Islam Safak Bayram

Revenue Optimization Frameworks for Multi-Class PEV Charging Stations

The charging power of plug-in electric vehicles (PEVs) decreases significantly when the state of charge (SoC) gets closer to the fully charged state, which leads to a longer charging duration. Each time when the battery is charged at high rates, it incurs a significant degradation cost that shortens the battery life. Furthermore, the differences between demand preferences, battery types, and charging technologies make the operation of the charging stations a complex problem. Even though some of these issues have been addressed in the literature, the charging station modeling with battery models and different customer preferences have been neglected. To that end, this paper proposes two queueing-based optimization frameworks. In the first one, the goal is to maximize the system revenue for single class customers by limiting the requested SoC targets. The PEV cost function is composed of battery degradation cost, the waiting cost in the queue, and the admission fee. Under this framework, the charging station is modeled as a

Demand side management for peak reduction and PV integration in Qatar

M/G/S/K

On the evaluation of plug-in electric vehicle data of a campus charging network

queue, and the system performance is assessed based on the numerical and simulation results. In the second framework, we describe an optimal revenue model for multi-class PEVs, building upon the approach utilized in the first framework. Two charging strategies are proposed: 1) a dedicated charger model and 2) a shared charger model for the multi-class PEVs. We evaluate and compare these strategies. Results show that the proposed frameworks improve both the station performance and quality of service provided to customers. The results show that the system revenue is more than doubled when compared with the baseline scenario which includes no limitations on the requested SoC.

A survey on behind the meter energy management systems in smart grid

The electricity demand in Qatar has grown more than twofold within merely a decade. Highly subsidized electricity tariffs combined with decreased government revenues has urged local authorities to reduce the energy consumption, improve the energy efficiency, and deploy environmentally friendly renewable energy alternatives. In this study, we present the role of demand-side management (DSM) techniques for energy savings and photovoltaic (PV) renewable energy integration. DSM techniques are classified into technological, economical, and social layers. The technology layer has been supported by the government while incentive/pricing based economic layer options are yet to be deployed. To pave the way for such studies, we present evidence from Bahrains, a neighboring country with similar climate, government and social structures, new pricing policy to show the customer responsiveness on electricity prices. Moreover, we present a case study on how to use direct load control framework during summer months in Qatar. In the last section, we present the output of Qatars first large-scale PV deployment in Education City. The results show that due to local weather conditions, large-scale PV adoption can easily affect the grid operations and a more responsive load is required to mitigate the potential impacts.

Planning, Operation, and Protection of Microgrids: An Overview☆

The mass adoption of plug-in electric vehicles (PEVs) requires the deployment of public charging stations. Such facilities are expected to employ distributed generation and storage units to reduce the stress on the grid and boost sustainable transportation. While prior work has made considerable progress in deriving insights for understanding the adverse impacts of PEV chargings and how to alleviate them, a critical issue that affects the accuracy is the lack of real world PEV data. As the dynamics and pertinent design of such charging stations heavily depend on actual customer demand profile, in this paper we present and evaluate the data obtained from a 17 node charging network equipped with Level 2 chargers at a major North American University campus. The data is recorded for 166 weeks starting from late 2011. The result indicates that the majority of the customers use charging lots to extend their driving ranges. Also, the demand profile shows that there is a tremendous opportunity to employ solar generation to fuel the vehicles as there is a correlation between the peak customer demand and solar irradiation. Also, we provided a more detailed data analysis and show how to use this information in designing future sustainable charging facilities.