Tools for Analysis and Optimization of Standalone Green Microgrids
Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.
[1] C. Walsh, “Microgrid Regulatory Policy in the U.S.,” CIVICSOLAR, 2014.
[2] A. Dolara, F. Grimaccia, G. Magistrati, and G. Marchegiani, “Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming,” Energies, vol.10, issue 2, 2017, p.1-20. www.mdpi.com/1996-1073/10/2/241/pdf (accessed January 26, 2017).
[3] Z. Maheshwari, and R. Ramakumar, “Smart Integrated Renewable Energy Systems (SIRES): A Novel Approach for Sustainable Development,” Energies, vol.10, issue 8 2017, p.1-22. www.mdpi.com/1996-1073/10/8/1145 (accessed January 26, 2017).
[4] J. Lin, Y. Wu, H. Lin, “Successful Experience of Renewable Energy Development in Several Offshore Islands.” Energy Procedia, vol.100, 2016, pp.8–13. doi.org/10.1016/j.egypro.2016.10.137 (accessed January 26, 2017).
[5] S. Small, and P.K. Hota, “Design and Analysis of solar PV-Fuel Cell and Wind Energy based Microgrid System for Power Quality Improvement,” Cogent Engineering, vol.4, 2017, pp.1-21. doi.org/10.1080/23311916.2017.1402453 (accessed January 25, 2018).
[6] P. Marcon, Z. Szabo, I. Vesely, F. Zezulka, O. Sajdl, Z. Roubal, and P. Dohnal, “A Real Model of a Micro-Grid to Improve Network Stability,” Applied Sciences, vol.7, 2017, pp.1-16. www.mdpi.com/2076-3417/7/8/757 (accessed January 26, 2017).
[7] C. S. E., Bale, L. Varga, and T. J. Foxon, “Energy and Complexity: New Ways Forward,” Applied Energy, vol. 138, January 2015, pp. 150-159.
[8] E. A. Kremers, “Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models,” Karlsruhe, KIT Scientific Publishing, 2013.
[9] P. Lilienthal, T. Lambert, “HOMER: The Micropower Optmization Model,” National Renewable Energy Laboratory (NREL) Innovation for Energy Future Fact Sheet, NREL/FS-710-35406. March, 2004, www.nrel.gov/docs/fy04osti/35406.pdf (accessed on July 5, 2017)
[10] H. Lund, “Renewable Energy Systems,” 2nd edition, Academic Press, 2014.
[11] P. Wijayatunga, L. George, A. Lopez, and J. A. Aguado, “Integrating Clean Energy in Small Island Power Systems: Maldives Experience,” Energy Procedia, vol.103, 2016, pp.274–279. doi.org/10.1016/j.egypro.2016.11.285 (accessed January 26, 2017).
[12] S. Kilkis, “Exergy Transition Planning for net-zero Districts,” Energy, vol. 92, part 3, 2015, pp.515-531. doi.org/10.1016/j.energy.2015.02.009 (accessed February 12, 2018).
[13] HOMER Energy, www.homerenergy.com/user_interface.html (accessed October 19, 2017).
[14] D. Connolly, EnergyPLAN: Finding and Inputting Data in EnergyPLAN, Aalborg University, Denmark, 2013, www.energyplan.eu/wp-content/uploads/2013/06/Finding-and-Inputting-Data-into-the-EnergyPLAN-Tool-v5.pdf (accessed July 5, 2017).
[15] Energy PLAN: Advanced Energy System Analysis Computer Model. Department of Development and Planning, Aalborg University, Denmark, 2016, www.energyplan.eu (accessed on July 5, 2017).
[16] The Isle of Eigg, www.isleofeigg.org/eigg-electric/ (accessed on September 15, 2017).
[17] Sunny Portal, www.sunnyportal.com (accessed on September 17, 2017).
[18] Solar Radiation Data, www.soda-pro.com/web-services/meteo-data/merra (accessed on September 17, 2017).
[1] C. Walsh, “Microgrid Regulatory Policy in the U.S.,” CIVICSOLAR, 2014.
[2] A. Dolara, F. Grimaccia, G. Magistrati, and G. Marchegiani, “Optimization Models for Islanded Micro-Grids: A Comparative Analysis between Linear Programming and Mixed Integer Programming,” Energies, vol.10, issue 2, 2017, p.1-20. www.mdpi.com/1996-1073/10/2/241/pdf (accessed January 26, 2017).
[3] Z. Maheshwari, and R. Ramakumar, “Smart Integrated Renewable Energy Systems (SIRES): A Novel Approach for Sustainable Development,” Energies, vol.10, issue 8 2017, p.1-22. www.mdpi.com/1996-1073/10/8/1145 (accessed January 26, 2017).
[4] J. Lin, Y. Wu, H. Lin, “Successful Experience of Renewable Energy Development in Several Offshore Islands.” Energy Procedia, vol.100, 2016, pp.8–13. doi.org/10.1016/j.egypro.2016.10.137 (accessed January 26, 2017).
[5] S. Small, and P.K. Hota, “Design and Analysis of solar PV-Fuel Cell and Wind Energy based Microgrid System for Power Quality Improvement,” Cogent Engineering, vol.4, 2017, pp.1-21. doi.org/10.1080/23311916.2017.1402453 (accessed January 25, 2018).
[6] P. Marcon, Z. Szabo, I. Vesely, F. Zezulka, O. Sajdl, Z. Roubal, and P. Dohnal, “A Real Model of a Micro-Grid to Improve Network Stability,” Applied Sciences, vol.7, 2017, pp.1-16. www.mdpi.com/2076-3417/7/8/757 (accessed January 26, 2017).
[7] C. S. E., Bale, L. Varga, and T. J. Foxon, “Energy and Complexity: New Ways Forward,” Applied Energy, vol. 138, January 2015, pp. 150-159.
[8] E. A. Kremers, “Modelling and Simulation of Electrical Energy Systems through a Complex Systems Approach using Agent-Based Models,” Karlsruhe, KIT Scientific Publishing, 2013.
[9] P. Lilienthal, T. Lambert, “HOMER: The Micropower Optmization Model,” National Renewable Energy Laboratory (NREL) Innovation for Energy Future Fact Sheet, NREL/FS-710-35406. March, 2004, www.nrel.gov/docs/fy04osti/35406.pdf (accessed on July 5, 2017)
[10] H. Lund, “Renewable Energy Systems,” 2nd edition, Academic Press, 2014.
[11] P. Wijayatunga, L. George, A. Lopez, and J. A. Aguado, “Integrating Clean Energy in Small Island Power Systems: Maldives Experience,” Energy Procedia, vol.103, 2016, pp.274–279. doi.org/10.1016/j.egypro.2016.11.285 (accessed January 26, 2017).
[12] S. Kilkis, “Exergy Transition Planning for net-zero Districts,” Energy, vol. 92, part 3, 2015, pp.515-531. doi.org/10.1016/j.energy.2015.02.009 (accessed February 12, 2018).
[13] HOMER Energy, www.homerenergy.com/user_interface.html (accessed October 19, 2017).
[14] D. Connolly, EnergyPLAN: Finding and Inputting Data in EnergyPLAN, Aalborg University, Denmark, 2013, www.energyplan.eu/wp-content/uploads/2013/06/Finding-and-Inputting-Data-into-the-EnergyPLAN-Tool-v5.pdf (accessed July 5, 2017).
[15] Energy PLAN: Advanced Energy System Analysis Computer Model. Department of Development and Planning, Aalborg University, Denmark, 2016, www.energyplan.eu (accessed on July 5, 2017).
[16] The Isle of Eigg, www.isleofeigg.org/eigg-electric/ (accessed on September 15, 2017).
[17] Sunny Portal, www.sunnyportal.com (accessed on September 17, 2017).
[18] Solar Radiation Data, www.soda-pro.com/web-services/meteo-data/merra (accessed on September 17, 2017).
@article{"International Journal of Electrical, Electronic and Communication Sciences:77550", author = "William Anderson and Kyle Kobold and Oleg Yakimenko", title = "Tools for Analysis and Optimization of Standalone Green Microgrids", abstract = "Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.", keywords = "Microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks.", volume = "12", number = "6", pages = "402-8", }
