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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Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.