This study presents a spatially resolved multiphysical framework for a hybrid system combining a photovoltaic (PV) panel and a plant-based microbial fuel cell (P-MFC), known as a biopile, deployed within an IoT-based residential virtual power plant (VPP) in C？te d’Ivoire. The biopile is modeled as a one-dimensional continuous soil medium (thickness d = 0.05 m, N = 21 nodes) solving for six coupled state fields: temperature, volumetric water content, dissolved oxygen, soluble substrate, proton concentration, and planktonic microbial biomass. The comprehensive dynamic system includes 129 state variables, incorporating Nernst equilibrium potentials, Butler-Volmer charge transfer kinetics, Archie’s law for ion transport, Monod-type microbial kinetics, Richards-type moisture transport, and thermal conduction. The quasi-static electrical subproblem is solved analytically using Kirchhoff’s law, providing a closed-form expression for the internal resistance and cell current. The model, validated using high-frequency field data (N > 6000 samples over 5.79 hours) from a residential VPP combining a PV panel and a Costusspicatus biopile selected from among four plant matrices, yielded several results. Of the four proposed configurations: 4S3P, 8S3P, 6S2P and 12S2P, only the 6S2P was validated with measurements of 8.5 V and 0.3 A by the series/parallel topological scaling law without parameter adjustment. The model qualitatively distinguishes the diurnal response between the PV branch and the biopile. A simple calibration of the effective active area with Ae ≈ 0.18 - 0.20 m2 aligns the measured and predicted operating points, although this represents a parametric adjustment rather than an independent validation. The Nernst model systematically overestimates the open-circuit voltage by ΔV = +0.585 V due to anodic passivation and unmodeled mixed-potential losses. This model provides a framework for the topological sizing of biofuel cells and their complementarity with photovoltaics in virtual power plants, but does not yet allow for quantitative prediction of the power delivered under load.
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