A multi-disciplinary analysis of UK grid mix scenarios with large-scale PV deployment. (March 2018)

• Record Type: Journal Article
• Authors: Raugei, Marco; Leccisi, Enrica; Azzopardi, Brian; Jones, Christopher; Gilbert, Paul; Zhang, Lingxi; Zhou, Yutian; Mander, Sarah; Mancarella, Pierluigi
• Is Part Of: Energy policy. Volume 114(2018)
• Page Start: 51
• Page End: 62
• Publication Date: 2018-03
• Subjects: Grid mix -- LCA -- EROI -- Prospective -- Consequential -- Scenarios; Energy policy -- Periodicals; Politique énergétique -- Périodiques; Electronic journals; 333.79
• DOI: 10.1016/j.enpol.2017.11.062
• Languages: English
• ISSNs: 0301-4215

Abstract

Abstract: The increasing contribution of renewable energies to electricity grids in order to address impending environmental challenges implies a reduction in non-renewable resource use and an alignment with a global transition toward a low-carbon electric sector. In this paper, four future UK grid mix scenarios with increased photovoltaic (PV) installed capacity are assessed and compared to a benchmark "Low PV" scenario, from 2016 to 2035. The complexity of the issue requires a multi-disciplinary approach to evaluate the availability of net energy, environmental aspects and technical performance. Hence, the comparison between scenarios includes short-term and long-term energy metrics as well as greenhouse gas (GHG) and technical metrics. Also, the paper considers the viewpoints offered by both an "integrative" and a "dynamic" approach to net energy analysis. Results for all five analysed scenarios indicate that increased PV deployment will not be detrimental to the UK grid performance from the points of view of a wide range of system-level technical (% renewable energy curtailment to ensure grid stability), energy (energy return on investment and non-renewable cumulative energy demand) and environmental (greenhouse gas emissions) metrics. Highlights: Four future UK grid mix scenarios with increased PV capacity are assessed and compared. A range of energy, emission and technical metrics are defined and employed. All scenarios lead to improved energy use and reduced GHG emissions.