In interconnected microgrids, facade thermal photovoltaics (TPVs) systems have to be efficiently scaled and allocated for cost-effective building energy consumption and network operation. This paper aims at defining pertinent innovative solutions for reducing the undesired severe voltage dips and minimizing the relevant total costs of the PVs allocation within interconnected microgrids. To optimally place and size the TPVs, different meta-heuristic optimization tools are considered. Dealing with several scenarios of loads and solar energy output uncertainties, the ability of the novel modified meta-heuristic optimizer based on coronavirus herd immunity optimizer (CHIO) to capture a global optimal solution is evaluated. Using Matlab numerical simulations, fair comparison with grey wolf optimization, particle swarm optimization, arithmetic optimization algorithm, and chimp optimization algorithm is presented. The coronavirus herd immunity optimizer tool surpasses the other algorithms in terms of fulfilling the objective function, convergence, and the execution time for the large-scale 295–bus system, which is established of the interconnected IEEE 141–bus, IEEE 85–bus, and IEEE 69–bus subsystems. With the flexible penetration of the building facade TPVs, the voltage profile at all buses is significantly improved. Regarding the overall operational expenses, the CHIO is deemed applicable, replicable and efficient. When compared to the grey wolf optimizer, the CHIO reports expenses of 18.8M$ with savings of 59.67%. The operational voltage level of the studied distributed network is maintained properly by a resilient cluster of 491 clean energy buildings with each having facade area of 200m .