TITLE:
Robust Dynamic Electricity Pricing under Uncertainty: A Stochastic-Behavioral Optimization Approach for Senegal
AUTHORS:
Dimitry Diassy, Moussa Touré, Ndeye Thiam, Aly Touré, Fatma Sow, Sokhna Khady Fal, Mamadou Lamine Samb
KEYWORDS:
Pyomo, Stochastic Optimization, Dynamic Pricing, Demand Response, Renewable Energy Integration, Power System Resilience, Developing Countries
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.16 No.4,
April
29,
2026
ABSTRACT: This study develops a stochastic and behavioral extension of a Pyomo-based mixed-integer linear programming (MILP) framework to optimize dynamic electricity tariffs in Senegal under uncertainty. Building on a previously established deterministic model for the 2022-2050 period, the proposed approach explicitly incorporates uncertainties related to electricity demand, fuel prices, renewable availability, and climate stress, while accounting for heterogeneous consumer responses to price signals. Three configurations are compared: a deterministic benchmark (M0), a stochastic model (M1), and a stochastic-behavioral model (M2). Results show that all configurations support a highly renewable transition by 2050, with renewable shares between 78% and 80%, but with different trade-offs between efficiency and robustness. While the deterministic benchmark achieves the lowest average cost under central conditions (68.9 FCFA/kWh), the stochastic-behavioral model provides the best overall performance under uncertainty, combining high renewable penetration (79.5%), competitive costs (≈69.5 - 70.0 FCFA/ kWh), reduced subsidies (≈7.1%), and the highest peak reduction (15.8%). Under adverse scenarios, M2 consistently outperforms the other configurations, demonstrating superior resilience. Sensitivity analysis further identifies storage capacity, gas prices, and demand elasticity as key drivers of system robustness. Overall, the findings highlight that tariff design should be approached as an adaptive policy instrument integrating uncertainty management, demand flexibility, and social protection. The proposed Pyomo framework offers a reproducible tool to support predictive tariff regulation and resilient energy planning in Senegal.