The Ethiopia energy mix is dominated by hydro-generation, which is largely reliant on water resources and their availability. This article aims to examine the impacts of severe drought on electric power generation by developing a Drought Scenario. OSeMOSYS (an open source energy modelling tool) was used to perform the analyses. The results were then compared with an existing reference scenario called “New Policy Scenario”. The study looked at how power generation and CO₂ emissions would be altered in the future if reservoir capacity was halved due to drought. Taking this into account, the renewable energy share decreased from its 90% in 2050 to 81% in 2065, which had been 98% to 89% in the case of New Policy Scenario. In another case, CO₂ emissions also increased from 0.42 Mt CO₂ in 2015 to 7.3 Mt CO₂ in 2065, a 3.3 Mt CO₂ increase as compared to the New Scenario. The results showed how a prolonged period of drought would reduce the river flows and lead to an energy transition that may necessitate the installation of other concurrent alternative power plants. The study suggested ways to approach energy mix, particularly for countries with hydro-dominated power generation and those experiencing drought.

Intermittent power interruptions and blackouts with long outage durations are very common, especially on weak distribution grids such as in developing countries. This paper proposes a hybrid photovoltaic (PV)-battery-system sizing optimization through a genetic algorithm to address the reliability in fragile grids measured by the loss of power supply probability (LPSP) index. Recorded historical outage data from a real stochastic grid in Ethiopia and measured customer load is used. The resulting hybrid-system Pareto solutions give the flexibility for customers/power utilities to choose appropriate sizes based on the required reliability level. To evaluate the sizing solutions’ robustness, this work considers and compares grid outage modeling through two different approaches. The first is a Markov model, developed to be minimally implemented with limited outage data available. The second is a Weibull model, commonly used to describe extreme phenomena and failure analysis. It is more faithful in reproducing the dispersion of outage events. Using these models, the effectiveness and performance of the PV-battery system is verified on a large number of simulated outage scenarios, to estimate the real performance of the optimized design. It leads to a more accurate evaluation of the behavior of a renewable power system to a weak and unreliable electrical grid.

This paper proposes a photovoltaic-battery storage system (PV-BSS) to improve the reliability of a power distribution network with frequent outages, encountered in many developing countries. The system sizing methodology takes into account the loss of power supply probability (LPSP) and the cost of energy. A genetic algorithm is used for achieving improved reliability with an optimal cost of energy by determining the Pareto solutions set. A detailed outage survey characterized by the number and duration of outages experienced by a typical customer on a selected weak distribution system is examined. For this study, real load demand and outage data have been recorded for a university building in Ethiopia. Moreover, long-term outage data was analyzed and studied to understand the overall outage situation, which clearly showed its randomness. The proposed PV-BSS helps to reduce the loss of power and achieve a reliable power supply with zero LPSP for the case considered. Currently, without any standby and backup system, the building has a poor power supply with an erratic outage of about 6% LPSP.

Amélioration de la fiabilité de l'alimentation électrique par le développement d'une solution multi-sources sur un réseau électrique faible: Les pannes de réseau stochastiques, fréquentes et de longue durée, sont très courantes dans les pays en développement, ce qui pose un problème insupportable de fiabilité de l'alimentation électrique. Les clients sont souvent obligés d'utiliser des générateurs diesel pour surmonter ces pannes imprévisibles et récurrentes. Cette thèse propose un système multi-source solaire photovoltaïque et batterie intégré à un réseau faible basé sur des données de pannes stochastiques enregistrées et un profil de charge. L'objectif de dimensionnement est de trouver le meilleur compromis entre les contraintes technico-économiques, d'assurer la continuité de l'approvisionnement (réduction des pannes) et la minimisation du coût de l'énergie, ce qui met en œuvre une optimisation de dimensionnement multi-objectifs pour obtenir un ensemble de solutions Pareto optimales. Ce travail porte également sur la modélisation et la simulation stochastiques des pannes de réseau. Deux modèles présentant les avantages d'une mise en œuvre minimale sans nécessiter de données détaillées sur les pannes et la capacité de prendre en compte les événements extrêmes dans les pannes sont développés. Le modèle de Markov (matrice de transition de Markov) est capable de capturer les répartitions jour-nuit démontrées dans le test de périodicité. Le modèle de Weibull est capable de prendre en compte de manière précise les événements extrêmes des pannes tels que les durées de pannes longues mais moins fréquentes. Les résultats nous permettent également de tester l'efficacité et la performance du système multi-source (PV-batterie) conçu sur un grand nombre de profils de pannes de scénarios potentiels réalisables pendant la durée de vie du système renouvelable.