# Site selection optimization for 100% renewable energy sources

## TL;DR

This paper proposes an optimized plan for renewable energy site selection in Turkey to achieve sustainability and reduce environmental impact by 2050.

## Contribution

A novel weighted goal programming model integrating regARIMA forecasts and risk analysis for RES site selection in Turkey.

## Key findings

- The model recommends 53% wind energy, 23% biomass, and 13% hydroelectric energy installations.
- The regARIMA model shows high accuracy with an R2 close to 1 and MAPE below 4%.
- The optimization model effectively minimizes costs, time, and risk while maximizing carbon reduction.

## Abstract

The increase in the use of Renewable Energy Sources (RES) provides many advantages such as reducing the environmental problems and sustainability. In this study, a long-term optimum RES settlement strategic plan is conducted for 81 provinces in Turkey by considering real data. Biomass energy, solar energy, hydroelectric energy, geothermal energy, and wind energy are considered RES sources. Energy consumption until the 2050 year is estimated with the regARIMA method, and then a weighted goal programming model was developed in which the outputs of the regARIMA method and risk analysis are integrated. The results of the regARIMA method are tested, and the test results indicate that an R2 value close to 1 indicates that the model is suitable, a low and negative MPE value is neutral, and a MAPE value below 4% indicates high accuracy of the model. Using GAMS 23.5 optimization software program, the developed weighted goal programming model is solved optimally. In this integrated model developed, the objectives of minimizing the installation time, minimizing the investment cost, minimizing the annual cost, maximizing the carbon emission reduction, maximizing the usage time, and minimizing the risk are considered. When the results obtained regarding the number of installations according to the model are examined, the decisions are made for 53% wind energy, 23% biomass energy, 13% hydroelectric energy, 9% solar energy, and 2% geothermal energy. Computational results show that the effective solutions are obtained by minimizing the sum of goals values, covering all provinces in Turkey, and considering real data.