EMSx: a numerical benchmark for energy management systems

Franc, Adrien Le; Carpentier, Pierre; Chancelier, Jean-Philippe; Lara, Michel De

doi:10.1007/s12667-020-00417-5

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…What if we had compared SDDP not only with "ordinary" MPC but with its stochastic variant, Stochastic Model Predictive Control (SMPC)? A systematic analysis done in [30], using a large dataset of microgrids, reveals that our conclusions remain valid: algorithms based on the offline computation of cost-to-go functions (SDP, SDDP) outperform lookahead algorithms (MPC, SMPC).…”

Section: Discussionmentioning

confidence: 75%

Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches

Pacaud¹,

Carpentier²,

Chancelier³

et al. 2022

Preprint

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In this study, a microgrid with storage (battery, hot water tank) and solar panel is considered. We benchmark two algorithms, MPC and SDDP, that yield online policies to manage the microgrid, and compare them with a rule based policy. Model Predictive Control (MPC) is a well-known algorithm which models the future uncertainties with a deterministic forecast. By contrast, Stochastic Dual Dynamic Programming (SDDP) models the future uncertainties as stagewise independent random variables with known probability distributions. We present a scheme, based on out-of-sample validation, to fairly compare the two online policies yielded by MPC and SDDP. Our numerical studies put to light that MPC and SDDP achieve significant gains compared to the rule based policy, and that SDDP overperforms MPC not only on average but on most of the out-of-sample assessment scenarios.

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Section: Discussionmentioning

confidence: 75%

Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches

Pacaud¹,

Carpentier²,

Chancelier³

et al. 2022

Preprint

Self Cite

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“…Electricity price data has been downloaded from the "ENTSO-E Transparency Platform" [13,19] and refer to the IT-Centre-North bidding zone in 2017. The load and renewable generator profiles refer to the site 15 of the EMSx benchmark dataset [22], provided by Schneider Electric, collecting historical observations of real microgrids in the United States and in Europe. The price profile is presented in Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

Learning-based MPC using Differentiable Optimisation Layers for Microgrid Energy Management

Casagrande,

Boem

2023

2023 European Control Conference (ECC)

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In this paper we present a learning-based Model Predictive Control (MPC) algorithm based on differentiable optimisation layers. Recent works show that it is possible to include an optimisation problem as a network layer in a Neural Network (NN) architecture. Here the MPC optimisation problem is integrated on the last layer of a NN which is used to estimate the uncertain parameters of the objective function. The NN is then trained online, end-to-end (E2E), based on previous control actions performance. We show that directly targeting the optimality of the control actions leads to improved control results with respect to the standard method of estimating the uncertain parameters and then perform the optimisation. The effectiveness of the proposed method is illustrated on a microgrid energy management problem where the future profile of the electricity price is not known.

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“…According to [11], some Energy Management System (EMS) optimization approaches are: Model Predictive Control (MPC), Open Loop Feedback Control (OLFC, sometimes referred to as stochastic MPC) and stochastic dynamic programming (SDP). The mentioned stochastic methods can solve the optimization problem considering multiple scenarios with their inherent probabilities of occurrence.…”

Section: State Of the Artmentioning

confidence: 99%

“…The use of scenarios can be done as shown in [9], where N s scenarios s and their associated probabilities π s are applied to represent forecasted values, and [11], where σ scenarios w σ and their probabilities π σ are used in the stochastic optimization for microgrid management.…”

Section: E Integration Of the Forecasting With Other Applicationsmentioning

confidence: 99%

Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids

Parejo,

García,

Personal

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization approaches, stochastic and probabilistic methods are experiencing a growth in their use. However, one of the problems when applying these approaches is the complexity of creating and evaluating the quality of the required stochastic forecasts compared to deterministic forecasts. To mitigate this difficulty, this paper proposes a probabilistic forecasting framework that integrates model creation, their evaluation, and the selection of the best model for predicting. Additionally, two novel methods are proposed for creating scenario sets, and a new metric is defined for evaluating and selecting which model to use. The proposed framework is applied in a case study over a dataset of ten secondary distribution substations from a real distribution network located in Manzanilla (Spain), showing the effect of the selection criteria over the forecasting quality.Note to Practitioners-This article was motivated by the challenge of probabilistic forecasting inclusion in automatic management systems applied to power distribution networks and microgrids. Modern stochastic management optimization methods are fed with probabilistic forecasts, which offer richer information than classic deterministic forecasting. Therefore, the management systems should be able to automatically train a certain number of forecasting models (e.g., machine learning models), evaluate and compare them, and apply the best ones for obtaining the forecasts to feed the management optimization system. Considering the variety of models, techniques, probabilistic forecast types, and evaluation metrics, it can be unclear how to perform this process. For these reasons, this article proposes a probabilistic forecasting framework that integrates methods for the construction of diverse types of predictions (quantiles, intervals, and scenario sets), their evaluation, and the selection of the best model for performing each required prediction for feeding optimization systems. This framework could help to facilitate the implantation of modern stochastic optimization management systems for distribution networks and microgrids, as it simplifies the forecasting process.

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EMSx: a numerical benchmark for energy management systems

Cited by 8 publications

References 15 publications

Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches

Optimization of a domestic microgrid equipped with solar panel and battery: Model Predictive Control and Stochastic Dual Dynamic Programming approaches

Learning-based MPC using Differentiable Optimisation Layers for Microgrid Energy Management

Probabilistic Forecasting Framework Oriented to Distribution Networks and Microgrids

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