Reducing the Vulnerability of Electric Power Infrastructure against Natural Disasters by Promoting Distributed Generation
Abstract
Natural disasters cause significant damage to the electrical power infrastructure every year. Therefore, there is a crucial need to reduce the vulnerability of the electric power grid against natural disasters. Distributed generation (DG) represents small-scale decentralized power generation that can help reduce the vulnerability of the grid, among many other benefits. Examples of DG include small-scale photo-voltaic (PV) systems. Accordingly, the goal of this paper is to investigate the benefits of DG in reducing the vulnerability of the electric power infrastructure by mitigating against the impact of natural disasters on transmission lines. This was achieved by developing a complex system-of-systems (SoS) framework using agent-based modeling (ABM) and optimal power flow (OPF). N-1 contingency analysis and optimization were performed under two approaches: The first approach determined the minimum DG needed at any single location on the electric grid to avoid blackouts. The second approach used a genetic algorithm (GA) to identify the minimum total allocation of DG distributed over the electric grid to mitigate against the failure of any transmission line. Accordingly, the model integrates ABM, OPF, and GA to optimize the allocation of DG and reduce the vulnerability of electric networks. The model was tested on a modified IEEE 6-bus system as a proof of concept. The outcomes of this research are intended to support the understanding of the benefits of DG in reducing the vulnerability of the electric power grid. The presented framework can guide future research concerning policies and incentives that can strategically influence consumer decision to install DG and reduce the vulnerability of the electric power infrastructure.
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Data Availability Statement
All data and models generated or analyzed during the study are included in the published paper.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Award Contract # 1901740. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.
References
Abbey, C., D. Cornforth, N. Hatziargyriou, K. Hirose, A. Kwasinski, E. Kyriakides, G. Platt, L. Reyes, and S. Suryanarayanan. 2014. “Powering through the storm: Microgrids operation for more efficient disaster recovery.” IEEE Power Energy Mag. 12 (3): 67–76. https://doi.org/10.1109/MPE.2014.2301514.
Abdmouleh, Z., A. Gastli, L. Ben-Brahim, M. Haouari, and N. A. Al-Emadi. 2017. “Review of optimization techniques applied for the integration of distributed generation from renewable energy sources.” Renewable Energy 113 (5): 266–280. https://doi.org/10.1016/j.renene.2017.05.087.
Abraham, Y. S., C. J. Anumba, and S. Asadi. 2018. Exploring agent-based modeling approaches for human-centered energy consumption prediction, 368–378. Reston, VA: ASCE.
Agarwal, A., and K. Khandeparkar. 2021. “Distributing power limits: Mitigating blackout through brownout.” Sustainable Energy Grids Networks 26 (10): 100451. https://doi.org/10.1016/j.segan.2021.100451.
Ahmed, M. O., I. H. El-adaway, K. T. Coatney, and M. S. Eid. 2016. “Construction bidding and the winner’s curse: Game theory approach.” J. Constr. Eng. Manage. 142 (2): 04015076. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001058.
Ali, G. G., and I. H. El-adaway. 2020. “Relationship between electric-power sector development and socioeconomic parameters: Statistical analysis approach.” J. Energy Eng. 146 (5): 04020045. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000690.
Aliabadi, D. E., M. Kaya, and G. Sahin. 2017b. “Competition, risk and learning in electricity markets: An agent-based simulation study.” Appl. Energy 195 (Apr): 1000–1011. https://doi.org/10.1016/j.apenergy.2017.03.121.
Aliabadi, D. E., M. Kaya, and G. Şahin. 2017a. “An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms.” Energy Policy 100 (4): 191–205. https://doi.org/10.1016/j.enpol.2016.09.063.
Arghandeh, R., M. Brown, A. D. Rosso, G. Ghatikar, E. Stewart, A. Vojdani, and A. Meier. 2014. “The local team: Leveraging distributed resources to improve resilience.” IEEE Power Energy Mag. 12 (5): 76–83. https://doi.org/10.1109/MPE.2014.2331902.
ASCE. 2020. Failure to act: Electric infrastructure investment gaps in a rapidly changing environment. Reston, VA: ASCE.
ASCE. 2021. Policy statement 518—Unified definitions for critical infrastructure resilience. Reston, VA: ASCE.
Asgari, S. 2016. Modeling construction competitive bidding: An agent-based approach. New York: Columbia Univ.
Awwad, R., S. Asgari, and A. Kandil. 2015. “Developing a virtual laboratory for construction bidding environment using agent-based modeling.” J. Comput. Civ. Eng. 29 (6): 04014105. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000440.
Azar, E., and H. Al Ansari. 2017. “Multilayer agent-based modeling and social network framework to evaluate energy feedback methods for groups of buildings.” J. Comput. Civ. Eng. 31 (4): 04017007. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000651.
Azar, E., and C. C. Menassa. 2012. “Agent-based modeling of occupants and their impact on energy use in commercial buildings.” J. Comput. Civ. Eng. 26 (4): 506–518. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000158.
Bakkensen, L. A., C. Fox-Lent, L. K. Read, and I. Linkov. 2017. “Validating resilience and vulnerability indices in the context of natural disasters.” Risk Anal. 37 (5): 982–1004. https://doi.org/10.1111/risa.12677.
Batouli, M., and A. Mostafavi. 2014. “A hybrid simulation framework for integrated management of infrastructure networks.” In Proc., Winter Simulation Conf. 2014, 3319–3330. New York: IEEE.
Berglund, E. Z. 2015. “Using agent-based modeling for water resources planning and management.” J. Water Resour. Plann. Manage. 141 (11): 04015025. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544.
Bernhardt, K. L. S., and S. McNeil. 2008. “Agent-based modeling: Approach for improving infrastructure management.” J. Infrastruct. Syst. 14 (3): 253–261. https://doi.org/10.1061/(ASCE)1076-0342(2008)14:3(253).
Bjarnadottir, S., Y. Li, and M. G. Stewart. 2013. “Hurricane risk assessment of power distribution poles considering impacts of a changing climate.” J. Infrastruct. Syst. 19 (1): 12–24. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000108.
Boussaïd, I., J. Lepagnot, and P. Siarry. 2013. “A survey on optimization metaheuristics.” Inf. Sci. 237 (Jul): 82–117. https://doi.org/10.1016/j.ins.2013.02.041.
Burke, P. J., and A. Abayasekara. 2018. “The price elasticity of electricity demand in the United States: A three-dimensional analysis.” Energy J. 39 (2). https://doi.org/10.5547/01956574.39.2.pbur.
Carmenate, T., P. Inyim, N. Pachekar, G. Chauhan, L. Bobadilla, M. Batouli, and A. Mostafavi. 2016. “Modeling occupant-building-appliance interaction for energy waste analysis.” Proc. Eng. 145 (Sep): 42–49. https://doi.org/10.1016/j.proeng.2016.04.012.
Choi, B., and S. Lee. 2018. “An empirically based agent-based model of the sociocognitive process of construction workers’ safety behavior.” J. Constr. Eng. Manage. 144 (2): 04017102. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001421.
Clausen, A., A. Umair, Y. Demazeau, and B. N. Jørgensen. 2017. “Agent-based integration of complex and heterogeneous distributed energy resources in virtual power plants.” In Advances in practical applications of cyber-physical multi-agent systems: The PAAMS collection, 43–55. Berlin: Springer.
Driesen, J., and F. Katiraei. 2008. “Design for distributed energy resources.” IEEE Power Energy Mag. 6 (3): 30–40. https://doi.org/10.1109/MPE.2008.918703.
Du, J., and M. El-Gafy. 2012. “Virtual organizational imitation for construction enterprises: Agent-based simulation framework for exploring human and organizational implications in construction management.” J. Comput. Civ. Eng. 26 (3): 282–297. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000122.
Du, J., and Q. Wang. 2011. “Exploring reciprocal influence between individual shopping travel and urban form: Agent-based modeling approach.” J. Urban Plann. Develop. 137 (4): 390–401. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000084.
Dunn, S., S. Wilkinson, D. Alderson, H. Fowler, and C. Galasso. 2018. “Fragility curves for assessing the resilience of electricity networks constructed from an extensive fault database.” Nat. Hazards Rev. 19 (1): 04017019. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000267.
EIA (US Energy Information Administration). 2015. “EIA electricity data now include estimated small-scale solar PV capacity and generation.” Accessed February 11, 2021. https://www.eia.gov/todayinenergy/detail.php?id=23972.
EIA (US Energy Information Administration). 2020. “Electricity data.” Accessed September 6, 2020. https://www.eia.gov/electricity/data.php.
Eid, M. S., and I. H. El-adaway. 2017a. “Integrating the social vulnerability of host communities and the objective functions of associated stakeholders during disaster recovery processes using agent-based modeling.” J. Comput. Civ. Eng. 31 (5): 04017030. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000680.
Eid, M. S., and I. H. El-adaway. 2017b. “Sustainable disaster recovery decision-making support tool: Integrating economic vulnerability into the objective functions of the associated stakeholders.” J. Manage. Eng. 33 (2): 04016041. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000487.
Eid, M. S., and I. H. El-adaway. 2017c. “Sustainable disaster recovery: Multiagent-based model for integrating environmental vulnerability into decision-making processes of the associated stakeholders.” J. Urban Plann. Develop. 143 (1): 04016022. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000349.
Eid, M. S., and I. H. El-adaway. 2018. “Decision-making framework for holistic sustainable disaster recovery: Agent-based approach for decreasing vulnerabilities of the associated communities.” J. Infrastruct. Syst. 24 (3): 04018009. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000427.
Eid, M. S., and I. H. El-adaway. 2021. “Discussion of ‘Multiobjective optimization of postdisaster reconstruction processes for ensuring long-term socioeconomic benefits’ by Pedram Ghannad, Yong-Cheol Lee, Carol J. Friedland, Jin Ouk Choi, and Eunhwa Yang.” J. Manage. Eng. 37 (3): 07021001. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000918.
El-adaway, I. H., C. Sims, M. S. Eid, Y. Liu, and G. G. Ali. 2020. “Preliminary attempt toward better understanding the impact of distributed energy generation: An agent-based computational economics approach.” J. Infrastruct. Syst. 26 (1): 04020002. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000527.
Elsayegh, A., C. H. Dagli, and I. H. El-adaway. 2020. “An agent-based model to study competitive construction bidding and the winner’s curse.” Proc. Comput. Sci. 168 (15): 147–153.
Erdelj, M., E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz. 2017. “Help from the sky: Leveraging UAVs for disaster management.” IEEE Pervasive Comput. 16 (1): 24–32. https://doi.org/10.1109/MPRV.2017.11.
Esmalian, A., M. Ramaswamy, K. Rasoulkhani, and A. Mostafavi. 2019. Agent-based modeling framework for simulation of societal impacts of infrastructure service disruptions during disasters, 16–23. Reston, VA: ASCE.
Frank, S., and S. Rebennack. 2016. “An introduction to optimal power flow: Theory, formulation, and examples.” IIE Trans. 48 (12): 1172–1197. https://doi.org/10.1080/0740817X.2016.1189626.
Ganguly, S., and D. Samajpati. 2015. “Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm.” IEEE Trans. Sustainable Energy 6 (3): 688–697. https://doi.org/10.1109/TSTE.2015.2406915.
Gao, W., R. Zhou, and D. Zhao. 2017. “Heuristic failure prediction model of transmission line under natural disasters.” IET Gener. Transm. Distrib. 11 (4): 935–942. https://doi.org/10.1049/iet-gtd.2016.0872.
Ghodoosi, F., S. Abu-Samra, M. Zeynalian, and T. Zayed. 2018. “Maintenance cost optimization for bridge structures using system reliability analysis and genetic algorithms.” J. Constr. Eng. Manage. 144 (2): 04017116. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001435.
Goldfarb, D., and A. Idnani. 1983. “A numerically stable dual method for solving strictly convex quadratic programs.” Math. Program. 27 (1): 1–33. https://doi.org/10.1007/BF02591962.
Gupta, R., A. Bruce-Konuah, and A. Howard. 2019. “Achieving energy resilience through smart storage of solar electricity at dwelling and community level.” Energy Build. 195 (4): 1–15. https://doi.org/10.1016/j.enbuild.2019.04.012.
Hagberg, A., P. Swart, and S. Chult. 2008. Exploring network structure, dynamics, and function using networks. Los Alamos, New Mexico: Los Alamos National Lab.
Harper, C. D., C. T. Hendrickson, and C. Samaras. 2018. “Exploring the economic, environmental, and travel implications of changes in parking choices due to driverless vehicles: An agent-based simulation approach.” J. Urban Plann. Develop. 144 (4): 04018043. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000488.
Harrison, G. P., A. Piccolo, P. Siano, and A. R. Wallace. 2007. “Distributed generation capacity evaluation using combined genetic algorithm and OPF.” Int. J. Emerging Electric Power Syst. 8 (2). https://doi.org/10.2202/1553-779X.1517.
Harrison, G. P., A. Piccolo, P. Siano, and A. R. Wallace. 2008. “Hybrid GA and OPF evaluation of network capacity for distributed generation connections.” Electric Power Syst. Res. 78 (3): 392–398. https://doi.org/10.1016/j.epsr.2007.03.008.
Hedman, K. W., R. P. O’Neill, E. B. Fisher, and S. S. Oren. 2009. “Optimal transmission switching with contingency analysis.” IEEE Trans. on Power Syst. 24 (3): 1577–1586. https://doi.org/10.1109/TPWRS.2009.2020530.
Hegazy, T. 1999. “Optimization of resource allocation and leveling using genetic algorithms.” J. Constr. Eng. Manage. 125 (3): 167–175. https://doi.org/10.1061/(ASCE)0733-9364(1999)125:3(167).
Hines, P., J. Apt, and S. Talukdar. 2008. Trends in the history of large blackouts in the United States. New York: IEEE.
Howell, S., Y. Rezgui, J. L. Hippolyte, B. Jayan, and H. Li. 2017. “Towards the next generation of smart grids: Semantic and holonic multi-agent management of distributed energy resources.” Renewable Sustainable Energy Rev. 77 (4): 193–214. https://doi.org/10.1016/j.rser.2017.03.107.
Huang, J., Z. Liu, X. Fu, and K. Zhang. 2019. Agent-based simulation for optimization of bus transit lines, 1210–1222. Reston, VA: ASCE.
Hunter, J. D. 2007. “Matplotlib: A 2D graphics environment.” Comput. Sci. Eng. 9 (3): 90. https://doi.org/10.1109/MCSE.2007.55.
Kedir, N. S., M. Raoufi, and A. R. Fayek. 2020. “Fuzzy agent-based multicriteria decision-making model for analyzing construction crew performance.” J. Manage. Eng. 36 (5): 04020053. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000815.
Khan, M. R. B., R. Jidin, and J. Pasupuleti. 2016. “Multi-agent based distributed control architecture for microgrid energy management and optimization.” Energy Convers. Manage. 112 (3): 288–307. https://doi.org/10.1016/j.enconman.2016.01.011.
Khurshaid, T., A. Wadood, S. G. Farkoush, C. H. Kim, N. Cho, and S. B. Rhee. 2019. “Modified particle swarm optimizer as optimization of time dial settings for coordination of directional overcurrent relay.” J. Electr. Eng. Technol. 14 (1): 55–68. https://doi.org/10.1007/s42835-018-00039-z.
Kiel, E. S., and G. Hovin Kjølle. 2019. “The impact of protection system failures and weather exposure on power system reliability.” In Proc., 2019 IEEE Int. Conf. on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1–6. New York: IEEE.
Kim, K., and B. C. Paulson. 2003. “Agent-based compensatory negotiation methodology to facilitate distributed coordination of project schedule changes.” J. Comput. Civ. Eng. 17 (1): 10–18. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:1(10).
Kiomjian, D., I. Srour, and F. J. Srour. 2020. “Knowledge sharing and productivity improvement: An agent-based modeling approach.” J. Constr. Eng. Manage. 146 (7): 04020076. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001866.
Kluyver, T., B. Ragan-Kelley, F. Pérez, B. E. Granger, M. Bussonnier, J. Frederic, K. Kelley, J. B. Hamrick, J. Grout, and S. Corlay. 2016. Jupyter Notebooks-a publishing format for reproducible computational workflows. Amsterdam, Netherlands: IOP Press.
Koch, Z., M. Yuan, and E. Bristow. 2020. “Emergency response after disaster strikes: Agent-based simulation of ambulances in New Windsor, NY.” J. Infrastruct. Syst. 26 (3): 06020001. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000565.
Langlois, S. 2006. Effect of high intensity winds on overhead transmission lines. Montréal, Canada: McGill University.
Leeton, U., T. Ratniyomchai, and T. Kulworawanichpong. 2010. “Optimal reactive power flow with distributed generating plants in electric power distribution systems.” In Proc., 2010 Int. Conf. on Advances in Energy Engineering. New York: IEEE. https://doi.org/10.1109/ICAEE.2010.5557590.
Lijesen, M. G. 2007. “The real-time price elasticity of electricity.” Energy Econ. 29 (2): 249–258. https://doi.org/10.1016/j.eneco.2006.08.008.
Liu, X., K. Hou, H. Jia, J. Zhao, L. Mili, X. Jin, and D. Wang. 2020. “A planning-oriented resilience assessment framework for transmission systems under typhoon disasters.” IEEE Trans. Smart Grid 11 (6): 5431–5441. https://doi.org/10.1109/TSG.2020.3008228.
Liu, Y., H. Dong, N. Lohse, and S. Petrovic. 2015. “Reducing environmental impact of production during a rolling blackout policy—A multi-objective schedule optimisation approach.” J. Cleaner Prod. 102 (4): 418–427. https://doi.org/10.1016/j.jclepro.2015.04.038.
Liu, Y., and C. Singh. 2020. “Evaluation of hurricane impact on failure rate of transmission lines using fuzzy expert system.” In Proc., 2009 15th Int. Conf. on Intelligent System Applications to Power Systems, 1–6. New York: IEEE.
Liu, Z., C. C. Jacques, S. Szyniszewski, J. K. Guest, B. W. Schafer, T. Igusa, and J. Mitrani-Reiser. 2016. “Agent-based simulation of building evacuation after an earthquake: Coupling human behavior with structural response.” Nat. Hazards Rev. 17 (1): 04015019. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000199.
Lopes, F., and H. Coelho. 2018. Electricity markets with increasing levels of renewable generation: Structure, operation, agent-based simulation, and emerging designs. Berlin: Springer.
Ma, L., P. Bocchini, and V. Christou. 2020. “Fragility models of electrical conductors in power transmission networks subjected to hurricanes.” Struct. Saf. 82 (Jan): 101890. https://doi.org/10.1016/j.strusafe.2019.101890.
Mantawy, A. H., and M. S. Al-Ghamdi. 2003. “A new reactive power optimization algorithm.” In Proc., 2003 IEEE Bologna Power Tech Conf. New York: IEEE.
Mardaneh, M., and G. B. Gharehpetian. 2004. “Siting and sizing of DG units using GA and OPF based technique.” In Proc., 2004 IEEE Region 10 Conf. TENCON 2004, 331–334. New York: IEEE.
McKinney, W. 2011. Pandas: A foundational Python library for data analysis and statistics. Koln, Germany: Deutsches Zentrum für Luft- und Raumfahrt.
Miller, R. M. 2021. ‘Massive failure’: Why are millions of people in Texas still without power?. Tysons, VA: USA Today.
Millman, K. J., and M. Aivazis. 2011. “Python for scientists and engineers.” Comput. Sci. Eng. 13 (2): 9–12. https://doi.org/10.1109/MCSE.2011.36.
Moradi-Sepahvand, M., T. Amraee, and S. S. Gougheri. 2022. “Deep learning based hurricane resilient coplanning of transmission lines, battery energy storages, and wind farms.” IEEE Trans. Ind. Inf. 18 (3): 2120–2131. https://doi.org/10.1109/TII.2021.3074397.
Mostafavi, A., and D. Abraham. 2010. Frameworks for systemic and structural analysis of financial innovations in infrastructure. Miami, FL: Florida International Univ.
Mostafavi, A., D. Abraham, and D. DeLaurentis. 2012a. Toward sustainable financial innovation policies in infrastructure: A framework for ex-ante analysis, 41–50. Reston, VA: ASCE.
Mostafavi, A., D. Abraham, and D. DeLaurentis. 2014. “Ex-ante policy analysis in civil infrastructure systems.” J. Comput. Civ. Eng. 28 (5): A4014006. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000350.
Mostafavi, A., D. Abraham, D. DeLaurentis, J. Sinfield, and C. Queiroz. 2012b. Innovation policy assessment for civil infrastructure system-of-systems, 2300–2309. Reston, VA: ASCE.
Nazari-Heris, M., M. Sadat-Mohammadi, M. A. Mirzaei, S. Asadi, B. Mohammadi-Ivatloo, and H. Jebelli. 2020. “Robust energy management of integrated power infrastructure and gas networks with high penetration of renewable energy sources.” In Proc., Construction Research Congress 2020: Infrastructure Systems and Sustainability, 501–511. Reston, VA: ASCE.
NERC (North American Electric Reliability Corporation). 2020. “North American electric reliability corporation 2020.” Accessed November 14, 2020. https://www.nerc.com/Pages/default.aspx.
Nicklow, J., et al. 2010. “State of the art for genetic algorithms and beyond in water resources planning and management.” J. Water Resour. Plann. Manage. 136 (4): 412–432. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000053.
NOAA (National Oceanic and Atmospheric Administration). 2021. “U.S. billion-dollar climate disasters 1980-2021.” Accessed April 29, 2021. www.ncdc.noaa.gov/billions/events.pdf.
Norouziasl, S., A. Jafari, and C. Wang. 2019. Analysis of lighting occupancy sensor installation in building renovation using agent-based modeling of occupant behavior, 593–601. Reston, VA: ASCE.
Nosratabadi, S. M., R.-A. Hooshmand, and E. Gholipour. 2017. “A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems.” Renewable Sustainable Energy Rev. 67 (8): 341–363. https://doi.org/10.1016/j.rser.2016.09.025.
Oliphant, T. E. 2006. A guide to NumPy. Washington, DC: Trelgol.
Oliphant, T. E. 2007. “Python for scientific computing.” Comput. Sci. Eng. 9 (3): 10–20. https://doi.org/10.1109/MCSE.2007.58.
Padhy, N. P. 2004. “Unit commitment-a bibliographical survey.” IEEE Trans. Power Syst. 19 (2): 1196–1205. https://doi.org/10.1109/TPWRS.2003.821611.
Pan, X., C. S. Han, and K. H. Law. 2012. A multi-agent based simulation framework for the study of human and social behavior in egress analysis, 1–12. Reston, VA: ASCE.
Panteli, M., and P. Mancarella. 2017. “Modeling and evaluating the resilience of critical electrical power infrastructure to extreme weather events.” IEEE Syst. J. 11 (3): 1733–1742. https://doi.org/10.1109/JSYST.2015.2389272.
Pereyra, J., X. He, and A. Mostafavi. 2016. Multi-agent framework for the complex adaptive modeling of interdependent critical infrastructure system, 1556–1566. Reston, VA: ASCE.
Phillips, L., H. Link, R. Smith, and L. Welland. 2006. Agent-based control of distributed infrastructure resources. Albuquerque, NM: Sandia National Laboratories.
Pisica, I., C. Bulac, and M. Eremia. 2009. “Optimal distributed generation location and sizing using genetic algorithms.” In Proc., 2009 15th Int. Conf. on Intelligent System Applications to Power Systems, 1–6. New York: IEEE.
Poyrazoglu, G., and H. Oh. 2015. “Optimal topology control with physical power flow constraints and N-1 contingency criterion.” IEEE Trans. Power Syst. 30 (6): 3063–3071. https://doi.org/10.1109/TPWRS.2014.2379112.
Raoufi, M., and A. R. Fayek. 2018. “Fuzzy agent-based modeling of construction crew motivation and performance.” J. Comput. Civ. Eng. 32 (5): 04018035. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000777.
Raoufi, M., and A. R. Fayek. 2020. “Fuzzy Monte Carlo agent-based simulation of construction crew performance.” J. Constr. Eng. Manage. 146 (5): 04020041. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001826.
Rasoulkhani, K., B. Logasa, M. P. Reyes, and A. Mostafavi. 2017. “Agent-based modeling framework for simulation of complex adaptive mechanisms underlying household water conservation technology adoption.” Winter Simul. Conf. 2017 (1): 1109–1120.
Rasoulkhani, K., B. Logasa, M. P. Reyes, and A. Mostafavi. 2018. “Understanding fundamental phenomena affecting the water conservation technology adoption of residential consumers using agent-based modeling.” Water 10 (8): 993. https://doi.org/10.3390/w10080993.
Salman, A. M., and Y. Li. 2017. “Assessing climate change impact on system reliability of power distribution systems subjected to Hurricanes.” J. Infrastruct. Syst. 23 (1): 04016024. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000316.
Seabold, S., and J. Perktold. 2010. “Statsmodels: Econometric and statistical modeling with python.” In Proc., 9th Python in Science Conf. Austin, TX: Scipy.
Sharma, N. K., D. S. Babu, and S. C. Choube. 2012. “Application of particle swarm optimization technique for reactive power optimization.” In Proc., IEEE-Int. Conf. on Advances in Engineering, Science and Management (ICAESM-2012), 88–93. New York: IEEE.
Sheblé, G. B. 1999. “Economic dispatch, unit commitment, and optimal power flow as auctions.” In Computational auction mechanisms for restructured power industry operation. 107–164. Berlin: Springer.
Sheppard, K. 2017. “Linear model estimation—Linearmodels 4.5 documentation.” Accessed January 30, 2020. https://bashtage.github.io/linearmodels/doc/index.html#.
Siegfried, R. 2014. Modeling and simulation of complex systems: A framework for efficient agent-based modeling and simulation. Berlin: Springer.
Smith, J. L., and J. T. Brokaw. 2012. Agent based simulation of human movements during emergency evacuations of facilities, 1–10. Reston, VA: ASCE.
Son, J., and E. M. Rojas. 2011. “Evolution of collaboration in temporary project teams: An agent-based modeling and simulation approach.” J. Constr. Eng. Manage. 137 (8): 619–628. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000331.
Sun, J., and L. Tesfatsion. 2006. DC optimal power flow formulation and solution using QuadProgJ. Duluth, MN: AgEcon.
Sun, J., and L. Tesfatsion. 2007. “An agent-based computational laboratory for wholesale power market design.” In Proc., 2007 IEEE Power Engineering Society General Meeting, 1–6. New York: IEEE.
Sweda, T. M., and D. Klabjan. 2015. “Agent-based information system for electric vehicle charging infrastructure deployment.” J. Infrastruct. Syst. 21 (2): 04014043. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000231.
Tesfatsion, L. 2006. “Agent-based computational economics: A constructive approach to economic theory.” In Handbook of computational economics, edited by L. Tesfatsion, and K. L. Judd, 831–880. Amsterdam, Netherlands: Elsevier.
Tofis, Y., S. Timotheou, and E. Kyriakides. 2017. “Minimal load shedding using the swing equation.” IEEE Trans. Power Syst. 32 (3): 2466–2467. https://doi.org/10.1109/TPWRS.2016.2614886.
Tungadio, D. H., B. P. Numbi, M. W. Siti, and A. A. Jimoh. 2015. “Particle swarm optimization for power system state estimation.” Neuro Comput. 148 (10): 175–180. https://doi.org/10.1016/j.neucom.2012.10.049.
Van Der Walt, S., S. C. Colbert, and G. Varoquaux. 2011. “The NumPy array: A structure for efficient numerical computation.” Comput. Sci. Eng. 13 (2): 22. https://doi.org/10.1109/MCSE.2011.37.
Vaskovskaya, T., P. G. Thakurta, and J. Bialek. 2018. “Contribution of transmission and voltage constraints to the formation of locational marginal prices.” Int. J. Electr. Power Energy Syst. 101 (Apr): 491–499. https://doi.org/10.1016/j.ijepes.2018.04.004.
Virtanen, P., et al. 2020. “SciPy 1.0: Fundamental algorithms for scientific computing in python.” Nat. Methods 17 (3): 261–272. https://doi.org/10.1038/s41592-019-0686-2.
Wang, Y., C. Chen, J. Wang, and R. Baldick. 2016. “Research on resilience of power systems under natural disasters—A review.” IEEE Trans. on Power Syst. 31 (2): 1604–1613. https://doi.org/10.1109/TPWRS.2015.2429656.
Wang, Z., and X. Ye. 2018. “Social media analytics for natural disaster management.” Int. J. Geogr. Inf. Sci. 32 (1): 49–72. https://doi.org/10.1080/13658816.2017.1367003.
Waskom, M., O. Botvinnik, D. O’Kane, P. Hobson, J. Ostblom, S. Lukauskas, D. C. Gemperline, T. Augspurger, Y. Halchenko, and J. B. Cole. 2018. “mwaskom/seaborn: v0. 9.0 (July 2018).” Zenodo. https://doi.org/10.1109/ACCESS.2018.2858860.
Watkins, M., A. Mukherjee, N. Onder, and K. Mattila. 2009. “Using agent-based modeling to study construction labor productivity as an emergent property of individual and crew interactions.” J. Constr. Eng. Manage. 135 (7): 657–667. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000022.
Wong, L. 2011. A review of transmission losses in planning studies. Sacramento, CA: California Energy Commission.
Xiao, Y., L. Fang, and K. W. Hipel. 2018. “Agent-based modeling approach to investigating the impact of water demand management.” J. Water Resources Plann. Manage. 144 (3): 04018006. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000907.
Xiao, Y., and P. Wang. 2004. “Effect of transmission network on nodal market power in a deregulated power market.” In Proc., 2004 Int. Conf. on Power System Technology, 408–412. New York: IEEE.
Yang, Y., W. Tang, Y. Liu, Y. Xin, and Q. Wu. 2018. “Quantitative resilience assessment for power transmission systems under typhoon weather.” IEEE Access 6: 40747–40756. https://doi.org/10.1109/ACCESS.2018.2858860.
Yang, Z., M. Nazemi, P. Dehghanian, and M. Barati. 2020. “Toward resilient solar-integrated distribution grids: Harnessing the mobility of power sources.” In Proc., 2020 IEEE/PES Transmission and Distribution Conf. and Exposition (TD), 1–5. New York: IEEE.
Yin, L. 2013. “Assessing walkability in the city of Buffalo: Application of agent-based simulation.” J. Urban Plann. Develop. 139 (3): 166–175. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000147.
Yu, B., Z. Guo, Z. Peng, H. Wang, X. Ma, and Y. Wang. 2019. “Agent-based simulation optimization model for road surface maintenance scheme.” J. Transp. Eng. Part B: Pavements 145 (1): 04018065.
Yu, M., C. Yang, and Y. Li. 2018. “Big data in natural disaster management: A review.” Geosciences 8 (5): 165. https://doi.org/10.3390/geosciences8050165.
Yu, Z. Y., Z. P. Li, T. Qian, K. C. Huang, X. Y. Chen, and W. H. Tang. 2021. “Optimal restoration strategy based on resilience improvement for power transmission systems under extreme weather events.” IOP Conf. Ser.: Earth Environ. Sci. 645 (1): 012042. https://doi.org/10.1088/1755-1315/645/1/012042.
Yuan, Y., L. Wu, W. Song, and Z. Jiang. 2009. “Collaborative control of microgrid for emergency response and disaster relief.” In Proc., 2009 Int. Conf. on Sustainable Power Generation and Supply, 1–5. New York: IEEE.
Zhang, H., L. Cheng, S. Yao, T. Zhao, and P. Wang. 2020. “Spatial–temporal reliability and damage assessment of transmission networks under hurricanes.” IEEE Trans. Smart Grid 11 (2): 1044–1054. https://doi.org/10.1109/TSG.2019.2930013.
Zhang, L., G.-L. Chang, S. Zhu, C. Xiong, L. Du, M. Mollanejad, N. Hopper, and S. Mahapatra. 2013. “Integrating an agent-based travel behavior model with large-scale microscopic traffic simulation for corridor-level and subarea transportation operations and planning applications.” J. Urban Planning Develop. 139 (2): 94–103. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000139.
Zhu, J., and A. Mostafavi. 2015. An integrated framework for the assessment of the impacts of uncertainty in construction projects using dynamic network simulation, 355–362. Reston, VA: ASCE.
Zhu, J., and A. Mostafavi. 2016. Dynamic meta-network modeling for an integrated project performance assessment under uncertainty, 2340–2350. Reston, VA: ASCE.
Zhu, J., and A. Mostafavi. 2018. “Performance assessment in complex engineering projects using a system-of-systems framework.” IEEE Syst. J. 12 (1): 262–273. https://doi.org/10.1109/JSYST.2017.2671738.
Zhu, L., X. Zhao, and D. K. H. Chua. 2016. “Agent-based debt terms’ bargaining model to improve negotiation inefficiency in PPP projects.” J. Comput. Civ. Eng. 30 (6): 04016014. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000571.
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Published In
Natural Hazards Review
Volume 24 • Issue 2 • May 2023
Copyright
© 2022 American Society of Civil Engineers.
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Received: Jul 8, 2021
Accepted: Oct 31, 2022
Published online: Dec 27, 2022
Published in print: May 1, 2023
Discussion open until: May 27, 2023
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