Designing Renewables for an Uncertain World: Adaptive Co-Design Formulation and Marine Energy Case Study

Abstract Renewable energy systems are subject to uncertainty arising both from modeling simplifications/imperfections and a stochastic environment. To maximize energy production under this uncertainty, real-time plant and controller adaptability can be introduced. While the inclusion of plant and controller adaptability has been demonstrated to enhance the energetic performance of both wind and marine energy systems, this comes at an economic cost, due to the need for additional actuators and adaptive control software development. In this work, a co-design framework is presented to identify the system design, model refinement technique, and degree of plant and controller adaptability that minimize the expected levelized cost of energy (LCOE) of a renewable energy system. This framework is then applied to a detailed case study involving an installation of marine hydrokinetic (MHK) kites, using a detailed cost model and a computationally efficient surrogate model to estimate energetic performance. The study resulted in identified LCOE-optimal levels of plant and controller adaptability, in addition to an optimal level of model refinement following the plant freeze date (recognizing that this model refinement comes at a development cost). To fully realize the range of adaptation built into the design of the optimized kite system, a real-time extremum-seeking-based adaptation strategy is presented. Through dynamic simulation results, we show that the energetic performance of the kite converges to the predictions generated by the surrogate model, thus validating the use of the surrogate model in the co-design optimization.