Holistic MILP-based approach for rural electrification planning

This study proposes a procedure that can help address the challenge of achieving universal energy access by facilitating the process of rural electrification. Our holistic methodology enables the analysis of electrification processes and tailored for the usage of open-source data, allowing for efficient “green-field” and “brown-field” expansion planning. To apply this methodology to a non-electrified rural area, population distribution, terrain, existing infrastructure, and accessible information from public databases are necessary inputs. This methodology adopts a cluster-oriented approach and utilises multi-parameter cost surfaces to represent terrain in order to perform routing of medium-voltage (MV) and low-voltage (LV) networks for unelectrified communities. The solution from the routing algorithm is enhanced utilising advanced features such as road tracking and pole-sharing. Following the energy demand assessment of each community, both the options of interconnection to the national grid and supplying loads with standalone microgrids were evaluated and compared to define the most cost-effective solution. This study proposes a combination of bottom-up and top-down approaches, where the solution for each community separately, in terms of the optimal microgrids’ generation portfolio and grid routing, is then used as input for an integrated optimisation to obtain a realistic and cost-effective electrification plan. The overall electrification is achieved by solving a nonconvex mixed integer linear programming (MILP) optimisation constrained by electrical parameters, considering the loading of elements and voltage drops along the lines. Finally, the procedure provides an estimate of the overall materials and costs incurred, as well as a georeferenced deployment of electrical equipment. To validate the procedure and demonstrate its feasibility, the approach was applied to the electrification plan of the Butha-Buthe region in Lesotho. The proposed tool is shared on GitHub to support the adoption of a comprehensive electrification approach.