Data-driven seasonal scenario generation-based static operation of hybrid energy systems

Abstract

Integrating intermittent wind and solar energy into hybrid energy system has introduced significant operational uncertainties. This paper develops a static operation model incorporating biomass gasification-based combined heat and power as a coupling center based on conceptual utility grid-connected real data in Sacramento, California. This study involves generating typical scenarios with seasonal characteristics and demand correlations to capture key input parameters accurately. Subsequently, the Newton-Raphson method was developed to calculate the energy flow within these scenarios. Simulation results demonstrate the proposed method achieves over 70 % construction accuracy across different seasonal scenarios. The economic results that the winter electricity loads increased by 44.8 % compared to summer, with corresponding rises in gas and heat loads by 360.6 % and 372.3 %, respectively, resulting in the hybrid energy system economic cost increase of 58.9 %. These results confirm the model's robustness in effectively managing intermittent energy sources and addressing the economic impacts of seasonal demand variations.