Two-stage optimization for collaborative operations of an integrated energy system maximizing recovery efficiency of waste heat

• A proposed two-stage optimization can improve IES collaborative operation. • A proposed operation strategy improves energy efficiencies. • Increasing renewable energy penetration can improve waste heat recovery efficiency. • The recovery efficiency of waste heat is improved up to 91.13%. A reasonable configuration of all IES devices and an optimal operation strategy are critical to exploiting the potential of collaborative operation. Therefore, this study proposes a two-stage optimization strategy that synthesizes the two abovementioned points to exploit the potential of IES collaborative operation. The optimization strategy simultaneously enhances the utilization efficiency of gas-fired electricity and waste heat under multi-energy coordination and tackles the uncertainties associated with renewable energy and building loads. The strategy consists of an upper-level optimization to configure all IES devices under constraints such as maximum heating and cooling loads using a mathematical model, and a lower-level optimization to enhance the utilization efficiencies of waste heat and gas-fired power, together with solar power and coal-fired power. This efficiency improvement is achieved through a proposed GT-based cooperative operation strategy (GTCOS) that embeds the subsequent electric load strategy. The results show that the proposed two-stage optimization strategy, which integrates an improved multi-objective particle swarm optimization algorithm with GTCOS, achieves average savings of up to 21.51% in primary energy and 27.80% in specific exergo-environmental cost per MWh of energy consumption, compared to an existing study. Adopting the optimal strategy under uncertainty to save energy and costs while reducing carbon emissions, the system achieves a peak penetration rate of 91.89%. Concurrently, waste heat recovery efficiency is improved to 88.17%, and gas-fired electricity is fully utilized. These findings highlight the potential of the proposed strategy to optimize IES collaborative operations in the presence of uncertainties.