Skip to main content

Advertisement

SpringerLink
Log in

Multiobjective Scheduling-based Energy Management System Considering Renewable Energy and Energy Storage Systems: A Case Study and Experimental Result

  • Published:
Journal of Control, Automation and Electrical Systems Aims and scope Submit manuscript

Abstract

The fast-growing development of smart grid and renewable energy increases the challenge in balancing the production on local energy consumption. The power scheduling of energy storage has directed to growing interests in energy storage system to increase the use of renewables. In this study, a practical laboratory energy management system considering renewable energy and battery is established. Besides, two control strategies including ‘scheduling’ and ‘ON/OFF’ operation of the grid in the photovoltaic–wind–battery hybrid systems are modeled. This paper proposes a day-ahead optimizing planning using mixed-integer linear programming, aiming to achieve economic benefit by reducing operational costs of the grid. Related to demand-side management, a control technique is developed for a proper scheduling of the power from the hybrid system. The ultimate objective of the aimed strategy is to maximize the advantages of renewable energy in different running conditions such as weather fluctuation and grid support. In addition, a day-ahead optimization for operational costs, as well as a prediction model for PV and WT, is used. The data of renewable productions and load demand are used. The obtained results prove that applying the scheduling strategy for PV–WT–battery and grid operation control models, significant grid decreasing can be achieved related to the case where the grid is managed alone to satisfy the same load demand.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Abbreviations

OF:

Objective function

T :

Time scheduling

\( \Delta t \) :

Duration of interval

I :

Index of units of res

\( C_{\text{sell}}^{\text{grid}} \) :

Cost of selling energy

\( C_{\text{buy}}^{\text{grid}} \) :

Cost of buying energy

\( P_{\text{res}} \) :

Power from renewables (kW)

\( P_{\text{grid}} \) :

Power from the utility (kW)

\( P_{\text{grid/buy}} \) :

Power bought from the utility (kW)

\( P_{\text{grid/sell}} \) :

Power sold to the utility (kW)

\( P^{\text{bat}} \) :

Power of the battery (kW)

\( P_{\text{l}} \) :

Power load (kW)

\( ES_{ \hbox{max} }^{\text{bat}} \) :

Maximum battery energy level

\( ES_{ \hbox{min} }^{\text{bat}} \) :

Minimum battery energy level

\( \eta^{{{\text{bat}},{\text{ch}}}} \) :

Battery charging efficiency

\( \eta^{{{\text{bat}},{\text{disch}}}} \) :

Battery discharging efficiency

\( \lambda_{\text{grid}} \left( t \right),\; B_{t} \) :

Binary variable

\( X_{\text{load}} \) :

Connection load demand

MG:

Microgrid

REs:

Renewable energies

ESS:

Energy storage system

EMS:

Energy management system

PV:

Photovoltaic

WT:

Wind turbine

SOC:

State of charge

DERs:

Distributed energy resources

References

  • Beltran, H., Bilbao, E., Belenguer, E., Etxeberria-Otadui, I., & Rodriguez, P. (2013). Evaluation of storage energy requirements for constant production in PV power plants. IEEE Transactions on Industrial Electronics, 60(3), 1225–1234.

    Article  Google Scholar 

  • Beyer, H., Martínez, J., Suri, M., Torres, J., Lorenz, E., Müller, S., Hoyer-Klick, C., & Ineichen, P. (2009). Report on benchmarking of radiation products. In Sixth framework programme MESOR, management and exploitation of solar resource knowledge. [Online]. Available at: http://www.mesor.org/docs/MESoR_Benchmarking_of_radiation_products.pdf.

  • Borowy, B. S., & Salameh, Z. M. (1996). Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system. IEEE Transactions on Energy Conversions, 11(2), 367–375.

    Article  Google Scholar 

  • Bueno, C., & Carta, J. A. (2005). Technical-economic analysis of wind-powered pumped hydrostorage systems. Part I: Model development. Solar Energy, 78, 382–395.

    Article  Google Scholar 

  • Castillo-Cagigal, M., Caamao-Martin, E., Matallanas, E., Masa-Bote, D., Gutirrez, A., Monasterio-Huelin, F., et al. (2011). PV self-consumption optimization with storage and active DSM for the residential sector. Solar Energy, 85(9), 2338–2348.

    Article  Google Scholar 

  • Chaoui, H., & Gualous, H. (2017). Adaptive fuzzy logic control for a class of unknown nonlinear dynamic systems with guaranteed stability. Journal of Control, Automation and Electrical Systems. https://doi.org/10.1007/s40313-017-0342-y.

    Article  Google Scholar 

  • Choi, S., Kim, B., Cokkinides, G. J., & Meliopoulos, A. P. S. (2011). Feasibility study: Autonomous state estimation in distribution systems. IEEE Transactions on Power Systems, 26(4), 2109–2117.

    Article  Google Scholar 

  • Chou, K. C., & Corotis, R. B. (1981). Simulation of hourly wind speed and array wind power. Solar Energy, 26, 199–212.

    Article  Google Scholar 

  • Das, R., Thirugnanam, K., Kumar, P., et al. (2014). Mathematical modeling for economic evaluation of electric vehicle to smart grid interaction. IEEE Transactions on Smart Grid, 5, 712–721.

    Article  Google Scholar 

  • De Matos, J. G., Ribeiro, L. A. D. S., & Gomes, E. C. (2013). Power control in AC autonomous and isolated microgrids with renewable energy sources and energy storage systems. In Proceedings of the IEEE 39th annual conference on industrial electronics society, Vienna, Austria (pp. 1827–1832).

  • El Kafazi, I., Bannari, R., & Abouabdellah, A. (2016). Modeling and forecasting energy demand. In 4th Edition of the international renewable and sustainable energy conference (IRSEC’16 November 14-17, 2016) (pp. 746–750).

  • El Kafazi, I., Bannari, R., Abouabdellah, A., Aboutafail, M. O., & Guerrero, J. M. (2017). Energy production a comparison of forecasting methods using the polynomial curve fitting and linear regression. In 5th Edition of the international renewable and sustainable energy conference (IRSEC’17December 04-07, 2017).

  • El Kafazi, I., Bannari, R., Lassioui, A., & Aboutafail, M. O. (2018). Power Scheduling for Renewable Energy Connected to the grid. In 2018 3rd international conference on power and renewable energy. https://doi.org/10.1051/e3sconf/20186408008.

    Article  Google Scholar 

  • Farzin, H., Firuzabad, M. F., & MoeiniAghtaie, M. (2017). A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids. IEEE Transactions on Smart Grid, 8(1), 117–127.

    Article  Google Scholar 

  • Fathima, A. H., & Palanisamy, K. (2015). Optimization in microgrids with hybrid energy systems: A review. Renewable and Sustainable Energy Reviews, 45, 431–446.

    Article  Google Scholar 

  • Hocaoglu, Faith O., Gerek, Omer N., & Kurban, Mehmet. (2009). A novel hybrid (wind-photovoltaic) system sizing procedure. Solar Energy, 83, 2019–2028.

    Article  Google Scholar 

  • Hooshmand, A., Asghari, B., & Sharma, R. (2014). Experimental demonstration of a tiered power management system for economic operation of grid-tied microgrids. IEEE Transactions on Sustainable Energy, 5(4), 1319–1327.

    Article  Google Scholar 

  • ISCC21. (2008). Guide for optimizing the performance and life of lead-acid batteries in remote hybrid power systems. IEEE Std 1561-2007, pp. C1–25.

  • Jiang, Q., Xue, M., & Geng, G. (2013). Energy management of microgrid in grid-connected and stand-alone modes. IEEE Transactions on Power Systems, 28(3), 3380–3389.

    Article  Google Scholar 

  • Kakigano, H., Miura, Y., & Ise, T. (2013). Distribution voltage control for DC microgrids using fuzzy control and gain-scheduling technique. IEEE Transactions on Power Electronics, 28, 2246–2258.

    Article  Google Scholar 

  • Katiraei, F., Iravani, R., Hatziargyriou, N., & Dimeas, A. (2008). Microgrids management. IEEE Power and Energy Magazine, 6(3), 54–65.

    Article  Google Scholar 

  • Lasnier, F., & Ang, T. G. (1990). Photovoltaic engineering handbook. Bristol: Routledge.

    Google Scholar 

  • Li, G., Xi, F., Li, X., et al. (2012). Coordinated control of battery storage system and diesel generators in ac island microgrid. In 7th Proceedings of the IEEE international power electronics and motion control conference, China (pp. 112–117).

  • Lin, Lu, Yang, Hongxing, & Burnett, John. (2002). Investigation on wind power potential on Hong Kong islands—An analysis of wind power and wind turbine characteristics. Renewable Energy, 27, 1–12.

    Article  Google Scholar 

  • Markvard, T. (2000). Solar electricity (2nd ed.). London: Wiley.

    Google Scholar 

  • Marra, F., & Yang, G. (2015). Decentralized energy storage in residential feeders with photovoltaics. In P. D. Lu (Ed.), Energy storage for smart grids (pp. 277–294). Boston: Academic Press.

    Chapter  Google Scholar 

  • Nejabatkhah, F., & Li, Y. W. (2015). Overview of power management strategies of hybrid AC/DC microgrid. IEEE Transactions on Power Electronics, 30(12), 7072–7089.

    Article  Google Scholar 

  • Nikraz, M., Dehbonei, H., & Nayar, C. V. (2003). A DSP controlled PV system with MPPT. In Australian power engineering conference, Christchurch (pp. 1–6).

  • Pahasa, J., & Ngamroo, I. (2015). PHEVs bidirectional charging/discharging and SoC control for microgrid frequency stabilization using multiple MPC. IEEE Transactions on Smart Grid, 6(2), 526–533.

    Article  Google Scholar 

  • Sangwongwanich, A., Yang, Y., & Blaabjerg, F. (2015). High-performance constant power generation in grid-connected PV systems. IEEE Transactions on Power Electronics, 99, 1.

    Google Scholar 

  • Sarker, M. R., Ortega-Vazquez, M. A., & Kirschen, D. S. (2015). Optimal coordination and scheduling of demand response via monetary incentives. IEEE Transactions on Smart Grid, 6(3), 1341–1352.

    Article  Google Scholar 

  • Shi, W., Xie, X., Chu, C.-C., & Gadh, R. (2015). Distributed optimal energy management in microgrids. IEEE Transactions on Smart Grid, 6(3), 1137–1146.

    Article  Google Scholar 

  • Siano, P., Cecati, C., Yu, H., & Kolbusz, J. (2012). Real time operation of smart grids via FCN networks and optimal power flow. IEEE Transactions on Industrial Informatics, 8(4), 944–952.

    Article  Google Scholar 

  • Singh, M., Kumar, P., & Kar, I. (2013). A multi charging station for electric vehicles and its utilization for load management and the grid support. IEEE Transactions on Smart Grid, 4, 1026–1037.

    Article  Google Scholar 

  • Tani, A., Camara, M. B., & Dakyo, B. (2015). Energy management in the decentralized generation systems based on renewable energy ultra-capacitors and battery to compensate the wind/load power fluctuations. IEEE Transactions on Industry Applications, 51, 1817–1827.

    Article  Google Scholar 

  • Triki, A., Maidi, A., Belharet, K., & Corriou, J.-P. (2017). Robust control strategy for a conduction–convection system based on the scenario optimization. Journal of Control, Automation and Electrical Systems. https://doi.org/10.1007/s40313-017-0317-z.

    Article  Google Scholar 

  • Tsikalakis, A. G., & Hatziargyriou, N. D. (2008). Centralized control for optimizing micro grids operation. IEEE Transactions on Energy Conversion, 23, 241–248.

    Article  Google Scholar 

  • Vieira, P. A. V., Pinheiro, B., Perez, F., & Bortoni, E. C. (2018). Sizing and evaluation of battery energy storage integrated with photovoltaic systems. International Journal of Smart Grid and Sustainable Energy Technologies, 2, 67–72.

    Google Scholar 

  • Xu, L., & Chen, D. (2011). Control and operation of a DC micro grid with variable generation and energy storage. IEEE Transactions on Industry Applications, 26, 2513–2522.

    Google Scholar 

  • Zhang, Y., Gatsis, N., & Giannakis, G. (2013). Robust energy management for microgrids with high-penetration renewables. IEEE Transactions on Sustainable Energy, 4(4), 944–953.

    Article  Google Scholar 

  • Zhao, Z. (2012). Optimal energy management for microgrids. Ph.D. dissertation, Clemson University.

  • Zhou, W., Yang, H. X., & Fang, Z. H. (2007). A Novel model for photovoltaic array performance prediction. Applied Energy, 84(12), 1187–1198.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory Systems Engineering, ENSA, Ibn Tofail University Kenitra, Kenitra, Morocco

    I. El Kafazi & R. Bannari

Authors
  1. I. El Kafazi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Bannari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to I. El Kafazi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Kafazi, I., Bannari, R. Multiobjective Scheduling-based Energy Management System Considering Renewable Energy and Energy Storage Systems: A Case Study and Experimental Result. J Control Autom Electr Syst 30, 1030–1040 (2019). https://doi.org/10.1007/s40313-019-00524-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40313-019-00524-4

Keywords

Search

Navigation