Co‐optimisation of wind farm micro‐siting and cabling layouts

Abstract

Wind farm layout optimisation (WFLO) is carried out in this study considering the wake effect, and cabling connections and losses. The wind farm micro-siting optimisation problem is formulated with the aid of Jensen's wake model. Cabling between the wind turbines and the point of common coupling is an important aspect of the wind farm design as it affects the capital investment as well as income over the lifetime of the wind farm. The cabling layout must satisfy the connection of the wind turbines to the point of common coupling in such a way that the total cable length is reduced while reliability is maintained. Introducing the cabling layout optimisation to the WFLO, further complicates the optimisation problem. An integrated tool is developed to optimise the wind farm layout and cabling simultaneously. The main contribution of this work is the development of an integrated tool that maximizes the energy production of the wind farm via optimal allocation of wind turbines with optimal cable routing. This tool considers the capital cost of wind turbines and cabling, wind farm power production, and power losses in the cabling over the lifetime of the wind farm. The proposed co-optimisation problem is solved using genetic algorithm. The decision variables are the wind farm layout, cable paths and sizes, and the location of the point of common coupling within the land perimeter. A case study incorporating a multi-speed and multi-direction wind profile is carried out to demonstrate the applicability of the proposed approach. Moreover, the proposed methodology is compared to the separate optimisation method where the WFLO and cabling optimisation are solved sequentially with two separate steps. It is shown that the co-optimisation method is superior in terms of cable power losses, overall wind farm cost, and compactness (land use). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Optimal Sizing of Hybrid Wind-Solar Power Systems to Suppress Output Fluctuation

“…Jensen's model is one of the recommended models with a strong performance [25,26]. Hence, this model is the one used for further analysis in this study, and its mathematical model is described in detail in [27].…”

“…According to the available literature, the objective function and the parameters of Grady et al's [28] study are widely used as a benchmark. Therefore, the objective function in Grady et al's study was used to cross-check the developed wake model and the formulated optimization problem [27].…”

Optimization of Offshore Wind Farms Configuration Minimizing the Wake Effect

“…Part of the kinetic energy is converted into mechanical energy by WTs when the air passes through the blades. The wake effect reflects the interference that the wind passing through one turbine exerts on another, reducing the air mass flow and wind speed, reducing wind energy production [16], [17]. The Jensen model, illustrated in Figure 2, is used to model the wake effect in wind farms.…”

“…Fig. 2 -Principle of the Jensen wake effect model (top view) [17] Figure 2 represents a single wake effect where Ti is located at coordinates (xi, yi) and Tn at coordinates (xn, yn), representing the upstream and downstream turbines, respectively. The wake effect is axisymmetric.…”

“…The objective function in the optimization model is given by 𝑚𝑎𝑥𝑖𝑚𝑖𝑧𝑒 ∑(𝐸 𝑖 𝑥 𝑖 − 𝑤 𝑖 𝐸 𝑖 ) 𝑖∈𝑁 (18) To ensure that it is valid (17), the following constraint is considered…”