Research Article

Decentralized Energy and Water Networks for Community Resilience against Natural Disasters

Govind Joshi
Colorado School of Mines, USA
Salman Mohagheghi
Colorado School of Mines, USA
* Corresponding author
DOI icon 10.24018/ejenergy.2022.2.4.76

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Submitted 2022-06-05
Published 2022-09-05

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Article Main Content

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

References

  1. 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: https://www.europarl.europa.eu/meetdocs/2004_2009/documents/dv/ieep_cc_natural_disasters_/ieep_cc_natural_disasters_en.pdf.
     Google Scholar
  2. Banholzer S, Kossin J, Donner S. Reducing disaster: Early warning systems for climate change, Springer, 2014, pp. 21–49.
     Google Scholar
  3. Halverson JB, Rabenhorst T. Hurricane Sandy: The science and impacts of a superstorm. Weatherwise, 2013; 66(2):14–23.
     Google Scholar
  4. Vigdor J. The economic aftermath of Hurricane Katrina. J. Econ. Perspect. 2008; 22(4):135–154.
     Google Scholar
  5. Satake K, Atwater BF. Long-term perspectives on giant earthquakes and tsunamis at subduction zones,” Annu. Rev. Earth Planet. Sci. 2007; 35:349–374.
     Google Scholar
  6. 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.
     Google Scholar
  7. Thomas DSK, Phillips BD, Fothergill A, Blinn-Pike L. Social vulnerability to disasters. CRC Press; 2009.
     Google Scholar
  8. 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.
     Google Scholar
  9. 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.
     Google Scholar
  10. Fordham M, Lovekamp WE, Thomas DSK, Phillips BD. Understanding social vulnerability. Soc. vulnerability to disasters. 2013; 2:1–29.
     Google Scholar
  11. Rodríguez H, Russell CN. Understanding disasters: vulnerability, sustainable development, and resiliency. Public Sociol. Read. 2006; 193–211.
     Google Scholar
  12. 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.
     Google Scholar
  13. Venkataramanan G, Marnay C. A larger role for microgrids. IEEE Power Energy Mag. 2008; 6(3):78–82.
     Google Scholar
  14. 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.
     Google Scholar
  15. Falco GJ, Webb WR. Water microgrids: The future of water infrastructure resilience. Procedia Eng. 2015; 118:50–57.
     Google Scholar
  16. Chen SX, Gooi HB, Wang M. Sizing of energy storage for microgrids. IEEE Trans. Smart Grid. 2011; 3(1):142–151.
     Google Scholar
  17. 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.
     Google Scholar
  18. 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.
     Google Scholar
  19. 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.
     Google Scholar
  20. 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.
     Google Scholar
  21. 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.
     Google Scholar
  22. Hartmann B, Dán A. Methodologies for storage size determination for the integration of wind power. IEEE Trans. Sustain. Energy. 2013; 5(1):182–189.
     Google Scholar
  23. 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.
     Google Scholar
  24. 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.
     Google Scholar
  25. Jordehi AR. Optimisation of demand response in electric power systems, a review,” Renew. Sust. Energy Rev. 2019; 103:308–319.
     Google Scholar
  26. Denholm P, Mai T. Timescales of energy storage needed for reducing renewable energy curtailment. Renew. Energy. 2019; 130:388–399.
     Google Scholar
  27. Fooladivanda D, Taylor JA. Energy-optimal pump scheduling and water flow. IEEE Trans. Control Netw. Syst. 2017; 5(3):1016–1026.
     Google Scholar
  28. Jowitt PW, Germanopoulos G. Optimal pump scheduling in water-supply networks. J. Water Resour. Plan. Manag. 1992; 118(4):406–422.
     Google Scholar
  29. Moosavian SA. Optimal design of water distribution networks under uncertainty. PhD Thesis. University of British Columbia; 2018.
     Google Scholar
  30. 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.
     Google Scholar
  31. 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.
     Google Scholar
  32. 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.
     Google Scholar
  33. Yerri S, Piratla KR. Decentralized water reuse planning: Evaluation of life cycle costs and benefits. Resour. Conserv. Recycl. 2019; 141:339–346.
     Google Scholar
  34. 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.
     Google Scholar
  35. Hakanen J, Sahlstedt K, Miettinen K. Wastewater treatment plant design and operation under multiple conflicting objective functions. Environ. Model. Softw. 2013; 46:240–249.
     Google Scholar
  36. 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.
     Google Scholar
  37. 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.
     Google Scholar
  38. 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.
     Google Scholar
  39. 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.
     Google Scholar
  40. Moazeni F, Khazaei J. Optimal operation of water-energy microgrids; a mixed integer linear programming formulation. J. Clean. Prod. 2020; 275:122776.
     Google Scholar
  41. 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: https://data.openei.org/submissions/153.
     Google Scholar
  42. NSRDB. National solar radiation data base [Internet]. 2005 [cited 2021 November 15]. Available from: https://rredc.nrel.gov/solar/old_data/nsrdb/1991-2005/tmy3/.
     Google Scholar
  43. National Renewable Energy Lab. Wind integration national dataset toolkit [Internet]. 2021 [cited 2021 Nov 15]. Available from: https://www.nrel.gov/grid/wind-toolkit.html.
     Google Scholar
  44. U. S. C. Bureau. Characteristics of new housing, [Internet]. 2021 [cited 2021 Nov 15]. Available from: https://www.census.gov/construction/chars/.
     Google Scholar
  45. Wind Energy Market Intelligence. The wind power [Internet]. 2018 [cited 2021 Nov 15]. Available from: www.thewindpower.net/turbine_en_348_nordtank_ntk300-31.php.
     Google Scholar
  46. Ryse Energy. Ryse Energy 5 kW wind turbines [Internet]. 2021 [cited 2021 Nov 15]. Available from: https://www.ryse.energy/5kw-wind-turbines/.
     Google Scholar
  47. USGS. Water science school [Internet]. 2020 [cited 2021 November 15]. Available from: https://www.usgs.gov/special-topic/water-science-school/science.
     Google Scholar
  48. Milnes M. The mathematics of pumping water [Internet]. 2017 [cited 2021 Nov 15]. Available from: http://www.raeng.org.uk/ publications/other/17-pumping-water.
     Google Scholar
  49. Commission California Energy. California code of regulations title 20 [Internet]. 2016 [cited 2021 November 15]. Available from: https://www.epa.gov/sites/production/files/2017-10/documents/ws-commercialbuildings-waterscore-residential-kitchen-laundry-guide.pdf.
     Google Scholar
  50. Peacock B. Energy and cost required to lift or pressurize water [Internet]. 1996 [cited 2021 November 15]. Available from: https://cetulare.ucanr.edu/files/82040.pdf.
     Google Scholar