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The role of residential distributed energy resources in Pakistan's energy transition

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  • Mehmood, Fahad
  • Umar, Muhammad
  • Dominguez, Cristina
  • Kazmi, Hussain

Abstract

In light of climate change concerns and falling costs, many low-income countries, such as Pakistan, have adopted a number of policies to incentivise distributed energy resources and generation. These policies have meant that the installed base of grid-connected distributed solar generation in Pakistan has grown rapidly, from only 1 MW in 2015–2016 to 232 MW in 2020–2021. However, it still constitutes a paltry 0.5% of the overall electricity mix. In this paper, using high-resolution demand and generation profiles from Lahore (the second most populous city in Pakistan and the one with the most distributed generation), we show that distributed generation offers low payback periods for urban households, even without net metering. This calculus is aided by likely future tariff hikes. Additionally, distributed generation helps support the grid, even without storage or demand response, due to a close alignment between supply and demand. Based on this analysis, we recommend the regulator to provide longer term visibility on the continuation of incentives such as net metering, and how this will eventually be shifted towards solar mandates. Likewise, it is prudent for distribution companies to provide targeted incentives for uptake of distributed generation to alleviate issues such as non-technical losses and transformer overloading.

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  • Mehmood, Fahad & Umar, Muhammad & Dominguez, Cristina & Kazmi, Hussain, 2022. "The role of residential distributed energy resources in Pakistan's energy transition," Energy Policy, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:enepol:v:167:y:2022:i:c:s0301421522002798
    DOI: 10.1016/j.enpol.2022.113054
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    2. Marcelino, C.G. & Leite, G.M.C. & Wanner, E.F. & Jiménez-Fernández, S. & Salcedo-Sanz, S., 2023. "Evaluating the use of a Net-Metering mechanism in microgrids to reduce power generation costs with a swarm-intelligent algorithm," Energy, Elsevier, vol. 266(C).

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