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FelixVeaux/Uganda-Solar-Panel-Placement-Per-District

Domaine:

environment and energygeospatial

Type de record:

project
Créateur:
Fel
Hôte:
Linear Optimization with Gurobi to position Solar Panels across district to support the Ugandan Electrical Transformation Plan (ETP) # Optimizing Solar Panel Placement in Uganda: A Linear Programming Approach to Achieve Electrification Goals Linear Optimization with Gurobi to position Solar Panels across district to support the Ugandan Electrical Transformation Plan (ETP) ## Introduction Access to electricity is fundamental for economic development, health, social equality, environment, job creation, and quality of life. In Uganda, 80% of the population does not have access to electricity, mainly due to the limited reach of the network in rural areas. This has led to a rise in micro-grids and individual solar home systems for tasks such as crop irrigation for farmers. Building on this success, the project aims to further decentralize solar energy production so that communities can access a reliable local power source. Increasing solar generation in Uganda can catalyze better healthcare (through vaccine refrigeration and reliable hospital power), higher school enrollment (lighting for evening classes and studying), and economic growth (local businesses and irrigation). It also reduces reliance on bioenergy—benefiting both public health and the environment. By investing in robust solar infrastructure, Uganda and its partners can bridge the rural-urban divide in energy access. Our aim is to guide government bodies, NGOs, social businesses, and international development agencies in making more informed, data-driven decisions on solar infrastructure. ## Placement Plan By employing optimization techniques and spatial analysis tools, we have identified where solar panels will yield the most impact at the lowest cost. This article outlines how a Linear Programming model using Gurobi, combined with ArcGIS mapping, supports strategic solar deployment to meet Uganda’s ambitious electrification targets. To help achieve this, we developed an optimization model that provides: - Location-based recommendations for installing solar panels - Cost estimations that factor in both installation and transportation …

Visit

github.com

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