A multiperiodal management method at user level for storage systems using artificial neural network forecasts

Grazia Belli, Giovanni Brusco, Alessandro Burgio, Daniele Menniti, Anna Pinnarelli, Nicola Sorrentino, Pasquale Vizza

Abstract


The increase of renewable non-programmable production and the necessity to locally self-consume the produced energy led to utilize ever more storage systems. To correctly utilize storage systems, an opportune management method has to be utilized. This paper implements a multi-period management method for storage systems, using different management strategies. The method aims to minimize the total absorbed and supplied energy or the peak power exchanged with the grid. The results show the effectiveness of the method in diminishing the energy exchanged with the grid and also the possibility to optimize the performance of the storage systems

Keywords


Storage management; multi-pediod scheduling; prosumer; optimal energy management

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References


D. Menniti, A. Pinnarelli, N. Sorrentino, A. Burgio, and G. Brusco, “Energy Management System for an Energy District With Demand Response Availability”, Smart Grid, IEEE Transactions on, vol. 5(5), 2014, pp. 2385-2393.

D. Menniti, A. Pinnarelli, N. Sorrentino, G. Belli, A. Burgio, “Demand Response Program in an Energy District with storage availability”, International Review of Electrical Engineering, in press.

A. Nottrott, J. Kleissl, and B. Washom, “Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems”, Renewable Energy, vol. 55, 2013, pp. 230-240.

N. Jayasekara, P. Wolfs, and M.A.S. Masoum, “An optimal management strategy for distributed storages in distribution networks with high penetrations of PV”, Electric Power Systems Research, vol. 116, 2014, pp. 147-157.

P. Vytelingum, T.D. Voice, S.D. Ramchurn, A. Rogers, and N.R. Jennings, “Agent-based micro-storage management for the smart grid”, in Proc. of the 9th International Conference on Autonomous Agents and Multiagent Systems, vol. 1, International Foundation for Autonomous Agents and Multiagent Systems, pp. 39-46, May 2010.

I. Atzeni, L.G. Ordóñez, G. Scutari,D.P. Palomar, and J.R. Fonollosa, “Demand-side management via distributed energy generation and storage optimization”, Smart Grid, IEEE Transactions on, vol. 4(2), 2013, pp. 866-876.

A. Mohamed, and O. Mohammed, "Real-time energy management scheme for hybrid renewable energy systems in smart grid applications", Electric Power Systems Research, Vol. 96, 2013, pp. 133-143.

D.Menniti, A. Pinnarelli, N. Sorrentino, A. Burgio, G. Brusco, “The economic viability of a feed-in tariff scheme which solely awards the self-consumption for promoting the use of integrated photovoltaic-battery systems”, Applied Energy, in press.

S. Makridakis, and M. Hibon, “Evaluating accuracy (or error) measures”, Working paper 95/18/TM, INSEAD, (1995) France.

Y. Zhang, M. Beaudin, Raouf Taheri, H. Zarcipour, and D. Wood, “Day-Ahead Power PV power production Output Forecasting for Small Scale Soar Photovoltic Electricity Generators”, IEEE Transactions on Smart Grid, Vol. 6, no. 5, September 2015

C. Chen, S. Duan, T. Cai, and B. Liu, “Online 24-h solar power forecasting based on weather type classification using artificial neural network”, Solar Energy, Vol. 85, no. 11, 2011, pp. 2856-2870.

C. W. Chow, B. Urquhart, J. Kleissl, M. Lave, A. Dominguez, J. Shields, and B. Washom, “Intra‐hour forecasting with a total sky imager at the UC San Diego solar energy testbed”, Solar Energy, Vol 85, no. 11, 2011, pp 2881–2893.

J. J. Moré, “The Levenberg-Marquardt algorithm: Implementation and theory”, in Lecture Notes in Mathematics, No. 630–Numerical Analysis, Springer-Verlag, 1978, pp. 105–116.

N. Hatziarg, Microgrids: Architectures and Control, Wiley-IEEE Press, February 2014.

H. S. Hippert, C. E. Pedreira, and R.C. Souza, “Neural networks for short-term load forecasting: A review and evaluation”, Power Systems, IEEE Transactions on, vol. 16(1), 2011, pp 44-55.

H. Chitsaz, H. Shaker, H. Zareipour, D. Wood, and N. Amjady, “Short-term electricity load forecasting of buildings in microgrids”, Energy and Buildings, vol. 99, 2015, pp. 50-60.




DOI: http://dx.doi.org/10.22149/teee.v1i4.60

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Copyright (c) 2016 Grazia Belli, Giovanni Brusco, Alessandro Burgio, Daniele Menniti, Anna Pinnarelli, Nicola Sorrentino, Pasquale Vizza

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