Open Access Open Access  Restricted Access Subscription or Fee Access

Multi-Objective Optimization of a Microgrid Considering MBESS Efficiencies, the Initial State of Charge, and Storage Capacity


(*) Corresponding author


Authors' affiliations


DOI: https://doi.org/10.15866/iree.v17i3.22053

Abstract


In this paper, the NSGA-III algorithm is implemented in order to find optimal operation in a grid-connected microgrid with Distributed Energy Resources DER in terms of minimal power imports and power losses. The optimal location and sizing of elements in electric networks, such as distributed generators or energy storage systems, is typically a long-term planning task for distribution networks. However, in this paper, that problem is reformulated in order to find an optimal operational scheme (location, and the hourly State of Charge) for Mobile Battery Energy Storage Systems MBESS in a microgrid considering charge/discharge efficiencies, three initial States of Charge SOC, three capacities for BESS, PV generation, power import/export costs and demand profiles. Then, the NSGA-III algorithm is adapted to find solution fronts to the location and dispatch of energy within BESS units. The numeric results obtained have shown that efficiencies constraint strongly the operation of MBESS units in microgrids in terms of the cost of active power imports, while the analysis without efficiencies allowed a more flexible range of operation in both objectives. In comparison with the system without MBESS, reductions of up to 10% in power losses and up to 16% in power import costs have been found. Overall, optimal placement and operation of MBESS might bring further improvements in already optimized networks, and flexibility to the operation of microgrids by enhancing the use of distributed generators and relieving the network's load during peak demand.
Copyright © 2022 Praise Worthy Prize - All rights reserved.

Keywords


Microgrid Hourly Operation; Mobile Battery Storage Systems (MBESS); Multi-Objective Optimization; Power Imports Minimization; Power Loss Minimization

Full Text:

PDF


References


O. Gandhi, D. S. Kumar, C. D. Rodríguez-Gallegos, and D. Srinivasan, Review of power system impacts at high PV penetration Part I: Factors limiting PV penetration, Sol. Energy, vol. 210, no. February, pp. 181-201, 2020.
https://doi.org/10.1016/j.solener.2020.06.097

M. E. Birk and R. D. Tabors, Impact of Distributed Energy Resources on Locational Marginal Prices and Electricity Networks, 2016.

A. Anestis and V. Georgios, Economic benefits of Smart Microgrids with penetration of DER and mCHP units for non-interconnected islands, Renew. Energy, vol. 142, pp. 478-486, 2019.
https://doi.org/10.1016/j.renene.2019.04.084

J. Newman and P. MacDougall, Increasing DER integration through discrete intraday settlements, Electr. J., vol. 34, no. 4, p. 106932, 2021.
https://doi.org/10.1016/j.tej.2021.106932

M. Azimian, V. Amir, and S. Javadi, Economic and Environmental Policy Analysis for Emission-Neutral Multi-Carrier Microgrid Deployment, Appl. Energy, vol. 277, no. August, p. 115609, 2020.
https://doi.org/10.1016/j.apenergy.2020.115609

W. J. Farmer and A. J. Rix, Impact of continuous stochastic and spatially distributed perturbations on power system frequency stability, Electr. Power Syst. Res., vol. 201, no. August, p. 107536, 2021.
https://doi.org/10.1016/j.epsr.2021.107536

O. D. Montoya, W. Gil-González, and L. F. Grisales-Noreña, Relaxed convex model for optimal location and sizing of DGs in DC grids using sequential quadratic programming and random hyperplane approaches, Int. J. Electr. Power Energy Syst., vol. 115, no. July 2019, p. 105442, 2020.
https://doi.org/10.1016/j.ijepes.2019.105442

M. N. Alam, B. Das, and V. Pant, Protection scheme for reconfigurable radial distribution networks in presence of distributed generation, Electr. Power Syst. Res., vol. 192, no. October 2020, p. 106973, 2021.
https://doi.org/10.1016/j.epsr.2020.106973

O. D. Montoya, W. Gil-González, and L. F. Grisales-Noreña, An exact MINLP model for optimal location and sizing of DGs in distribution networks: A general algebraic modeling system approach, Ain Shams Eng. J., vol. 11, no. 2, pp. 409-418, 2020.
https://doi.org/10.1016/j.asej.2019.08.011

Z. A. Obaid, L. M. Cipcigan, L. Abrahim, and M. T. Muhssin, Frequency control of future power systems: reviewing and evaluating challenges and new control methods, J. Mod. Power Syst. Clean Energy, vol. 7, no. 1, pp. 9-25, 2019.
https://doi.org/10.1007/s40565-018-0441-1

M. A. Hamidan and F. Borousan, Optimal planning of distributed generation and battery energy storage systems simultaneously in distribution networks for loss reduction and reliability improvement, J. Energy Storage, vol. 46, no. December 2021, p. 103844, 2022.
https://doi.org/10.1016/j.est.2021.103844

S. Sharma, K. R. Niazi, K. Verma, and T. Rawat, Coordination of different DGs, BESS and demand response for multi-objective optimization of distribution network with special reference to Indian power sector, Int. J. Electr. Power Energy Syst., vol. 121, 2020.
https://doi.org/10.1016/j.ijepes.2020.106074

T. Aziz, N. Al Masood, S. R. Deeba, W. Tushar, and C. Yuen, A methodology to prevent cascading contingencies using BESS in a renewable integrated microgrid, Int. J. Electr. Power Energy Syst., vol. 110, no. January, pp. 737-746, 2019.
https://doi.org/10.1016/j.ijepes.2019.03.068

A. C. Duman, H. S. Erden, Ö. Gönül, and Ö. Güler, Optimal sizing of PV-BESS units for home energy management system-equipped households considering day-ahead load scheduling for demand response and self-consumption, Energy Build., p. 112164, 2022.
https://doi.org/10.1016/j.enbuild.2022.112164

A. Kumar et al., Strategic allocation and energy management of BESS for the provision of ancillary services in active distribution networks, Energy Procedia, vol. 158, pp. 2972-2978, 2019.
https://doi.org/10.1016/j.egypro.2019.01.963

N. Savvopoulos and N. Hatziargyriou, Estimating Operational Flexibility from Active Distribution Grids, Int. Conf. Eur. Energy Mark. EEM, vol. 2020-Septe, 2020.
https://doi.org/10.1109/EEM49802.2020.9221991

Y. C. Tsao, V. Van Thanh, and Q. Wu, Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs, Int. J. Electr. Power Energy Syst., vol. 125, no. March 2020, p. 106418, 2021.
https://doi.org/10.1016/j.ijepes.2020.106418

S. Chowdhury, S. P. Chowdhury, and P. Crossley, Microgrids and active distribution networks. 2009.
https://doi.org/10.1049/PBRN006E

C. K. Das, O. Bass, T. S. Mahmoud, G. Kothapalli, M. A. S. Masoum, and N. Mousavi, An optimal allocation and sizing strategy of distributed energy storage systems to improve performance of distribution networks, J. Energy Storage, vol. 26, no. September, p. 100847, 2019.
https://doi.org/10.1016/j.est.2019.100847

H. M. H. Farh, A. M. Al-Shaalan, A. M. Eltamaly, and A. A. Al-Shamma'A, A Novel Crow Search Algorithm Auto-Drive PSO for Optimal Allocation and Sizing of Renewable Distributed Generation, IEEE Access, vol. 8, pp. 27807-27820, 2020.
https://doi.org/10.1109/ACCESS.2020.2968462

M. Khasanov, S. Kamel, M. Tostado-Veliz, and F. Jurado, Allocation of Photovoltaic and Wind Turbine Based DG Units Using Artificial Ecosystem-based Optimization, Proc. - 2020 IEEE Int. Conf. Environ. Electr. Eng. 2020 IEEE Ind. Commer. Power Syst. Eur. EEEIC / I CPS Eur. 2020, pp. 1-5, 2020.
https://doi.org/10.1109/EEEIC/ICPSEurope49358.2020.9160696

A. Ali, M. U. Keerio, and J. A. Laghari, Optimal Site and Size of Distributed Generation Allocation in Radial Distribution Network Using Multi-objective Optimization, J. Mod. Power Syst. Clean Energy, vol. 9, no. 2, pp. 404-415, 2021.
https://doi.org/10.35833/MPCE.2019.000055

M. A. Aderibigbe, A. U. Adoghe, F. Agbetuyi, and A. E. Airoboman, A review on optimal placement of distributed generators for reliability improvement on distribution network, 2021 IEEE PES/IAS PowerAfrica, PowerAfrica 2021, 2021.
https://doi.org/10.1109/PowerAfrica52236.2021.9543266

M. Yin and K. Li, Optimal Allocation of Distributed Generations with SOP in Distribution Systems, IEEE Power Energy Soc. Gen. Meet., vol. 2020-Augus, pp. 15-19, 2020.
https://doi.org/10.1109/PESGM41954.2020.9281656

M. M. Rana, M. F. Romlie, M. F. Abdullah, M. Uddin, and M. R. Sarkar, A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system, Energy, vol. 234, p. 121157, 2021.
https://doi.org/10.1016/j.energy.2021.121157

H. Alharthi, A. Alzahrani, S. Shafiq, and M. Khalid, Optimal Allocation of Batteries to Facilitate High Solar Photovoltaic Penetration, in 2019 9th International Conference on Power and Energy Systems, ICPES 2019, 2019.
https://doi.org/10.1109/ICPES47639.2019.9105479

A. Alzahrani, H. Alharthi, and M. Khalid, Optimal battery energy storage placement in highly PV - Penetrated distribution networks, in 2020 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2020, 2020.
https://doi.org/10.1109/ISGT45199.2020.9087725

M. Stecca, L. Ramirez Elizondo, T. Batista Soeiro, P. Bauer, and P. Palensky, A Comprehensive Review of the Integration of Battery Energy Storage Systems into Distribution Networks, IEEE Open J. Ind. Electron. Soc., vol. 1, no. February, pp. 1-1, 2020.
https://doi.org/10.1109/OJIES.2020.2981832

E. M. Krieger and C. B. Arnold, Effects of undercharge and internal loss on the rate dependence of battery charge storage efficiency, J. Power Sources, vol. 210, pp. 286-291, 2012.
https://doi.org/10.1016/j.jpowsour.2012.03.029

A. Allahham, D. Greenwood, C. Patsios, and P. Taylor, Adaptive receding horizon control for battery energy storage management with age-and-operation-dependent efficiency and degradation, Electr. Power Syst. Res., vol. 209, no. March, p. 107936, 2022.
https://doi.org/10.1016/j.epsr.2022.107936

Y. K. Wu and K. T. Tang, Frequency support by BESS - Review and analysis, Energy Procedia, vol. 156, no. September 2018, pp. 187-198, 2019.
https://doi.org/10.1016/j.egypro.2018.11.126

K. Doenges, I. Egido, L. Sigrist, E. Lobato Miguelez, and L. Rouco, Improving AGC Performance in Power Systems with Regulation Response Accuracy Margins Using Battery Energy Storage System (BESS), IEEE Trans. Power Syst., vol. 35, no. 4, pp. 2816-2825, 2020.
https://doi.org/10.1109/TPWRS.2019.2960450

M. Al-Saffar, M. Al-Saffar, and P. Musilek, Reinforcement Learning-Based Distributed BESS Management for Mitigating Overvoltage Issues in Systems with High PV Penetration, IEEE Trans. Smart Grid, vol. 11, no. 4, pp. 2980-2994, 2020.
https://doi.org/10.1109/TSG.2020.2972208

M. Ovaskainen, J. Öörni, and A. Leinonen, Superposed control strategies of a BESS for power exchange and microgrid power quality improvement, Proc. - 2019 IEEE Int. Conf. Environ. Electr. Eng. 2019 IEEE Ind. Commer. Power Syst. Eur. EEEIC/I CPS Eur. 2019, pp. 3-8, 2019.
https://doi.org/10.1109/EEEIC.2019.8783764

H. Saboori and S. Jadid, Mobile and self-powered battery energy storage system in distribution networks-Modeling, operation optimization, and comparison with stationary counterpart, J. Energy Storage, vol. 42, no. August, p. 103068, 2021.
https://doi.org/10.1016/j.est.2021.103068

K. E. Adetunji, I. W. Hofsajer, A. M. Abu-Mahfouz, and L. Cheng, A Review of Metaheuristic Techniques for Optimal Integration of Electrical Units in Distribution Networks, IEEE Access, vol. 9, pp. 5046-5068, 2021.
https://doi.org/10.1109/ACCESS.2020.3048438

M. Dorigo, M. Birattari, and T. Stützle, Metaheuristic, in Encyclopedia of Machine Learning and Data Mining, C. Sammut and G. I. Webb, Eds. Boston, MA: Springer US, 2017, pp. 817-818.
https://doi.org/10.1007/978-1-4899-7687-1_537

M. Gendreau and J. Y. Potvin, Metaheuristics in combinatorial optimization, Ann. Oper. Res., vol. 140, no. 1, pp. 189-213, 2005.
https://doi.org/10.1007/s10479-005-3971-7

K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., vol. 6, no. 2, pp. 182-197, 2002.
https://doi.org/10.1109/4235.996017

C. A. C. Coello, G. T. Pulido, and M. S. Lechuga, Handling multiple objectives with particle swarm optimization, IEEE Trans. Evol. Comput., vol. 8, no. 3, pp. 256-279, Jun. 2004.
https://doi.org/10.1109/TEVC.2004.826067

N. Srinivas and K. Deb, Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms., Evol. Comput., vol. 2, no. 3, 1994.
https://doi.org/10.1162/evco.1994.2.3.221

K. Deb and H. Jain, An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints, IEEE Trans. Evol. Comput., vol. 18, no. 4, pp. 577-601, 2014.
https://doi.org/10.1109/TEVC.2013.2281535

I. Das and J. E. Dennis, Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optim., vol. 8, no. 3, pp. 631-657, 1998.
https://doi.org/10.1137/S1052623496307510

W. Gil-González, A. Garces, O. D. Montoya, and J. C. Hernández, A mixed-integer convex model for the optimal placement and sizing of distributed generators in power distribution networks, Appl. Sci., vol. 11, no. 2, pp. 1-15, 2021.
https://doi.org/10.3390/app11020627

K. A. Hernandez Hernandez and J. S. Carrillo Cruz, "Analysis Of The Electrical Demand Curve For Residential Users Stratum 4 In The City Of Bogotá In Different Scenarios Of Consumption Habits," Universidad Distrital Francisco José De Caldas, 2017.

M. Kalami Heris, NSGA-III: Non-dominated Sorting Genetic Algorithm, the Third Version - MATLAB Implementation, 2016. (accessed Mar. 24, 2022).
https://yarpiz.com/456/ypea126-nsga3

R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education, IEEE Trans. Power Syst., vol. 26, no. 1, pp. 12-19, Feb. 2011.
https://doi.org/10.1109/TPWRS.2010.2051168


Refbacks

  • There are currently no refbacks.


Please send any question about this web site to info@praiseworthyprize.com
Copyright © 2005-2024 Praise Worthy Prize