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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.

Abstract 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.

Abstract 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.

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.

Abstract The performance indicators of Improved Cook Stoves (ICSs) for Developing Countries are commonly evaluated and compared using the arithmetic average of replicated tests performed using a standardized laboratory-based test, commonly the Water Boiling Test (WBT). Possibility theory is here employed to examine energy data retrieved from the WBT-based literature regarding the results of laboratory tests on ICSs and traditional Three-Stone Fire (TSF) stoves; fifty-seven comparisons of stoves are analysed. Chebyshev and uniform possibility distributions are employed to represent energy data affected by epistemic uncertainty. The extension principle of fuzzy set theory is applied to obtain possibility distributions of the saving of fuel use parameter for each comparison of cookstoves. The results indicate that at 90%, 95% and 99% degree of confidence, only 22.22%, 15.00% and 15.00% of all the supposed “improved” stoves emerged respectively as real ICSs at most, while the percentage of “improved” stoves obtained by considering the mean values of the WBT is among 3 and 6 times higher than the percentage resulted by taking into account the epistemic uncertainties. The work suggests how neglecting intrinsic uncertainties of tests’ results might lead to misinterpret and report non-comprehensive information about ICSs’ thermal energy performance, and to reveal some concerns about their effective improvements over traditional devices.

Abstract 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.

Abstract 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.

Abstract 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.