A multi-objective planning method for multi-energy complementary distributed energy system: Tackling thermal integration and process synergy

Abstract

This study proposes a multi-objective optimization methodology for planning multi-energy complementary distributed energy systems considering process synergy and thermal integration. The process integration technique is integrated into the Energy Hub model to deal with the multi-process synergy and temporal source-load matching. The system design and dispatch strategy are optimized by an augmented ε-constraint method with three objectives (economics, carbon emission, and fossil fuel consumption), and then the optimal tradeoff solution is identified by the Technique for Order Preference by Similarity to an Ideal Solution. Moreover, a novel multi-energy complementary distributed energy system is developed, which includes comprehensive utilization of solar energy (photovoltaic, photothermal, and thermochemical) and middle-low temperature heat utilization technologies, as well as hybrid energy storage technologies. Finally, a case study located in Beijing is selected as an illustrated example. The obtained single-objective optimization solutions and Pareto optimal solutions are further analyzed and compared in terms of system configuration, hourly/yearly energy balance, and thermal integration condition. The results show that the multi-energy complementary distributed energy system presents an economic benefit (reducing 25% of the annual total cost) compared to a gas turbine-based integrated energy system. Considering thermal integration contributes to 5.13% of the cost reduction. The configuration of the energy storage devices will reduce 18% energy supply cost, 9% fossil fuel consumption, and 42% carbon emission with the storage devices' boundary increase from 2 MWh to 60 MWh. Moreover, the optimal design of the system provides a reference for decision-making and a basis for flexible operation. The annual total cost, carbon emission, and fossil fuel consumption of the optimal solution in the Pareto frontier are 8.19 million CNY, 2.91 kt CO2-eq./year, and 18.4 GWh, respectively.