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With the rapid development of renewable energy and the increasing maturity of energy storage technology, microgrids are quickly becoming popular worldwide. The stochastic scheduling problem of microgrids can increase operational costs and resource wastage. In order to reduce operational costs and optimize resource utilization efficiency, the real-time scheduling of microgrids becomes particularly important. After collecting extensive data, reinforcement learning (RL) can provide good strategies. However, it cannot make quick and rational decisions in different environments. As a method with generalization ability, meta-learning can compensate for this deficiency. Therefore, this paper introduces a microgrid scheduling strategy based on RL and meta-learning. This method can quickly adapt to different environments with a small amount of training data, enabling rapid energy scheduling policy generation in the early stages of microgrid operation. This paper first establishes a microgrid model, including components such as energy storage, load, and distributed generation (DG). Then, we use a meta-reinforcement learning framework to train the initial scheduling strategy, considering the various operational constraints of the microgrid. The experimental results show that the MAML-based RL strategy has advantages in improving energy utilization and reducing operational costs in the early stages of microgrid operation. This research provides a new intelligent solution for microgrids' efficient, stable, and economical operation in their initial stages. [ABSTRACT FROM AUTHOR]

This paper proposes a novel cloud computingbased parallel deep reinforcement learning (DRL) energy management strategy (EMS) for connected plug-in hybrid vehicles. First, a proximal policy optimization (PPO) algorithm is developed. Since the cloud computing can reduce the computational burden of the connected vehicles, the PPO is deployed in the proposed cloud computing-based EMS. In order to improve the strategy adaptation, a parallel mechanism is proposed to achieve the information interaction with multiple vehicles. Considering the real-time control requirements, thread pool is proposed and applied in the cloud computing based parallel EMS. The thread pool-based strategy provides an efficient real-time control ability and strategy improvement solution. To verify the PPO based EMS, the dynamic programming, deep Q-network and double deep Q-network strategies are developed for comparison. It is found that the PPO can achieve similar fuel efficiency improvement with the DP strategy among the three DRL algorithms. For parallel training of multiple connected vehicles, the cloud computing-based parallel EMS improves fuel economy by approximately 7.7%. Threadpool based parallel real-time EMS reduces average time for computational interactions by 20% and further improves the fuel efficiency. The proposed strategy has the advantages of realtime control, adaptability and continuous learning for improved fuel efficiency. [ABSTRACT FROM AUTHOR]

This article proposed the architecture of a stand-alone photovoltaic connected system (SPVS) with energy storage. An SPVS with energy storage requires power management for various operating modes. A coordinate controller is often necessary to manage the change in control architecture depending on the operating mode. This proposed system contains a boost converter (BC) and a bidirectional switched quasi-Z-source DC-DC converter (BSQZSDC). The BC is used for two purposes. The first is extracting the maximum power from the PV. The second is to control the DC link voltage when the SPVS is not in MPPT mode; dual-loop PI controllers are used for this. The BSQZSDC is used for energy management and regulates DC link voltage when SPVS operates in MPPT mode. This paper addresses about the effective control approach that BSQZSDC used, i.e., the double-integral sliding mode control, to regulate the DC link voltage and battery current in various operating modes. Experimental results were carried out to depict that the controller reasonably achieves the control objectives and proper energy management. [ABSTRACT FROM AUTHOR]

The Smart Grid AMI is at the forefront of modernizing energy management for efficient energy distribution and maintaining grid reliability. However, the seamless flow of data within AMI introduces critical security challenges, necessitating innovative approaches to ensure secure data transmission. This paper addresses these challenges by proposing a Quantum Key Distribution (QKD) based on Multi-Objective Optimization (MOO) and Reinforcement Learning (RL) framework for secure data transmission within the Smart Grid AMI. Primarily, we formulate the routing problem as a MOO challenge with four primary objectives namely, energy efficiency, latency minimization, reliability, and security to capture the essence of efficient and secure data transmission. Further, we introduce an RL agent based on the Proximal Policy Optimization (PPO) algorithm for robust policy learning. The RL agent by exploring diverse routing actions and receiving rewards optimizes the routing decisions within the network environment based on the objectives specified in the multi-objective function (MOF). A novel MOF quantifies trade-offs between security and performance metrics, integrating QKD-based security metrics with traditional optimization objectives. This function guides the RL agent in making informed routing decisions. Through extensive simulations using the NS-2 simulator in a realistic Smart Grid AMI environment, the proposed approach obtained energy consumption of 750 J, latency of 55 ms, and security level of 96% and also revealed significant improvements compared to conventional techniques widely employed for secured communication in AMI networks. Overall, the simulation results exhibited that the proposed method showed outstanding performance by achieving energy efficiency of 0.95, latency reduction of 0.92, reliability improvement of 0.94, and security enhancement of 0.96. Overall, this integration offers a pioneering solution to address the evolving security challenges in the Smart Grid landscape and contributes to the advancement of secure data transmission in Smart Grid AMI networks, fostering a resilient and secure energy ecosystem. [ABSTRACT FROM AUTHOR]

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS. [ABSTRACT FROM AUTHOR]

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy. [ABSTRACT FROM AUTHOR]

Energy generation and pollutant emissions are two faces of the same coin, as the current energy sources i.e., fossil energy, are still considered to be major sources of greenhouse gases (GHG). Therefore, shifting to cleaner energy sources imposes itself as an inevitable solution to reduce this environmental cost. In this paper, a hybrid system based on the multi-agent approach and Long Short-Term Memory (LSTM) neural networks to forecast the energy production and its carbon dioxide (CO2) emissions and simulate the potential emission reduction in case of switching to renewable energy sources is presented. The proposed system's architecture consists of combining LSTM models with the agent-based technology, where multiple LSTM forecasting models were trained to forecast the production of each type of the studied energies and then estimate the equivalent emitted CO2 and calculate the influence of the renewable energy inputs on the carbon emissions and the fossil fuels consumption. The simulation process consists of two phases: firstly, each forecasting agent uses a specific LSTM model to forecast short-term energy production. Secondly, these agents send the forecasted values to the coordination agent who is responsible for calculating the total CO2 emissions and the benefits of the renewable energy inputs. [ABSTRACT FROM AUTHOR]

Non-Intrusive Load Monitoring (NILM), which provides sufficient load for the energy consumption of an entire building, has become crucial in improving the operation of energy systems. Although NILM can decompose overall energy consumption into individual electrical sub-loads, it struggles to estimate thermal-driven sub-loads such as occupants. Previous studies proposed Non-Intrusive Thermal Load Monitoring (NITLM), which disaggregates the overall thermal load into sub-loads; however, these studies evaluated only a single building. The results change for other buildings due to individual building factors, such as floor area, location, and occupancy patterns; thus, it is necessary to analyze how these factors affect the accuracy of disaggregation for accurate monitoring. In this paper, we conduct a fundamental evaluation of NITLM in various realistic office buildings to accurately disaggregate the overall thermal load into sub-loads, focusing on occupant thermal load. Through experiments, we introduce NITLM with deep learning models and evaluate these models using thermal load datasets. These thermal load datasets are generated by a building energy simulation, and its inputs for the simulation were derived from realistic data like HVAC on/off data. Such fundamental evaluation has not been done before, but insights obtained from the comparison of learning models are necessary and useful for improving learning models. Our experimental results shed light on the deep learning-based NITLM models for building-level efficient energy management systems. [ABSTRACT FROM AUTHOR]

Renewable energy systems are mostly used in the world due to their inexhaustible and nonpolluting production. As a result of a large utilization of these energy sources in different areas, the electricity production rate is increasing every day. Previous studies clarified uses, modeling, configuration, energy management operation, and optimization objectives based on different energy sources. For this reason, this paper focuses on an overview of multi energy systems as renewable and conventional power sources with the integration of an energy storage system coupled to the on-off electrical network. Furthermore, a survey is done regarding global energy production, configuration energy systems, energy storage systems, power management strategies, and optimization methods based on different hybrid energy systems. Multiple optimization approaches have been implemented to reach the global best solution for the hybrid power systems. To ensure the best optimization result, it is preferable to take hybrid optimization methods into consideration. These methods have been invented recently and have proved their efficacy and performance mainly in power systems. [ABSTRACT FROM AUTHOR]

The service life and fuel consumption of fuel cell system (FCS) are the main factors limiting the commercialization of fuel cell electric vehicles (FCEV). Effective energy management strategies (EMS) can reduce fuel consumption during the cycle and prolong the service life of FCS. This paper proposes an energy management strategy based on the deep reinforcement learning (DRL) algorithm, deep Q-learning (DQL). Considering the unstable performance of conventional DQL during the training process, a new algorithm called Double Deep Q Learning (DDQL) is introduced. The DDQL uses a target evaluation network to evaluate output actions and a delayed update strategy to improve the convergence and stability of DRL. This article trains the strategy using UDDS cycle, tests it using combined cycles UDDS-WLTC-NEDC, and compares it with traditional ECM-based EMS. The results demonstrate that under the combined cycle, the strategy effectively reduced FCS voltage degradation by 50%, maintained fuel economy, and ensured consistency between the initial and final state of charge (SOC) of LIB. [ABSTRACT FROM AUTHOR]

Energy management strategies (EMSs) are one of the key technologies for the development of plug-in hybrid electric buses (PHEBs). This paper addresses the issue of optimal energy distribution for PHEBs under significant variations in passenger load at different bus stations, which cannot be solved by a single equivalent factor equivalent fuel consumption minimization energy management strategy (ECMS). An adaptive equivalent factor equivalent fuel consumption minimization energy management strategy (A-ECMS) considering passenger load is proposed. First, the powertrain system of the PHEB is modeled, and the accuracy of the model is verified in a simulation environment. Then, the reference SOC descent trajectory of the battery is obtained using a dynamic programming (DP) algorithm, the recursive least squares (RLS) method is employed to identify the passenger load, and the influence of different loads on the state of charge (SOC) trajectory under a single equivalent factor is analyzed. Finally, a genetic algorithm (GA) is used to establish the correspondence between passenger load, bus station, and equivalent factor, enabling the actual SOC to follow the reference SOC descent trajectory, thereby achieving optimal energy distribution. Simulation results demonstrate that the A-ECMS reduces fuel consumption of the PHEB per 100 km by 2.59% and 10.10% compared to the ECMS and rule-based EMS, respectively, validating the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

With the progress of modern times, automobile technology has become integral to human society. At the same time, the need for energy has also grown. In parallel, the total amount of waste energy that is liberated from different parts of the automobile has also increased. In this ever-increasing energy demand pool, future energy shortages and environmental pollution are the primary concerns. A thermoelectric generator (TEG) is a promising technology that utilizes waste heat and converts it into useful electrical power, which can reduce fuel consumption to a significant extent. This paper comprehensively reviews automobile thermoelectric generators and their technological advancements. The review begins by classifying different waste heat technologies and discussing the superiority of TEGs over the other existing technologies. Then, we demonstrate the basic concept of and advancements in new high-performance TEG materials. Following that, improvements and associated challenges with various aspects, such as the heat exchanger design, including metal foam, extended body, intermediate fluid and heat pipe, leg geometry design, segmentation, and multi-staging, are discussed extensively. Finally, the present study highlights research guidelines for TEG design, research gaps, and future directions for innovative works in automobile TEG technologies. [ABSTRACT FROM AUTHOR]

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS. • A controllable neural model for energy management of fuel cell vehicles is proposed. • Neural regressors and classifiers are trained under single-step and multi-step inputs. • A shooting method and an adaptive method for neural models are proposed. • The proposed method is comprehensively analyzed and compared with benchmarks. [ABSTRACT FROM AUTHOR]

This paper proposes an energy management strategy (EMS) based on double‐deep Q‐Networks (DDQN) with demand torque prediction (DTP) to optimize the fuel consumption of hybrid electric vehicles (HEVs) by online utilizing vehicle‐to‐vehicle (V2V) information. The main framework of the EMS is designed as DDQN, combining Q‐learning with the deep neural network to realize real‐time training and online optimization of the torque distribution between the internal combustion engine and the electric motor, and given that the model training easily falls into local minima and overestimation, two different Q‐networks are designed to decouple action selection and target evaluation. Meanwhile, BP‐network predicts future demand torque using the ego vehicle speed, front vehicle speed, and distance between the two vehicles to reduce the burden of model training due to the introduction of traffic information. The predicted demand torque is introduced into the DDQN‐based EMS as states, together with the distance traveled in the driving cycle and the current information on HEV. The effectiveness and adaptability to actual driving cycles of the proposed strategy are verified by comparison with DDQN‐based EMS without DTP and rule‐based EMS. The results show that the fuel consumption using DDQN‐based EMS without DTP is reduced by 4.1% compared with rule‐based EMS. The introduction of demand torque prediction resulted in a further 3.9% reduction in fuel consumption. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Intelligent load scheduling for the smart city is essential due to the growth of industrialization, which directly consumes energy from the grid. The energy management system in industrialization is enhanced using the Internet of things in the same domain. The general need in a smart city is to maintain the energy utilization and cost that the industrial Internet of things could achieve for industry application. Researchers have also found a solution to this problem, but every time the generator cannot sell energy, the industry purchases the same. There is a chance for the industry to supply its energy to other industries or the grid. By keeping this constraint, an intelligent approach to load scheduling based on optimized Q-Net is proposed in this paper, comprising the supplies as renewable energy, battery, and industrial energy. Also, the reactive power flow of smart cities during scheduling is mitigated by classifying the energy available in the bus network. The proposed method scheduled demand power with 99% accuracy with the lowest cost of 13.7041$/KW for peak hr. scheduling with a demand of 6000KW/hr. [ABSTRACT FROM AUTHOR]

Forecasts and their corresponding optimized operation plans for energy plants never match perfectly, especially if they have a horizon of several days. In this paper, we suggest a concept to cope with uncertain load forecasts by reserving a share of the energy storage system for short-term balancing. Depending on the amount of uncertainty in the load forecasts, we schedule the energy system with a specific reduced storage capacity at the day-ahead market. For the day of delivery, we examine the optimal thresholds when the remaining capacity should be used to balance differences between forecast and reality at the intraday market. With the help of a case study for a simple sector-coupled energy system with a demand for cooling, it is shown that the energy costs could be reduced by up to 10% using the optimal reserve share. The optimal reserve share depends on the forecast quality and the time series of loads and prices. Generally, the trends and qualitative results can be transferred to other systems. However, of course, an individual evaluation before the realization is recommended. [ABSTRACT FROM AUTHOR]

This paper presents innovative machine-learning solutions to enhance energy efficiency in electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural model's loss function, a classifier tailored to precisely recommend optimal loss functions based on the measurement data is designed. This classifier recommends which model, equipped with given loss functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in the optimal adjustment of the loss function to a specific measurement vector, which allows for better reconstructions than that by traditional models trained based on a constant loss function. This study presents a methodology enabling the development of an optimal loss function classifier to determine the optimal model and loss function for specific datasets. The approach eliminates the randomness inherent in traditional methods, leading to more accurate and reliable reconstructions. In order to achieve the set goal, four models based on a simple LSTM network structure were first trained, each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared error). The best classifier training results were obtained for support vector machines. The quality of the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the classifier demonstrated effective performance, consistently recommending models that produced reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in improved energy management and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, an event-triggered distributed secondary control, along with an energy management algorithm, was developed to ensure the voltage stability and power management of a DC microgrid containing batteries and renewable energy sources, such as PV systems and wind turbines. The energy management algorithm, employing fuzzy logic control, governs power flow based on the generation status of sources and the charging rate of the battery. Consequently, the control algorithm shields the battery from overcharging and over-discharging situations, simultaneously ensuring energy quality within the microgrid. Sampled-data-based event-triggered control is integrated into the proposed distributed secondary control to alleviate communication burdens between controllers, effectively avoiding Zeno behavior. To demonstrate the efficacy of the proposed control algorithm, several experimental studies were conducted on a real DC microgrid prototype. The results obtained confirmed the controller's effectiveness. With the proposed control algorithm, autonomous control has been developed to ensure the safe and continuous operation of loads in island-mode microgrids, incorporating PV systems, wind turbines, and batteries, while also minimizing communication overhead. This control system adeptly manages power flow, safeguards the battery against overcharging and over-discharging, and optimizes the efficiency of intermittent energy sources. [ABSTRACT FROM AUTHOR]

The DOC (diesel oxidation catalyst), DPF (diesel particulate filter), SCR (selective catalytic reduction), and ASC (ammonia slip catalyst) are widely used in diesel exhaust after-treatment systems. The thermal management of after-treatment systems using DOC, DPF, SCR, and ASC were investigated to improve the efficiency of these devices. This paper aims to identify the challenges of this topic and seek novel methods to control the temperature. Insulation methods and catalysts decrease the energy required for thermal management, which improves the efficiency of thermal management. Thermal insulation decreases the heat loss of the exhaust gas, which can reduce the after-treatment light-off time. The DOC light-off time was reduced by 75% under adiabatic conditions. A 400 W microwave can heat the DPF to the soot oxidation temperature of 873 K at a regeneration time of 150 s. An SCR burner can decrease NOx emissions by 93.5%. Electrically heated catalysts can decrease CO, HC, and NOx emissions by 80%, 80%, and 66%, respectively. Phase-change materials can control the SCR temperature with a two-thirds reduction in NOx emissions. Pt-Pd application in the catalyst can decrease the CO light-off temperature to 113 °C. Approaches of catalysts can enhance the efficiency of the after-treatment systems and reduce the energy consumption of thermal management. [ABSTRACT FROM AUTHOR]

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP. [ABSTRACT FROM AUTHOR]

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station. [ABSTRACT FROM AUTHOR]

The hybrid combination of hydrogen fuel cells (FCs) and batteries has emerged as a promising solution for efficient and eco-friendly power supply in maritime applications. Yet, ensuring high-quality and cost-effective energy supply presents challenges. Addressing these goals requires effective coordination among multiple FC stacks, batteries, and cold-ironing. Although there has been previous work focusing it, the unique maritime load characteristics, variable cruise plans, and diverse fuel cell system architectures introduce additional complexities and therefore worth to be further studied. Motivated by it, a two-layer energy management system (EMS) is presented in this paper to enhance shipping fuel efficiency. The first layer of the EMS, executed offline, optimizes day-ahead power generation plans based on the vessel's next-day cruises. To further enhance the EMS's effectiveness in dynamic real-time situations, the second layer, conducted online, dynamically adjusts power splitting decisions based on the output from the first layer and instantaneous load information. This dual-layer approach optimally exploits the maritime environment and the fuel cell features. The presented method provides valuable utility in the development of control strategies for hybrid powertrains, thereby enabling the optimization of power generation plans and dynamic adjustment of power splitting decisions in response to load variations. Through comprehensive case studies, the effectiveness of the proposed EMS is evaluated, thereby showcasing its ability to improve system performance, enhance fuel efficiency (potential fuel savings of up to 28%), and support sustainable maritime operations. [Display omitted] • Hybrid PEM fuel cell/battery system enables zero-emission shipping. • Two-layer EMS enhances fuel efficiency while satisfying real-time load requirements. • Rotational sequential distribution prevents uneven degradation. • Rotational sequential distribution enhances computational efficiency. [ABSTRACT FROM AUTHOR]

This paper presents a framework for the efficient design and evaluation of a standalone hybrid renewable energy system (HRES) to meet the energy requirements of a rural community in the north-eastern region of Nigeria. The proposed microgrid system incorporates solar photovoltaic, wind turbines, biomass gasifier, fuel cell, and Battery storage. The sizing of each component is determined through the utilization of real local meteorological data and the load demand over a year, employing the Levy flight-salp swarm algorithms (LF-SSA). The optimization objective is to minimize the annualized system cost (ASC) of the HRES, while also considering the reliability constraint of Loss of power supply probability (LPSP). The comparative outcomes demonstrate that the LF-SSA surpasses other examined algorithms, namely the salp swarm, genetic algorithm, and HOMER software by achieving substantial cost savings of $29033, $50796, $191771 respectively in relation to the proposed HRES. The result further shows that, the LF-SSA provides the lowest LCOE of $0.933162/kWh, in contrast to SSA at $0.947737/kWh, GA at $0.958660/kWh, and HOMER at $1.075351/kWh. Additionally, the results indicate that the implemented Energy Management System (EMS) has successfully facilitated the establishment of an environmentally friendly and cost-effective energy system. • An efficient energy storage system is essential for managing intermittent energy supply. • The fuel cell can generate DC power by combining hydrogen with oxygen. • The amount of power generated by a PV panel depends on temperature and solar radiation intensity. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

The global aluminium industry faces a serious challenge in reducing greenhouse gas emissions as the demand for aluminium continues to increase. The aluminium industry has a responsibility to streamline its energy consumption, especially in the production process. There have been many studies discussing energy consumption performance in the industry. However, most of them only discuss energy saving partially, without involving energy consumption with various items produced. This paper proposes an energy savings measurement in the manufacturing industry. An energy baseline consumed per unit volume has been developed using the equivalent volume method with an energy management control system (EMCS). The study takes a case example from the automotive--aluminium component industry. The steps taken in the study are: examining the production process, converting the production volume to equivalent, calculating the energy consumption ratio, developing an energy baseline, simulating the savings performance, and then proposing an EMCS with key performance indicators (KPI) for sustainability. The results show that the development of a baseline using the ratio of energy and an equivalent volume of production gives a better data correlation with an R² value close to one. From the baseline, the bestdemonstrated performance (BDP) can be used as a reference to set energy goals. Furthermore, the data and the deviant deleted have the same baseline value. They differ in energy reduction goal percentages. The practical application of this study is not only in the manufacturing industry but in other industries as well, such as building management. This study contributes to energy savings achieved with EMCS. [ABSTRACT FROM AUTHOR]

• Provide an overview of NMG as a cyber-physical system and power balance methods. • Comprehensive review of hierarchical EMS for NMG and bidirectional PX scenarios. • Discuss the future hierarchical EMS trends for NMGs. A networked microgrid (NMG) is a novel cyber-physical system that provides power to urban and rural communities. The benefits of NMG that coupled with hierarchical energy management system (EMS) include increased renewable energy utilization, reliability, system efficiency, and flexibility in inter-microgrid (MG) power exchange (PX). Nevertheless, achieving secure and optimal NMG operation would require hierarchical EMS strategies that can manage uncertainties such as renewable generation fluctuations, load variation, energy prices, and flexible bidirectional PX. This paper outlines the NMG concept and various power balance methods. Subsequently, a comprehensive review of hierarchical EMS designs for NMG is presented. Four NMG models are introduced, and a comparison of various EMS strategies based on objectives, constraints, and optimization methods is also elaborated. Additionally, this paper addresses flexible bidirectional power flow between multiple MGs and the utility grid (UG) within three PX scenarios (MG-to-MG, MG-to-UG, and UG-to-MG). The advantages and challenges of hierarchical EMS for NMG with bidirectional power flow are also discussed considering grid stability, economic viability, and environmental impact. Finally, future research directions for enhancing the NMG model for optimal inter-MG PX are established. It highlights the efficient hierarchical EMS strategies and optimization algorithms for more reliable, affordable, and sustainable NMG operation. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

• A hierarchical convex optimization framework for HEV-PCC is proposed. • A TSI-MPC approach is designed to address the speed planning problem. • An energy management strategy that combines regression model with PMP is designed. Predictive Cruise Control (PCC) achieves automated driving with improved vehicle economy by incorporating road gradient information from high-precision maps. However, existing research on PCC has primarily centered around conventional gasoline vehicles. Hybrid Electric Vehicle (HEV) offer superior fuel efficiency compared to their traditional counterparts. To further enhance vehicle economy by integrating the PCC system with HEVs, this paper proposes a hierarchical control architecture for HEV-PCC based on convex optimization. Firstly, considering spatial slope information from the road ahead, this paper introduces a Time-Space Integration Model Predictive Control (TSI-MPC) based on the principles of kinetic energy. Secondly, the paper combines a current regression model with the Pontryagin Minimum Principle (PMP) to present a Regression Analysis - Pontryagin Minimum Principle (RA-PMP) approach for the direct analytical solution of the energy management problem. Finally, the strategy is validated through simulation analysis and Hardware-in-Loop (HIL) testing on various real road scenarios. The result indicate that, in comparison to the Equivalent Consumption Minimization Strategy (ECMS) with constant speed cruising (CC), this method achieves an average fuel economy improvement of 9.45%. [ABSTRACT FROM AUTHOR]

In recent years, the severe environmental degradation and high levels of fossil fuel consumption linked to conventional ship energy systems have drawn attention to the advancement of alternative ship energy systems. Consequently, ship energy systems based on the use of an electrical microgrid are coming to the fore as an increasingly popular alternative solution. However, managing the energy flows within a shipboard microgrid can be highly challenging due to the multiple energy sources (including renewable energy sources) and types of loads involved. Additionally, the integration of an energy storage system has been identified as an effective solution for improving the reliability of shipboard power systems, pointing out the important role of energy storage systems in maritime microgrids and their potential to enhance the energy management process. As such, effective energy management strategies are necessary to optimize performance and ensure that the vessel's energy demands are met while minimizing fuel consumption and reducing cost and environmental impacts. This paper presents a comprehensive review of such strategies and methods recently presented in the literature associated with energy management in shipboard microgrids integrating energy storage systems and examine the different techniques that can be utilized to achieve optimal system performance. Moreover, the paper also sheds light on the future challenges and perspectives imposed by the modern shipboard microgrids operation on the energy/power management of these systems. The aim of this paper is to provide a helpful guideline for researchers and engineers conducting research in the field of electrically powered ships. [Display omitted] • A comprehensive review of EMS for electrical and hybrid ship is presented. • A comprehensive review of ESS which can be used in such ships is presented. • Technologies of ESS and their suitability to ships are discussed and commented. • Future challenges and perspectives imposed by modern SMG operation are presented. [ABSTRACT FROM AUTHOR]

• TIP-EMS enhances EV energy efficiency by integrated prediction and energy management. • Significant driving economy boost; 1.6%-4.2% better than state-of-the-art PEMS. • TIP-EMS shows robust performance with varying prediction weight adjustments. To enhance the energy efficiency of electrified vehicles (EVs), developing effective energy management strategies (EMS) for hybrid storage systems is essential. Predictive EMS (PEMS) that foresee future vehicle speeds have demonstrated substantial potential in boosting EMS performance. However, traditional PEMS models, employing a sequential approach of speed prediction followed by energy allocation, are hampered by cumulative errors. These errors from the initial speed predss this issue, this paper introduces a novel solution: the trainable integrated preiction negatively impact the efficiency of subsequent energy management. To addrediction and energy management strategy (TIP-EMS). Contrasting with conventional sequential PEMS, TIP-EMS features a dual-branch, integrated neural network, which is fully trainable. This network processes driving status inputs via attention layers, with one branch dedicated to energy management objectives using a reinforcement learning (RL) algorithm, and the other to vehicle speed prediction. Both branches are trained simultaneously, but post-training, only the RL branch is activated for energy management. Implemented with a soft actor-critic RL algorithm, TIP-EMS is applied to a fuel cell EV for optimized energy management. The validation involved training TIP-EMS using 27 driving profiles, which developed its prediction and energy management capabilities, followed by tests in untrained scenarios. The results show that TIP-EMS surpasses conventional sequential PEMS by up to 4.2% in scenarios where prediction accuracies are comparable, highlighting the efficacy of the trainable integrated mechanism. In addition, TIP-EMS exhibits superior energy conservation compared to non-predictive RL strategies. Lastly, TIP-EMS exhibits robustness to adjustments in the weight given to the prediction objective, further confirming its practical applicability. [ABSTRACT FROM AUTHOR]

Integrating tasks with different functions and criticality levels onto the same hardware platform forms mixed-criticality systems (MCSs). Semi-clairvoyant scheduling, in which the mode transition is revealed at the arrival of high-criticality tasks, has attracted researchers' attention. Most studies focus on the schedulability test of MCS and do not consider energy consumption. In this paper, we simultaneously consider fault tolerance, energy management, and semi-clairvoyant scheduling in MCS. Firstly, we propose a schedulability test based on compensating adaptive mixed-criticality called CAMC-sem-cp. Secondly, we propose an energy-efficient scheduling algorithm based on the CAMC-sem-cp, called EE-CAMC-sem-cp. Finally, we conduct experiments to evaluate the performance of the proposed algorithms. The experimental results show that EE-CAMC-sem-cp can save 47.84% of the energy consumption compared to CAMC-sem-cp. [ABSTRACT FROM AUTHOR]

• Unique approach: load response focus in microgrid energy management. • Economic load response model: simplicity with price elasticity. • Balancing simplicity and efficiency: expressing microgrid operations. • Mathematical load response modeling: assessing consumer behavior. • Economic load response for consumption changes estimation with trade-offs. This paper introduces a pioneering approach to power system optimization by integrating microgrids and demand-side management, with a focus on its novel contributions. Emphasizing cost reduction, the study targets effective demand management through load shifting. A unique microgrid energy management system is proposed for peak-shaving, leveraging the cost advantages of lower controllable unit expenses. The system actively involves microgrid units in responsive load management, reducing peak consumption during high-demand periods. Evaluation of various demand-side management techniques, especially peak-time rebate programs, is conducted through presented indices measuring the energy management solution's effectiveness. To address the complexity, a particle swarm-based algorithm is introduced for computational efficiency. Validation of the proposed method and model in diverse scenarios showcases significant cost reduction, demonstrating the practicality and innovation of the integrated microgrid and demand-side management strategy. The study highlights a substantial reduction in peak load through load response program participation. At a 20 % participation rate with a 10-cent incentive, there's a significant decrease in peak load during specific hours. With increasing participation and incentives, peak load reduction intensifies, reaching optimal results at a 40 % participation rate and a 20-cent incentive. [ABSTRACT FROM AUTHOR]

The environmental impact, economic considerations, and passenger comfort of hybrid electric propulsion systems (HEPSs) are significantly influenced by the design phase. However, a design method solely focused on minimizing annual fuel consumption may inadvertently lead to increased life-cycle cost and decreased annual pure-electric sailing time. To address this issue, this paper presents a tri-objective optimization design method to achieve an optimal trade-off among the objectives for a polar mini-cruise ship. First, a scalable mathematical model is established for a hybrid diesel engines/batteries/shore power propulsion system. Then, a refined, rule-based energy management strategy is developed to control the power flow among energy sources. Next, a tri-objective optimization model with detailed descriptions of objectives, variables, constraints, and method is formulated. Finally, the performances of three optimal designs, obtained through tri-objective optimization, single-objective optimization, and conventional heuristics, are compared through simulations based on a typical real-world navigation case. The results highlight the clear advantages of the proposed method over the other two methods. Furthermore, sensitivity analysis underscores that the parameters of the diesel engine, battery, and gear ratio exert a greater impact on HEPS performances than other factors. • A scalable mathematical model of a hybrid electric propulsion system for a polar mini-cruise ship is established in detail. • In addition to the annual fuel consumption and life-cycle cost, annual pure-electric sailing time is considered. • A refined rule-based energy management strategy is established to coordinate energy flow among two engines and batteries. • A tri-objective optimal design with the interaction between the NSGA-II algorithm and the scalable HEPS model is proposed. • Sufficient comparisons verify the superiority of the proposed method based on a typical real-world navigation case. [ABSTRACT FROM AUTHOR]

This paper presents a new prosumer-centric peer-to-peer (P2P) energy trading framework incorporating the network-secure export–import limits, known as dynamic operating envelope (DOE), for prosumers considering their preferences to trade in the local market. In the proposed framework, a DOE-integrated energy management algorithm is developed for prosumers considering electric vehicles (EVs) charge–discharge and photovoltaic (PV) systems. The framework allows prosumers to operate within defined DOE, aligning with the preferences of EV-owning prosumers by managing the desired EV battery charge levels at their departure. An auction-based P2P energy trading mechanism for prosumers is proposed incorporating the allocated DOE to ensure secure network operation and valuing prosumers' preferences to benefit them. The proposed framework is implemented on a 78-node low-voltage (LV) residential distribution network to evaluate its performance. The results and comparative analyses verify the effectiveness of the proposed framework in addressing network issues such as voltage and line congestion problems, meeting prosumers' preferences in asset management, and providing benefits to all participants in the energy trading process. • A prosumer-centric P2P energy trading using network-secure export–import limits. • Hybrid DER marketplace model-based framework consisting of the DSO and the DMO. • Energy management within the limits considering EVs charge–discharge and PV systems. • DOE-integrated Double auction-based market mechanism for P2P energy. • Fairness in allocating DOEs with high RPI, PIEI, QoS, QoE and MiM values. [ABSTRACT FROM AUTHOR]

In this paper, the ELEKTRA framework is introduced, a novel community energy management system that organizes the prosumers into autonomous communities and determines the prosumers' optimal energy procurement in a distributed manner. Our motivation stems from the increasing complexity of the energy procurement for prosumers and the need for more efficient and decentralized approaches to optimize their energy consumption. The first element of the ELEKTRA framework implements an autonomous hedonic game-theoretic model at the prosumers' home energy management controllers, considering the prosumers' individual energy consumption and generation patterns, as well as the utility-provided rewards for proactive participation. Specifically, the prosumer grouping is modeled as a hedonic game, demonstrating the existence of a Nash-stable and individually-stable prosumer grouping. The second element of the ELEKTRA framework employs a distributed non-cooperative game-theoretic approach. This addresses how prosumers strategically consume energy to meet their needs while maximizing their payoff by procuring additional energy from the utility company. Also, utilizing the theory of n -person concave games allows for a thorough evaluation of accuracy, performance, and complexity in determining optimal energy consumption for prosumers. A comprehensive evaluation of the ELEKTRA framework is conducted using real data from the southwest region of USA. The results demonstrate the operational effectiveness of the ELEKTRA framework, showcasing its superiority in optimizing prosumer payoff compared to existing models. The performance assessment, grounded in real-world data, provides valuable insights into the efficacy of the ELEKTRA framework in contrast to current state-of-the-art. [ABSTRACT FROM AUTHOR]

Energy demand from the built environment is among the most important contributors to greenhouse gas emissions. One promising way to curtail these emissions is through innovative energy management systems (EMS's). These systems often rely on access to real-world demand data, which remains elusive in practice. Even when available, energy demand data typically suffers from missing data as well as many irregularities and anomalies. This precludes the application of many off-the-shelf machine learning algorithms for time series analysis and modelling, necessary for downstream energy management. Transforming energy demand time series to low dimensional feature matrices has been shown to work well in determining similar buildings and predicting meta-data, both of which can be used to create better forecast algorithms used as input in EMS's. These studies are, however, often marred by the limited size of datasets, as well as the non-interpretable nature of extracted features. This paper addresses these concerns and makes several important contributions: (1) it collates several open-source datasets to create a large meta-analysis dataset containing energy demand data for over 13,000 buildings; (2) it investigates the use of different interpretable feature extraction methods on this collated dataset; and (3) it shows that this feature matrix can be used more generally to determine similar buildings and predict building properties such as missing meta-data. The large feature matrix resulting from the work is open-sourced as part of a web-based dashboard to enable the community to reproduce and further develop our results. • A large building energy demand dataset containing over 13,000 buildings is created. • Different interpretable time series feature extraction methods are investigated. • Features show considerable discriminative power in detecting similar buildings. • Extracted features can be used to predict missing meta-data in building datasets. [ABSTRACT FROM AUTHOR]

The design of energy management strategy (EMS) that can be applied in real-time with nearly optimal fuel economy for connected plug-in hybrid electric vehicles (C-PHEVs) is an urgent need at present, especially in the context where the benefits of rapid development of intelligent transportation system are increasingly evident. Equivalent consumption minimization strategy (ECMS) is able to achieve the desired performance, on the premise that the equivalent factor (EF) is adjusted reasonably. To achieve this goal, a variable optimization domain-based cooperative EMS for C-PHEVs is proposed in this paper. First, an offline sub-cycle division method is designed, which is effectively utilized to construct a fitting function to output reference EF based on the latest driving information. Second, the theoretical relationship between EF and operating efficiency of the driving components is explored, and the variable optimization domain of EF is designed to avoid inefficient operation of engine. Third, the EF optimization is then simplified into a quadratic problem within the designed domain, which is rapidly solved online with the golden section search algorithm. Finally, the comparison work is conducted under two real-world driving cycles. The results exhibit that the proposed strategy improves the fuel economy of C-PHEV by 6.440 % and 7.936 % over that using the conventional adaptive ECMS (CA-ECMS), respectively. The average engine fuel consumption rates of the proposed strategy are significantly reduced compared with that of CA-ECMS. Moreover, the effectiveness of the proposed strategy is verified on the experiment platform, demonstrating its application prospects. • A sub-cycle division method is proposed to construct fitting function for online optimization. • The theoretical relationship between EF and engine efficiency is explored. • A variable optimization domain-based cooperative EMS is designed for C-PHEV. • The application performance of the proposed strategy is verified on the hybrid test bench. [ABSTRACT FROM AUTHOR]

Fuel cell electric vehicles have earned substantial attentions in recent decades due to their high-efficiency and zero-emission features, while the high operating costs remain the major barrier towards their large-scale commercialization. In such context, this paper aims to devise an energy management strategy for an urban postal-delivery fuel cell electric vehicle for operating cost mitigation. First, a data-driven dual-loop spatial-domain battery state-of-charge reference estimator is designed to guide battery energy depletion, which is trained by real-world driving data collected in postal delivery missions. Then, a fuzzy C-means clustering enhanced Markov speed predictor is utilized to project the upcoming velocity. Lastly, combining the state-of-charge reference and the forecasted speed, a model predictive control-based cost-optimization energy management strategy is established to mitigate vehicle operating costs imposed by energy consumption and power-source degradations. Validation results have shown that 1) the proposed strategy could mitigate the operating cost by 4.43 % and 7.30 % in average versus benchmark strategies, denoting its superiority in term of cost-reduction and 2) the computation burden per step of the proposed strategy is averaged at 0.123 ms, less than the sampling time interval 1s, proving its potential of real-time applications. • A cost-optimization energy management is devised for postal-delivery FCEVs. • A data-driven dual-loop spatial-domain battery SoC reference estimator is devised. • Both energy consumption and power source durability are accounted. • A comprehensive evaluation of power-allocation and operating costs is conducted. [ABSTRACT FROM AUTHOR]

In this paper, a real time implementable load coordination strategy is developed for the optimization of electric demands in a smart home. The strategy minimizes the electricity cost to the home owner, while limiting the disruptions associated with the deferring of flexible power loads. A multi-objective nonlinear mixed integer programming is formulated as a sequential model predictive control, which is then solved using genetic algorithm. The load shifting benefits obtained by deploying an advanced coordination strategy are compared against a baseline controller for various home characteristics, such as location, size and equipment. The simulation study shows that the deployment of the smart home energy management strategy achieves approximately 5% reduction in grid cost compared to a baseline strategy. This is achieved by deferring approximately 50% of the flexible loads, which is possible due to the use of the stationary energy storage. • Real-time load coordination strategy for optimizing electrical loads in smart homes. • Inclusion of renewable energy sources and stationary batteries in the coordination. • Definition of generalizable metrics for the evaluation of load shifting strategy. • Improvements in improved energy efficiency and reduced electricity costs. • Comprehensive analysis that considers diverse home characteristics such as location. [ABSTRACT FROM AUTHOR]

Data analysis is one of the most important tools that can contribute to unveiling critical relationships. Econometric approaches are evolving continuously, while older methods although are not completely abandoned, they may be used vis-à-vis the new ones for comparison purposed. This special issue has called for papers that have used novel research methods in a panel data framework for the studying and modelling renewable energy use and management. This special issue is useful for readers because it provides them an overview of the those novel methodologies employed in the field of renewable energy economics and econometrics. [ABSTRACT FROM AUTHOR]

• An MPC energy management strategy for power-split hybrid electric vehicles is proposed. • A universal parametric internal model for multimode power-split transmissions is used. • The discrete mode switch is integrated within the continuous MPC framework. • The transmission meshing losses are included. • A comparison of MPC internal models with different complexity is performed. Model predictive control (MPC) is one of the most promising energy management strategies for hybrid electric vehicles. However, owing to constructive complexity, the multimode power-split powertrain requires dedicated mathematical tools to model the mode switch and transmission power losses within the internal model of the controller. Thus, the transmission losses are usually neglected and the mode switch is optimised through offline simulations. This paper proposes an MPC internal model relying on a parametric approach available in the literature, which provides a unique formulation for modelling any power-split transmission and assesses the transmission meshing losses. The objectives, which cover a gap in the literature, are: 1) to integrate the discrete problem of the mode switch in a continuous formulation of the internal model; 2) to compare MPC internal models with different complexity, and evaluate how the consideration of meshing losses and efficiency of the electric machines affect the controller performance. The results on a case study vehicle, i.e., the Chevrolet Volt, suggest that a simplified internal model deteriorates the fuel consumption performance by less than 2 %, while the integrated mode switch is comparable to the offline strategy. [ABSTRACT FROM AUTHOR]

Energy management algorithms of microgrids have attracted growing attention for the efficient operation and coordination of active end-users. In developing these algorithms, leader–follower game approaches could provide effective strategies when the behavioral and operational characteristics of active end-users are integrated into the methodology. However, since these approaches require the exact implementation of the strategy determined by the leader, it is of great importance to deal with the uncertainty of renewable energy generation, charging/discharging energy of storage systems, and load consumption. Thus, possible negative effects of these uncertainties on the outcome can be minimized with an appropriate billing procedure. In this paper, a Stackelberg game approach is proposed for the energy sharing management of a microgrid including prosumers and plug-in electric vehicle (PEV) charging stations (CSs), and a proper billing scheme is designed to increase the robustness of the approach on real-time applications. In the hereby game-theoretic approach, the energy demands of PEV CSs are determined as variable demands considering the operational characteristics of CSs. The energy planning of CSs is based on determining the number of PEVs that each station can accept over the vehicle charging levels for a specified time interval. Also, by the necessary modifications identified for the presented approach, the microgrid can operate independently from the utility grid and satisfy the requirements of islanded mode for the time slots in which the microgrid exchanges energy with the grid based on the end-game results. The energy management strategy proposed in this study presents a comprehensive solution capable of optimizing energy sharing in microgrids while taking into account end-users' operational characteristics and network requirements. Furthermore, the designed billing scheme ensures that the accuracy of the proposed game approach remains unaffected by the uncertainties, thus preserving the microgrid's profit. • Energy sharing management problem of a microgrid is modeled as a Stackelberg game. • Loads of PEV charging stations are determined by an integer programming problem. • The microgrid can operate independently from the utility grid under islanded mode. • A billing scheme is proposed to minimize the negative effects caused by uncertainties. [ABSTRACT FROM AUTHOR]

• HRES with PV-Wind and different types of energy storage systems. • Optimal size and efficient performance of HRES. • Bi-level mixed integer nonlinear programming optimization approach. • Real application of a stand-alone HRES in a real case study, Jordan. This paper aims to design and run a standalone hybrid renewable energy System (HRES) feeding residential building load. The suggested HRES encompasses photovoltaic panels and wind turbines as energy sources, combined with a hydrogen subsystem and a bank of batteries for energy storage. The meteorological input data includes irradiance, temperature, and wind speed for four seasonal weeks. These data sets are collected for a rural site situated in the town of Ras Munif, northern Jordan. The load profiles characterize a selected benchmark residential building. Consequently, a bi-level mixed integer nonlinear programming (BMINLP) problem is formulated to incorporate the designing problem with the energy management process. The upper level represents the sizing problem, while the lower level problem denotes the energy management strategy (EMS). Each problem has its independent objective function, constraints, and optimization variables. The EMS is an economic-based single objective optimization problem expressed as a mixed integer linear programming (MILP) problem. On the contrary, a mixed integer nonlinear programming (MINLP) approach frames the sizing problem to optimize a techno-economic-based multi-objective function. The interaction between the two problems is that the lower problem is an embedded constraint of the upper problem. Moreover, the candidate solutions from the upper problem are treated as fixed parameters in the lower problem. A multi-objective genetic algorithm is used to execute the overall problem by the MATLAB toolbox. The technical and economic performance parameters, including the investment cost, maintenance, operating costs, and others, are assessed based on the best findings. The key finding demonstrates that the summer week has the lowest total cost and energy loss factor, with around 132,000 $ and 0, respectively. Furthermore, the findings reveal that the entire system's energy loss factor for each sample period is less than 0.0009. This value is substantially less than the permitted limit of 0.01. [ABSTRACT FROM AUTHOR]

Traditional dynamic energy management methods optimize the energy usage in wireless sensor nodes adjusting their behavior to the operating conditions. However, this comes at the cost of losing the predictability in the operation of the sensor nodes. This loss of predictability is particularly problematic for the battery life, as it determines when the nodes need to be serviced. In this paper, we propose an energy and relevance-aware monitoring method, which leverages the principles of self-awareness to address this challenge. On one hand, the relevance-aware behavior optimizes how the monitoring efforts are allocated to maximize the monitoring accuracy; while on the other hand, the power-aware behavior adjusts the overall energy consumption of the node to achieve the target battery life. The proposed method is able to balance both behaviors so as to achieve the target battery life, at the same time is able to exploit variations in the collected data to maximize the monitoring accuracy. Furthermore, the proposed method coordinates two different adaptive schemes, a dynamic sampling period scheme, and a dual prediction scheme, to adjust the behavior of the sensor node. The evaluation results show that the proposed method consistently meets its battery lifetime goal, even when the operating conditions are artificially changed, and is able to improve the mean square error of the collected signal by up to 20% with respect to the same method with the relevance-aware behavior disabled, and of up to 16% with respect the same algorithm with just the adaptive sampling period or the dual prediction scheme enabled. Consequently showing the ability of the proposed method of making appropriate decisions to balance the competing interest of its two behaviors and coordinate the two adaptive schemes to improve their performance. • The monitoring method complies with energy goals while improving monitoring accuracy. • The adaptive actions combine an adaptive sampling rate and a dual prediction scheme. • The dual prediction scheme is altered to operate with a fixed transmission rate. • The proposed monitoring method is evaluated using three different data sources. [ABSTRACT FROM AUTHOR]

With the technological advances of flexible multiple energy conversion and utilization, hydrogen energy has attracted increasing attention. The hydrogen-based integrated energy system (HIES) consisting of multiple microgrids (MGs) with hydrogen exchanges among MGs is considered a promising hydrogen utilization paradigm. To facilitate the coordination among multiple MGs, this paper proposed a multi-stage and multi-timescale energy management for a HIES considering the electricity-heat-hydrogen supply-demand balance and demand uncertainties. The proposed solution consists of three stages, i.e. the day-ahead scheduling stage, model predictive control (MPC) based intraday rolling dispatch stage and intraday real-time adjustment stage to participate in the electricity and hydrogen market. In the HIES, hydrogen energy can be dispatched and utilized across MGs to enable flexible energy management and improve energy utilization efficiency. The proposed solution is extensively assessed through the IEEE 33-bus test network with a HIES compared with three benchmark solutions and the numerical results confirm its effectiveness and economic benefits. • A multi-stage and multi-timescale energy management solution is developed. • A HIES with multiple MGs in the electricity and hydrogen market is exploited. • Hydrogen trading among MGs are considered. • Daily operational cost of the HIES is minimized. [ABSTRACT FROM AUTHOR]

[Display omitted] • A "production, storage and utilization" hydrogen integrated utilization system. • Steady-state, and dynamic analysis for proposed system. • Integrated heat recovery of electrolyzer and fuel cell. • Energy efficiency of electrolyzer and fuel cell improved by 6.3% and 24.5%. • Reduced electrolyzer voltage drop by 0.19 mV in 168 h of fluctuating operation. In integrated hydrogen energy utilization systems, due to the low efficiency of hydrogen/electricity conversion, coordination of energy management and efficient waste heat recovery is required to optimize performance. To address this challenge, this paper presents a comprehensive and sophisticated modeling and energy management strategy to enhance the off-grid energy utilization rate while prolonging the main components' lifetime. The developed model incorporates multiphase flow and heat transport balance for electricity and heat production, enabling a highly accurate representation of real-world behaviors of the system. The proposed off-grid operation strategy is complemented by a designed heat recovery scheme, ensuring the use of energy resources and waste heat. In addition, the proposed energy management strategy monitors the real-time status of each subsystem, actively reducing the number of harmful start-stop cycles of the hydrogen production system, thereby mitigating short-term power impacts and delaying its aging. Specifically, the voltage degradation of the reduction cell is reduced from 4.67 mV to 4.48 mV, the energy utilization rate is increased from 47.6 % to 53.9 %, and the energy efficiency of fuel cells significantly increases from 53.6 % to 78.1 %. [ABSTRACT FROM AUTHOR]

Metal-organic frameworks (MOFs) hold great promise for diverse applications when combined with polymers. However, a persistent challenge lies in the susceptibility of exposed MOF pores to molecule and polymer penetration, compromising the porosity and overall performance. Here, we design a molecular-caged MOF (MC-MOF) to achieve contracted window without sacrificing the MOF porosity by torsional conjugated ligands. These molecular cages effectively shield against the undesired molecule penetration during polymerization, thereby preserving the pristine porosity of MC-MOF and providing outstanding light and thermal management to the composites. The polymer containing 0.5 wt % MC-MOF achieves an 83% transmittance and an exceptional haze of 93% at 550 nanometers, coupled with remarkable thermal insulation. These MC-MOF/polymer composites offer the potential for more uniform daylighting and reduced energy consumption in sustainable buildings when compared to traditional glass materials. This work delivers a general method to uphold MOF porosity in polymers through molecular cage design, advancing MOF-polymer applications in energy and sustainability. [ABSTRACT FROM AUTHOR]