New Method Coordinated Control of Battery Energy Storage Systems and Distributed Generations in Distribution Networks

Truong Ngoc-Hung

Abstract


This paper studies on the microgrid operation modes based on a distribution network integrated with battery energy storage systems (BESSs) and distributed generations (DGs) such as solar and wind power plants. The location and capacity of DGs and BESSs are properly optimized to operate the distribution network. The IEEE 33-node distribution network integrated with solar power plants, wind power plants, and BESSs are modeled and simulated using the daily load and generation profiles. The mathematical modeling of the distribution network is applied to run power flow based on the practical load and generation profiles. For grid-following mode, the optimal location and capacity selection of the BESSs will improve the operation efficiency of the network. After integrating DGs and BESSs, the total active power loss is reduced by 27.6% and the voltage quality is also improved by 3.1%. Besides, the BESSs are capable to maintain the stability operation of the distribution network during a specified duration under a microgrid via the coordination between the BESSs and DGs after occurring a failure in the main grid. The simulation results have proven the capability of the BESSs to stabilize and improve the operation efficiency of the microgrid in both grid-following and grid-forming modes.

Keywords


Battery energy storage system; Distributed generation; Microgrid; Solar power; Wind power

Full Text:

PDF

References


Yatimi H. and E.H. Aroudam. 2015. A detailed study and modeling of photovoltaic module under real climatic conditions. International Journal of Electronics Electrical Engineering 3(3): 171-176.

Boucenna K., Sebbagh T., and Benchouia N.E., 2023. Modeling, optimization, and techno-economic assessment of a hybrid system composed of photovoltaic-wind-fuel cell and battery bank. Journal Européen des Systèmes Automatisés 56(1): 29.

Ayanlade S.O., Jimoh A., Ogunwole E.I., Jimoh A.B., and Ezekiel S.O., 2023. Mathematical modelling of the distribution network for three-phase power flow analysis. In Proceedings of the 11th International Conference on Mathematical Modeling In Physical Sciences. Belgrade, Serbia, 5-8 September.

Mahdavi M., Alhelou H.H., Hatziargyriou N.D., and Al-Hinai A., 2021. An efficient mathematical model for distribution system reconfiguration using AMPL. IEEE Access 9:79961-79993.

Belhadjer Y., Ait Abbas H., Laroussi K., Bousbaine A., Fergani O., and Mazari A., 2024. Optimizing DC microgrid systems for efficient electric vehicle battery charging in Ain El Ibel, Algeria. International Journal of Engineering 37(10): 1891-1900.

Mahmoud M.S., Hussain S.A., and Abido M.A., 2014. Modeling and control of microgrid: An overview. Journal of the Franklin Institute 351(5): 2822-2859.

Moradian M., Tabatabaei F.M., and Moradian S., 2013. Modeling, control and fault management of microgrids. Smart Grid and Renewable Energy 4(1): 99-112.

Ma Q., Wei W., Chen X., and Mei S., 2022. The admissible set of parameters guaranteeing small‐signal stability of a microgrid. IET Renewable Power Generation 16(13): 2721-2731.

Bui V.-H., Song N.-O., Lee J.-H., and Kim H.-M., 2015. Mathematical modeling of real-time scheduling for microgrid considering uncertainties of renewable energy sources. International Journal of Smart Home 9(7): 271-284.

El Sayed A., Poyrazoglu G., and Ahmed E.E., 2024. An integrated framework for techno-enviro-economic assessment in nanogrids. International Journal of Renewable Energy Development 13(2): 340-350.

Lei Y., Mullane A., Lightbody G., and Yacamini R., 2006. Modeling of the wind turbine with a doubly fed induction generator for grid integration studies. IEEE Transactions on Energy Conversion 21(1): 257-264.

Chaudhuri A., Datta R., Kumar M.P., Davim J.P., and Pramanik S., 2002. Energy conversion strategies for wind energy system: Electrical, mechanical and material aspects. Materials 15(3): 1232.

Singh M. and S. Santoso. 2011. Dynamic models for wind turbines and wind power plants. National Renewable Energy Lab.(NREL), Golden, CO (United States).

Chong C., Rigit A., and Ali I., 2021. Wind turbine modelling and simulation using Matlab/SIMULINK. In Proceedings of the 13th International UNIMAS Engineering Conference 2020 (ENCON 2020), Kuching, Malaysia, 27-28 October. IOP Publishing.

Muller S., Deicke M., and De Doncker R.W., 2002. Doubly fed induction generator systems for wind turbines. IEEE Industry applications magazine 8(3): 26-33.

Garcia D. and J. Luis. 2009. Modeling and control of squirrel cage induction generator with full power converter applied to windmills, University of OULU.

Kwon D.Y., Bang T.K., Kim C.W., Shin K.H., and Choi J.Y., 2020. Experimental verification and electromagnetic characteristics analysis of wound-rotor synchronous generator using magnetic equivalent circuit method. AIP Advances 10(1): 015014.

Ahmadigorji M. and M. Mehrasa. 2023. A robust renewable energy source-oriented strategy for smart charging of plug-in electric vehicles considering diverse uncertainty resources. International Journal of Engineering 36(4): 709-719.

Shin S.S., Oh J.S., Jang S.H., Cha J.H., and Kim J.E., 2017. Active and reactive power control of ESS in distribution system for improvement of power smoothing control. Journal of Electrical Engineering Technology 12(3): 1007-1015.

Akagi S., Yoshizawa S., Yoshinaga J., Ito M., Fujimoto Y., Hayashi Y., Yano T., Nakahata H., Hisada T., and Tran X.M., 2016. Capacity determination of a battery energy storage system based on the control performance of load leveling and voltage control. Journal of International Council on Electrical Engineering 6(1): 94-101.

Zulkifly Z., Yusoff S.H., Tumeran N.L., and Razali N.S.I., 2023. Battery energy storage system (BESS) modeling for microgrid. IIUM Engineering Journal 24(1): 57-74.

Gao J.T., Shih C.H., Lee C.W., and Lo K.Y., 2022. An active and reactive power controller for battery energy storage system in microgrids. IEEE Access 10: 10490-10499.

Akdogan M.E. and S. Ahmed. 2021. Energy storage system (ess) for compensating unbalanced multi-microgrids using modified reverse droop control. In Proceedings of 2021 IEEE Applied Power Electronics Conference and Exposition (APEC), Phoenix, AZ, USA, 14-17 June: IEEE.

Eyisi C., Al-Sumaiti A.S., Turitsyn K., and Li Q., 2019. Mathematical models for optimization of grid-integrated energy storage systems: a review. In Proceedings of 2019 North American Power Symposium (NAPS), Wichita, KS, USA, 13-15 October: IEEE.

Mansour O.M.A.A., 2016. Determining the power and energy capacity of a battery energy storage system utilizing a smoothing feeder profile to accommodate high photovoltaic penetration on a distribution feeder. MS Thesis, Portland State University, US.

Biroon R.A., Pisu P., and Schoenwald D., 2020. Inter-area oscillation damping via hybrid LQR state feedback control of large-scale battery. In 2020 52nd North American Power Symposium (NAPS), 2021, pp. 1-6: IEEE.

Foomani A.H., Moradlou M., and Nazarian P., 2024. Utilizing a new voltage stability index in distribution power system in presence of wind turbine units. International Journal of Engineering 37(4): 711-724.

Dharageshwari K. and C. Nayanatara. 2015. Multiobjective optimal placement of multiple distributed generations in IEEE 33 bus radial system using simulated annealing. In Proceedings of 2015 International Conference on Circuits, Power and Computing Technologies. Nagercoil, India, 19-20 March: IEEE.

Almabsout E.A., El-Sehiemy R.A., An O.N.U., and Bayat O., 2020. A hybrid local search-genetic algorithm for simultaneous placement of DG units and shunt capacitors in radial distribution systems. IEEE Access 8: 54465-54481.

Reguieg Z., Bouyakoub I., and Mehedi F., 2025. Integrated optimization of power quality and energy management in a photovoltaic-battery microgrid. Renewable Energy 241: 122358.