Search

In the context of last-mile electrification of rural communities, stand-alone solutions like Solar Home Systems are often considered an equivalent option for access to electricity as other technologies, such as minigrids and connection to the main grid. In this work, the authors question this paradigm and propose an alternative approach that takes into account the limited service, and in turn, limited development possibilities, that Solar Home Systems grant to their users. A monetary penalty is applied to this technology based on the missed services. The conceived approach is applied to a case study in the district of Cochabamba, Bolivia, thanks to the cooperation with a local NGO. Results show how the inclusion of shadow costs influences the optimal electrification strategy in terms of users connected to the main grid by up to 33%, highlighting how standard approaches tend to overlook key aspects of access to electricity linked to the possibility of development. • Different electrification strategies are not equivalent in adequacy of service. • Least-cost methods cannot effectively compare grid connection and Solar Home Systems. • We define a method to make technologies economically comparable through shadow costs. • A two-stage MILP is applied to the case study of Omereque, Bolivia. • Through this methodology more equitable electrification planning can be achieved. [ABSTRACT FROM AUTHOR]

Electricity is crucial for each country's economic and social development. More than half of the population in Sub-Saharan African (SSA) lack access to energy, which has dire consequences for the local population's living conditions, as well as affecting the local economy and increasing poverty. Renewable energy-based mini-grid (REBM) systems are the promising solution to mitigate the issue of energy access in rural areas, however, the expansion of renewable energy (RE) systems and the rate of energy access are both advancing at a slow pace. This study used PESTLE analysis (Political, Economic, Social, Technological, Legal, and Environmental) in the context of Mozambique, a SSA country, to identify the most important categories and factors to support decision-making processes by breaking down existing barriers and hurdles in the energy sector enabling them to plan and implement measures, subdivided in primary, secondary and tertiary for the development of REBM in remote settlements. Even though PESTLE analysis is rather often worldwide applied to the energy sector, its application to the Sub-Saharan context is rare, while its application to the Mozambique energy sector is absent in scientific literature. Besides, within our study, energy experts from different professional backgrounds and nationalities have been interviewed using an online questionnaire: 62 energy experts (48 Africans, and 14 Non-African) from 14 different countries assessed the categories and factors. The experts questioned identified the political category as the most crucial for policy-makers to prioritize, with the top three global weighting factors being, Clear Government policies (plan, regulation, priority, strategy) 5.79%, National financial scheme for RES investments 4.93%, and cost of investment 4.86%. The findings can support policy-makers breakdown barriers in the energy sector, planning and implementing measures for the REBM development, and contribute to achieving the SDG7 target. • Determine the categories that should be prioritized (PESTLE analysis). • Factors to promote the development of renewable energies. • Energy expert opinions for the development of mini-grids. • Propose adequate and suitable mini-grid for rural electrification. • Propose recommendation to policy-makers to mitigate renewable energy barriers. [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]

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]