4,668 research outputs found
An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning
For short-term solar irradiance forecasting, the traditional point
forecasting methods are rendered less useful due to the non-stationary
characteristic of solar power. The amount of operating reserves required to
maintain reliable operation of the electric grid rises due to the variability
of solar energy. The higher the uncertainty in the generation, the greater the
operating-reserve requirements, which translates to an increased cost of
operation. In this research work, we propose a unified architecture for
multi-time-scale predictions for intra-day solar irradiance forecasting using
recurrent neural networks (RNN) and long-short-term memory networks (LSTMs).
This paper also lays out a framework for extending this modeling approach to
intra-hour forecasting horizons thus, making it a multi-time-horizon
forecasting approach, capable of predicting intra-hour as well as intra-day
solar irradiance. We develop an end-to-end pipeline to effectuate the proposed
architecture. The performance of the prediction model is tested and validated
by the methodical implementation. The robustness of the approach is
demonstrated with case studies conducted for geographically scattered sites
across the United States. The predictions demonstrate that our proposed unified
architecture-based approach is effective for multi-time-scale solar forecasts
and achieves a lower root-mean-square prediction error when benchmarked against
the best-performing methods documented in the literature that use separate
models for each time-scale during the day. Our proposed method results in a
71.5% reduction in the mean RMSE averaged across all the test sites compared to
the ML-based best-performing method reported in the literature. Additionally,
the proposed method enables multi-time-horizon forecasts with real-time inputs,
which have a significant potential for practical industry applications in the
evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to
years, when different components needing maintenance should be taken out of
operation. Its objective is to minimize operation-cost expectation while
satisfying reliability-related constraints. We propose a distributed
scenario-based chance-constrained optimization formulation for this problem. To
tackle tractability issues arising in large networks, we use machine learning
to build a proxy for predicting outcomes of power system operation processes in
this context. On the IEEE-RTS79 and IEEE-RTS96 networks, our solution obtains
cheaper and more reliable plans than other candidates
Enhancing Energy Production with Exascale HPC Methods
High Performance Computing (HPC) resources have become the key actor for achieving more ambitious challenges in many disciplines. In this step beyond, an explosion on the available parallelism and the use of special purpose
processors are crucial. With such a goal, the HPC4E project applies new exascale HPC techniques to energy industry simulations, customizing them if necessary, and going beyond the state-of-the-art in the required HPC exascale
simulations for different energy sources. In this paper, a general overview of these methods is presented as well as some specific preliminary results.The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) under the HPC4E Project (www.hpc4e.eu), grant agreement n° 689772, the Spanish Ministry of
Economy and Competitiveness under the CODEC2 project (TIN2015-63562-R), and
from the Brazilian Ministry of Science, Technology and Innovation through Rede
Nacional de Pesquisa (RNP). Computer time on Endeavour cluster is provided by the
Intel Corporation, which enabled us to obtain the presented experimental results in
uncertainty quantification in seismic imagingPostprint (author's final draft
Distributed Stochastic Market Clearing with High-Penetration Wind Power
Integrating renewable energy into the modern power grid requires
risk-cognizant dispatch of resources to account for the stochastic availability
of renewables. Toward this goal, day-ahead stochastic market clearing with
high-penetration wind energy is pursued in this paper based on the DC optimal
power flow (OPF). The objective is to minimize the social cost which consists
of conventional generation costs, end-user disutility, as well as a risk
measure of the system re-dispatching cost. Capitalizing on the conditional
value-at-risk (CVaR), the novel model is able to mitigate the potentially high
risk of the recourse actions to compensate wind forecast errors. The resulting
convex optimization task is tackled via a distribution-free sample average
based approximation to bypass the prohibitively complex high-dimensional
integration. Furthermore, to cope with possibly large-scale dispatchable loads,
a fast distributed solver is developed with guaranteed convergence using the
alternating direction method of multipliers (ADMM). Numerical results tested on
a modified benchmark system are reported to corroborate the merits of the novel
framework and proposed approaches.Comment: To appear in IEEE Transactions on Power Systems; 12 pages and 9
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