Large fluctuations may occur on the energy supply and the load sides when large-scale renewable energies are integrated, leading to great challenges in power systems. The renewable power curtailment is especially numerous in the integrated electricity-heat energy system (IEHES) on account of electricityheat coupling. The flexible resources (FRs) on both the energy supply and load sides are introduced into the optimal dispatch of the IEHES and further modeled to alleviate the renewable fluctuations in this paper. On the energy supply side, three kinds of FRs based on electricity-heat coordination are modeled and discussed. On the load side, the shiftable electricity demand resource is characterized. On this basis, the solution for FRs participating in IEHES dispatch is given, with goals of maximizing the renewable penetration ratio and lowering operation costs. Two scenarios are performed, and the results indicate that the proposed optimal dispatch strategy can effectively reduce the renewable energy curtailment and improve the flexibility of the IEHES. The contribution degrees of different FRs for renewable integration are also explored.

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To address climate change and environmental pollution, an increasing number of renewable energy source generations are connected to the grid; meanwhile, the need for carbon capture and pollutant reduction for traditional energy has increased in urgency. In this study, the dispatch problem for an integrated energy system (IES) is expanded considering renewable penetration, carbon capture, and pollutant reduction. First of all, detailed models of carbon and pollutants reductions systems are set up. Specifically, the carbon capture system’s characteristics, which contribute more flexibility for the conventional power plants, are clarified. In addition, the treatment process of pollutants containing SO2 and NOx is elaborated. Moreover, the structure of an evolutionary IES containing pollutants treatment, battery and thermal energy storage, and carbon capture and storage systems are put forward. On this basis, the model of IES for renewable energy penetration and environmental protection considering the constraint of pollutant ultra-low emissions is set up. Finally, the simulation results show that the proposed approach can improve renewable energy penetration and restrain carbon and pollutants emissions.

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The control of flue gas emission in thermal power plants has been a topic of concern. Selective catalytic reduction technology has been widely used as an effective flue gas treatment technology. However, precisely controlling the amount of ammonia injected remains a challenge. Too much ammonia not only causes secondary pollution but also corrodes the reactor equipment, while too little ammonia does not effectively reduce the NOx content. In recent years, deep reinforcement learning has achieved better results than traditional methods in decision making and control, which provides new methods for better control of selective catalytic reduction systems. The purpose of this research is to design an intelligent controller using reinforcement learning technology, which can accurately control ammonia injection, and achieve higher denitrification effect and less secondary pollution. To train the deep reinforcement learning controller, a high-precision virtual denitration environment is first constructed. In order to make the virtual environment more realistic, this virtual environment was designed as a special structure with two decoders and a unique approach was used in fitting the virtual environment. A deep deterministic policy agent is used as an intelligent controller to control the amount of injected ammonia. To make the intelligent controller more stable, the actor-critic framework and the experience pool approach were adopted. The results show that the intelligent controller can control the emissions of nitrogen oxides and ammonia at the outlet of the reactor after training in virtual environment.