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Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures

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  • Georgiou, Giorgos S.
  • Rouvas, Constantinos
  • Nathanael, Demetris

Abstract

Concerning the growth rate in Renewable Energy share, especially in the EU, it is fact that Rooftop PV systems have a significant role to play. Nevertheless, it is also accepted that with current policies, schemes and scientific methods, today's Renewable Energy growth rate is still far from the desired one. Based on that, this study proposes a novel integration of a Mixed-Integer Linear Programming based model in a Public Tender Procedure, which enables the bulk installation and thus, the rapid expansion of Rooftop PV systems, in a least-cost manner. The main objective and contribution of this paper is to provide an economic solution to interested Public Bodies for utilizing the rapid growth of rooftop PV systems through novel mechanisms and hence, to better achieve the desired Renewable Energy targets. To further highlight the capabilities of the proposed method, the results from a real-life project, showed that the bulk installation of PV systems, in 404 public schools with a total PV capacity of 4.8 MWp, can be realized at an optimum cost. The main achievements are: (i) high-level competition, due to the ability of the proposed model to select multiple PV Installers, (ii) a 35% lower project cost compared to the initial estimated budget and (iii) a significantly lower unit cost of about 0.7 million €/MWp, compared to about 0.91 and 1.54 million €/MWp obtained from other similar projects. Finally, the outcomes of the proposed MILP model are validated by those obtained from an exhaustive optimal solution search procedure.

Suggested Citation

  • Georgiou, Giorgos S. & Rouvas, Constantinos & Nathanael, Demetris, 2022. "Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures," Renewable Energy, Elsevier, vol. 187(C), pages 347-361.
    • Handle: RePEc:eee:renene:v:187:y:2022:i:c:p:347-361
    DOI: 10.1016/j.renene.2022.01.038
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    References listed on IDEAS

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    1. Pavlos Nikolaidis, 2025. "AI-Enhanced Photovoltaic Power Prediction Under Cross-Continental Dust Events and Air Composition Variability in the Mediterranean Region," Energies, MDPI, vol. 18(14), pages 1-30, July.

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