Abstract
This paper presents an approach to develop an optimal control strategy for a Battery Energy Storage System (BESS) within a photovoltaic (PV)-battery microgrid in Finland. The BESS is used to assist the joint optimization of self-consumption and revenues provided by selling primary frequency reserves in the normal Frequency Containment Reserve (FCR) market. To participate in this frequency control market, a day-ahead planning of the reserve that will be held in the battery during the following day has to be submitted to the transmission system operator. To develop the optimal day-ahead reserve profile, the primary challenge we face is the random nature of the next-day frequency deviations, which we model as an AR (Autoregressive) process. We tackle these uncertainties by employing a two-stage stochastic programming formulation for the reserve planning and battery control optimization problem. Closed-loop simulations are then used to validate the energy management strategy, together with a Model-Predictive Controller (MPC) that is updated every 15 min to take into account an updated system state and new forecasts for load, PV production and electricity market prices. The results show that the considered stochastic programming approach significantly reduces energy costs and increases reliability when compared to a standard deterministic model.
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
Notes
- 1.
Considering thus the average value of the frequency deviation time series and, therefore, assuming that there will be no frequency deviations.
Abbreviations
- N :
-
Number of scenarios for optimization
- \(N'\) :
-
Sample size for out-of-sample simulation
- M :
-
Sample size for closed-loop simulation
- H :
-
Number of time steps in prediction horizon
- \(C_\text {TOU}\) :
-
Time-of-Use (variable) energy price [$]
- \(P_\text {G}\) :
-
Grid power consumption [kW]
- \(C_\text {R}\) :
-
Remuneration for provided reserve [$/kW]
- R :
-
Allocated frequency reserve
- \(P_\text {L}\) :
-
Load power
- \(P_\text {pv}\) :
-
PV power
- \(P_\text {B}\) :
-
Battery power
- b :
-
Stored energy in battery
- \(\tau \) :
-
Time step
- \(P_\text {Ch}\) :
-
Battery charging power
- \(P_\text {Disch}\) :
-
Battery discharging power
- \(\eta _\text {Ch}\) :
-
Charging efficiency
- \(\eta _\text {Disch}\) :
-
Discharging efficiency
- \(P_\text {N}\) :
-
Nominal battery charging power
- \(P_\text {R}\) :
-
Frequency reserve power provided by the battery
- \(P_\text {min}\) :
-
Minimum battery power (i.e. max. discharging power)
- \(P_\text {max}\) :
-
Maximum battery charging power
- \(b_\text {min}\) :
-
Lower bound on energy stored in the battery
- \(b_\text {max}\) :
-
upper bound on energy stored in the battery
- \(\varDelta f\) :
-
Normalized frequency deviation
References
van Ackooij, W., de Oliveira, W., Song, Y.: Adaptive partition-based level decomposition methods for solving two-stage stochastic programs with fixed recourse. Inf. J. Comput. 30(1), 57–70 (2017)
Cheng, B., Powell, W.B.: Co-optimizing battery storage for the frequency regulation and energy arbitrage using multi-scale dynamic programming. IEEE Trans. Smart Grid 9(3), 1997–2005 (2016)
Conte, F., Massucco, S., Silvestro, F.: Day-ahead planning and real-time control of integrated PV-storage systems by stochastic optimization. IFAC-PapersOnLine 50(1), 7717–7723 (2017)
Engels, J., Claessens, B., Deconinck, G.: Combined stochastic optimization of frequency control and self-consumption with a battery. IEEE Trans. Smart Grid 10(2), 1971–1981 (2017)
Khalid, M., Savkin, A.V.: Model predictive control based efficient operation of battery energy storage system for primary frequency control. In: 2010 11th International Conference on Control Automation Robotics & Vision, pp. 2248–2252. IEEE (2010)
Kumar, R., Wenzel, M.J., Ellis, M.J., ElBsat, M.N., Drees, K.H., Zavala, V.M.: A stochastic MPC framework for stationary battery systems
de Oliveira, W., Sagastizábal, C., Scheimberg, S.: Inexact bundle methods for two-stage stochastic programming. SIAM J. Optim. 21(2), 517–544 (2011)
Shapiro, A., Dentcheva, D., Ruszczyński, A.: Lectures on stochastic programming: modeling and theory. SIAM (2009)
Shi, Y., Xu, B., Wang, D., Zhang, B.: Using battery storage for peak shaving and frequency regulation: joint optimization for superlinear gains. IEEE Trans. Power Syst. 33(3), 2882–2894 (2017)
Thorbergsson, E., Knap, V., Swierczynski, M., Stroe, D., Teodorescu, R.: Primary frequency regulation with li-ion battery based energy storage system-evaluation and comparison of different control strategies. In: Intelec 2013; 35th International Telecommunications Energy Conference, SMART POWER AND EFFICIENCY, pp. 1–6. VDE (2013)
Vilppo, O., Rautiainen, A., Rekola, J., Markkula, J., Vuorilehto, K., Järventausta, P.: Profitable multi-use of battery energy storage in outage mitigation and as frequency reserve. Int. Rev. Electr. Eng. 13(3), 185–194 (2018)
Walawalkar, R., Apt, J., Mancini, R.: Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy 35(4), 2558–2568 (2007)
Wang, T., Kamath, H., Willard, S.: Control and optimization of grid-tied photovoltaic storage systems using model predictive control. IEEE Trans. Smart Grid 5(2), 1010–1017 (2014)
White, C.D., Zhang, K.M.: Using vehicle-to-grid technology for frequency regulation and peak-load reduction. J. Power Sour. 196(8), 3972–3980 (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Bousnina, D., de Oliveira, W., Pflaum, P. (2020). A Stochastic Optimization Model for Frequency Control and Energy Management in a Microgrid. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2020. Lecture Notes in Computer Science(), vol 12565. Springer, Cham. https://doi.org/10.1007/978-3-030-64583-0_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-64583-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-64582-3
Online ISBN: 978-3-030-64583-0
eBook Packages: Computer ScienceComputer Science (R0)