Application of multi-agent EADRC in flexible operation of combined heat and power plant considering carbon emission and economy. (15th January 2023)

• Record Type: Journal Article
• Authors: Hou, Guolian; Huang, Ting; Zheng, Fumeng; Gong, Linjuan; Huang, Congzhi; Zhang, Jianhua
• Is Part Of: Energy. Volume 263:Part B(2023)
• Publication Date: 2023-01-15
• Subjects: Flexible and low-carbon operation -- Combine heat and power plant -- Error-based active disturbance rejection control -- Multi-agent system; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2022.125711
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Flexibility enhancement of combined heat and power (CHP) plant is extremely urgent to accommodate renewable energy and maintain the stability of power grid. In this paper, a new control strategy seamlessly integrates the error-based active disturbance rejection control (EADRC), Kalman filter and reinforcement learning algorithm is proposed and applied to CHP plant for operational flexibility promotion. Firstly, in EADRC, load fluctuation, uncertainties and heating supply interference are regarded as a total disturbance precisely estimated and compensated in real-time. Secondly, the optimal parameters of controller are obtained via the twin delayed deep deterministic policy gradient algorithm based multi-agent system. Under the demand of flexible operation, a comprehensive optimization objective including general control objectives and carbon reduction requirements is constructed for CHP plant. By setting the multi-objective function as the reward function of multi-agent system, plant is guided to the Utopia point which emblems the flexible, economic and low-carbon operation. The new controller effectively fuses the features from each component, namely state estimation, dynamic optimization, and continuous learning. Thirdly, the stability analysis of proposed controller based on singular perturbation theory is provided. Finally, the flexible and low–carbon operation capacity of CHP plant with the new control scheme under wide-load operating conditions is successfully verified through extensive simulations. Highlights: Flexible operation problem of CHP plant is formulated as Markov decision process. An innovative controller fusing reinforcement learning and EADRC is constructed. Energy-saving and carbon reduction are considered in multi-objective function. A multi-agent system is used to obtain the optimal parameters of controller. The flexibility of CHP plant is verified under wide-load operating conditions.