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The power of efficiency: Optimizing environmental and social benefits through demand-side-management

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  • Miara, Ariel
  • Tarr, Craig
  • Spellman, Rachel
  • Vörösmarty, Charles J.
  • Macknick, Jordan E.

Abstract

Substantial social and environmental benefits can be achieved through regional DSM (demand-side management) strategies. Here, three DSM scenarios that vary in capital investment costs of technology retrofits were tested for the contemporary Northeastern US. These resulted in an 8.3–16.5% decrease in summertime regional electricity consumption. The lower power consumption achieved through DSM was analyzed under an additional five SPR (strategic power reduction) scenarios to explore how the reduced electricity demand could be optimized through different modalities of thermoelectric power production that lower human health risks, thermal water pollution, carbon emissions or system costs (operation and maintenance) of power plants. SPR scenarios show potential to lower health risks to nearly two million people with corresponding avoided external costs of $11 billion per year, lower carbon emissions (31%, maximum) and thermal water pollution (37%, maximum). By internalizing external costs, some unfavorable investments (NPV (net present value) < 0) turned into favorable ones (NPV > 0). Results show that integrating tradeoffs of DSM beyond the building scale unveil considerable social and environmental benefits that are ignored in typical financial valuations. This, in turn, can provide more holistic assessments and identify actionable policy alternatives of value to energy and environmental planners that aim to achieve sustainable development.

Suggested Citation

  • Miara, Ariel & Tarr, Craig & Spellman, Rachel & Vörösmarty, Charles J. & Macknick, Jordan E., 2014. "The power of efficiency: Optimizing environmental and social benefits through demand-side-management," Energy, Elsevier, vol. 76(C), pages 502-512.
    • Handle: RePEc:eee:energy:v:76:y:2014:i:c:p:502-512
    DOI: 10.1016/j.energy.2014.08.047
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    3. Scott, Michael J. & Daly, Don S. & Hathaway, John E. & Lansing, Carina S. & Liu, Ying & McJeon, Haewon C. & Moss, Richard H. & Patel, Pralit L. & Peterson, Marty J. & Rice, Jennie S. & Zhou, Yuyu, 2015. "Calculating impacts of energy standards on energy demand in U.S. buildings with uncertainty in an integrated assessment model," Energy, Elsevier, vol. 90(P2), pages 1682-1694.
    4. Wei, Sun & Yanfeng, Xu, 2017. "Research on China's energy supply and demand using an improved Grey-Markov chain model based on wavelet transform," Energy, Elsevier, vol. 118(C), pages 969-984.
    5. Rajanna, S. & Saini, R.P., 2016. "Employing demand side management for selection of suitable scenario-wise isolated integrated renewal energy models in an Indian remote rural area," Renewable Energy, Elsevier, vol. 99(C), pages 1161-1180.
    6. Rama Curiel, José Adrián & Thakur, Jagruti, 2022. "A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions," Energy, Elsevier, vol. 258(C).
    7. U. G. D. Madushika & Thanuja Ramachandra & Gayani Karunasena & P. A. D. S. Udakara, 2023. "Energy Retrofitting Technologies of Buildings: A Review-Based Assessment," Energies, MDPI, vol. 16(13), pages 1-16, June.
    8. Jun Dong & Huijuan Huo & Sen Guo, 2016. "Demand Side Management Performance Evaluation for Commercial Enterprises," Sustainability, MDPI, vol. 8(10), pages 1-23, October.
    9. Ioannis Panapakidis & Nikolaos Asimopoulos & Athanasios Dagoumas & Georgios C. Christoforidis, 2017. "An Improved Fuzzy C-Means Algorithm for the Implementation of Demand Side Management Measures," Energies, MDPI, vol. 10(9), pages 1-42, September.

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