Abstract
In this paper, a model predictive control approach is presented to optimize generator and storage operation in the German transmission grid over time spans of hours to several days. In each optimization, a full AC model with typical OPF constraints such as voltage or line capacity limits is used. With given RES and load profiles, inter-temporal constraints such as generator ramping and storage energy are included. Jacobian and Hessian matrices are provided to the solver to enable a fast problem formulation, but the computational bottleneck still lies in solving the linear Newton step. The deviation in storage operation when comparing the solution over the entire horizon of 96 h against the model predictive control is shown in the German transmission grid. The results show that horizons of around 24 h are sufficient with today’s storage capacity, but must be extended when increasing the latter.
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References
Carpentier J (1962) Contribution a l’etude du dispatching economique. Bull Soc Francaise des Electriciens 3:431–447
Duff IS (2004) Ma57—a code for the solution of sparse symmetric definite and indefinite systems. ACM Trans Math Softw 30(2):118–144
Gill S, Kockar I, Ault G (2014) Dynamic optimal power flow for active distribution networks. IEEE Transactions on Power Systems 29(1):121–131
Glavic M, Hajian M, Rosehart W, Van Cutsem T (2011) Receding-horizon multi-step optimization to correct nonviable or unstable transmission voltages. Power Systems, IEEE Transactions on 26(3):1641–1650
Kuhn HW, Tucker AW (1951) Nonlinear programming. In: Proceedings of the Second Berkeley Symposium on Mathematical Statistics and Probability, University of California Press, Berkeley, Calif., pp 481–492
Meyer-Huebner N, Suriyah M, Leibfried T (2015a) On efficient computation of time constrained optimal power flow in rectangular form. In: IEEE PowerTech conference, Eindhoven
Meyer-Huebner N, Suriyah M, Leibfried T, Slednev V, Bertsch V, Fichtner W, Gerstner P, Schick M, Heuveline V (2015b) Time constrained optimal power flow calculations in the german transmission grid. In: International ETG Congress, Bonn
Olivares D, Lara J, Canizares C, Kazerani M (2015) Stochastic-predictive energy management system for isolated microgrids. Smart Grid, IEEE Transactions on 6(6):2681–2693
Qin Z, Yang Y (2008) Vectorization implementation of optimal power flow in rectangular form based on interior point method. In: IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, pp 1–8
Qin Z, Hou Y, Lu E, Luo C, Cheng S (2014) Solving long time-horizon dynamic optimal power flow of large-scale power grids with direct solution method. IET Generation, Transmission Distribution 8(5):895–906
Rawlings J (2000) Tutorial overview of model predictive control. Control Systems, IEEE 20(3):38–52
Wang H, Murillo-Sanchez C, Zimmerman R, Thomas R (2007) On computational issues of market-based optimal power flow. IEEE Transactions on Power Systems 22(3):1185–1193
Xie K, Song Y (2001) Dynamic optimal power flow by interior point methods. IEE Proceedings- Generation, Transmission and Distribution 148(1):76–84
Acknowledgements
The authors kindly acknowledge the support for this work from the German Research Foundation (DFG) under the Project Number LE1432/14-1.
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Meyer-Hübner, N. et al. (2017). Optimal Storage Operation with Model Predictive Control in the German Transmission Grid. In: Bertsch, V., Fichtner, W., Heuveline, V., Leibfried, T. (eds) Advances in Energy System Optimization. Trends in Mathematics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-51795-7_3
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DOI: https://doi.org/10.1007/978-3-319-51795-7_3
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