Impact of Growing Renewable Energy Penetration on Optimal Design and Operation of Grid-Connected RIES via a Bi-Level Dynamic Optimization Model

This study extends an established bi-level dynamic optimization framework for grid-connected regional integrated energy systems (RIES) to address the escalating renewable energy penetration (REP) within integrated power systems (IPS). While traditional models treat REP as static, our approach integrates its dynamic growth into both the design and operational scheduling phases, utilizing a genetic algorithm paired with the Gurobi solver. Applied to a case study in Changsha, the extended model is systematically benchmarked against conventional static REP scenarios. The research shows that the introduction of REP growth factors in the optimization model can increase the installation capacity of ground source heat pumps (GSHPs), reduce the installation capacity of combined heat and power units and absorption chillers, and reduce the initial investment of the system by 12.20%. Affected by the difference in equipment capacity configuration, the primary energy consumption and total cost of the grid-connected RIES decrease during the planning period, while the cumulative carbon dioxide emissions show a slight increase. The primary energy consumption and total cost under winter operating conditions were reduced by 2.44% and 2.38%, respectively. In addition, with the increase of REP growth rate in IPS, the reductions in the system’s primary energy consumption, carbon dioxide emissions, and total cost all increase accordingly. Therefore, in macroscopic RIES planning, incorporating dynamic REP reveals a clear trade-off: improving economic and energy efficiency may temporarily increase carbon emissions when renewable penetration is low.