• Energy Research

The fast globalization of renewable energy-based technologies has enabled its wide speared utilization as well. This has shaped a new prospect of operation in the modern electricity system. But, its dependency on environmental factors leads to an uncertain scenario in the day-ahead electricity market. During this period, compromises are made in the genuine process of expenditure and resources of the producers to offset the capacity that decreases the profits for the producers. In general, a significant variety of scenarios need to be taken into account when describing uncertainty, thereby necessitating the need for techniques of scenario reduction. Therefore, to manage the intractable effects of solar radiation and wind speed instability, the function of the Beta and Weibull distribution of probability is implemented, respectively, and scenarios are minimized using forward-reduction algorithms. Besides, an underestimation and overestimation of the cost function are used to calculate the deviation of renewable influence. Thus, this paper is suggesting a valuable bidding strategy to maximize the remuneration of electricity producers in the presence of rival competitors and the instability of solar and wind energy. This problem has been prepared by taking the benchmark IEEE 30-bus network with and without renewable energy sources, and this problem has been solved by using the Gravitational Search Algorithm. The observations of the outcome demonstrate the appropriateness of the projected bid strategy in the presence of volatility of renewable energy.

This paper presents a comprehensive reliability framework for incorporating different plug-in electric vehicle (PEV) charging load models into the evaluation of generation adequacy. The proposed framework comprises special treatment and innovative models to achieve an accurate determination of the impact of PEV load models on reliability. First, a goodness-of-fit statistical model determines the probability distribution functions (PDFs) that best reflect the main characteristics of driver behavior. Second, robust and detailed stochastic methods are developed for modeling different charging scenarios (uncontrolled charging and charging based on time-of-use (TOU) pricing). These models are based on the use of a Monte Carlo simulation in conjunction with the fitted PDFs to generate and assess a large number of possible scenarios, while handling the uncertainties associated with driver behavior, penetration levels, charging levels, battery capacities, and customer response to TOU pricing. A novel reliability-based framework for the application of dynamic critical event call programs for use with PEV charging loads is also proposed. The effectiveness of the proposed framework with respect to improving system reliability is demonstrated using several case studies applied on the IEEE Reliability Test System.

In this paper, a self-learning multi-class intelligent model for wind turbine fault diagnosis is proposed by using MFQL (Modified-Fuzzy-Q-Learning) technique. The MFQL is adaptive in nature and extension of fuzzy-Q-learning method where look-up table of Q-learning is conquered by fuzzy based approximation strategy to reduce the curse of dimensionality of the Q-learning. The proposed MFQL classifier diagnoses the mechanical and imbalance faults without using mechanical sensors. Proposed methodology is addressed with relying on PMSG (Permanent Magnet Synchronous Generator) stator current signals, which is already being used by protection system of wind turbines. According to the aforementioned description, non-stationary current signals of PMSG have been pre-processed to extract the input features by empirical mode decomposition followed with J48 algorithm based most relevant input feature selection. For the one-step ahead performance demonstration of the proposed MFQL approach, results have been compared with neural network, support vector machines, fuzzy logic, and conventional Fuzzy-Q-Learning techniques. Demonstrated results outperform the capability of proposed MFQL approach. Moreover, MFQL is developed first time to implement in the area of WTGS fault diagnosis in the literature.