Microgrids play a pivotal role in the attempts to achieve universal access to electricity; hence, their accurate planning, which also includes the evolution of the system over the project lifetime, is essential for maximizing the profitability of the investments. This paper proposes an iterative multi-year mixed-integer linear programming (MILP) optimization, in which power-dependent efficiency and capacity degradation of storage as functions of the operative strategy are considered. In this study, the computational burden of such a large problem is significantly reduced by means of an external loop that accounts for the hourly storage efficiency and residual capacity and serves to update the parameters for the MILP optimization of the following iteration. This approach is tested on a case study of a community in Soroti, in central-eastern Uganda. Comparison with the output of a one-shot MILP and with a procedure that overlooks the effects of the dispatching strategy on storage characteristics is shown, highlighting the benefits gained with the proposed algorithm in terms of both simulation time and accuracy of the design.

Optimization analyses are commonly used in microgrids to identify the most efficient and reliable operation of the available energy resources. Unfortunately, most of the times these programming problems rely on input parameters which are not accurately known. In this context, advanced computing paradigms for solving uncertainty optimization problems represent the most promising enabling methodology. These techniques may show their effectiveness during both the dispatch and the pre-dispatch phase, when operators need to solve the unit-commitment and the economic dispatch problems. To this aim, this paper discusses and compares experimentally some promising existing alternatives to deterministic methods to deal with the solution of optimization problems in the presence of data uncertainty.

This paper presents two innovative procedures developed for rural electrification planning. To address the challenges of processing vast geospatial data, handling complex and computationally intensive network design, and making detailed yet accessible economic assessments, this work introduces a Buffering plugin for community identification and a Grid Routing and Cost Allocation plugin for network design and economic assessment, both integrated into the open-source QGIS platform. The first enables the identification of potential electrification zones through dual methodologies, while the second introduces three key processes: hierarchical clustering, a modified minimum spanning tree, and a novel cost allocation methodology that provides village-specific LCOE calculations. Testing in Zambia has proven that this approach is not only effective but also—compared to existing tools—offers significant advantages in terms of computational efficiency and accessibility, while providing practical solutions to large-scale challenges. This synergistic approach enables planners to move from granular geospatial data to actionable electrification decisions through a streamlined process. The analysis covered over 3 million buildings, grouped into 162,142 settlement clusters, and subsequently determined optimal electrification strategies for 3025 villages—40.4% connected to grid extensions and 59.6% to mini-grids—serving a total population of 18 million people.

As of today, universal access to electricity is still a great challenge in developing countries, where grid extension is often a too expensive and time-consuming investment. Isolated systems are an effective solution to address the energy poverty issue. Hybrid microgrids, integrating conventional units and RES (Renewable Energy Sources) generators, are the most suitable option because they exploit resources which are locally available, clean, free and usually abundant. Both the planning and the operation phase are made particularly challenging by the intermittent nature of RES production and by the unpredictability of the demand behaviour and evolution in developing contexts. This work tackles this issue by developing a robust scheduling algorithm and providing a solution which guarantees the requested energy even in the worst possible condition, i.e. maximum demand and minimum RES production. A complete analysis of the reformulation procedure has been performed, in order to describe how to get a tractable counterpart by exploiting the strong duality theorem. The model has been applied to the case study of a missionary centre in Wau, South Sudan. It has been implemented in GAMS and the results have been compared with the output of the deterministic version of the algorithm.