The mutual benefits of renewables and carbon capture: Achieved by an artificial intelligent scheduling strategy

Abstract Renewable power and carbon capture are key technologies to transfer the power industry into low carbon generation. Renewables have been developed fast, however, the intermittent nature has imposed higher requirement for the flexibility of the power grid. Retrofitting carbon capture technologies to existing fossil-fuel fired power plants is an important solution to avoid the “lock-in” of emissions, but the high operating costs hinders their large scale application. The coexistence of renewable power and carbon capture opens up a new avenue that the deployment of carbon capture can provide additional flexibility for better accommodation of renewable power while excess renewables can be used to reduce the operating costs of carbon capture. To this end, this paper proposes an artificial intelligence based optimal scheduling strategy for the power plant-carbon capture system in the context of renewable power penetration to show that the mutual benefits between carbon capture and renewable power can be achieved when the carbon capture process is made fully adjustable. An artificial intelligent deep belief neural network is used to reflect the complex interactions between carbon, heat and electricity within the power plant carbon capture system. Multiple operating goals are considered in the scheduling such as minimizing the operating costs, renewable power curtailment and carbon emission, and the particle swarm heuristic optimization is employed to find the optimal solution. The impacts of carbon capture constraint mode, carbon emission penalty coefficient, carbon dioxide production constraints and renewable power installed capacity are investigated to provide broader insight on the potential benefit of carbon capture in future low-carbon energy system. A case study using real world data of weather condition and load demand shows that renewable power curtailment can be reduced by 51% with the integration of post-combustion capture systems and 35% of total carbon emission are captured by the use of excess renewable power through optimal scheduling. This paper points out a new way of using artificial intelligent technologies to coordinate the couplings between carbon and electricity for efficient and environmentally friendly operation of future low-carbon energy system.