This paper proposes a method to evaluate the worth of installing renewable distributed generation (DG) in distribution networks. Moreover, the work optimally allocates these DG units in the distribution network to maximize the worth of the connection to the local distribution company (LDC), as well as the customers connected to the system. The proposed methodology helps the LDC to better assess the benefits of the renewable DG units' proposed connections and to identify the optimal buses on which to connect these DG units. The benefits considered in this paper are deferral of upgrade investments, reduction of the cost of energy losses, and reliability improvement, which is represented by the interruption cost reduction. The proposed methodology takes into consideration the uncertainty and variability associated with the output power of renewable DG as well as the load variability. The planning problem of determining the optimal location and sizes of DG units is defined as multi-objective mixed integer programming.

Unlike the existing research that focuses on detecting electricity theft cyber-attacks in the consumption domain, this paper investigates electricity thefts at the distributed generation (DG) domain. In this attack, malicious customers hack into the smart meters monitoring their renewable-based DG units and manipulate their readings to claim higher supplied energy to the grid and hence falsely overcharge the utility company. Deep machine learning is investigated to detect such a malicious behavior. We aim to answer three main questions in this paper: a) What are the cyber-attack functions that can be applied by malicious customers to the generation data in order to falsely overcharge the utility company? b) What sources of data can be used in order to detect these cyber-attacks by the utility company? c) Which deep machine learning-model should be used in order to detect these cyber-attacks? Our investigation revealed that integrating various data from the DG smart meters, meteorological reports, and SCADA metering points in the training of a deep convolutional-recurrent neural network offers the highest detection rate (99.3%) and lowest false alarm (0.22%).

This paper proposes an efficient planning algorithm for allocating smart electric vehicle (EV) charging stations in remote communities. The planning problem jointly allocates and sizes a set of distributed generators (DGs) along with the EV charging stations to balance the supply with the total demand of regular loads and EV charging. The planning algorithm specifies optimal locations and sizes of the EV charging stations and DG units that minimize two conflicting objectives: 1) deployment and operation costs and 2) associated green house gas emissions, while satisfying the microgrid technical constraints. This is achieved by iteratively solving a multi-objective mixed integer non-linear program. An outer sub-problem determines the locations and sizes of the DG units and charging stations using a non-dominated sorting genetic algorithm. Given the allocation and sizing decisions, an inner sub-problem ensures smart, reliable, and eco-friendly operation of the microgrid by solving a non-linear scheduling problem. The proposed algorithm results in a Pareto frontier that captures the tradeoff between the conflicting planning objectives. Simulation studies investigate the performance of the proposed planning algorithm in order to obtain a compromise planning solution.

The electrification of the transportation sector is an important step to ensure a sustainable system. It offers a promising solution to reduce oil consumption, decrease Greenhouse Gas (GHG) emissions, improve air quality, increase transportation sector efficiency, and it can play an important role in the integration of renewable energy resources into the electric grid. However, one of the biggest bottlenecks of the electrification of vehicles is the requirement for various charging infrastructure options. Three technologies have emerged as game-changers: Dynamic Wireless Charging (DWC), Battery Swapping Stations, and Fast Charging Stations (FCSs). These advancements have the potential to increase the adoption of Electric Vehicles (EVs) by addressing critical challenges such as range anxiety and charging downtime issues of EV users. The primary focus of this work is on FCS, which currently represents the most readily available option in the market. To ensure the optimal utilization of this charging infrastructure, it is important to address the challenges related to routing EVs to the nearest FCS that can meet the EV charging requirements, such as wait time, charging duration and charging cost. Vehicle-to-Everything (V2X) and the Internet of Vehicles (IoV) will play a critical role in enhancing EV routing to the FCS. Additionally, the coordination of EV charging demand is essential to achieve grid load balancing and prevent grid overload. The main focus of this paper is to present the vision and future trends of Smart Green IoV in the era of electric vehicles for sustainable V2X connectivity, grid integration, vehicular networking, data security, and services.

This paper proposes a novel online coordination method for the charging of plug-in electric vehicles (PEVs) in smart distribution networks. The goal of the proposed method is to optimally charge the PEVs in order to maximize the PEV owners' satisfaction and to minimize system operating costs without violating power system constraints. Unlike the solutions reported in the literature, the proposed charging architecture guarantees the feasibility of the charging decisions by means of a novel prediction unit that can forecast future PEVs power demand and through an innovative two-stage optimization unit that ensures effective charging coordination. Coordinated PEV discharging also enables improved utilization of power system resources. Simulation results for a typical distribution network are provided as a demonstration of the effectiveness of the proposed architecture.

This paper proposes a novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming.