Abstract
Sustainable energy policies are becoming of paramount importance for our future, shaping the environment around us, underpinning economic growth, and increasingly affecting the geopolitical considerations of governments world-wide. Renewable energy diffusion and energy efficiency measures are key for obtaining a transition toward low carbon energy systems.
A number of policy instruments have been devised to foster such a transition: feed-in-tariffs, tax exemptions, fiscal incentives, grants. The impact of such schemes on the actual adoption of renewable energy sources is affected by a number of economic and social factors.
In this paper, we propose a novel approach to model the diffusion of residential PV systems and assess the impact of incentives. We model the diffusion’s environment using an agent-based model and we study the emergent, global behaviour emerging from the interactions among the agents. While economic factors are easily modelled, social ones are much more difficult to extract and assess. For this reason, in the model we have inserted a large number of social parameters that have been automatically tuned on the basis of past data. The Emilia-Romagna region of Italy has been used as a case study for our approach.
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Iachini, V., Borghesi, A., Milano, M. (2015). Agent Based Simulation of Incentive Mechanisms on Photovoltaic Adoption. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_11
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DOI: https://doi.org/10.1007/978-3-319-24309-2_11
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