In the last two decades, the number of strategies for planning the maintenance of power systems have increased considerably. As societal dependence on power system infrastructure continues to grow, there is an increased need to identify the best practices in the field of power system maintenance planning to ensure the continued reliable operation of the grid. This paper presents a comprehensive and systematic state-of-the-art review of advances in power system maintenance and the significance for the field of power system reliability. As the main contributions, this paper systematically organizes the published literature, and analyses the most relevant milestones in the context of power systems adequacy and security enhancement, producing a taxonomy of the different maintenance strategies. This includes detailing existing approaches for power system maintenance planning, and providing clear definitions, models, methods, and characteristics of maintenance policy. The review also includes the most relevant standards employed in the power system maintenance field. Finally, areas requiring further research are identified alongside emerging trends in power system maintenance, to inform industry practice and support further research.

This paper provides a comprehensive review of machine learning strategies and optimization formulations employed in energy management systems (EMS) tailored for plug-in hybrid electric vehicles (PHEVs). EMS stands as a pivotal component facilitating optimized power distribution, predictive and adaptive control strategies, component health monitoring, and energy harvesting, thereby enabling the maximal exploitation of resources through optimal operation. Recent advancements have introduced innovative solutions such as Model Predictive Control (MPC), machine learning-based techniques, real-time optimization algorithms, hybrid optimization approaches, and the integration of fuzzy logic with neural networks, significantly enhancing the efficiency and performance of EMS. Additionally, multi-objective optimization, stochastic and robust optimization methods, and emerging quantum computing approaches are pushing the boundaries of EMS capabilities. Remarkable advancements have been made in data-driven modeling, decision-making, and real-time adjustments, propelling machine learning and optimization to the forefront of enhanced control systems for vehicular applications. However, despite these strides, there remain unexplored research avenues and challenges awaiting investigation. This review synthesizes existing knowledge, identifies gaps, and underscores the importance of continued inquiry to address unanswered research questions, thereby propelling the field toward further advancements in PHEV EMS design and implementation.

Aircraft taxiing is conventionally performed using the main engines’ inefficient idle thrust. Therefore, in line with greener aviation, the electrification of taxiing is the most viable option to reduce emissions, noise, and fossil fuel consumption during ground operations. This paper studies the potential of hybridising the conventional electric taxiing system, which is currently driven by the Auxiliary Power Unit, with an electrical energy storage system, comprising commercial high-energy and high-power lithium-ion batteries, for the purpose of reducing fuel consumption. Hence, a power distribution optimisation is formulated to minimise fuel consumption over a typical worst-case taxi-out profile. Three different energy management strategies are presented for a narrow-body aeroplane. The optimisation is performed for the selection of off-the-shelf batteries so that their impact on fuel savings can be evaluated in the early design stage. The study showed that a wide range of savings is achievable according to the selected strategy, the added weight allowance, and the battery characteristics. Considering a 180 kg added weight allowance and covering the three investigated strategies, up to 72% of taxiing fuel is saved.

Aircraft electrification requires novel designs to supply the growing demand for electric power onboard through efficient and reliable production and distribution of electrical energy. Moreover, the aircraft power system will be a key enabler for the integration of future technologies. Pledging to these intentions, we propose a formulation to synthesize a power system architecture that complies with safety specifications following a platform-based design methodology that optimizes the main aerospace drivers. Due to the nonlinear nature of the design problem, this article presents reliability-based MILP network design formulations for topology synthesis. The novelty of this approach relies on the adoption of network design optimization for MEA power system construction that allows explicit design formulations as MILP problems. This approach will provide an effective way to include safety specifications by introducing reliability and resiliency constraints.