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1. Investigating the impact of wind speed variability on optimal sizing of hybrid wind-hydrogen microgrids for reliable power supply

   https://doi.org/10.1016/j.ijhydene.2025.01.444  

   Eniola, Victor; Cimorelli, Jack; Niezrecki, Christopher; Willis, David; Jin, Xinfang (March 2025, International Journal of Hydrogen Energy)

   Addressing resource intermittency is crucial for designing effective and economical renewable energy systems for many applications. Hydrogen as long-term energy storage medium shows promise for increasing renewables penetration into the grid. Cost-effective hybrid wind-hydrogen microgrids (HWHMs) require system-level sizing of each subcomponent. This study employs low-order HWHM component models in a system-level framework to predict HWHM performance. It introduces a novel approach to investigate the optimal sizing of HWHMs. The study uniquely addresses the impact of wind speed fluctuation amplitudes and frequency variations on system design – an area not previously explored. The model is run for 7 days using several different wind speed profiles and real load demand data from an off-grid Naval facility on an island in California. In our test cases, the findings indicate that fewer wind turbines and more hydrogen tanks are required to successfully meet demand when wind speed fluctuations increase. For example, when the wind speed fluctuation increases from 0.68 to 2.04 m/s, and the wind turbine is expected to maintain an average power equivalent to 90% of the peak load, the turbine capacity drops by 17%, requiring a 304% rise in the number of tanks. However, the frequency of wind speed variation has a negligible effect on the optimal HWHM configuration. Through a rule-based optimization algorithm, this research offers important insights for designing reliable microgrids capable of meeting critical loads despite highly variable wind conditions. 

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2. Ensemble Methods for Probabilistic Solar Power Forecasting: A Comparative Study

   https://doi.org/10.1109/PESGM52003.2023.10253133  

   Ahmad, Tawsif; Zhou, Ning (July 2023, IEEE)

   To guide the selection of probabilistic solar power forecasting methods for day-ahead power grid operations, the performance of four methods, i.e., Bayesian model averaging (BMA), Analog ensemble (AnEn), ensemble learning method (ELM), and persistence ensemble (PerEn) is compared in this paper. A real-world hourly solar generation dataset from a rooftop solar plant is used to train and validate the methods under clear, partially cloudy, and overcast weather conditions. Comparisons have been made on a one-year testing set using popular performance metrics for probabilistic forecasts. It is found that the ELM method outperforms other methods by offering better reliability, higher resolution, and narrower prediction interval width under all weather conditions with a slight compromise in accuracy. The BMA method performs well under overcast and partially cloudy weather conditions, although it is outperformed by the ELM method under clear conditions. 

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3. Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks

   Pinceti, A.; Sankar, L.; Kosut, O (January 2021, 2021 IEEE Power & Energy Society General Meeting (PESGM))

   null (Ed.)

   A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications. 

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4. Experimental evaluation of power distribution to reactive loads in a network-controlled delivery grid

   https://doi.org/10.1109/FMEC.2018.8364065  

   Jiang, Zhengqi; Shah, Haard; Rojas-Cessa, Roberto; Grebel, Haim; Mohamed, Ahmed (April 2018, Conference Paper published Apr 2018 in 2018 Third International Conference on Fog and Mobile Edge Computing (FMEC))

   We present experiments with combined reactive and resistive loads on a testbed based on the Controlled-Delivery power Grid (CDG) concept. The CDG is a novel data-based paradigm for distribution of energy in smart cities and smart buildings. This approach to the power grid distributes controlled amounts of power of loads following a request-grant protocol performed through a parallel data network. This network is used as a data plane that notifies the energy supplier about requests and inform loads of the amount of granted power. The energy supplier decides the load, amount, and the time power is granted. Each load is associated with a network address, which is used at the time when power is requested and granted. In this way, power is only delivered to selected loads. Knowing the amount of power being supplied in the CDG requires knowing the precise amount of power demand before this is requested. While the concept works well for an array of resistive loads, it is unclear how to apply it to reactive loads, such as motors, whose power consumption varies over time. Therefore, in this paper, we implement a testbed with multiple loads, two light bulbs as resistive loads and an electrical motor as a reactive load. We then propose to use power profiles for the adoption of the request-grant protocol in the CDG concept. We adopt the use of power profiles to leverage the generation of power requests and evaluate the efficiency of the request-grant protocol on the amount of supplied power. In addition, the deviation of delivered power in the data and power planes is evaluated and results show that the digitized power profile of the reactive loads enables the issuing of power requests for such loads with high accuracy. 

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5. Uncertainty-Aware Day-Ahead Datacenter Workload Planning with Load-Following Small Modular Reactors

   https://doi.org/10.1145/3757892.3757905  

   Yang, Yijie; Wang, Dan; Shi, Jian; Wu, Chenye; Han, Zhu (July 2025, ACM SIGEnergy Energy Informatics Review)

   The rapid rise of AI applications has driven datacenters to unprecedented energy demands, which has prompted major tech companies to adopt on-site nuclear power plants (NPPs) alongside grid electricity. While existing research focuses on off-site NPPs in multi-energy systems optimized for investment returns, recent advances in small modular reactors (SMRs), particularly load-following SMRs (LF-SMRs), offer flexible, reliable power tailored for datacenter co-location. However, LF-SMRs are governed by a set of physical constraints, such as ramp rate and stability limits, making them unsuitable as fully dispatchable sources. This paper proposes a novel day-ahead workload scheduling approach that jointly coordinates datacenter operations and LF-SMR output, explicitly modeling these constraints. We develop a two-stage formulation that forecasts carbon-free grid energy from the grid using conformal prediction in the first stage and then optimizes LF-SMR output and workload scheduling via mixed-integer programming in the second stage. Evaluation on real workload traces shows that our method reduces carbon-based energy consumption by up to 43.44% compared to baselines that omit nuclear integration or ignore SMR limitations. 

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