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Security-constrained unit commitment (SCUC) is a key component of operating electricity markets. By increasing the share of renewable energies on the generation side, and emergence and growth of new stochastic loads on the demand side, stochastic SCUC has become more important for secure-optimal operation of the market. Scenario-based techniques have been suggested widely for stochastic SCUC in the literature. However, they are usually very time-consuming. This problem is escalated in large-scale power systems with high penetration of stochastic generation and loads. To mitigate the computational burden of SCUC problem, this paper develops an algorithm, based on point estimation method and Bender's decomposition technique. The proposed approach breaks the probabilistic problem into a few deterministic points with much lower computation burden yet with minimal loss of accuracy. The proposed approach is implemented on a six-bus system as the first numerical study, and on a modified IEEE 118-bus system with 94 probabilistic variables as the second case study. The efficacy of proposed algorithm is confirmed, especially in the last test case with notable reduction in computational burden without considerable loss of precision. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Virtual power plant (VPP) is the owner and/or manager of a group of distributed generations as well as some end consumers, which may have random behavior. In addition to the uncertainties associated with generation and demand, the market price is also a source of uncertainty in VPP scheduling. In this paper, a novel stochastic operating strategy is proposed for VPPs to participate in day-ahead market, considering different uncertainties in generation, consumption, and electricity market price. Highly stochastic consumption of plug-in hybrid electric vehicles (PHEVs) is also considered in the present study, as an emerging source of uncertainty. To mitigate the loading effect of PHEVs on the network, a smart charging strategy is proposed, which encourages the owners to charge their PHEVs in off-peak hours, and the results of this strategy are used in estimating the load. To achieve the maximum profit for the VPP in an electricity market, different methods based on point estimation method and Monte Carlo simulation are analyzed to handle the uncertainties. Moreover, three strategies are proposed for charging PHEVs, and their effect on VPP's total cost is compared. The proposed stochastic programming is solved by a modified version of teaching-learning-based optimization algorithm. Numerical simulations on modified 18-bus distribution system corroborate the efficacy of the proposed methodology. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY With increasing penetration of wind turbine in power systems and micro grids, it is inevitable to investigate effects of wind turbines on micro-grid performance. This paper investigates the impact of doubly fed induction generator (DFIG) on transient stability of the micro-grid. In the first step; simulation results show that the more penetration of DFIG in a micro-grid, the less critical clearing time (CCT). In the second step, in order to improve the transient stability of the DFIG, a Fuzzy controller is introduced and added to the conventional proportional and integral (PI) controller of rotor side of the DFIG, which increases the CCT. In proposed algorithm, the speed and acceleration of the rotor are used as the inputs of the fuzzy PI controller. Numerical simulations on a typical micro-grid shows the benefits of the proposed controller. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY: This article proposes a novel approach for price‐based market partitioning to mitigate the congestion problem. The conventional fuzzy C‐means clustering algorithm is reported in previous works for market partitioning, but it may be inefficient or unstable especially in large‐scale power systems. Intrazonal congestion and fragmented zones are two important reported deficiencies of market partitioning algorithms. To enhance the congestion management, the proposed algorithm takes directly the effect of congested lines using a suitable index. In this approach, the fuzzy C‐means is used in a new manner to stabilize and improve the effectiveness of the algorithm and to mitigate the reported deficiencies of previously market partitioning methods. Numerical studies on IEEE 118‐bus test system show the algorithm efficacy. The results do not include any intrazonal congestion or fragmented zone. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]