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Coordination of networked microgrids (MGs) offers a promising solution to utilize distributed resources flexibilities and accommodate renewable energy. This paper studies real-time coordination of distribution system operation (DSO) and MGs considering multivariate uncertainty. Current researches suffer from inadaptability to dynamic system uncertainty, extensive iterations, and dependence on prediction. To fill these gaps, A novel multi-agent learning based stochastic dynamic programming (MASDP) is proposed to obtain the optimal policy for entities. Specifically, transactive energy control (TEC) mechanism, which requires only market-based information interactions, is employed to facilitate coordination between MG and DSO. A data-driven offline self-learning is proposed for entities to learn how to manage resources in response to system uncertainty. After sufficient offline learning, online operation of MASDP can be run in both non-iterative and iterative manners, by which near-optimal/optimal real-time solutions of DSO and MGs can be given sequentially and distributedly. Numerical comparisons with state-of-art policies and TEC algorithms verify the optimality, efficiency, adaptability, and scalability of MASDP. [ABSTRACT FROM AUTHOR]

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]

Transactive Energy Control (TEC) as a market-based control is a critical notion for scheduling Multi-Carrier Energy Systems (MCESs) in local networks and forming an Energy Hub (EH). Nevertheless, implementing TEC for scheduling and controlling MCESs is extremely difficult due to the lack of a cooperative TEC model that accounts for network constraints and the uncertainty of Renewable Energy Sources (RESs). This paper defines and formulates Prosumer-Based Multi-Carrier Energy Systems (PB-MCESs), which include electricity, heat, cooling, and gas hubs to enable internal coordination of resources and flexibility extraction for PB-MCESs. Subsequently, Nash Bargaining Game Theory is employed to construct a cooperative TEC that prioritizes P2P energy trade. In addition to P2P energy trading, PB-MCESs can trade their reserve in a P2P fashion to mitigate their uncertainty. PB-MCESs estimate the level of uncertainty using stochastic programming and allot a reserve capacity based on this estimation in order to manage their uncertainty via P2P reserve trading and internal reserves. PB-MCES can also control their risk by altering their risk-taking factor in accordance with the Conditional Value-at-Risk (CVAR) index. Implementations have demonstrated that the proposed cooperative TEC decreases total costs by 17.14% and that the proposed P2P reserve trading reduces total costs by 16.32%. [Display omitted] • Prosumer-based multi-carrier energy modeling. • A fair P2P energy + reserve trading. • Nash bargaining game-based market. • Convex system constraints modeling. • Risk assessment using CVAR. [ABSTRACT FROM AUTHOR]

Transactive Energy Control (TEC) paradigm enables involving Microgrids (MGs) in the energy management procedure to realize the transition of energy systems using market-based mechanisms. This paper, in this regard, proposes a review including state-of-the-art energy management using the TEC concept in MGs, highlighting the types of interactions among prosumers and MG operators, uncertainty models, and Energy Management System (EMS) designs. The proposed review illustrates that the distributed style of interactions with prosumer-centric uncertainty management has scarcely been investigated, while it is more beneficial in terms of financial, computational, and communication aspects. Hence, to pave the path for future research, a distributed TEC is proposed based on the consensus Alternative Direction Method of Multipliers (ADMM), wherein prosumers can solve their prosumption problem using a novel EMS and the MG operator acts as a coordinator to oversee system constraints. The EMS relies on a two-stage stochastic model, whereby prosumers can personally manage the associated risk using conditional value-at-risk (CVaR). The implementation results indicate that the accuracy, runtime, and communication burden are improved using the proposed distributed TEC by 32.76%, 15.68%, and 1094.6% compared to the decentralized TEC. Moreover, prosumers can save a further 5.75% on their bills using prosumer-centric uncertainty management. Consequently, the proposed review illuminates the research direction, while the proposed distributed TEC with the prosumer-centric uncertainty management elaborates on the identified gaps to accelerate the pace of improvement in this area. [Display omitted] • A survey on energy management in networked microgrids using transactive control. • A prosumer-oriented transactive control considering microgrid constraints. • An energy management system based on predictive and non-predictive control. • A new approach for managing prosumer-side uncertainties. • A two-stage stochastic optimization with risk consideration. [ABSTRACT FROM AUTHOR]