CN-121981505-A - Photovoltaic power station optimization method and device integrating component layout and electricity price response

CN121981505ACN 121981505 ACN121981505 ACN 121981505ACN-121981505-A

Abstract

The application provides a photovoltaic power station optimization method and device integrating component layout and electricity price response. The method comprises the steps of applying a collaborative optimization model, combining real-time electricity price data and a power grid dispatching instruction, adjusting current space layout parameters of a photovoltaic power station to meet target space layout parameters enabling a first net present value to reach the maximum value, determining space hot spots and time hot spots for generating electricity discarding of the photovoltaic power station according to the target space layout parameters, calculating index values for carrying out full life cycle economic evaluation on the photovoltaic power station, and adjusting the space layout parameters and/or the power dispatching strategy of the photovoltaic power station by combining the space hot spots and the time hot spots according to the index values which do not meet preset standards. The application can improve the economic benefit of the photovoltaic power station.

Inventors

GAO WENXIANG
PENG HUAIWU
Jiang Yingsha
ZHAO WEI
ZHANG ZHENSHI
GUO YUEHAN
LIU NAIJING
LI YUJIN

Assignees

中国电建集团西北勘测设计研究院有限公司

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (10)

1. The photovoltaic power station optimization method integrating the component layout and the electricity price response is characterized by comprising the following steps of: Applying a collaborative optimization model, and combining real-time electricity price data and a power grid dispatching instruction, and adjusting current space layout parameters of the photovoltaic power station to meet target space layout parameters for enabling a first net present value to reach a maximum value; The first net present value characterizes the productivity benefit of the photovoltaic power station, the collaborative optimization model is defined as a mathematical relationship among the first net present value, the real-time electricity price data, the maximum schedulable resource quantity of the power grid and the space layout parameter, and the maximum schedulable resource quantity of the power grid is obtained based on the power grid scheduling instruction; Determining a space hot spot and a time hot spot for generating waste electricity of the photovoltaic power station according to the target space layout parameters, and calculating an index value for carrying out full life cycle economic evaluation on the photovoltaic power station; responding to the index value not meeting a preset standard, and combining the spatial hot spot and the time hot spot to adjust the spatial layout parameters and/or the power scheduling strategy of the photovoltaic power station; the mathematical relationship of the collaborative optimization model is expressed as: ; Wherein, the Is the maximum value of the first net present value, eta (alpha, D) is the power generation efficiency determined by the inclination angle and the spacing, the physical significance of the power generation efficiency is that the influence of the inclination angle alpha and the row spacing D of the component on the power generation efficiency is quantified, the power generation efficiency is calculated by a geometrical optical model in solar engineering or is determined based on an empirical fitting formula, T is a period mark, T is the number of period samples, G is a period pattern For the effective irradiation quantity of the surface of the component in the t period, the effective irradiation quantity is derived from the output of meteorological monitoring data or a numerical meteorological model, and the topographic shielding effect is corrected by combining topographic mapping data, P The real-time electricity price of the period t is obtained from the electric power market transaction data; covering the performance loss caused by the power grid dispatching instruction and the power discarding economic loss and layout parameters for the power discarding loss function; the initial investment cost corresponds to the construction cost of the photovoltaic power station; obtaining the maximum resource quantity which can be scheduled for the power grid in the t period based on a power grid scheduling instruction or a prediction model; The Lagrangian multiplier is used for balancing the objective function and the constraint requirement of the power grid in the optimization process.
2. The method of claim 1, wherein in the mathematical relationship, a factor term of the spatial layout parameter is positively correlated with the first net present value, a factor term of the real-time electricity price data is positively correlated with the first net present value, and a factor term of the maximum amount of schedulable resources of the grid is positively correlated with the first net present value.
3. The method of claim 1, wherein the index value comprises a power rejection rate, a flatness electrical cost, and a second net present value, the second net present value characterizing a project benefit of the photovoltaic power plant.
4. A method according to claim 3, wherein the leveling electric cost is obtained by summing an accumulated value of initial investment cost and operation maintenance cost, and then performing quotient calculation with an accumulated value of generated energy; And the second net present value is obtained by carrying out numerical accumulation along with time after the quotient of the difference value of the gain and the cost and the factor item of the discount rate.
5. The method of claim 4, wherein the spatial hot spot is used to provide a regional reference for component adjustment and the temporal hot spot is used to provide a temporal reference for component adjustment; and in response to the index value not meeting a preset standard, adjusting a spatial layout parameter and/or a power scheduling strategy of the photovoltaic power station in combination with the spatial hot spot and the time hot spot, including: In response to the leveling electrical cost exceeding a cost threshold, increasing component tilt angle, extending component cleaning period, and/or moderately compressing component row spacing in low occlusion regions; and/or increasing component row spacing of the high-power-rejection risk area, reducing component row spacing of the low-power-rejection risk area and/or configuring buffer energy storage in response to the power rejection rate being higher than a power rejection rate threshold, wherein the buffer energy storage is used for storing power when the power grid acceptance capacity is insufficient and delivering power to the power grid when the power grid capacity is insufficient, and/or in response to the fluctuation amplitude of the second net present value exceeding an amplitude threshold, selling power preferentially in a high-power-price period, converting the power capacity into self-use or energy storage in a low-power-price period and/or obtaining benefit subsidies in response to the power grid frequency modulation requirement.
6. The method of claim 1, wherein determining a spatial hot spot and a temporal hot spot for generating a discard of electricity for the photovoltaic power plant based on the target spatial layout parameter comprises: Based on historical irradiation curve data of the photovoltaic power station, component arrangement topological information and a power grid electricity limiting instruction time stamp, constructing a three-dimensional matrix comprising component coordinates, time and electricity limiting rate, and respectively calculating electricity limiting light discarding quantity and planned maintenance light discarding quantity; Inputting the target space layout parameters, the three-dimensional matrix, the electricity limiting waste amount and the planned maintenance waste amount to a machine learning algorithm, carrying out waste characteristic analysis based on the machine learning algorithm, and outputting a space hot spot and a time hot spot of waste generated by the photovoltaic power station; The power limiting waste amount is obtained by solving a difference between the power limiting target power of the power grid and the actual output power of the inverter and then solving a derivative based on a non-power grid power limiting time period; The planned maintenance light rejection amount is obtained by solving a difference between theoretical generated power and actual output power of the inverter and then solving a derivative based on a non-power grid electricity limiting time period; the amount of electric-power-limiting light is calculated by: Limited light rejection = ; Wherein [ t0, t1] represents a non-power grid electricity limiting time period; the actual output power of the inverter; the planned overhaul waste amount is calculated by the following method: Planned overhaul waste amount = ; Wherein, the The power is ideal power without loss; the power supply system is used for outputting the actual power of the inverter, and [ t0, t1] is a non-power grid electricity limiting period.
7. The method according to claim 1, characterized in that the current spatial layout parameters of the photovoltaic power plant are obtained in the following way: acquiring optical resource data of a photovoltaic power station and preprocessing the optical resource data to obtain preprocessed optical resource data; according to the preprocessed light resource data, performing three-dimensional shadow modeling on the photovoltaic power station to obtain a three-dimensional shadow model of the photovoltaic power station; And carrying out shadow shielding analysis on the photovoltaic power station through the three-dimensional shadow model, and optimizing initial spatial layout parameters of the photovoltaic power station based on the shadow shielding analysis result to obtain current spatial layout parameters of the photovoltaic power station.
8. A photovoltaic power plant optimizing apparatus that fuses a component layout and an electricity price response, comprising: the system comprises a collaborative optimization module, a real-time power price data and a power grid scheduling instruction, wherein the collaborative optimization module is used for applying a collaborative optimization model, combining the real-time power price data and the power grid scheduling instruction, and adjusting the current space layout parameter of the photovoltaic power station to meet a target space layout parameter enabling a first net present value to reach the maximum value, the first net present value represents the productivity benefit of the photovoltaic power station, and the collaborative optimization model is defined as a mathematical relation among the first net present value, the real-time power price data, the power grid maximum schedulable resource quantity and the space layout parameter, wherein the power grid maximum schedulable resource quantity is obtained based on the power grid scheduling instruction; the hot spot identification and evaluation module is used for determining a space hot spot and a time hot spot for generating power discarding of the photovoltaic power station according to the target space layout parameters, and calculating an index value for carrying out full life cycle economic evaluation on the photovoltaic power station; And the parameter adjustment module is used for adjusting the spatial layout parameters and/or the power scheduling strategy of the photovoltaic power station by combining the spatial hot spot and the time hot spot in response to the index value not meeting the preset standard.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to implement the method of any one of claims 1-7.
10. A computer readable storage medium having stored thereon a computer program/instruction which, when executed by a processor, implements the method of any of claims 1-7.

Description

Photovoltaic power station optimization method and device integrating component layout and electricity price response Technical Field The application relates to the technical field of photovoltaic power generation system optimization, in particular to a photovoltaic power station optimization method and device integrating component layout and electricity price response. Background The current energy field is subjected to high-speed development of the photovoltaic industry, the current national installed capacity is more than 10 hundred million kilowatts, the current national installed capacity accounts for 30% of the total power generation installed capacity, and the photovoltaic is a second largest power source from the installation perspective. However, the problems of large power development, such as mismatching of the fluctuation of the photovoltaic output and the power grid capacity, large amount of light rejection (such as the rejection rate exceeding 10% in northwest areas) and high land cost, which also causes difficulty in reducing the cost of the photovoltaic, are also accompanied by the strong development. Meanwhile, in the aspect of system design, space parameters such as component inclination angles, spacing and the like are separated and optimized with electricity price policies and energy storage configuration by the traditional design, the multi-factor dynamic coupling effect is ignored, and the calculated benefits under the static electricity price assumption cannot reflect the actual benefits potential of time-sharing electricity price and market bidding policies. Therefore, the application provides a photovoltaic power station optimization method and device integrating component layout and electricity price response, so as to solve one of the technical problems. Disclosure of Invention In order to overcome the problems in the related art, the application provides a photovoltaic power station optimization method and device integrating component layout and electricity price response. According to a first aspect of an embodiment of the present application, there is provided a photovoltaic power station optimizing method for fusing component layout and electricity price response, the method comprising: The method comprises the steps of applying a collaborative optimization model, combining real-time electricity price data and a power grid dispatching instruction, adjusting current space layout parameters of a photovoltaic power station to meet target space layout parameters enabling a first net present value to reach the maximum value, wherein the first net present value represents productivity benefits of the photovoltaic power station, the collaborative optimization model is defined as mathematical relations among the first net present value, the real-time electricity price data, the maximum power grid dispatching resource amount and the space layout parameters, the maximum power grid dispatching resource amount is obtained based on the power grid dispatching instruction, determining space hot spots generated by the photovoltaic power station and time hot spots for carrying out full life cycle economic evaluation on the photovoltaic power station according to the target space layout parameters, and adjusting the space layout parameters and/or the power dispatching strategy of the photovoltaic power station by combining the space hot spots and the time hot spots according to the fact that the index value does not meet preset standards. In one embodiment, in the mathematical relationship, a factor term of the spatial layout parameter is positively correlated with the first net present value, a factor term of the real-time electricity price data is positively correlated with the first net present value, and a factor term of the maximum schedulable resource amount of the power grid is positively correlated with the first net present value. In one embodiment, the index value comprises a power rejection rate, a leveling electrical cost, and a second net present value, the second net present value characterizing a project benefit of the photovoltaic power plant. In one embodiment, the leveling degree electricity cost is obtained by summing an accumulated value of initial investment cost and operation maintenance cost and then carrying out quotient calculation on the sum and an accumulated value of generated energy, and the second net present value is obtained by carrying out numerical accumulation along with time period after carrying out quotient calculation on a factor item of a difference value of income and cost and a discount rate. In one embodiment, the spatial hot spot is used for providing regional reference for component adjustment, the time hot spot is used for providing time period reference for component adjustment, the response to the index value not meeting a preset standard is combined with the spatial hot spot and the time hot spot, the adjustment of the spatial layout parameters a