Cost-Effective Adaptive Systems for Renewable Energy

Renewable energy systems like wind and marine (wave and ocean current) generators routinely face uncertainty in the form of ever-changing environments and imperfect system models. Real-time plant and controller adaptability can help account for this uncertainty, but such systems come with additional cost. In a recent paper in the Journal of Dynamic Systems, Measurement, and Control, Exponent's Jacob Fine and coauthor Chris Vermillion of the University of Michigan, Ann Arbor, describe a framework to identify the system design, model refinement technique, and degree of plant and controller adaptability that can minimize the expected levelized cost of energy (LCOE) of a renewable energy system.

The authors applied their framework to a case study involving marine energy harvesting kites, deploying a cost model and a computational model to estimate performance. The authors were able to find the best cost-performance balance for designing an adaptive renewable energy system. They identified how much adaptability should be built into both the physical system and its control system to achieve the lowest LCOE. They also determined how much the system model should be improved after the plant design is frozen, while accounting for the fact that further model refinement costs time and money.

To enable the optimized kite leverage its built-in adaptability, the authors proposed a real-time control strategy that continually adjusts the system to improve performance. Simulations showed that the kite's energy performance matched what the simplified predictive model forecast, supporting the model's usefulness in the optimization process. 

From the publication: "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."