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Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service

Author

Listed:
  • Chavid Leewiraphan

    (School of Renewable Energy and Smart Grid Technology (SGtech), Naresuan University, Phitsanulok 65000, Thailand)

  • Nipon Ketjoy

    (School of Renewable Energy and Smart Grid Technology (SGtech), Naresuan University, Phitsanulok 65000, Thailand)

  • Prapita Thanarak

    (School of Renewable Energy and Smart Grid Technology (SGtech), Naresuan University, Phitsanulok 65000, Thailand)

Abstract

Rising fossil energy prices and the significantly decreasing prices of energy technology have resulted in electricity consumers having the option to install solar PV rooftops to rely on the self-consumption of clean energy. However, the increase in this amount is affecting the revenue of electricity as a utility, which must adapt and develop its business model to accommodate the situation. If the utility cannot be adapted in time, it may lead to a loss of income from services and the sale of electricity from fossil energy. The utility in Thailand’s electricity market that acts as the distribution system operator (DSO) is known as the Provincial Electricity Authority (PEA), and the Metropolitan Electricity Authority (MEA) is responsible for managing distribution networks and customers. There are four types of solar rooftop-as-a-service (RaaS) business perspectives they could consider as opportunities through which to minimize revenue impact. The business services were designed for the DSO customer as follows: Consulting, Design, and Installation (CDI); Operation and Maintenance (O&M); Energy Service Company (ESCO); and Power Purchase Agreement (PPA). The model comprises four customer segments: residential buildings and small-, medium-, and large-scale commercial buildings. This paper applies SWOT, Five Forces, 4P marketing, and economic impact analyses to identify the possibilities when using the DSO business model. The SWOT analysis demonstrates that ESCO and PPA are strengths in the DSO’s performance characteristics and existing customer data. In the electricity industry, both models offer enormous customer bargaining power in terms of a Five Forces analysis. The main reason is that there is currently high competition in the installation service. In the 4P analysis result, the price per unit is found to be significantly lower than in residential scenarios. Therefore, there is a format for presenting promotions with an advantage over competitors. Deploying an after-sales service that brings convenience to all customer segments is needed. The economic analysis conducted using Cournot competition game theory shows a significant differential in the Medium (M) and Large (L) customer sectors’ competition due to lower technology prices. In conclusion, with the current regulatory framework and criteria, the ESCO and PPA show the best practical model from a utility business perspective. The recommendation for DSO is to create a strategic ecosystem and to link it with private companies as their partnership business.

Suggested Citation

  • Chavid Leewiraphan & Nipon Ketjoy & Prapita Thanarak, 2023. "Business Perspectives of Distributed System Operators for Solar Rooftop-as-a-Service," Energies, MDPI, vol. 17(1), pages 1-15, December.
    • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:52-:d:1304864
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    References listed on IDEAS

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    1. Sonsaard, Parinya & Ketjoy, Nipon & Mensin, Yodthong, 2023. "Market strategy options to implement Thailand demand response program policy," Energy Policy, Elsevier, vol. 173(C).
    2. Chaianong, Aksornchan & Tongsopit, Sopitsuda & Bangviwat, Athikom & Menke, Christoph, 2019. "Bill saving analysis of rooftop PV customers and policy implications for Thailand," Renewable Energy, Elsevier, vol. 131(C), pages 422-434.
    3. Lazar Gitelman & Mikhail Kozhevnikov, 2023. "New Business Models in the Energy Sector in the Context of Revolutionary Transformations," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    4. Zhong, Teng & Zhang, Zhixin & Chen, Min & Zhang, Kai & Zhou, Zixuan & Zhu, Rui & Wang, Yijie & Lü, Guonian & Yan, Jinyue, 2021. "A city-scale estimation of rooftop solar photovoltaic potential based on deep learning," Applied Energy, Elsevier, vol. 298(C).
    5. Bankel, Amanda & Mignon, Ingrid, 2022. "Solar business models from a firm perspective – an empirical study of the Swedish market," Energy Policy, Elsevier, vol. 166(C).
    6. Galen Barbose & Andrew J. Satchwell, 2020. "Benefits and costs of a utility-ownership business model for residential rooftop solar photovoltaics," Nature Energy, Nature, vol. 5(10), pages 750-758, October.
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