## Spatio-temporal analysis of PV diffusion patterns: an integrated neural networks and agent-based model

Ali Alderete Peralta, Cranfield University Photovoltaic (PV) panels offer significant potentials for contributing to the UK’s energy policy goals relating to decarbonisation of the energy system, security of supply and affordability. The substantive drop in the cost of panels since 2007, coupled with the introduction of the Feed-in Tariff (FiT) Scheme in 2010, has resulted in a rapid increase in installation of PV panels in the UK from 16.1MW in 2010 January to 12.4GW by 2017 December. Yet, spatial and temporal diffusion of PVs show significant differences across the UK. By creating reverse flows on the networks, especially at low voltage distribution networks, domestic PVs present a key challenge for network operators to manage the grid such that there is enough capacity and voltage headroom available to accommodate these flows. That’s why understanding spatio-temporal diffusion of PVs can provide valuable insights to both network operators and policy makers with a view to predict and shape their future deployment. To date, different approaches have been used for analysing PV diffusion process, including (i) spatial regression, (ii) agent-based modelling (ABM) and (iii) Read more…

Categories: Academic Papers, Energy modelling

Tags: Energy Consumers - Domestic, Energy Distribution, energy modelling, Energy policy, solar

AlderetePeralta_SPATIO-TEMPORAL_ANALYSIS_OF_PV_DIFFUSION_PATTERNS.pptx*1.79 MB*Peralta-Spatio-temporal-analysis-of-PV-diffusion-patterns.pdf

*503.89 KB*

Sep

2018