The literature covering Plug-in Electric Vehicles (EVs) contains many charging/discharging strategies. However, none of the review papers covers such strategies in a complete fashion where all patterns of EVs charging/discharging are identified. Filling a gap in the literature, we clearly and systematically classify such strategies. After providing a clear definition for each strategy, we provide a detailed comparison between them by categorizing differences as follows: complexity; economics and power losses on the grid side; ability to provide ancillary services for integrity of the power grid; operation aspects (e.g., charging timing); and detrimental impact on the EV, the power grid, or the environment. Each one of these comparison categories is subdivided into even more detailed aspects. After we compare the EV charging/discharging strategies, we further provide recommendations on which strategies are suitable for which applications. Then, we provide ratings for each strategy by weighting all aspects of comparison together. Our review helps authors or aggregators explore likely choices that might suit the specific needs of their systems or test beds.

In recent years, air pollution and climate change issues have pushed people worldwide to switch to using electric vehicles (EVs) instead of gas-driven vehicles. Unfortunately, most distribution system facilities are neither designed nor well prepared to accommodate these new types of loads, which are characterized by random and uncertain behavior. Therefore, this paper provides a comprehensive investigation of EVs’ effect on a realistic distribution system. It provides a technical evaluation and analysis of a real distribution system’s load and voltage drop in the presence of EVs under different charging strategies. In addition, this investigation presents a new methodology for managing EV loads under a dynamic response strategy in response to the distribution system’s critical hours. The proposed methodology is applied to a real distribution network, using the Monte Carlo method and the CYME program. Random driver behavior is taken into account in addition to various factors that affect EV load parameters. Overall, the results show that the distribution system is significantly affected by the addition of EV charging loads, which create a severe risk to feeder limits and voltage drop. However, a significant reduction in the impact of EVs can be achieved if a proper dynamic demand response programme is implemented. We hope that the outcomes of this investigation will provide decision-makers and planners with prior knowledge about the expected impact of using EVs and, consequently, enable them to take the proper actions needed to manage such load.

This study proposes an improved single‐diode modelling approach for photovoltaic (PV) modules suitable for a broad range of the PV technologies available today, including modules based on tandem cell structures. After establishing the model (which has an overall of seven parameters), this study devises a methodology to estimate its parameters using Standard Test Conditions (STC) data, Nominal Operating Cell Temperature (NOCT) data, and temperature coefficient values as provided in most manufacturers’ datasheets. Simulation results and their comparison with a previous work show a very accurate prediction of critical points in the current‐voltage characteristics curve. The precise prediction happens for both STC and NOCT conditions and the error in predicting maximum power point (MPP) lies within 1% limit, and the error in its corresponding voltage and current is almost always within 2% limit. Further, for both MPP and open‐circuit voltage, the statistical variance around manufacturer measurements due to temperature changes is demonstrated to be low for five various module technologies.

The literature contains different methods for estimating logistic functions parameters to fit the power‐speed characteristics of a wind turbine (WT). However, their disadvantages are: (1) they require a large amount of supervisory control and data acquisition data; (2) the parameter range needed for constrained optimization is not systematically determined; and/or (3) they do not guarantee monotonically increasing relationship between wind speed and predicted WT output power. This paper proposes a systematic approach to fitting the five‐ and the six‐parameter logistic functions to power‐speed data of WTs. The authors introduce new limits on the parameters of these functions, which guarantee their monotonicity. Most of these limits are determined analytically and are obtained from manufacturer datasheets. Afterwards, these limits are passed as optimization constraints for subspace trust region algorithm. The results show that the authors' method generally provides better accuracy with mean absolute percentage error values below 0.02 for the five‐parameter logistic function and below 0.005 for the six‐parameter logistic function. The authors present accurate fits for a group of WTs of different ratings (from 275 kW to 3000 kW) and 12 unique manufacturers, which proves the versatility of the authors' method.

This paper addresses two problems of interest in the present time, namely, the characterization of price variations and the corresponding load response to them. The paper begins by defining Price Elasticity Matrices (PEMs) and shows how they can be used to model demands deviation from their scheduled levels due to price differentials. It then explains the reason as to why extending PEMs characterization is necessary. Afterward, we show how Normal distribution lacks accuracy in modeling the variation of market clearing prices from one day to the next. Based on empirical data from the Midcontinent Independent System Operator (MISO) area, we propose the use of Stable distribution and demonstrate, how such statistical model is very accurate to characterize electricity market price variations. The models mentioned above are used in a Monte Carlo (MC) simulation to find the probability that different Direct Load Control (DLC) levels would not be enough to maintain the peak demand below its reference value. Since MC simulations are so computationally intensive, we also implement Importance Sampling Monte Carlo (ISMC) to substantially reduce the computational burden without sacrificing much of the accuracy.