heterogeneous resources. The long-term planning decision includes determining equipment capacity, and the short-term operational decision includes equipment scheduling strategies under multiple operational scenarios. The equipment capacity serves as the boundary constraint of the equipment scheduling strategy.

[0040] The objective function of the collaborative optimization model is to maximize the net present value over the entire life cycle. The net present value comprehensively considers the investment costs related to the equipment capacity configuration scheme and the operating benefits and maintenance costs related to the equipment scheduling strategy.

[0041] Furthermore, heterogeneous resources include wind turbine units, photovoltaic units, battery energy storage systems, and electrolyzers. Matching constraints include: matching the rapid backup capacity provided by photovoltaic units and battery energy storage systems with rapid backup requirements, and matching the continuous backup capacity provided by wind turbine units and electrolyzers with continuous backup requirements.

[0042] Furthermore, the collaborative optimization model also includes a physical feasible domain model (backup capacity model) for wind power units, photovoltaic units, battery energy storage systems, and electrolyzers, used to calculate the backup capacity of each resource.

[0043] First, we construct equipment models for wind power units, photovoltaic units, battery energy storage systems, and electrolyzers. Then, based on these equipment models, we further construct physical feasible domain models for wind power units, photovoltaic units, battery energy storage systems, and electrolyzers.

[0044] For example, the construction process of the physical feasible domain model for wind power units, photovoltaic units, battery energy storage systems, and electrolyzers is as follows: 1. Wind power units and photovoltaic units can be modeled using the upper bound of output: Based on the collected and preprocessed wind power resource data and photovoltaic resource data, the collaborative decision-making platform calculates the upper bound of available output for wind power units and photovoltaic units under each typical operating day and each scheduling period, and uses the upper bound of available output as the basic boundary for subsequent steady-state output and reserve capacity calculations.

[0045] Among them, the wind power unit is in the first A typical operating day, the first The upper bound of available output within a scheduling period is represented as follows: ; In the formula, For wind power units in the first A typical operating day, the first The upper limit of available output within a scheduling period; This refers to the installed capacity of the wind power unit; For the first A typical operating day, the first Equivalent wind speed within each scheduling period; This is the unit capacity output coefficient function of the wind power unit.

[0046] Equivalent wind speed This is obtained by unifying the time scale of the collected raw wind speed time series data; for the collected continuous wind speed sequences... ,have: ; In the formula, Preset scheduling duration; It is a continuous-time variable.

[0047] Wind power unit capacity output coefficient function Determined based on the power curve of the wind turbine, and expressed as: ; In the formula, To cut in wind speed; Rated wind speed; To determine the wind speed, the collaborative decision-making platform can directly calculate the upper limit of available output of wind power units for each scheduling period based on the collected wind speed data using the above formula.

[0048] Photovoltaic unit in the first A typical operating day, the first The upper bound of available output within a scheduling period is represented as follows: ; In the formula, In the first A typical operating day, the first The upper limit of available output within a scheduling period; This refers to the installed capacity of the photovoltaic unit; In the first A typical operating day, the first Equivalent solar irradiance within a scheduling period; In the first A typical operating day, the first Photovoltaic cell temperature during each scheduling period; This is the unit capacity output coefficient function of the photovoltaic unit.

[0049] Equivalent solar irradiance It is obtained by unifying the time scale of the collected raw irradiance time series data; for the collected continuous irradiance sequence ,have: ; In the formula, Preset scheduling duration; It is a continuous-time variable.

[0050] Photovoltaic cell temperature Based on calculations of ambient temperature and solar irradiance, the preferred expression is: ; In the formula, In the first A typical operating day, the first Ambient temperature during each scheduling period; This refers to the nominal operating temperature of the photovoltaic unit.

[0051] Photovoltaic unit unit capacity output coefficient function The preferred option is expressed as: ; In the formula, Reference irradiance under standard test conditions; The reference temperature under standard test conditions; β The temperature power coefficient of the photovoltaic unit is given by the above formula. Using this formula, the collaborative decision-making platform can directly calculate the upper limit of the available output of the photovoltaic unit for each scheduling period based on the collected irradiance and ambient temperature data.

[0052] Based on the available power output upper bound of wind power units and photovoltaic units, the collaborative decision-making platform further establishes their steady-state power output boundary: ; ; In the formula, and The wind power unit and the photovoltaic unit were respectively in the first A typical operating day, the first Steady-state output within a scheduling period.

[0053] 2. The collaborative decision-making platform models the electrolyzer as an adjustable load, whose operating power is constrained by rated capacity and minimum stable load. The electrolyzer in the... A typical operating day, the first The operating power within each scheduling period must meet the following requirements: ; In the formula, For the electrolytic cell in the first A typical operating day, the first Operating power within a scheduling period; This refers to the installed capacity of the electrolytic cell; This is the minimum stable load factor for the electrolytic cell; For the electrolytic cell in the first A typical operating day, the first The start / stop status variable within a scheduling period (e.g., start is 1, stop is 0).

[0054] The collaborative decision-making platform further establishes a mapping relationship between electrolyzer power and hydrogen production: ; In the formula, For the electrolytic cell in the first A typical operating day, the first Hydrogen production during each scheduling period; and represents the fitting coefficient for the hydrogen production characteristics of the electrolyzer.

[0055] 3. The collaborative decision-making platform establishes charging and discharging power constraints and state of charge constraints for the battery energy storage system. A typical operating day, the first The energy storage charging and discharging power within each scheduling period must meet the following requirements: ; ; ; In the formula, and For the battery energy storage system in the first A typical operating day, the first Charging power and discharging power within each scheduling period; The power capacity of the battery energy storage system; and For the battery energy storage system in the first A typical operating day, the first The charging and discharging state variables within each scheduling period. The energy storage state of charge satisfies: ; ; In the formula, For battery energy storage systems in the first A typical operating day, the first Energy storage during a scheduling period; The self-discharge rate of the energy storage; and These are charging efficiency and discharging efficiency, respectively. For scheduling duration; The energy capacity of the battery energy storage system; and These represent the minimum and maximum states of charge of a battery energy storage system, respectively.

[0056] 4. Backup Capacity Modeling. Based on the current operating point and remaining margin of each device, the collaborative decision-making platform calculates the backup capacity of wind power units, photovoltaic units, electrolyzers, and battery energy storage systems during each scheduling period.

[0057] The upward backup capacity for wind power units and photovoltaic units are as follows: ; ; The upward backup of the electrolytic cell is an adjustable power released by reducing the load, satisfying the following: ; Upward backup of battery energy storage systems is constrained by both remaining discharge power margin and sustainable discharge energy.

[0058] After obtaining the physical feasible domain model of the wind power unit, photovoltaic unit, battery energy storage system and electrolyzer, a collaborative optimization model can be further constructed.

[0059] For example, the construction process of the collaborative optimization model is as follows: 1. First-stage capacity configuration variable construction. The collaborative decision-making platform establishes the first-stage capacity configuration variables, which must include at least the installed capacity of wind power units. Installed capacity of photovoltaic units Power capacity of battery energy storage system Energy capacity of battery energy storage system and the installed capacity of electrolytic cells The capacity configuration variables for the first phase are represented as follows: ; In the formula, This is the set of capacity configuration variables for the first phase. These capacity configuration variables characterize the physical construction scale of various equipment in the Shagohuang New Energy System and serve as the unified boundary for the steady-state scheduling variables in the second phase.

[0060] 2. Construction of second-stage steady-state scheduling variables. The collaborative decision-making platform establishes second-stage steady-state scheduling variables for each operational scenario, each typical operational day, and each scheduling period.

[0061] When performing multi-scenario decomposition, a set of scenario-related scheduling variables is obtained: ; In the formula, and The wind power unit and the photovoltaic unit were respectively in the first The first operating scenario, the first A typical operating day, the first Steady-state output within a scheduling period; and For the battery energy storage system in the first The first operating scenario, the first A typical operating day, the first Charging power and discharging power within each scheduling period; For battery energy storage systems in the first The first operating scenario, the first A typical operating day, the first Energy storage during a scheduling period; For the electrolytic cell in the first The first operating scenario, the first A typical operating day, the first Operating power within a scheduling period; For the first The first operating scenario, the first A typical operating day, the first Power transmitted via VSC-HVDC within a scheduling period.

[0062] The second-stage steady-state scheduling variables are used to describe the operation plan for each scenario and time period under the known capacity configuration results.

[0063] 3. Coupling of Capacity Configuration Variables and Steady-State Scheduling Variables. The collaborative decision-making platform uses the capacity configuration variables from the first stage as the unified constraint boundary for the steady-state scheduling variables in the second stage, thereby establishing a coupling relationship between capacity configuration and steady-state scheduling. Specifically, the operation of wind power units, photovoltaic units, battery energy storage systems, and electrolyzers in each scenario must not exceed their corresponding installed capacity and equipment operating boundaries; the operating variables for any operating scenario, any typical operating day, and any scheduling period in the second stage are all constrained by the equipment model. This step ensures that a single capacity configuration result can simultaneously constrain the steady-state scheduling behavior under all operating scenarios.

[0064] 4. Collaborative Constraint Construction. Due to the varying response speeds and sustained support capabilities of different resources within the new energy base, the collaborative decision-making platform further divides available backup capacity into rapid backup capacity and sustained backup capacity. Rapid backup capacity characterizes the power support that can be rapidly injected or released in the initial stages of a disturbance, while sustained backup capacity characterizes the support capability that can maintain output as the frequency continues to evolve to its lowest point. Considering that the battery energy storage system and photovoltaic units can rapidly adjust their power according to the converter power command within tens of milliseconds, rapid backup capacity is jointly provided by the battery energy storage system and photovoltaic units. Considering the large inertia of wind turbines, the speed at which active power adjusts according to power commands is approximately several hundred milliseconds to several seconds. Furthermore, considering the risk of cell explosion due to rapid fluctuations and imbalances in the partial pressure of hydrogen and oxygen within the cell caused by rapid power adjustments in the electrolyzer, the electrolyzer and wind power units in this platform are only responsible for sustained backup capacity.

[0065] Preferably, the first The first operating scenario, the first A typical operating day, the first Rapid standby capability under each scheduling period Represented as: ; In the formula, and The battery energy storage system and photovoltaic unit were respectively in the first The first operating scenario, the first A typical operating day, the first Rapid backup capability provided within each scheduling period.

[0066] Among them, the battery energy storage system is in the first The first operating scenario, the first A typical operating day, the first The rapid standby capability provided within a scheduling period is represented as follows: ; In the formula, The power capacity of the battery energy storage system; and For the battery energy storage system in the first The first operating scenario, the first A typical operating day, the first Discharge power and charging power within each scheduling period; For battery energy storage systems in the first The first operating scenario, the first A typical operating day, the first Energy storage during a scheduling period; This represents the minimum state of charge of a battery energy storage system. The energy capacity of the battery energy storage system; For rapid response time windows; Let be the discharge efficiency of the battery energy storage system. This formula shows that the rapid backup capability of energy storage is limited by both the remaining power margin and the amount of electricity that can be released in a short period of time.

[0067] Photovoltaic unit in the first The first operating scenario, the first A typical operating day, the first Rapid standby capability within a scheduling period Represented as: ; In the formula; For photovoltaic units in the first A typical operating day, the first The upper limit of available output within a scheduling period; For photovoltaic units in the first The first operating scenario, the first A typical operating day, the first The steady-state output over a scheduling period. This formula shows that the rapid backup capability of a photovoltaic unit comes from the adjustment margin it retains relative to the upper limit of available output at the current operating point.

[0068] No. The first operating scenario, the first A typical operating day, the first Continuous backup capacity during each scheduling period Represented as: ; In the formula, and These are the electrolytic cell and the wind power unit, respectively. The first operating scenario, the first A typical operating day, the first Continuous backup capacity provided within each scheduling period.

[0069] This formula shows that the continuous backup capacity of energy storage comes from the available electricity that it can continuously release during the time window of the lowest frequency point.

[0070] The continuous standby capability of an electrolyzer stems from its capacity to reduce load while remaining at or above the minimum stable load. The electrolyzer in the... The first operating scenario, the first A typical operating day, the first Continuous backup capacity during each scheduling period Represented as: ; In the formula, For the electrolytic cell in the first The first operating scenario, the first A typical operating day, the first Operating power during each scheduling period For the electrolytic cell in the first The first operating scenario, the first A typical operating day, the first Start-up and shutdown status variables under each scheduling period; This is the minimum stable load factor for the electrolytic cell; This refers to the installed capacity of the electrolytic cell.

[0071] The continuous reserve capacity of a wind turbine unit derives from the regulation margin it retains relative to the upper limit of available output at the current operating point, and is used to provide continuous support in the subsequent stages of a disturbance. The wind turbine unit in the... The first operating scenario, the first A typical operating day, the first Continuous backup capacity during each scheduling period Represented as: ; In the formula, For wind power units in the first A typical operating day, the first The upper bound of available output under each scheduling period; For wind power units in the first The first operating scenario, the first A typical operating day, the first Steady-state output under each scheduling period.

[0072] After obtaining rapid standby capability and continuous standby capability, the collaborative decision-making platform matches them with rapid standby requirements and continuous standby requirements, respectively. The optimal matching constraint is expressed as: ; ; In the formula, and They were respectively in the second The first operating scenario, the first A typical operating day, the first Rapid backup requirements and continuous backup requirements under each scheduling period.

[0073] Through the above constraints, the collaborative model can ensure that frequency safety boundaries are met under different operating scenarios while satisfying power balance.

[0074] 6. Objective Function Construction. The collaborative decision-making platform constructs an objective function with the goal of achieving economic efficiency throughout the entire life cycle. Preferably, the optimization objective is to maximize the project's net present value, and its expression is: ; In the formula, The objective function value; For one-time investment costs; For the first Annual operating revenue; For the first Annual operating costs; The discount rate; The project lifespan. This includes the initial investment cost. Calculated by combining the installed capacity and unit investment cost of various types of equipment, and directly derived from the capacity configuration results of the first phase, it is expressed as: ; In the formula, , , , and These are the unit investment costs for wind power units, photovoltaic units, electrolytic cells, energy storage power capacity, and energy storage energy capacity, respectively. , , , and These refer to the installed capacity of wind power units, photovoltaic units, electrolytic cells, energy storage power capacity, and energy storage energy capacity, respectively.

[0075] No. Annual operating costs The cost is calculated from the combined operating and maintenance costs of various types of equipment, and is derived from the annual maintenance expenditures of the configured equipment during its lifespan. It is expressed as: ; In the formula, , , and These are the operation and maintenance cost coefficients for wind power units, photovoltaic units, electrolytic cells, and battery energy storage systems, respectively.

[0076] No. Annual operating revenue The hydrogen production revenue is obtained by weighting the hydrogen production revenue under various operating scenarios, typical operating days, and scheduling periods. It originates from the electrolyzer operating power and corresponding hydrogen production determined by the second-stage steady-state scheduling results, and is specifically expressed as follows: ; In the formula, For the first For example, the probability of each operating scenario is set as follows: weak AC interconnection is the main mode of normal system operation, with a probability of 50%; planned islanding occurs during regular maintenance, debugging, or specific scheduling modes, with a probability of 20%; and full power electronic islanding is induced by severe faults or emergency operations, with a low probability of occurrence, set at 30%. For the first The number of days in a year corresponding to a typical operating day; The selling price of hydrogen; In the first The first operating scenario, the first A typical operating day, the first Hydrogen production during each scheduling period.

[0077] Step S500: Solve the collaborative optimization model to obtain collaborative decision results, which include equipment capacity configuration schemes and equipment scheduling strategies for different operating scenarios.

[0078] The collaborative decision-making platform invokes the solver's collaborative optimization model for joint solution. Under the conditions of satisfying equipment operating boundaries, power balance constraints, frequency safety constraints, and electro-hydrogen coupling constraints, it obtains the optimal or suboptimal capacity configuration and steady-state scheduling results for the objective function. The joint solution simultaneously determines the first-stage capacity configuration variables and the second-stage steady-state scheduling variables, thus ensuring that the output results have consistent capacity boundaries and executable operating plans across multiple operating scenarios.

[0079] The collaborative decision-making platform extracts capacity configuration results from the joint solution results. These results include the installed capacity of wind power units, photovoltaic units, the power and energy capacities of battery storage systems, and the installed capacity of electrolyzers. The capacity configuration results are used to guide the determination of the physical construction scale of various equipment in remote renewable energy systems.

[0080] The collaborative decision-making platform extracts steady-state scheduling results from the joint solution results for each operating scenario, typical operating day, and scheduling period. These steady-state scheduling results include wind power output plans (steady-state output of wind turbine units), photovoltaic power output plans (steady-state output of photovoltaic units), energy storage charging and discharging plans (charging and discharging power of battery energy storage systems), energy storage state-of-charge trajectory (energy stored in battery energy storage systems), electrolyzer operation plans (operating power of electrolyzers), VSC-HVDC power transmission plans, and rapid and continuous reserve allocation results. The steady-state scheduling results are used to characterize the system operation mode under different operating scenarios and resource-load conditions.

[0081] The collaborative decision-making platform outputs the verified capacity configuration results to the engineering design unit for equipment capacity determination and system construction plan formulation. Simultaneously, it outputs the verified scenario-based steady-state scheduling results to the station-level energy management system to generate day-ahead or intraday scheduling plans, reserve configuration plans, and VSC-HVDC power transmission plans for different operating scenarios. This step completes a closed-loop output from input information across multiple operating scenarios to capacity configuration results, scenario-based scheduling results, and reserve allocation results.

[0082] It should be noted that the above steps are mainly offline steps. After the capacity configuration and scenario-based scheduling strategy are determined offline, this method may also include online real-time coordination and control (step S600).

[0083] First, to detect disturbances promptly, the collaborative decision-making platform collects the VSC-HVDC DC bus voltage in real time. Since power imbalances in the receiving-end grid are first transmitted through the DC lines, causing transients in DC link energy and deviations in the DC bus voltage, the deviation of the DC bus voltage can be monitored to construct an equivalent sensing signal for frequency changes in the receiving-end grid. For example, in a fully power electronic islanded scenario, the DC bus voltage deviation can be directly mapped to the equivalent frequency deviation of the sending-end system; in scenarios with synchronous machine support, the voltage deviation can be converted into a correction for the active power of the sending-end converter, enabling the sending-end frequency response to quickly follow changes in the receiving-end frequency. This approach reduces reliance on long-distance, high-latency frequency communication channels, improving the timeliness and reliability of disturbance detection.

[0084] Once a frequency disturbance is detected, the collaborative decision-making platform generates differentiated real-time power adjustment commands based on the equivalent sensing signal and the reserve margins of each resource pre-determined in the scheduling strategy. This differentiation is reflected in the allocation of commands according to the different characteristics of each resource. Specifically, the command-based battery energy storage system employs a combination of virtual inertia and droop control, prioritizing the response rate of frequency change to suppress the initial rapid frequency decline. Its command amplitude is strictly constrained by the energy storage power capacity and the current state of charge to avoid over-discharge. Simultaneously, the command-based electrolyzer uses droop control to reduce its load power, providing continuous power support to the system. However, its adjustment rate is limited by a maximum ramp-up limit, and the final power must not fall below its minimum stable load to ensure equipment safety. Furthermore, if wind turbines and photovoltaic units have reserved upward adjustment margins at the current operating point, commands will also be generated to enable them to participate in auxiliary support via droop control, with the command amplitude not exceeding their available reserve capacity.

[0085] Finally, before all generated power adjustment commands are sent to the controllers of each device for execution, they are compared with the upper limit of the reserve capacity set by the collaborative optimization model. The smaller value is then sent out. This is an important safety limiting step, ensuring that the control commands do not exceed the physical capacity of the equipment. Simultaneously, the system monitors the VSC-HVDC DC bus voltage in real time. If it is found that the voltage exceeds the safety limit due to power adjustment, higher-priority protection logic is triggered, such as prioritizing the adjustment of DC power or limiting the adjustment depth of the electrolyzer, to ensure the voltage stability and safety of the entire system. Through the above real-time control steps, this invention achieves a complete closed loop from multi-scenario frequency safety requirement modeling, capacity configuration and steady-state scheduling collaborative decision-making, to real-time coordinated frequency control, enabling the remote new energy system to maintain frequency stability under various operating scenarios while also considering operational economy.

[0086] In summary, the present invention has the following technical effects: 1. Improved frequency security of remote renewable energy systems under variable operating environments. By constructing multiple operating scenarios and establishing scenario-related frequency security constraints, this invention internalizes the security requirements under different grid strengths into capacity configuration and scheduling decisions, ensuring that the system still has sufficient backup capacity under harsh scenarios such as weak grids and isolated areas, fundamentally avoiding the risk of frequency instability.

[0087] 2. Improved engineering feasibility and economic efficiency of the decision-making scheme. The collaborative optimization model adopted in this invention unifies long-term capacity planning and short-term operation scheduling, and refines the physical feasible domain of heterogeneous resources such as wind, solar, energy storage, and hydrogen. This makes the output capacity configuration and scheduling plan not only physically executable, but also achieves a coordinated balance between safety and economy by maximizing the net present value over the entire life cycle.

[0088] 3. It achieves optimized collaboration and efficient utilization of heterogeneous resources. By decomposing backup demand into two categories, rapid and continuous, and accurately matching them with the dynamic response characteristics of different resources, this invention can fully leverage the rapid response capability of battery energy storage and the continuous regulation capability of electrolyzers and wind power, forming a complementary frequency support system and improving the overall utilization efficiency of backup resources.

[0089] 4. Enhanced real-time frequency control capability of the system. By constructing an equivalent frequency sensing signal based on DC bus voltage deviation and generating differentiated multi-resource coordinated control commands, this invention achieves rapid and accurate response to frequency disturbances, effectively reduces reliance on long-distance communication, and improves the robustness and timeliness of the control system.

[0090] In one exemplary embodiment, a collaborative decision-making platform is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0091] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0092] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0093] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0094] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0095] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0096] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0097] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for frequency security and coordinated control of a remote new energy system, characterized in that, include: Construct multiple operating scenarios and configure different frequency security requirement parameters for different operating scenarios; Based on different stages of frequency dynamic response, the total frequency security reserve requirement is decomposed into two types of reserve requirements with different response time scales. Both types of reserve requirements with different response time scales are calculated based on frequency security requirement parameters. A collaborative optimization model is established that couples long-term planning decisions with short-term operational decisions. In the collaborative optimization model, a matching constraint is established to match the backup demand with two different response time scales with the backup capacity of heterogeneous resources. The long-term planning decision includes determining equipment capacity, and the short-term operational decision includes equipment scheduling strategies under multiple operational scenarios. The equipment capacity serves as the boundary constraint of the equipment scheduling strategy. Solve the collaborative optimization model to obtain collaborative decision results, which include the equipment capacity configuration scheme and the equipment scheduling strategy for different operating scenarios.

2. The method for frequency security and coordinated control of a remote new energy system according to claim 1, characterized in that, Two types of standby requirements with different response time scales include rapid standby requirements and continuous standby requirements. The rapid backup requirement is the rapid power support capability needed to suppress a rapid frequency decline after a disturbance occurs; the continuous backup requirement is the continuous power support capability needed to limit the drop in frequency to the lowest point after a rapid frequency decline.

3. The method for frequency security and coordinated control of a remote new energy system according to claim 1, characterized in that, The heterogeneous resources include wind power units, photovoltaic units, battery energy storage systems, and electrolyzers; The matching constraints include: matching the rapid backup capability provided by the photovoltaic unit and the battery energy storage system with the rapid backup requirement, and matching the continuous backup capability provided by the wind power unit and the electrolyzer with the continuous backup requirement.

4. The method for frequency security and coordinated control of a remote new energy system according to claim 3, characterized in that, The collaborative optimization model also includes a physical feasible domain model for the wind power unit, the photovoltaic unit, the battery energy storage system, and the electrolyzer, used to calculate the backup capacity of each resource.

5. The method for frequency security and coordinated control of a remote new energy system according to claim 1, characterized in that, The objective function of the collaborative optimization model is to maximize the net present value over the entire life cycle. The net present value comprehensively considers the investment costs related to the configuration scheme of the equipment capacity and the operating benefits and maintenance costs related to the equipment scheduling strategy.

6. The method for frequency security and coordinated control of a remote new energy system according to claim 2, characterized in that, The frequency safety requirements parameters include inertia level, acceptable frequency change rate limit, frequency minimum point constraint, extreme disturbance violation probability, and maximum permissible frequency deviation. The calculation steps for the rapid standby requirement include: constructing a probability distribution of net power disturbance based on the prediction errors of wind power, photovoltaic power and load; extracting credible extreme disturbances from the probability distribution of net power disturbances according to the extreme disturbance default probability; and calculating the rapid standby requirement based on the credible extreme disturbances, the inertia level and the acceptable frequency change rate limit. The calculation steps for the continuous reserve requirement include: calculating the continuous reserve requirement based on the minimum frequency constraint and the maximum allowable frequency deviation.

7. The method for frequency security and coordinated control of a remote new energy system according to claim 2, characterized in that, Also includes: By monitoring the deviation of the VSC-HVDC DC bus voltage in real time, an equivalent sensing signal for the frequency change of the receiving-end power grid is constructed. Based on the equivalent sensing signal and the pre-determined backup allocation in the equipment scheduling strategy, a differentiated real-time power adjustment command is generated. The differentiated real-time power adjustment commands include commands to provide virtual inertia and rapid sag support for energy storage, and commands to provide sag support for the electrolyzer under process constraints.

8. The method for frequency security and coordinated control of a remote new energy system according to claim 1, characterized in that, Also includes: Multiple typical operating days are extracted based on historical resource and load data to characterize the changes in resources and load over time, and these typical operating days are coupled and mapped with the operating scenarios.

9. The method for frequency security and coordinated control of a remote new energy system according to claim 8, characterized in that, The typical operating day is also divided into several different scheduling periods.

10. A collaborative decision-making platform, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the remote renewable energy system frequency security and coordinated control method according to any one of claims 1-9.

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