Accounting for Optimal Control in the Sizing of Isolated Hybrid Renewable Energy Systems Using Imitation Learning

Decarbonization of isolated or off-grid energy systems through phase-in of large shares of intermittent solar or wind generation requires co-installation of energy storage or continued use of existing fossil dispatchable power sources to balance supply and demand. The effective CO2 emission reduction depends on the relative capacity of the energy storage and renewable sources, the stochasticity of the renewable generation, and the optimal control or dispatch of the isolated energy system. While the operations of the energy storage and dispatchable sources may impact the optimal sizing of the system, it is challenging to account for the effect of finite horizon, optimal control at the stage of system sizing. Here, we present a flexible and computationally efficient sizing framework for energy storage and renewable capacity in isolated energy systems, accounting for uncertainty in the renewable generation and the optimal feedback control. To this end, we implement an imitation learning approach to stochastic neural model predictive control (MPC) which allows us to relate the battery storage and wind peak capacities to the emissions reduction and investment costs while accounting for finite horizon, optimal control. Through this approach, decision makers can evaluate the effective emission reduction and costs of different storage and wind capacities at any price point while accounting for uncertainty in the renewable generation with limited foresight. We evaluate the proposed sizing framework on a case study of an offshore energy system with a gas turbine, a wind farm and a battery energy storage system (BESS). In this case, we find a nonlinear, nontrivial relationship between the investment costs and reduction in gas usage relative to the wind and BESS capacities, emphasizing the complexity and importance of accounting for optimal control in the design of isolated energy systems.

On a time-frequency blurring operator with applications in data augmentation

Inspired by the success of recent data augmentation methods for signals which act on time-frequency representations, we introduce an operator which convolves the short-time Fourier transform of a signal with a specified kernel. Analytical properties including boundedness, compactness and positivity are investigated from the perspective of time-frequency analysis. A convolutional neural network and a vision transformer are trained to classify audio signals using spectrograms with different augmentation setups, including the above mentioned time-frequency blurring operator, with results indicating that the operator can significantly improve test performance, especially in the data-starved regime.

Four ways to recover the symbol of a non-binary localization operator

We present a set of results on how the symbol of a localization operator can be recovered from spectral information, the image of white noise or the image of an orthonormal basis. This extends earlier results which have been limited to the case where the symbol is a binary mask. Moreover, we present some numerical aspects of the different methods and discuss their performance.