The meaning of compatibility in systems of taper and volume equations has been extended. It is desirable and possible to develop completely compatible taper and volume equation systems that have algebraic compatibility and numeric consistency among all the component equations. Two such taper and volume systems were developed for slash pine in the southeastern United States. One was derived from a recently developed merchantable volume equation (Zhao and Kane 2017), and the other was derived from the well-known Max and Burkhart segmented polynomial taper equation (1976). Three fitting methods were used to obtain numerically consistent estimates of parameters in these two systems. The performance of the systems associated with fitting methods were evaluated with taper, merchantable volume and total volume predictions, and bias trends over different dbh classes, total height classes, and different relative height intervals. The new system outperformed the Max-Burkhart taper-based system for merchantable and total volume predictions and was competitive in diameter prediction. Optimization of parameters of the system for both taper and cumulative volume simultaneously is preferable to separate optimization for taper or volume only.

Offshore wind energy is a promising option for emission-free power generationbecause its potential can satisfy the entire US energy demand. However, deep offshore wind energy is mostly left untapped due to the high levelized cost of energy (LCOE) of floating offshore wind turbines (FOWTs). To address the challenge, individual blade pitch control (IPC) of each blade is necessary to reduce fatigue due to the nonlinear dynamics involving unbalanced nonstationary wind/wave loading. In this paper, a machine learning control (MLC) method is proposed that utilizes a genetic program to selectively evolve promising control law candidates from simulated FOWT sensor data. The proposed method utilizes unique MLC features including an efficient selection of design load cases (DLCs) to accelerate the evolution, DLC-based elitism to identify promising candidates, and the use of internal state feedback to learn effective controller properties across all the target DLCs. These features are used to reduce evaluation time on the complex nonlinear FOWT model. The feasibility of the method is demonstrated by reducing fatigue and ultimate loading by 41% under the representative DLCs provided by the Aerodynamic Turbines with Load Attenuation Systems (ATLAS) competition hosted by the Advanced Research Projects Agency–Energy (ARPA-E). Unlike the methods founded upon black-box models [e.g., artificial neural networks (ANNs)], the proposed method provides interpretable results; thus, it can contribute to learning important design features to guide the future controller design. For example, the best individual driven during the feasibility study exhibits a nonlinear proportional controller to a sigmoidlike platform pitch signal, which can be a starting point for more advanced IPC design for FOWTs. The proposed method will improve the cost-effectiveness of FOWTs and be applied to similar nonlinear control problems, such as unmanned aerial vehicle (UAV) control.

Due to the uncontrollable generators, islanded microgrids powered only by renewable energy require costly energy storage systems. Energy storage needs are amplified when load and generation are misaligned on hourly, monthly, or seasonal timescales. Diversification of both loads and generation can smooth out such mismatches. However, the ideal type of battery to smooth out remaining generation deficits will depend on the duration(s) that energy is stored. This study presents a controls co-design approach to design an islanded microgrid, showing the benefit of hybridizing tidal and solar generation and hybridizing lithium-ion and flow battery energy storage. The optimization of the microgrid’s levelized cost of energy is initially studied in grid-search slices to understand convexity and smoothness. Then, a particle swarm optimization is proposed and used to study the sensitivity of the hybrid system configuration to variations in component costs. The study highlights the benefits of controls co-design, the need to model premature battery failure, and the importance of using battery cost models that are applicable across orders of magnitude variations in energy storage durations. The results indicate that such a hybrid microgrid would currently produce energy at five times the cost of diesel generation, but flow battery innovations could bring this closer to only twice the cost while using 100% renewable energy.

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