Stochastic Optimal Energy Planning of the Multi-connected Grids by the Presence of Bi-facial PV Panels: Interaction of Micro-nano and Main Grid

Abstract

The increasing greenhouse gas (GHG) emissions from fossil fuel-based energy systems have accelerated the global push toward cleaner technologies. Bi-facial photovoltaic (BPV) panels, capable of capturing solar irradiance from both sides, have emerged as a promising solution due to their higher energy yield and comparable costs to traditional PV systems. This paper explores the integration of BPV panels into a multi-connected grid comprising nano-, micro-, and main grid layers. A stochastic optimization framework is developed to address the uncertainties of solar irradiance. The problem is formulated as a Mixed-Integer Linear Programming (MILP) model and solved using the Augmented Epsilon Constraint (AEC) method in the General Algebraic Modeling System (GAMS) environment. Results demonstrate that incorporating BPV panels reduces microgrid operational costs by approximately 20%, boosts nano-grid profits by about 81%, and cuts emissions by about 10%, highlighting their potential to enhance system efficiency, flexibility, and sustainability.