Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling

Paul Rowley; Philip Leicester; Diane Palmer; Paul Westacott; Chiara Candelise; Thomas Betts; Ralph Gottschalg

Multi-domain analysis of photovoltaic impacts via integrated spatial and probabilistic modelling

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Author(s): Paul Rowley ¹ ; Philip Leicester ¹ ; Diane Palmer ¹ ; Paul Westacott ² ; Chiara Candelise ² ; Thomas Betts ¹ ; Ralph Gottschalg ¹
- Affiliations: 1: CREST, School of Electronic, Electrical and Systems Engineering, Loughborough University, UK;
  2: Centre for Energy Policy and Technology, Imperial College, London, UK
Source: Volume 9, Issue 5, July 2015, p. 424 – 431
DOI: 10.1049/iet-rpg.2014.0374 , Print ISSN 1752-1416, Online ISSN 1752-1424

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

Received 03/11/2014, Accepted 19/03/2015, Revised 27/01/2015, Published 13/05/2015

replace with: Currently, the impacts of wide-scale implementation of photovoltaic (PV) technology are evaluated in terms of such indicators as rated capacity, energy output or return on investment. However, as PV markets mature, consideration of additional impacts (such as electricity transmission and distribution infrastructure or socio-economic factors) is required to evaluate potential costs and benefits of wide-scale PV in relation to specific policy objectives. This study describes a hybrid GIS spatio-temporal modelling approach integrating probabilistic analysis via a Bayesian technique to evaluate multi-scale/multi-domain impacts of PV. First, a wide-area solar resource modelling approach utilising GIS-based dynamic interpolation is presented and the implications for improved impact analysis on electrical networks are discussed. Subsequently, a GIS-based analysis of PV deployment in an area of constrained electricity network capacity is presented, along with an impact analysis of specific policy implementation upon the spatial distribution of increasing PV penetration. Finally, a Bayesian probabilistic graphical model for assessment of socio-economic impacts of domestic PV at high penetrations is demonstrated. Taken together, the results show that integrated spatio-temporal probabilistic assessment supports multi-domain analysis of the impacts of PV, thereby providing decision makers with a tool to facilitate deliberative and systematic evidence-based policy making incorporating diverse stakeholder perspectives.

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