Multi-objective windfarm optimization simultaneously optimizing coe and land footprint of wind farms under different land plot availability

Wind farm planning involves multiple performance objectives, such as (i) average annual energy production, (ii) lifetime costs, and (iii) net impact on surroundings. In general, planning a commercial-scale wind farm can take several years. Undesirable concept-to-installation delays are often attributed to the lack of an upfront understanding of howdifferent factors (e.g., wind conditions, choice of turbines) collectively affect the perfor-mance objectives of a wind farm. Moreover, it is necessary to understand the balance between the socio-economic, engineering, and environmental objectives at an early stage in the planning process − e.g., the tradeoffs between the production capacity, costs, and land footprint of the wind farm. In this paper, we investigate how layout optimization (i.e., optimal selection and siting of turbines) can be exploited to accomplish desirable bal-ance between the following two objectives: (i) cost of energy (COE) and (ii) land area pe MW installed (LAMI). The COE is estimated using the Wind Turbine Design Cost and Scaling Model (WTDCS) and the energy production model offered by the Unrestricted Wind Farm Layout Optimization (UWFLO) framework. The LAMI is computed using an Layout-based land usage model, where LAMI is estimated based on the turbine locations in a candidate layout. The tradeoff between COE and LAMI is investigated specifically under different landowner participation constraints on land plot availability. The Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm is used to perform the wind farm layout optimization. The investigation provides important insights into the most attractive land plots at a site in terms of COE and land footprint.