New energy base unit optimization method and device based on frequency safety and voltage stiffness joint constraint, terminal equipment and storage medium

Abstract

The application discloses a new energy base unit optimization method and device based on frequency safety and voltage stiffness joint constraints, a terminal equipment and a storage medium, and belongs to the technical field of power system operation optimization. The method comprises the following steps: acquiring frequency response parameters and electrical basic parameters of a new energy base to be optimized; generating frequency safety constraints after constructing a frequency response model according to the frequency response parameters; generating voltage stiffness constraints after constructing a quantitative mapping relationship between new energy station output power and grid connection point voltage stiffness according to the electrical basic parameters; constructing a unit combination optimization model and corresponding thermal power unit operation and energy storage operation constraints according to time sequence operation parameters, electrical basic parameters and operation cost data; and solving the optimization model to obtain a unit start-stop strategy and a power output distribution strategy and to perform dispatching optimization in combination with the frequency safety and voltage stiffness constraints and taking the minimum total operation cost as the target. Through implementation of the application, the problem that frequency and voltage collaborative protection cannot be realized in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization technology, and in particular to a method, apparatus, terminal equipment and storage medium for optimizing generating units in new energy bases based on joint constraints of frequency security and voltage stiffness. Background Technology

[0002] Against the backdrop of a global effort to transform energy structures and address climate change, accelerating the development and large-scale utilization of renewable energy has become a core pathway to achieving dual-carbon goals. Coastal regions, possessing advantages in natural resources such as offshore wind power and onshore photovoltaics, are gradually becoming important areas for large-scale new energy development. Coastal new energy bases, as key platforms for aggregating and efficiently utilizing diverse new energy sources, play an indispensable role in promoting the construction of a new energy system.

[0003] Frequency stability is a key indicator for measuring the safety level of power system operation and directly affects the reliability of power supply. Voltage stability is also an important dimension for ensuring the safe operation of power systems. Voltage stiffness, as a quantitative indicator characterizing the ability of node voltage to resist disturbances, reflects the inherent ability of the power system to maintain bus voltage stability under reactive power injection disturbances, and its essence is highly correlated with the system short-circuit capacity. With the large-scale replacement of traditional synchronous units by new energy units, the system short-circuit capacity is showing a downward trend, resulting in a continuous weakening of the voltage stiffness of key nodes. When introducing voltage stiffness constraints into existing unit combination optimization models, the inherent coupling effect between voltage stiffness constraints and frequency safety constraints is often ignored, making it difficult to achieve coordinated protection of frequency and voltage, resulting in the inability to simultaneously guarantee the frequency and voltage safety of new energy bases. Summary of the Invention

[0004] This invention provides a method, apparatus, terminal equipment, and storage medium for optimizing new energy base units based on joint constraints of frequency security and voltage stiffness. It can effectively solve the problem that existing technologies neglect the inherent coupling effect between voltage stiffness constraints and frequency security constraints, making it difficult to achieve coordinated protection of frequency and voltage.

[0005] One embodiment of the present invention provides an optimization method for generating units in a new energy base based on joint constraints of frequency security and voltage stiffness, comprising: Obtain frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized; A frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model. Based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed, and voltage stiffness constraints are generated based on the quantitative mapping relationship. Based on the time-series operating parameters, the electrical basic parameters, and the operating cost data, a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints are constructed. Under the constraints of frequency safety, voltage stiffness, thermal power unit operation, and energy storage operation, with the goal of minimizing the total operating cost within the scheduling cycle, the unit combination optimization model is solved to obtain the unit start-up and shutdown strategy and the power output allocation strategy. The scheduling optimization of the new energy base to be optimized is carried out based on the unit start-up and shutdown strategy and the power output allocation strategy.

[0006] Furthermore, the frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; A frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

[0007] Furthermore, the electrical basic parameters include equivalent impedance parameters, grid connection point reference voltage parameters, and power factor of the new energy power station; Based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship is constructed between the output power of the new energy power station and the voltage stiffness at the grid connection point. Voltage stiffness constraints are then generated based on this quantitative mapping relationship, including: Based on the equivalent impedance parameters and the grid connection point reference voltage parameters, a voltage stiffness index is constructed to characterize the ability of node voltage to resist power injection disturbances. Based on the power factor of the new energy power station and the voltage stiffness index, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed. Based on the quantitative mapping relationship and the preset voltage stiffness safety threshold, a voltage stiffness constraint is generated.

[0008] Furthermore, the operating constraints of the thermal power unit include upper and lower limits of active power output constraints and ramping power constraints.

[0009] Furthermore, the energy storage operation constraints include upper and lower limits of energy storage charging and discharging power, upper and lower limits of state of charge, mutual exclusion of charging and discharging, energy balance constraints at the beginning and end of the scheduling cycle, and constraints on the maximum number of charging and discharging cycles.

[0010] Furthermore, the unit combination optimization model is as follows: in, This represents the total operating cost within the scheduling period. This represents the total start-up and shutdown costs of thermal power units. This represents the total cost of electricity generation. The total penalty cost for abandoning wind and solar power plants; For scheduling periods; The scheduling period; The number of thermal power plants; and The first The start-up and shutdown costs of a thermal power plant; Binary variables used to control the start-up and shutdown of individual thermal power plants; The output power of a single thermal power plant; This represents the power generation cost coefficient for thermal power plants. and These represent the index numbers of wind turbine units and photovoltaic units, respectively; and These represent the total number of wind turbine units and photovoltaic units, respectively. and These represent the predicted output of wind turbines and photovoltaic units, respectively. and These represent the actual output of the wind turbine and the photovoltaic unit, respectively. and These represent the decommissioning penalty costs for wind turbines and photovoltaic units, respectively.

[0011] As an improvement to the above solution, another embodiment of the present invention provides a new energy base unit optimization device based on joint constraints of frequency security and voltage stiffness, comprising: The data acquisition module is used to acquire frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized. A frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model. The voltage stiffness constraint module is used to construct a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point based on the electrical basic parameters, and to generate voltage stiffness constraints based on the quantitative mapping relationship. The unit combination optimization model construction module is used to construct the unit combination optimization model and the corresponding thermal power unit operation constraints and energy storage operation constraints based on the time-series operating parameters, the electrical basic parameters and the operating cost data. The joint constraint solving module is used to solve the unit combination optimization model under the constraints of frequency security, voltage stiffness, thermal power unit operation and energy storage operation, with the goal of minimizing the total operating cost within the scheduling cycle, to obtain the unit start-up and shutdown strategy and the power output allocation strategy. The unit scheduling optimization module is used to perform scheduling optimization on the new energy base to be optimized based on the unit start-up and shutdown strategy and the power output allocation strategy.

[0012] Furthermore, the frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; The frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

[0013] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a new energy base unit optimization method based on joint constraints of frequency security and voltage stiffness as described in the above embodiments.

[0014] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness described in the above embodiment.

[0015] By implementing this invention, at least the following beneficial effects are achieved: This invention provides a method, apparatus, terminal equipment, and storage medium for optimizing new energy base units based on joint constraints of frequency security and voltage stiffness. The method acquires frequency response parameters, electrical foundation parameters, time-series operating parameters, and operating cost data of the new energy base to be optimized; constructs a frequency response model based on the frequency response parameters, and generates frequency security constraints based on the frequency response model; constructs a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point based on the electrical foundation parameters, and generates voltage stiffness constraints based on the quantitative mapping relationship; then, based on the time-series operating parameters, the electrical foundation parameters, and the operating cost data, constructs a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints; under the frequency security constraints, the voltage stiffness constraints, the thermal power unit operating constraints, and the energy storage operating constraints, the unit combination optimization model is solved with the objective of minimizing the total operating cost within the scheduling cycle to obtain unit start-up and shutdown strategies and power output allocation strategies; finally, the new energy base to be optimized is scheduled and optimized based on the unit start-up and shutdown strategies and the power output allocation strategies. First, a frequency response model is constructed based on frequency response parameters, and then frequency safety constraints are constructed based on the frequency response model. Simultaneously, based on electrical fundamental parameters, a quantitative mapping relationship is established between renewable energy output and grid connection point voltage stiffness. Based on this quantitative mapping relationship, voltage stiffness requirements are converted into voltage stiffness constraints, satisfying both frequency stability and voltage support constraints, thus achieving coupling between voltage and frequency. Under the joint constraints of frequency safety and voltage stiffness, combined with thermal power unit operation constraints and energy storage operation constraints, the unit combination optimization model is solved with the goal of minimizing total operating cost. This yields unit start-up and shutdown strategies and power output allocation strategies that satisfy both frequency and voltage safety, achieving optimized scheduling of the renewable energy base and thereby improving its operational safety. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness, provided by an embodiment of the present invention. Figure 2 This is an operational structure diagram of a coastal new energy base provided in an embodiment of the present invention; Figure 3 This is a frequency response framework diagram of a coastal new energy base provided in an embodiment of the present invention; Figure 4 This is a diagram showing the optimized combination of generating units in a coastal new energy base, considering the joint constraints of frequency security and voltage stiffness, provided by an embodiment of the present invention. Figure 5 This is a diagram showing the optimized combination of generating units in a coastal new energy base, without considering the joint constraints of frequency security and voltage stiffness, according to an embodiment of the present invention.

[0017] Figure 6 This is a schematic diagram of the structure of a new energy base unit optimization device based on the joint constraints of frequency security and voltage stiffness, provided in an embodiment of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] See Figure 1 To address the problem that existing technologies neglect the inherent coupling effect between voltage stiffness constraints and frequency safety constraints, making it difficult to achieve coordinated protection of frequency and voltage, an embodiment of the present invention provides a flowchart illustrating an optimization method for new energy base units based on joint constraints of frequency safety and voltage stiffness, including: S1. Obtain the frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized; Specifically, frequency response parameters represent various fundamental parameters depicting the dynamic frequency changes after power disturbances in the power system. These are core inputs characterizing the power system's frequency regulation capability, including system frequency deviation, frequency response time, power increments from renewable energy plants participating in frequency regulation, and droop coefficients. Electrical fundamental parameters are parameters representing the inherent electrical characteristics of the power grid, node voltage reference levels, and equipment operating characteristics. These are the underlying data for analyzing voltage stability issues, including equivalent impedance parameters, grid connection reference voltage parameters, and the power factor of renewable energy plants. Time-series operational parameters are time-period-by-period operational forecast data within the scheduling cycle, serving as the time-series driving input for optimized scheduling. Operating cost data are parameters representing the economic costs of operating various power sources, providing a quantitative basis for optimization objectives.

[0020] This example illustrates the acquisition of frequency response parameters, electrical infrastructure parameters, time-series operating parameters, and operating cost data for the new energy base to be optimized. Four types of core data need to be collected from the base's energy management system, equipment ledger, and forecasting system to ensure that the data's time scale matches the scheduling cycle and that the data accuracy meets the requirements of optimization calculations.

[0021] S2. Construct a frequency response model based on the frequency response parameters, and generate frequency safety constraints based on the frequency response model; Specifically, the frequency response model describes the frequency variation over time after a power system disturbance, and can reproduce the entire process from the occurrence of the disturbance to the restoration of steady state. Frequency security constraints are the constraints that ensure the power system frequency remains within the allowable range, and are the safety boundaries that dispatching schemes must satisfy.

[0022] Indicatively, a frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model. Starting from physical characteristics, the frequency regulation capability of various power supplies is quantified into a mathematical model, and then transformed into frequency safety constraints that can be directly called in the optimization problem.

[0023] Preferably, the frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; A frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

[0024] Specifically, system frequency deviation is the difference between the actual operating frequency and the rated frequency of the power system, and is a direct indicator of frequency stability, measured in Hertz (Hz). Frequency response time is a parameter describing how quickly a frequency-modulated power supply responds to frequency deviations; a smaller value indicates a faster response. Frequency regulation power increment is the amount of active power adjusted by the frequency-modulated power supply when a frequency deviation occurs, directly reflecting its frequency regulation capability. The droop coefficient characterizes the output adjustment range corresponding to a unit frequency deviation; a smaller droop coefficient indicates a larger frequency regulation output per unit frequency deviation. Primary frequency regulation characteristics are the dynamic characteristics of the power supply automatically adjusting its active power output based on frequency deviations, forming the first line of defense for system frequency stability.

[0025] Specifically, the maximum frequency deviation is the maximum deviation of the frequency from the rated value after a disturbance occurs, and it is the core indicator of transient frequency safety. The maximum frequency change rate is the fastest rate of frequency change at the initial moment of the disturbance, reflecting the system's inertia level and is an important indicator of frequency safety. The steady-state frequency deviation is the deviation of the frequency from the rated value after frequency adjustment ends and the system enters a steady state, reflecting the overall capability of the system's primary frequency regulation. The permissible frequency safety threshold is the maximum permissible value of frequency indicators specified according to industry standards or operating procedures, and it is the standard for determining whether the frequency is safe.

[0026] Schematic, based on the system frequency deviation, frequency response time, power increment, and droop coefficient, an equivalent fitting is performed on the primary frequency regulation characteristics of each power unit within the new energy base to be optimized, constructing a frequency response model. The power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. Specifically, for the four types of power sources—thermal power units, wind farms, photovoltaic power plants, and supporting energy storage—their respective droop coefficients and response time constants are collected. A parameter identification method is used to uniformly fit the frequency regulation dynamic characteristics of each type of power source into the form of a first-order inertial element. The advantage of this approach is that the frequency regulation characteristics of different types of power sources can be expressed in a unified mathematical form, facilitating subsequent superposition and calculation of the overall system frequency response. Furthermore, thermal power units rely on turbine governors for primary frequency regulation, resulting in a relatively slow response but strong sustainability; new energy and energy storage rely on power electronic control for frequency regulation, resulting in a fast response but limited by reserve capacity. After unified fitting, the frequency regulation contributions of each type of power source can be clearly compared.

[0027] Based on the frequency response model, the maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the occurrence of the disturbance are analyzed. After obtaining a unified frequency modulation model for various power sources, and combining the system's equivalent inertia and damping coefficient, the overall frequency response differential equation of the system is established. Through analytical derivation, three key indicators during the frequency change process under a given unbalanced power disturbance are calculated: the maximum rate of frequency change at the moment of disturbance, the lowest point of frequency drop (i.e., maximum frequency deviation), and the steady-state frequency deviation after final stabilization. These three indicators correspond to the initial, transient, and steady-state stages of the frequency process, respectively, covering the entire process of frequency disturbance and providing more comprehensive frequency safety assurance than a single indicator.

[0028] Frequency safety constraints are generated based on the maximum frequency deviation, the maximum rate of frequency change, the steady-state frequency deviation, and a preset frequency safety allowable threshold. The three calculated frequency indicators are then compared with the preset safety limits, transforming them into three inequality constraints. These constraints require that for any optimized scheduling scheme, the corresponding three frequency indicators must be less than or equal to the safety limits. This transforms the physical-level frequency safety requirements into mathematical constraints that the optimization model can directly recognize.

[0029] In a preferred embodiment of the present invention, a frequency response model characterizing transient frequency modulation characteristics is constructed. The frequency response characteristics of various types of units, such as thermal power, wind power, photovoltaic, and energy storage, are equivalently fitted to a first-order inertial element using the least squares method. Analytical expressions for frequency safety indicators such as maximum frequency deviation, maximum frequency change rate, and steady-state frequency deviation are derived to establish frequency safety constraints.

[0030] Preferably, this embodiment is applicable to coastal new energy bases. Figure 2 This is a schematic diagram of the operational structure of a coastal new energy base, which consists of thermal power plants, wind power plants, photovoltaic power plants, and their supporting energy storage power plants. For various types of generating units, the least squares method is used to identify the parameters of their dynamic frequency response characteristics. The frequency response characteristics of all types of units are then uniformly and equivalently fitted into a first-order inertial element model, the mathematical expression of which is as follows: in, For the Laplace operator; , and These are the power increment, droop coefficient, and response time constant for thermal power plants, wind power plants, wind power-supported energy storage power stations, photovoltaic power stations, and photovoltaic-supported energy storage power stations participating in primary frequency regulation, respectively. This represents the system's frequency deviation.

[0031] Preferably, Figure 3 For the frequency response framework of the coastal new energy base, after load disturbance, multiple types of units jointly provide inertia support and primary frequency regulation power. Its dynamic frequency response equation is shown below: In the formula, The system frequency response time; The equivalent inertial time constant of the system; The damping coefficient of the system; This represents the unbalanced power of the system at the moment the disturbance occurs.

[0032] The frequency response model was analyzed, and the maximum frequency deviation was derived. Maximum frequency change rate at the initial moment of the disturbance and steady-state frequency deviation The parsing expression: In the formula, This represents the maximum frequency deviation. The maximum rate of change of frequency; This refers to the steady-state frequency deviation. The initial frequency of the system; This refers to the total rated capacity of all generating units; , , , , These are intermediate variables introduced during the derivation process; This is the operating status variable for thermal power plants. It is set to 1 when the plant starts up and 0 when it stops. This is the moment of lowest frequency, that is, the point in time when the system frequency drops to its maximum value after the disturbance occurs; This refers to the number of the thermal power plant. This refers to the number of thermal power plants.

[0033] To ensure system frequency security, the following frequency security constraints must be imposed on the above analytical expression: In the formula, , , These represent the maximum permissible frequency deviation, the maximum permissible rate of frequency change, and the maximum permissible steady-state frequency deviation for coastal new energy bases, respectively.

[0034] In a preferred embodiment of the present invention, the droop coefficient of thermal power units is 4%, and the response time constant is 2s; the droop coefficient of wind farms is 5%, and the response time constant is 0.5s; the droop coefficient of photovoltaic power stations is 5%, and the response time constant is 0.3s; and the droop coefficient of energy storage power stations is 2%, and the response time constant is 0.1s. The frequency regulation characteristics of the four types of power sources are uniformly fitted into a first-order inertial element model. Combined with the system equivalent inertia time constant of 4s and the damping coefficient of 15pu, the disturbance scenario is set as a sudden load increase of 10% of the rated capacity. Through analytical calculation, the maximum frequency change rate is 0.18Hz / s, the maximum frequency deviation is 0.19Hz, and the steady-state frequency deviation is 0.12Hz. Compared with the preset frequency safety allowable threshold, the frequency change rate is ≤0.2Hz / s, the maximum frequency deviation is ≤0.2Hz, and the steady-state deviation is ≤0.2Hz. All three indicators meet the requirements and are converted into corresponding inequality constraints and incorporated into the optimization model. In the final optimized plan, by reasonably arranging the continuous operation of two thermal power units and coordinating with energy storage for rapid frequency regulation, it is possible to ensure that all three frequency indicators are within the safe range even during periods of high renewable energy generation, while avoiding the cost waste caused by operating multiple units.

[0035] Compared to existing methods that only consider single or dual frequency indicators, this embodiment constructs a comprehensive frequency security protection system. Most existing methods only focus on the maximum frequency deviation, neglecting issues such as malfunction of low-frequency load shedding devices and generator disconnection that may result from excessively rapid frequency change rates. This embodiment simultaneously manages indicators across three stages: initial rate of change, transient minimum point, and steady-state deviation, achieving full-process protection from disturbance occurrence to frequency recovery, providing more comprehensive frequency security assurance. By uniformly fitting the frequency regulation characteristics of different power sources into a first-order inertial element, complex high-order model solutions are avoided. The analytical derivation method offers fast calculation speed, suitable for day-ahead dispatching scenarios in engineering applications. Through parameter fitting by type, the frequency regulation capabilities of thermal power, new energy, and energy storage can be clearly quantified, facilitating targeted allocation of frequency regulation resources by dispatchers and providing a quantitative basis for the subsequent participation of new energy in frequency regulation.

[0036] S3. Based on the electrical basic parameters, construct a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point, and generate voltage stiffness constraints based on the quantitative mapping relationship; Specifically, voltage stiffness is the ratio of the voltage amplitude at the grid connection point of a grid-connected device (such as a wind farm or photovoltaic power station) before and after the device is connected to the grid, assuming the system's equivalent impedance remains constant. It is used to quantitatively assess the node's voltage's ability to withstand changes in power injection; a higher value indicates a more stable voltage and stronger disturbance resistance. The quantitative mapping relationship is the numerical correspondence between the output power of the renewable energy power station and the voltage stiffness at the grid connection point, which can be calculated from known power levels. Voltage stiffness constraints are conditions that ensure the grid connection point voltage stiffness is not lower than a safe lower limit, used to prevent voltage instability risks.

[0037] Indicatively, based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship between the output power of new energy power plants and the voltage stiffness at the grid connection point is constructed. Voltage stiffness constraints are generated based on this quantitative mapping relationship. Starting from the physical essence of voltage stability, the measurement standard for voltage stiffness is first clarified, then the impact of the amount of electricity generated by new energy on the voltage stiffness at the grid connection point is calculated, and finally, a minimum safety standard is set to avoid the problem of insufficient voltage support capacity when new energy generates a large amount of electricity.

[0038] Preferably, the electrical basic parameters include equivalent impedance parameters, grid connection point reference voltage parameters, and power factor of the new energy power station; Based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship is constructed between the output power of the new energy power station and the voltage stiffness at the grid connection point. Voltage stiffness constraints are then generated based on this quantitative mapping relationship, including: Based on the equivalent impedance parameters and the grid connection point reference voltage parameters, a voltage stiffness index is constructed to characterize the ability of node voltage to resist power injection disturbances. Based on the power factor of the new energy power station and the voltage stiffness index, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed. Based on the quantitative mapping relationship and the preset voltage stiffness safety threshold, a voltage stiffness constraint is generated.

[0039] Specifically, the equivalent impedance parameter is the equivalent impedance viewed from the grid connection point towards the system side, reflecting the electrical strength of the system. A smaller impedance indicates a stronger system and a larger short-circuit capacity. The grid connection point reference voltage is the rated operating voltage of the grid connection point and serves as the benchmark value for calculating voltage deviation and voltage stiffness. The power factor is the ratio of active power to apparent power at a renewable energy power plant, reflecting the proportional relationship between active and reactive power output. The voltage stiffness index is typically defined as the ratio of voltage amplitude before and after a disturbance; a value closer to 1 indicates better voltage stability. The voltage stiffness safety threshold is the minimum allowable value for voltage stiffness; values ​​below this threshold indicate insufficient node voltage support capacity and a risk of instability.

[0040] Schematic, a voltage stiffness index is constructed based on the equivalent impedance parameters and the grid connection point reference voltage parameters to characterize the ability of node voltage to withstand power injection disturbances. In specific implementation, firstly, the system equivalent impedance of the grid connection point, including amplitude and phase information, is extracted from the grid topology parameters. Then, using the rated reference voltage of the grid connection point as a reference, the voltage stiffness index is defined according to the ratio of the voltage amplitude of the grid connection point before and after equipment connection. Furthermore, the essence of voltage stiffness is directly related to the system short-circuit capacity. The higher the short-circuit capacity, the smaller the equivalent impedance, the stronger the voltage stiffness, and the less prone the voltage is to fluctuations. As new energy replaces traditional generating units, the system equivalent impedance increases, the short-circuit capacity decreases, and the voltage stiffness weakens accordingly.

[0041] Based on the power factor of the renewable energy power plants and the voltage stiffness index, a quantitative mapping relationship between the output power of the renewable energy power plants and the voltage stiffness at the grid connection point is constructed. The renewable energy power plant is equated to a source injecting active and reactive power into the grid. Combining the operating power factor of the power plant, the output power is converted into an equivalent impedance form. Substituting this into the previously defined voltage stiffness formula, the correspondence between the power plant's output power and the voltage stiffness at the grid connection point is derived. In simple terms, this calculates how much the voltage stiffness at the grid connection point decreases when 1MW of renewable energy is generated, thus allowing for control of renewable energy output to ensure that voltage stiffness meets standards.

[0042] Based on the quantitative mapping relationship and the preset voltage stiffness safety threshold, voltage stiffness constraints are generated. Based on the derived quantitative mapping relationship, it is required that the voltage stiffness of the grid connection point cannot be lower than the preset voltage stiffness safety threshold during any scheduling period, which is converted into corresponding inequality constraints and incorporated into the optimization model. In this way, the optimization model will automatically consider the impact on voltage stiffness when scheduling renewable energy output and the start-up and shutdown of thermal power units, avoiding situations where excessive renewable energy generation or excessive shutdown of thermal power units leads to insufficient voltage stiffness.

[0043] In a preferred embodiment of the present invention, based on the physical definition of voltage stiffness, a quantitative relationship between the output power of new energy power plants and the voltage stability at the grid connection point is derived, and a voltage safety constraint with voltage stiffness threshold as the core is established to prevent voltage instability caused by the mismatch between the start-up and shutdown plan of traditional thermal power units and the grid-connected power of new energy.

[0044] Preferably, voltage stiffness The calculation formula is as follows: In the formula, and These represent the grid connection point voltages before and after the equipment is connected; and These represent the amplitude and phase of the equivalent impedance of the grid-connected unit, respectively. and These represent the magnitude and phase of the system's equivalent impedance, respectively.

[0045] If we consider a grid-connected device (taking a new energy generating unit as an example) as a device that injects active and reactive power, its equivalent impedance can be expressed as: In the formula, Indicates the output power of the grid-connected unit; Indicates the voltage at the grid connection point; Indicates the power factor angle; It is the imaginary unit.

[0046] To ensure the voltage support capability of nodes when a high proportion of new energy sources are connected to the grid, the voltage stiffness must not be lower than the minimum safety limit. The constraints, namely: .

[0047] In a preferred embodiment of the present invention, the system equivalent impedance amplitude at the main grid connection point is 0.12 pu, the phase is 80°, the grid connection point reference voltage is 220 kV, and the rated power factor of the renewable energy power station is 0.95. First, the grid connection point voltage stiffness index is defined as the ratio of voltage amplitude before and after renewable energy access. Substituting the system equivalent impedance parameters, the baseline calculation formula for voltage stiffness is obtained. Then, combined with the renewable energy power factor of 0.95, the quantitative relationship between renewable energy output power and voltage stiffness is derived: for every 100 MW increase in renewable energy output, the grid connection point voltage stiffness decreases by approximately 0.015. Setting the voltage stiffness safety threshold to 0.9, substituting into the mapping relationship yields the constraint: under any combination of renewable energy output and thermal power unit operating capacity at any given time period, the voltage stiffness must not be lower than 0.9. In the final optimized scheme, during the peak period of renewable energy generation at noon, three thermal power units are kept in operation. At this time, the voltage stiffness at the grid connection point is 0.93, which meets the safety requirements. If the traditional scheme without voltage constraints is followed, only two units are operated, and the voltage stiffness will drop to 0.88, which is lower than the safety threshold and poses a risk of voltage instability, thus verifying the necessity of the constraint.

[0048] Most existing methods use reactive power limits to approximate voltage stability constraints, failing to accurately reflect the impact of renewable energy grid connection on voltage support capacity. This implementation method, based on the physical definition of voltage stiffness, directly quantifies the impact of power injection on node voltage stability, resulting in more precise constraints and better reflecting the actual scenario of high-proportion renewable energy grid connection. By establishing a quantitative mapping between power and voltage stiffness, the voltage stiffness level for each time period can be calculated in advance during the scheduling planning stage, proactively adjusting unit start-up and shutdown and renewable energy output, avoiding the lag of reactive adjustments after voltage problems occur during operation. Since the start-up and shutdown of thermal power units simultaneously affect inertia and short-circuit capacity, this implementation method links voltage stiffness constraints and frequency constraints, jointly constraining the minimum number of thermal power units that can be started, avoiding scheduling conflicts caused by the two types of constraints operating independently.

[0049] S4. Based on the time-series operating parameters, the electrical basic parameters, and the operating cost data, construct a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints; Specifically, the unit combination optimization model is a mathematical optimization model with the goal of optimizing operating costs and constrained by various safety and operating rules, used to solve for the optimal unit start-up, shutdown, and output schemes.

[0050] Schematic, based on the aforementioned time-series operating parameters, electrical infrastructure parameters, and operating cost data, a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints are constructed, integrating the optimization objectives and various limitations into a single unit combination optimization model. The operating constraints of thermal power units and energy storage are determined by the physical characteristics of the power source itself and belong to the conventional constraints of the optimization model. Together with the aforementioned frequency and voltage safety constraints, they constitute the complete constraint system of the model.

[0051] Preferably, the operating constraints of the thermal power unit include upper and lower limits of active power output constraints and ramping power constraints.

[0052] Specifically, the upper and lower limits of active power output constraints are the maximum and minimum active power output limits that enable a single thermal power unit to operate stably, determined by the physical characteristics of the boiler and turbine. The ramp power constraint, also known as the ramp rate constraint, refers to the maximum allowable adjustment range of active power output of a thermal power unit between two adjacent scheduling periods, reflecting the unit's output adjustment speed.

[0053] Schematic illustration: The active power output upper and lower limit constraints require that the output power of each thermal power unit in operation must be greater than or equal to the minimum technical output and less than or equal to the rated maximum output. When the unit operates below the minimum technical output, boiler combustion becomes unstable, easily leading to problems such as flameout and substandard denitrification efficiency; therefore, a lower limit must be set. The upper limit is the unit's rated capacity, and operation beyond the rated output is not permitted. The ramp-up power constraint requires that the output change of a thermal power unit between two adjacent time periods cannot exceed the maximum ramp-up rate limit. Output adjustment of a thermal power unit requires adjustments to fuel quantity, steam valve opening, etc., and cannot be changed drastically instantaneously. The ramp-up rate is a limit on this adjustment speed, usually expressed as a percentage of rated capacity adjustable per minute. Both types of constraints are inherent physical constraints of thermal power units. Incorporating them into the optimization model ensures that the obtained output scheme can directly guide the actual operation of the unit, preventing output commands that the unit cannot execute.

[0054] In a preferred embodiment of the present invention, the operating constraints of the thermal power unit include upper and lower limits of the active power output and ramp rate constraints, the mathematical expressions of which are: In the formula, Let be the start-up / shutdown status variable of the i-th thermal power unit in the thermal power plant during time period t. A value of 1 indicates that the thermal power unit is in the start-up state, and a value of 0 indicates that the thermal power unit is in the shutdown state. This is the maximum ramp rate limit for the i-th thermal power unit; , These are the active power output values ​​of the i-th unit at time t and time t-1, respectively. and These represent the maximum and minimum output of the thermal power unit, respectively.

[0055] Preferably, the energy storage operation constraints include upper and lower limits of energy storage charging and discharging power, upper and lower limits of state of charge, mutual exclusion of charging and discharging, energy balance constraints at the beginning and end of the scheduling cycle, and constraints on the maximum number of charging and discharging cycles.

[0056] Specifically, the upper and lower limit constraints of charging and discharging power are the maximum charging and discharging power limits of the energy storage power station, which are determined by the rated capacity of the energy storage converter. The upper and lower limit constraints of the state of charge refer to the allowable range of the ratio of the remaining capacity of the energy storage battery to the rated capacity. Generally, overcharging or over-discharging is not allowed, otherwise the battery life will be damaged. The charging and discharging mutual exclusion constraint means that at the same time, the energy storage can only be in one of the three states of charging, discharging and idling, and cannot be charged and discharged at the same time. The energy balance constraint at the beginning and end of the scheduling period means that the state of charge of the energy storage remains consistent at the start and end of the scheduling period, so as to ensure the continuity of the next scheduling period. The maximum constraint on the number of charging and discharging cycles is the maximum number of complete charging and discharging cycles of the energy storage within the scheduling period, which is used to control battery loss and extend the service life.

[0057] Illustratively, the upper and lower limit constraints of charging and discharging power require that neither the charging power nor the discharging power can exceed the rated maximum power of the converter, so as to ensure the safe operation of the equipment. The upper and lower limit constraints of the state of charge require that the remaining capacity of the energy storage is always within the allowable range, which is usually set between 10% and 90% in the industry to avoid damage to the battery caused by deep charging and discharging. The charging and discharging mutual exclusion constraint is realized by binary state variables, and the charging state and the discharging state cannot take effect at the same time at the same moment, so as to avoid logical contradiction. The energy balance constraint at the beginning and end of the scheduling period requires that the state of charge at the start of the scheduling period is equal to the state of charge at the end of the scheduling period. For example, in the day-ahead scheduling, the energy storage capacity at zero o'clock is consistent with the capacity at zero o'clock the next day, so that the daily scheduling can be smoothly connected, and the situation that the power is used up one day and cannot be used the next day will not occur. The maximum constraint on the number of charging and discharging cycles limits the number of complete charging and discharging of the energy storage within one scheduling period, reduces the cycle loss of the battery, and lowers the full life cycle cost.

[0058] In a preferred embodiment of the present invention, the operation constraints of the energy storage system include the upper and lower limit constraints of charging and discharging power of the energy storage device, the upper and lower limit constraints of the state of charge, the mutual exclusion constraint of charging and discharging, the energy balance constraint at the beginning and end of the scheduling period, and the maximum constraint on the number of charging and discharging cycles. Taking the energy storage system supporting each wind farm station as an example, the mathematical expression of its operation constraints is as follows: In the formula, and are respectively the electric energy storage capacity of the energy storage supporting the y-th wind farm station at time t and time t-1; and are respectively the discharging power and charging power of the y-th wind farm station at time t-1; and are respectively the discharging efficiency and charging efficiency of the energy storage supporting the wind farm; and are respectively the discharging power and charging power of the energy storage supporting the wind farm; and are respectively the charging efficiency and discharging efficiency of the energy storage equipped in the wind farm; and respectively represent the minimum and maximum state of charge of the energy storage equipped in the wind farm station; is the rated capacity of the energy storage equipped in the wind farm station; and are respectively the discharging power and charging power of the y-th wind farm station at time t; is a binary variable for controlling energy storage charging, where 1 indicates that the energy storage system is in a charging state, and 0 indicates no charging; is a binary variable for controlling energy storage discharging, where 1 indicates that the energy storage system is in a discharging state, and 0 indicates no discharging; represents the maximum number of charge-discharge cycles of the energy storage system.

[0059] S5. Under the frequency safety constraint, the voltage stiffness constraint, the thermal power unit operation constraint and the energy storage operation constraint, with the goal of minimizing the total operation cost within the scheduling period, the unit commitment optimization model is solved to obtain a unit start-stop strategy and a power supply output allocation strategy; Specifically, the scheduling period is the total time range covered by one optimal scheduling, which is usually 24 hours in the day-ahead scheduling scenario. The unit start-stop strategy is the start-up and shutdown state scheme of thermal power units in each time period within the scheduling period. The power supply output allocation strategy is the active power output allocation scheme of various power sources in each time period within the scheduling period.

[0060] Illustratively, under the frequency safety constraint, the voltage stiffness constraint, the thermal power unit operation constraint and the energy storage operation constraint, with the goal of minimizing the total operation cost within the scheduling period, the unit commitment optimization model is solved to obtain the unit start-stop strategy and the power supply output allocation strategy. All constraint conditions and optimization objectives are substituted into a solving tool, so as to calculate the most economical scheduling scheme that meets all safety and operation rules.

[0061] Preferably, the unit commitment optimization model is as follows: wherein, is the total operation cost within the scheduling period; is the total sum of start-up and shutdown costs of thermal power units; is the total sum of power generation costs; is the total sum of curtailment penalty costs for wind power and photovoltaic stations; is a scheduling time period; is the scheduling period; is the number of thermal power stations; and are respectively the start-up and shutdown costs of the th thermal power station; A binary variable used to control the start-up and shutdown of a single thermal power plant; 1 indicates plant startup, and 0 indicates plant shutdown. The output power of a single thermal power plant; This represents the power generation cost coefficient for thermal power plants. and These represent the index numbers of wind turbine units and photovoltaic units, respectively; and These represent the total number of wind turbine units and photovoltaic units, respectively. and These represent the predicted output of wind turbines and photovoltaic units, respectively. and These represent the actual output of the wind turbine and the photovoltaic unit, respectively. and These represent the decommissioning penalty costs for wind turbines and photovoltaic units, respectively.

[0062] Specifically, the total operating cost is the sum of all operation-related costs of the renewable energy base within the scheduling cycle, and is the core objective of optimization. The total start-up and shutdown cost is the sum of the costs incurred by all thermal power units during startup and shutdown within the scheduling cycle. The total power generation cost is the sum of the costs incurred by thermal power units during power generation within the scheduling cycle. The total curtailment penalty cost, also known as the curtailment penalty cost, is a penalty cost set for wind and solar power curtailment, used to incentivize renewable energy consumption. The scheduling period is the smallest time unit within the scheduling cycle, such as 1 hour or 15 minutes, with each period corresponding to a set of output data. The start-up / shutdown binary variable represents the start-up / shutdown status of the thermal power unit; a value of 1 indicates the unit is running, and 0 indicates it is shut down. The curtailment penalty cost coefficient is the penalty cost corresponding to a unit of curtailed power; the higher the coefficient, the more the model tends to reduce curtailment and increase renewable energy consumption.

[0063] Schematic, the unit combination optimization model is solved centrally using an optimization solver to obtain the optimal unit start-up and shutdown plan and the output scheme of each type of unit (thermal power, wind power, photovoltaic, and energy storage). Thus, under the premise of ensuring the safe and stable operation of the system frequency and voltage, the operating state variables of the thermal power plant are obtained. Binary variables for starting and stopping thermal power units Binary variables for controlling energy storage charging Binary variables for controlling energy storage discharge To achieve optimal operational economy.

[0064] S6. Based on the unit start-up and shutdown strategy and the power output allocation strategy, the new energy base to be optimized is scheduled and optimized.

[0065] Schematic illustration: Based on the unit start-up and shutdown strategy and the power output allocation strategy, the scheduling optimization of the new energy base to be optimized is carried out. The obtained start-up and shutdown commands and power output commands are issued to each station to guide actual operation, ultimately achieving economically optimized operation under safety constraints. By introducing dual safety constraints of frequency and voltage, the coordinated optimization of traditional thermal power units, new energy units and their supporting energy storage is realized. This can effectively maintain the system inertia level and improve the voltage stiffness of key nodes, thereby ensuring the safe and stable operation of the coastal new energy base while taking into account economic efficiency.

[0066] In a preferred embodiment of the present invention, in order to verify the effectiveness of the proposed model and method, step S5 is performed on an improved IEEE 39-node standard test system for simulation. Figure 4 To consider the combined constraints of frequency security and voltage stiffness, the unit combination optimization results of coastal new energy bases are presented. Figure 5 This represents the unit combination optimization results for coastal new energy bases without considering the joint constraints of frequency security and voltage stiffness. Figure 4 and Figure 5 The comparison shows that, after considering joint constraints, the situation where all thermal power units shut down simultaneously will no longer occur. Furthermore, the number of start-ups and shutdowns of thermal power units within a single scheduling cycle is significantly reduced, indicating that this invention can improve operational economy while maintaining system inertia.

[0067] This invention achieves synergistic optimization and quantitative assurance of frequency and voltage safety. Existing research mostly focuses on single frequency safety constraints or voltage stiffness constraints, making it difficult to achieve synergy between these two key safety indicators. This invention derives joint analytical constraints for maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation by constructing a frequency response model that considers multiple types of power sources. Simultaneously, based on the physical definition of voltage stiffness, it derives a quantitative relationship between renewable energy output and grid connection voltage stability. By embedding these two sets of constraints into the unit combination optimization model, it achieves synergistic, quantitative assessment, and proactive defense of frequency stability and voltage stability at the scheduling level, effectively addressing the systemic safety risks brought about by a high proportion of renewable energy grid connection.

[0068] This invention improves the economic efficiency of system operation and the capacity for renewable energy absorption. Traditional, overly conservative dispatching strategies, prioritizing safety, sacrifice economic efficiency. This invention, through a unit combination optimization model, collaboratively optimizes the start-up and shutdown plans of thermal power units, the reduced-load operation of renewable energy, and the rapid charging and discharging of energy storage. Under strict safety constraints, the optimization model reduces unnecessary unit start-ups and shutdowns through refined cost optimization, while incentivizing the maximization of renewable energy absorption.

[0069] This invention enhances the adaptability and operability of the dispatching scheme to the actual operating conditions of the power grid. The model of this invention combines traditional constraints such as power balance and generator ramping with voltage stiffness constraints, enabling optimized generator combinations and output schemes to automatically meet the topological and electrical characteristics limitations of the power grid. Furthermore, through optimized dispatching of energy storage in low-storage, high-generation scenarios, it not only leverages the peak-shaving function but also utilizes its rapid power response capability to support transient frequency stability, thus fully realizing the value of energy storage.

[0070] By implementing this embodiment, frequency response parameters, electrical foundation parameters, time-series operating parameters, and operating cost data of the new energy base to be optimized are obtained; a frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model; a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness of the grid connection point is constructed based on the electrical foundation parameters, and voltage stiffness constraints are generated based on the quantitative mapping relationship; then, based on the time-series operating parameters, the electrical foundation parameters, and the operating cost data, a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints are constructed; under the frequency safety constraints, the voltage stiffness constraints, the thermal power unit operating constraints, and the energy storage operating constraints, the unit combination optimization model is solved with the objective of minimizing the total operating cost within the scheduling cycle to obtain the unit start-up and shutdown strategy and the power output allocation strategy; finally, the new energy base to be optimized is scheduled and optimized based on the unit start-up and shutdown strategy and the power output allocation strategy. First, a frequency response model is constructed based on frequency response parameters, and then frequency safety constraints are constructed based on the frequency response model. Simultaneously, based on electrical fundamental parameters, a quantitative mapping relationship is established between renewable energy output and grid connection point voltage stiffness. Based on this quantitative mapping relationship, voltage stiffness requirements are converted into voltage stiffness constraints, satisfying both frequency stability and voltage support constraints, thus achieving coupling between voltage and frequency. Under the joint constraints of frequency safety and voltage stiffness, combined with thermal power unit operation constraints and energy storage operation constraints, the unit combination optimization model is solved with the goal of minimizing total operating cost. This yields unit start-up and shutdown strategies and power output allocation strategies that satisfy both frequency and voltage safety, achieving optimized scheduling of the renewable energy base and thereby improving its operational safety.

[0071] See Figure 6 This is a schematic diagram of a new energy base unit optimization device based on joint constraints of frequency security and voltage stiffness provided in an embodiment of the present invention, comprising: The data acquisition module is used to acquire frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized. A frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model. The voltage stiffness constraint module is used to construct a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point based on the electrical basic parameters, and to generate voltage stiffness constraints based on the quantitative mapping relationship. The unit combination optimization model construction module is used to construct the unit combination optimization model and the corresponding thermal power unit operation constraints and energy storage operation constraints based on the time-series operating parameters, the electrical basic parameters and the operating cost data. The joint constraint solving module is used to solve the unit combination optimization model under the constraints of frequency security, voltage stiffness, thermal power unit operation and energy storage operation, with the goal of minimizing the total operating cost within the scheduling cycle, to obtain the unit start-up and shutdown strategy and the power output allocation strategy. The unit scheduling optimization module is used to perform scheduling optimization on the new energy base to be optimized based on the unit start-up and shutdown strategy and the power output allocation strategy.

[0072] Preferably, the frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; The frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

[0073] This invention provides a new energy base unit optimization device based on joint constraints of frequency security and voltage stiffness. The device acquires frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized using a data acquisition module. In the frequency security constraint construction module, a frequency response model is constructed based on the frequency response parameters, and frequency security constraints are generated based on the frequency response model. In the voltage stiffness constraint module, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed based on the electrical foundation parameters, and voltage stiffness constraints are generated based on the quantitative mapping relationship. The device also includes a unit combination optimization model construction module. Based on the time-series operating parameters, the electrical infrastructure parameters, and the operating cost data, a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints are constructed. Through a joint constraint solving module, under the frequency safety constraints, voltage stiffness constraints, thermal power unit operating constraints, and energy storage operating constraints, the unit combination optimization model is solved with the objective of minimizing the total operating cost within the scheduling cycle, yielding unit start-up and shutdown strategies and power output allocation strategies. Finally, in the unit scheduling optimization module, the new energy base to be optimized is scheduled and optimized based on the unit start-up and shutdown strategies and the power output allocation strategies. First, a frequency response model is constructed based on frequency response parameters, and then frequency safety constraints are constructed based on the frequency response model. Simultaneously, based on electrical fundamental parameters, a quantitative mapping relationship is established between renewable energy output and grid connection point voltage stiffness. Based on this quantitative mapping relationship, voltage stiffness requirements are converted into voltage stiffness constraints, satisfying both frequency stability and voltage support constraints, thus achieving coupling between voltage and frequency. Under the joint constraints of frequency safety and voltage stiffness, combined with thermal power unit operation constraints and energy storage operation constraints, the unit combination optimization model is solved with the goal of minimizing total operating cost. This yields unit start-up and shutdown strategies and power output allocation strategies that satisfy both frequency and voltage safety, achieving optimized scheduling of the renewable energy base and thereby improving its operational safety.

[0074] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0075] Those skilled in the art will understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0076] Another embodiment of the present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a new energy base unit optimization method based on joint constraints of frequency security and voltage stiffness as described in the above embodiments. The terminal device can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. The terminal device may include, but is not limited to, a processor and a memory.

[0077] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0078] The memory can be used to store the computer program. The processor implements various functions of the terminal device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device or other volatile solid-state storage device.

[0079] Another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness described in the above embodiment.

[0080] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0081] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. An optimization method for generating units in a new energy base based on joint constraints of frequency security and voltage stiffness, characterized in that, include: Obtain frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized; A frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model. Based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed, and voltage stiffness constraints are generated based on the quantitative mapping relationship. Based on the time-series operating parameters, the electrical basic parameters, and the operating cost data, a unit combination optimization model and corresponding thermal power unit operating constraints and energy storage operating constraints are constructed. Under the constraints of frequency safety, voltage stiffness, thermal power unit operation, and energy storage operation, with the goal of minimizing the total operating cost within the scheduling cycle, the unit combination optimization model is solved to obtain the unit start-up and shutdown strategy and the power output allocation strategy. The scheduling optimization of the new energy base to be optimized is carried out based on the unit start-up and shutdown strategy and the power output allocation strategy.

2. The optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 1, characterized in that, The frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; A frequency response model is constructed based on the frequency response parameters, and frequency safety constraints are generated based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

3. The optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 1, characterized in that, The electrical basic parameters include equivalent impedance parameters, grid connection point reference voltage parameters, and power factor of new energy power stations; Based on the aforementioned electrical fundamental parameters, a quantitative mapping relationship is constructed between the output power of the new energy power station and the voltage stiffness at the grid connection point. Voltage stiffness constraints are then generated based on this quantitative mapping relationship, including: Based on the equivalent impedance parameters and the grid connection point reference voltage parameters, a voltage stiffness index is constructed to characterize the ability of node voltage to resist power injection disturbances. Based on the power factor of the new energy power station and the voltage stiffness index, a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point is constructed. Based on the quantitative mapping relationship and the preset voltage stiffness safety threshold, a voltage stiffness constraint is generated.

4. The optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 1, characterized in that, The operating constraints of the thermal power units include upper and lower limits of active power output constraints and ramping power constraints.

5. The optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 1, characterized in that, The energy storage operation constraints include upper and lower limits of energy storage charging and discharging power, upper and lower limits of state of charge, mutual exclusion of charging and discharging, energy balance constraints at the beginning and end of the scheduling cycle, and constraints on the maximum number of charging and discharging cycles.

6. The optimization method for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 1, characterized in that, The unit combination optimization model is as follows: in, This represents the total operating cost within the scheduling period. This represents the total start-up and shutdown costs of thermal power units. This represents the total cost of electricity generation. The total penalty cost for abandoning wind and solar power plants; For scheduling periods; The scheduling period; The number of thermal power plants; and The first The start-up and shutdown costs of a thermal power plant; Binary variables used to control the start-up and shutdown of individual thermal power plants; The output power of a single thermal power plant; This represents the power generation cost coefficient for thermal power plants. and These represent the index numbers of wind turbine units and photovoltaic units, respectively; and These represent the total number of wind turbine units and photovoltaic units, respectively. and These represent the predicted output of wind turbines and photovoltaic units, respectively. and These represent the actual output of the wind turbine and the photovoltaic unit, respectively. and These represent the decommissioning penalty costs for wind turbines and photovoltaic units, respectively.

7. A new energy base unit optimization device based on joint constraints of frequency security and voltage stiffness, characterized in that, include: The data acquisition module is used to acquire frequency response parameters, electrical foundation parameters, timing operation parameters, and operating cost data of the new energy base to be optimized. A frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model. The voltage stiffness constraint module is used to construct a quantitative mapping relationship between the output power of the new energy power station and the voltage stiffness at the grid connection point based on the electrical basic parameters, and to generate voltage stiffness constraints based on the quantitative mapping relationship. The unit combination optimization model construction module is used to construct the unit combination optimization model and the corresponding thermal power unit operation constraints and energy storage operation constraints based on the time-series operating parameters, the electrical basic parameters and the operating cost data. The joint constraint solving module is used to solve the unit combination optimization model under the constraints of frequency security, voltage stiffness, thermal power unit operation and energy storage operation, with the goal of minimizing the total operating cost within the scheduling cycle, to obtain the unit start-up and shutdown strategy and the power output allocation strategy. The unit scheduling optimization module is used to perform scheduling optimization on the new energy base to be optimized based on the unit start-up and shutdown strategy and the power output allocation strategy.

8. The optimization device for new energy base units based on joint constraints of frequency security and voltage stiffness as described in claim 7, characterized in that, The frequency response parameters include system frequency deviation, frequency response time, power increment of new energy power plants participating in frequency regulation, and droop coefficient; The frequency security constraint construction module is used to construct a frequency response model based on the frequency response parameters and generate frequency security constraints based on the frequency response model, including: Based on the system frequency deviation, the frequency response time, the power increment, and the droop coefficient, the primary frequency regulation characteristics of each power unit in the new energy base to be optimized are equivalently fitted to construct a frequency response model; wherein, the power units include thermal power plants, wind power plants, photovoltaic power plants, and energy storage power plants. The maximum frequency deviation, maximum rate of frequency change, and steady-state frequency deviation at the time of the disturbance are analyzed based on the frequency response model. Frequency safety constraints are generated based on the maximum frequency deviation, the maximum frequency change rate, the steady-state frequency deviation, and the preset frequency safety allowable threshold.

9. A terminal device, characterized in that, The system includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a method for optimizing new energy base units based on joint constraints of frequency security and voltage stiffness as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform a new energy base unit optimization method based on joint constraints of frequency security and voltage stiffness as described in any one of claims 1 to 6.

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