This study develops a Pyomo-based Mixed Integer Linear Programming (MILP) model to optimize electricity tariffs in Senegal, aiming to design a framework that is economically efficient, socially equitable, and environmentally sustainable. The model integrates generation, storage, and dynamic pricing mechanisms into a unified optimization structure covering the period 2022–2050. Five tariff scenarios are simulated - Reference, Progressive, Feed-in Tariff, Static Hybrid, and Dynamic Hybrid -allowing a comparative assessment of their technical and financial performance. Results demonstrate that the Dynamic Hybrid scenario achieves the most favorable outcomes. By 2050, renewable energy reaches 80% of the total generation mix, while the average cost of electricity decreases by 18% (from 83.8 to 68.9 FCFA/kWh). Public subsidies fall dramatically, from 27.5%to 6.8% of sector revenues. Dynamic hourly pricing reduces peak demand by 12–15%, limits reliance on thermal generation, and improves system flexibility through expanded energy storage (10% of the mix by 2050). Moreover, the social lifeline tariff (65 FCFA/kWh for the first 50 kWh/month) remains fiscally sustainable, ensuring protection for low-income households. Overall, the study highlights that dynamic tariff optimization, enabled by open-source algorithmic tools such as Pyomo, can serve as a strategic instrument for predictive regulation and sustainable energy governance. Policy recommendations are proposed for institutional strengthening, data-driven tariff setting, and regional integration within ECOWAS, positioning Senegal as a potential model for resilient energy transition in West Africa.
Diassy et al. (Tue,) studied this question.