The fast deployment of distributed energy resources in the electric power system has highlighted the need for an efficient energy trading transactive model, without the need for centralized dispatch. In this field, a particular challenge is the determination of an effective pricing scheme that is able to produce benefits for all participants. In this paper, a novel dynamic pricing methodology is presented, offering a market-oriented means to drive decentralized energy trading and to optimize financial benefits for owners of distributed energy resources. Firstly, a price-responsive model for each type of distributed energy resource is investigated. Particularly, the decoupled State of Charge function is proposed to calculate the value of a single charging/discharging action for energy storage systems. In addition, an adaptable three-tiered framework is designed, including micro-grid balancing, aggregator scheduling, and trading optimization. By launching Tier I, II, and III, the spot prices for participants are iteratively updated and optimized in inner-micro-grid, inner-aggregator, and inter-aggregators level. The framework is able to maximize the financial savings from renewable energy, and meanwhile, provide a dynamic price signal to assist stakeholders in determining response actions and trading strategies. A realistic case is simulated using Java Agent Development framework based multi-agent modeling. The results indicate that the presented methodology enables decentralized energy trading and permits easier marketization of micro-grids with a high share of distributed energy resources.

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Total transfer capability (TTC) is a vital security indicator for power exchange among areas. It characterizes time-variants and transient stability dynamics, and thus is challenging to evaluate efficiently, which can jeopardize operational safety. A leaning-aided optimal power flow method is proposed to handle the above challenges. At the outset, deep learning (DL) is utilized to globally establish real-time transient stability estimators in parametric space, such that the dimensionality of dynamic simulators can be reduced. The computationally intensive transient stability constraints in TTC calculation and their sensitivities are therewith converted into fast forward and backward processes. The DL-aided constrained model is finally solved by nonlinear programming. The numerical results on the modified IEEE 39-bus system demonstrate that the proposed method outperforms several model-based methods in accuracy and efficiency.

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Electric vehicles (EVs) have been rapidly developed during the last few years due to the low-carbon industry and smart grid initiatives. Meanwhile, the impact of large-scale EVs' integration on the reliability and safety of power grids is becoming increasingly prominent. To address and solve these problems, challenges on EV charging control have been presented. Besides, the EV charging load modelling with improved accuracy and rationality is required. To investigate the influencing factors of EV charging load, this survey summarises the existing EV charging load modelling methods. In addition, a new research framework for a scale EV evolution model of charging load is proposed, with an emphasis on reducing the deficiencies of the existing research in dealing with the EV scale development. Moreover, the future research prospect of EV charging load modelling on power system planning, operation, and market design has also been discussed.

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