• Energy Research

Rural electrification in remote areas of developing countries has several challenges which hinder energy access to the population. For instance, the extension of the national grid to provide electricity in these areas is largely not viable. The Kenyan Government has put a target to achieve universal energy access by the year 2020. To realize this objective, the focus of the program is being shifted to establishing off-grid power stations in rural areas. Among rural areas to be electrified is Habaswein, which is a settlement in Kenya’s northeastern region without connection to the national power grid, and where Kenya Power installed a stand-alone hybrid mini-grid. Based on field observations, power generation data analysis, evaluation of the potential energy resources and simulations, this research intends to evaluate the performance of the Habaswein mini-grid and optimize the existing hybrid generation system to enhance its reliability and reduce the operation costs. The result will be a suggestion of how Kenyan rural areas could be sustainably electrified by using renewable energy based off-grid power stations. It will contribute to bridge the current research gap in this area, and it will be a vital tool to researchers, implementers and the policy makers in energy sector.

Reducing costs and risks to foster rural electrification in developing countries is one of the major hurdles for researchers, firms and practitioners aiming to develop business with rural minigrids, which are local networks that supply energy demand by exploiting local sources. Predictive rolling-horizon operating strategies have proven to reduce operating costs and better cope with uncertainties in renewable sources and demand than standard priority-list approaches, when forecasts are accurate enough. Their main drawback is the need for more powerful controlling devices, which, however, is becoming acceptable for modern operation purposes. Yet, calculating the optimal design of the minigrid operated with predictive strategies can easily require much larger computational requirements than with priority-list rules. In the present paper, we describe a two-stage procedure to reduce the computational requirements of design methodologies based on predictive strategies. First, an optimization is developed using a priority-list strategy, namely load-following, whose results are used to refine the second-stage optimization based on an advanced rolling-horizon strategy. Results suggest significant reduction in computational requirements, up to 84% with respect to standard predictive approaches, with negligible reduction in the optimality of results.

The present paper proposes and describes a new method, called L2A (listen-to-apprise), conceived of in order to improve the commitment of all the participants involved in an educational process specifically dedicated to sustainability and the green economy. The first stage consists in listening to the students and, when possible, in listening to the environment, the people, and the territory, while, in the second stage, the acquired information is used to exploit everyone’s talents and to achieve the maximum advantage and satisfaction for all. The first phase of L2A is mainly dedicated to listening to the students via the submission of an on-line questionnaire that measures their learning preferences, self-efficacy and sensitivity to the themes of sustainability and the green economy. The investigation has been extended from Italy to Honduras, where the authors are involved in the development of major projects concerning sustainability. The results of the survey have been analyzed by means of standard significance and correlation analysis, and therefore, significant differences among the groups and correlations within each group have been detected. The results have been discussed in order to explain how the L2A method works and how useful and powerful a tool it could be to improve teaching, learning and practical activities.

Artículos en revistas The governments of developing countries struggle to guarantee the universal access to electricity on their territory and 1.2 billion people are still without any service, especially in remote areas. Hybrid mini-grids can be an effective solution since they exploit local renewable resources integrated with energy storage devices, reduce the use of fuel generators, and defer the construction of long and expensive grids until the growth of demand makes it profitable. Off-grid mini-grids are typically operated with simple load-following dispatching strategies, but predictive approaches can provide better performances, although at the expense of additional computational requirements. This paper investigates the benefits of using rolling-horizon dispatching strategies during the mini-grid design stage, also comparing how the optimal size of components is affected by several technical and economical parameters. Moreover, we propose the use of a stochastic sizing procedure that captures the uncertainties related to the load, to the renewable generation, and to the time required for the fuel procurement and delivery. A case study with real load data collected from an existing mini-grid placed in Habaswein, Kenya, is presented and discussed. The optimal sizing of some components turns out to be almost unaffected by the operational strategies, so their preliminary design can be simplified to avoid time-consuming simulations. Conversely, the optimal sizing of the diesel generator and of its fuel tank is strongly related to both the local economic parameters and the operational strategy of the mini-grid, which must be properly simulated. info:eu-repo/semantics/publishedVersion

Abstract The paper discusses a combined experimental–numerical analysis of an innovative solar thermal device to be used as an Integrated Collector Storage (ICS) system providing domestic hot water. In this equipment the collector acts also as a storage unit, without requiring an external vessel. Due to its simple configuration, the ICS device was successfully used in several circumstances, especially in extreme situations such as in post-earthquake tent cities or to reach remote users in Africa. In order to assess the efficiency of this collector, the draw-off process was investigated measuring the value of the mean temperature of the water discharging from the tap as cold water filled the collector. In the present configuration the draw-off is not completely optimised and a detailed analysis was carried out in order to investigate the mixing of cold and hot water in the solar collector during the discharge phase. A series of thermocouples was placed in selected positions around the shield of the collector to investigate the evolution of the near wall temperature. Furthermore, a numerical analysis based on Large-Eddy Simulation (LES) of the mixing process inside the collector was carried out using an open source, in-house, finite-volume computational code. Even if some restrictive hypotheses were made on the thermal boundary conditions and the absence of stratification, the LES results gave interesting findings to improve the collector performance.

This study develops a sustainable housing model for the Mosquitia region of Honduras, aimed at improving the living conditions of indigenous communities and reducing their vulnerability to the effects of climate change. The improved housing efficiency and resilience will contribute to strengthen sanitation and hygiene, improve the living comfort and reduce environmental impact, particularly focusing on preserving the forest biodiversity. The project was developed following the criteria of environmental, technical, social and economic sustainability to propose a shared model, reproducible by the beneficiaries living in different kinds of Mosquitian ecosystems. Increased building efficiency is obtained through optimization of construction techniques and improvement of materials’ performances. The main material of the proposed dwelling is wood, coherently with the “Miskita” tradition; the increase of its durability, which is obtained by proper seasoning and protection from atmospheric agents and parasites, contributes to the optimization of the use of this natural resource and to reducing the impact on deforestation, which threatens forest biodiversity. The data collection campaign, conducted before developing the housing model design and which has aimed at obtaining information on technical and social aspects related to residences’ welfare and health conditions, has highlighted the great importance of improving the construction model. Despite the advance in sanitary conditions and the economic level, the data collection campaign revealed that even the most developed communities amongst those visited have been perpetrating substantial construction errors, which reduce the resilience of structures to extreme natural phenomena, such as tropical storms and hurricanes, which frequently affect the Mosquitia region. This unexpected discovery increased the importance of housing model design in order to correct these improper construction techniques and avoid their application in the newly-started village growth.

With the aim of maximizing profits of specific business applications, economics, and sometimes reliability and environmental constraints, have been widely guiding developers when designing microgrids. However, mathematical indicators alone, yet relevant, may not be able to fully capture the sociopolitical and geographical circumstances under which developers operate, especially in rural areas of developing countries. In this paper, we propose a methodology for obtaining microgrid designs that not only achieves the traditional economic-efficient optimal solution but also suggests multiple design options that increase the eligibility for developers, which can select an option given their particular circumstances. Based on a consolidated heuristic method, Particle Swarm Optimization, our algorithm identifies several design options of the microgrid's components, by using an iterative approach that stores the simulations occurring in each iteration. The results from an illustrative numerical case study highlight that significantly different designs can lead to similar values of the objective function, i.e. investment and operational costs. Our proposed methodology is of particular interest for developers, who have the opportunity to choose among a set of different technological solutions, but similar in economic terms.