Abstract
Virtual Power Plants (VPP) are one of the main components of future smart electrical grids, connecting and integrating several types of energy sources, loads and storage devices. A typical VPP is a large industrial plant with high (partially shiftable) electric and thermal loads, renewable energy generators and electric and thermal storages. Optimizing the use and the cost of energy could lead to a significant economic impact. This work proposes a VPP Energy Management System (EMS), based on a two-step optimization model that decides the minimum-cost energy balance at each point in time considering the following data: electrical load, photovoltaic production, electricity costs, upper and lower limits for generating units and storage units. The first (day-ahead) step models the prediction uncertainty using a robust approach defining scenarios to optimize the load demand shift and to estimate the cost. The second step is an online optimization algorithm, implemented within a simulator, that uses the optimal shifts produced by the previous step to minimize, for each timestamp, the real cost while fully covering the optimally shifted energy demand. The system is implemented and tested using real data and we provide analysis of results and comparison between real and estimated optimal costs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aloini, D., Crisostomi, E., Raugi, M., Rizzo, R.: Optimal power scheduling in a virtual power plant. In: 2011 2nd IEEE PES International Conference and Exhibition on Innovative Smart Grid Technologies, pp. 1–7, December 2011
Awerbuch, S., Preston, A.: The Virtual Utility: Accounting, Technology and Competitive Aspects of the Emerging Industry, vol. 26. Springer Science & Business Media, Berlin (2012). https://doi.org/10.1007/978-1-4615-6167-5
Bai, H., Miao, S., Ran, X., Ye, C.: Optimal dispatch strategy of a virtual power plant containing battery switch stations in a unified electricity market. Energies 8(3), 2268–2289 (2015). http://www.mdpi.com/1996-1073/8/3/2268
Bordin, C., Anuta, H.O., Crossland, A., Gutierrez, I.L., Dent, C.J., Vigo, D.: A linear programming approach for battery degradation analysis and optimization in offgrid power systems with solar energy integration. Renew. Energy 101, 417–430 (2017)
Bracewell, R.N., Bracewell, R.N.: The Fourier Transform and its Applications, vol. 31999. McGraw-Hill, New York (1986)
Edwards, R.E., New, J., Parker, L.E.: Predicting future hourly residential electrical consumption: a machine learning case study. Energy Buildings 49, 591–603 (2012)
Espinosa, A., Ochoa, L.: Dissemination document low voltage networks models and low carbon technology profiles. Technical report, University of Manchester, June 2015
Gamou, S., Yokoyama, R., Ito, K.: Optimal unit sizing of cogeneration systems in consideration of uncertain energy demands as continuous random variables. Energy Convers. Manag. 43(9), 1349–1361 (2002)
Jain, R.K., Smith, K.M., Culligan, P.J., Taylor, J.E.: Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Appl. Energy 123, 168–178 (2014)
Jurkovi, K., Pandi, H., Kuzle, I.: Review on unit commitment under uncertainty approaches. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 1093–1097, May 2015
Kaplanis, S., Kaplani, E.: A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values. Renew. Energy 32(8), 1414–1425 (2007)
Lombardi, P., Powalko, M., Rudion, K.: Optimal operation of a virtual power plant. In: Power and Energy Society General Meeting, PES 2009, pp. 1–6. IEEE (2009)
Palma-Behnke, R., Benavides, C., Aranda, E., Llanos, J., Sez, D.: Energy management system for a renewable based microgrid with a demand side management mechanism. In: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–8, April 2011
Zhao, C., Guan, Y.: Unified stochastic and robust unit commitment. IEEE Trans. Power Syst. 28(3), 3353–3361 (2013)
Zheng, Q.P., Wang, J., Liu, A.L.: Stochastic optimization for unit commitment, a review. IEEE Trans. Power Syst. 30(4), 1913–1924 (2015)
Zhou, Z., Zhang, J., Liu, P., Li, Z., Georgiadis, M.C., Pistikopoulos, E.N.: A two-stage stochastic programming model for the optimal design of distributed energy systems. Appl. Energy 103, 135–144 (2013). http://www.sciencedirect.com/science/article/pii/S0306261912006599
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
De Filippo, A., Lombardi, M., Milano, M., Borghetti, A. (2017). Robust Optimization for Virtual Power Plants. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds) AI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017. Lecture Notes in Computer Science(), vol 10640. Springer, Cham. https://doi.org/10.1007/978-3-319-70169-1_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-70169-1_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-70168-4
Online ISBN: 978-3-319-70169-1
eBook Packages: Computer ScienceComputer Science (R0)