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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]

Abstract In this article, the economic and environmental implications due to the projected evolution of the power sector in Egypt until 2040 are assessed and discussed. The Reference Energy System (RES) of the Egyptian power sector has been defined and implemented in the Open Source Energy Modelling System (OSeMOSYS), based on two different energy scenarios. To increase the accuracy of the analysis, the discount rate on capital investments for the energy technologies has been imposed as a time dependent exogenous variable. Moreover, the robustness of the obtained results has been tested through a sensitivity analysis on the main exogenous parameters. It is found that Combined Cycles, Wind, and Photovoltaic rooftop systems are compatible technologies to be included in the future Egypt's power generation mix. In particular, based on the abundant Egypt's renewables resources endowments, wind power technology comes first in achieving the proposed target on renewables penetration in the country's generation mix, and it might be a feasible alternative to replace a part of the natural gas share. Moreover, the significant impact of discount rate on capitals on the final results is highlighted: low values of discount rate would skew generation mix to include higher investment cost technologies and vice versa. Highlights • Implications of future energy scenarios in Egypt have analyzed based on OSeMOSYS. • Sensitivity analysis has performed on relevant techno-economic scenario parameters. • Combined cycles, wind and PV are key technologies in future Egypt's power mix. • Efficiency of combined cycles is relevant in reducing power cost and emissions. • Wind technology may replace a part of natural gas share in the electricity mix. [ABSTRACT FROM AUTHOR]

The decarbonisation of residential heat through integration with the power system and deployment of refurbishment policies is at the core of European energy policies. Yet, heat-electricity integration may be challenged, in practice, by the large variability of heat demand across weather years. Current approaches for residential heat demand simulation fail to provide insights about the extent of such variability across many weather years and about the benefits potentially brought about by nearly zero-energy buildings. To fill this gap, this work develops an open-source space-heating demand simulation workflow that is applicable to any country's building stock. The workflow, based on a well-established lumped-parameter thermodynamic model, allows capturing sub-national weather-year variability and the mitigation effects of refurbishment. For Italy, different weather years lead to variations in heat demand up to 2 TWh/day, lasting for several days. Moreover, some weather regimes produce spatial asymmetries that may further complicate heat-electricity integration. The refurbishment of about 55% of buildings constructed before 1975 could substantially mitigate such oscillations, leading to a 31–37% reduction of yearly heat demand, primarily in colder regions. Intra-day heat demand variations, driven by user behaviour, are not substantially impacted by refurbishment, calling for the simultaneous deployment of flexible heat generating technologies. • Heat demand models fail to capture weather variability and refurbishment benefits. • This study provides an open, context-adaptable workflow that allows to do so. • For Italy, large and long-lasting demand variations occur across weather regimes. • Refurbishment can reduce absolute demand by up to 37% and mitigate oscillations. • All such effects vary spatially and must be evaluated at sub-national resolution. [ABSTRACT FROM AUTHOR]