In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupli...In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling.In light of this,this study focuses on electro-thermal-hydrogen trigeneration systems,first modelling the system's scheduling optimization problem as a Markov decision process,thereby transforming it into a sequential decision problem.Based on this,this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement,aiming to minimize system operating costs and enhance the system's sustainable operation capability.Experimental results show that compared to traditional reinforcement learning algorithms,the reinforcement learning algorithm based on deep deterministic policy gradient improvement achieves improvements of 12.5%and 22.8%in convergence speed and convergence value,respectively.Additionally,under uncertainty scenarios ranging from 10%to 30%,cost reductions of 2.82%,3.08%,and 2.52%were achieved,respectively,with an average cost reduction of 2.80%across 30 simulated scenarios.Compared to the original algorithm and rule-based algorithms in multi-uncertainty environments,the reinforcement learning algorithm based on improved deep deterministic policy gradients demonstrated superiority in terms of system operating costs and continuous operational capability,effectively enhancing the system's economic and sustainable performance.展开更多
基金the Science and Technology Projects of State Grid Jiangsu Electric Power Company “Design, Regulation and Application of Electric-Hydrogen-Heat Integrated Energy Systems for Low Carbon Buildings, J2024184”.
文摘In the field of low-carbon building systems,the combination of renewable energy and hydrogen energy systems is gradually gaining prominence.However,the uncertainty of supply and demand and the multi-energy flow coupling characteristics of this system pose challenges for its optimized scheduling.In light of this,this study focuses on electro-thermal-hydrogen trigeneration systems,first modelling the system's scheduling optimization problem as a Markov decision process,thereby transforming it into a sequential decision problem.Based on this,this paper proposes a reinforcement learning algorithm based on deep deterministic policy gradient improvement,aiming to minimize system operating costs and enhance the system's sustainable operation capability.Experimental results show that compared to traditional reinforcement learning algorithms,the reinforcement learning algorithm based on deep deterministic policy gradient improvement achieves improvements of 12.5%and 22.8%in convergence speed and convergence value,respectively.Additionally,under uncertainty scenarios ranging from 10%to 30%,cost reductions of 2.82%,3.08%,and 2.52%were achieved,respectively,with an average cost reduction of 2.80%across 30 simulated scenarios.Compared to the original algorithm and rule-based algorithms in multi-uncertainty environments,the reinforcement learning algorithm based on improved deep deterministic policy gradients demonstrated superiority in terms of system operating costs and continuous operational capability,effectively enhancing the system's economic and sustainable performance.
