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  5. Optimization model for deployment and delivery of alternative energy with use of spatial data

Optimization model for deployment and delivery of alternative energy with use of spatial data

S. Minukhin, M. Syzrantsev
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Description: The approach and model of determination of the location of alternate sources of power based on the use of spatial data are considered. For data processing the system PRISM datasets which provide estimates of climate elements precipitation, temperature, mean dew point, vapor pressure deficit is selected. To achieve the goal of constructing an optimization model for the selection and delivery of alternative energy sources, a model including loading data from PRISM datasets and converting them into a spatial database table; finding the most suitable places for placement of equipment for the extraction of alternative energy according to the criteria; transformation of the found places into the points of the future graph; construction of the tree of the shortest paths by the Dijkstra's algorithm. To implementation of the model, Entity Framework 6 - Microsoft-based ObjectOriented Data Access for Microsoft .NET Framework was selected. For building a distance alternative source of energy to consumers Entity Framework STDistance is used. To build the edges of the graph, the SQL Builder API is allows to build spatial types. The results of the application of the proposed model for the location and supply of energy in the southwestern US territory - possible locations for the equipment for the production of solar energy, location of consumer settlements, optimal route (route) of delivery of alternative energy - are presented.


Keywords: GIS, spatial data, alternative energy sources, Entity Framework 6, Azure SQL Database, PRISM, Dijkstra's algorithm

Reference:
Minukhin, S.V. and Syzrantsev, M.H. (2018), “Optymizatsiina model roztashuvannia ta postachannia alternatyvnoi enerhii z vykorystanniam prostorovykh danykh” [Optimization model for deployment and delivery of alternative energy with use of spatial data], Information Processing Systems, Vol. 1(152), pp. 61-67. https://doi.org/10.30748/soi.2018.152.09.