According to this paper, the dragon-shape strategy is the optimized option of China＇s future strategy with respect to the geographic distribution of regional economy.
In this study, China＇s current macro-environ- mental policies as well as their implementation and management tools are analyzed. By using the basic economic methodology, detailed studies are conducted focusing on the...In this study, China＇s current macro-environ- mental policies as well as their implementation and management tools are analyzed. By using the basic economic methodology, detailed studies are conducted focusing on the implementation effect of contemporary China＇s typical environmental policy of the total pollutant discharge quantity control type, and also the two types of environmental management tools are compared from the perspective of implementation costs and policy uncertainty. By introduction of distributed management tools into the implementation of environmental policies, marketoriented means and the methods of economic analysis are introduced into environmental policy decision-making mechanisms, which could afford a new method for changing the current relatively low efficiency of environ- mental policy, solving the problem of＂government failure＂ in environmental policy implementation, and providing a new way to make environmental policy system more flexible and more efficient. It is of great practical significance to solve China＇s current structural, complex and accumulative environmental problems.展开更多
The optimal dispatch methods of integrated energy systems(IESs) currently struggle to address the uncertainties resulting from renewable energy generation and energy demand. Moreover, the increasing intensity of the g...The optimal dispatch methods of integrated energy systems(IESs) currently struggle to address the uncertainties resulting from renewable energy generation and energy demand. Moreover, the increasing intensity of the greenhouse effect renders the reduction of IES carbon emissions a priority. To address these issues, a deep reinforcement learning(DRL)-based method is proposed to optimize the low-carbon economic dispatch model of an electricity-heat-gas IES. In the DRL framework, the optimal dispatch model of the IES is formulated as a Markov decision process(MDP). A reward function based on the reward-penalty ladder-type carbon trading mechanism(RPLT-CTM) is introduced to enable the DRL agents to learn more effective dispatch strategies. Moreover, a distributed proximal policy optimization(DPPO) algorithm, which is a novel policy-based DRL algorithm, is employed to train the DRL agents. The multithreaded architecture enhances the exploration ability of the DRL agents in complex environments. Experimental results illustrate that the proposed DPPO-based IES dispatch method can mitigate carbon emissions and reduce the total economic cost. The RPLT-CTM-based reward function outperforms the CTM-based methods, providing a 4.42% and 6.41% decrease in operating cost and carbon emission, respectively. Furthermore, the superiority and computational efficiency of DPPO compared with other DRL-based methods are demonstrated by a decrease of more than 1.53% and 3.23% in the operating cost and carbon emissions of the IES, respectively.展开更多
文摘According to this paper, the dragon-shape strategy is the optimized option of China＇s future strategy with respect to the geographic distribution of regional economy.
文摘In this study, China＇s current macro-environ- mental policies as well as their implementation and management tools are analyzed. By using the basic economic methodology, detailed studies are conducted focusing on the implementation effect of contemporary China＇s typical environmental policy of the total pollutant discharge quantity control type, and also the two types of environmental management tools are compared from the perspective of implementation costs and policy uncertainty. By introduction of distributed management tools into the implementation of environmental policies, marketoriented means and the methods of economic analysis are introduced into environmental policy decision-making mechanisms, which could afford a new method for changing the current relatively low efficiency of environ- mental policy, solving the problem of＂government failure＂ in environmental policy implementation, and providing a new way to make environmental policy system more flexible and more efficient. It is of great practical significance to solve China＇s current structural, complex and accumulative environmental problems.
基金supported in part by the National Natural Science Foundation of China (No.61102124)。
文摘The optimal dispatch methods of integrated energy systems(IESs) currently struggle to address the uncertainties resulting from renewable energy generation and energy demand. Moreover, the increasing intensity of the greenhouse effect renders the reduction of IES carbon emissions a priority. To address these issues, a deep reinforcement learning(DRL)-based method is proposed to optimize the low-carbon economic dispatch model of an electricity-heat-gas IES. In the DRL framework, the optimal dispatch model of the IES is formulated as a Markov decision process(MDP). A reward function based on the reward-penalty ladder-type carbon trading mechanism(RPLT-CTM) is introduced to enable the DRL agents to learn more effective dispatch strategies. Moreover, a distributed proximal policy optimization(DPPO) algorithm, which is a novel policy-based DRL algorithm, is employed to train the DRL agents. The multithreaded architecture enhances the exploration ability of the DRL agents in complex environments. Experimental results illustrate that the proposed DPPO-based IES dispatch method can mitigate carbon emissions and reduce the total economic cost. The RPLT-CTM-based reward function outperforms the CTM-based methods, providing a 4.42% and 6.41% decrease in operating cost and carbon emission, respectively. Furthermore, the superiority and computational efficiency of DPPO compared with other DRL-based methods are demonstrated by a decrease of more than 1.53% and 3.23% in the operating cost and carbon emissions of the IES, respectively.
