City smart power grid planning method on basis of land conversion risk

Abstract

The invention discloses a city smart power grid planning method on the basis of land conversion risk. The city smart power grid planning method includes acquiring an electronic map of an area to be planned, rasterizing the electronic map to obtain a grid map, and endowing current land use information to each grid as attribute information; subjecting each grid to simulative prediction on the basis of the land conversion risk; manufacturing a load density curve of different types of land; calculating a first total load prediction value by means of the simulative prediction results of the land conversion risk and the load density curve; calculating a second total load prediction value of the area to be planned by a weighting method; comparing the first total load prediction value and the second total load prediction value, and determining the optimal planning scheme of the power grid according to planning target functions of a city power distribution grid when comparison results meet the setting conditions. The city smart power grid planning method solves the problem that the load prediction results cannot provide effective data for economic evaluation of the power grid planning scheme.

Description

technical field [0001] The invention belongs to the technical field of urban grid planning and design, and in particular relates to an urban smart grid planning method based on land conversion risks. Background technique [0002] At present, urban power grid planning firstly predicts the power load in the planning area. The total investment and operating cost are minimized as the objective function to select the power grid planning scheme. [0003] In terms of power load forecasting, traditional load forecasting methods include trend extrapolation method, time series method, regression model forecasting method and intelligent forecasting methods such as gray model forecasting method and artificial neural network forecasting method. These methods belong to the forecasting of total regional power load, but they are powerless to predict the spatial distribution of power load containing geographic space information such as the time and location of load appearance. Among the cu...

AI Technical Summary

Problems solved by technology

However, whether it is the previous grid planning economic evaluation model, the improved grid planning economic evaluation model or the flexible constraint programming model, they only calculate the cost flow and ignore the income flow, and take the minimum cost instead of the maximum benefit as the objective function. Comprehensively reflect the economic value and investment efficiency of each

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