"background": "Power distribution networks in sub-Saharan Africa face persistent challenges with reliability and technical losses, yet robust field methodologies for evaluating equipment interventions are lacking. Existing studies often rely on modelled or aggregated data, which may not capture real-world performance under local operating conditions. ", "purpose and objectives": "This article presents a novel methodological framework for conducting a randomised field trial to empirically evaluate the impact of specific equipment upgrades on network reliability and yield (energy delivered) within a national utility. The objective is to establish a rigorous, replicable protocol for causal inference in a live distribution network. ", "methodology": "The methodology employs a cluster-randomised design, assigning feeder sections to treatment (equipment upgrade) or control groups. Key performance indicators, including System Average Interruption Duration Index (SAIDI) and energy delivered, are monitored via smart meters and outage management systems. The primary analysis uses a difference-in-differences model: Y{it = \0 + \1 + \2 + \3 (\) +, where inference is based on cluster-robust standard errors. ", "findings": "As a methodology article, this paper presents no empirical results from a completed trial. However, the proposed design is shown through simulation to be adequately powered (≥80%) to detect a minimum detectable effect of a 15% reduction in SAIDI, assuming an intra-cluster correlation coefficient of 0. 1. ", "conclusion": "The outlined randomised field trial methodology provides a technically sound and operationally feasible framework for obtaining high-quality evidence on the efficacy of distribution network interventions in contexts similar to Kenya. ", "recommendations": "Utilities and researchers should adopt randomised designs for large-scale infrastructure evaluations to move beyond observational studies. Pilot phases are recommended to refine data collection logistics and validate the proposed clustering strategy before full trial deployment. ", "key
Mwangi et al. (Wed,) studied this question.