CN-121998181-A - New energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling

CN121998181ACN 121998181 ACN121998181 ACN 121998181ACN-121998181-A

Abstract

The invention provides a new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling, which comprises the steps of respectively obtaining a GIS data set and rasterization data of a research area, obtaining a historical load data set, electric equipment characteristics and a historical electric power parameter set of a receiving area, constructing an elastic load curve according to the historical load data set, the electric equipment characteristics, the new energy equipment parameters and the historical electric power parameters, obtaining a constructable area according to the GIS data set, an transmission and distribution channel and the rasterization data, constructing an optimization model comprising minimizing total investment cost, minimizing ecological influence and maximizing power supply reliability according to the elastic load curve and the constructable area to obtain an optimal planning scheme, and planning the new energy base in the research area. The method can effectively avoid damage to the ecological environment, reduce construction difficulty and risk, and the obtained planning scheme achieves optimal balance in the aspects of economy, ecology, power supply and the like.

Inventors

LI RUITENG
FAN ZITIAN
WANG JIXUE
LI XIAOLEI
YANG SHUO
ZHAO YITING
LIU ZIJIAN
ZHOU YUHAN
LIANG HANG
HAN LU

Assignees

电力规划总院有限公司

Dates

Publication Date: 20260508
Application Date: 20260119

Claims (10)

1. A new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling is characterized by comprising the steps of acquiring parameters of a research area, an input and output channel, a receiving end area and new energy equipment; Acquiring a GIS data set and rasterized data based on the research area respectively; Acquiring a historical load data set, electric equipment characteristics and a historical power parameter set based on the receiving end region; Constructing an elastic load curve according to the historical load data set, the electric equipment characteristics, the new energy equipment parameters and the historical electric power parameters; acquiring a constructable area according to the GIS data set, the transmission and distribution channel and the grid data; Constructing an optimization model according to the elastic load curve and the constructable region, wherein the optimization targets of the optimization model comprise minimizing the total investment cost, minimizing the ecological influence and maximizing the power supply reliability; obtaining an optimal planning scheme according to the optimization model; and planning a new energy base in the research area based on the optimal planning scheme.
2. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 1, wherein constructing an elastic load curve according to the historical load data set, the electric equipment characteristic, the new energy equipment parameter and the historical electric power parameter comprises: Preprocessing the historical load data set to obtain a time sequence data set; obtaining a data change rule for the time sequence data set; Obtaining a reference load curve according to the time sequence data set and the data change rule; Obtaining a first value range according to the historical power parameter set and the time sequence data set; acquiring a power parameter response threshold according to the characteristics of the electric equipment and the data change rule; acquiring a power parameter response coefficient based on the first value range; constructing a demand side response adjustment quantity according to the characteristics of the electric equipment, the data change rule, the power parameter response coefficient and the power parameter response threshold; constructing an elastic load curve model according to the reference load curve, the demand side response adjustment quantity and the new energy equipment parameter; And obtaining the elastic load curve according to the time sequence data set, the data change rule and the elastic load curve model by adopting a machine learning algorithm.
3. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 2, wherein the data change rule comprises a load increase trend, a load fluctuation characteristic and a load adjustment capability.
4. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 3, wherein the load growth trend is a long-term growth trend of load.
5. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 3, wherein the load fluctuation characteristics comprise a daily load fluctuation characteristic, a cyclic load fluctuation characteristic and a monthly load fluctuation characteristic.
6. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 3, wherein a piecewise linear model is adopted to construct the elastic load curve model.
7. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 2, wherein the rasterized data comprises at least two grid units; obtaining the constructable region according to the GIS data set, the transmission and distribution channel and the grid data comprises the following steps: Acquiring a forbidden construction area according to the GIS data set; obtaining a region to be planned according to the rasterized data and the forbidden construction region; Respectively obtaining a resource score and an ecological sensitivity score of each grid unit in the area to be planned according to the GIS data set; Obtaining a distance score of each grid unit in the area to be planned according to the transmission and distribution channel; Obtaining a suitability score of each grid unit in the area to be planned according to the resource score, the ecological sensitivity score and the distance score based on a preset weight; And obtaining the constructable area according to the suitability score.
8. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 7, wherein the step of obtaining an optimal planning scheme according to the optimization model comprises the following steps: s1, obtaining at least one intermediate planning scheme according to the optimization model; S2, verifying the intermediate planning scheme corresponding to the highest suitability score by adopting a time sequence production simulation method to obtain a verification result; when the verification result meets the construction requirement, executing a step S3; When the verification result does not meet the construction requirement, the optimization model is adjusted according to the verification result, and the step S1 and the step S2 are repeatedly executed; and step S3, obtaining an optimal planning scheme according to the intermediate planning scheme corresponding to the highest suitability score.
9. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 8, wherein adjusting the optimization model comprises adjusting the power parameter response coefficient, the power parameter response threshold and the preset weight.
10. The new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling according to claim 7, wherein the construction-forbidden areas comprise ecological protection areas and topography factor areas.

Description

New energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling Technical Field The invention relates to the technical field of new energy base planning, in particular to a new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling. Background The method takes a novel electric power system and ecological management cooperative system as a core target, the Sha Gehuang area is positioned as a main battlefield for new energy development, and in the Sha Gehuang large-base planning field, the existing technical scheme mainly focuses on the consideration of a single factor. For example, a Chinese patent application with publication number of CN119106756A is a method for directly planning and optimizing parameters of a new-energy-source base network storage of a sand-gossypii, and the optimization method mainly establishes a capacity configuration and layout optimization model of a wind-light power station by collecting local load data and outgoing power data of an energy base to be planned Sha Gehuang so as to realize optimal configuration and layout of the wind-light power station. However, the optimizing method is used for configuration and layout planning schemes, potential damage to the ecological environment is possibly caused due to insufficient rationality of space layout, meanwhile, the difficulty and risk of construction are increased, the new energy base is difficult to effectively match load requirements in actual operation, the power supply advantages of the new energy cannot be fully exerted, the reliability and economy of power supply are reduced, and in addition, the optimizing method is difficult to obtain an optimal balanced planning scheme, and the complex requirements of new energy base planning in Sha Gehuang areas cannot be met. Disclosure of Invention Aiming at the problems existing in the prior art, the invention provides the new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling, which ensures that the space layout of the new energy base is more reasonable, the load fluctuation can be better adapted, the reliability and the economy of power supply are improved, the damage to the ecological environment can be effectively avoided, the construction difficulty and the risk are reduced, and the obtained planning scheme achieves the optimal balance in the aspects of economy, ecology, power supply and the like. The technical scheme adopted by the invention is that the new energy base multi-objective optimization planning method based on GIS data processing and elastic load curve modeling comprises the steps of obtaining parameters of a research area, a transmission and distribution channel, a receiving end area and new energy equipment; Acquiring a GIS data set and rasterized data based on the research area respectively; Acquiring a historical load data set, electric equipment characteristics and a historical power parameter set based on the receiving end region; Constructing an elastic load curve according to the historical load data set, the electric equipment characteristics, the new energy equipment parameters and the historical electric power parameters; acquiring a constructable area according to the GIS data set, the transmission and distribution channel and the grid data; Constructing an optimization model according to the elastic load curve and the constructable region, wherein the optimization targets of the optimization model comprise minimizing the total investment cost, minimizing the ecological influence and maximizing the power supply reliability; obtaining an optimal planning scheme according to the optimization model; and planning a new energy base in the research area based on the optimal planning scheme. In some of these embodiments, constructing an elastic load curve from the historical load dataset, the powered device characteristics, the new energy device parameters, and the historical power parameters includes: Preprocessing the historical load data set to obtain a time sequence data set; obtaining a data change rule for the time sequence data set; Obtaining a reference load curve according to the time sequence data set and the data change rule; Obtaining a first value range according to the historical power parameter set and the time sequence data set; acquiring a power parameter response threshold according to the characteristics of the electric equipment and the data change rule; acquiring a power parameter response coefficient based on the first value range; constructing a demand side response adjustment quantity according to the characteristics of the electric equipment, the data change rule, the power parameter response coefficient and the power parameter response threshold; constructing an elastic load curve model according to the reference load curve, the demand side response adjus