Result: Hybrid solar chp microgrid optimization: python code framework using real equipment data

This paper presents a dynamic optimization methodology, developed in Python, for hybrid microgrid systems that combine Photovoltaic (PV) generation with Combined Heat and Power (CHP) engines. The proposed method, in contrast to traditional models that depend on theoretical assumptions or fixed software platforms, directly integrates manufacturer-specific data for chillers and CHP engines, thus improving precision in equipment sizing and operational planning. The methodology is supported by case studies conducted in three cities: In August, Alexandria’s maximum cooling load was 935.5 kW with 184.5 kW of excess power, while Kuwait’s cooling demand was higher at 2810.1 kW with 168.6 kW of excess energy. In January, however, 3360 kW were required to meet Calgary’s heating needs. These results demonstrate how the framework can improve system performance in a range of regional and seasonal settings. By combining real-time climate modelling with actual manufacturer data, the study fills a clear gap in the literature. It offers a more practical substitute for multi-software optimization techniques. The model’s precision and pertinence are validated through comparison with industry catalogues. The framework is advised for evaluation using other manufacturers’ datasets and should be expanded to incorporate thermal storage, demand-side management, and battery systems.