CN-122000935-A - Power grid peak regulation and frequency modulation cooperative method and device considering multiple types of power generation energy sources

CN122000935ACN 122000935 ACN122000935 ACN 122000935ACN-122000935-A

Abstract

The invention belongs to the field of electric power, and discloses a grid peak regulation and frequency modulation cooperative method and device for multiple types of power generation energy, which comprise the steps of inputting wind-solar load prediction data and conventional parameters of each unit into an upper flexible resource decision model, taking minimized net load curve variance as a target, coordinating each flexible resource to carry out peak clipping and valley filling to obtain an output plan of the multiple types of power generation energy and an optimized net load curve, transmitting the optimized net load curve to a lower thermal power unit decision model, carrying out linearization treatment on a fuel cost function and a three-section type deep peak regulation interval of the thermal power unit by introducing a square auxiliary variable and a large M method, optimizing the thermal power unit decision model by taking optimal thermal power running cost as a target to obtain a power generation plan and frequency modulation spare capacity of the thermal power unit, and carrying out peak regulation and frequency modulation on the power grid based on the output plan of the multiple types of the power generation energy, the power generation plan and the frequency modulation spare capacity of the thermal power unit.

Inventors

ZHANG JIANGFENG
WANG TIANYU
LU MIN
ZHAO HONGYU
SU YE
DING NING
ZHENG KEKE
WANG QI

Assignees

国网浙江省电力有限公司电力科学研究院
国网浙江省电力有限公司

Dates

Publication Date: 20260508
Application Date: 20260211

Claims (10)

1. The utility model provides a power grid peak regulation frequency modulation cooperative method considering multiple types of power generation energy, which is characterized by comprising the following steps: Inputting wind-solar-load prediction data and conventional parameters of each unit into an upper flexible resource decision model, and coordinating each flexible resource to carry out peak clipping and valley filling with the aim of minimizing the variance of a payload curve to obtain an output plan of a plurality of types of power generation energy sources and an optimized payload curve, wherein the upper flexible resource decision model takes into consideration power rejection rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint; Transmitting the optimized net load curve to a lower layer thermal power unit decision model, and carrying out linearization treatment on a fuel cost function and a three-section type depth peak regulation interval of the thermal power unit by introducing a square auxiliary variable and a large M method; Optimizing the lower layer thermal power unit decision model by taking optimal running cost of the thermal power unit as a target to obtain a power generation plan and frequency modulation reserve capacity of the thermal power unit, wherein the lower layer thermal power unit decision model considers system frequency modulation reserve demand constraint, power balance constraint and thermal power unit running constraint; and carrying out peak regulation and frequency modulation on the power grid based on the output plans of the multi-type power generation energy sources, the power generation plans of the thermal power generating unit and the frequency modulation standby capacity.
2. The method of claim 1, wherein the objective function of the upper layer flexible resource decision model is to minimize a payload variance expressed as: ; Wherein, the For the net load of the grid in period t, As an average value of the net load, As the original load for the period t, For the wind power consumption amount in the t period, For the photovoltaic consumption of the period t, For the output power of the virtual power plant in period t, The output power of the pumped storage is used for the period t, And storing the output power for the t period.
3. The method of claim 1, wherein the power rejection constraint comprises a range of values of [0,1] for the wind-solar power rejection rate, and the total air rejection rate and the total light rejection rate are not greater than a preset ratio of the predicted power of the wind-solar power rejection rate, and the expression is: ; ; Wherein, the The electricity rejection rate of the wind power at the time t is calculated, Photovoltaic power rejection rate at time t ; ; Wherein, the In order to allow for a maximum rate of wind curtailment, In order to allow for a maximum light rejection rate, The output power of the photovoltaic power station is output, And (5) outputting power for the wind farm.
4. The method of claim 1, wherein the energy storage operation constraint comprises a charge-discharge power constraint, a state of charge (SOC) constraint, a charge-discharge balance constraint in a period and a capacity upper limit constraint, the pumped storage operation constraint comprises an output power constraint and a reservoir capacity constraint, and the virtual power plant operation constraint comprises an output upper limit constraint and a output lower limit constraint, wherein the output upper limit and the output lower limit are in a proportional relation with the total wind-solar output.
5. The method of claim 1, wherein the linearizing the fuel cost function and the three-stage deep peak shaving interval of the thermal power unit by introducing square auxiliary variables and a large M method comprises: For the quadratic term in the fuel cost of the thermal power generating unit Square auxiliary variables were introduced: , linearizing product after introducing auxiliary variable by large M method ; In the fuel cost of thermal power generating unit Instead of And converts the nonlinear product to a linear constraint and preserves the convex quadratic term in the fuel cost to place the problem in the MIQP category.
6. The method of claim 5, wherein the linearizing the fuel cost function and the three-stage deep peak shaving interval of the thermal power unit by introducing square auxiliary variables and a large M method comprises: Defining three mutually exclusive binary variables 、、 Respectively showing that the thermal power generating unit is in a state of basic peak regulation, no oil feeding depth peak regulation and oil feeding depth peak regulation; And setting an activation constraint formed by a large M method for the output interval corresponding to each state, introducing a minimum epsilon to prevent the interval from overlapping, and ensuring that only one interval is activated at any time.
7. The method of claim 1, wherein the objective function of the lower thermal power plant decision model is to minimize total thermal power running cost, expressed as: ; Wherein, the The running cost of the unit i in the period t is considered for the depth peak shaving cost.
8. A grid peak shaving and frequency modulation cooperative device for accounting for multiple types of power generation energy sources, the device comprising: The peak clipping unit inputs wind-solar load prediction data and conventional parameters of each unit into an upper flexible resource decision model, and coordinates each flexible resource to clip peaks and fill valleys with the aim of minimizing the variance of a payload curve to obtain an output plan of a multi-type power generation energy source and an optimized payload curve, wherein the upper flexible resource decision model considers the constraint of a power rejection rate, the constraint of energy storage operation, the constraint of pumped storage operation and the constraint of virtual power plant operation; The processing unit is used for transmitting the optimized net load curve to a lower layer thermal power unit decision model, and carrying out linearization processing on a fuel cost function and a three-section type depth peak regulation interval of the thermal power unit by introducing a square auxiliary variable and a large M method; The optimizing unit is used for optimizing the lower layer thermal power unit decision model by taking the optimal running cost of the thermal power unit as a target to obtain a power generation plan and a frequency modulation reserve capacity of the thermal power unit, wherein the lower layer thermal power unit decision model considers the constraint of the frequency modulation reserve requirement of the system, the constraint of power balance and the constraint of the running of the thermal power unit; And the frequency modulation unit is used for carrying out peak regulation and frequency modulation on the power grid based on the output plans of the multiple types of power generation energy sources, the power generation plans of the thermal power generating unit and the frequency modulation standby capacity.
9. An electronic device, comprising: A processor; A memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method according to any of claims 1-7 by executing the executable instructions.
10. A computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of any of claims 1-7.

Description

Power grid peak regulation and frequency modulation cooperative method and device considering multiple types of power generation energy sources Technical Field The invention belongs to the field of electric power, and particularly relates to a grid peak regulation and frequency modulation cooperative method and device considering multiple types of power generation energy sources. Background With the large-scale grid connection of renewable energy sources such as wind power, photovoltaic and the like, an electric power system faces increasingly severe peak regulation and frequency modulation pressure. The traditional thermal power generating unit bears main peak regulation and frequency modulation tasks, but is limited by technical constraints such as climbing speed, minimum output and the like, and has the problem of insufficient flexibility when coping with high-proportion renewable energy fluctuation. Meanwhile, the randomness and fluctuation of wind power and photovoltaic lead to the increase of peak-valley difference of a net load curve of the system, and the phenomenon of wind and light discarding is serious. In the related art, peak shaving and frequency modulation are generally carried out by adopting a single resource or a simple combination mode, and cooperative optimization of multiple types of flexible resources is lacked. For example, partial schemes only consider the peak shaving effect of energy storage or pumped storage, do not fully utilize the aggregate regulation capability of the virtual power plant, or only pay attention to peak shaving and ignore the frequency modulation standby requirement, resulting in insufficient system safety margin. In addition, the deep peak regulation of the thermal power generating unit relates to nonlinear cost functions and segmentation constraint, the traditional optimization method is difficult to solve efficiently, and practical application of multi-energy collaborative scheduling is limited. Disclosure of Invention In view of the above, the invention discloses a grid peak regulation and frequency modulation cooperative method and device considering multiple types of power generation energy sources, which can solve the defects in the related art. In order to achieve the above purpose, the technical scheme disclosed by the invention is as follows: According to a first aspect of the present invention, a grid peak shaving and frequency modulation collaboration method is provided, which takes into account multiple types of power generation energy sources, including: Inputting wind-solar-load prediction data and conventional parameters of each unit into an upper flexible resource decision model, and coordinating each flexible resource to carry out peak clipping and valley filling with the aim of minimizing the variance of a payload curve to obtain an output plan of a plurality of types of power generation energy sources and an optimized payload curve, wherein the upper flexible resource decision model takes into consideration power rejection rate constraint, energy storage operation constraint, pumped storage operation constraint and virtual power plant operation constraint; Transmitting the optimized net load curve to a lower layer thermal power unit decision model, and carrying out linearization treatment on a fuel cost function and a three-section type depth peak regulation interval of the thermal power unit by introducing a square auxiliary variable and a large M method; Optimizing the lower layer thermal power unit decision model by taking optimal running cost of the thermal power unit as a target to obtain a power generation plan and frequency modulation reserve capacity of the thermal power unit, wherein the lower layer thermal power unit decision model considers system frequency modulation reserve demand constraint, power balance constraint and thermal power unit running constraint; and carrying out peak regulation and frequency modulation on the power grid based on the output plans of the multi-type power generation energy sources, the power generation plans of the thermal power generating unit and the frequency modulation standby capacity. According to a second aspect of the present invention, there is provided a grid peak shaving and frequency modulation collaboration device taking into account multiple types of power generation energy, the device comprising: The peak clipping unit inputs wind-solar load prediction data and conventional parameters of each unit into an upper flexible resource decision model, and coordinates each flexible resource to clip peaks and fill valleys with the aim of minimizing the variance of a payload curve to obtain an output plan of a multi-type power generation energy source and an optimized payload curve, wherein the upper flexible resource decision model considers the constraint of a power rejection rate, the constraint of energy storage operation, the constraint of pumped storage operation and the constraint of virtua