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.

Public Safety Power Shutoffs (PSPS), also known as proactive de-energizations, proactively de-energize a portion of power systems to mitigate the risk of catastrophic wildfires caused by electric infrastructure. Since the first practice in Southern California in 2012, PSPS have been widely discussed in government and industry, but seldom in the academic literature. This paper surveys the PSPS program in California, including its history, policies, and practices. In practice, PSPS present strong interactions between electric utilities and customers, yielding PSPS a trade-off problem to balance the risk of power-line-ignited wild-fires (i.e., wildfire risk) against the harms of power shutoff (i.e., PSPS risk). In this regard, this paper summarizes the industry-standard and research methods for PSPS studies, including models, data sources, and test systems. It is suggested to integrate engineering solutions with social-economic science, such as energy innovation, energy equity, and PSPS uncertainties, in future PSPS studies.

The randomness of user behaviors plays a significant role in electric vehicle (EV) scheduling problems, especially when the power supply for EV supply equipment (EVSE) is limited. Existing EV scheduling methods do not consider this limitation and assume charging session parameters, such as stay duration and energy demand values, are perfectly known, which is not realistic in practice. In this paper, based on real-world implementations of networked EVSEs on University of California at Los Angeles campus, we developed a predictive scheduling framework, including a predictive control paradigm and a kernel-based session parameter estimator. Specifically, the scheduling service periodically computes for cost-efficient solutions, considering the predicted session parameters, by the adaptive kernel-based estimator with improved estimation accuracies. We also consider the power sharing strategy of existing EVSEs and formulate the virtual load constraint to handle the future EV arrivals with unexpected energy demand. To validate the proposed framework, 20-fold cross validation is performed on the historical dataset of charging behaviors for over one-year period. The simulation results demonstrate that average unit energy cost per kWh can be reduced by 29.42% with the proposed scheduling framework and 66.71% by further integrating solar generations with the given capacity, after the initial infrastructure investment. The effectiveness of kernel-based estimator, virtual load constraint, and event-based control scheme are also discussed in detail.

A battery/supercapacitor hybrid energy storage system is developed to mitigate the battery degradation for electric vehicles. By coordinating the battery and supercapacitor, the proposed system avoids using the large bidirectional DC/DC. Through the improved topology and two added controlled switches, the battery current can be managed flexibly. Based on the battery and supercapacitor voltage, seven operation modes of battery and capacitor cooperation are designed. The control strategy is redesigned to match the modes, in which the key control parameters are calibrated based on three standard driving cycles. During driving, the proposed system calls the predefined parameter set by the cycle recognition technique. The objective of the cycle-related control is to maximize the harvest of the braking energy and minimize battery degradation in various driving styles. Taking the battery case solely as a benchmark and the infinite supercapacitors case as the largest battery degradation mitigation scenario, the battery degradation quantification of the proposed energy storage system shows more than 80% mitigation of the maximum theoretical battery degradation mitigation on urban dynamometer driving schedule (UDDS), highway fuel economy cycle (HWFET), and high-speed (US06) driving cycle, respectively. During the tested driving cycles, the simulation result indicates the battery degradation reduced by 30% more than the battery solely scenario, which proves the benefit of the proposed system.

It is a crucial yet challenging task to ensure commercial load resilience during high-impact, low-frequency extreme events. In this paper, a novel safe reinforcement learning (SRL)-based resilient proactive scheduling strategy is proposed for commercial buildings (CBs) subject to extreme weather events. It deploys the correlation between different CB components with demand response capabilities to maximize the customer comfort levels while minimizing the energy reserve cost. It also develops an SRL-based algorithm by combining deep-Q-network and conditional-value-at-risk methods to handle the uncertainties in the extreme weather events such that the impact from extreme epochs in the learning process is greatly mitigated. As a result, an optimum control decision can be derived that targets proactive scheduling goals, where exploration and exploitation are considered simultaneously. Extensive simulation results show that the proposed SRL-based proactive scheduling decisions can ensure the resilience of a commercial building while maintaining comprehensive comfort levels for the occupants.

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies.