Research On Integrated Energy System And Low-carbon Economic Operation Considering Carbon Capture And P2G

In recent years,environmental problems have become increasingly prominent,and the traditional energy industry operation mode is difficult to meet the development needs of energy conservation and emission reduction.The integrated energy system is a multi-energy coupling system including electricity,heat and natural gas.Compared with the traditional energy system,the adjustment method is more flexible.Through the coordinated control of each energy system,it can improve the energy efficiency and reduce the carbon emissions of the system.In summary,this paper studies the low-carbon economy with carbon capture and P2G integrated energy systems,starting from the supply and demand side of multiple energy sources,and fully taps the low-carbon adjustment potential of the integrated energy system.The main contents are as follows:Starting from the supply side of the integrated energy system,the operation characteristics of combined heat and power（CHP）,power-to-gas（P2G）and carbon capture system（CCS）are analyzed.A joint operation mode of CHP-CCS-P2G（CCP）is proposed.The electricity-gas-heat output characteristics and carbon emissions of CHP units under the CCP combined operation mode are analyzed.A day-ahead low-carbon dispatch model of an integrated energy system considering carbon trading is established under the CCP joint operation mode.The results show that the CCP joint operation mode can significantly improve the system economic benefits and reduce carbon emissions,and can effectively reduce the high cost of adding CCS to coal-fired units.Research on the low-carbon economic operation strategy of the integrated energy system considering the emission of air pollutants.The emission characteristics of SO₂,NO_x,flue gas dust and CO₂ in the integrated energy system are analyzed.A multi-time scale scheduling model of an integrated energy system considering air pollutant emissions is established.In the day-ahead optimal scheduling,the emission costs of the system’s SO₂,NO_x,flue gas dust,and CO₂are added to the objective function,and the lowest operating cost of the integrated energy system is established.the objective function.In intraday optimal scheduling,the highest renewable energy consumption is taken as the objective function.In order to alleviate the influence of day-ahead source load forecast output error on scheduling,the intraday scheduling model adopts distributed model predictive control（DMPC）.Scrolling optimization method.It is verified by simulation that the proposed optimal scheduling model considering air pollutant emissions can effectively reduce the influence of day-ahead forecast errors on the control effect,and at the same time improve the environmental benefits of the system.In daily optimal scheduling,this paper proposes a low-carbon optimization framework for integrated energy systems based on deep reinforcement learning without considering the system dynamics model and uncertainty factor modeling.The deep reinforcement learning method based on Actor-Critic framework is analyzed.The low-carbon economic scheduling problem of integrated energy system is described as a Markov decision process,the state space,action space and reward function of the agent are defined,and the solution is solved by the asynchronous advantage actor-critic algorithm（A3C）.The simulation verifies that the proposed optimization method can obtain good dynamic control effect under the condition of renewable energy and load fluctuation.A low-carbon dispatch model for an integrated energy system considering reliability constraints and integrated demand response is proposed.A comprehensive demand response model based on price and incentives is established.The objective function is established with the lowest operating cost and carbon emission of the integrated energy system,and the load loss rate of the power system is added to the objective function as a criterion for measuring the reliability of the system.The optimization problem is then described as a reinforcement learning framework,solved by a deep reinforcement learning algorithm based on the twin delayed deep deterministic policy gradient algorithm（TD3）.The results show that after considering the demand response,the CO₂ emission can be further reduced while ensuring the reliability of the system.