Reducing the computational burden of a microgrid energy management system

McIlvenna, Amelia; Herron, Andrew; Hambrick, Joshua; Ollis, Ben; Ostrowski, James

doi:10.1016/j.cie.2020.106384

Cited by 10 publications

(5 citation statements)

References 21 publications

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“…There is research which focuses on reducing computational burden, such as that by A. McIlvenna et al [33], who modified a forecasting system through the reduction of integer variables by relaxation. This paper aims to optimize the use of a previously built forecasting system regardless of which one it is.…”

Section: Literature Reviewmentioning

confidence: 99%

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Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

et al. 2022

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Electrical energy is consumed at the same time as it is generated, since its storage is unfeasible. Therefore, short-term load forecasting is needed to manage energy operations. Due to better energy management, precise load forecasting indirectly saves money and CO2 emissions. In Europe, owing to directives and new technologies, prediction systems will be on a quarter-hour basis, which will reduce computation time and increase the computational burden. Therefore, a predictive system may not dispose of sufficient time to compute all future forecasts. Prediction systems perform calculations throughout the day, calculating the same forecasts repeatedly as the predicted time approaches. However, there are forecasts that are no more accurate than others that have already been made. If previous forecasts are used preferentially over these, then computational burden will be saved while accuracy increases. In this way, it will be possible to optimize the schedule of future quarter-hour systems and fulfill the execution time limits. This paper offers an algorithm to estimate which forecasts provide greater accuracy than previous ones, and then make a forecasting schedule. The algorithm has been applied to the forecasting system of the Spanish electricity operator, obtaining a calculation schedule that achieves better accuracy and involves less computational burden. This new algorithm could be applied to other forecasting systems in order to speed up computation times and to reduce forecasting errors.

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Section: Literature Reviewmentioning

confidence: 99%

“…The forecast evaluation allows the creation of an an optimal forecast schedule that reduces computational burden and increases overall accuracy, instead of focusing on improving computationally a single model, such as in the work by A. McIlvenna et al [33].…”

Section: Paper Contributionsmentioning

confidence: 99%

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

et al. 2022

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“…The problem is solved using mixed integer non-linear programming (MINLP) and simulation results are provided for both grid-connected and IMGs. On the other hand, in [127] it is proposed to relax the DG's binary variables to reduce the complexity of the RHC solved with MILP. The simulation results show a decrement of the computational burden without largely degrading the accuracy in comparison with the traditional RHC in an IMG.…”

Section: Rolling Horizon Controlmentioning

confidence: 99%

Energy management system optimization in islanded microgrids: An overview and future trends

Raya-Armenta¹,

Bazmohammadi²,

Aviña-Cervantes

et al. 2021

Renewable and Sustainable Energy Reviews

104

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“…Data from the PV system are subject to exploratory analyses and cleaning as outlined in the authors' prior work for similarly sized PV systems [34]- [38]. These include imputing missing values [39] and statistical curve fitting [40]- [42]. Time-series data on X = {I, W S, AT , RH, AP }, and Y = Gen from the month of September 2019 were used for this study.…”

Section: A Data Preparationmentioning

confidence: 99%

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

Sundararajan

Ollis

2021

IEEE Access

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Photovoltaic (PV) power prediction has a constantly evolving solutions landscape with a myriad of data-driven techniques. Each technique leverages a self-adaptive algorithm that must retrain in intervals, be it each day, week, or season, to avoid the model generalizing poorly because of overfitting, underfitting, or concept drift. This paper aims to improve the generalization capability of PV power predictors such as autoencoders used widely in the industry by introducing feature-enhanced ensemble learning (FEEL) after the feature selection step. This framework uses a combination of nonparametric regression and generalized additive models, and an ensemble of weak regularized multilayer perceptron models. Once trained, the framework can reliably generalize on test data across long time periods without any significant degradation in performance. The proposed framework was validated against the baseline autoencoder-based feature enhancement model on a real PV system from a smart neighborhood in Alabama for September 2019. The FEEL framework performed three times better than the baseline, but when applied to the baseline, its performance improved by two times on average. Furthermore, the framework generalized consistently better than five other feature enhancement strategies. Despite fluctuations in weather, the FEEL framework's R-square score had a range of 8.1%, whereas that of the baseline was 48.3%. The mutual information and Minkowski distance scores attempted to quantify concept and model drift, respectively. These scores show that the FEEL framework generalized the ensemble learning models at least two times better than the baseline across the different test days. These results form the first step toward decentralized intelligence for smart grid applications that could free up resources for other expensive analytics in the field. INDEX TERMSFeature enhancement, nonparametric regression, ensemble learning, generalized additive model, concept drift, photovoltaic power prediction. Nomenclature Unless otherwise specified, the following nomenclature is applied throughout this paper. Abbreviations AE Autoencoder, also denotes one of the feature enhancement strategies ARIMA Autoregressive integrated moving average ARIMAX ARIMA with extragenous inputs ERM Ensemble of regularized multilayer perceptrons FEEL Feature-enhanced ensemble learning GAM Generalized additive model

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Reducing the computational burden of a microgrid energy management system

Cited by 10 publications

References 21 publications

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

Reduction of Computational Burden and Accuracy Maximization in Short-Term Load Forecasting

Energy management system optimization in islanded microgrids: An overview and future trends

Regression and Generalized Additive Model to Enhance the Performance of Photovoltaic Power Ensemble Predictors

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