احسان جمشيدي شورستاني

Given the escalating global concerns about climate change, environmental pollution, an‎d the depletion of natural resources, ensuring a sustainable energy supply has emerged as one of the greatest challenges of the 21st century. Simultaneously, the exhaustion of fossil fuel reserves, increasing oil an‎d gas prices, an‎d the environmental repercussions of extensive reliance on non-renewable energy sources have rendered the transition to renewable energy sources an unavoidable requirement in economic, environmental, an‎d industrial planning at the national level. Among renewable sources, wind energy has gained a leading role due to its environmental friendliness, low operational an‎d maintenance costs, an‎d adaptability across diverse geographical regions. These advantages have attracted considerable interest from policymakers, industry professionals, an‎d researchers. Wind farms, the primary means of exploiting wind energy, necessitate accurate an‎d optimized designs regarding both site selec‎tion an‎d turbine arrangement, as factors such as budgetary constraints, limited lan‎d availability, an‎d turbine wake interactions significantly influence their overall efficiency an‎d productivity. This study aims to develop an effective model for optimizing wind turbine layouts through two main scenarios: (1) maximizing power generation with a fixed number of turbines an‎d (2) achieving a predefined power output with the fewest possible turbines. For spatial modeling, a 2×2 km² wind farm is considered, divided into 100 discrete cells. Various wind conditions representing different climates are analyzed, an‎d the "Wake Jensen" model is applied to eva‎luate the impact of wake interference among turbines. The turbine arrangement is then optimized using the Genetic Algorithm, a robust combinatorial optimization technique, with the objective of maximizing power output while minimizing both installation an‎d operational costs. A notable innovation of this research is the inclusion of turbine height as a decision variable in the optimization process. Its effects are assessed under two conditions—fixed height an‎d variable height—an‎d thoroughly examined in terms of output power an‎d total system costs. In the final stage of the research, a novel method is proposed. Initially, a preliminary turbine layout is designed to meet the target power output without accounting for wake effects. Subsequently, the actual power output (including wake interference) is calculated, an‎d additional turbines are gradually incorporated to achieve the target output. This procedure is performed for both fixed- an‎d variable-height scenarios, after which the outcomes from each approach are compared an‎d analyzed.

بهينه‌سازي , مزرعه‌بادي , الگوريتم‌ ژنتيك , اثر ويك

Optimization , Wind farm , Genetic Algorithm , Wake effect

Ehsan jamshidi

S.Mojtaba Mirhoseini