Probabilistic evaluation of distribution network reliability in the presence of renewable energy sources, energy storage and electric vehicles
| dc.contributor.author | Pariyarath, Anand Maniyam | |
| dc.contributor.examiningcommittee | Filizadeh, Shaahin (Electrical and Computer Engineering) Bassuoni, Mohamed (Civil Engineering) Karki, Rajesh (Electrical and Computer Engineering, University of Saskatchewan) | en_US |
| dc.contributor.supervisor | Rajapakse, Athula (Electrical and Computer Engineering) Bagen, Bagen (Electrical and Computer Engineering) | en_US |
| dc.date.accessioned | 2021-04-19T21:25:02Z | |
| dc.date.available | 2021-04-19T21:25:02Z | |
| dc.date.copyright | 2021-04-12 | |
| dc.date.issued | 2021-03 | en_US |
| dc.date.submitted | 2021-04-12T12:32:05Z | en_US |
| dc.degree.discipline | Electrical and Computer Engineering | en_US |
| dc.degree.level | Doctor of Philosophy (Ph.D.) | en_US |
| dc.description.abstract | Increased penetration of electric vehicles (EVs) and renewable energy sources (RESs) in power systems can directly affect the system reliability and impose additional complexities to planning and operation due to their uncertainties. The traditional planning methods based on deterministic analysis fail to accurately capture the impact of the aforementioned uncertainty on the system reliability. In this thesis, a reliability-oriented distribution system analysis methodology that captures the complex interactions between EVs, photovoltaic (PV) power production, and energy storage is proposed. Firstly, a two-layer stochastic EV charging demand estimation model is proposed. The model comprises of a traffic layer representing the spatial-temporal distributions of EVs and an electrical network layer describing the impact of EV charging demand on electrical network. A Dynamic Hidden Markov model is used to capture the EV movements in the traffic layer. The ability of the traffic layer model to faithfully represent the random travel pattern of actual vehicles used by different types of drivers is examined. Secondly, a novel stochastic solar radiation model based on probability distributions of the first-order differences of hourly global solar horizontal radiation is proposed to calculate the stochastic power output of the PV system. Measured solar radiation data from four different locations with varying climate characteristics were used to evaluate the proposed model in comparison to two previously reported models. Additionally, various computational models such as the EV charging station model, reliability evaluation model, and economic evaluation model are developed to support the reliability and economic evaluation with necessary inputs. Monte Carlo simulation (MCS) is used to analyze a range of best to worst-case scenarios for more optimal outcomes. A range of sensitivity analysis is performed to illustrate the reliability and economic impact due to EV charging, PV power production and various operating strategies. Several new reliability indices are proposed to quantify the impact of EV charging characteristics, RES penetration, and energy storage system (ESS) on the reliability performance of distribution systems. Finally, an optimization algorithm along with developed stochastic models and MCS framework is used for the optimization of the resource sizes considering EV charging stations (EVCSs) life-cycle costs, reliability and emissions. | en_US |
| dc.description.note | October 2021 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1993/35451 | |
| dc.language.iso | eng | en_US |
| dc.rights | open access | en_US |
| dc.subject | Electric vehicles | en_US |
| dc.subject | Distribution system reliability | en_US |
| dc.subject | Monte Carlo simulation | en_US |
| dc.subject | Particle swam optimization | en_US |
| dc.subject | Photovoltaic generation | en_US |
| dc.subject | Battery storage | en_US |
| dc.title | Probabilistic evaluation of distribution network reliability in the presence of renewable energy sources, energy storage and electric vehicles | en_US |
| dc.type | doctoral thesis | en_US |