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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]

Laboratory protocols based on water heating procedures represent the most widespread tool for the evaluation of Improved Cooking Stoves (ICSs) thermal performance. Nevertheless, the performance of the cooking system can vary substantially when the boundary conditions – ambient conditions, burn sequence – differ from those experienced in a specific lab. Consequently, we developed and experimentally validated a Cooking Stoves Thermal Performance Simulator (Cook-STePS), based on a 1-D heat and mass transfer model and implemented in an Excel ® and VBA environment, in order to provide additional information about how the performance of a cooking system would change, in selected conditions, as compared to the baseline laboratory performance. The study shows how the tool can be effectively applied to a generic cooking system through a two-steps procedure, based on (i) a preliminary set of experimental tests in controlled conditions and (ii) the simulation of performance in the desired conditions. [ABSTRACT FROM AUTHOR]

Russia's invasion of Ukraine fuelled an energy crisis, which considerably impacted Europe given its heavy reliance on Russian natural gas imports. This study uses an ensemble of four global integrated assessment models, which are further soft-linked to two sectoral models, and explores the synergies and trade-offs among three approaches to living without Russian gas in Europe: (a) replacing with other gas imports, (b) boosting domestic energy production, and (c) reducing demand and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking further fossil-fuel lock-ins, despite featuring the lowest gas price spikes and potentially reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production on the other hand would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, could offer considerable space for further emissions cuts at the lowest power-sector investment costs; nonetheless, an energy efficiency-driven strategy would also risk relocation of energy-intensive industries, an aspect of increasing relevance to EU policymakers. • Three 'corner' strategies to replacing Russian natural gas in the EU are explored. • EU climate targets can still be reached in all three strategies, but costs increase. • Promoting energy efficiency or domestic renewables can boost end-use decarbonisation. • An 'energy efficiency first' response may save millions € in annual electricity costs. • Southern Europe is the most vulnerable to high electricity prices across strategies. [ABSTRACT FROM AUTHOR]

Energy access projects in remote off-grid areas would benefit from the adoption of a multi-energy system perspective, addressing all energy needs – not only lighting and power appliances, but also water-heating and cooking – by means of a mix of energy vectors. However, multi-energy analyses in remote areas are hindered by a lack of models allowing for the generation of multi-energy load profiles based on interview-based information characterised by high uncertainty. This study proposes a novel open-source bottom-up stochastic model specifically conceived for the generation of multi-energy loads for systems located in remote areas. The model is tested and validated against data obtained from a real system, showing a very good approximation of measured profiles, with percentage errors consistently below 2% for all the selected indicators, and an improved accuracy compared to existing approaches. In particular, some innovative features – such as the possibility to define and modulate throughout the day appliances' duty cycles – seem to be determinant in marking a difference with previous approaches. This might arguably be even more beneficial for case studies characterised by a larger penetration of appliances that are subject to complex and unpredictable duty cycle behaviour. • A model for generating load profiles for systems located in remote areas is designed. • The model is stochastic, open-source and conceived for multi-energy analyses. • Innovative features allow to model also complex and variable duty-cycles. • Validation against measured profiles proves consistent model accuracy. • Open-source nature allows customisability and context adaptability. [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]

Thanks to their modularity and their capacity to adapt to different contexts, hybrid microgrids are a promising solution to decrease greenhouse gas emissions worldwide. To properly assess their impact in different settings at country or cross-country level, microgrids must be designed for each particular situation, which leads to computationally intractable problems. To tackle this issue, a methodology is proposed to create surrogate models using machine learning techniques and a database of microgrids. The selected regression model is based on Gaussian Processes and allows to drastically decrease the computation time relative to the optimal deployment of the technology. The results indicate that the proposed methodology can accurately predict key optimization variables for the design of the microgrid system. The regression models are especially well suited to estimate the net present cost and the levelized cost of electricity (R 2 = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R 2 = 0.92 and 0.86). A least-cost path towards 100% electrification coverage for the Bolivian lowlands mid-size communities is finally computed, demonstrating the usability and computational efficiency of the proposed framework. • A novel methodology to create surrogate models for energy planning is presented. • Gaussian regression is compared to multi-variable regression. • The surrogate models for Economic indicators have a high prediction accuracy. • PV and Batteries installed capacity have lower prediction accuracy. • Surrogate models are created and coupled to a rural energy planning tool. [ABSTRACT FROM AUTHOR]

Integrated systems modelling has provided insights on pathways for the sustainable use of energy, land and water resources. Among the existing modelling tools, some have been widely used to engage policy makers, due to their open source and basic structure. Yet, in the attempt of simplifying the representation of integrated systems, essential features significantly influencing the dynamics between systems may have been left aside. This study proposes an improved formulation of the Open Source Energy Modelling System (OSeMOSYS), for a better representation of energy and resource storage processes. In particular, the focus of this work is the storage losses introduction for both dams for hydropower generation and batteries for electricity storage. The modifications were applied to a case study representing key features of both a developed and developing country. The results highlight that, with low additional computational effort, a much more accurate representation of the storage technologies can be achieved. Despite the introduction of losses, renewable energy technologies tend to have a high penetration in the future energy mix thanks to storage applications that remedy their unpredictability and seasonality. • An improved storage formulation in the OSeMOSYS modelling framework is proposed. • Storage losses for water dams and batteries have been implemented in OSeMOSYS. • The improved formulation has been tested and verified through the Atlantis case study. • An accurate storage representation can be performed with few computational effort. [ABSTRACT FROM AUTHOR]

Efforts towards ensuring clean and affordable electricity for all have been progressing slowly in rural, off grid areas of developing countries. In this context, hybrid microgrids may offer reliable and potentially clean electricity for isolated locations. Nevertheless, the process of planning and operation of these systems faces several challenges, often due to the uncertainties related to the renewable resources and to the stochastic nature of electricity consumption in rural contexts. This paper tackles this problem and contributes to the literature in bridging the gap between field practices and two-stage stochastic modeling approaches by identifying an open-source modeling framework which is then applied to real local data. As reference case-study, we consider a microgrid built in 2015 in Bolivia. Overall, the optimal system results from a compromise between the Net Present Cost, the peak capacity installed and the flexibility (to balance variable generation). Different approaches to size isolated microgrids are tested, with the conclusion that methods accounting for the uncertainty in both demand and renewable generation may lead to a more robust configuration with little impacts on the final cost for the community. • The historical monitoring data from a remote PV-diesel microgrid in Bolivia for rural electrification is presented. • A novel two-stage optimization framework is developed for the robust design of such systems under uncertainty. • Components models and performance curves are derived from the monitoring data. • Among different model formulations, stochastic MILP models generate the most robust system configuration. • Recommendations are formulated regarding the optimal configuration with respect to the existing system. [ABSTRACT FROM AUTHOR]