Electric Vehicles (EVs) and hybrid Electric vehicles (HEVs) are going to reshape the future of the transportation sector. However, adopting large numbers of EVs and HEVs will impact the electric utilities as well. Managing the charging/discharging of substantial numbers of distributed batteries will be critical for the successful adoption of EVs and HEVs. Therefore, this paper presents a review study about the recent control and optimization strategies for managing the charging/discharging of EVs. The paper covers different control and operation strategies reported in the literature as well as issues related to the real time dispatching of EVs in the smart grids. In addition, challenges related to the stochastic nature of the driving characteristics of EVs are considered. Finally, some open problems related to the energy management of EVs will be presented.

Power losses in electric distribution systems are categorized into technical and non-technical losses. Technical losses (TL) refer to the heat losses in the distribution system components. Non-technical losses (NTL), however, refer to losses that come in different shapes and forms, such as tampering with smart meters, attacking the data transmitted from smart meters, malfunctioning of smart meters, and tapping service cables. To accurately detect and correct NTL, it is essential to first estimate the TL. Estimating TL requires knowledge of the network topology and cable impedances, which might not be readily available for the concerned utility company. This work proposes an algorithm with the following functions: remotely characterizing and updating the cable impedances, detecting and classifying the type of losses, estimating the TL and NTL when a smart meter is in error, estimating these losses due to tapping a power cable by a registered or an unregistered user, and estimating these losses due to a cyber-attack. Both active and reactive powers are considered. The proposed algorithm deploys a modified version of the Hamming code, which is originally used in detection and correction of errors in binary data transmission. Simulation results verify the accuracy and effectiveness of the proposed algorithm.

This paper introduces a planning model that can be used by an investor who would like to provide ancillary services (AS) to electricity markets. The proposed model helps the investor evaluate two potential options: aggregating distributed batteries in electric vehicles (EVs) or using a dedicated energy storage system (ESS). For EV aggregations in AS markets, the targeted EV fleet size, which is a function of energy tariff charged by the EV aggregator (EVA), is a key planning consideration. In the case of ESS, the physical size is the main planning consideration. Using the proposed model, the two options for maximizing the investor’s long-term payoffs are analyzed and compared by assuming the same initial investment cost for both options. Accordingly, the investor’s daily bidding strategy in AS markets is optimized for each case. The net present worth is used as the basis for comparing the two investment options, and sensitivity analyses are carried out to study the impact of planning and operation variables on the feasibility of the two options. Simulation results consider both options and discuss the results for providing AS to electricity markets.

Handling the variability and uncertainty associated with integrating large capacities of renewable energy sources (RES) into the power grid is a challenge that is increasingly influencing the power systems operation. At the same time, the growing need for desalinated water in arid areas increases the importance of suitable energy sources for sustainable operation of water desalination plants. However, as power and water system operators have traditionally operated their systems in isolation, there is a lack of understanding of the interdependence and interactions between these two systems.This paper addresses this gap by proposing a risk-based two-stage stochastic co-optimization framework that coordinates the operation of a renewable-rich power system with the operation of grid-connected reverse-osmosis water desalination plants (RO-WDP) to minimize their combined operational costs and maximize the utilization of RES. From the power system operation standpoint, the RO-WDPs are considered as controllable demand, and the proposed model integrates the energy flexibility of RO-WDPs in the day-ahead power system operation. The proposed model considers the operational constraints of both power and water desalination systems, thus co-optimizing their operation without compromising the reliable supply of power and water to end-users, while taking into account the uncertainty of the demands and RES.

The proliferation of distributed renewable generation poses many challenges to power distribution networks (DN), such as network congestion and voltage violations. Recent advances in battery technologies have made battery energy storage systems (BESS) more economically viable than ever before, which makes them suitable for many grid-scale applications. By providing grid support, distributed BESS can serve as enablers for higher penetrations of renewables at the DN. Distributed BESS can also provide ancillary services (AS), such as frequency regulation and reserves, to the bulk power grid. This article proposes a budget-constrained planning model for optimally sizing and siting distributed BESS in a DN to provide grid support for a renewable-rich DN and provide AS to the electricity market. The proposed model, formulated as a mixed-integer linear programming problem, maximizes the total profit of participating in the AS markets while supporting the operation of an unbalanced DN. Simulation results and sensitivity analyses demonstrate the merits of the proposed planning model. The accuracy of the linearized unbalanced DN model is also validated.