Heat and hydrological extremes in warm climates: Lessons from paleoclimate simulations

Extreme climate events and their impacts are currently arising as a critical feature of climate change. Paleoclimate studies are essential for understanding global environmental change and predicting extreme’s trends as the paleo-studies determine the factors that caused changes in the climate. Many studies have suggested that the mid-Pliocene and last interglacial (LIG) can be potentially used as an analogue for the future climates, but the extreme climate events are often missing in these studies. This thesis aims to show whether the LIG and mid-Pliocene are considered as analogues for the future of two extreme climate indices, including summer days index and heavy precipitation index. The MPI-ESM and COSMOS are employed to simulate the LIG, mid-Pliocene, pre-industrial, and future climates. First, the anomalies of temperature, precipitation, and selected indices are plotted for the simulations with respect to PI. In general, the summer days and heavy precipitation patterns are similar to the temperature and precipitation patterns, respectively. The probability density functions of climate variables and extreme indices in the centre of North America and Africa, the south of Africa, and Malaysia, clearly show that the increases in the average temperature and precipitation result in a growth in the corresponding extreme index. Comparing the anomaly plots for different simulations, the LIG can be only considered as analogue for future of summer days index in the northern-hemisphere regions such as the centre of North America. The mid-Pliocene not only is a good analogue for the summer days at the global scale but also can be used regionally for the prediction of heavy precipitation events. Due to the different characteristics of models employed in this project, there are some discrepancies in the results of similar simulations produced by MPI-ESM and COSMOS

Performance of heuristic optimization in coordination of plug-in electric vehicles charging

A heuristic load management (H-LMA) algorithm is presented for coordination of Plug-in Electric Vehicles (PEVs) in distribution networks to minimize system losses and regulate bus voltages. The impacts of optimization period T (varied from 15 minutes to 24 hours) and optimization time interval (varied 15 minutes to one hour) on the performance, accuracy and speed of the H-LMA is investigated through detailed simulations considering enormous scenarios. PEV coordination is performed by considering substation transformer loading while taking PEV owner priorities into consideration. Starting with the highest priority consumers, HLMA will use time intervals to distribute PEV charging within three designated high, medium and low priority time zones to minimize total system losses over period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results generated in MATLAB are presented for a 449 node distribution network populated with PEVs in residential feeders

A heuristic approach for coordination of plug-in electric vehicles charging in smart grid

In this paper, a heuristic load management algorithm (H-LMA) is proposed for Plug-in Electric Vehicles (PEVs) charging coordination. The proposed approach is aimed to minimize system losses over a period T (e.g., 24 hours) through re-optimizing the system at time intervals (e.g., 15 minutes) while regulating bus voltages through future smart grid communication system by exchanging signals with individual PEV chargers. Scheduling is performed based on the allowable substation transformer loading level and taking into account PEV owner preference/priority within three designated charging time zones. Starting with the highest priority consumers, H-LMA will distribute charging of PEVs within the selected priority time zones to minimize total system losses over a period T while maintaining network operation criteria such as power generation and bus voltages within their permissible limits. Simulation results are presented for different charging scenarios and are compared to demonstrate the performance of H-LMA for the modified IEEE 23 kV distribution system connected to several low voltage residential networks populated with PEVs. The main contribution of this paper lies in the detailed simulations / analyses of the smart grid under study and highlighting the impacts of and T values on the performance of the proposed coordination approach in terms of accuracy and coordination execution time

Online coordination of plug-in electric vehicles considering grid congestion and smart grid power quality

© 2018 MDPI AG. All rights reserved. This paper first introduces the impacts of battery charger and nonlinear load harmonics on smart grids considering random plug-in of electric vehicles (PEVs) without any coordination. Then, a new centralized nonlinear online maximum sensitivity selection-based charging algorithm (NOL-MSSCA) is proposed for coordinating PEVs that minimizes the costs associated with generation and losses considering network and bus total harmonic distortion (THD). The aim is to first attend the high priority customers and charge their vehicles as quickly as possible while postponing the service to medium and low priority consumers to the off-peak hours, considering network, battery and power quality constraints and harmonics. The vehicles were randomly plugged at different locations during a period of 24 h. The proposed PEV coordination is based on the maximum sensitivity selection (MSS), which is the sensitivity of losses (including fundamental and harmonic losses) with respect to the PEV location (PEV bus). The proposed algorithm uses the decoupled harmonic power flow (DHPF) to model the nonlinear loads (including the PEV chargers) as current harmonic sources and computes the harmonic power losses, harmonic voltages and THD of the smart grid. The MSS vectors are easily determined using the entries of the Jacobian matrix of the DHPF program, which includes the spectrums of all injected harmonics by nonlinear electric vehicle (EV) chargers and nonlinear industrial loads. The sensitivity of the objective function (fundamental and harmonic power losses) to the PEVs were then used to schedule PEVs accordingly. The algorithm successfully controls the network THDv level within the standard limit of 5% for low and moderate PEV penetrations by delaying PEV charging activities. For high PEV penetrations, the installation of passive power filters (PPFs) is suggested to reduce the THDv and manage to fully charge the PEVs. Detailed simulations considering random and coordinated charging were performed on the modified IEEE 23 kV distribution system with 22 low voltage residential networks populated with PEVs that have nonlinear battery chargers. Simulation results are provided without/with filters for different penetration levels of PEVs

Fuzzy Approach for Online Coordination of Plug-In Electric Vehicle Charging in Smart Grid

This paper proposes an online fuzzy coordination algorithm (OL-FCA) for charging plug-in electric vehicles (PEVs) in smart grid networks that will reduce the total cost of energy generation and the associated grid losses while maintaining network operation criteria such as maximum demand and node voltage profiles within their permissible limits. A recently implemented PEV coordination algorithm based on maximum sensitivity selection (MSS) optimization is improved using fuzzy reasoning. The proposed OL-FCA considers random plug-in of vehicles, time-varying market energy prices, and PEV owner preferred charging time zones based on priority selection. Impacts of uncoordinated, MSS, and fuzzy coordinated charging on total cost, gird losses, and voltage profiles are investigated by simulating different PEV penetration levels on a 449-node network with three wind distributed generation (WDG) systems. The main advantage of OL-FCA compared with the MSS PEV coordination is the reduction in the total cost it introduces within the 24h

Online Coordinated Charging of Plug-In Electric Vehicles in Smart Grid to Minimize Cost of Generating Energy and Improve Voltage Profile

This Ph.D. research highlights the negative impacts of random vehicle charging on power grid and proposes four practical PEV coordinated charging strategies that reduce network and generation costs by integrating renewable energy resources and real-time pricing while considering utility constraints and consumer concerns

Online Transformer Internal Fault Detection Based on Instantaneous Voltage and Current Measurements Considering Impact of Harmonics

This paper investigates the performance of a recently proposed online transformer internal fault detection technique and examines impact of harmonics through detailed nonlinear simulation of a transformer using three-dimensional finite element modelling. The proposed online technique is based on considering the correlation between the instantaneous input and output voltage difference (ΔV) and the input current of a particular phase as a finger print of the transformer that could be measured every cycle to identify any incipient mechanical deformation within power transformers. To precisely emulate real transformer operation under various winding mechanical deformations, a detailed three-dimensional finite-element model is developed. Detailed simulations with (non)sinusoidal excitation are performed and analysed to demonstrate the unique impact of each fault on the ΔV-I locus. Impact of harmonic order, magnitude and phase angle is also investigated. Furthermore, practical measurements have been performed to validate the effect of winding short circuit fault on the proposed ΔV-I locus without and with the impact of system harmonics