Optimization, Implementation and Utilization of Home-based Solar- powered Microgrid
Kio, Onisodumeya Onisobuana
Abstract
This study focuses on the optimization, implementation, and utilization of a home-based solar- powered microgrid to enhance energy reliability and efficiency in remote areas. The research evaluates different energy storage systems (ESS) lead-acid (PbA), lithium-ion (Li-ion), hybrid-ion batteries, and ultra-capacitors to determine their feasibility based on operational cost, lifecycle, and fuel efficiency. A Schiffer weighted Ah throughput model was incorporated into the energy management system (EMS) to account for real-time battery degradation, and various charging strategies (weekly, bi-weekly, monthly, and auto-threshold cycling) were analyzed to assess their impact on system performance. To achieve these objectives, the study employed a deterministic optimization model to minimize total operational costs while maximizing battery lifespan and generator fuel efficiency. Simulations were conducted using data from existing microgrid systems, with key parameters including battery wear cost, throughput, and generator fuel consumption. Different battery cycling strategies were tested to identify the most effective method for reducing energy losses and extending battery life. The results show that frequent full charging mitigates battery degradation, with auto-threshold cycling being the most cost-effective strategy. This optimization reduced the yearly operational cost of a 142 kWh PbA battery-based microgrid by 0.62% ($826) and decreased generator fuel consumption by 82 gallons annually. Among the storage technologies analyzed, Li-ion batteries were found to be 2.55% more cost-effective and 1.5% more fuel-efficient than hybrid-ion alternatives, making them the preferred choice for sustainable microgrid operations.
Keywords
Optimization
implementation
utilization
solar powered microgrd
energy storage
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systems utilizing different battery types. Energy Conversion and Management, 112, 435–
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integration with renewable electricity: Quantifying expert opinions. Environmental
Innovation and Societal Transitions, 3, 29–49.
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applications. In AIAA Aerospace Sciences Meeting and Exhibit.
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control in PV-diesel hybrid mini-grids (PhD thesis). Citeseer.
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simulation of lead-acid batteries. Journal of Power Sources, 176(1), 373–380.
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Hittinger, E., Wiley, T., Kluza, J., & Whitacre, J. (2015). Evaluating the value of batteries in
microgrid electricity systems using an improved energy systems model. Energy
Conversion and Management, 89, 458–472. https://doi.org/10.1016/j.enconman.
2014.09.076
Homer Energy. (2015). HOMER software for microgrid design and optimization.
http://www.homerenergy.com/
Huff, G. (2016). The role of storage in energy system flexibility. International Energy Agency.
https://www.iea.org/media/workshops/2014/egrdenergystorage/huff.pdf
Jenkins, D., Fletcher, J., & Kane, D. (2019). Lifetime prediction and sizing of lead-acid batteries
for microgeneration storage applications. Renewable Power Generation, 2(3), 191–200.
https://doi.org/10.1049/iet-rpg:20070079
Kim, K.-H., Rhee, S. B., Song, K.-B., & Lee, K. Y. (2012). An efficient operation of a micro grid
using heuristic optimization techniques: Harmony search algorithm, PSO, and GA. In
Power and Energy Society General Meeting
Langella, R., Testa, A., & Ventre, C. (2014). A new model of lead-acid batteries lifetime in smart
grid scenario. In Energy Conference (ENERGYCON) (1343–1348).
Li, P., Xu, D., Zhou, Z., Lee, W. J., & Zhao, B. (2016). Stochastic optimal operation of microgrid
based on chaotic binary particle swarm optimization. Smart Grid, 7(1), 66–73.
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off-grid renewable energy systems. Solar Energy, 81(11), 1409–1425.
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storage and battery storage for a renewable energy powered island. Energy Conversion
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Mohamed, F. A., & Koivo, H. N. (2012). Multiobjective optimization using mesh adaptive direct
search for power dispatch problem of microgrid. International Journal of Electrical
Power & Energy Systems, 42(1), 728–735. https://doi.org/10.1016/ j.ijepes. 2012.04.008
Mouser electronics. (2015). http://www.mouser.com/search/ refine. aspx? Ntk=P+Mar Com
&Ntt=107638639
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charging/discharging scheme for wind power producers in real-time markets. Energies,
5(12), 5439–5452.
Palma-Behnke, R., Benavides, C., Lanas, F., Severino, B., Reyes, L., Llanos, J., & Sáez, D. (2013).
A microgrid energy management system based on the rolling horizon strategy. Smart
Grid, 4(2), 996–1006.
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mini-grid: System performance and fuel saving based on one year of monitored data.
Sustainable Energy, 3(1), 167–175.
Power generation by renewable energy sources. (2016). University of Leeds Engineering
Department.http://www.engineering.leeds.ac.uk/electronic/postgraduate/reading-list
/documents/PVgenerator-1-11-12.doc
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suitable and cost-effective battery. Journal of Power Sources, 144(2), 373–384.
Sanchez, I. (2016). Microgrid technology: Enabling energy reliability and security – Opportunities
in campus, commercial, and industrial communities. Journal of Renewable Energy
Systems, 10(2), 45–6
Schiffer, J., Sauer, D. U., Bindner, H., Cronin, T., Lundsager, P., & Kaiser, R. (2007). Model
prediction for ranking lead-acid batteries according to expected lifetime in renewable
energy systems and autonomous power-supply systems. Journal of Power Sources,
168(1), 66–78.
Sedaghat, B., Jalilvand, A., & Noroozian, R. (2012). Design of a multilevel control strategy for
integration of stand-alone wind/diesel system. International Journal of Electrical Power
& Energy Systems, 35(1), 123–137.
Shahan, Z. (2015). 38,000 Tesla Powerwall reservations in under a week (Tesla/Elon Musk
transcript). CleanTechnica. http://cleantechnica.com/2015/05/07/38000-tesla-powe r wal
-reservations -in-under-a-week-tesla-elon-musk-transcript/
Shi, Z., Peng, Y., & Wei, W. (2014). Optimal sizing of DGs and storage for microgrid with
interruptible load using improved NSGA-II. In Evolutionary Computation (CEC), IEEE
(2108–2115).
Siemens, R. H. (2011). Smart distribution: Coupled microgrids. Proceedings of the IEEE, 99(6),
1074–1082.
Simpkins, T., Cutler, D., Hirsch, B., Olis, D., & Anderson, K. (2015). Cost-optimal pathways to
75% fuel reduction in remote Alaskan villages. In IEEE Conference on Technologies for
Sustainability (SusTech) (125–130).
Sun Xtender. (2015). Sun Xtender PVX-2580L AGM sealed battery. http://www.solar-
electric.com/concorde-sunxtender-pvx-2580l.html
Suzuki, S., Ueda, Y., & Takamitsu, I. (2019). Grid stabilization for large-scale PV generation
plant. In 23rd European Photovoltaic Solar Energy Conference (3276–3280).
Svoboda, V., Wenzl, H., Kaiser, R., Jossen, A., Baring-Gould, I., Manwell, J., Su, W., Wang, J.,
& Roh, J. (2014). Stochastic energy scheduling in microgrids with intermittent renewable
energy resources. Smart Grid, 5(4), 1876–1883
Tesla. (2015). Tesla Powerwall. Tesla Journal of Energy Storage, 1(1), 1–10.
Tonkoski, R. (2011). Impact of high penetration of photovoltaics on low voltage systems and
remedial actions (PhD thesis). Citeseer.
Tufte, E. D. (2014). Impacts of low load operation of modern four-stroke diesel engines in
generator configuration.
U.S. Department of Energy. (2014). Energy storage safety strategic plan. rom http://
energy.gov/oe/downloads/energy-storage-safety-strategic-plan-december-2014
Walsh, B. (2016). Building a country by switching on the lights. Internal Journal of Time USA
Time, 187(5), 42–45.
Wenzl, H., Baring-Gould, I., Kaiser, R., Liaw, B. Y., Lundsager, P., & Manwell, J. (n.d.). from
http://www.iea.org/topics/energypoverty/
Witmer, D., & Watson, S. (2019). Rural energy conference project. University of Alaska Technical
Report.
Woodruff, A. (2007). An economic as
