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Industrial energy management is critical to ensuring efficient energy usage, market viability, and greenhouse gas emission reductions. In this study, the aim is to investigate energy management practices in paper and pulp industries (PPI) of Bangladesh. The study identifies several barriers to adopt energy management practices; "Lack of governmental attention", "Inadequate staff awareness" and "limited access to capital" are identified as the major constraints. In addition, HVAC systems and compressed air systems present higher barriers compared to electrical motor and pump systems while looking at the barriers by technology areas. On the other hand, "Owner's demand", "Commitment from the top management" and "Pressure from the customers and different non-Governmental organization" are found to be the dominant drivers of energy management in the studied PPI. Energy management practices can enhance the energy efficiency approximately by 4–5 % according to the study. Furthermore, this study reveals that the majority of PPIs are unaware of the energy service companies (ESCO) with the lack of a standardized system for energy audit, as well as a lack of trust and information, appearing to be the obstacles behind this. • Energy management practices in paper and pulp industries of Bangladesh are shown. • Barriers and drivers to energy management practices in paper and pulp industry are presented. • Barriers to energy efficiency measures by technology area are discussed. • Barriers to consult energy service companies (ESCOs) are presented. [ABSTRACT FROM AUTHOR]

Plant operators and policy makers frequently use energy benchmarking to assess the potential for reducing energy consumption from industrial plants. As benchmarking studies require considerable resources and the cooperation of plant operators it is tempting to try to merge or compare data from different studies. This paper reviews published benchmarks and energy-saving estimates from the paper and pulp industries to explore how comparable data from independent studies are. A literature review was conducted which identified that benchmarks were either produced through a top-down process using annual production and fuel consumption data or through a bottom-up process from process-level data. It was concluded that top-down benchmarks are useful in measuring national trends but are of little value to individual plants. For common process such as Kraft pulp production it is possible to compare values from different studies but only if sufficient information is given in the original studies to confirm that their scope is identical. However, it is unlikely that improvement rates in energy use can be inferred from the difference between studies that use different sources, as the degree of disagreement between contemporary studies is of the same order as the identified potential energy savings. Benchmarking studies were found to provide good summaries of potential technological improvements although there is some inconsistency in estimations of potential impacts. [ABSTRACT FROM AUTHOR]

Research has revealed the existence of an energy-efficiency gap – the difference between optimal and actual energy end-use, suggesting that energy efficiency can be improved. Energy management (EnM) is a means for improving industrial energy efficiency. However, due to various barriers, the full potential of EnM is not realised. Several studies have addressed drivers and barriers to energy efficiency but few to EnM. This study aims to identify EnM practices, the most important perceived drivers and barriers for EnM, and relations among them in the energy-intensive Swedish pulp and paper industry (PPI), which has the longest experience internationally of practising EnM systems, and has worked according to the standards since 2004. Our results show that, altogether, the PPI works regularly and continuously with EnM, with a clear division of responsibilities. The highest maturity for EnM practices was for energy policy, followed by organization, investments, and performance measurement. The study also shows that communication between middle management and operations personnel has potential for improvement. The most important categories of drivers were economic, whereas for barriers they were organizational. Nevertheless, knowledge-related barriers and drivers were amongst the most important, suggesting that the absorptive capacity for energy issues could be improved. Image 1029 • Energy is managed continuously, with clear policies and responsibilities. • The main drivers are economical while main barriers are organizational. • Knowledge related barriers and drivers are amongst the next most important. • Employees' absorptive capacity for energy issues could be improved. • Link between energy policy and performance measurement could be improved. [ABSTRACT FROM AUTHOR]

The Swedish pulp and paper industry (PPI) must increase energy efficiency to remain competitive on the global market, which has experienced entries from countries with cheaper energy and raw material supplies. Interactions among variables for energy use, production, energy management, electricity price and firm characteristics (FC), in different types of mills, i.e., pulp, paper and integrated mills, in Sweden from 2006 to 2015 indicate that correlations among the studied variables were different in different types of mills. This difference between types of mills seemed to originate partly from varying accessibility to production residue that could be used for energy. For all types of mills, variation of electricity prices did not correlate significantly with energy efficiency during the study period. The studied FC were firm's age, number of employees, number of companies in company group, net sales and profit for the year. Energy efficiency was more affected by the variables characterizing energy and production compared to the variables representing FC. This study also suggested presence of possible discrepancies between FC that were perceived as barriers to energy management towards energy efficiency, according to previous studies, and what was shown by the data combining variables representing energy use, production and FC. [ABSTRACT FROM AUTHOR]

Abstract: The systematic, transparent and accurate on-line energy information of the production process is quite the basis of mill energy management and conservation. An Energy Management System (EMS) was built according to the characteristics of the energy usage in a typical newsprint paper mill. The EMS first realized the process data acquisition and integration, second fulfilled the on-line energy information calculation from the integrated data. A water and steam properties query component based on IAPWS-IF97 was developed and plugged into the EMS to facilitate the energy calculation of water and steam. The practical performance in the paper mill showed that the integrated energy information of the whole mill was gained and the on-line energy supervisory was achieved. In the end a systematic, transparent and accurate energy usage profile is obtained and it would provide the valuable fundamental energy data for the further energy analysis and optimization in the EMS. [Copyright &y& Elsevier]

Abstract: Alarmingly low pulp prices in early 2009 left pulp and paper mills across North America desperate for any way to improve thin profit margins. One solution that continues to gain popularity among the industry is improved energy management systems for cogeneration systems, which use steam for two purposes – to provide heat for the pulping process and to generate electricity for sale to regional providers. This paper presents an energy optimization algorithm for use in a pulp and paper mill cogeneration system. The algorithm is applicable to a number of popular mill configurations, power sale contracts, and fuel purchasing scenarios. The method is also extended to address weather-dependent cooling limitations encountered by a mill cogeneration facility, in which case an iterative solution is proposed in order to maintain convexity of the optimization problem. Results are presented in the form of three case studies. [ABSTRACT FROM AUTHOR]

The pulp and paper industry faces several challenges linked to climate and environmental impact, resource efficiency, rising energy prices, increased competition for biomass resources, and declining demand for traditional printed paper products. However, these challenges also offer strategic opportunities for the industry to develop into a competitive, resource-efficient, and low-carbon industry in line with a biobased economy. Against this background, this paper aims to analyse current energy strategies in the pulp and paper industry in Sweden. Specifically, the paper analyses how companies combine continuous process efficiency to reduce energy costs with activities that could be developed into new energy-related products to increase revenue. Most of the analysed companies work to various degrees with both these strategies, employing methods that include improving energy efficiency, energy security, and energy conversion, as well as developing a wide range of biobased energy products. However, our study indicates that there is an untapped potential associated with energy product development, and we conclude that energy efficiency measures can free up resources, enabling the development of new energy products. Finally, several potential managerial outcomes and implications are outlined. • The paper analyses the energy strategies in the pulp and paper industry in Sweden. • Energy strategies focus both to improve energy efficiency and develop new energy-products. • The companies' energy strategies combined exploitation and exploration. • A number of managerial implications are presented. [ABSTRACT FROM AUTHOR]

Deploying renewable energy to unit design energy-efficient technologies may fulfill the additional demand of various energy-intensive industries for their sustainability and resiliency. Multiple Stage Evaporator (MSE) is one of the most energy-intensive units used to extract the water content of the weak liquor in various industries, including the paper industry. This energy intensiveness may be reduced by integrating various Energy Reduction Schemes (ERSs). Hence, this work proposes a ERSs integrated MSE model to analyze its energy efficiency. These ERSs includes Thermo-Vapor Compressor, Steam-, Feed- Split, Feed Preheater, and Flash Tanks. Further, the performance of the proposed model is investigated under two important real-time plant complexities: Boiling Point Elevation and Fouling to achieve more realistic results. The performance analysis is initiated with the formulation of a nonlinear constrained optimization problem to increase the steam economy (SE). Also, the simulation is extended to validate at two different product concentrations (52% and 65%) by employing state-of-art optimization algorithms: CONOPT, and SCA in GAMS and MATLAB respectively. The simulated results shows an increment 1.64% and 1.37% of SE for both 52% and 65% concentration of weak liquor respectively in case of CONOPT than SCA. Also, the waste heat of the condensate, feed, and product may be further utilized for the heat recovery by incorporating the flash tanks which leads to a countable energy saving. Eventually, integrating the solar fields: PTC and LFR ensures a notable reduction in conventional heat utilization by 85.96% and 92.85%, respectively and hence, enhance the energy efficiency. • Expounded energy modeling of ERSs based MSE with BPE and Fouling. • Implementation of NLP and metaheuristic approaches for performance optimization. • Reutilization of waste heat to maximize the energy efficiency. • Integration of solar fields (PTC and LFR) to reduce conventional fuel utilization. • Prospecting the challenges and direction of energy management optimization. [ABSTRACT FROM AUTHOR]

Increasing energy prices and market volatilities force energy intensive industries to implement energy management systems in order to ensure competitiveness. In this paper, we focus on the strategic level of energy management, specifically, on the analysis of the internal and external conditions that form the basis for strategic development. For this, we selected the case of the Austrian paper and pulp industry. We applied a hybrid method by combining an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process which was based on a survey of expert opinion. The results show that cost-related factors predominate. According to our analysis, the four most important factors are all directly linked to energy costs, energy efficiency, and the energy market. Experts pay little attention to environmental issues or to energy market volatilities. Apart from that, the combination of an analysis of strengths, weaknesses, opportunities and threats with an analytic hierarchy process promises to be a valuable tool for strategic energy management. This is especially true for energy intensive companies and/or sectors, since it enables decision makers to take strategic decisions based on a systematic understanding of the main issues. [ABSTRACT FROM AUTHOR]

The pulp and paper industry is one of the five most energy-intensive industries world-wide. In Sweden, most pulp and paper mills were certified with a standardized energy management system already in 2005. As Swedish mills have more than a decade of experience with energy management systems and energy key performance indicators (KPIs), studying KPIs within Swedish pulp and paper mills will enable both a state-of-the-art positioning of best-practice in relation to energy KPIs in pulp and paper mills, but also spot potential barriers and drivers in the utilization of energy KPIs. This paper studies the current level of implementation and operationalization of energy-related KPIs in the Swedish pulp and paper industry. The results show a potential for improvement. • Pulp and paper mills' level of implementation of energy-related KPIs is studied. • 25% of the mills apply best practice regarding the establishment of energy KPIs. • Pulp and paper mills can potentially improve their monitoring of energy KPIs. • Lack of resources is a major barrier to implementing relevant energy KPIs. [ABSTRACT FROM AUTHOR]

Abstract: The economic assessment of an energy enhancement program envisaged for a Kraft wood pulping mill has been performed. The retrofit projects which have been analysed include reduction of the process steam demand, steam exportation to a district heating network and cogeneration. An automated MILP optimisation model has been developed to quantify the economic benefit of the projects independently and in combinations. Three cogeneration systems were considered: single back-pressure steam turbine, single steam condensing turbine and, a two-turbine system. A sensitivity analysis has been conducted for two modalities of key cost parameters (steam demand reduction level, fuel and power purchase costs, steam and power sales price) to determine the impact of those parameters on the economics of co-generation options. Results show that the configuration with the single back-pressure steam turbine has the shortest payback time, but the combination of two turbines produces more power and gives higher benefits in the long term. [Copyright &y& Elsevier]

The clean transformation of building energy consumption is a critical pathway for achieving deep decarbonization. In light of the diversity of building energy needs, this paper proposes an integrated zero-carbon energy supply building architecture that involves the coupling of electrical, hydrogen, heat, and cooling energy sources. To address challenges in real-time supply-demand balancing and reliability of supply under extreme or boundary conditions due to complex energy couplings, the paper introduces an energy management framework with two control layers comprising mixed-integer linear programming (MILP) and model predictive control (MPC). This framework conducts hierarchical optimization to synergize economic and technical objectives. At the economic optimization layer, optimal economic parameters are formulated based on MILP; while at the MPC optimization layer, these parameters serve as reference states. By incorporating state relaxation method, the framework accommodates the uncertainties associated with system operation. Simulation analyses of typical weeks during summer and winter seasons demonstrate that the energy optimization management approach proposed in this paper can achieve highly reliable and economically stable operation of the building energy supply system. • A zero-carbon energy supply building energy structure is constructed. • Dynamic evolution model of electricity-hydrogen-heat-cooling multi-energy coupling system is established. • A double-layer energy management method including economic optimization and MPC optimization is proposed. [ABSTRACT FROM AUTHOR]

This paper considers an electric-hydrogen hybrid energy storage system composed of supercapacitors and hydrogen components (e.g., electrolyzers and fuel cells) in the context of a microgrid with photovoltaic generators. To manage the power and hydrogen flows within the microgrid and coordinate the coupling between the microgrid and a hydrogen refueling station, this paper proposes an energy management framework. The outer layer of the framework takes the hydrogen compressor power as part of the microgrid load and optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. The inner layer develops a two-stage scheme to optimize the power allocation between the electric and hydrogen systems within the microgrid. A low pass filter with an adaptive cutoff frequency is utilized to split the net load power of the microgrid into high-frequency and low-frequency components, which are then assigned to the electric and hydrogen systems, respectively. The adaptive cutoff frequency is determined based on the power split factor tuned twice during the two stages of the power allocation scheme based on the supercapacitor state of charge. The effectiveness of the proposed framework is validated by performing real-time simulations using a dSPACE platform. • Proposes an energy management framework for electric-hydrogen systems. • Optimizes the hydrogen flow from the microgrid to the hydrogen refueling station. • Develops a two-stage power allocation scheme based on artificial potential field. • Validates the framework using a dSPACE DS1202 real-time simulation system. [ABSTRACT FROM AUTHOR]

This paper presents a detailed review of the plasma gasification process for waste disposal. Due to Shifting economies and consumerism culture, ample change can be seen across the globe in the form of rapid industrialisation and burgeoning urbanisation, which results in a massive increase in waste generation. Plasma gasification technology is an emerging solution for processing a wide range of waste making it a highly sustainable, efficient, and ecologically sound process. This literature review discusses the feasibility of the plasma gasification method and its superiority over other conventional waste disposal techniques. A comprehensive analysis of different methods of plasma generation for gasification is done in this literature review. The review paper presents a detailed coverup of the entire process of plasma gasification and post-processes. The article discusses the notable advancement, current scenario, and possibilities in plasma gasification methodology in terms of the design of plasma torch, modelling of gasifiers, generation of syngas, and power production. It provides an overview of advanced simulation tools helpful in the analysis of the Waste-to-Energy (WtE) process. This literature review aims to provide an overview of the recent advances in plasma gasification, highlighting the technical aspects, environmental benefits, and economic viability of the technology. It also reviews the latest research and developments in plasma gasification and discusses the challenges and limitations of the technology, as well as the future prospects and potential applications. • Plasma gasification methodology for waste treatment is discussed in detail. • Prominent thermal and non-thermal practices are discussed and compared in this literature review. • Selective use of plasma gas for enhanced efficiency and optimised gas yield/composition. • Hybrid systems offer versatility in processing diverse feedstocks and can achieve higher energy efficiencies. • Plasma-catalysis approach with NTP shows desirable syngas yield. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies point out a large (untapped) potential for energy efficiency in industry through the adoption of energy management practices. The aim of this paper is to describe and analyze energy management practices in two different Swedish energy-intensive industries: the pulp and paper industry and the foundry industry. The results show that one third of the studied mills and about two fifths of the studied foundries do not allocate energy costs by means of sub-metering, which probably contributes to reinforce the split incentive problem. Moreover, about one fifth of the mills and about half of the foundries lack a long-term energy strategy. The results also show that only about 40% and 25% respectively of the studied mills and foundries may be categorized as successful when it comes to energy management practices. If energy management is not fully prioritized even in energy-intensive industries – such as the studied foundry and pulp- and paper industry it will, in all probability, not be prioritized in less energy-intensive industrial sectors or countries either, indicating a large untapped potential in regard to cleaner, more environmentally sound, production in the industrial sector. [Copyright &y& Elsevier]

By advancement of Internet of Things (IoT) technology in smart life such as smart city, smart home, smart healthcare and smart transportation, interconnections between smart things are growing that complicate evaluation of efficiency factors on the intelligent systems. Energy consumption as one of the most challenging issues is increasing with the growing IoT devices and existing interconnections between cloud data centers, mobile applications and human activities. Managing energy efficiency and power consumption is one of the important issues in green IoT-enabled technologies. This paper presents an overview on the energy management solutions in the IoT based on Systematic Literature Review (SLR). The main goal of this SLR-based overview is to recognize significant research trends in the field of energy management and power consumption techniques which need additional consideration to highlight more efficient and effective methods in IoT. Also, a taxonomy is proposed to categorize the existing research studies on energy management solutions. A statistical and technical analysis of reviewed existing papers are provided, and evaluation factors and attributes are discussed. We observed that variety of published research papers in smart home have highest percentage to evaluate energy management in the IoT. Also, deep learning and clustering methods are must popular techniques that were applied to evaluate the energy management in IoT case studies. Finally, new challenges and forthcoming issues of the energy management and efficient power consumption methods are presented. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy management strategy satisfying real-time and highly efficient energy management strategies for fuel cell hybrid electric vehicles (FCHEVs). The strategy is based on model predictive control (MPC), which integrates long short-term memory (LSTM) and dynamic programming (DP). A high-precision powertrain model of the investigated FCHEV is established for subsequent simulations. After training under several typical working conditions, LSTM is designed to forecast future power demands of the entire vehicle. Using the prediction results, the DP algorithm calculates the control scheme based on model predictive control. Considering the economy and durability of power sources, the results of four different control strategies are compared: thermostat, power following, traditional DP, and MPC. The MPC proposed in this paper reduces the total usage cost per 100 km on the test set by 9%, 33.5%, and −4.6%. • The economy and durability of vehicular fuel cell system are both considered. • The LSTM neural network is imported into MPC theory for vehicular power prediction. • The key parameters of MPC and LSTM are determined by referring to the greedy algorithm. • The computational load in MPC optimisation is reduced by simplifying DP algorithm. • The real-time performance of the supposed EMS on-board is validated. [ABSTRACT FROM AUTHOR]

Environmental working conditions have a great impact on the power demand and fuel consumption of vehicles, and the rational use of road terrain information plays an important role in the improvement of vehicle economy and energy sources durability. In this paper, a fuel cell hybrid electric bus (FCHEB) energy management strategy (EMS) based on deep deterministic policy gradient (DDPG) algorithm taking into account future terrain information is proposed based on cyber-physical system (CPS) to reduce the economic cost of FCHEB. First, this paper implements the information exchange between the vehicle system and the network layer through CPS to obtain the environmental working conditions of the vehicle operation. Second, future terrain information is introduced into the framework for the first time with hydrogen consumption, battery degradation and fuel cell durability constraints to rationally allocate the power of the FCHEB. The results show that the proposed strategy improves the power battery durability by 7.39% and reduces the total operating cost by 5.76% compared to the EMS that ignores the future terrain information. • An energy management strategy that considers future terrain information is proposed. • The proposed strategy achieves an effective combination with cyber physical system. • A trade-off between hydrogen consumption and energy sources durability is achieved. • The proposed strategy reduces the total cost by 7.81% compared to NTI EMS. [ABSTRACT FROM AUTHOR]

Fuel cell hybrid electric vehicles (FCHEV) are one of the most promising new energy vehicles. The cost and lifetime of its powertrain have limited its commercial development. This paper proposed an energy management strategy based on nonlinear model predictive control (NMPC) technology to solve the economy and durability problem of FCHEVs. Based on Markov Monte Carlo(MCMC) method, a prediction model of multi-scale operating conditions is established, and dynamic programming(DP) is used to realize the optimal control in the predicted time domain. The "constant speed prediction" is innovatively adopted in the transition stage to improve the prediction accuracy and enable the model to be realized online. The ways to reduce calculating amount of NMPC are also discussed in this paper. This simplification leads to suboptimal fuel economy and durability of control system but can have obvious reduction in calculating time. The simulation results show that, compared with the thermostat strategy and the power following strategy, the degradation cost decrease of 11.1% and 23.9% and the total operation cost of NMPC decrease of 11.0% and 23.5% respectively. The NMPC strategy has better economy and durability than the rule-based energy management strategy, is close to the global optimal result obtained by dynamic programming and can meet the requirements of real-time control. • The economy and durability of vehicular fuel cell system are both considered. • Markov Chain Monte Carlo is imported into MPC theory for vehicular power prediction. • Adaptability to changing conditions is strengthened via "constant speed prediction". • The computational load in MPC optimization is reduced by simplifying DP algorithm. [ABSTRACT FROM AUTHOR]

Renewable energy systems (RESs) have played a vital role in meeting the continuously fast-rising energy demand. While RESs provide limitless and ecologically good possibilities, putting RESs in place has proven to be a challenging task due to their intermittent energy supply. Battery energy storage systems have widely been used in microgrids to manage the issue of intermittent energy supply caused by the RESs. The disadvantages of batteries include, large size, limited lifespan, and high cost. For this reasons, energy planners are looking into hydrogen-based storage systems as a potential solution. This paper aims to provide the recent advancements in the sizing and energy management of hybrid RES (HRES), as well as conventional methods. To achieve this, the authors have employed a review strategy that involves several specific tasks, such as preparing a database of articles related to Hybrid RESs with hydrogen storage, designing inclusion/exclusion criteria to filter out relevant articles, and analyzing each article from critical viewpoints. Thus, this paper elaborates on the general formulation framework for optimization, the classification and review of different optimization methods, and the literature on the application and research of Energy Management Systems (EMS). Additionally, the review presents an analysis and comparison of various system optimization methods and EMS through a study of several cases from the literature. It is hoped that the review will act as a basic framework for researchers looking to investigate HRES incorporated with hydrogen storage. • An efficient energy storage system is essential for managing intermittent energy supply. • A fuel cell generates electricity by combining hydrogen and oxygen to form water. • Soft computing techniques use both single and hybrid algorithms. • Deterministic rule-based controllers operate based on pre-defined rules. • Energy Management System coordinate the flow of power between different components. [ABSTRACT FROM AUTHOR]

In the context of renewable energy systems, microgrids (MG) are a solution to enhance the reliability of power systems. In the last few years, there has been a growing use of energy storage systems (ESSs), such as hydrogen and battery storage systems, because of their environmentally-friendly nature as power converter devices. However, their short lifespan represents a major challenge to their commercialization on a large scale. To address this issue, the control strategy proposed in this paper includes cost functions that consider the degradation of both hydrogen devices and batteries. Moreover, the proposed controller uses scenarios to reflect the stochastic nature of renewable energy resources (RESs) and load demand. The objective of this paper is to integrate a stochastic model predictive control (SMPC) strategy for an economical/environmental MG coupled with hydrogen and battery ESSs, which interacts with the main grid and external consumers. The system's participation in the electricity market is also managed. Numerical analyses are conducted using RESs profiles, and spot prices of solar panels and wind farms in Abu Dhabi, UAE, to demonstrate the effectiveness of the proposed controller in the presence of uncertainties. Based on the results, the developed control has been proven to effectively manage the integrated system by meeting overall constraints and energy demands, while also reducing the operational cost of hydrogen devices and extending battery lifetime. • Implementation of an SMPC strategy based on a scenario-based approach. • Reduction of operation and maintenance costs and integration of uncertainties. • Minimization of the overall operational costs and maximization the profits. • Reduction of complexity and computational time. • Implementation of a unique control architecture to manage a microgrid in different modes. [ABSTRACT FROM AUTHOR]

Efficient management of hybrid energy sources, including fuel cells, batteries, and supercapacitors, is crucial for optimizing fuel consumption during emergency aircraft landings. Researchers are increasingly recognizing metaheuristic optimization algorithms for their effective convergence properties. This paper introduces a Parasitism-Predation Optimization Algorithm (PPOA)-based Energy Management System (EMS) hybridized with an Equivalent Consumption Minimization Strategy (ECMS) and an External Energy Maximization Strategy (EEMS) to reduce fuel consumption in Hybrid Energy Sources (HES). The proposed PPOA-EMS is developed using MATLAB/SIMULINK and validated on the Opal-RT 5700 real-time Hardware-in-the-Loop (HIL) simulator. The PPOA-ECMS configuration achieved fuel consumption of 17.5 g in simulations and 17.44 g in real-time simulation, with an efficiency of 88.61%. Similarly, the PPOA-EEMS configuration further reduces the fuel consumption to 15.2 g in simulations and 15.36 g in real-time simulations, with an efficiency of 93.17%. This work highlights the PPOA-EMS optimization in hydrogen fuel consumption when compared to conventional and metaheuristic algorithms, including State Machine Control (SMC), External Energy Maximization Strategy (EEMS), Equivalent Consumption Minimization Strategy (ECMS), Classical PI Controller (CPI), Gray Wolf Algorithm (GWO), Mine Blast Algorithm (MBA), and Slaps Swarm Algorithm (SSA). Finally, the findings demonstrate the effectiveness of PPOA-EMS in enhancing fuel efficiency and ensuring robust energy management. • Hybrid Power System is formed using Fuel cell, battery, and supercapacitor. • This study presents PPOA algorithm to optimize fuel consumption of a fuel cell. • PPOA is hybridized with ECMS and EEMS energy management techniques. • PPOA is tested using MATLAB/Simulink and compered with OPAL-RT Real time simulator. • PPOA-ECMS attained least H 2 consumption (15.36 gm) compared to Existing algorithms. [ABSTRACT FROM AUTHOR]

Overcoming the use of conventional internal combustion engines for future transportation is mandatory to achieve sustainability. In this regard, hybrid electric vehicles comprising hydrogen fuel cells with Li-ion battery storage are gaining more attention. The paper proposes a robust approach for economic power-sharing between these sources. A predictive speed control strategy is realized to improve the vehicle's overall operation compared to conventional proportional–integral–derivative controller and tested for various standardized driving cycles. Additionally, a comprehensive techno-economic analysis using total cost of ownership is performed. Results suggest that hybrid vehicles with fuel cells and batteries are more economical with costs($) of 5. 81 e 04 , 2. 17 e 04 , and 2. 04 e 05 , for transient, modal, and manual driving cycles, respectively compared with internal combustion engines and batteries having 6. 23 e 04 , 3. 21 e 04 , and 2. 54 e 05. Furthermore, for evaluating the performance of the proposed predictive controller in terms of error minimization, various performance indices are obtained, compared, and tested using a hardware-in-loop platform. • Predictive control-based optimal power management approach. • Economic benefits through hydrogen fuel cells (HFCs) as an auxiliary energy source. • The controller's performance investigation through performance indices (PIs). • Validation using simulation and Hardware-in-loop results. • Techno-economic analysis for full lifetime use of the HEV. [ABSTRACT FROM AUTHOR]

This research paper presents an comprehensive energy management system (EMS) for standalone renewable energy systems, integrating photovoltaic (PV) panels, fuel cells (FC), batteries, and supercapacitors (SC). The study evaluates four EMS strategies: Proportional-Integral (PI) approach, an Equivalent Consumption Minimization Strategy (ECMS), an ECMS optimized with Harris Hawks Optimization (ECMS_HHO), and State Machine Control (SMC) Strategy. The main goal is to minimize fuel consumption and boost system efficiency. Simulations results show that ECMS- based on Harris Hawks Optimization excels, achieving the lowest hydrogen consumption and highest efficiency: 7.1797 g at 86.8601% efficiency in spring, and 8.5521 g at 88.3974% in summer. SMCS, however, has the highest hydrogen consumption, indicating lower efficiency. ECMS_HHO's strategic battery discharge yields a final state of charge of 44.1051% in spring and 44.5713% in summer, emphasizing hydrogen conservation. Thus, ECMS_HHO is identified as a superior, sustainable solution for efficient energy management in the standalone renewable systems. • Optimal energy management of DC microgrid is proposed. • The consumed hydrogen is optimally managed and decreased. • The overall efficiency of the system is increased. [ABSTRACT FROM AUTHOR]

To achieve the 8000-h proton exchange membrane fuel cell stack (PEM FCS) life target set by the U.S DoE and promote fuel cell hybrid electric vehicles (FCHEVs) massive introduction in the automotive market, using multi-fuel cell stack (MFCS) systems instead of single-fuel cell stack systems seems to be an interesting solution that deserves to be explored. MFCS systems' concept combines several small FCSs modules instead of using a single high-powered FCS module. The modularity in such systems can be exploited through energy management to improve their durability and extend their good energy-efficiency power range. However, FCSs' multiplicity makes it challenging to implement effective energy management strategies (EMSs). This paper proposes a remaining useful life (RUL) prognostic-based EMS to extend MFCS systems' lifetime while keeping their hydrogen consumption reasonable. For this purpose, a prognostic algorithm is developed to predict PEM FCSs' RUL in real-world automotive application scenarios. Then a rule-based EMS allocates the demand between stacks using prognostic results. The proposed strategy's performance is evaluated on a hybrid MFCS/battery system using Matlab/Simulink's environment. Simulation results show that implementing the proposed strategy instead of conventional EMSs can extend MFCS systems' lifetime by at least a factor of 2.35 while keeping their hydrogen consumption reasonable. © 2001 Elsevier Science. All rights reserved. • A post-prognostic decision-making strategy is proposed for MFCS systems. • RUL predictions are performed in real-world automotive scenarios. • The MFCS system's lifetime is significantly extended with the proposed EMS. • RUL is a suitable parameter for adaptive energy management decision-making. [ABSTRACT FROM AUTHOR]

To improve energy utilization efficiency for fuel cell vehicles (FCVs), it is crucial to pay particular attention to the coordination relationships between speed planning and energy management on the road with multiple signal lights. However, the coordination relationships are complex optimization problems with large computational loads, which make it challenging to consider speed planning and energy management simultaneously. Then, a co-optimization method based on Pontryagin's minimum principle (PMP) with the cooperative optimization algorithm is proposed for speed planning and energy management in the FCVs. An optimization control model for the speed planning problem online is established using the signal light timing model and solved using the PMP method to reduce the number of accelerations and decelerations, get closer to the desired speed, and decrease the demand power. Meanwhile, the optimization objective function for energy management is built to achieve the minimum hydrogen consumption, and the PMP method is re-applied to achieve a reasonable distribution of the output power of the fuel cell and lithium battery. Additionally, a cooperative optimization algorithm by conjunct use of the Whale optimization algorithm (WOA) and the Grey wolf optimization (GWO) is put forward to optimize the parameters in the PMP method. The simulation results indicate that the proposed method avoids waiting at red lights during the vehicle speed planning phase, minimizes speed fluctuations, and improves driving comfort. Moreover, compared to the Quadratic Programming (QP), when this method is utilized for power distribution between two sources, the FCHEV consumes 7.51% less hydrogen in route one and 1.35% less hydrogen in route 2. Besides, the method yields a more stable output power from the fuel cell. In summary, the method proposed in this paper fulfills the real-time optimization requirements for speed and energy. [Display omitted] • A co-optimization approach for speed planning and energy management is presented. • Speed planning and energy management are investigated using optimized PMP. • A cooperative WOA-GWO is proposed to optimize the parameters in the PMP. • Lower hydrogen consumption and faster calculation speed are achieved. [ABSTRACT FROM AUTHOR]

In recent years, Electric Vehicles (EVs) powered by a Proton Exchange Membrane Fuel Cell (PEMFC) which has high energy density and efficiency have become an attractive solution for the automotive industry. However, since the power density of the PEMFC is not high enough for different road conditions such as accelerating and going uphill, a supercapacitor (SC) is preferred as the auxiliary energy source for high power density in EVs. Thus, in this paper, a hybrid power system consisting of the Fuel Cell (FC) and SC for a Four-Wheel Drive Electric Vehicle (4WDEV) is proposed. Since energy requirement of the EV depends on the travel route, an effective Energy Management System can be designed by using a Geographic Information System (GIS). According to the road trajectory, the power requirement of the EV is estimated and thus, energy consumption is reduced. The proposed system is validated by using the Matlab Simscape Toolbox. • A hybrid power system consisting of a PEM Fuel Cell and SC for a 4WDEV is proposed. • An effective energy management system is designed using a GIS based on road route. • The high power demand of the EV is met from the SC in the sloping roads. • The energy consumption and the stresses of the power converters are reduced. [ABSTRACT FROM AUTHOR]

With further penetration of low-carbon energy conversion, microgrids (MGs) have become a necessary tool for expanding the consumption of renewable energies. In this paper, an optimal operation model for a microgrid-based multi-agent system is proposed. The goal is to save the total energy cost, which is expressed as a sum of locally observable convex functions. Therefore, improving the operational efficiency of microgrids is the key to promoting renewable energy development. This paper develops a three-layer multi-agent system model considering energy storage system and power thermal load demand response to solve the energy management problem of microgrids. In order to investigate the effect of energy storage system and demand response in microgrids, this paper designs three simulation cases, namely infrastructure case, energy storage case and demand response case. In order to prove the effectiveness of the proposed method, this paper uses the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) the multi-agent system model can realize the joint optimization of "resource, network, load and storage". (2) The introduction of energy storage and demand response system in microgrids can stabilize the output. Renewable energy units promote the use of renewable energy and reduce the overall operating cost of microgrids. Moreover, the results clearly demonstrate that the proposed algorithm has far better performance than other optimization methods. Also, the analysis obtained from the results shows that the cost is reduced by 1.82%. PV and WT output increased by 14.54% and 2.42%. In addition, their standard deviation decreases after ESS participation. The proposed approach is very effective through a simulation case study, which shows high potential for applications. [ABSTRACT FROM AUTHOR]

Demand side management (DSM) is one of the trending economic strategies which shifts the elastic demand to the off-peak hours from the peak hours so as to reduce the overall generation cost of the system. The work done in this paper can be categorized in three phases. In the first phase, various wind speed to power conversion mathematical models available in literature are analysed to find out the one with maximum level of wind penetration. For second phase, an economic DSM strategy is implemented to restructure the forecasted load demand model for various participation levels. In the final phase the cost-effective optimization of two microgrid distribution systems are percolated. As an optimization tool, novel hybrid CSAJAYA has been used to carry on the study. Different types of grid participating and pricing strategies along with valve point loading effect and wind energy uncertainty are considered to amplify the complexity and practicality of the study. The generation costs reduced from 3 to 5% when the forecasted demand was reformed with 20% DSM participation for both the test systems. A detailed comparison with the results from various optimization tools studied confirms the effectiveness of the proposed hybrid approach. The hybrid optimization tool presented in this paper performs better in terms of central tendencies, nonparametric statistical analysis, and algorithm execution time. • Five different wind speed-to-power mathematical modelling have been studied. • Effects of DSM in reducing the microgrid generation cost was analysed. • Grid participation strategies and their impact on microgrid generation cost was analysed. • Grid pricing strategies and their impact on microgrid generation cost was analysed. [ABSTRACT FROM AUTHOR]

This paper carries out a comprehensive analysis on an offshore wind farm equipped with a hybrid storage comprised of hydrogen and battery, from the perspective of economic effectiveness. To rapidly evaluate the system economy, a computationally efficient convex program that takes the nonlinear storage efficiencies into account is provided, which can simultaneously and synergistically optimize the storage sizing and energy management over a long offshore wind cycle. In the analysis, a case study on the optimal configuration and operation of the hybrid storage is thoroughly investigated, answering what the scalings are and how the storage functions in the offshore wind farm. Comparisons to other offshore wind farms with none or only one storage type further demonstrate the advantage of combining hydrogen plant and battery. Influences of the offshore wind electricity price of grid parity and hydrogen price on the system economies, in the terms of total annual cost, net annual profit and hydrogen production cost, are also discussed, revealing sensitivity and dependency of the scalings. Finally, this paper presents the future potential of applying hydrogen plant in the offshore wind farm, from the angles of hydrogen production cost and energy saving. • Nonlinear modeling of the offshore wind-hydrogen-battery system is presented. • Convex programming is applied to optimize the plant sizes and control of the OWHBS. • Comparative analysis on the economics of the OWHBS is provided. • Potential of the hybrid hydrogen-battery storage is assessed. [ABSTRACT FROM AUTHOR]

Green hydrogen is a potential contributor to a more sustainable transportation system. In this paper, we present our experiences and insights gained from an industrial-scale green hydrogen refilling station, marked by the innovative hybrid Edge–Cloud electrolyser system (EMS) employed in this context. Hybrid EMS can improve system performance, adapt to dynamic conditions, and continuously improve efficiency, even under unpredictable load and low storage situations. It also allows the centralized monitoring of future hydrogen production sites to facilitate the operation and maintenance and, in the long-term vision, to enable the joint coordination of the plants flexibilities in the service market as a virtual plant, thus increasing the cost efficiency. Through a real-time digital-model based assessment of the EMS, this study offers valuable lessons learned and findings, contributing to a deeper understanding of EMS in green hydrogen infrastructure. • Description of a commercial Energy Management System (EMS) developed by the company ABB. • Demonstration of the benefits of the hybrid edge–cloud EMS architecture. • Application of an EMS to a hydrogen fueling station with on-site electrolysis. • Real-time digital-model assessment, offering valuable lessons learned contributing to a deeper understanding of EMS for H2. [ABSTRACT FROM AUTHOR]

Energy hubs (EHs) represent a pivotal paradigm in achieving optimal resource utilization across various energy domains. This paper presents an advanced framework for the optimal management of a smart island, leveraging the synergies within the water-electricity nexus. By integrating diverse resources, including electricity, water, heat, and hydrogen, the proposed EH model aims to meet the multifaceted demands of consumers on the island. To enhance operational efficiency, this study delves into the nuanced impacts of key EH components, elucidating their roles in meeting demand profiles and minimizing operational costs. Formulated as a mixed integer linear programming (MILP) model, the EH optimization problem is addressed using the GAMS optimization tool. The overarching objective is to fulfill consumer demand while concurrently optimizing resource utilization, considering factors such as storage degradation costs and emissions from fossil-fuel-based units. In addition to strategic optimization, this study pioneers a novel approach to stochastic parameter forecasting, integrating convolutional neural networks (CNNs) and long-short-term memory networks (LSTMs). By harnessing the capabilities of these advanced forecasting techniques, the EH model can anticipate dynamic changes in demand patterns with heightened accuracy and precision. The empirical results underscore the transformative potential of the proposed EH framework, showcasing significant reductions—up to 30%—in emission costs. Moreover, the study underscores the pivotal role of EHs as enablers for scaling up renewable energy penetration, offering a robust foundation for sustainable energy transitions in island communities and beyond. Additionally, implementing a load-shifting demand response program can lower total costs by approximately $257 per day, offering significant savings for EHs over extended periods. [Display omitted] • Modeling a mixed integer linear programming for energy management of an energy hub. • Comprehensive study on hydrogen and electric storage systems. • Uncertainty modeling using convolutional and long short-term memory networks. • Modeling water–electricity nexus to meet water and electricity demand. [ABSTRACT FROM AUTHOR]

Energy Management Control Strategies (EMCS) play a significant role in improving the economy of Fuel Cell Heavy-duty Truck (FCHT). In this paper, a Single Fuzzy (S-Fuzzy) control strategy is introduced for the FCHT by considering the output characteristics of the fuel cell as well as the charging and discharging power characteristics of the power battery at the current state of charge (SOC). On this basis, the real-time difference between the current SOC and the target SOC of the power battery during the actual operation of the vehicle was further considered, the real-time correction coefficient of the fuel cell output power was introduced, and a Compound Fuzzy (C-Fuzzy) control strategy suitable for FCHT was developed. Meanwhile, the established vehicle model was verified dynamically and economically to ensure its accuracy, with the maximum speed error lower than 2%, the SOC error within 4.9%, and the relative dynamic simulation error of 1.3%. Utilizing co-simulation platforms Cruise and Matlab, the research demonstrates that the C-Fuzzy control strategy significantly outperforms the S-Fuzzy strategy, reducing Equivalent Hydrogen Consumption (EHC) by 1.63% under C-WTVC conditions and by 3.12% under CHTC-TT conditions. To counteract efficiency loss from fuel cell load fluctuations, a real-time EMCS based on C-Fuzzy control and multi-level power output was developed. Validated over a 122-km route, the real-time EMCS reduced the equivalent hydrogen consumption per 100 km of the FCHT to 10.14 kg, maintained the average fuel cell efficiency at 51.02%, and ensured stable SOC of the power battery within the predetermined target range. • Developed FCHT simulation model, validated against real data for accuracy. • Proposed S-Fuzzy control strategy for FCHT based on fuel cell and battery characteristics. • Proposed C-Fuzzy control strategy for FCHT, improving efficiency by further reducing hydrogen consumption. • Demonstrated C-Fuzzy's superiority in reducing hydrogen consumption in FCHT under typical conditions. • Evaluated EMCS in a 122 km real vehicle test, confirming fuel cell efficiency and SOC stability. [ABSTRACT FROM AUTHOR]

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

Hydrogen energy storage systems (HESS) play an essential role in Microgrid (MG) systems to address the inherent generation characteristics of renewable energy sources. Also, the integration of Demand Response (DR) into the Energy Management System (EMS) of a renewable-based Multi-Microgrid (MMG) can lead to substantial technical and economic benefits. This paper proposes a modified optimization algorithm for optimizing MMG Energy Management (EM). The proposed algorithm is a modified version of the Student Psychology-Based Optimization (SPBO) technique called Modified Student Psychology-Based Optimization (MSPBO). This modification aims to improve issues such as slow convergence, low solution accuracy, lack of diversity, and getting stuck in local optima. The proposed MSPBO method incorporates a local escape operator and a collaborative student class to achieve a better balance between exploiting known solutions and exploring new possibilities. The MSPBO algorithm is applied to address the EM challenge within a MMG context. Considering the integration of renewable sources as Wind turbines and solar photovoltaic and the HESS, the EM problem is formulated as a two-stage multi-objective optimization: minimizing the operating cost of conventional generators and power transactions cost in addition to the cost of HESS, while maximizing operator benefits and peak load reduction. This multi-objective problem is tackled using a hybrid ε-lexicography–weighted-sum approach that avoids the need for normalization. The performance of the proposed MSPBO is evaluated using CEC 2017 benchmark test functions, utilizing various statistical measures such as best, average, worst, rank, and standard deviation (SD) of fitness values, along with Wilcoxon's rank-sum test. The MSPBO technique is compared with other optimization algorithms for these test functions, highlighting its efficiency and adeptness in achieving a harmonious trade-off between exploiting known solutions and exploring new ones. Furthermore, the MSPBO method is applied to solve two case studies. In Case 1, which involves a single stage with conventional demand response optimization, the results achieved using MSPBO are benchmarked against other optimization techniques, revealing its superior efficacy in addressing the EM challenge. In Case 2, a more complex two-stage multi-objective problem is tackled using MSPBO, and the assessment of the integration of HESS in the MMG is evaluated. In the second stage, there is a notable enhancement in peak load reduction percentage (PRP), from 13.9% to 16.13% without the HESS and from 12.68% to 16.46% with the integration of HESS. [ABSTRACT FROM AUTHOR]

Among the global carbon emissions in different fields, the carbon emissions in the transportation sector account for more than 1/4. Only the development of passenger car technology makes it difficult to achieve the climate goals. E-bike is considered to be one of the effective solutions. Hydrogen energy, as one of the representatives of new energy, has the advantages of cleanliness, high efficiency, and high energy density. Therefore, in this paper, a long-range hydrogen-electric hybrid bike and its energy management strategy are designed to achieve optimal performance. Herein, two energy management strategies, including the threshold-based and fuzzy logic optimized threshold-based strategy, are designed and compared by evaluating the parameters of equivalent hydrogen consumption, state of charge fluctuation, and the final state of the control system. The results show that with an initial SOC of 50% for Li-ion battery, the fuzzy logic optimized threshold-based strategy reduces equivalent hydrogen consumption by up to 16% compared to the threshold-based strategy. Besides, the fluctuation of the Li-ion battery's state of charge could be reduced by up to 54.7%, indicating that the fuzzy logic optimized threshold-based strategy is a promising energy management strategy for fuel cell hybrid E-bike. [Display omitted] • A long-range hydrogen-electric hybrid E-bike with metal hydride hydrogen storage is designed. • A fuzzy logic optimized threshold-based energy management strategy (EMS) is proposed. • Li-ion battery SOC fluctuations could be reduced by up to 54.7% via fuzzy logic optimized EMS. • Fuzzy logic optimized EMS shows self-adaptability and economic H 2 consumption for hybrid E-bike. [ABSTRACT FROM AUTHOR]

For saving fuel and extending the fuel cells (FC)/battery lifetime, this paper proposes a real-time cost-optimal predictive energy management strategy of FC hybrid electric vehicles based on continuation/general minimal residuals (C/GMRES) algorithm. To ensure the preferable continuation for algorithm application, the continuity method is proposed for accurate model approximations. The external penalty method is employed to transform the inequality constraints as an equivalent index cost. Then, the continuous and unconstrained model predictive control problem is reformulated, and the C/GMRES algorithm is proposed for real-time optimization. Given the output, the expected FC control command can be decided by the designed postprocessing rules. The performance of the proposed strategy is validated under simulations and hardware-in-the-loop (HIL) tests. The results manifest that the proposed strategy can effectively save the total cost for the predictive horizon of 5s–60s even when the neural network-based predictive velocity is adopted. Besides, compared with the interior point method, the proposed C/GMRES algorithm achieves similar solving effects while exhibiting more than 100 times computing efficiency. In addition, the execution time of the proposed strategy in each step is less than 1.2 ms under HIL tests, indicating its real-time applicability. • State of health for fuel cells and battery are modeled and optimized. • Continuity method of health-aware system model is proposed. • Real-time optimization algorithm is proposed for this highly nonlinear problem. • Results about horizon length and predictive errors are gained and discussed. • HIL test verifies the control effects and real-time applicability. [ABSTRACT FROM AUTHOR]

In this paper, certain issues envisaged in the framework of development of design methodology for intelligent autonomous distributed hybrid power complexes (ADHPC) with green hydrogen and energy storage, functioning in grid mode and in the mode of interaction with the global (national) grid (GN) have been solved. Depending on their energy deficit or surplus, relative to the global grid, ADHPCs can operate as a load or as an energy source, respectively, as follows: based on the analysis of the energy balance management process in the ADHPC, a reasonable choice of the structure of the system of accumulation and distribution of power flows (SADCF) was made from the point of view of increasing the reliability of its functioning and ensuring the physical feasibility of the energy balance management process in this structure, i.e. keeping the actual power consumption of the consumers close to the required rated power at each given time t. This is achieved by including a condenser connected to the SADCF system on its assembly and distribution bus and a storage system BS with double-level ((BS1, BS2), double-circuit ((BS1(1), BS1(2)), (BS2(1), BS2(2))) structure, whereby: BS1of the level 1 - to manage the capacity balance in the SADCF under normal ADHPC regime and the variation of green hydrogen and consumption capacities within their confidence intervals assessed at the design stage; BS2 of the level 2 - to coordinate ADHPC and GN modes of operation and to control, together with BS1 of the level 1, GN, diesel generator (DG), the power balance in the SADCF when the ADHPC fails and when RES and consumption power are outside their confidence intervals; alternating charge/discharge operation of the parallel circuits will extend the life of the BS system; – a comprehensive definition of optimal ADHPC system situational energy balance management task is formulated. [ABSTRACT FROM AUTHOR]

Electricity is establishing ground as a means of energy, and its proportion will continue to rise in the next generations. Home energy usage is expected to increase by more than 40% in the next 20 years. Therefore, to compensate for demand requirements, proper planning and strategies are needed to improve home energy management systems (HEMs). One of the crucial aspects of HEMS are proper load forecasting and scheduling of energy utilization. Energy management systems depend heavily on precise forecasting and scheduling. Considering this scenario, this article was divided into two parts. Firstly, this article gives a thorough analysis of forecasting models in HEMs with the primary goal of determining whichever model is most appropriate in a given situation. Moreover, for optimal utilization of scheduling strategies in HEMs, the current literature has discussed a number of scheduling optimization approaches. Therefore, secondly in this article, these approaches will be examined thoroughly to develop effective operating scheduling and to make wise judgments regarding usage of these techniques in HEMs. Finally, this paper also presents the future technical advancements and research gaps in load forecasting and scheduling and how they affect HEMs activities in the near future. [ABSTRACT FROM AUTHOR]

• Metaheuristic Multi-Start algorithm for the demand consumption planning. • Determining the optimum PV installation sizing. • Nearly Zero Energy Building using photovoltaic energy. The energy consumption planning of smart homes connected to a grid with flexible power pricing, and complemented with a local PV installation, is studied. Advanced energy management is necessary to implement the Nearly Zero Energy Building (nZEB) concept in these smart homes. To do so, in the Energy Management System, it is essential to consider the flexibility provided by power pricing schemes, the distributed generation from renewable energies and the use of smart household appliances. This paper studies a novel formulation that depends on the comfort index required by the user, the PV power production, and the price variation of the grid energy. The study is performed on two levels: in the first, a metaheuristic multi-start, consisting of two steps, constructive heuristics and refinement heuristics (local search), is proposed to regulate the power demand of household appliances. The second level consists of determining the optimum PV installation that ensures the economic viability of the plant. The novelty of the paper is that the user defines a desired comfort level as input data, and the algorithm defines a demand plan that meets this comfort at the lowest possible cost. Six real case studies were carried out, considering the insertion of photovoltaic generation, to evaluate the performance of the proposed model from a technical and economic point of view. The results show that the proposed approach allows user requirements to be fulfilled and the energy production cost to be minimized. Without needing to reduce user comfort, the proposed demand planning algorithm can reduce the energy cost by 48.07 %. In this same scenario, combining the proposed method and the insertion of a photovoltaic system composed of 15 modules (Scenario 6), the total energy cost is reduced by 73.90 %. [ABSTRACT FROM AUTHOR]

In this paper, we put forward a deep reinforcement learning (DRL) based energy management system (EMS) solution for a typical Korean net-zero residential micro-grid (NZR-MG). We model NZR-MG EMS to extract a profitable business model that respects whole stakeholders' interests and meets Korean power system regulations and specifications. We deployed the value-based DRL technique, dual deep Q-learning (DDQN), as a solution for our EMS problem since of its simplicity, stability in the learning process, and non-dependency on hyper-parameter selection compared to actor–critic methods. Due to the implementation of mixed-integer nonlinear programming (MINLP) to solve the reward function in this paper, DDQN, despite other DRL methods, provides precise, explicit, and meaningful rewards. In addition to encouraging the agent to choose profitable actions, this approach releases the proposed DRL-based method from the hindrance of redesigning the reward function experimentally in any future extension of the environment elements. Moreover, attaching transfer learning (TL) to the process of training DDQN agent defeat the MINLP imposed latency in training convergence. An extensive benchmark is proposed to test the superiority of the proposed method versus other DRL algorithms. [Display omitted] • This paper presents a profitable business model for net-zero residential micro-grids considering whole stakeholders' interests based on Korean power system regulations. • The double deep Q-learning technique is hired as a principle optimization method to solve arranged net-zero residential micro-grid energy management cost function. • The accuracy of double deep Q-learning is enhanced by deploying mixed integer non-linear programming as a reward estimator to feasible hiring explicit reward for our optimization solution. • Transfer learning is applied to accelerate double deep Q-learning convergence rate by sequentially training agent action selection. [ABSTRACT FROM AUTHOR]

Road freight transport on hilly routes represents a significant challenge for the advancement of fuel cell electric trucks because of the high-performance requirements for fuel consumption, vehicle lifetime, and battery charge control. Therefore, it is essential to optimize the vehicle design and energy management, which greatly influence the driving performance and total cost of ownership. This paper focuses on the cost-optimal design and energy management of fuel cell electric trucks, considering five key influencing factors: powertrain component sizing, driving cycle, vehicle weight, component degradation, and market prices. The cost optimization relies on a novel predictive energy management scheme based on dynamic programming and the systematic calibration of control parameters. The paper analyzes the simulation results to highlight three main findings for fuel cell electric trucks: 1) cost-optimal energy management is essential to define the best trade-off between fuel consumption and component degradation; 2) the total cost of ownership is significantly influenced by component sizing, driving cycles, vehicle weight, and market prices; 3) predictive energy management is highly beneficial in challenging road topographies for substantial cost-saving and lower component size requirements. [Display omitted] • Investigation on total cost of ownership of heavy-duty fuel cell electric vehicles. • Optimal vehicle design and control in demanding routes for performance requirements. • Energy management optimization for minimum ownership cost in realistic driving scenarios. • Trade-off analysis between fuel consumption and expected powertrain components life. • Significant benefits of predictive energy management in challenging road topographies. [ABSTRACT FROM AUTHOR]

Increasing environmental concerns, scarcity of fossil fuel resources, and uncontrolled demand growth have led to upgrading and restructuring existing energy systems. Sustainable multi-energy systems (MESs) will dominate energy production in the future. Integrated management systems must be provided to plan and control the sustainable MESs for optimal operation. Energy hubs (EHs) represent one of the most promising concepts for optimal management systems in sustainable MESs. They can play a great role in moving towards sustainable MES models. This paper provides a comprehensive review of the concepts of EHs and their applications, also benefits gained from the integration of different energies. In addition, the paper discusses the effect of other related options like renewable energy resources, MESs, demand-side management, and energy storage systems. Finally, the paper presents the most common research areas, methods of solving the EHs' associated problems, presents real applications of EHs and explores the future challenges that face the EHs. [ABSTRACT FROM AUTHOR]

The problems of energy shortage, severe pollution, and global warming are becoming increasingly severe. Renewable energy systems with hydrogen have been widely used. In recent years, much literature has described the energy management of renewable energy systems with hydrogen in a comprehensive way. However, most of them are proposed and discussed from an academic point of view. There are likewise several different approaches and ideas in the patents that address the energy management of hydrogen renewable energy systems. Moreover, most patents are oriented toward industrial applications and still need to be reviewed and analyzed. To fill this gap, this paper reviews relevant US patents to find potential and industrial hydrogen applications and energy management strategies in renewable energy systems. The work presented in this paper will provide solutions and guidance in solving energy management problems in renewable energy systems. • Review of U.S. patents for hydrogen-containing renewable energy systems management methods. • Review of U.S. patents for application of hydrogen-containing. • Energy management strategy based on object classification. • The industry mainly adopts rules-based energy management strategies. [ABSTRACT FROM AUTHOR]