The adoption of electric vehicles (EVs) is increasing due to governmental policies focused on curbing climate change. EV batteries are retired when they are no longer suitable for energy-intensive EV operations. A large number of EV batteries are expected to be retired in the next 5–10 years. These retired batteries have 70–80% average capacity left. Second-life use of these battery packs has the potential to address the increasing energy storage system (ESS) demand for the grid and also to create a circular economy for EV batteries. The needs of modern grids for frequency regulation, power smoothing, and peak shaving can be met using retired batteries. Moreover, these batteries can also be employed for revenue generation for energy arbitrage (EA). While there are articles reviewing the general applications of retired batteries, this paper presents a comprehensive review of the research work on applications of the second-life batteries (SLBs) specific to the power grid and SLB degradation. The power electronics interface and battery management systems for the SLB are also thoroughly reviewed.

In this paper, the application and future vision of Artificial Intelligence (AI)-based techniques in microgrids are presented from a cyber-security perspective of physical devices and communication networks. The vulnerabilities of microgrids are investigated under a variety of cyber-attacks targeting sensor measurements, control signals, and information sharing. With the inclusion of communication networks and smart metering devices, the attack surface has increased in microgrids, making them vulnerable to various cyber-attacks. The negative impact of such attacks may render the microgrids out-of-service, and the attacks may propagate throughout the network due to the absence of efficient mitigation approaches. AI-based techniques are being employed to tackle such data-driven cyber-attacks due to their exceptional pattern recognition and learning capabilities. AI-based methods for cyber-attack detection and mitigation that address the cyber-attacks in microgrids are summarized. A case study is presented showing the performance of AI-based cyber-attack mitigation in a distributed cooperative control-based AC microgrid. Finally, future potential research directions are provided that include the application of transfer learning and explainable AI techniques to increase the trust of AI-based models in the microgrid domain.

With the development of the electric vehicle industry, the number of batteries that are retired from vehicles is increasing rapidly, which raises critical environmental and waste issues. Second-life batteries recycled from automobiles have eighty percent of the capacity, which is a potential solution for the electricity grid application. To utilize the second-life batteries efficiently, an accurate estimation of their performance becomes a crucial portion of the optimization of cost-effectiveness. Nonetheless, few works focus on the modeling of the applications of second-life batteries. In this work, a general methodology is presented for the performance modeling and degradation prediction of second-life batteries applied in electric grid systems. The proposed method couples an electrochemical model of the battery performance, a state of health estimation method, and a revenue maximization algorithm for the application in the electric grid. The degradation of the battery is predicted under distinct charging and discharging rates. The results show that the degradation of the batteries can be slowed down, which is achieved by connecting numbers of batteries together in parallel to provide the same amount of required power. Many works aim for optimization of the operation of fresh Battery Energy Storage Systems (BESS). However, few works focus on the second-life battery applications. In this work, we present a trade-off between the revenue of the second-life battery and the service life while utilizing the battery for distinct operational strategies, i.e., arbitrage and peak shaving against Michigan’s DTE electricity utility’s Dynamic Peak Pricing (DPP) and Time of Use (TOU) tariffs. Results from case studies show that arbitrage against the TOU tariff in summer is the best choice due to its longer battery service life under the same power requirement. With the number of retired batteries set to increase over the next 10 years, this will give insight to the retired battery owners/procurers on how to increase the profitability, while making a circular economy of EV batteries more sustainable.

The increasing penetration of modern Variable Speed Wind Turbines (VSWTs) in microgrids creates the problem of frequency stabilization due to reduced inertia of the power system. To emulate the Inertia Response of the conventional synchronous machines, wind turbines can be provided with an inertia emulation controller. The modelling presented in this paper aims at equipping the modern Type D wind turbine with inertia response and primary frequency control (PFC) capabilities. Two controllers — inertial and droop, are implemented and their frequency control capabilities are compared in an isolated power system which consists of a conventional steam turbine generator and a wind farm. The results suggest that proposed controllers help in better frequency control performance in the microgrid

Many electric vehicle (EV) batteries will retire in the next 5–10 years around the globe. These batteries are retired when no longer suitable for energy-intensive EV operations despite having 70–80% capacity left. The second-life use of these battery packs has the potential to address the increasing demand for battery energy storage systems (BESSs) for the electric grid, which will also create a robust circular economy for EV batteries. This article proposes a two-layered energy management algorithm (monthly layer and daily layer) for demand charge reduction for an industrial consumer using photovoltaic (PV) panels and BESSs made of retired EV batteries. In the proposed algorithm, the monthly layer (ML) calculates the optimal dispatch for the whole month and feeds the output to the daily layer (DL), which optimizes the BESS dispatch, BESSs’ degradation, and energy imported/exported from/to the grid. The effectiveness of the proposed algorithm is tested as a case study of an industrial load using a real-world demand charge and Real-Time Pricing (RTP) tariff. Compared with energy management with no consideration of degradation or demand charge reduction, this algorithm results in 71% less degradation of BESS and 57.3% demand charge reduction for the industrial consumer.