Abstract
Inserting renewable energy in the electric grid in a decentralized manner is a key challenge of the energy transition. However, at local scale, both production and demand display erratic behavior, which makes it challenging to match them. It is the goal of Energy Management Systems (EMS) to achieve such balance at least cost. We present EMSx, a numerical benchmark for testing control algorithms for the management of electric microgrids equipped with a photovoltaic unit and an energy storage system. EMSx is made of three key components: the EMSx dataset, provided by Schneider Electric, contains a diverse pool of realistic microgrids with a rich collection of historical observations and forecasts; the EMSx mathematical framework is an explicit description of the assessment of electric microgrid control techniques and algorithms; the EMSx software EMSx.jl is a package, implemented in the Julia language, which enables to easily implement a microgrid controller and to test it. All components of the benchmark are publicly available, so that other researchers willing to test controllers on EMSx may reproduce experiments easily. Eventually, we showcase the results of standard microgrid control methods, including Model Predictive Control, Open Loop Feedback Control and Stochastic Dynamic Programming.
Similar content being viewed by others
Change history
06 March 2021
A Correction to this paper has been published: https://doi.org/10.1007/s12667-021-00430-2
Notes
When the horizon extends further than the period, we truncate the lookahead window to \(\min (H, T-t{+}1)\).
We recall that gains were defined relatively to the cost performance of a dummy controller.
References
Bertsekas, D.P.: Dynamic Programming and Optimal Control, vol. 1. Athena Scientific, Belmont, MA (1995)
Bertsekas, D.P.: Dynamic programming and suboptimal control: a survey from ADP to MPC. Eur. J. Control 11(4–5), 310–334 (2005)
Bertsekas, D.P.: Dynamic Programming and Optimal Control: Approximate Dynamic Programming, 4th edn. Athena Scientific, Belmont (2012)
Bezanson, J., Karpinski, S., Shah, V.B., Edelman, A.: Julia: a fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145 (2012)
Carpentier, P., Chancelier, J.P., Cohen, G., De Lara, M.: Stochastic multi-stage optimization. In: Asmussen, S., Glynn, P.W., Kurtz, T.G., Le Jan, Y. (eds.) Probability Theory and Stochastic Modelling, vol. 75. Springer, Cham (2015)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning. Springer series in statistics, New York (2001)
Garcia, C.E., Prett, D.M., Morari, M.: Model predictive control: theory and practice—a survey. Automatica 25(3), 335–348 (1989)
Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with lstm recurrent networks. J. Mach. Learn. Res. 3(Aug), 115–143 (2002)
Haessig, P., Kovaltchouk, T., Multon, B., Ahmed, H.B., Lascaud, S.: Computing an optimal control policy for an energy storage. arXiv preprint arXiv:1404.6389 (2014)
Hafiz, F., Awal, M., de Queiroz, A.R., Husain, I.: Real-time stochastic optimization of energy storage management using rolling horizon forecasts for residential pv applications. In: 2019 IEEE Industry Applications Society Annual Meeting, pp. 1–9. IEEE (2019)
Heymann, B., Bonnans, J.F., Silva, F., Jimenez, G.: A stochastic continuous time model for microgrid energy management. In: 2016 European Control Conference (ECC), pp. 2084–2089. IEEE (2016)
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J.: Probabilistic energy forecasting: global energy forecasting competition 2014 and beyond. Int. J. Forecas. 32(3), 896–913 (2016)
Kriett, P.O., Salani, M.: Optimal control of a residential microgrid. Energy 42(1), 321–330 (2012)
Lohndorf, N., Shapiro, A.: Modeling time-dependent randomness in stochastic dual dynamic programming. Eur. J. Oper. Res. 273(2), 650–661 (2019)
Olivares, D.E., Mehrizi-Sani, A., Etemadi, A.H., Cañizares, C.A., Iravani, R., Kazerani, M., Hajimiragha, A.H., Gomis-Bellmunt, O., Saeedifard, M., Palma-Behnke, R., et al.: Trends in microgrid control. IEEE Trans. Smart Grid 5(4), 1905–1919 (2014)
Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming, 1st edn. Wiley, New York (1994)
Rigaut, T., Carpentier, P., Chancelier, J.P., De Lara, M., Waeytens, J.: Stochastic optimization of braking energy storage and ventilation in a subway station. IEEE Trans. Power Syst. 34(2), 1256–1263 (2018)
Shapiro, A.: Analysis of stochastic dual dynamic programming method. Eur. J. Oper. Res. 209(1), 63–72 (2011)
Shapiro, A., Dentcheva, D., Ruszczynski, A.: Lectures on Stochastic Programming: Modeling and Theory, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2014)
Staid, A., Watson, J.P., Wets, R.J.B., Woodruff, D.L.: Generating short-term probabilistic wind power scenarios via nonparametric forecast error density estimators. Wind Energy 20(12), 1911–1925 (2017)
Woodruff, D.L., Deride, J., Staid, A., Watson, J.P., Slevogt, G., Silva-Monroy, C.: Constructing probabilistic scenarios for wide-area solar power generation. Solar Energy 160, 153–167 (2018)
Acknowledgements
We thank Efficacity and Schneider Electric for the PhD funding of Adrien Le Franc. Additionally, we are grateful for the feedbacks and data supply from Peter Pflaum and Claude Le Pape (Schneider Electric) and we thank our colleague Tristan Rigaut (Efficacity) for insightful tips about the Julia language. We thank the Guest Editor and the Reviewers for their insightful comments that helped improve the manuscript.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The original online version of this article was revised: The co-author name should spelled as “Michel De Lara”.
Rights and permissions
About this article
Cite this article
Le Franc, A., Carpentier, P., Chancelier, JP. et al. EMSx: a numerical benchmark for energy management systems. Energy Syst 14, 817–843 (2023). https://doi.org/10.1007/s12667-020-00417-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12667-020-00417-5