New energy power grid optimization scheduling method considering AGC frequency base value adjustment

Abstract

The invention discloses a new energy power grid optimization scheduling method considering AGC frequency base value adjustment, which belongs to the technical field of new energy power grid optimization, realizes source and load collaborative optimization by dynamically adjusting a system frequency base value, and comprises the following steps: establishing a load and frequency dynamic response model; setting a safety boundary of frequency base value adjustment; constructing an optimization model taking a frequency base value as a core; and solving a dynamic optimization result. According to the method, firstly, the incidence relation between the load power and the frequency is quantitatively analyzed, meanwhile, the frequency safety constraint is considered, and a power grid frequency base value active adjustment model is established; and then, establishing a power balance constraint of interaction of thermal power, wind power and load, taking the sum of the thermal power cost and the wind curtailment penalty as a target, providing a power grid optimization scheduling method, reducing the wind curtailment rate, and improving the effectiveness and feasibility in the aspects of operation economy.

Description

Technical Field

[0001] This invention belongs to the field of new energy power grid optimization technology, and specifically relates to a new energy power grid optimization scheduling method that considers AGC frequency base value adjustment. Background Technology

[0002] In the power system field, AGC, or Automatic Generation Control, is crucial for maintaining frequency stability. The AGC system monitors the actual frequency of the power grid in real time and compares it with the rated frequency (50Hz in China). By adjusting the output of generator units in real time, it dynamically matches power generation with load demand, thereby keeping the system frequency within a safe range. Traditional dispatch models forcibly fix the system frequency to a 50Hz reference value, leading to the following problems:

[0003] Load regulation capacity is idle: the adjustable component of the load power (linear / double proportional load, such as motors and transformers) cannot respond to frequency changes, thus losing the opportunity to optimize power balance by utilizing load self-regulation;

[0004] Limited absorption of new energy sources: When the power generation of new energy sources surges (such as during the peak season of wind and solar power), the fixed frequency constraint prevents thermal power from flexibly reducing its output, forcing the curtailment of wind and solar power.

[0005] The current AGC is only used as a "correction tool" and cannot actively participate in economic dispatch. It has a lag in response and only passively adjusts the unit output after the frequency deviates (such as increasing the output only when the frequency drops to 49.8Hz). It cannot prevent fluctuations, lacks optimization objectives, and the control strategy takes "maintaining 50Hz" as the single objective. It does not consider the economic impact of frequency regulation on cost / wind curtailment rate, which leads to contradictions between wind / solar curtailment and thermal power regulation. The specific contradiction is that thermal power units are limited by minimum output and ramp-up rate, and the speed of output reduction cannot keep up with the fluctuations of new energy. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the prior art and provide a new energy power grid optimization scheduling method that considers AGC frequency base value adjustment, thus solving the problems mentioned in the background art.

[0007] The objective of this invention is achieved as follows: a new energy power grid optimization scheduling method considering AGC frequency base value adjustment, comprising: acquiring power grid frequency data and load data, wherein the frequency data comes from the AGC system and the load data includes historical load curves; based on the frequency data and load data, establishing a dynamic response model of load and frequency to quantify the impact of frequency changes on load power; according to the dynamic response model, setting a safety boundary for frequency base value adjustment to ensure that the system frequency is within the allowable range; constructing an optimization model with the objective of minimizing total cost, wherein the total cost includes the operating cost of thermal power units and the cost of wind curtailment penalties; solving the optimization model to generate frequency adjustment commands and unit scheduling schemes.

[0008] The dynamic response model of the load and frequency is as follows:

[0009]

[0010] In the formula: P t D_load P represents the load power at time t at the actual frequency. t DN_1 The load power at the reference frequency; f t f is the actual frequency of the system at time t; N The system reference frequency; a0, a1, and a2 represent the proportions of the first three types of loads; used to determine the subsequent adjustment frequency f. t This lays the foundation for controlling load power;

[0011] By quantifying the impact of actual frequency changes on load power through a dynamic response model, a computational basis is provided for proactively adjusting frequency values ​​to achieve load control. This allows for real-time changes in the total load demand of the entire network. Combined with an optimization model, it dynamically balances renewable energy fluctuations with thermal power output, thereby reducing wind curtailment rates and operating costs. Breaking away from the passive mode of existing AGC (Automatic Generation Control) that merely maintains frequency stability, this approach transforms the frequency base value into an active control variable, forming a scheduling method of "dynamic frequency optimization and source-load coordination." This solves the technical problem of conflicting wind / solar curtailment and thermal power regulation caused by a high proportion of renewable energy integration.

[0012] The safety boundary for adjusting the frequency base value is:

[0013]

[0014] In the formula: f max and f min These are the maximum and minimum values ​​of the system frequency; used to ensure system stability; the safety boundary for adjusting the frequency base value is used to ensure that the actual operating frequency of the system is always within the allowable safe range, preventing frequency exceedance from causing equipment damage or system crash.

[0015] The optimization model is as follows:

[0016] Objective: To minimize the cost of thermal power generation and the losses from wind curtailment.

[0017]

[0018] In the formula: a i b i and c i c is the cost coefficient for thermal power units. loss This is the wind curtailment penalty coefficient;

[0019] Constraints: Based on load, frequency response relationship and safety boundary, combined with thermal power unit constraints, wind curtailment constraints and nodal power balance.

[0020] By solving the dynamic optimization results, including real-time adjustment of f t , so that the load power P t D_load By coordinating with the frequency base value, source-load matching optimization is achieved.

[0021] The constraints included in the optimization model are: thermal power unit operation constraints, thermal power unit start-up and shutdown constraints, wind farm curtailment constraints, and power balance constraints at grid nodes.

[0022] The operating constraints of the thermal power units are as follows: at any node, the actual power generation of the thermal power unit at the dispatch time must simultaneously meet the lower limit condition and the upper limit condition. The lower limit condition is that the actual power generation is not lower than the preset minimum allowable output of the unit multiplied by the operating state value at the moment. The upper limit condition is that the actual power generation is not higher than the preset maximum allowable output of the unit multiplied by the operating state value at the moment.

[0023] The optimization objective of the optimization model is to minimize the total cost of grid operation, which includes the operating cost of thermal power units and the cost of wind curtailment penalty. The operating cost of thermal power units includes secondary fuel cost, linear fuel cost, and fixed start-up and shutdown cost. The cost of wind curtailment penalty is the actual amount of wind curtailed power multiplied by the unit wind curtailment penalty coefficient.

[0024] Specifically, the constraints included in the optimization model are: thermal power unit operation constraints, wind farm curtailment constraints, and power balance constraints at grid nodes.

[0025] The operating constraints of the thermal power units include:

[0026] Power limit is

[0027] Climbing restrictions are

[0028] In the formula: Let be the power generation of thermal power unit i at time t; and These are the upper and lower limits of the output of thermal power units, respectively. and These represent the upward and downward ramp rates, respectively. Operating constraints of thermal power units can stimulate load growth, such as accelerating motor equipment, while frequency reduction can suppress load demand, such as reducing the energy consumption of resistor equipment, thus making the load side a "hidden energy storage resource".

[0029] Specifically, the start-stop constraints of the thermal power unit are as follows:

[0030]

[0031] In the formula: T on Minimum continuous runtime; T off Minimum continuous downtime; The start-up and shutdown status of the i-th thermal power unit during time period t is 1 for running and 0 for shutting down; i is the equipment index, either thermal power unit or wind farm; k is a time period loop variable.

[0032] The start-up and shutdown constraints of thermal power units include minimum continuous operating time constraints and minimum continuous shutdown time constraints. The minimum continuous operating time constraint requires that when a thermal power unit switches from a shutdown state to an operating state during a scheduling period, the unit must maintain its operating state for a continuous period starting from the switching time. The minimum continuous shutdown time constraint requires that when a thermal power unit switches from an operating state to a shutdown state during a scheduling period, the unit must maintain its shutdown state for a continuous period starting from the switching time.

[0033] By using a dual-constraint coupled model with frequency security domain and transmission capacity domain, line overload or frequency instability can be avoided during the optimization process.

[0034] By setting coordinated constraints for unit start-up and shutdown and frequency regulation, such as minimum operating time T... on It is necessary to adapt to frequency regulation requirements and ensure the flexible adjustment capability of thermal power plants.

[0035] Specifically, the wind curtailment constraint is as follows:

[0036]

[0037] By curtailing wind power An objective function is explicitly introduced to directly quantify the contribution of frequency regulation to the consumption of new energy sources.

[0038] The power balance constraint of the power grid node is:

[0039]

[0040] In the formula: This refers to the power output of the wind curtailment. Let be the wind power generation of the wind farm at node i at time t; Let i be the active power output of the thermal power unit at node i during time period t; Predict the power generation of the wind farm at node i during time period t; The active power output of the photovoltaic power station at node i during time period t; Let ∑ be the active power of the load at node i during time period t; j P ij,t Let be the total line power flow from node i to its neighboring node j.

[0041] The power balance constraint of a power grid node is that for any node in the power grid, during the dispatch period, the total power generation of the node is equal to the total power consumption of the node plus the power transmitted by the node. The total power generation of the node includes the active power output of thermal power units, the predicted wind power output of wind farms, the active power output of photovoltaic power plants, and the negative value of wind curtailment power. The total power consumption of the node is the active power consumed by the current load of the node. The power transmitted by the node is the sum of the power flow transmitted to directly adjacent nodes through all transmission lines.

[0042] Specifically, the line power flow calculation is as follows:

[0043]

[0044] And satisfy:

[0045] In the formula: P ij,t θ represents the active power flow on the lines connected to nodes i and j at time t; i,t Let θ be the voltage phase angle at node i at time t; j,t X is the voltage phase angle at node j at time t; ij θ represents the corresponding line impedance. max and θ min These are the upper and lower limits of the phase angle. The line transmission limitations are:

[0046]

[0047] P ij,max and P ij,min These represent the upper and lower limits of the transmission power of lines i and j.

[0048] Power flow calculation and voltage phase angle constraints include active power flow calculation of interconnected lines and node voltage phase angle security constraints, including:

[0049] For the active power flow calculation of interconnected lines, the transmission power of the line during the dispatch period for any two directly connected grid nodes is determined by the following physical relationship: the transmission power value is equal to the node voltage phase angle value minus the node voltage phase angle value and then divided by the line impedance value.

[0050] The node voltage phase angle safety constraint stipulates that for any node in the power grid, the voltage phase angle value during the dispatch period must meet both a lower limit condition and an upper limit condition. The lower limit condition is that the voltage phase angle is not lower than the minimum allowable value preset by the system, and the upper limit condition is that the voltage phase angle is not higher than the maximum allowable value preset by the system.

[0051] By embedding a frequency influence term into the traditional DC power flow model, the load Containing f t This enables precise tracking of power flow across the entire network.

[0052] Specifically, the load proportioning coefficients satisfy the following: a0 represents frequency-independent loads, a1 represents linearly frequency-dependent loads, and a2 represents quadratic frequency-dependent loads, with a0 + a1 + a2 = 1. The frequency regulation strategy is to increase f during peak periods of new energy output. t Reduce wind curtailment and lower f during off-peak hours for renewable energy output. t Save on thermal power costs.

[0053] By using load classification response coefficients (a0, a1, a2) to guide differentiated frequency regulation, the uncontrolled load fluctuations caused by a "one-size-fits-all" frequency adjustment are avoided. A revolutionary strategy is proposed to shift from "source follows load" to "load follows source."

[0054] When wind power generation is at its peak, the frequency is increased to 50.5Hz, the load increases by 5%, and wind curtailment is reduced by 20%.

[0055] When wind power is insufficient, the frequency is reduced to 49.8Hz, the load is reduced by 3%, and the start-up cost of thermal power is saved.

[0056] The transmission power limit for interconnected lines is as follows: for any two physically connected nodes in the power grid, during any scheduling period, the following conditions apply: minimum power guarantee: the actual transmission power of the line shall not be lower than the preset minimum transmission capacity lower limit of the line; maximum power cut-off: the actual transmission power of the line shall not exceed the preset maximum transmission capacity upper limit of the line.

[0057] Dynamic frequency regulation strategies based on the dynamic response model of load and frequency, and load frequency characteristic classification rules include:

[0058] A constant proportional load means that power demand does not change with frequency.

[0059] For linear proportional loads, power demand is directly proportional to frequency changes.

[0060] For a secondary proportional load, the power demand is proportional to the square of the frequency change.

[0061] The dynamic frequency regulation strategy implements differentiated frequency control based on fluctuations in new energy power generation, including:

[0062] During peak periods of renewable energy output: proactively increase the system frequency reference value and reduce wind curtailment by decreasing the total power demand of linear and quadratic proportional loads;

[0063] During periods of low renewable energy output: proactively reduce the system frequency benchmark value and decrease the power generation capacity of thermal power units to reduce the cost of thermal power fuel.

[0064] Specifically, the implementation platform is the IEEE 30-node system, comprising 7 thermal power units, 2 wind farms, and 2 photovoltaic power plants, with a scheduling cycle of 24 hours and a step size of 1 hour. Complex scheduling verification of the 7 thermal power units and wind / solar power plants was achieved within the IEEE 30-node system. Dynamic frequency adjustment within the range of 49.5Hz to 50.5Hz did not affect system stability, and the overall cost reduction stemmed from fuel savings in thermal power and reduced wind curtailment penalties. By transforming frequency into a flexible adjustment resource connecting sources and loads, a significantly innovative systemic solution was formed, breaking through the traditional dilemma of "rigid source-load opposition" in renewable energy grid scheduling.

[0065] The beneficial effects of this invention are as follows: By establishing power balance constraints based on the interaction of thermal power, wind power, and loads, and aiming to minimize the sum of thermal power costs and wind curtailment penalties, a grid optimization scheduling method is proposed, demonstrating effectiveness and feasibility in reducing wind curtailment rates and improving operational economics. It breaks through the existing passive mode of AGC (Automatic Generation Control) that only maintains frequency stability, transforming the frequency base value into an active control variable, forming a new scheduling paradigm of "dynamic frequency optimization and source-load coordination," providing a new path to resolve the contradiction between wind / solar curtailment and thermal power regulation caused by high-proportion renewable energy access. By solving the dynamic optimization results, including real-time adjustment of f... t , so that the load power P t D_load By coordinating with the frequency base value, source-load matching optimization is achieved. The first load dynamic model incorporating a frequency adjustment factor is proposed, revealing that frequency increase can stimulate load growth, such as accelerating motor-type equipment, while frequency decrease can suppress load demand, such as reducing the energy consumption of resistive equipment, making the load side a "hidden energy storage resource." A pioneering dual-constraint coupled model with a frequency security domain and transmission capacity domain is implemented to avoid line overload or frequency instability during optimization. Coordination constraints between unit start-up / shutdown and frequency regulation are set, such as the minimum operating time T. on To adapt to frequency regulation requirements and ensure the flexible adjustment capability of thermal power, this invention first quantitatively analyzes the correlation between load power and frequency, and takes into account frequency safety constraints. It establishes an active adjustment model of the grid frequency base value, which solves the technical problems in the existing technology where wind / solar curtailment and thermal power regulation are contradictory, and thermal power units are limited by minimum output and ramp rate, and the output reduction speed cannot keep up with the fluctuations of new energy sources. Attached Figure Description

[0066] Figure 1 This is the IEEE 30-node wiring diagram of the present invention;

[0067] Figure 2 This is the new energy and load forecast power diagram of the present invention;

[0068] Figure 3 This is the power distribution diagram of Scheme 1 of the present invention;

[0069] Figure 4 This is the actual frequency diagram of the system of the present invention;

[0070] Figure 5 This is the power distribution diagram of Scheme 2 of the present invention. Detailed Implementation

[0071] The present invention will now be described in further detail with reference to the accompanying drawings. It should be noted that this is only for the purpose of more clearly illustrating and explaining the present invention.

[0072] Example 1

[0073] like Figure 1-5 As shown in the figure, this embodiment discloses a new energy power grid optimization scheduling method considering AGC frequency base value adjustment, including: acquiring power grid frequency data and load data, wherein the frequency data comes from the AGC system and the load data includes historical load curves; based on the frequency data and load data, establishing a dynamic response model of load and frequency to quantify the impact of frequency changes on load power; according to the dynamic response model, setting a safety boundary for frequency base value adjustment to ensure that the system frequency is within the allowable range; constructing an optimization model with the objective of minimizing total cost, wherein the total cost includes the operating cost of thermal power units and the cost of wind curtailment penalty; solving the optimization model to generate frequency adjustment commands and unit scheduling schemes.

[0074] The dynamic response model of the load and frequency is as follows:

[0075]

[0076] In the formula: P t D_load P represents the load power at time t at the actual frequency. t DN_1 The load power at the reference frequency; f t f is the actual frequency of the system at time t; N The system reference frequency; a0, a1, and a2 represent the proportions of the first three types of loads; used to determine the subsequent adjustment frequency f. t This lays the foundation for controlling load power;

[0077] By quantifying the impact of actual frequency changes on load power through a dynamic response model, a computational basis is provided for proactively adjusting frequency values ​​to achieve load control. This allows for real-time changes in the total load demand of the entire network. Combined with an optimization model, it dynamically balances renewable energy fluctuations with thermal power output, thereby reducing wind curtailment rates and operating costs. Breaking away from the passive mode of existing AGC (Automatic Generation Control) that merely maintains frequency stability, this approach transforms the frequency base value into an active control variable, forming a scheduling method of "dynamic frequency optimization and source-load coordination." This solves the technical problem of conflicting wind / solar curtailment and thermal power regulation caused by a high proportion of renewable energy integration.

[0078] The safety boundary for adjusting the frequency base value is:

[0079]

[0080] In the formula: f max and f min These are the maximum and minimum values ​​of the system frequency; used to ensure system stability; the safety boundary for adjusting the frequency base value is used to ensure that the actual operating frequency of the system is always within the allowable safe range, preventing frequency exceedance from causing equipment damage or system crash.

[0081] The optimization model is as follows:

[0082] Objective: To minimize the cost of thermal power generation and the losses from wind curtailment.

[0083]

[0084] In the formula: a i b i and c i c is the cost coefficient for thermal power units. loss This is the wind curtailment penalty coefficient;

[0085] Constraints: Based on load, frequency response relationship and safety boundary, combined with thermal power unit constraints, wind curtailment constraints and nodal power balance.

[0086] By solving the dynamic optimization results, including real-time adjustment of f t , so that the load power P t D_load By coordinating with the frequency base value, source-load matching optimization is achieved.

[0087] The constraints included in the optimization model are: thermal power unit operation constraints, thermal power unit start-up and shutdown constraints, wind farm curtailment constraints, and power balance constraints at grid nodes.

[0088] The operating constraints of the thermal power units are as follows: at any node, the actual power generation of the thermal power unit at the dispatch time must simultaneously meet the lower limit condition and the upper limit condition. The lower limit condition is that the actual power generation is not lower than the preset minimum allowable output of the unit multiplied by the operating state value at the moment. The upper limit condition is that the actual power generation is not higher than the preset maximum allowable output of the unit multiplied by the operating state value at the moment.

[0089] The optimization objective of the optimization model is to minimize the total cost of grid operation, which includes the operating cost of thermal power units and the cost of wind curtailment penalty. The operating cost of thermal power units includes secondary fuel cost, linear fuel cost, and fixed start-up and shutdown cost. The cost of wind curtailment penalty is the actual amount of wind curtailed power multiplied by the unit wind curtailment penalty coefficient.

[0090] Specifically, the constraints included in the optimization model are: thermal power unit operation constraints, wind farm curtailment constraints, and power balance constraints at grid nodes.

[0091] The operating constraints of the thermal power units include:

[0092] Power limit is

[0093] Climbing restrictions are

[0094] In the formula: Let be the power generation of thermal power unit i at time t; and These are the upper and lower limits of the output of thermal power units, respectively. and These represent the upward and downward ramp rates, respectively. Operating constraints of thermal power units can stimulate load growth, such as accelerating motor equipment, while frequency reduction can suppress load demand, such as reducing the energy consumption of resistor equipment, thus making the load side a "hidden energy storage resource".

[0095] Specifically, the start-stop constraints of the thermal power unit are as follows:

[0096]

[0097] In the formula: T on Minimum continuous runtime; T off Minimum continuous downtime; The start-up and shutdown status of the i-th thermal power unit during time period t is 1 for running and 0 for shutting down; i is the equipment index, either thermal power unit or wind farm; k is a time period loop variable.

[0098] The start-up and shutdown constraints of thermal power units include minimum continuous operating time constraints and minimum continuous shutdown time constraints. The minimum continuous operating time constraint requires that when a thermal power unit switches from a shutdown state to an operating state during a scheduling period, the unit must maintain its operating state for a continuous period starting from the switching time. The minimum continuous shutdown time constraint requires that when a thermal power unit switches from an operating state to a shutdown state during a scheduling period, the unit must maintain its shutdown state for a continuous period starting from the switching time.

[0099] By using a dual-constraint coupled model with frequency security domain and transmission capacity domain, line overload or frequency instability can be avoided during the optimization process.

[0100] By setting coordinated constraints for unit start-up and shutdown and frequency regulation, such as minimum operating time T... on It is necessary to adapt to frequency regulation requirements and ensure the flexible adjustment capability of thermal power plants.

[0101] Specifically, the wind curtailment constraint is as follows:

[0102]

[0103] By curtailing wind power An objective function is explicitly introduced to directly quantify the contribution of frequency regulation to the consumption of new energy sources.

[0104] The power balance constraint of the power grid node is:

[0105]

[0106] In the formula: This refers to the power output of the wind curtailment. Let be the wind power generation of the wind farm at node i at time t; Let i be the active power output of the thermal power unit at node i during time period t; Predict the power generation of the wind farm at node i during time period t; The active power output of the photovoltaic power station at node i during time period t; Let ∑ be the active power of the load at node i during time period t; j P ij,t Let be the total line power flow from node i to its neighboring node j.

[0107] The power balance constraint of a power grid node is that for any node in the power grid, during the dispatch period, the total power generation of the node is equal to the total power consumption of the node plus the power transmitted by the node. The total power generation of the node includes the active power output of thermal power units, the predicted wind power output of wind farms, the active power output of photovoltaic power plants, and the negative value of wind curtailment power. The total power consumption of the node is the active power consumed by the current load of the node. The power transmitted by the node is the sum of the power flow transmitted to directly adjacent nodes through all transmission lines.

[0108] Specifically, the line power flow calculation is as follows:

[0109]

[0110] And satisfy:

[0111] In the formula: P ij,t θ represents the active power flow on the lines connected to nodes i and j at time t; i,t Let θ be the voltage phase angle at node i at time t; j,t X is the voltage phase angle at node j at time t; ij θ represents the corresponding line impedance. max and θ min These are the upper and lower limits of the phase angle. The line transmission limitations are:

[0112]

[0113] P ij,max and P ij,min These represent the upper and lower limits of the transmission power of lines i and j.

[0114] Power flow calculation and voltage phase angle constraints include active power flow calculation of interconnected lines and node voltage phase angle security constraints, including:

[0115] For the active power flow calculation of interconnected lines, the transmission power of the line during the dispatch period for any two directly connected grid nodes is determined by the following physical relationship: the transmission power value is equal to the node voltage phase angle value minus the node voltage phase angle value and then divided by the line impedance value.

[0116] The node voltage phase angle safety constraint stipulates that for any node in the power grid, the voltage phase angle value during the dispatch period must meet both a lower limit condition and an upper limit condition. The lower limit condition is that the voltage phase angle is not lower than the minimum allowable value preset by the system, and the upper limit condition is that the voltage phase angle is not higher than the maximum allowable value preset by the system.

[0117] By embedding a frequency influence term into the traditional DC power flow model, the load Containing f t This enables precise tracking of power flow across the entire network.

[0118] Specifically, the load proportioning coefficients satisfy the following: a0 represents frequency-independent loads, a1 represents linearly frequency-dependent loads, and a2 represents quadratic frequency-dependent loads, with a0 + a1 + a2 = 1. The frequency regulation strategy is to increase f during peak periods of new energy output. t Reduce wind curtailment and lower f during off-peak hours for renewable energy output. t Save on thermal power costs.

[0119] By using load classification response coefficients (a0, a1, a2) to guide differentiated frequency regulation, the uncontrolled load fluctuations caused by a "one-size-fits-all" frequency adjustment are avoided. A revolutionary strategy is proposed to shift from "source follows load" to "load follows source."

[0120] When wind power generation is at its peak, the frequency is increased to 50.5Hz, the load increases by 5%, and wind curtailment is reduced by 20%.

[0121] When wind power is insufficient, the frequency is reduced to 49.8Hz, the load is reduced by 3%, and the start-up cost of thermal power is saved.

[0122] The transmission power limit for interconnected lines is as follows: for any two physically connected nodes in the power grid, during any scheduling period, the following conditions apply: minimum power guarantee: the actual transmission power of the line shall not be lower than the preset minimum transmission capacity lower limit of the line; maximum power cut-off: the actual transmission power of the line shall not exceed the preset maximum transmission capacity upper limit of the line.

[0123] Dynamic frequency regulation strategies based on the dynamic response model of load and frequency, and load frequency characteristic classification rules include:

[0124] A constant proportional load means that power demand does not change with frequency.

[0125] For linear proportional loads, power demand is directly proportional to frequency changes.

[0126] For a secondary proportional load, the power demand is proportional to the square of the frequency change.

[0127] The dynamic frequency regulation strategy implements differentiated frequency control based on fluctuations in new energy power generation, including:

[0128] During peak periods of renewable energy output: proactively increase the system frequency reference value and reduce wind curtailment by decreasing the total power demand of linear and quadratic proportional loads;

[0129] During periods of low renewable energy output: proactively reduce the system frequency benchmark value and decrease the power generation capacity of thermal power units to reduce the cost of thermal power fuel.

[0130] Specifically, the implementation platform is the IEEE 30-node system, including 7 thermal power units, 2 wind farms, and 2 photovoltaic power stations, with a scheduling cycle of 24 hours and a step size of 1 hour. Complex scheduling verification of the 7 thermal power units and wind and solar power stations was achieved within the IEEE 30-node system. Dynamic frequency adjustment within the range of 49.5Hz to 50.5Hz did not affect system stability, and the overall cost reduction stemmed from fuel savings in thermal power and reduced wind curtailment penalties. By transforming frequency into a flexible adjustment resource connecting sources and loads, a significantly innovative systemic solution was formed, breaking through the traditional dilemma of "rigid source-load opposition" in renewable energy grid scheduling. It solved the technical problem in existing technologies where wind / solar curtailment and thermal power regulation contradict each other, and thermal power units are limited by minimum output and ramp-up rates, resulting in output reduction speeds that cannot keep up with renewable energy fluctuations.

[0131] This invention first quantitatively analyzes the correlation between load power and frequency, while also considering frequency security constraints, and establishes an active adjustment model for the power grid frequency base value. Next, it establishes power balance constraints for the interaction of thermal power, wind power, and loads, aiming to minimize the sum of thermal power costs and wind curtailment penalties, and proposes an optimized power grid dispatching method. Finally, through comparative analysis of typical simulation examples, the effectiveness and feasibility of the proposed method in reducing wind curtailment rates and improving operational economics are verified, providing a reference for efficient dispatching strategies for large power grids.

[0132] In this embodiment, the simulation is performed. Equation (1) of the thermal power unit model represents the power generation limit constraint of the thermal power unit, Equation (2) represents the ramp rate constraint of the thermal power unit, and Equations (3) to (4) represent the minimum start-stop time constraint.

[0133]

[0134] In the formula: Let be the power generation of thermal power unit i at time t; and These are the upper and lower limits of the output of thermal power units, respectively. and These represent the upward and downward climbing rates, respectively.

[0135] The wind power model, Equation (5) represents the wind curtailment power limit of the wind farm.

[0136]

[0137] In the formula: Let be the wind power generation of the wind farm at node i at time t; For wind curtailment power; frequency base value active adjustment model.

[0138] When a system is in steady-state operation, the characteristic of the active power of the load changing with frequency is called the active power-frequency static characteristic, or simply the load's frequency static characteristic. Based on the relationship between active power and frequency, loads can be classified into the following categories:

[0139] (1) Loads that are not affected by frequency, such as incandescent lamps and electric heaters.

[0140] (2) Loads that are proportional to frequency, such as motors for metal cutting machine tools and grinding mills.

[0141] (3) Loads that are proportional to the square of the frequency, such as eddy current losses in transformers.

[0142] (4) Loads that are proportional to high powers of frequency, such as electric motors with blowers or centrifugal pumps.

[0143] Since the proportion of loads that are directly proportional to high powers of frequency in actual production and daily life is small and can generally be ignored, the relationship between system load power and frequency is as follows:

[0144]

[0145] a0 + a1 + a2 = 1 (7)

[0146] In the formula: f t P represents the actual frequency of the system at time t. t D_load f is the load power at time t at the actual frequency; N P is the system reference frequency; t DN_1 The load power is at the reference frequency; a0, a1, and a2 represent the proportions of the first three types of loads.

[0147] When the power grid is operating normally, the frequency needs to meet the safety constraint of equation (8).

[0148]

[0149] In the formula: f max and f min These represent the maximum and minimum system frequencies.

[0150] Equation (9) is the objective function of the scheduling model, which minimizes the energy consumption cost and wind curtailment penalty cost of thermal power units.

[0151]

[0152] In the formula: a i b i and c i c is the cost coefficient for thermal power units. loss This represents the wind curtailment penalty coefficient.

[0153] Equations (10) to (13) represent the network constraints of the scheduling model, where equation (10) represents the node power balance constraint, equation (11) represents the node voltage phase angle constraint, and equations (9) to (10) represent the line power flow constraint.

[0154]

[0155] In the formula: P ij,t X represents the active power flow on the lines connected to nodes i and j at time t; ij θ represents the corresponding line impedance. i,t Let θ be the voltage phase angle at node i at time t; max and θ min The upper and lower limits of the phase angle; P ij,max and P ij,min These are the upper and lower limits of the transmission power of line ij.

[0156] To verify the superiority of the proposed scheduling method, a simulation example using the simulation parameters was built using the IEEE 30-node system. The system includes 7 thermal power units, 2 photovoltaic power plants, and 2 wind farms. The parameters of the thermal power units are shown in Table 1, including the upper and lower limits of unit output, ramp rate, minimum start-up and shutdown time, and cost coefficient. The total installed capacity of the wind farms is 500MW, and the total installed capacity of the photovoltaic power plants is 900MW. The predicted curves for new energy sources and load are shown below. Figure 2 As shown. The scheduling cycle is 24 hours, and the scheduling step size is 1 hour. The optimization model is solved using Matlab with the Yalmip toolbox and the Gurobi solver.

[0157] Table 1 Parameters of Thermal Power Units

[0158]

[0159]

[0160] This paper sets up two schemes to verify the superiority of the proposed scheduling strategy:

[0161] Option 1: By adopting the scheduling scheme proposed in this paper, the system frequency can be actively adjusted.

[0162] Option 2: The system always operates at the reference frequency.

[0163] Analysis of Results of Scheme 1

[0164] Figure 5 The diagram illustrates the power balance relationship between thermal power, wind power, and photovoltaic power output and load. As shown in the diagram, thermal power units flexibly adjust to compensate for fluctuations in renewable energy output at different times to ensure a consistently balanced load.

[0165] Figure 4This demonstrates the actual frequency variations of the system. Combined with... Figure 3 Analysis shows that during periods of high wind and solar power generation, the system frequency will be increased as much as possible to reduce wind curtailment because Scheme 1 considers active frequency adjustment. Similarly, during periods of low wind and solar power generation, the system frequency will be reduced to decrease the output of thermal power units.

[0166] During the dispatching cycle, the total operating cost of the power grid was RMB 9,751,353,384, of which the power generation cost of thermal power units was RMB 9,751,283,109 and the wind curtailment penalty cost was RMB 69,175, as shown in Table 2.

[0167] Table 2 Cost Comparison

[0168] Cost of thermal power units (yuan) Cost of wind curtailment penalty / yuan Total cost / yuan Option 1 9751283109 69175 9751353384 Option 2 9990867856 71990 9990939847

[0169] Analysis of Results of Scheme 2

[0170] Figure 5 The system power allocation of Scheme 2 is presented. Compared to Scheme 1, during the peak renewable energy generation period (9-15 hours), Scheme 1 effectively reduces wind curtailment due to its active frequency adjustment. Within the dispatch cycle, the total dispatch cost of Scheme 2 is 9,990,939,847 yuan, which is 239,586,463 yuan (2.4%) higher than Scheme 1. During the peak renewable energy generation period (3-4 hours), the minimum start-up and shutdown time constraints of thermal power units lead to increased wind curtailment in Scheme 1, resulting in higher wind curtailment penalty costs. However, the power generation costs of thermal power units are significantly reduced, resulting in a substantial decrease in the total cost.

[0171] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A new energy power grid optimization scheduling method considering AGC frequency base value adjustment, characterized in that, include: Acquire frequency data and load data of the power grid, wherein the frequency data comes from the AGC system and the load data includes historical load curves; Based on the frequency and load data, a dynamic response model of load and frequency is established to quantify the impact of frequency changes on load power. Based on the dynamic response model, a safety boundary for frequency base value adjustment is set to ensure that the system frequency is within the allowable range, thereby preventing equipment damage or system crash caused by frequency exceeding the limit. An optimization model is constructed with the objective of minimizing the total cost, which includes the operating cost of thermal power units and the cost of wind curtailment penalty. The constraints of the optimization model include: thermal power unit operation constraints, and the actual power generation of the thermal power unit at any node at the scheduling time, satisfying the lower limit condition and the upper limit condition. The lower limit condition is that the actual power generation is not lower than the preset minimum allowable output of the unit multiplied by the current operating state value, and the upper limit condition is that the actual power generation is not higher than the preset maximum allowable output of the unit multiplied by the current operating state value. Solve the optimization model to generate frequency regulation commands and unit scheduling schemes.

2. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 1, characterized in that, The operating cost of the thermal power unit includes secondary fuel cost, linear fuel cost, and fixed start-stop cost; The wind curtailment penalty cost is the actual amount of wind curtailment multiplied by the unit wind curtailment penalty coefficient.

3. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 1, characterized in that, The constraints of the optimization model also include start-up and shutdown constraints of thermal power units, wind curtailment constraints of wind farms, and power balance constraints of grid nodes. The start-up and shutdown constraints of the thermal power unit include: minimum continuous operating time constraint and minimum continuous shutdown time constraint; The minimum continuous operating time constraint is that when a thermal power unit switches from a shutdown state to an operating state during a scheduling period, the unit must maintain its operating state unchanged for a continuous period starting from the switching time. The minimum continuous downtime constraint is that when a thermal power unit switches from the operating state to the shutdown state during a scheduling period, the unit must remain in the shutdown state for a continuous period starting from the switching time.

4. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 3, characterized in that, The start-stop constraint formula for the thermal power unit is: In the formula: T on Minimum continuous runtime; T off Minimum continuous downtime; The start-up and shutdown status of the i-th thermal power unit during time period t is 1 for running and 0 for shutting down; i is the equipment index, either thermal power unit or wind farm; k is a time period loop variable.

5. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 3, characterized in that: The wind curtailment constraint of the wind farm is: The power balance constraint of the power grid node is: In the formula: This refers to the power output of the wind curtailment. Let be the wind power generation of the wind farm at node i at time t; Let i be the active power output of the thermal power unit at node i during time period t; Predict the power generation of the wind farm at node i during time period t; The active power output of the photovoltaic power station at node i during time period t; Let ∑ be the active power of the load at node i during time period t; j P ij,t Let be the total line power flow from node i to its neighboring node j.

6. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 1, characterized in that, Power flow calculation and voltage phase angle constraints include: For the active power flow calculation of interconnected lines, the transmission power of the line during the dispatch period for any two directly connected grid nodes is determined according to the following physical relationship: the transmission power value is equal to the node voltage phase angle value minus the node voltage phase angle value and then divided by the line impedance value. The node voltage phase angle safety constraint stipulates that for any node in the power grid, the voltage phase angle value during the scheduling period must meet both a lower limit condition and an upper limit condition. The lower limit condition is that the voltage phase angle is not lower than the minimum allowable value preset by the system, and the upper limit condition is that the voltage phase angle is not higher than the maximum allowable value preset by the system.

7. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 6, characterized in that, The transmission power of interconnecting lines is limited to any two physically connected nodes in the power grid during any given scheduling period, including: Minimum power guarantee: The actual transmission power of the line shall not be lower than the preset minimum transmission capacity limit of the line. Maximum power cut-off: The actual power transmitted by the line must not exceed the maximum transmission capacity limit preset for the line.

8. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 1, characterized in that, The dynamic frequency regulation strategy of the load and frequency dynamic response model includes: A constant proportional load means that power demand does not change with frequency. For linear proportional loads, power demand is directly proportional to frequency changes. For a secondary proportional load, the power demand is quadratically related to the frequency change.

9. The new energy power grid optimization scheduling method considering AGC frequency base value adjustment according to claim 8, characterized in that, The dynamic frequency regulation strategy implements differentiated frequency control based on fluctuations in new energy power generation, including: During peak periods of renewable energy output, the system frequency reference value is proactively increased to reduce wind curtailment by decreasing the total power demand of linear and quadratic proportional loads. During periods of low renewable energy output, the system frequency benchmark is proactively lowered to reduce the power generation capacity of thermal power units and lower the cost of thermal power fuel.

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