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With the increasing penetration of renewable energy sources and electric vehicles (EVs), coordinated operation of EV aggregator (EVA) and distribution system operation (DSO) becomes a complex multistage and multidimensional stochastic problem. The motivation behind this paper is to develop a decentralized mechanism to offer a computationally efficient and almost optimal on-line policy for such problem under the framework of transactive energy control (TEC). First, a heterogeneous decomposition-based TEC is designed by utilizing the heterogeneous interactions between DSO and EVA. Then, a decentralized approximate dynamic programming-based algorithm is proposed to offer almost optimal dynamic TEC policies. A decentralized value function approximation approach with temporal difference learning is further employed for entities to learn how to utilize the flexibilities of their resources in response to the stochastic exogenous information. Case studies demonstrate the effectiveness of the proposed algorithm in terms of optimality, robustness, computation efficiency, and scalability. [ABSTRACT FROM AUTHOR]

Collaboration of networked microgrids (NMGs) with diverse generation sources is a promising solution to smooth volatile generation output and enhance the utilization efficiency of renewable energies. In addition to centralized and decentralized collaboration mechanisms, transactive energy control (TEC) is an emerging and effective market-based control to enable energy transactions among distributed entities such as NMGs. However, existing studies on TEC suffer from several major weaknesses such as unconstrained/simplified model formulations and slow convergence rates. This paper proposes a novel TEC mechanism to tackle these weaknesses. First, the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism to coordinate the operation of NMGs are briefly reviewed and modeled by a scenario-based stochastic optimization method. A new TEC mechanism is then proposed, consisting of a TEC framework, mathematical model, pricing rule, and algorithm. The optimality of the proposed TEC mathematical model and pricing rule is demonstrated. The effectiveness of the proposed TEC mechanism is verified in case- studies where the NMGs operate in grid-connected, islanded, and congested modes. The advantages of the proposed TEC mechanism are also illustrated through comparisons with the centralized mechanism, decentralized mechanism, and subgradient-based TEC mechanism. [ABSTRACT FROM AUTHOR]