Abstract
A significant advancement regarding the electrification of transportation has occurred in recent years due to technological developments, environmental concerns, and geopolitical issues in the energy areas all over the world. In this study, a new concept for the integration of rail-based public transportation systems with electric vehicle (EV) parking lots operated by a park and ride strategy is propounded, including also renewable resources based energy production. In the proposed structure, the charging power demand of the EV parking lot is supplied by different charging strategies considering the existing unused energy infrastructure capacity and the regenerative braking energy of the railway system, altogether. Here, the design of a photovoltaic (PV) based carport type renewable energy production unit is also realized in the existing local parking area. The development of an optimal energy management system to effectively manage these inputs is realized and the uncertainties pertaining to EVs' demand are also taken into account. To demonstrate its efficacy, the concept is tested considering a bench of case studies and comprehensive results are obtained.

Abstract
The fast-charging units have become a more efficient and attractive option recently for reducing the challenges due to the long charging time of electric vehicles (EVs). To evaluate the impacts of the EV fast charging stations (EVFCS) on the power grid and also to assess their contributions to the system operation through the vehicle-to-grid (V2G) technology, two control methods, namely, sliding mode control (SMC) and fuzzy logic control (FLC), are developed in this study for a DC microgrid including EVFCS and distributed generation sources. In these methods, the EV battery is used as a DC source of a distribution static compensator (D-STATCOM) with the objective of mitigating the voltage sag in the microgrid. Various simulations are conducted in MATLAB Simulink/SimPowerSystems environment in order to examine the effectiveness of the proposed control approaches in terms of ensuring the stability and improving the dynamic performance of the EVFCS. The results show that considerable improvements can be achieved, especially in the case of using the SMC method.

Abstract
The large-scale introduction of distributed photovoltaic (DPV) increases the need for retailers to consider and quantify the differences in monthly electricity consumption of customers to maximize their interests in trading in the forward electricity market. For customers with DPV, retailers need to predict net electricity consumption (NEC), which is actual electricity consumption (AEC) minus DPV generation. However, the DPV is behind the meter and DPV generation data is invisible to retailers. Therefore, the issue of how to distinguish the transition of customers from no DPV to with DPV and their DPV installation information needs to be addressed. To better capture the additions of DPV timely under high penetration of DPV, a decoupling-based monthly NEC prediction model considering the DPV installation update is proposed. Firstly, the features are extracted from the hourly NEC data of known customers with DPV to distinguish other customers whether installing DPV. Secondly, an online update framework of DPV installation evaluated by two validations is proposed. Thirdly, based on the difference in the electricity consumption series before and after the installation of DPV, the NEC is decoupled into AEC and DPV generation. Finally, the monthly DPV generation prediction results are subtracted from the monthly AEC prediction results to obtain the final monthly NEC results. Different scenarios of DPV penetration are set in case studies to test the performance between the proposed model and other direct models. The results indicate the superiority of the proposed method under high penetration of DPV.

Abstract
The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration.

Abstract
Yearly, the number of Distributed Energy Resources (DER) integrated into the power grid increases has increased, having a large impact on power generation globally, promoting the introduction of renewable energy resources (RER). To increase the flexibility of the power system with integrated RER, the introduction of energy storage systems (ESS) is essential. Demand response (DR) programs also help to increase grid flexibility, resulting in increased grid reliability as grid congestion and losses decrease. However, this new paradigm shift needs further research and careful analysis. In this work, two types of DR programs are addressed to promote greater participation by different consumers features. To interconnect the different consumers, DR aggregators are inserted to ensure that individual consumers have influence on the power market. All these aspects, if accompanied by information, measurement, communication, and control systems, give rise to the smart grids, playing an essential role. The results show, considering the worst uncertainty case scenario, that there is a suitable total RER of 2151.50 kW, against 3227.30 kW, by not considering the RER uncertainty.

Abstract
Due to the considerable increase of distributed energy resources, a new model of energy trading called peer-to-peer (P2P) has emerged in local energy communities that play a key role in the proliferation of renewable energy sources. However, although local and distributed power trading allows for a more decentralized and open grid, these models have a significant impact on the control, operation, and planning of the electricity distribution grid. Thus, reducing the demand for power at an affordable price is one of the main objectives of P2P markets, considering the different voltage limits and possible congestion existing in the distribution system. Thus, the main goal of this work is to evaluate the impact of the P2P market on the distribution network operation. This work includes an energy community in a neighborhood involving nine connected houses and one school, involving different renewable technologies and energy storage systems installed in each consumer and/or prosumer. The simulation results indicate that in the presence of local distributed generation and the inclusion of energy storage devices and electric vehicles allow a high-cost reduction (16%) and a very positive impact on the distribution system in terms of congestion and voltage deviations.