Wind plant annual energy production maximisation and efficiency improvement with considerations for thrust and turbulence intensity - Charles, Mfon Okokon (Department of Electrical Engineering, 2025)
Power maximisation in a wind plant (WP) is a necessary and continuous procedure to maintain a cost/benefit ratio required for profit making on the capital investment. Limited land availability with a good wind resource or high land leasing costs imposes close turbine deployments onshore, which increases the wind power plant power (WPP) density. However, the resulting shorter turbine-to-turbine (T-2-T) distances advance increased wake interactions between turbines that increase turbulence intensities and consequently loads within the WP hence hampering its power and energy efficiency. Although sparse turbine deployments are common offshore to reduce wake effects and maximise efficiency, these resulting large T-2-T distances could increase operational and maintenance costs due to long cabling and tedious movement between turbines. Current WPP optimisation studies employ layout optimization, turbine-level control, and plant-wide set-point optimisation to maximise WP power and/or minimise turbine loads, with a majority favouring layout optimisation over regular layouts. Layout optimisation, however, does not guarantee consistently large T-2-T distances throughout the wind plant in any wind direction and would not promote computational costs reduction imposed by turbine set-point optimisation in multi-directional wind plants. However, besides their more appealing outlook, regular layouts could afford directions of large T-2-T distances which promote low TI, low deficit, and where non-optimized operational benefits (e.g. reduced computational and communication overhead) can be enjoyed, along with directions of tight T-2-T distances to also enjoy optimized operational benefits (e.g. high turbine and power density). To exploit this feature, this study proposes and implements an axial induction-based cooperative power maximisation for annual energy production (AEP) maximisation in a hexagonally deployed multi-directional wind site with space restrictions, using particle swarm optimization (PSO) and genetic algorithm (GA). Initial results confirm a significant increase in wind plant power and AEP, accompanied by increase in turbine-level turbulence intensity, thrust coefficient, and thrust as consequences imposed by the unconstrained optimisation-enhanced power increments. These observations have not been previously highlighted nor addressed in the literature of WP power optimisation. In addition to implementing a thrust coefficient-constrained WP power maximisation to manage turbine thrust coefficient and thrust, a novel upstream sectored implementation of a well-studied wake model is applied to improve the mean wind speeds at turbine positions, by eliminating upstream turbines' wake effects at a downstream turbine based on their axial and radial distances from the downstream turbine, and furthermore, based on their axial induction factors. Final results show a WP AEP gain of up to 25%, with significant improvements in WP energy efficiency that compares favourably with the conventional 7D non-optimised deployment typical of conventional WPs. A reduction in TI and turbine thrust magnitude is also achieved. These findings have implications on turbine loads, capital and operational expenditure, land use and space optimisation, visual aesthetics and environmental impact, and the economic viability of hexagonally deployed WPs in comparison to optimised layout WPs and conventionally deployed WPs.