Decentralized Energy and Water Networks for Community Resilience against Natural Disasters


  •   Govind Joshi

  •   Salman Mohagheghi


Large-scale natural disasters can severely damage the energy and water infrastructure, leading to disruption of services. In addition to raising possible health risks, lack of access to electricity and water can impede or prolong recovery from the disaster. To be resilient against such events, the electric power grid and the water distribution network must be able to continue operating with minimal impact on end-users and with constricted costs. Naturally, one approach is to reinforce the energy and water infrastructure against natural hazards. However, this may be cost-prohibitive or even infeasible. An alternative solution is to allocate sufficient localized resources such that these networks can continue operating at a decentralized scale until the main network is repaired and restored. In this paper, a solution is proposed to design a localized water and energy system that can serve a community affected by a natural disaster, with little external support. An optimization model is developed to optimally allocate resources, e.g., distributed energy resources and water storage capacity, based on the needs of the community and subject to operational constraints. Such decentralized systems can significantly improve the resilience of the energy and water networks and assist affected communities in the aftermath of disaster events.

Keywords: Distributed generation, electric microgrid, natural disasters, renewable energy resources, resilience, water micronet


Anderson J, Bausch C. Climate change and natural disasters: Scientific evidence of a possible relation between recent natural disasters and climate change. [Internet]. 2006 [cited 2021 Nov 15]. Available from:

Banholzer S, Kossin J, Donner S. Reducing disaster: Early warning systems for climate change, Springer, 2014, pp. 21–49.

Halverson JB, Rabenhorst T. Hurricane Sandy: The science and impacts of a superstorm. Weatherwise, 2013; 66(2):14–23.

Vigdor J. The economic aftermath of Hurricane Katrina. J. Econ. Perspect. 2008; 22(4):135–154.

Satake K, Atwater BF. Long-term perspectives on giant earthquakes and tsunamis at subduction zones,” Annu. Rev. Earth Planet. Sci. 2007; 35:349–374.

Ohnishi T. The disaster at Japan’s Fukushima-Daiichi nuclear power plant after the March 11, 2011 earthquake and tsunami, and the resulting spread of radioisotope contamination. Radiat. Res. 2012; 177(1):1–14.

Thomas DSK, Phillips BD, Fothergill A, Blinn-Pike L. Social vulnerability to disasters. CRC Press; 2009.

Veenema TG, Thornton CP, Lavin RP, Bender AK, Seal S, and Corley A. Climate change–related water disasters’ impact on population health. J. Nurs. Scholarsh. 2017; 49(6):625–634.

Benevolenza MA, DeRigne L. The impact of climate change and natural disasters on vulnerable populations: A systematic review of literature. J. Hum. Behav. Soc. Environ. 2019; 29(2):266–281.

Fordham M, Lovekamp WE, Thomas DSK, Phillips BD. Understanding social vulnerability. Soc. vulnerability to disasters. 2013; 2:1–29.

Rodríguez H, Russell CN. Understanding disasters: vulnerability, sustainable development, and resiliency. Public Sociol. Read. 2006; 193–211.

Quashie M, Joos G. A methodology to optimize benefits of microgrids. Proceedings of the 2013 IEEE power & energy society general meeting, pp. 1–5, Vancouver, BC, Canada, July 2013.

Venkataramanan G, Marnay C. A larger role for microgrids. IEEE Power Energy Mag. 2008; 6(3):78–82.

Choobineh M, Mohagheghi S. Emergency electric service restoration in the aftermath of a natural disaster,” Proceedings of the 2015 IEEE Global Humanitarian Technology Conference, pp. 183–190, Seattle, WA, USA, October 2015.

Falco GJ, Webb WR. Water microgrids: The future of water infrastructure resilience. Procedia Eng. 2015; 118:50–57.

Chen SX, Gooi HB, Wang M. Sizing of energy storage for microgrids. IEEE Trans. Smart Grid. 2011; 3(1):142–151.

Kahrobaee S, Asgarpoor S, Qiao W. Optimum sizing of distributed generation and storage capacity in smart households. IEEE Trans. Smart Grid. 2013; 4(4):1791–1801.

Atia R, Yamada N. Sizing and analysis of renewable energy and battery systems in residential microgrids. IEEE Trans. Smart Grid. 2016; 7(3):1204–1213.

Rodríguez-Gallegos CD, Yang D, Gandhi O, Bieri M, Reindl T, Panda SK. A multi-objective and robust optimization approach for sizing and placement of PV and batteries in off-grid systems fully operated by diesel generators: An Indonesian case study. Energy. 2018; 160:410–429.

Alharbi H, Bhattacharya K. Optimal sizing of battery energy storage systems for microgrids. Proceedings of the 2014 IEEE Electrical Power and Energy Conference, pp. 275–280, Calgary, AB, Canada, November 2014.

Bludszuweit H, Domínguez-Navarro JA. A probabilistic method for energy storage sizing based on wind power forecast uncertainty. IEEE Trans. Power Syst. 2010; 26(3):1651–1658.

Hartmann B, Dán A. Methodologies for storage size determination for the integration of wind power. IEEE Trans. Sustain. Energy. 2013; 5(1):182–189.

Bahramirad S, Reder W, Khodaei A. Reliability-constrained optimal sizing of energy storage system in a microgrid. IEEE Trans. Smart Grid. 2012; 3(4):2056–2062.

Cao B, Dong W, Lv Z, Gu Y, Singh S, Kumar P. Hybrid microgrid many-objective sizing optimization with fuzzy decision. IEEE Trans. Fuzzy Syst. 2020; 28(11):2702–2710.

Jordehi AR. Optimisation of demand response in electric power systems, a review,” Renew. Sust. Energy Rev. 2019; 103:308–319.

Denholm P, Mai T. Timescales of energy storage needed for reducing renewable energy curtailment. Renew. Energy. 2019; 130:388–399.

Fooladivanda D, Taylor JA. Energy-optimal pump scheduling and water flow. IEEE Trans. Control Netw. Syst. 2017; 5(3):1016–1026.

Jowitt PW, Germanopoulos G. Optimal pump scheduling in water-supply networks. J. Water Resour. Plan. Manag. 1992; 118(4):406–422.

Moosavian SA. Optimal design of water distribution networks under uncertainty. PhD Thesis. University of British Columbia; 2018.

Batchabani E, Fuamba M. Optimal tank design in water distribution networks: review of literature and perspectives. J. water Resour. Plan. Manag. 2014; 140(2):136–145.

Cunha M, Marques J. A new multiobjective simulated annealing algorithm—MOSA‐GR: Application to the optimal design of water distribution networks. Water Resour. Res. 2020; 56(3):1–29.

Yuksel E, Eroglu V, Sarikaya HZ, Koyuncu I. Current and future strategies for water and wastewater management of Istanbul City. Environ. Manage. 2004; 33(2):186–195.

Yerri S, Piratla KR. Decentralized water reuse planning: Evaluation of life cycle costs and benefits. Resour. Conserv. Recycl. 2019; 141:339–346.

Kendrick DA, Rao HS, Wells CH. Optimal operation of a system of waste water treatment facilities. Proceedings of the 1970 IEEE Symposium on Adaptive Processes Decision and Control, Austin, TX, USA, December 1970.

Hakanen J, Sahlstedt K, Miettinen K. Wastewater treatment plant design and operation under multiple conflicting objective functions. Environ. Model. Softw. 2013; 46:240–249.

Zohrabian A, Plata SL, Kim DM, Childress AE, Sanders KT. Leveraging the water‐energy nexus to derive benefits for the electric grid through demand‐side management in the water supply and wastewater sectors. Wiley Interdiscip. Rev. Water. 2021; 8(3): e1510.

da Silveira APP, Mata-Lima H. Energy audit in water supply systems: a proposal of integrated approach towards energy efficiency. Water Policy. 2020; 22(6):1126–1141.

Fooladivanda D, Domínguez-García AD, Sauer PW. Utilization of water supply networks for harvesting renewable energy. IEEE Trans. Control Netw. Syst. 2018; 6(2):763–774.

Wang F, Xu J, Liu L, Yin G, Wang J, Yan J. Optimal design and operation of hybrid renewable energy system for drinking water treatment. Energy. 2021; 219:119673.

Moazeni F, Khazaei J. Optimal operation of water-energy microgrids; a mixed integer linear programming formulation. J. Clean. Prod. 2020; 275:122776.

Office of Energy Efficiency & Renewable Energy. Commercial and residential hourly load profiles for all TMY3 locations in the United States [Internet]. 2014 [cited 2021 November 15]. Available from:

NSRDB. National solar radiation data base [Internet]. 2005 [cited 2021 November 15]. Available from:

National Renewable Energy Lab. Wind integration national dataset toolkit [Internet]. 2021 [cited 2021 Nov 15]. Available from:

U. S. C. Bureau. Characteristics of new housing, [Internet]. 2021 [cited 2021 Nov 15]. Available from:

Wind Energy Market Intelligence. The wind power [Internet]. 2018 [cited 2021 Nov 15]. Available from:

Ryse Energy. Ryse Energy 5 kW wind turbines [Internet]. 2021 [cited 2021 Nov 15]. Available from:

USGS. Water science school [Internet]. 2020 [cited 2021 November 15]. Available from:

Milnes M. The mathematics of pumping water [Internet]. 2017 [cited 2021 Nov 15]. Available from: publications/other/17-pumping-water.

Commission California Energy. California code of regulations title 20 [Internet]. 2016 [cited 2021 November 15]. Available from:

Peacock B. Energy and cost required to lift or pressurize water [Internet]. 1996 [cited 2021 November 15]. Available from:


How to Cite
Joshi, G., & Mohagheghi, S. (2022). Decentralized Energy and Water Networks for Community Resilience against Natural Disasters. European Journal of Energy Research, 2(4), 39–48.