Scheduling Power-Intensive Operations of Battery Energy Storage Systems and Application to Hybrid Hydropower Plants (2403.16821v2)
Abstract: This paper proposes a novel set of power constraints for Battery Energy Storage Systems (BESSs), referred to as Dynamic Power Constraints (DPCs), that account for the voltage and current limits of the BESS as a function of its State of Charge (SOC). These constraints are formulated for integration into optimization-based BESS scheduling problems, providing a significant improvement over traditional static constraints. It is shown that, under mild assumptions typically verified during practical operations, DPCs can be expressed as a linear function of the BESS power, thus making it possible to retrofit existing scheduling problems without altering their tractability property (i.e., convexity). The DCPs unify voltage and current constraints into a single framework, filling a gap between simplified models used in BESS schedulers and more advanced models in real-time controllers and Battery Management Systems (BMSs). By improving the representation of the BESS's power capability, the proposed constraints enable schedulers to make more reliable and feasible decision, especially in power-intensive applications where the BESS operates near its rated power. To demonstrate the effectiveness of the DPCs, a simulation-based performance evaluation is conducted using a hybrid system comprising a 230 MW Hydropower Plant (HPP) and a 750 kVA/500 kWh BESS. Compared to state-of-the-art formulations such as static power constraints and DPC formulations without voltage constraints the proposed method reduces BESS constraint violations by 93% during real-time operations.
- D. U. Sauer, P. Birke, M. Keller, O. Bohlen, and J. B. Gerschler, “Robust algorithms for a reliable battery diagnosis : managing batteries in hybrid electric vehicles,” in Moving to sustainable mobility : EVS 22, the 22nd International Battery, Hybrid and Fuel Cell Electric Vehicle Symposium and Exposition ; proceedings, October 23 - 28, 2006, Yokohama, Japan,. Tokyo: Japan Automobile Research Institute, 2006.
- X. Liu, Y. Yang, G. Wang, Y. Li, and W. Ma, “Method for estimating the maximum output of a battery for a hybrid electric vehicle,” Patent CN101 133 514B, Dec 16, 2009.
- J. P. Christophersen, “Battery test manual for electric vehicles, revision 3,” 6 2015.
- F. Sun, R. Xiong, H. He, W. Li, and J. E. E. Aussems, “Model-based dynamic multi-parameter method for peak power estimation of lithium–ion batteries,” Applied Energy, vol. 96, pp. 378–386, 2012, smart Grids.
- P. Malysz, J. Ye, R. Gu, H. Yang, and A. Emadi, “Battery state-of-power peak current calculation and verification using an asymmetric parameter equivalent circuit model,” IEEE Transactions on Vehicular Technology, vol. 65, no. 6, pp. 4512–4522, 2016.
- R. D. Anderson, Y. Zhao, X. Wang, X. G. Yang, and Y. Li, “Real time battery power capability estimation,” in 2012 American Control Conference (ACC), 2012, pp. 592–597.
- M. Kazemi, H. Zareipour, N. Amjady, W. D. Rosehart, and M. Ehsan, “Operation scheduling of battery storage systems in joint energy and ancillary services markets,” IEEE Transactions on Sustainable Energy, vol. 8, no. 4, pp. 1726–1735, 2017.
- M. Elsaadany and M. R. Almassalkhi, “Battery optimization for power systems: Feasibility and optimality,” in 2023 62nd IEEE Conference on Decision and Control (CDC). IEEE, 2023, pp. 562–569.
- M. Nick, R. Cherkaoui, and M. Paolone, “Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support,” IEEE Transactions on Power Systems, vol. 29, no. 5, pp. 2300–2310, 2014.
- F. Sossan, E. Namor, R. Cherkaoui, and M. Paolone, “Achieving the dispatchability of distribution feeders through prosumers data driven forecasting and model predictive control of electrochemical storage,” IEEE Transactions on Sustainable Energy, vol. 7, no. 4, pp. 1762–1777, 2016.
- F. Berglund, S. Zaferanlouei, M. Korpås, and K. Uhlen, “Optimal operation of battery storage for a subscribed capacity-based power tariff prosumer—a norwegian case study,” Energies, vol. 12, no. 23, p. 4450, 2019.
- F. Zheng, Y. Xing, J. Jiang, B. Sun, J. Kim, and M. Pecht, “Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries,” Applied energy, vol. 183, pp. 513–525, 2016.
- M. Kraning, Y. Wang, E. Akuiyibo, and S. Boyd, “Operation and configuration of a storage portfolio via convex optimization,” IFAC Proceedings volumes, vol. 44, no. 1, pp. 10 487–10 492, 2011.
- S. Cassano and F. Sossan, “Stress-informed control of medium- and high-head hydropower plants to reduce penstock fatigue,” Sustainable Energy, Grids and Networks, vol. 31, p. 100688, 2022.
- ——, “Model predictive control for a medium-head hydropower plant hybridized with battery energy storage to reduce penstock fatigue,” Electric Power Systems Research, vol. 213, p. 108545, 2022.
- C. Nicolet, “Hydroacoustic modelling and numerical simulation of unsteady operation of hydroelectric systems,” Ph.D. dissertation, EPFL, Lausanne, 2007.
- S. Cassano, F. Sossan, C. Landry, and C. Nicolet, “Performance assessment of linear models of hydropower plants,” in 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe). IEEE, 2021, pp. 01–06.
- M. Pignati, M. Popovic, S. Barreto, R. Cherkaoui, G. D. Flores, J.-Y. Le Boudec, M. Mohiuddin, M. Paolone, P. Romano, S. Sarri et al., “Real-time state estimation of the epfl-campus medium-voltage grid by using pmus,” in 2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). IEEE, 2015.
- ENTSO-E, Ed., “ENTSO-E Statistical Factsheet 2018”, Brussels, 2019.
- C. Nicolet, R. Berthod, N. Ruchonnet, and F. Avellan, “Evaluation of possible penstock fatigue resulting from secondary control for the grid,” Proceedings of HYDRO, 2010.