| Title |
A MILP algorithm for the optimal sizing of an off-grid hybrid renewable energy system in South Tyrol |
| ID_Doc |
15926 |
| Authors |
Alberizzi, JC; Rossi, M; Renzi, M |
| Title |
A MILP algorithm for the optimal sizing of an off-grid hybrid renewable energy system in South Tyrol |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.egyr.2019.08.012 |
| Abstract |
The exploitation of renewable energy sources through sustainable energy technologies are taking the field to decrease the pollutions' emissions into the Earth's environment. To offset the limitations of such resources, hybrid energy systems are becoming fundamental in grid-connected applications as well as in off-grid ones. However, the unsteady behavior of renewable sources, such as Sun and Wind, complicates the prediction of the energy production's trend. The main factors and components involved in the design of hybrid energy systems are: (i) type of generators, (ii) their optimal number, (iii) storage systems and (iv) optimal management strategies. All of them have to be considered simultaneously to develop the optimal solution aimed at either reducing the dependence from fossil fuels or granting the supply of energy. In this paper, a methodology based on the Mixed Integer Linear Programming (MILP) is presented and adopted to meet the electric demand of a mountain lodge located in a remote area in South-Tyrol (Italy). The methodology has been developed implementing an algorithm through the Matlab (c) software. The algorithm is capable of evaluating the optimal size of a hybrid off-grid Solar-Wind system with battery storage in order to replace an Internal Combustion Engine (ICE) fueled by diesel. (C) 2019 Published by Elsevier Ltd. |
| Author Keywords |
Hybrid off-grid energy system; Mixed integer linear programming; Matlab (c); Optimization algorithm; Renewable energy |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000518455400003 |
| WoS Category |
Energy & Fuels |
| Research Area |
Energy & Fuels |
| PDF |
https://doi.org/10.1016/j.egyr.2019.08.012
|