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The electrification-based literature reports a limited knowledge about the mechanisms of evolution of electricity demand in off-grid settings, especially in remote contexts of developing countries, due to the lack of robust and appropriate modelling frameworks. Such lack of understanding and modelling endeavour contributes to an inefficient allocation of resources for electrification projects and inappropriate off-grid sizing processes. As a first step towards the development of a more appropriate electricity demand model, we present a comparative study of two approaches for modelling different diffusion mechanisms of electricity connections: system-dynamics and agent-based models. The latter includes the modelling of social network archetypes in the simulation of diffusion processes. We model different scenarios of diffusion and we use them for evaluating the impact on the sizing process of an off-grid hydroelectric system. The results suggest that the structure of the social network can represent a crucial parameter that can impact on timing needed to complete the diffusion of electricity access – from few months to even >10 years. This affects the sizing process and the long-term sustainability of the power system, leading to variation of the hydroelectric capacity and the battery size up to around 55% and 100%, respectively. Our results indicate that the agent-based approach allows a more diversified representation of diffusion processes, but the limitations and scarcity of data can be an obstacle to their prompt application for energy application in unelectrified areas. On the contrary, system-dynamics can represent a more appropriate method since it requires less quantitative data and it provides a more structural and holistic modelling framework for conceptualising and formulating in a the determinants and complexities affecting the evolution of electricity demand in unelectrified areas. • Innovation diffusion theory is used to define local off-grid electricity access scenarios. • Agent-based modelling and system-dynamics approaches are compared. • The modelling of social interactions can impact on diffusion mechanisms. • The energy optimisation process is affected by the networks structure as diffusion processes get more complex. • System-dynamics can provide a more holistic understanding of the energy-development nexus. [ABSTRACT FROM AUTHOR]

Abstract Electric cooking (or e-cooking) based on renewable sources and highly-efficient cooking devices could represent a sustainable and reliable option to achieve the universal access to clean cooking facilities by 2030. Still, the techno-economic feasibility of e-cooking has never been evaluated through (i) a high-resolution assessment of the electric and cooking load profiles, coupled with (ii) a least-cost energy system optimisation for the related electricity supply. Hence, the present study aims at accurately investigating the techno-economic potential of a fully-renewable solar micro-grid, ensuring an integrated access to electricity and clean cooking, based on two representative case studies in Tanzania, namely: (i) a residential case study, and (ii) a community-service case study. Bottom-up stochastic load profiles are generated for the two contexts by expanding the existing LoadProGen model with a novel complementary algorithm for the computation of high-resolution cooking loads. The cost-optimised results prove the cost-competitiveness of e-cooking, especially for the community-service case, where it would require only an additional 17% capital investment due to the pre-existence of other energy-intensive activities. Moreover, the range of Levelised Cost for Cooking a Meal (LCCM) obtained for e-cooking considering the different scenarios lies within 0.16 ÷ 0.70 USD/meal, a range that is comparable with that of all other cooking options – including firewood –, and even more cost-competitive than LPG. Highlights • We propose a novel high-resolution model for electric cooking load profiles. • We couple electric cooking profiles with PV-based micro-grid optimisation. • We prove that micro-grid-based e-cooking is cost-competitive with other options. • Effect on system sizing is limited if other energy-intensive appliances are present. [ABSTRACT FROM AUTHOR]

As it emerges from the literature, electricity access in rural contexts is deeply intertwined with socioeconomic dynamics. However, the advent of a reliable and sufficient source of electricity is not the sole driver that might contribute to local development. Indeed, complementary activities might have a crucial role in sustaining the development of rural communities as well as the electricity access. The current research addresses the lack of counterfactual scenarios in which the impact of complementary activities on electrification projects can be investigated. The authors introduce the case study of Matembwe village, a rural community in the Njombe region of Tanzania. The data collection includes the electricity consumption, number of electricity connections, and number of income-generating activities in a timespan ranging from 1989 to 2015. The analysis is based on system dynamics. The study considers different scenarios representing the dynamics related to the following complementary actions: access to market measures, access to credit measures, and access to usable skills. On the one hand, the study reveals that the effectiveness of the considered complementary actions is limited except from the access to microcredit which fosters an increase in electricity connections by 17%. On the other hand, both access to microcredit and the starting up of a local cooperative by CEFA Onlus that reinvests its profits in the local market impact the socio-economic dimension by 69% and 22%, respectively. [ABSTRACT FROM AUTHOR]

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]