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1. CESAR: A Convolutional Echo State AutoencodeR for High‐Resolution Wind Forecasting

   https://doi.org/10.1029/2026JH001234  

   Bonas, Matthew; Giani, Paolo; Crippa, Paola; Castruccio, Stefano (August 2026, Journal of Geophysical Research: Machine Learning and Computation)

   An accurate and timely assessment of wind speed and energy output allows an efficient planning and management of this resource on the power grid. Wind energy, especially at high resolution, calls for the development of nonlinear statistical models able to capture complex dependencies in space and time. This work introduces a Convolutional Echo State AutoencodeR (CESAR), a spatio‐temporal, neural network‐based model which first extracts the spatial features with a deep convolutional autoencoder, and then models their dynamics with an echo state network. We also propose a two‐step approach to also allow for computationally affordable inference, while also performing uncertainty quantification. We focus on a high‐resolution simulation in Riyadh (Saudi Arabia), an area where wind farm planning is currently ongoing, and show how CESAR is able to provide improved forecasting of wind speed and power for proposed building sites by up to 17% against the best alternative methods. 

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2. Valuing the Capacity Contribution of Renewable Energy Systems with Storage.

   Baker, T; Shittu, E.; Greenwood, S. (January 2020, IISE transactions)

   L. Cromarty, R. Shirwaiker (Ed.)

   The growth of renewable energy technologies creates significant challenges for the stability of the system because of their intermittency. Nonetheless, we can value these technologies with storage systems. We model the supply by a renewable technology, wind, into a storage facility using the leaky bucket mechanism. The bucket is synonymous with storage while the leakage is equivalent to meeting load. Modelica is used to capture: (i) the time-dependence of the state of the bucket based on a physical model of storage; (ii) the stochastic representation of wind energy using wind speed data that is fed into a physical model of a wind technology; and (iii) the load, modeled as a resistor-inductor circuit. The strength of Modelica in using non-causal equations for basic sub-systems that are linked together is harnessed through its libraries. We find that there is a diminishing return to storage. Beyond a certain level of storage, the integration of a reliable baseload power supply is required to diminish the risk due to reduced reliability. The need for storage systems as a hedge against intermittency is dependent on the interplay between the supply volatilities and the stochastic load to guarantee an acceptable level of quality of service and reliability. 

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3. Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

   https://doi.org/10.1109/TII.2022.3195695  

   Alhazmi, Mohannad; Dehghanian, Payman; Nazemi, Mostafa; Oikonomou, Konstantinos (January 2022, IEEE Transactions on Industrial Informatics)

   The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. The closely-intertwined ecosystem of water and power infrastructures is commonly known as water-energy nexus. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a Boolean solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. The suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks. 

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4. Spatial modeling sensitivity analysis: Copula selection for wind speed dependence

   https://doi.org/10.1063/5.0185710  

   Shahirinia, Amir; Farahmandfar, Zeinab; Bina, M. Tavakoli; Henderson, Saul B.; Ashtary, Mohammad (April 2024, AIP Advances)

   As the adoption of wind energy as a key renewable energy source accelerates, precise power flow analysis becomes crucial for accurate power delivery forecasting. This paper addresses the inherent uncertainties in wind speed data at different wind farm locations by conducting a sensitivity analysis to assess wind farm pairs. The analysis accommodates various data sizes, namely, short, medium, and large, and diverse spatial relationships between wind farms. By leveraging National Renewable Energy Laboratory wind speed data from nine distinct wind farms, the dependence structure between wind farm pairs is modeled using copulas. This modeling takes both the wind speed knowledge level and the various spatial interplays among the wind farm pairs into consideration. The findings indicate an inverse proportionality between the strength of dependence and the distance separating the wind farm pairs. 

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5. Non-Contact Wind Turbine Blade Crack Detection Using Laser Doppler Vibrometers

   https://doi.org/10.3390/en17092165  

   Zabihi, Ali; Aghdasi, Farhood; Ellouzi, Chadi; Singh, Nand Kishore; Jha, Ratneshwar; Shen, Chen (May 2024, Energies)

   In response to the growing global demand for both energy and a clean environment, there has been an unprecedented rise in the utilization of renewable energy. Wind energy plays a crucial role in striving for carbon neutrality due to its eco-friendly characteristics. Despite its significance, wind energy infrastructure is susceptible to damage from various factors including wind or sea waves, rapidly changing environmental conditions, delamination, crack formation, and structural deterioration over time. This research focuses on investigating non-destructive testing (NDT) of wind turbine blades (WTBs) using approaches based on the vibration of the structures. To this end, WTBs are first made from glass fiber-reinforcement polymer (GFRP) using composite molding techniques, and then a short pulse is generated in the structure by a piezoelectric actuator made from lead zirconate titanate (PZT-5H) to generate guided waves. A numerical approach is presented based on solving the elastic time-harmonic wave equations, and a laser Doppler vibrometer (LDV) is utilized to collect the vibrational data in a remote manner, thereby facilitating the crack detection of WTBs. Subsequently, the wave propagation characteristics of intact and damaged structures are analyzed using the Hilbert–Huang transformation (HHT) and fast Fourier transformation (FFT). The results reveal noteworthy distinctions in damaged structures, where the frequency domain exhibits additional components beyond those identified by FFT, and the time domain displays irregularities in proximity to the crack region, as detected by HHT. The results suggest a feasible approach to detecting potential cracks of WTBs in a non-contact and reliable way. 

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