New Plug-in Electric Vehicles Charging Models Based on Demand Response Programs for System Reliability Improvement
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Recent years have seen a dramatic worldwide increase in the use of plug-in electric vehicles (PEVs). Their tremendous social, economic, and environmental benefits have made PEVs highly promising alternatives to conventional automobiles powered by internal combustion engines. Continuing government initiatives and technological advances are expected to lead to an even more rapid rise in the PEV penetration in the near future. Despite the important advantages of PEVs, however, their integration also raises new concerns and presents a number of special difficulties to the power system reliability. There is in fact recognized need to address the challenges imposed by PEV charging loads, to study their adverse impact on overall system reliability, and to determine whether existing generation capacity is sufficient for accommodating these new types of loads with their high penetration levels and different uncertainty characteristics. This thesis presents a comprehensive reliability framework for incorporating different 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 analysis determines the probability distribution functions (PDFs) that best reflect the main characteristics of driver behaviour. Second, robust and detailed stochastic methods are developed for modeling different charging scenarios (uncontrolled charging and charging based on 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 behaviour, penetration levels, charging levels, battery capacities, and customer response to TOU pricing. When PEV charging loads become a significant factor in power systems and PEV charging times are uncontrolled, they are expected to cause a severe risk to system reliability, especially at higher PEV penetration and charging levels. Solutions that maintain an acceptable level of system reliability and ensure adequate generation capacity must therefore be found. Proposed in this thesis is novel reliability-based frameworks for the application of different DR programs for use with PEV charging loads. The proposed frameworks are in line with the recent trend toward investigating solutions at the demand side and exploiting the existing flexibility to help improve reliability. The first framework is proposed for incorporating PEV charging loads to respond to dynamic critical events. The framework involves two models: the first determines the time and demand for critical system events, when system supply facilities are unable to meet PEV loads, and the second assesses the feasibility of PEV owner response to critical events. The second framework is proposed for designing time-of-use (TOU) schedules to mitigate the impact of uncontrolled PEV charging load. The proposed framework involves the use of different stochastics simulation models, visualization approaches, and expert rules that help to arrive at proper TOU schedules for PEV charging load.
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Abdulaziz Almutairi (2018). New Plug-in Electric Vehicles Charging Models Based on Demand Response Programs for System Reliability Improvement. UWSpace. http://hdl.handle.net/10012/13894