针对多微电网市场新能源出力不确定性以及参与主体间利益关联与冲突导致的市场运行风险大、效率低等问题,提出基于图卷积神经网络与长短时记忆网络GCN-LSTM(graph convolutional neural network and long short-term memory network)时...针对多微电网市场新能源出力不确定性以及参与主体间利益关联与冲突导致的市场运行风险大、效率低等问题,提出基于图卷积神经网络与长短时记忆网络GCN-LSTM(graph convolutional neural network and long short-term memory network)时空预测算法的多微电网市场主从博弈均衡优化策略。首先,从时空维度设计了基于深度强化学习算法的多微电网两阶段主从博弈均衡运行机制;然后,将多微电网市场中竞价主体间的相互作用构建两阶段滚动优化模型,根据决策阶段要求,以提升各微电网主体经济效益为目标,在实时调控阶段构建多主体主从博弈模型,对内部电价及设备出力进行调整,实现多微电网市场均衡独立优化运行。最后,通过算例分析表明,所提方法能有效降低微电网各发电主体出力的不确定性对市场稳定运行和新能源消纳的影响,提高市场经济效益。展开更多
This article addresses the autonomy of joint radio resource management （JRRM） between heterogeneous radio access technologies（RATs） owned by multiple operators. By modeling the inter-operator competition as a gene...This article addresses the autonomy of joint radio resource management （JRRM） between heterogeneous radio access technologies（RATs） owned by multiple operators. By modeling the inter-operator competition as a general-sum Markov game, correlated-Q learning（CE-Q） is introduced to generate the operators＇ pricing and admission policies at the correlated equilibrium autonomically. The heterogeneity in terms of coverage, service suitability, and cell capacity amongst different RATs are considered in the input state space, which is generalized using multi-layer feed-forward neural networks for less memory requirement. Simulation results indicate that the proposed algorithm can produce rational JRRM polices for each network under different load conditions through the autonomic learning process. Such policies guide the traffic toward an optimized distribution and improved resource utilization, which results in the highest network profits and lowest blocking probability compared to other self-learning algorithms.展开更多
文摘针对多微电网市场新能源出力不确定性以及参与主体间利益关联与冲突导致的市场运行风险大、效率低等问题,提出基于图卷积神经网络与长短时记忆网络GCN-LSTM(graph convolutional neural network and long short-term memory network)时空预测算法的多微电网市场主从博弈均衡优化策略。首先,从时空维度设计了基于深度强化学习算法的多微电网两阶段主从博弈均衡运行机制;然后,将多微电网市场中竞价主体间的相互作用构建两阶段滚动优化模型,根据决策阶段要求,以提升各微电网主体经济效益为目标,在实时调控阶段构建多主体主从博弈模型,对内部电价及设备出力进行调整,实现多微电网市场均衡独立优化运行。最后,通过算例分析表明,所提方法能有效降低微电网各发电主体出力的不确定性对市场稳定运行和新能源消纳的影响,提高市场经济效益。
基金This work is supported by the National Natural Science Foundation of China （60632030）；the Integrated Project of the 6th Framework Program of the European Commission （IST-2005-027714）；the Hi-Tech Research and Development Program of China （2006AA01Z276） ；the China-European Union Science and Technology Cooperation Foundation of Ministry of Science and Technology of China （0516）.
文摘This article addresses the autonomy of joint radio resource management （JRRM） between heterogeneous radio access technologies（RATs） owned by multiple operators. By modeling the inter-operator competition as a general-sum Markov game, correlated-Q learning（CE-Q） is introduced to generate the operators＇ pricing and admission policies at the correlated equilibrium autonomically. The heterogeneity in terms of coverage, service suitability, and cell capacity amongst different RATs are considered in the input state space, which is generalized using multi-layer feed-forward neural networks for less memory requirement. Simulation results indicate that the proposed algorithm can produce rational JRRM polices for each network under different load conditions through the autonomic learning process. Such policies guide the traffic toward an optimized distribution and improved resource utilization, which results in the highest network profits and lowest blocking probability compared to other self-learning algorithms.
