This paper presents the service value method (SVM) as a novel means to gather and interpret end-user needs, aspirations, and contextual factors to improve engineering design practice of energy access systems for the Global South. The method adopts a service-oriented approach and consists of a rapid and effective field exercise to gather qualitative and quantitative data from end users in focus groups. This exercise is suitable for enabling end-user participation in Global South contexts. The data are interpreted as service maps that capture end-user preferences to inform tradeoffs of different design criteria, guiding the preliminary design of the energy system. The method ensures end-user needs and contexts are integrated into the design process early on. A case study is presented, where the SVM was used to design solar nanogrids in Kenya and Bangladesh.

In this paper, power losses of a multiport DC – DC converter are analysed. The converter has four bidirectional ports and consists of a triple active bridge and four half bridge converters. As a result, sixteen different operating modes, defined by all combinations of power flow direction, are possible. The converter power losses and resulting efficiencies vary widely with operating conditions. This paper presents methods to accurately calculate the efficiency of the four-port converter in each operating mode using component datasheet information, taking into account losses in the transformer of the triple active bridge converter, passive components and semiconductors. A 200 W converter prototype is used to experimentally validate the loss model for one specific operating mode.

Due to various weather perturbation effects, the stochastic nature of real-life solar irradiance has been a major issue for solar photovoltaic (PV) system planning and performance evaluation. This paper aims to discover clearness index (CI) patterns and to construct centroids for the daily CI profiles. This will be useful in being able to provide a standardized methodology for PV system design and analysis. Four years of solar irradiance data collected from Johannesburg (26.21 S, 28.05 E), South Africa are used for the case study. The variation in CI could be significant in different seasons. In this paper, cluster analysis with Gaussian mixture models (GMM), K-Means with Euclidean distance (ED), K-Means with Manhattan distance, Fuzzy C-Means (FCM) with ED, and FCM with dynamic time warping (FCM DTW) are performed for the four seasons. A case study based on sizing a stand-alone solar PV and storage system with anaerobic digestion biogas power plants is used to examine the usefulness of the clustering results. It concludes that FCM DTW and GMM can determine the correct PV farm rated capacity with an acceptable energy storage capacity, with 36 and 46 rather than 1457 solar irradiance profiles, respectively.