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Due to increasing load and characteristic stagnation and fluctuations of existing generation systems capacity, the reliability assessment of generation systems is crucial to system adequacy. Furthermore, a rapid load increase could amount to a consequent sudden deficit in the generation supply before the next scheduled assessment. Hence, a reliability assessment is conducted at regular and close intervals to ensure adequacy. This study simulates and establishes the relationship between the load growth and generation capacity using the generation and load data of the IEEE reliability test system (IEEE RTS ‘96 standard). The generation capacity states and the risk model were obtained using the sequential Monte Carlo simulation (MCS) method. The load was gradually increased stepwise and is simulated against the constant generation capacity. In each case, the reliability index was recorded in terms of loss-of-load evaluation (LOLE). The recorded reliability index was thereafter fitted with the load-growth trend by the linear regression approach. A predictive assessment approach is thereafter proffered through the obtained fitting equation. In addition, a reliability threshold is effectively determined at a yield point for a reliability benchmark.

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In the power sector, microgrids play a supportive role in bridging the adequacy gap in the conventional electricity supply. Trading of the generated energy has recently been improved by blockchain technology which offers a new cheap, secure, and decentralized transaction approach. Its operation is however associated with an undesired inherent delay during energy transactions initiated by the prosumers, thus, failure to timely attend to incidences of urgent demand could end up in catastrophe at the consumer's side. This article thus proposes a cyber-enhanced transactive microgrid model using blockchain technology with optimized participants' permission protocol to ameliorate this challenge. It is demonstrated that the optimized blockchain participants' permission model leads to improved transaction speed and greater convenience. The transaction speed simulation is thereafter performed and it was also demonstrated that the node population has a greater effect than the transaction block size on the transaction speed improvement.

Abstract.It is reported i that the electricity cost to operate a cluster may well exceed its acquisition cost, and the processing of big data requires large scale cluster and long period. Therefore, energy efficient processing of big data is essential for the data owners and users. In this paper, we propose a novel algorithm MinBalance to processing I/O intensive big data tasks energy efficiently in heterogeneous cluster. In the former step, four greedy policies are used to select the proper nodes considering heterogeneity of the cluster. While in the latter step, the workloads of the selected nodes will be well balanced to avoid the energy wastes caused by waiting. MinBalance is a universal algorithm and cannot be affected by the data storage strategies. Experimental results indicate that MinBalance can achieve over 60% energy reduction for large sets over the traditional methods of powering down partial nodes.