TY - JOUR
TI - Sustainable renewable energy planning and wind farming optimization from a biodiversity perspective
AU - Dhunny, A
AU - Allam, Z
AU - Lobine, D
AU - Lollchund, M
T2 - Energy
AB - The impacts of climate change, largely fuelled by the consumption of fossil fuels and an unhealthy consuming lifestyle, are encouraging a wide and far ranging adoption of renewable energy sources. This is critical, and well received, for countries who are on the frontline of climate change, in particular Small Island Developing States (SIDS) and those with fragile, unique and complex natural ecosystems. While there has been research on the importance of renewable energy and on the increasing of its efficiency for bettering its adoption and resulting economic gain, there is however little literature on the design and its implementation in respect to the rich and fragile biodiversity of areas; which contribute greatly to local ecosystems, and even to liveability dimensions of fauna, flora as well as humans, regions and cities. To ensure optimal energy generation while preserving fragile ecosystems, a method for numerical Genetic Algorithm (GA) is proposed for wind farming optimization to determine both the sitting of wind turbines and the Levelised Cost of Energy (LCOE). Optimal sites were identified in the small island of Mauritius and the model was applied on a complex terrain around an airport to identify optimal sites for the sitting of wind turbines while analysing the energy offset in terms of demand and supply of the area to encourage decentralised and more stable energy networks. Based on a set of parameters including local regulations and guidelines, 3 scenarios with a layout of 12, 24 and 36 wind farming sitting arrangements were proposed with a minimal impact on the fauna and flora; including the pathways of migratory birds. This paper is aimed at energy, urban planners and policy makers looking at sustainable energy planning.
DA - 2019/10//
PY - 2019
VL - 185
SP - 1282
EP - 1297
UR - https://www.sciencedirect.com/science/article/pii/S0360544219314896
DO - 10.1016/j.energy.2019.07.147
LA - English
KW - Wind Energy
KW - Land-Based Wind
KW - Social & Economic Data
KW - Human Dimensions
ER -