Multi-Time Optimization Scheduling Strategy for Integrated Energy Systems Considering Multiple Controllable Loads and Carbon Capture Plants

Abstract

In response to the dual carbon targets, it is necessary not only to reduce carbon emissions but also to increase the proportion of renewable energy generation capacity, thereby exacerbating the scarcity of flexible resources in the power system. Addressing these challenges, this study proposes an operational optimization framework for an integrated energy system. This system encompasses wind/solar power plants, coal-fired power plants, carbon capture power plants, gas turbines, energy storage systems, and controllable loads, including reducible power loads, transferable power loads, electrolytic aluminum loads, transferable heat loads, and reducible loads. This study employs a system combining carbon capture plants with thermal power stations to supply flexible resources to the integrated energy system while reducing carbon emissions during the generation process of the thermal power units. A multi-timescale optimization scheduling approach is adopted to manage the uncertainties in wind, photovoltaic, and electric/thermal loads within the integrated energy system. The operational costs of the integrated energy system consider the capacity degradation costs of energy storage systems, the solvent degradation costs of carbon capture, and carbon costs. Finally, the cplex solver was used to solve the above model. The simulation results show that the consideration of five controllable loads leads to an increase of 7.22% in the interactive benefits with the power grid; the difference between the complete cost model and the incomplete overall benefits is 94.35%. It can be seen that the dispatching method proposed in this study can take advantage of the dispatching advantages of source-load adjustable resources and achieve the goal of low-carbon economic dispatching of the power system.

Keywords

• renewable energy
• carbon capture
• controllable loads
• multi-timescale scheduling