Temporal city-scale matching of solar photovoltaic generation and electric vehicle charging

The number of electric vehicles (EVs) and solar photovoltaic panels (PVs) are rapidly increasing in many power grids. An important emerging challenge is managing their less desirable consequences (e.g. grid instability and peak load), particularly in urban environments. We present a solution that matches the temporal nature of PV generation and EV charging. This solution is a simple coordination strategy for EV charging which minimally affects EV availability for drivers while maximizing the PV electricity generation absorbed by EV batteries. The strategy is benchmarked with high-resolution data from a medium-sized European city. We find that this coordination provides large benefits compared to commonly-observed uncoordinated charging patterns across seasons and PV and EV integration levels. With charging coordination, almost 71%–92% of the EV charging load can be provided by solar panels in the summer. However, winter’s lower solar irradiance results in a larger range of possibilities (13%–76%), with the exact value depending on the combination of PV and EV integration level. The gains compared to uncoordinated charging are generally highest in winter and similarly vary based on PV and EV integration levels (from 5 to 63 percentage points). Additionally, these benefits do not appear to come at a significant cost to EV availability for drivers

Active congestion quantification and reliability improvement considering aging failure in modern distribution networks

The enormous concerns of climate change and traditional resource crises lead to the increased use of distributed generations (DGs) and electric vehicles (EVs) in distribution networks. This leads to significant challenges in maintaining safe and reliable network operations due to the complexity and uncertainties in active distribution networks, e.g., congestion and reliability problems. Effective congestion management (CM) policies require appropriate indices to quantify the seriousness and customer contributions to congested areas. Developing an accurate model to identify the residual life of aged equipment is also essential in long-term CM procedures. The assessment of network reliability and equipment end-of-life failure also plays a critical role in network planning and regulation. The main contributions of this thesis include a) outlining the specific characteristics of congestion events and introducing the typical metrics to assess the effectiveness of CM approaches; b) proposing spatial, temporal and aggregate indices for rapidly recognizing the seriousness of congestion in terms of thermal and voltage violations, and proposing indices for quantifying the customer contributions to congested areas; c) proposing an improved method to estimate the end-of-life failure probabilities of transformers and cables lines taking real-time relative aging speed and loss-of-life into consideration; d) quantifying the impact of different levels of EV penetration on the network reliability considering end-of-life failure on equipment and post-fault network reconfiguration; and e) proposing an EV smart charging optimization model to improve network reliability and reduce the cost of customers and power utilities. Simulation results illustrate the feasibility of the proposed indices in rapidly recognizing the congestion level, geographic location, and customer contributions in balanced and unbalanced systems. Voltage congestion can be significantly relieved by network reconfiguration and the utilization of the proposed indices by utility operators in CM procedures is also explained. The numerical studies also verify that the improved Arrhenius-Weibull can better indicate the aging process and demonstrate the superior accuracy of the proposed method in identifying residual lives and end-of-life failure probabilities of transformers and conductors. The integration of EV has a great impact on equipment aging failure probability and loss-of-life, thus resulting in lower network reliability and higher cost for managing aging failure. Finally, the proposed piecewise linear optimization model of the EV smart charging framework can significantly improve network reliability by 90% and reduce the total cost by 83.8% for customers and power utilities

Towards Sustainable Transport and Mobility

Small island states are one of the most affected areas by sea-level rise, and sustainable transport development is crucial to their transition towards resiliency. However, their special spatial situations, insularity, geographic remoteness, small populations, and small economies resulted in high transport costs and car dependencies. The book moves away from the conventional focus on urban areas in the Global North and tourism. It gives a different perspective on sustainable transport, travelling, and commuting in the Caribbean and Europe. The authors provide research-based insights and show the state-of-the-art and future approaches for policy-makers, academics, and practitioners. Even beyond small island state research, the book offers an innovative outlook

New Plug-in Electric Vehicles Charging Models Based on Demand Response Programs for System Reliability Improvement

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