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Abstract To support the ongoing transition towards smart and decarbonised energy systems, energy models need to expand their scope and predictive capabilities. To this end, this study proposes a multi-layer modelling methodology that soft-links (i) a stochastic bottom-up load curves estimation model, (ii) a technology-rich energy system optimisation model (Calliope) and (iii) a Multi-Regional Input-Output model (Exiobase v.3), and applies it to investigate the economic and environmental consequences entailed by a massive replacement of traditional gas-fired kitchens with induction kitchens within the Italian residential sector. Two scenarios are considered for the analysis: (i) business as usual (BAU, 2015 energy system configuration), and (ii) national energy strategy (SEN, configuration prospected in 2030). The results show how the intervention produces positive net effects on the primary energy balance of the energy sector only when sustained by adequate shares of renewables, as in the SEN (−1.5 TWh∙y−1); otherwise, increased operation of fossil-fuel plants offsets gas savings (BAU, +2 TWh∙y−1). Nonetheless, feedbacks on other productive sectors entail additional energy consumption and emissions, thus counterpoising positive effects obtained within the energy sector even in the SEN scenario. Still, higher renewables penetration reduces overall additional emissions from 2.07 Mton∙y−1 for BAU to 0.88 Mton∙y−1 for the SEN. Highlights • A multi-layer methodology to link energy models and input-output models is proposed. • Italian electricity demand due to massive electrification of cooking devices is assessed. • Change in the Italian power production mix is assessed based on Calliope energy model. • The related economic/environmental implications are assessed with the Exiobase model. [ABSTRACT FROM AUTHOR]

Abstract Rural electricity plans are usually designed by relying on top-down rough and aggregated estimations of the electricity demand, which may fail to capture the real dynamics of local contexts. This study aims at soft-linking a bottom-up approach for short- and long-term forecasts of load profiles with an energy optimisation model in a more comprehensive rural energy planning procedure. The procedure is applied to a small Indian community, and it is based on three blocks: (i) a bottom-up model to project households' electrical appliances, which adopts socio-economic indicators to make long-term projections; (ii) a stochastic load profile generator, which employs correlations and users' habits for assessing the coincidence and load factors; (ii) an energy optimisation model based on OSeMOSYS to find the economic optimum. The simulations show that demand models based on socio-economic indicators lead to more structured and less arbitrary scenarios. The soft-link with the energy optimisation model confirms that when accounting for short- and long-term variabilities of electricity demand together, the optimal capacities and costs can vary up to 144% and 50% respectively. Integrating optimisation tools to bottom-up models based on socio-economic indicators for forecasting electricity demand is therefore pivotal to set more reliable investments plans in rural electrification. Graphical abstract Image 1 Highlights • A bottom-up model for local electricity demand in rural India is revised. • A stochastic model accounts for short-term uncertainty. • Using socio-economic indicators leads to less arbitrary demand scenarios. • A soft-link with a long-term rural energy optimisation model is developed. • The optimal planning is very sensitive to short- and long-term demand variations. [ABSTRACT FROM AUTHOR]