Search Results (64)

The increasing deployment of islanded microgrids in disaster-prone and infrastructure-constrained regions has elevated the importance of resilient energy storage systems capable of supporting autonomous operation. Grid-forming energy storage (GFES) units—designed to provide frequency reference, voltage regulation, and black-start capabilities—are emerging as critical assets for maintaining both energy adequacy and dynamic stability in isolated environments. However, conventional storage planning models fail to capture the interplay between uncertain renewable generation, time-coupled operational constraints, and control-oriented performance metrics such as virtual inertia and voltage ride-through. To address this gap, this paper proposes a novel distributionally robust optimization (DRO) framework that jointly optimizes the siting and sizing of GFES under renewable and load uncertainty. The model is grounded in Wasserstein-metric DRO, allowing worst-case expectation minimization over an ambiguity set constructed from empirical historical data. A multi-period convex formulation is developed that incorporates energy balance, degradation cost, state-of-charge dynamics, black-start reserve margins, and stability-aware constraints. Frequency sensitivity and voltage compliance metrics are explicitly embedded into the optimization, enabling control-aware dispatch and resilience-informed placement of storage assets. A tractable reformulation is achieved using strong duality and solved via a nested column-and-constraint generation algorithm. The framework is validated on a modified IEEE 33-bus distribution network with high PV penetration and heterogeneous demand profiles. Case study results demonstrate that the proposed model reduces worst-case blackout duration by 17.4%, improves voltage recovery speed by 12.9%, and achieves 22.3% higher SoC utilization efficiency compared to deterministic and stochastic baselines. Furthermore, sensitivity analyses reveal that GFES deployment naturally concentrates at nodes with high dynamic control leverage, confirming the effectiveness of the control-informed robust design. This work provides a scalable, data-driven planning tool for resilient microgrid development in the face of deep temporal and structural uncertainty. Full article

Adaptive Equivalent Consumption Minimization Strategies (A-ECMSs) are one of the best methodologies to optimize fuel consumption of plug-in hybrid vehicles (PHEVs) coupled with low computational requirements. In this paper, a review of A-ECMSs is proposed. Starting from an economic-environmental contextualization, hybrid vehicles are presented and classified, together with their modeling methodologies and the physical-mathematical representation of their components. Next, the control theory for hybrid vehicles is introduced and classified, deriving the A-ECMS approach. Several works accounting for different A-ECMS implementations, based on technology integration, time horizon, adaptivity mechanism, and technique, are addressed. The literature analysis shows a broad coverage of possibilities: the simple proportional-integral (PI) rule for equivalence factor adaptivity is often used, imposing a given battery state-of-charge (SoC); it is possible to optimally plan the battery SoC trajectory through offline optimization with optimal algorithms or by predicting ahead conditions with model predictive control (MPC) or neural networks (NNs); the integration with emerging technologies such as Vehicle-To-Everything (V2X) can be helpful, accounting also for car-following data and GPS information. Moreover, speed prediction is another common technique to optimally plan the battery SoC trajectory. Depending on available on-board computational power and data, it is possible to choose the best A-ECMS according to its application. Full article

This work presents a novel and fully virtual flow for extracting the SPICE model of a power MOSFET, starting exclusively from TCAD simulations. Unlike traditional approaches that rely on experimental silicon data, our methodology enables designers to optimize the device performance and extract accurate electrical parameters before any physical prototyping is required. By leveraging advanced TCAD tools, we generate a realistic device structure and obtain all the key electrical characteristics, which are then used for precise SPICE model extraction and macromodel integration. The extracted model is dynamically validated using a gate-charge test performed identically in both the TCAD and SPICE environments, demonstrating excellent agreement with less than a 2% error in the charge quantities, Q_gs and Q_gd. This approach proves that initial silicon prototyping can be confidently bypassed, and it is highly innovative because it enables designers to achieve highly faithful device simulations before hardware fabrication. This significantly reduces the need for costly and time-consuming prototyping and design re-spins, accelerating the development process while enhancing the accuracy in terms of the transient and dynamic characteristics of MOSFETs designed for specific applications; in our case, for an e-fuse to be integrated into a more complex system. Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

This study focuses on the participation of energy storage in primary frequency regulation of offshore wind farms. A frequency regulation performance evaluation indicator is designed, and the black-start capability of the wind farm after shutdown is also considered. By equivalently processing the black-start time, a black-start capability evaluation indicator is designed. An energy storage strategy is adopted to balance power charging and discharging during the primary frequency regulation cycle. Considering the service life of energy storage batteries and the maximum number of charge/discharge cycles, a multi-objective comprehensive optimization model is proposed, which integrates frequency regulation performance, annual average investment cost of energy storage, black-start capability, and wind energy utilization rate. The designed model is solved using a genetic algorithm. Finally, a case study of an offshore wind farm is given to compare and analyze the primary frequency regulation with energy storage participation and the joint frequency regulation of wind and energy storage, which verifies the effectiveness of the proposed designed model and algorithm. Full article

This study presents an experimental and numerical investigation into the performance and control optimization of an Organic Rankine Cycle (ORC)-based micro-combined heat and power (micro-CHP) system. A steady-state, off-design, charge-sensitive model is developed to design a control strategy for an ORC micro-CHP combi-boiler, aiming to efficiently meet real-time domestic hot water demands (up to 40 °C and 35 kW) while generating up to 2 kW of electricity. The system utilizes a natural gas burner to evaporate the working fluid (R245fa), with combustion heat power, volumetric pump speed, and expander speed as control variables. Experimental and numerical evaluations generate steady-state control maps to identify optimal operating regions. A PID-based dynamic control strategy is then developed to stabilize operation during start-ups and user demand variations. The results confirm that the strategy delivers hot water within 1.5 min in simple boiler mode and 3 min in cogeneration mode while improving electricity generation stability and outperforming manual control. The findings demonstrate that integrating steady-state modeling with optimized control enhances the performance, responsiveness, and efficiency of ORC-based micro-CHP systems, making them a viable alternative for residential energy solutions. Full article

Electric vehicles (EVs) play a crucial role in achieving sustainability goals, mitigating energy crises, and reducing air pollution. However, their rapid adoption poses significant challenges to the power grid, particularly during peak charging periods, necessitating advanced load management strategies. This study introduces an artificial intelligence (AI)-integrated optimal charging framework designed to facilitate fast charging and mitigate grid stress by smoothing the “duck curve”. Data from Caltech’s Adaptive Charging Network (ACN) at the National Aeronautics and Space Administration (NASA) Jet Propulsion Laboratory (JPL) site was collected and categorized into day and night patterns to predict charging duration based on key features, including start charging time and energy requested. The AI-driven charging strategy developed optimizes energy management, reduces peak loads, and alleviates grid strain. Additionally, the study evaluates the impact of integrating 1.5 million, 3 million, and 5 million EVs under various AI-based charging strategies, demonstrating the framework’s effectiveness in managing large-scale EV adoption. The peak power consumption reaches around 22,000 MW without EVs, 25,000 MW for 1.5 million EVs, 28,000 MW for 3 million EVs, and 35,000 MW for 5 million EVs without any charging strategy. By implementing an AI-driven optimal charging optimization strategy that considers both early charging and duck curve smoothing, the peak demand is reduced by approximately 16% for 1.5 million EVs, 21.43% for 3 million EVs, and 34.29% for 5 million EVs. Full article

The integration of renewable energy sources, such as wind and solar power, at high proportions has become an inevitable trend in the development of power systems under the new power system framework. The construction of a microgrid system incorporating hydrogen energy storage and battery energy storage can leverage the complementary advantages of long-term and short-term hybrid storage, achieving power and energy balance across multiple time scales in the power system. To prevent frequent start-stop cycles of hydrogen storage devices and lithium battery storage under overcharge and overdischarge conditions, a coordinated control strategy for power distribution in a microgrid with hydrogen storage is proposed. First, a fuzzy control algorithm is used for power distribution between hydrogen storage and lithium battery storage. Then, the hydrogen storage tank’s state of health (SOH) and the lithium battery’s state of charge (SOC) are compared, with the goal of selecting a multi-stack fuel cell system operating at its optimal efficiency point, where each fuel cell stack outputs 10 kW. This further ensures that the SOC and SOH remain within reasonable ranges. Finally, simulations are conducted in MATLAB/Simulink R2018b to verify that the proposed strategy maintains stability in the DC bus and alleviates issues of overcharge and overdischarge, ensuring that both the system’s SOC and SOH remain within a reasonable range, thereby enhancing equipment lifespan and system stability. Full article

The rapidly developing battery industry entails the need for battery testing. Battery charging and discharging require time-consuming testing, so it is necessary for the battery testing laboratory to take effective personnel protection, area isolation, fire and smoke extraction, remote monitoring, and other measures. The software plays a crucial role in optimizing the scheduling of battery test labs. It might start testing on a small scale initially, but later, as demands on the lab grow, an appropriate software will be needed to schedule testing for optimal usage. This article presents the design considerations and steps of a test lab for testing and analyzing low-voltage battery cells and packs, including all components, safety requirements for safety circuitry, and the purpose of laboratory testing for industrial and research purposes. Full article

In this paper, the problem of simultaneous charging of Electrical Vehicles (EVs) in distribution networks (DNs) is examined in order to depict congestion issues, increased power losses, and voltage constraint violations. To this end, this paper proposes an optimal EV charging schedule in order to allocate the charging of EVs in non-overlapping time slots, aiming to avoid overloading conditions that could stress the DN operation. The problem is structured as a linear optimization problem in GAMS, and the linear Distflow is utilized for the power flow analysis required. The proposed approach is compared to the one where EV charging is not optimally scheduled and each EV is expected to start charging upon its arrival at the residential charging spot. Moreover, the analysis is extended to examine the optimal siting of small-sized residential Photovoltaic (PV) systems in order to provide further relief to the DN. A mixed-integer quadratic optimization model was formed to integrate the PV siting into the optimization problem as an additional optimization variable and is compared to a heuristic-based approach for determining the sites for PV installation. The proposed methodology has been applied in a typical low-voltage (LV) DN as a case study, including real power demand data for the residences and technical characteristics for the EVs. The results indicate that both the DN power losses and the voltage profile are further improved in regard to the heuristic-based approach, and the simultaneously scheduled penetration of EVs and PVs could yield up to a 66.3% power loss reduction. Full article

In the evolving landscape of logistics, drone technology presents a solution to the challenges posed by traditional ground-based deliveries, such as traffic congestion and unforeseen road closures. This research addresses the Truck–Drone Delivery Problem (TDDP), wherein a truck collaborates with a drone, acting as a mobile charging and storage unit. Although the Traveling Salesman Problem (TSP) can represent the TDDP, it becomes computationally burdensome when nodes are dynamically altered. Motivated by this limitation, our study’s primary objective is to devise a model that ensures swift execution without compromising the solution quality. We introduce two meta-heuristics: the Strawberry Plant, which refines the initial truck schedule, and Genetic Algorithms, which optimize the combined truck–drone schedule. Using “Dataset 1” and comparing with the Multi-Start Tabu Search (MSTS) algorithm, our model targeted costs to remain within 10% of the optimum and aimed for a 73% reduction in the execution time. Of the 45 evaluations, 37 met these cost parameters, with our model surpassing MSTS in eight scenarios. In contrast, using “Dataset 2” against the CPLEX solver, our model optimally addressed all 810 experiments, while CPLEX managed only 90 within the prescribed time. For 20-customer scenarios and more, CPLEX encountered memory limitations. Notably, when both methods achieved optimal outcomes, our model’s computational efficiency exceeded CPLEX by a significant margin. As the customer count increased, so did computational challenges, indicating the importance of refining our model’s strategies. Overall, these findings underscore our model’s superiority over established solvers like CPLEX and the economic advantages of drone-assisted delivery systems. Full article

Low-temperature combustion (LTC) concepts, such as homogeneous charge compression ignition (HCCI) and partially premixed combustion (PPC), aim to reduce in-cylinder temperatures in internal combustion engines, thereby lowering emissions of nitrogen oxides (NO_x) and soot. These LTC concepts are particularly attractive for decarbonizing conventional diesel engines using renewable fuels such as methanol. This paper uses numerical simulations and a finite-rate chemistry model to investigate the combustion and emission processes in LTC engines operating with pure methanol. The aim is to gain a deeper understanding of the physical and chemical processes in the engine and to identify optimal engine operation in terms of efficiency and emissions. The simulations replicated the experimentally observed trends for CO, unburned hydrocarbons (UHCs), and NO_x emissions, the required intake temperature to achieve consistent combustion phasing at different injection timings, and the distinctively different combustion heat release processes at various injection timings. It was found that the HCCI mode of engine operation required a higher intake temperature than PPC operation due to methanol’s low ignition temperature in fuel-richer mixtures. In the HCCI mode, the engine exhibited ultra-low NO_x emissions but higher emissions of UHC and CO, along with lower combustion efficiency compared to the PPC mode. This was attributed to poor combustion efficiency in the near-wall regions and engine crevices. Low emissions and high combustion efficiency are achievable in PPC modes with a start of injection around a crank angle of 30° before the top dead center. The fundamental mechanism behind the engine performance is analyzed. Full article

A predictive model for the spatiotemporal distribution of electric vehicle (EV) charging load is proposed in this paper, considering multimodal travel behavior and microscopic traffic simulation. Firstly, the characteristic variables of travel time are fitted using advanced techniques such as Gaussian mixture distribution. Simultaneously, the user’s multimodal travel behavior is delineated by introducing travel purpose transfer probabilities, thus establishing a comprehensive travel spatiotemporal model. Secondly, the improved Floyd algorithm is employed to select the optimal path, taking into account various factors including signal light status, vehicle speed, and the position of starting and ending sections. Moreover, the approach of multi-lane lane change following and the utilization of cellular automata theory are introduced. To establish a microscopic traffic simulation model, a real-time energy consumption model is integrated with the aforementioned techniques. Thirdly, the minimum regret value is leveraged in conjunction with various other factors, including driving purpose, charging station electricity price, parking cost, and more, to simulate the decision-making process of users regarding charging stations. Subsequently, an EV charging load predictive framework is proposed based on the approach driven by electricity prices and real-time interaction of coupled network information. Finally, this paper conducts large-scale simulations to analyze the spatiotemporal distribution characteristics of EV charging load using a regional transportation network in East China and a typical power distribution network as case studies, thereby validating the feasibility of the proposed method. Full article

Aiming at the problem of source-load uncertainty caused by the increasing penetration of renewable energy and the large-scale integration of electric vehicles (EVs) into modern power system, a robust optimal operation scheduling algorithm for regional integrated energy systems (RIESs) with such uncertain situations is urgently needed. Based on this background, aiming at the problem of the irregular charging demand of EV, this paper first proposes an EV charging demand model based on the trip chain theory. Secondly, a multi-RIES optimization operation model including a shared energy storage station (SESS) and integrated demand response (IDR) is established. Aiming at the uncertainty problem of renewable energy, this paper transforms this kind of problem into a dynamic robust optimization with time-varying parameters and proposes an improved robust optimization over time (ROOT) algorithm based on the scenario method and establishes an optimal scheduling mode with the minimum daily operation cost of a multi-regional integrated energy system. Finally, the proposed uncertainty analysis method is verified by an example of multi-RIES. The simulation results show that in the case of the improved ROOT proposed in this paper to solve the robust solution of renewable energy, compared with the traditional charging load demand that regards the EVs as a whole, the EV charging load demand based on the trip chain can reduce the cost of EV charging by 3.5% and the operating cost of the multi-RIES by 11.7%. With the increasing number of EVs, the choice of the starting point of the future EV trip chain is more variable, and the choice of charging methods is more abundant. Therefore, modeling the charging demand of EVs under more complex trip chains is the work that needs to be studied in the future. Full article

Leasing has proven to be a business model that is perfectly suited to the circular economy. It significantly contributes to sustainable development by enabling the reuse of machinery and equipment after each lease period and by including preventive maintenance and overhauls within and between lease terms. This helps to extend the life cycle of equipment, promote value recovery, and reduce waste. This paper examines an imperfect preventive maintenance (PM) strategy applied to equipment rented under the terms of “free leasing”. In free leasing, the lessor makes the equipment available to the customer for a specified period of time without charging rent. In return, the customer is required to purchase the equipment’s consumables exclusively from the lessor. The lessor is also responsible for the maintenance of the equipment at the customer’s premises. The greater the quantity of consumables used by the customer, the more the equipment will deteriorate. Consequently, the lessor must be able to determine the most effective approach to preventive maintenance, ensuring that it aligns with the customer’s planned usage rate while maximizing profit. This work proposes a PM strategy to be adopted by the lessor during the free lease period. This strategy involves the performance of imperfect PM actions just before the start of the lease period and then periodically. Different packages of preventive actions can be applied each time, with each package having a different cost depending on the level of effectiveness in terms of rejuvenating the equipment. Minimal repairs are performed in the event of equipment failure. The decision variables are the PM period to be adopted and the maintenance efficiency level to be chosen for each preventive intervention. The objective is to determine, for a given customer with an estimated consumption rate profile of consumables, the optimal values of these decision variables so that the lessor maximizes their profit. A mathematical model is developed to express the lessor’s average profit over each lease period. A solution procedure is developed for small instances of the problem, and an Artificial Bee Colony algorithm is implemented for larger instances. A numerical example and a sensitivity analysis are presented. Full article