Investing in low-carbon technologies, including light-duty vehicles, is a strategy to decarbonize the residential sector and private mobility. This work aims to assess the parameters driving the economic convenience of battery electric vehicles (BEVs) for a household, and what are the economic implications of BEV adoption on the total cost of the residential energy system in case of house renovation. An archetypal household energy system has been modelled for the Italian context, where strong residential energy efficiency incentives have been put in place in recent years. Adopting a least-cost-oriented energy system optimization model, 33’600 residential energy system configurations have been analysed through an extensive sensitivity analysis carried out focusing on crucial input parameters, classified as behavioural (annual travelled distance, expected ownership time of the car), energy-related (electricity and heating demand, house location, PV installed capacity), and economic (grid electricity price, gasoline prices and incentives on BEV purchase). Results show that integration with PV installation is the parameter most strongly correlated with BEV adoption, followed by annual travelled distance and ownership time. Moreover, results suggest that an increase in electricity prices has a lower impact on disincentivizing BEVs adoption compared to how much an equivalent increase in gasoline prices disincentivizes ICEVs adoption. Valuable insights reveal that, within the range of the Italian average gasoline price, BEV-based energy systems remain competitive. This holds even with a high electricity price, provided a minimum of 3 kW photovoltaic capacity is installed. In light of the ongoing energy crisis in Europe, these findings are promising for BEV adoption, particularly if accompanied by BEV incentive policies.

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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.

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The mass-scale integration of electric vehicles into the power system is a key pillar of the European energy transition agenda. Yet, the extent to which such integration would represent a burden for the power system of each member country is still an unanswered question. This is mainly due to a lack of accurate and context-specific representations of aggregate mobility and charging patterns for large electric vehicle fleets. Here, we develop and validate against empirical data an open-source model that simulates such patterns at high (1-min) temporal resolution, based on easy-to-gather, openly accessible data. We hence apply the model – which we name RAMP-mobility – to 28 European countries, showing for the first time the existence of marked differences in mobility and charging patterns across those, due to a combination of weather and socio-economic factors. We hence quantify the impact that fully-electric car fleets would have on the demand to be met by each country's power system: an uncontrolled deployment of electric vehicles would increase peak demand in the range 35–51%, whilst a plausible share of adoption of smart charging strategies could limit the increase to 30–41%. On the contrary, plausible technology (battery density) and infrastructure (charging power) developments would not provide substantial benefits. Efforts for electric vehicles integration should hence primarily focus on mechanisms to support smart vehicle-to-grid interaction. The approach is applicable generally beyond Europe and can provide policy makers with quantitatively reliable insights about electric vehicles impact on the power system.@en

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 (R² = 0.99 and 0.98). Their accuracy is lower when predicting internal system variables such as installed capacities of PV and batteries (R² = 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.

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Coupling heat and electricity through power-to-heat (P2H) technologies is raising increasing attention. It allows, on the one hand, to substitute traditional heating technologies with highly-efficient heat pumps (HPs), while, on the other hand, exploiting cost-effective thermal storage options (TES) such as hot water tanks, ultimately increasing system flexibility and renewables penetration. Nonetheless, an accurate assessment of the benefits that may be ensured by a country-wide diffusion of P2H technologies is hindered by computational difficulties in representing large numbers of distributed HPs and TES systems within regionalor country-scale energy models. In this work, we simulate large numbers of individual HPs and TES systems and compute the realistic aggregate electricity consumption associated with those. Various relevant regulation logics are simulated, either thermostatically-controlled or aggregator-controlled. For the latter, we show that an equivalent virtual power plant (VPP) representation ensures sufficient accuracy for use in largescale energy models (NRMSE<10%). Finally, we evaluate the flexibility potential ensured by the different P2H configurations considered, by incorporating those into an open energy system optimisation model. We show that flexibility and decarbonisation benefits are achieved in all configurations, although they increase (up to -89.5 GWh/week of primary energy savings) with the degree of 'smartness' and PV-friendliness of P2H operation logics.

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The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines and resulting greenhouse gas emissions reduction. Nevertheless, allowing combinations of multiple flexibility options such as hydro for the long-term, electric vehicles and flexible thermal units for the short-term provides the best solution in terms of system adequacy, greenhouse gas emissions and operational costs.@en

Hybrid microgrids represent a cost-effective and viable option to ensure access to energy in rural areas located far from the main grid. Nonetheless, the sizing of rural microgrids is complicated by the lack of models capable of accounting for the evolution of the energy demand over time, which is likely to occur in such contexts as a result of the modification of users' lifestyles. To tackle this issue, the present study aims at developing a novel, long-term optimisation model formulation, capable of accounting for load evolution and performing suitable investment decisions for capacity expansion along the time horizon. Multiple scenarios of load evolution are considered to evaluate the beneficial effects of this novel approach, through the coupling of the model with a tool for stochastic load profiles generation. The results show how this implementation brings lower Net Present Cost to the project and improved correspondence between actual electricity demand and microgrid sizing. Finally, a sensitivity analysis evaluates the robustness of the approach with respect to input data variability and the Loss of Load parameter.

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With pressing priorities in the development agenda, policy makers in developing countries are in the difficult situation of prioritizing policy actions. Limited government and utility budgets need cost effective solutions to bring the desired development benefits of electrification, health, education and food security among others. Energy access is a prerequisite for economic activity and for human development as interacts in synergy with other development needs. As rural electrification models usually focus on the supply of electricity solely, thermal energy needs, such as cooking and water heating remain unattended and satisfied by non-renewable energy fuels. To this aim, we explore optimal electrification solutions addressing two types of energy demands, electricity and thermal energy demands for cooking. Our model builds on a 3-step electrification methodology proposed by Peña et al. including electricity as a modern source of clean energy for cooking in rural communities. The total investments needed to build and operate the microgrids, including distribution costs, is 332 million USD. This is equivalent to 1129 USD/per inhabitant. This amount does not account however the health and environment benefits that e-cooking can bring to inhabitants in Bolivian low-lands.

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For decades, electrification planning in the developing world has often focused on extending the national grid to increase electricity access. This article draws attention to the potential complementary role of decentralized alternatives – primarily micro-grids – to address universal electricity access targets. To this aim, we propose a methodology consisting of three steps to estimate the LCOE and to size micro-grids for large-scale geo-spatial electrification modelling. In the first step, stochastic load demand profiles are generated for a wide range of settlement archetypes using the open-source RAMP model. In the second step, stochastic optimization is carried by the open-source MicroGridsPy model for combinations of settlement size, load demand profiles and other important techno-economic parameters influencing the LCOE. In the third step, surrogate models are generated to automatically evaluate the LCOE using a multivariate regression of micro-grid optimization results as a function of influencing parameters defining each scenario instance. Our developments coupled to the OnSSET electrification tool reveal an important increase in the cost-competitiveness of micro-grids compared to previous analyses.@en

Designing highly renewable power systems involves a number of contested decisions, such as where to locate generation and transmission capacity. Yet, it is common to use a single result from a cost-minimizing energy system model to inform planning. This neglects many more alternative results, which might, for example, avoid problematic concentrations of technology capacity in any one region. To explore such alternatives, we develop a method to generate spatially explicit, practically optimal results (SPORES). Applying SPORES to Italy, we find that only photovoltaic and storage technologies are vital components for decarbonizing the power system by 2050; other decisions, such as locating wind power, allow flexibility of choice. Most alternative configurations are insensitive to cost and demand uncertainty, while dealing with adverse weather requires excess renewable generation and storage capacities. For policymakers, the approach can provide spatially detailed power system transformation options that enable decisions that are socially and politically acceptable. The planning of highly renewable power systems at any scale involves compromise across diverse stakeholders. We develop a method that generates a variety of spatially explicit, alternative system configurations that can be used to balance techno-economic feasibility with social and political goals. The application of our method to Italy reveals flexibility of choice for decisions like where to locate wind power and whether to invest in particular technologies. Technology substitution and complementarity is evident: solar photovoltaic and battery capacities expand together, as do wind and synthetic gas turbine capacities, all of which must notably increase to replace bioenergy's firm capacity. We also see that highly renewable systems rely on regional interconnectivity but that gas infrastructure is only useful at a fraction of current capacity. Our approach can be similarly applied to examine trade-offs in other national systems, as well as those at district and continental scales. We develop a computational method, which shows that there is a flexibility of choice to manage contested decisions arising when planning highly renewable power systems, such as where to locate wind capacity. Within this decision space, problematic technologies, such as bioenergy, are difficult and costly to replace and are not absolute must-haves. Expansion of PV is a must-have, coupled with battery when designed to cope with low-wind conditions. Carbon-neutral gas turbines can contribute to balancing but with a minor role compared with today's use.@en

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.@en

Robust sizing of rural micro-grids is hindered by uncertainty associated with the expected load demand and its potential evolution over time. This study couples a stochastic load generation model with a two-stage stochastic micro-grid sizing model to take into account multiple probabilistic load scenarios within a single optimisation problem. As a result, the stochastic-optimal sizing of the system ensures an increased robustness to shocks in the expected load compared to a best-case (lowest-demand) sizing, though with a lower cost and better dispatch flexibility compared to a worst-case (highest-demand) sizing. What is more, allowing just a 1% unmet demand enables to significantly improve the cost-competitiveness and the renewables penetration as all the not supplied energy is located in a negligible fraction of the unlikeliest highest demand scenarios.@en

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.@en

Distributed Generation is driving a paradigm shift in traditional power systems, allowing the production of renewable electricity and thermal energy close to the main energy consumption nodes (e.g., buildings). In this sense, thermal networks and electrical grids for Smart districts /cities have a key role, allowing the interconnections of distributed energy resources (renewables, combined heat and power generators, etc storage systems, and loads (electrical and thermal), locally dispatching supply and demand. In the present work, a multi-energy system for a new nearly zero energy and low carbon district near Milan (Italy) is proposed and analysed. After the evaluation of thermal and electrical energy needs of the district, an innovative thermal and electrical multi-energy system is designed with the specific aim to integrate multiple renewable energy sources (i.e., photovoltaics and groundwater energy) and energy storage technologies (i.e., sensible thermal storages), obtaining the best matching with thermal and electrical loads. Final results demonstrate the achievable benefits of the proposed solution, which can be successfully applied in several contexts.@en

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/kWh, a range that is comparable with that of all other cooking options – including firewood – and even more cost-competitive than LPG.

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Countries around the world are preparing to give the last leap to accomplish a 100 % of rural energy access. Nonetheless, country-wide electrification planning requires the analysis of hundreds of un-electrified villages with different social, economical and geographical backgrounds. State-of-the-art planning models typically handle this computationally challenging task relying on highly-simplified technological characterizations, at the expense of a proper estimation of the cost-optimal potential of off-grid technologies, particularly micro-grids. In this paper, we propose a machine-learning method to improve such technological characterization while keeping the computational tractability of the problem under control. Firstly, field surveys from rural un-electrified villages in Bolivia are used as an input for a stochastic load generator model, creating several demand scenarios for a set of different village archetypes; secondly, renewable energy time series for representative locations of Bolivia are created using the NASA database. For each demand and renewables potential combination, a two-stage stochastic sizing model is adopted to obtain the corresponding cost-optimal micro-grid configuration. Finally, these data are used to train a Gaussian process regression with the levelized cost of energy (LCOE) as dependent variable and the daily average demand, renewable energy, and techno-economic characteristics of the components as independent variables. The results show that the trained algorithm is ultimately able to identify the LCOE of microgrids in given conditions, out of the training dataset, with satisfying accuracy and limited computational effort.@en

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.@en

Improved Cooking Stoves (ICSs) represent the most commonly promoted solution to alleviate the burden associated with the use of traditional biomass in a short-term perspective. However, criticism is raising about the methodologies used for assessing their performance, with a particular focus on laboratory-based testing protocols. One of the key weaknesses of current protocols consists in the inaccurate and biased approach adopted for reporting and statistically analysing test results, which can lead to misleading conclusions about the actual improvements ensured by ICSs. This study proposes a robust procedure to deal with the statistical analysis of small sample sizes, and subsequently verify it through its application to an experimental comparison – based on the Water Boiling Test – between three models of stove. The results show that the current practice based on 3 or 5 replicates often produces biases in the analyses, as at least 13 replicates might be needed to achieve reliable results. Moreover, the study shows how the t-test is in most cases improperly applied, while the proposed procedure allows to deal both with normally and of non-normally distributed data sets in a robust way. In one case, the apparent improvement of an ICS model as compared to the three-stone fire, is refuted by the application of our procedure.@en

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.

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Around 2.7 billion people rely on biomass fuelled inefficient devices for cooking and heating. Improved Cooking Stoves are promoted as a means to mitigate the economic, environmental and social implications of this practice. However, their diffusion is hindered by a number of factors, including in particular the lack of agreement on performance evaluation methodologies. Laboratory protocols are designed to give useful indications to cookstoves developers, in order to improve their performance under controlled conditions, while field protocols provide the assessment of real performance of a cookstove in a given context. However, due to high time and finance requirements of the latter, lab results are often used also for stoves selection, also because of a general misunderstanding regarding their correct utilisation. In this work, we provide a review of all lab protocols officially published to date, comparing conceptual and technical aspects. We find that no protocol takes into account all the relevant factors at once. As a result, lab tests carry little information about real field performance, and can be misleading regarding stoves optimisation. Therefore, the analysis reveals the need to define better standards, regarding: (i) repeatability, metrics and statistical analysis of results; (ii) burn sequences calibrated from time to time according to the specific user.@en