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• 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]

• Providing an inclusive, up-to-date, and organized review of the published research. • Taxonomy of microgrid based on control structures, specifications and components. • Energy management summation and classification based on several significant factors. • Discussing some of the skilled methodologies and techniques developed or adopted. • Taxonomy of the different modeling and handling methods of the uncertainty. Energy is one of the essential foundations for the sustainable development of human society, so its management is necessary. Energy management system (EMS) can be explained as the procedure of optimizing, planning, controlling, monitoring, and saving energy to maximize operations and efficiency and minimize consumption. Microgrid (MG) requires EMS as an efficient and optimal tool owing to the stochastic nature of electrical loads and renewable sources. Moreover, energy management system is responsible for operation of a MG in reliable, secure and economical manner in either states of grid-connected or disconnected. Many literatures have recently focused on the expansion of advanced strategies of the MG energy management for establishing a self-sustained MG in both industrial and academic research. Thus, a comparative research is needed for having a 360° viewpoint of the energy management domain in MGs. In this regard, this research investigates a comparative and critical analysis of the developed strategies of the energy management for the MGs from different views and aspects from 2009 to 2022. The review strategy systematically adopted by the author includes: (i) Extracting research papers relevant to energy management in MGs; (ii) Filtering the significant papers to prepare a database of related research papers (iii) Classifying the used methods for EMS based on the technique, control strategies, and structure; (iv) Discussing potential directions for future studies. In a wider outlook, this research provides a systematic and updated review of energy management strategies for MGs developed by different researchers. The author hopes that academicians and practitioners can use the suggested framework as well as the offers presented for further studies on this significant yet sophisticated issue. [ABSTRACT FROM AUTHOR]

[Display omitted] • Feasibility study of cold ironing implementation on the ferry ship. • Cooperative cold ironing with green energy to enhance emission neutrality. • Reducing system and operational costs with a strategic energy management approach. • Two-stage management strategy combining system sizing and operation optimization. The cold ironing system is gaining interest as a promising approach to reduce emissions from ship transportation at ports, enabling further reductions with clean energy sources coordination. While cold ironing has predominantly been applied to long-staying vessels like cruise ships and containers, feasibility studies for short-berthing ships such as ferries are limited. However, the growing demand for short-distance logistics and passenger transfers highlights the need to tackle emissions issues from ferry transportation. Incorporating electrification technology together with integrated energy management systems can significantly reduce emissions from ferry operations. Accordingly, this paper proposes a cooperative cold ironing system integrated with clean energy sources for ferry terminals. A two-stage energy management strategy combining sizing and scheduling optimization is employed to reduce the port's emissions while minimizing system and operational costs. The proposed system configuration, determined through the sizing method, yields the lowest net present cost of $9.04 M. The applied energy management strategy managed to reduce operational costs by up to 63.402 %, while significantly decreasing emissions from both shipside and shoreside operations. From the shipside, emissions reductions of 38.44 % for CO 2 , 97.7 % for NO X , 96.69 % for SO 2 , and 92.1 % for PM were achieved. From the shoreside, the approach led to a 28 % reduction across all emission types. Thus, implementing cold ironing powered by clean energy sources is a viable solution for reducing emissions generated by ferry operations. The proposed energy management approach enables emissions reduction and delivering cost-effectiveness at ferry terminals. [ABSTRACT FROM AUTHOR]

Highlights • Graphene used in energy conversion and heat transfer are classified and compared. • Performances of energy equipment made of graphene products are investigated. • Further application of graphene products in the field of energy is summarized. • Heat transfer applications of nano-graphene and graphene coatings are discussed. Abstract Along with the chic fad energy industry field, the graphene application in energy conversion and management does bring about some benefits in terms of the energy transfer and storage development, the achievement it exerts on the multifaceted innovate approaches. Both current and previous studies on energy conversion, energy storage and energy transfer development, many research results are sufficient to show that all graphene products including graphene paper, film, foam and fiber have a significant role in improving performance. In the energy conversion research on the graphene paper, it has been recently demonstrated that putting the graphene into triboelectric nanogenerators practice possesses higher output performance than traditional generator, which has a maximum strain as large as 200% without evident degradation of the electrical properties. In terms of energy transfer, the graphene fiber application on the battery can significantly increase the charge and discharge rates with enhanced storage capacity of 763 F g−1. On the energy storage research, the graphene foam can enhance a high density of solar thermal storage up to 269.8 kJ kg−1 for long-term. Nano-graphene and graphene coating provide feasibility for the miniaturization of energy storage equipment which inspired the possibility for portable and foldable devices. The work done in this paper can effectively help promote the application of graphene in energy field. Based on our work, the researchers can choose graphene products (graphene paper, graphene film, graphene foam or graphene fiber) efficiently according to the research object in the future. [ABSTRACT FROM AUTHOR]

Highlights • An overview of methodology on how to model material and energy flow in industry. • Comprehensive summary of government, private, and trade association databases. • An overlook of connection between material/energy flow and energy purchased/sold. • Synopsis of activity-based process models to create historical pattern for industry. • An outline of key energy end uses including process related and non-process related. Abstract The manufacturing sector has been one of the most significant energy consuming and CO 2 emitting sectors over the past three decades. Therefore, it is important to have energy flow models of manufacturing processes to identify efficiency improvement and waste heat recovery opportunities with very specific targets. One of the best methods to pinpoint such locations is by referring to manufacturing process step models. Such models can be created on a large scale per industry and on small scale per process within an industry branch. Industry level modeling provides energy saving contingency by large magnitude where the impact on the economy and environment can be observed more visibly especially when sequential trends are identified. The methodology, reviewed in this paper, describes how to model energy flows on an industry scale by deriving the connection between key energy conversion activities on the one hand – including energy purchased/sold and onsite electricity generated, and the key end uses on the other hand. Industry-level energy flow model, referred to in this work, does not assume any specific system. That feature makes it scalable and allows analysis of manufacturing at various levels starting with single processes and finishing at sector and national economy levels. A combined analysis of the procedure is described in detail for creating process step models of manufacturing processes. The availability of U.S. national databases to calibrate these models is discussed and the challenges that arise in finding and using such databases are identified and analysed. In addition to addressing generic data issues associated with energy flow models, the paper also focuses on energy and material models and presents several examples of energy-intensive manufacturing processes, producing industrial gases, textiles, pulp and paper. Finally, importance of water use in onsite energy conversion and process end uses in various industries is addressed along with the sources of where this important data can be found to complete the energy flow modeling. [ABSTRACT FROM AUTHOR]

Highlights • Propose a novel sizing optimization approach for a standalone microgrid. • Propose an intelligent multi-objective energy management strategy. • Present the advantages of combining a hybrid optimization algorithms. • Validate the system performance based on a real case study under different scenarios. Abstract Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO 2 emission level. [ABSTRACT FROM AUTHOR]

This paper proposes a Multi-stage Energy Management System (MS-EMS) for power distribution in a smart microgrid comprising a photovoltaic system (PV), an Energy Storage System (ESS), and connected to an Electrical Power Grid (EPG). The proposed MS-EMS consists of two layers, the Anticipative Layer (AL) and the Reactive Layer (RL). The AL uses a Multi-objective particle swarm optimization (MOPSO) algorithm to determine the optimal power setpoints for each source over the next 24 h based on predicted energy demand and solar power generation. The RL utilizes real-time data collection and Extremum-Seeking optimization (ES) to compensate for prediction uncertainties by adjusting the ideal power setpoints in real-time. In addition, this paper addresses both the issues of management and prediction. It introduces a forecasting system that effectively predicts the next 24 h of load demand and PV power generation using actual data, by integrating a physical model with artificial neural networks (ANN). The efficiency of the proposed MS-EMS is highlighted using experimental data collected in real-time on a physical bench in Morocco. The performances of the proposed MS-EMS system are compared with those of existing EMS strategies, including an existing industrial solution. The comparison shows that the proposed MS-EMS substantially overperforms in terms of three criteria: energy bill, battery degradation cost, and peak-to-Average Ratio (PAR). Despite forecasting uncertainties, the experimental test shows a remarkable 6% gain in the reduction of the energy bill and PAR compared to an offline strategy (where uncertainties were not considered). Furthermore, it achieved a 5% and a 3% gain in the reduction of the energy bill and PAR, respectively compared to an online strategy (focused on real-time optimization). Additionally, compared to an existing industrial solution, the MS-EMS achieves a reduction in energy bill and PAR of 9% and 56% respectively while taking into account battery degradation constraints described in this study. • Multi-stage approach for Multi-Objectives Grid Management with Forecast Uncertainties. • Extremum Seeking approach for real-time energy management problem solving. • Artificial Intelligence for electrical load and PV power forecasting. • Experimental comparison of the proposed strategy with an existing industrial solution. • Experimental test shows a reduction in energy bill and PAR of 9% and 56% respectively. [ABSTRACT FROM AUTHOR]

• A two-layer framework is proposed for building HVAC and associated multi-energy consumption optimization. • 100% renewable complementarities are proposed for building multi-energy supplies. • A R-C thermodynamic network is formulated to model the building HVAC. • AC slow charging and DC fast charging types of EVs are considered and managed via a novel real-time supply–demand pricing. Building heating, ventilation, and air conditioning (HVAC) and associated energy consumption make up the more and more important part of the world, whose reduction provides a cost-effective path to the "dual carbon" goal. This paper proposes a two-layer management of HVAC-based multi-energy buildings under proactive demand response of fast/slow-charging electric vehicles (EVs). In this paper, the building HVAC is mathematically formulated via a R-C thermodynamic model, which coordinates with multi-energy converters and storages to form a 100% renewable building. The building management is a challenging optimization problem due to its severe constraints and strong spatio-temporal couplings. In the first layer, a day-ahead multi-energy dispatch is formulated to economically optimize the electrical, heat, gas energy carriers. In the second layer, alternating current (AC) slow charging and direct current (DC) fast charging types of EVs are considered and managed via a novel real-time supply–demand pricing mechanism. After acquiring the economical dispatch references in the first layer, the second layer implements a model predictive control (MPC)-based real-time scheduling to handle the multi-energy supply–demand fluctuations. The original two-layer optimization is further handled via mixed-integer linear program (MILP) reformulation for high-efficient solving. Comparisons have shown the advantageous performances of the proposed two-layer optimization over economics and practicability. Simulations results show that the overall system operating cost can be reduced by at most 3.01% with a higher operational flexibility in building management. [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]

• A model predictive control-based energy management for an electro-thermal microgrid. • The strategy minimizes operating costs and pollution avoiding battery degradation. • Analyze the state of health of the battery system to reduce degradation. • A comparison with a Unit Commitment approach under different generation scenarios. Access to electricity is a fundamental key to developing countries' growth. Microgrids (MG) have become potential solutions to provide energy to isolated rural areas in a safe and environmentally friendly way. Therefore, proposals for alternative solutions to bridge the electrification gap in the Ecuadorian Amazon – which has the most significant percentage of its population without access to electricity – are of significant interest. Consequently, this paper presents the design of an Energy Management System (EMS) based on Model Predictive Control (MPC) for an isolated electro-thermal microgrid comprising a photovoltaic generator, a diesel generator, a lithium-ion battery Energy Storage System (ESS), electrical loads, and a domestic hot water system. The EMS aims to supply energy reliably and safely, minimize the MG's operation costs, and extend the ESS's useful life while satisfying the end users' comfort. This study includes an estimated degradation model for the ESS's State of Health (SOH), an essential parameter contributing to reducing the microgrid's operating costs in the long term. Simulation results using one-year data present the influence of the prediction horizon on the MG's scheduling. In addition, the benefits of the proposed EMS are highlighted by comparing it with the results achieved by a Unit Commitment approach, where it is demonstrated that the proposed EMS presents a reduction in MG operating costs and greenhouse gas emissions while maximizing the utilization of renewable energy and extending the lifespan of the ESS. Finally, an experimental validation, using a Typhoon Hardware-in-the-loop HIL-402 device in real-time operation, stands out the effectiveness and feasibility of the proposed MPC-based EMS. [ABSTRACT FROM AUTHOR]

• An online EMS based on random forest is proposed for fuel cell vehicles. • A LVQ classifier is designed to determine the driving pattern in real-time. • A NSGA-II based optimization method is used to optimize the EMS. • The method can reduce fuel cell degradation and exhibit good fuel economy. An effective energy management strategy (EMS) is crucial for ensuring the safe and efficient operation of fuel cell vehicles. This paper proposes an online EMS for the fuel cell/battery hybrid system based on an improved random forest (RF) algorithm. First, in order to enhance the environmental adaptability of EMS, a learning vector quantization classifier is utilized to identify the driving mode under different road conditions. In each driving mode, the optimal control sequence is derived using the Pontryagin's minimum principle (PMP), forming a globally optimal training set based on the idea of minimizing fuel consumption. Then an online application solution based on the RF is proposed to learn the optimal control sequence and address the real-time limitation. Furthermore, A RF hyper-parameter optimization method based on the NSGA-II algorithm is proposed to improve the accuracy and robustness of the RF under different driving patterns. The simulation experiments under two test conditions showed that compared with PMP method, the proposed EMS reached 97.67% and 98.71% of benchmark in terms of hydrogen consumption, and fuel cell degree of aging was reduced by 5.9% and 4.9%. Hence, the proposed method is proved to be capable of reducing degradation and exhibiting good fuel economy. [ABSTRACT FROM AUTHOR]

• STO-IWGAN, aimed at enhancing energy optimization in fuel cell electric vehicles. • Maintaining the system's normal operation and hybrid system performance optimization. • Improving energy efficiency, reducing fuel usage, and enhancing overall FCEV. • Efficiency of proposed method at 95 %, surpasses that of other existing techniques. • Proposed technique is implemented using MATLAB, against contemporary methods. This paper introduces a novel hybrid approach, termed STO-IWGAN, aimed at enhancing energy optimization in fuel cell electric vehicles (FCEVs). The key to maintaining the system's normal operation is the energy management plan and hybrid system performance optimization. The STO-IWGAN method combines the Siberian Tiger Optimization (STO) and an enhanced Wasserstein Generative Adversarial Network (IWGAN). The primary objectives of this approach include improving energy efficiency, reducing fuel usage, and enhancing overall FCEV performance. The STO method is utilized to optimize the operating parameters of the fuel cell arrangement, while the IWGAN method predicts the vehicle's power demand. The proposed technique is implemented and evaluated using MATLAB, benchmarked against contemporary methods. Comparative analysis reveals that the STO-IWGAN method outperforms existing approaches such as heap-based optimization, particle swarm optimization, and wild horse optimization. This work aims to contribute to research on increasing the hybrid power system's energy utilization efficiency and fuel cell the lifespan, offer recommendations for optimal control strategy and structural design, and offer additional ideas for future energy management optimization. The efficiency value of the proposed method, recorded at 95 %, surpasses that of other existing techniques, indicating its superior performance in energy optimization for FCEVs. The Existing HBO method efficiency value is 85 %, PSO method efficiency value is 75 % and the WHO method efficiency value is 65 %. The proposed method efficiency value is higher than other existing method. [ABSTRACT FROM AUTHOR]

Operational flow chart strategy of solar monofacial (mPV) and bifacial (bPV) on-grid renewable energy systems. [Display omitted] • Optimal energy management of an on-grid mPV and bPV systems are investigated. • Realistic measured data are obtained from formal institutions. • Marine Predators Optimization algorithm minimizes Loss of Power Supply Probability. • The bPV is more reliable, feasible and more ecological than the mPV solar systems. • The bifacial PV technology is recommended for future PV installations. This paper introduces an optimal energy management and a comprehensive innovative comparison between on-grid monofacial and bifacial solar photovoltaic systems in terms of energy production, reliability, economic feasibility, ecological impact, footprint area, and other performance indicators. This is to satisfy the load demand of Al-Husainya city, Jordan. Realistic measured data are obtained from authoritative companies in Jordan. This includes ambient temperature, solar irradiance, and a utility-scale load demand. The optimal sizing and energy management strategy are achieved using Marine Predators optimization algorithm. This is done by minimizing Loss of Power Supply Probability. The decision variable is the number of photovoltaic modules. The unit investigation shows that the maximum power of the bifacial panel is increased compared with a monofacial module. So, this will highly affect system size and hence reliability, footprint required area, cost and ecological indicators are impacted too. Results show that the minimized values of the objective function of Loss of Power Supply Probability for monofacial and bifacial systems are 0.3534 % and 0.2511 %, respectively. The number of monofacial and bifacial panels are 12,940 and 11,584 respectively. In addition, the area required to install the bifacial array is decreased by 10.4791 % compared with the monofacial array. Finally, four additional optimization algorithms were used to validate and compare with the obtained results using the Marine Predators algorithm. The methodology described in this paper, which adopts the bifacial photovoltaic technology, is recommended and it can be applied anywhere to get highly reliable, feasible, and more environmentally friendly solar energy systems. [ABSTRACT FROM AUTHOR]

A vital element in making a sustainable world is correctly managing the energy in the domestic sector. Thus, this sector evidently stands as a key one for to be addressed in terms of climate change goals. Increasingly, people are aware of electricity savings by turning off the equipment that is not been used, or connect electrical loads just outside the on-peak hours. However, these few efforts are not enough to reduce the global energy consumption, which is increasing. Much of the reduction was due to technological improvements, however with the advancing of the years new types of control arise. Domestic appliances with the purpose of heating and cooling rely on thermostatic regulation technique. The study in this paper is focused on the subject of an alternative power management control for home appliances that require thermal regulation. In this paper a Model Predictive Control scheme is assessed and its performance studied and compared to the thermostat with the aim of minimizing the cooling energy consumption through the minimization of the energy cost while satisfying the adequate temperature range for the human comfort. In addition, the Model Predictive Control problem formulation is explored through tuning weights with the aim of reducing energetic consumption and cost. For this purpose, the typical consumption of a 24 h period of a summer day was simulated a three-level tariff scheme was used. The new contribution of the proposal is a modulation scheme of a two-level Model Predictive Control’s control signal as an interface block between the Model Predictive Control output and the domestic appliance that functions as a two-state power switch, thus reducing the Model Predictive Control implementation costs in home appliances with thermal regulation requirements. [ABSTRACT FROM AUTHOR]

The Conferences on Sustainable Development of Energy, Water and Environment Systems – SDEWES conferences at the beginning of the 21st century become a significant venue for researchers to meet, and initiate, discuss, share, and disseminate new ideas in various disciplines of sustainable development. In 2002, the first conference was organised in Dubrovnik, Croatia and following the tradition for odd years, the 10th SDEWES Conference again took place in Dubrovnik, Croatia from September 27th to October 2nd 2015. The total number of 1204 submitted abstracts resulted with 551 accepted manuscripts bringing 538 participants from 65 countries that participated in a number of oral and poster presentations, panels, invited lectures, and special events. Moreover, 17 special sessions were organised including 166 invited speakers. This editorial is based primarily upon 32 papers selected from among 551 contributions presented at the 10th SDEWES Conference but it also provides a wider picture on the contributions from previous SDEWES conferences. It summarises SDEWES published articles that have addressed identified problems or provided the background for the research that is reported in the current special issue. The main topics of these papers address sustainable combustion technologies, heat exchangers, waste heat recovery, sustainable buildings, campuses and communities, renewable energy technologies and sources, as well as energy system flexibility and energy storage. [ABSTRACT FROM AUTHOR]

• Fast Tri-level optimization approach. • HRES includes PV panels, wind turbines and HESS. • Real weather and load data of the stand-alone HRES. • The optimal solution for sizing and EMS stages. • Minimize the capital cost and maximize the efficiency of the whole system. Renewable energy sources penetrate our recent life very deeply. They have more expansion chances in the nearest future, especially in stand-alone and micro grid fields. Due to the uncertainty of the weather and the non-matching between the generated energy and demand, a hybrid energy storage system (HESS) is required. The optimization computations are the best way to select the optimal values for the capacity, sizes and quantities of the stand-alone hybrid renewable energy system (HRES) to satisfy the load demands of the available climate conditions. In this paper, the HRES which includes PV panels, wind turbines, battery banks, hydrogen tanks with fuel cells and electrolyzers has been tested and optimized using a tri-level optimization problem. Using Matlab software, the simulation results validate that the proposed method converged quickly and accurately to the optimal solution. The total capital cost was between $127,400 to $231,750. The maximum average efficiency was 83.53% at a minimum total capital cost of $127,400 and a minimum operating cost of $67 which were achieved in the same summer month presented in July. [ABSTRACT FROM AUTHOR]

The growing demand for sustainable systems due to climate change has led to increased reliance on renewable energy sources. However, this transition has raised concerns about power quality in power systems due to climate variations and the intermittent nature of renewables, photovoltaic energy generation in particular. In this context, uninterruptible power supply systems play a crucial role in ensuring reliable and high-quality energy supply. As an added benefit, photovoltaic energy generation may be integrated into uninterruptible power supply systems by sharing the inverter already present and storing generated energy in the batteries. In this paper, it is presented the design and management of photovoltaic energy, integrated into double-conversion uninterruptible power supplies. A method for selecting the suitable number of panels to be used in series in a given application is developed, considering the panel model used, and the environmental mission profile. A management algorithm for tracking the maximum power point of the photovoltaic generation while ensuring the hold-up time is proposed, introducing the dynamic hold-up time concept. Using the point for maximum power instead of the dynamic hold-up time at two different locations leads to voltage sags of 12.6% in the first and 24.3% in the second. Using the method to select the number of panels in series, 13 panels are selected for the first location, resulting in an annual generation that is only 2.3% smaller than the maximum power point. The results are validated using a hardware-in-the-loop platform and the dSpace Microlabbox 1202. • Design and management of photovoltaic energy in uninterruptible power supplies. • Development of algorithms for selecting the appropriate number of panels in series. • Proposal of the dynamic hold-up time concept. [ABSTRACT FROM AUTHOR]

• Deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. • The performance of the proposed approach is benchmarked against Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). • Using New York City cycle as a validation drive cycle, the approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. • The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. • The proposed method has 38% less training time when compared to the DDQN approach. Fuel cell hybrid electric vehicles offer a promising solution for sustainable and environment friendly transportation, but they necessitate efficient energy management strategies (EMSs) to optimize their fuel economy. However, designing an optimal leaning-based EMS becomes challenging in the presence of limited training data. This paper presents a deep stochastic reinforcement learning based approach to address this issue of epistemic uncertainty in a midsize fuel cell hybrid electric vehicle. The approach introduces a deep REINFORCE framework with a deep neural network baseline and entropy regularization to develop a stochastic policy for EMS. The performance of the proposed approach is benchmarked against three EMSs: i) a state-of- art deep deterministic reinforcement learning technique called Double Deep Q-Network (DDQN), Power Follower Controller (PFC) and Fuzzy Logic Controller (FLC). Using New York City cycle as a validation drive cycle, the deep REINFORCE approach improves fuel economy by 7.68%, 13.53%, and 10% compared to DDQN, PFC, and FLC, respectively. The deep REINFORCE approach improves fuel economy by 5.31 %,9.78 %, and 9.93 % compared to DDQN, PFC, and FLC, respectively under another validation cycle, Amman cycle. Moreover, the training results show that the proposed algorithm reduces training time by 38% compared to the DDQN approach. The proposed deep REINFORCE-based EMS shows superiority not only in terms of fuel economy, but also in terms of dealing with epistemic uncertainty. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explores RL-based EMS development in multi-power source vehicles. • Highlights general RL advantages over specific hybrid designs in EMS. • Discusses RL-EMS performance factors using PHEV and FCEV models. Reinforcement learning-based (RL-based) energy management strategy (EMS) is considered a promising solution for the energy management of electric vehicles with multiple power sources. Research and applications of reinforcement learning and deep reinforcement learning in energy management issues are emerging. However, previous studies have not systematically examined the essential elements of RL-based EMS. This paper presents a performance analysis of RL-based EMS in a Plug-in Hybrid Electric Vehicle (PHEV) and Fuel Cell Electric Vehicle (FCEV). The performance analysis is developed in four aspects: algorithm, perception and decision granularity, hyperparameters, and reward function. The results show that the Off-policy algorithm effectively develops a more fuel-efficient solution within the complete driving cycle compared with other algorithms. Improving the perception and decision granularity reduces the frequency of tabular-based policy updates but better balances battery power and fuel consumption. Setting a high initial SOC in training will effectively improve the performance of RL-based EMS. The construction of an equivalent energy loss reward function for RL-based EMS based on instantaneous State of Charge (SOC) variation should be approached with caution. This approach is highly sensitive to parameters and is more likely to lead to violations of SOC constraints. In contrast, an equivalent energy reward function based on overall SOC variation is a safer alternative. [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]

• A hierarchical predictive energy management is designed for fuel cell trucks under vehicle-following scenarios. • The speed planning considering future changes of leading vehicle is designed to ensure safe inter-vehicle distance and minimize power demand. • A driving-habit-conscious fuzzy C-means clustering enhanced Markov Chain speed prediction method is proposed to forecast future speed of leading vehicle. • The proposed hierarchical predictive energy management for host vehicle can reduce fuel cell degradation cost by 1.31–3.48% and total operation cost by 0.75–1.94%. Fuel cell hybrid electric heavy-duty vehicles play a key role to realize green and low-carbon travel. To cope with actual traffic environment, an advanced energy management strategy (EMS) is crucial to ensure vehicle's efficiency and economy. In this paper, a predictive co-optimization control method is designed to achieve speed planning and energy management for the host vehicle with a fuel cell/Li-ion battery hybrid energy storage system under vehicle-following scenarios. Firstly, the host vehicle obtains the real-time speed of leading vehicle by communication technology, and speed prediction for the leading vehicle is implemented by fuzzy C-means clustering enhanced Markov Chain (FCM-MC) considering driving habits. Secondly, based on speed prediction results, the speed planning which aims to ensure safe inter-vehicle distance and minimize the demanded vehicular power is implemented to derive the speed curve of host vehicle. Finally, according to speed planning results, predictive energy management is applied to achieve power allocation between fuel cell (FC) and battery. The simulation results denote that the proposed speed prediction method can decrease forecast error effectively. The speed planning ensures the safe inter-vehicle distance. With speed prediction accuracy improved, the proposed hierarchical energy management strategy can reduce fuel cell degradation cost by 1.31%-3.48% and total operation cost by 0.75%-1.94%. [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]

[Display omitted] • Novel predictive ECMS algorithm for hybrid trucks based on projected power demand. • Algorithm operation validated on commercial plug-in hybrid electric truck. • Up to 5.9% energy efficiency gains over a municipal operation profile. • Power-predictive ECMS for energy management brings real CO 2 benefits. This paper examines potential fuel consumption savings through the implementation of a predictive equivalent consumption minimisation strategy, as a control algorithm in the energy management system of hybrid heavy-duty vehicles. A plug-in hybrid electric truck simulation model was developed as a demonstrator and was validated with experimental data. The baseline vehicle model was based on the same algorithm as VECTO, which is further extended in the current study by novel factors that contain the average predicted power demand. The potential benefits of the proposed algorithm are examined for different heavy-duty categories over regulatory mission profiles in both charge-sustain and charge-depletion modes. The results show that the fuel consumption can be reduced by up to 5.9 % and 4.4% in charge sustaining and charge depleting modes, respectively, depending on the mission profile. If such an algorithm is adopted by commercial vehicles, this could lead to substantial CO 2 and fuel consumption savings on the road. [ABSTRACT FROM AUTHOR]

• A hierarchical energy management strategy with life balance control is proposed. • A dynamic threshold control of ultracapacitor SOC is designed for optimization. • Life-cycle average operating cost is defined to evaluate the comprehensive economy. • A hardware-in-the-loop platform is built to verify the effectiveness. • The proposed strategy has better life mileage compared with the DP strategy. Fuel cell (FC) is an ideal power source for electric vehicles with high efficiency and little pollution. However, with its weak dynamic reaction, it needs to be used in combination with other energy storage devices, and the coupling of multiple energy sources makes it difficult to fully utilize the system performance. Meanwhile, the cost and durability of FC hinder its commercialization. This paper takes the composite energy storage system composed of FC, battery (BAT) and ultracapacitor (UC) as the research object. In order to improve the system performance, a hierarchical energy management strategy is proposed based on the analysis of the relationship between UC output power and vehicle demand power under dynamic programming (DP) strategy, taking into account the vehicle energy consumption and energy source lifetime. A fuzzy control based on speed prediction and a dynamic state-of-charge (SOC) threshold control optimization algorithm of UC based on working condition recognition are designed to optimize the hierarchical control strategy. In the upper layer strategy, for realizing the online real-time distribution of UC output power, the generalized regression neural network is used to learn the relationship between UC output power and demand power under DP strategy; in the lower layer strategy, the power of the FC and BAT is distributed using an equivalent energy minimization strategy. Based on above strategy, this paper proposes a life balance control strategy for energy sources to maximize the lifetime of the composite energy storage system. Simulation and experimental results show that the proposed strategy has a comparable life-cycle average operating cost to the DP strategy with better life mileage. [ABSTRACT FROM AUTHOR]

In this paper, strategic energy management in a micro grid is proposed incorporating two types of storage elements viz. unitised regenerative fuel cell (URFC) and electric vehicle (EV). Rather than a simple approach of optimizing micro grid operation to minimize line loss in the micro grid, this paper deals with multi objective optimization to minimize line loss, operational cost and maximize the value of stored energy of URFC and EV simultaneously. Apart from URFC, two operation strategies are proposed for EV enabling V2G operation to reduce overall system cost of operation. To address the complexity, non-linearity and multi dimensionality of the objective function, particle swarm optimization-a heuristic approach based solution methodology along with forward and back sweep algorithm based load flow solution technique is developed. Combined with particle swarm optimization (PSO), forward and backward sweep algorithm resolves the complexity and multi dimensionality of the load flow analysis and optimizes the operational cost of micro grid. The simulation results are presented and discussed which are promising with regard to reduction in line loss as well as cost of operation. Scheduling strategy of the micro grid with both URFC and EV enabling V2G operation presents a promising approach to minimize line loss and cost of operation. [ABSTRACT FROM AUTHOR]

The droop method is an advantageous technique for stand-alone AC supply systems, allowing for power sharing among various inverters with no need for communication cables. However, in stand-alone systems with multiple distributed energy storage units, the conventional droop methods are unable to control the storage unit state-of-charge (SOC) in order to change simultaneously. Existing techniques endeavor to solve this problem by changing the slope of the P – f curve however this solution compromises the power response performance. As an alternative, this paper proposes a new SOC-based droop control, whereby the P – f curve is shifted either upwards or downwards according to the battery SOC. The proposed technique makes it possible to select the time constant for the battery SOC convergence and, at the same time, to optimize the power response performance. The paper also shows how the SOC changes when the ratios between the battery capacity and the inverter rated power are different and how the proposed technique can limit the SOC imbalance. Simulation and experimental results corroborate the theoretical analysis. [ABSTRACT FROM AUTHOR]

In this paper, a multi-objective energy management system is proposed in order to optimize micro-grid (MG) performance in a short-term in the presence of Renewable Energy Sources (RESs) for wind and solar energy generation with a randomized natural behavior. Considering the existence of different types of customers including residential, commercial, and industrial consumers can participate in demand response programs. As with declare their interruptible/curtailable demand rate or select from among different proposed prices so as to assist the central micro-grid control in terms of optimizing micro-grid operation and covering energy generation uncertainty from the renewable sources. In this paper, to implement Demand Response (DR) schedules, incentive-based payment in the form of offered packages of price and DR quantity collected by Demand Response Providers (DRPs) is used. In the typical micro-grid, different technologies including Wind Turbine (WT), PhotoVoltaic (PV) cell, Micro-Turbine (MT), Full Cell (FC), battery hybrid power source and responsive loads are used. The simulation results are considered in six different cases in order to optimize operation cost and emission with/without DR. Considering the complexity and non-linearity of the proposed problem, Multi-Objective Particle Swarm Optimization (MOPSO) is utilized. Also, fuzzy-based mechanism and non-linear sorting system are applied to determine the best compromise considering the set of solutions from Pareto-front space. The numerical results represented the effect of the proposed Demand Side Management (DSM) scheduling model on reducing the effect of uncertainty obtained from generation power and predicted by WT and PV in a MG. [ABSTRACT FROM AUTHOR]

Manufacturing, which is destined to play the most significant role in the reindustrialization of Serbia is also one of the largest energy consumers and environmental polluters. In accordance with this, a large number of energy and environment management initiatives have been implemented over the years. In developed countries, these initiatives are at an advanced level, but not in Serbia. A group of manufacturers in Serbia has recognized the significance of the environmental initiatives implementation, but the interest in energy management improvement has remained low. Although these initiatives can be used to achieve cost reduction in industry, not all the manufacturing sectors equally value the importance of energy management improvement. Among all the manufacturing sectors, it is necessary to prioritize those with the potentials for energy management improvement, which can be done using different methods. In this paper, the AHP (Analytic Hierarchy Process) method was used to prioritize manufacturing sectors in Serbia in the area of energy management improvement. Using a created AHP questionnaires criteria weights were selected. These questionnaires were completed by the experts from the Serbian Chamber of Commerce and Industry, providing us with the opportunity to evaluate the Serbian manufacturing sectors based on the real life data. The results of the AHP method, which was used as the prioritization instrument, and their analysis are presented in the paper. As a part of a wider study, aimed at the improvement of the energy management in Serbia, the three manufacturing sectors with the highest priority (“Manufacture of food products”, “Manufacture of motor vehicles, trailers and semi-trailers”, “Manufacture of other non-metallic mineral products”) will be analyzed in the future research in terms of energy process flows, energy management system implementation and other relevant issues. [ABSTRACT FROM AUTHOR]

This paper introduces a new electrical model of a PV array by simulating and tests it on one typical Micro-Grid (MG) to see its performance with regards of optimal energy management of Micro-Grids (MGS). In addition, it introduces a probabilistic framework based on a scenario-based method to overcome all the uncertainties in the optimal energy management of MGs with different renewable power sources, such as Photovoltaic (PV), Wind Turbine (WT), Micro Turbine (MT), and storage devices. Therefore, the uncertainty is considered for WT and PV output power variations, load demand forecasting error and grid bid changes at the same time. It is hard to solve MG problem with all its uncertainty for 24-h time intervals, and consider several equality and inequality at the same time. In fact, in order to resolve this issue, the problem needs one powerful technique that although it converges very fast, it escapes from the local optima. As a result, one modern Dolphin echolocation optimization algorithm (DEOA) is defined to explore all the search space globally. The DEO algorithm uses the ability of echolocation of the dolphins to find the best location. Additionally, the proposed modification method will be introduced in this paper. This method makes the algorithm work better and finds the locations faster. The proposed method is implemented on a test grid-connected MG and satisfying results can be seen after implementation. [ABSTRACT FROM AUTHOR]

• Sustainable energy systems, modelling and simulation concepts are discussed. • Modelling and simulation of energy systems are systematically presented. • The model is applied to a multi-vector industrial cluster case study. • Cluster energy efficiency is a crucial step towards decarbonisation. • Electrification of heat and renewable generation can enable grid island mode. Critical for achieving the United Kingdom's net-zero targets, decarbonising industrial clusters would require robust tools to assess the feasibility of decarbonisation technologies and investment solutions. This paper presents an integrated energy system planning tool for decarbonising industrial clusters. The adoption of the transfer functions method enables the development of individual component models for technologies, networks, and loads, facilitating the control and simulation of complex dynamics in multi-energy system operation, as demonstrated in a case study investigating heat and power demands of a dynamic hybrid cluster, with evaluation of decarbonisation implications including heat electrification, renewables, and fuel switching in both grid-connected and island modes to establish potential pathways for decarbonisation. With the implementation of these decarbonisation measures in the case study cluster, primary energy demand, costs, emissions, and energy losses were reduced by 42%, 71%, 53%, and 72% in grid mode and by 40%, 70%, 53%, and 63% in island mode, and higher losses in island mode is due to excess heat production by electric boilers intended to consume all available power. While outcomes might differ among various clusters due to their specific features, the study cluster, characterised by substantial heat demand compared to electricity and significant electricity exports, achieves significant emission reduction via heat electrification compared to other individual decarbonisation technology. Moreover, this tool will be instrumental in helping industrial clusters formulate comprehensive decarbonisation roadmaps based on informed decisions. [ABSTRACT FROM AUTHOR]

• A novel and integrated eco-driving strategy for fuel cell hybrid electric vehicle. • Power-varying equivalent H 2 conversion coefficient reduced 9.0% H 2 consumption. • It reached 94.2% of dynamic programming benchmark in terms of H 2 consumption. • The eco-driving strategy achieved best ride comfortability than traditional methods. Ecological driving (eco-driving) is a promising technology for transportation sector to save energy and reduce emission, which works by improving vehicle behaviors in traffic scenarios. Fuel cell hybrid electric vehicles (FCHEV) are receiving extensive attentions due to global fossil energy crisis, but whose implementations for eco-driving result in multiple objective collaborative optimization problems. In this paper, an eco-driving framework for FCHEV is proposed based on deep deterministic policy gradient (DDPG) algorithm. And it combines adaptive cruise control (ACC) and energy management strategy (EMS) into an integrated architecture. Firstly, in order to achieve excellent balance between driving behaviors and fuel economy, an appropriate weight coefficient value is determined after adequate explorations. Secondly, power-varying equivalent hydrogen conversion coefficient function is constructed to save fuel consumption by 8.97%. Thirdly, ablation experiments for health state of fuel cell system present 19.95% decrease in terms of health degradation. Then, comparison experiments indicate that the DDPG-based eco-driving strategy can reach 94.16% of that of dynamic programming with respect to equivalent hydrogen consumption, meanwhile with best ride comfortability. Moreover, simulation results under validation driving cycle manifest its excellent adaptability. [ABSTRACT FROM AUTHOR]

• An energy management of FC/SC/Bat hybrid vehicle is proposed. • The energy management strategy is based on a combination between three methods. • An interleaved boost converter and two bidirectional buck-boost converters are used. • The fast source (SC) is used to control the energy stored in the DC bus. • The experimental results exhibit the effectiveness of the proposed strategy. Managing the energy is one of the most important issues for the efficiency of vehicular applications using hybrid electric power systems. This paper deals with an analysis of an energy management for hybrid power system, which consists of fuel cell, ultra-capacitor and battery and dedicated to electrical vehicles. Thus, an energy management method is applied in order to split the energy flow between the three used sources. This strategy is based on a combination of three control methods; a fuzzy logic control, a flatness control and rule-based algorithm. For the fuzzy logic, the main idea is inspired from rule-based algorithms except that it is more flexible and less aggressive. The second technic is an innovative control using the flatness property for a Fuel cell/ supercapacitor/ Battery hybrid power system. For the rules based algorithm, it uses a supercapacitor state-of-charge control principle. Then, an experimental validation is done for the energy management method to show the efficiency of the proposed control strategy in term of the well share of the energy between sources to satisfy the load demand. A dSpace real-time card is used for management algorithm implementation and data acquisition. The obtained experimental results prove the efficiency and the validity of the proposed supervisor. [ABSTRACT FROM AUTHOR]

• Novel equivalent consumption minimization strategy with three power sources. • A test bench is built to validate the developed energy management strategy. • Online estimation of power sources' state of health by Unscented Kalman Filter. • Modify equivalent factor and fuel cell current change rate with power sources ageing. • Adaptive equivalent consumption minimization strategy with power sources degradation. The aim of this paper is to present an on-line adaptive equivalent consumption minimum strategy (AECMS) for fuel cell hybrid electric vehicle powered by fuel cell, battery and supercapacitor. In order to design the AECMS, an equivalent consumption minimum strategy (ECMS) without considering power sources degradation is firstly designed to decrease hydrogen consumption and degradation of power sources, which chooses fuel cell as the main power source to supply steady power, battery as the main energy storage source to buffer energy demand by vehicle and supercapacitor as the peak power supplier. A testbench is built to validate the developed ECMS. By contrastive experimental tests on ECMS, Rule Based Control Strategy (RBCS) and a Hybrid ECMS Operating mode control Strategy (HEOS) through the built test bench, hydrogen consumption of ECMS decreases 2.16% and 1.47% respectively and it also has the smoothest fuel cell current. Along with the degradation of fuel cell and battery, the charge sustenance objective of battery cannot be reached. Therefore, AECMS is finally designed to adjust equivalent factors and fuel cell dynamic current change rate along with the state of health (SOH) of fuel cell and battery, to make sure the charge sustenance of battery and prolong the lifetime of fuel cell. The method that on-line estimates their SOHs and the effects of their degradation on ECMS are also analyzed. [ABSTRACT FROM AUTHOR]

• First time the solar-wind-biogas-VRFB integrated micrgrid is modeled and validated. • The capacity optimization and energy management with lowest LCOE is done by HOMER. • Peak load shaving with optimal sources and grid is shown by PSCAD using real data. • Optimal schedule of renewable sources and VRFB ensures zero LPSP at consumer end. • Financial study of hybrid microgrid demonstrates project viability and up scaling. Uninterrupted access to electric power has become the basic need of today's world. Rural parts of many countries still do not have access to electricity or have electric power access to weak distribution grids with inadequate transmission and distribution system infrastructure. However, the countries where there is an abundance of solar radiation, a good potential of bio-degradable waste and average availability of wind source, access to electricity for those remote areas can be managed by distributed power generation. Considering the fact that the renewable energy sources (Solar, Wind etc.) are intermittent in nature, battery energy storage systems (BESS) and other reservoirs like biogas energy sources are the potential candidates to be integrated with the renewable sources to ensure continuous access to electricity and energy security. In this paper, a unique combination of Solar PV, Wind, Biomass and Vanadium Redox Flow Battery (VRFB) storage integrated hybrid Microgrid has been modeled and implemented practically for the first time. The capacity selection of different renewable sources for satisfying daily energy demand and their techno-commercial optimization has been performed through HOMER simulation. Further, the peak load shaving that is a limitation of HOMER model, has been established through PSCAD simulation by providing the real life data of different renewable sources, VRFB storage and the load profile as input to the model. The simulation model performances have been validated by a practical 10 kW P solar PV, 1 kW wind and 15 kVA Biogas generator integrated with 1 kW 6 h VRFB storage based Microgrid installed at India Institute of Engineering Science and Technology campus, India. In addition to these, zero loss of power supply probability (LPSP) has been ensured by implementing smart scheduling and controller considering the intermittency of the renewable sources. As a part of the financial analysis, project Investment on Return (IRR) and pay back has been calculated considering initial investment, operation and maintenance cost and revenue of generation. [ABSTRACT FROM AUTHOR]

Highlights • Performing a comprehensive review and analysis of the energy performance certification. • Developing an operational definition of the energy performance certification. • Illustrating the scope and data of the energy performance certification. • Classifying the energy performance certification. • Analyzing the practicability of the energy performance certification. Abstract The energy performance certification has been recognized as an effective assessment methodology and tool to systematically manage energy consumption and improve energy performance. In the process manufacturing industry and building industry, a large number of energy performance certifications have been applied worldwide with remarkable results achieved in energy saving and emissions mitigation. Mechanical manufacturing industry, which is characterised as a typical discrete manufacturing having a wide distribution in operations with large consumption of energy and low efficiency, has a considerable potential of benefiting from energy saving and emissions mitigation. The objective of this paper is to perform a review and analysis of energy performance certification in the mechanical manufacturing industry for evaluating its potentials and applicability for performance enhancement. We begin with analyzing energy performance certification and research gaps to develop an operational definition of energy performance certification. The scope of energy performance certification and the method for data acquisition are reviewed. Next, we establish the classification of energy performance certification from perspectives of the energy benchmarking, rating and labelling to lay a foundation for its implementation framework and evaluating its practicability. Through the systemic review and analysis, the current state of researching energy performance certification is provided with the methods for developing energy performance certification summarized and analyzed. These findings are useful references for managers to strengthen energy management and monitoring and improve energy performance in the mechanical manufacturing industry. [ABSTRACT FROM AUTHOR]

Highlights • Development of a modular model-based architecture for fuel cell vehicle optimization. • Co-optimization accounts for the interactions between design and control strategy. • Constrained optimization analyses explore an extended range of FCHV configurations. • Outcomes yield useful guidelines that support decision making in FCHV design process. • Specs independent control strategy valorized in multi-scenario optimization tasks. Abstract Developing successful fuel cell hybrid vehicles (FCHVs), to be destined to the widest deployment within the entire transport sector, is nowadays considered as a highly strategic target to fully meet well known environmental and regulatory constraints at international level. This happens thanks to the intrinsic overall superior features of fuel cell propulsion when compared to both electric and hybrid vehicles, such as high fuel-economy, reduced tank-to-wheel environmental impact and good ranges. Successful achievement of the above-introduced challenging goal motivates the research activity presented and discussed in this paper, namely the development of an advanced mathematical tool featuring co-optimization capabilities. The reason for such a requirement lies in the well-known strong interactions and mutual influence between selected design criteria and adopted control strategies. Therefore, a comprehensive model of a generic FCHV architecture and a specification independent control strategy, thus adaptable to different fuel cell system and battery sizes, were preliminary developed. Then, they were integrated and embedded within a modular constrained optimization algorithm, which was conceived in such a way as to simultaneously find the optimal FCHV powertrain design, as well as real-time applicable control strategies. Suitable design and energy management criteria were investigated on a selected driving cycle, in such a way to explore several powertrain configurations (i.e. more hybrid, as well as more plugin and range-extender like FCHV). This allowed verifying the suitability of the proposed procedure to yield solutions ensuring low hydrogen consumption (i.e. fuel economy as high as 135 km/kg) and full alignment with targeted energy management policies. Discussion of results, together with the physical meaning of main design and control variables provided as outcomes, underline the effectiveness of the proposed tool in providing a solid support in the preliminary design of cost-effective fuel cell powertrains destined to a variety of applications and driving habits. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Strategies for multi-stage reverse electrodialysis (MSRED) were compared and analyzed theoretically. • Serial control strategy is more feasible than independent control strategy in practice. • Thickening HC solution compartments can enhance the performance of MSRED. • Counter-flow arrangement is better than co-flow arrangement for MSRED. Abstract Reverse electrodialysis heat engine (REDHE) is an effective technology to harvest salinity gradient energy (SGE) driven by low grade heat. In order to improve the energy conversion efficiency of it, one practical way is to enhance the capturing SGE ability of the reverse electrodialysis (RED) unit. To achieve that, the promising multi-stage RED (MSRED) method is proposed. In this paper, two strategies for MSRED, i.e., individually control (Strategy I) and serial control (Strategy II) respectively, were theoretically compared and analyzed. The results showed that Strategy II is more suitable in practice, although its performance is slightly inferior compared with Strategy I. After that, the influence of feed solution velocity, thickness of high concentration solution compartments, concentrations of feed solutions and flow arrangement on the relevant key performance indicators of MSRED in the two strategies were investigated. And results demonstrated that thickening compartments of high concentration solution, increasing the concentration of high concentration feed solution and adopting counter-flow arrangement are all more effective for elevating the performance of MSRED than increasing feed solution velocity. Furthermore, increasing the concentration of low concentration feed solution overly has a significant negative impact on the performance of MSRED. [ABSTRACT FROM AUTHOR]

Highlights • Photovoltaic stand-alone microgrid with a Unitized Regenerative Fuel Cell. • Strip mall building in eight Köppen-Geiger climate zones as case study. • Matlab/Simulink® self-made simulation tool for the power plant management. • Technical, economic and environmental Simulink results comparison against HOMER and grid. Abstract Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector and, to save energy and mitigate the environmental impact of electricity consumption, distributed generation, involving renewable energy sources, might be a promising solution. In the present paper, a self-made simulation tool has been developed using Matlab/Simulink® to model a stand-alone polygeneration power plant for a strip mall. A photovoltaic panel array has been coupled to a battery and a unitized regenerative polymer electrolyte membrane fuel cell as primary storage/backup systems and a diesel generator as secondary backup system. The possibility of applying the same plant layout and control strategy to eight different climate zones in eight different States has then been assessed. Results show that even in the most favorable conditions in terms of daily radiation and average temperature, such as Miami, Las Vegas or Houston, the cost of electricity of the utility makes the implementation of these systems still inconvenient, unless a reduction in the initial investment costs of above 60% is pursued by means of incentives or a further establishment of the proposed technologies. [ABSTRACT FROM AUTHOR]

Highlights • Novel optimization method for off-grid renewable installations is presented. • Results are compared with an installed PV-battery system. • At least 9.7% reduction in the lifetime cost of the system is achieved. • Sensitivity analysis is performed to consider Li-ion battery price uncertainties. Abstract This paper proposes a new methodology to find the most economic system configuration and energy management strategy for Li-ion battery based off-grid renewable energy systems. A system level macroscopic model and a microscopic battery lifetime prediction model are incorporated into the optimization framework to simulate hourly performance of the system. Due to the computational efficiency of the model, optimization is carried out using enumerative method (evaluating all the possible combinations of components and control strategies) to ensure finding the global optimum solution of the problem. To investigate the effectiveness of the proposed methodology, the optimization results are compared with a baseline scenario which is an installed PV-battery system to provide electricity for an isolated house situated near Zaragoza, Spain. Results indicate that the optimized scenario leads to 9.7% reduction in the levelized cost of energy and 48.6% improvement in the battery service period in comparison with the baseline scenario. Moreover, by considering a 0.5% unmet load, the economic feasibility of the system and the battery longevity are enhanced to 14.6% and 78.4%, respectively. Finally, to evaluate the effect of battery unit price and future trends on the optimization results, sensitivity analysis is performed. [ABSTRACT FROM AUTHOR]

The aim of this paper is to compare an optimal and a sub-optimal strategy for the Fuel Cell Hybrid Power Systems based on Maximum Power Point tracking algorithms (with global feature or not) with the basic energy management strategy, namely the static Feed-Forward strategy considered as reference. The fuel economy is used as the unique performance indicator. The gaps in fuel economy for two Real-Time Optimization strategies based on Global Extremum Seeking algorithm and Perturb & Observe algorithm are compared to highlight the advantages of the global optimization strategies. Up to 5 L fuel economy was obtained for optimal strategies compared to sub-optimal ones. Also, the gaps in fuel economy are estimated for the proposed strategies using two levels of the FC current slope. The results of this study obtained for constant load are validated on a variable and unknown profile of the load power as well. [ABSTRACT FROM AUTHOR]

• Study of MVDC microgrid with PV, hydrogen system and electric vehicles. • Configuration based on Z-source converters that reduces the number of power converters. • Development of optimal energy management system (EMS) using biogeography-based optimization. • EMS achieved a notable improvement in the equivalent hydrogen consumption/generation. Currently, the technology associated with charging stations for electric vehicles (EV) needs to be studied and improved to further encourage its implementation. This paper presents a new energy management system (EMS) based on a Biogeography-Based Optimization (BBO) algorithm for a hybrid EV charging station with a configuration that integrates Z-source converters (ZSC) into medium voltage direct current (MVDC) grids. The EMS uses the evolutionary BBO algorithm to optimize a fitness function defining the equivalent hydrogen consumption/generation. The charging station consists of a photovoltaic (PV) system, a local grid connection, two fast charging units and two energy storage systems (ESS), a battery energy storage (BES) and a complete hydrogen system with fuel cell (FC), electrolyzer (LZ) and hydrogen tank. Through the use of the BBO algorithm, the EMS manages the energy flow among the components to keep the power balance in the system, reducing the equivalent hydrogen consumption and optimizing the equivalent hydrogen generation. The EMS and the configuration of the charging station based on ZSCs are the main contributions of the paper. The behaviour of the EMS is demonstrated with three EV connected to the charging station under different conditions of sun irradiance. In addition, the proposed EMS is compared with a simpler EMS for the optimal management of ESS in hybrid configurations. The simulation results show that the proposed EMS achieves a notable improvement in the equivalent hydrogen consumption/generation with respect to the simpler EMS. Thanks to the proposed configuration, the output voltage of the components can be upgraded to MVDC, while reducing the number of power converters compared with other configurations without ZSC. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel system is proposed for heating, electricity, fresh water, and ammonia production. • The PCMs are incorporated into the system to avoid performance fluctuations. • The effects of different parameters on the system outputs are investigated. • The solar-driven ammonia production shows a good potential for stabilizing non-dispatchable systems. This research paper designs and analyzes a novel integrated solar-driven system for ammonia synthesis, freshwater production and power generation. The trigeneration system contains a solar thermal subsystem, a thermal energy storage system where the molten salt is used as a heat transfer fluid and storage medium, a solar energy-driven Rankine cycle, a multistage flash distillation, a proton exchange membrane electrolyser array, and an ammonia synthesis unit. The proposed integrated system uses solar energy to charge the molten salt, which flows through a heat exchanger and generates steam for the topping cycle. The exhaust stream of the steam turbine provides heat for the multistage flash distillation (MFD) process. A 20 stage MFD unit is proposed where the seawater is utilized as the feedwater in this study. A portion of the produced freshwater is sent to the PEM electroylzer array for hydrogen production, which is used for ammonia production via the Haber-Bosch process. The thermodynamic analysis of the solar driven multigeneration system is analyzed through the Engineering Equation Solver (EES) and Aspen Plus software packages. The novelty of this study relies on the development of a new solar energy-based integrated system and its application for a selected community with over 10,000 residents to meet all of their needs to be self-sufficient and sustainable. Ammonia (for multiple purposes, such as fuel, energy carrier and feedstock), freshwater and electricity are the useful outputs as targeted in this paper. The study incorporates the phase change materials into the system for thermal energy storage purposes. The performance assessments based on energy and exergy efficiencies are carried out for the overall system and its components. Furthermore, the power, freshwater and ammonia production capacities are studied for better demand coverage. The overall exergy efficiency of the system is determined as 12.1 %, where the ammonia synthesis, power generation, and freshwater production capacities are 0.85 kg/s, 17.6 MW, and 143.97 kg/h respectively. Moreover, the study investigates the energy and exergy efficiencies with and without the presence of an air separation unit. The study results show that the air separation unit has less than 0.3% effect on the overall energy and exergy efficiencies. [ABSTRACT FROM AUTHOR]

• An online adaptive energy management strategy is proposed. • A dataset of 20 typical driving cycles and 20 self-testing driving cycles is constructed. • An improved k -means clustering approach is designed for driving pattern recognition. • The presented algorithms show superiority in terms of fuel consumption. An effective energy management strategy (EMS) is essential to ensure the safe and efficient operation of the fuel cell hybrid vehicles. In this paper, an online adaptive EMS is proposed for the fuel cell hybrid vehicles to minimize hydrogen consumption and adjust the strategy according to the driving conditions. Driving pattern recognition is realized by the improved k -means cluster approach which combines multiple k -means clusters with specific distances to serve as the classifier. In each driving pattern, separate machine learning (ML) models are trained to obtain the energy management regression learner. Comparison experiments are performed to determine the optimal ML model and input parameters. The effectiveness of the proposed EMS is evaluated using two compound test driving cycles. Results show that the proposed method achieves the lowest fuel consumption compared to the other five algorithms considered. Remarkably, it reduces hydrogen consumption by up to 5.66% when compared to commonly used methods. [ABSTRACT FROM AUTHOR]

The global energy consumption is increasing every day owing to the economic growth, urbanization, population growth, high standard of living and high income level. The conventional power plants cannot satisfy the world energy needs without some noticeable challenges such as greenhouse gas emissions, acid rain, health challenges, global warming, climate change and air pollution. The feasible alternative solution to meet ever increasing energy consumption in residential, commercial and industrial sectors of the economy is renewable energy sources (RESs) owing to the technical, political, societal, financial and environmental benefits. The hybrid energy system (HES) that is made up of wind turbine (WT), fuel cell (FC), photovoltaic (PV) and utility grid is proposed in the study to meet the energy needs of distinctive users in Cape Town, South Africa. The study's goal can be accomplished by making use of fmincon optimization technique to minimize the life cycle emission (LCE) of greenhouse gas (GHG), life cycle cost (LCC) and monetary value of energy purchased from the grid and maximize the job creation (JC), human development index (HDI), annual benefit (AB) and usage of PV, WT and FC. The effectiveness of the simulation technique applied in the paper can be assessed by comparing the HSE results with those from the base system. The outcomes of the HES offer a better performance with a payback period of 2.75 years, annual benefit of $329530, annual energy cost saving of $6899.3, annual electricity bill of $62747, potential energy saving of 52.37% and LCC of $419300. The annual energy purchased from the grid has reduced from 146,190 kWh to 59,593 kWh; this translates to 59.24% reduction when compared to the base system. The proposed HES with a non-renewable energy usage (NREU) of 40.78% and a renewable energy usage (REU) of 59.22% is financially feasible with a cost of energy (COE) of 0.0122 $/kWh and ecologically friendly with CO 2 , SO 2 and NOx LCE reductions of 58.85%, 58.81% and 58.29% when compared with the base system. This indicates that PV, FC and WT can be used in the traditional power system to lower the annual electricity bill, energy purchased from the utility grid and GHG emissions. The results obtained from the assessment of the proposed HES serve as crucial decision-supporting tools that can be used by designers and operators in choosing the right parts for their power systems. The outcomes of the study can be utilized to enhance sustainability of electricity supply and minimize poverty in the developing countries. [ABSTRACT FROM AUTHOR]

• A real-time energy scheduling strategy is proposed for a home energy management system (HEMS). • The HEMS integrates a supervised learning method to learn and mimic optimal actions of energy storage systems and electric vehicles. • The proposed method is validated using real-world data and compared with MADDPG-based and forecasting-based methods. • The results show the effectiveness of the proposed method for real-time energy scheduling. With rising energy costs and concerns about environmental sustainability, there is a growing need to deploy Home Energy Management Systems (HEMS) that can efficiently manage household energy consumption. This paper proposes a new supervised-learning-based strategy for optimal energy scheduling of an HEMS that considers the integration of energy storage systems (ESS) and electric vehicles (EVs). The proposed supervised-learning-based HEMS framework aims to optimize the energy costs of households by forecasting the energy demand and simultaneously scheduling the charging and discharging operations of ESS and EV. From the scenarios extracted from historical data, the HEMS optimization problem is solved using a mixed-integer linear programming (MILP) solver to collect the datasets on the optimal actions of the ESS and EV. Accordingly, a supervised learning method is used to learn the optimal actions of the MILP solver using deep neural networks (DNNs). Well-trained DNNs act as decision-making tools that are subsequently applied to predict near-optimal actions for ESS and EV based on real-time data. The effectiveness of the proposed method is demonstrated through simulation results and compared with deep reinforcement learning-based and forecasting-based methods. The results show that the proposed method can significantly reduce energy costs and improve the efficiency of ESS and EV operations. Overall, the proposed supervised-learning-based HEMS offers a practical and effective solution for residential energy management. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning-based energy management strategy (EMS) is essential for fuel cell hybrid electric vehicles to reduce hydrogen consumption, improve health performance and maintain charge. This is a complex nonlinear constrained optimization problem. In order to solve the problem of high bias caused by the inconsistency between the infinite support of stochastic policy and the bounded physics constraints of application scenarios, this paper proposes the Beta policy to improve standard soft actor critic (SAC) algorithm. This work takes hydrogen consumption, health degradation of both fuel cell system and power battery, and charge margin into consideration to design an EMS based on the improved SAC algorithm. Specifically, an appropriate tradeoff between money cost during driving and charge margin is firstly determined. Then, optimization performance differences between the Beta policy and the standard Gaussian policy are presented. Thirdly, ablation experiments of health constraints are conducted to show the validity of health management. Finally, comparison experiments indicate that, the proposed strategy has a 5.12% performance gap with dynamic programming-based EMS with respect to money cost, but is 4.72% better regarding to equivalent hydrogen consumption. Moreover, similar performances in validation cycle demonstrate good adaptability of the proposed EMS. [ABSTRACT FROM AUTHOR]

Recently, deep reinforcement learning has emerged as a popular approach for enhancing thermal energy management in buildings due to its flexibility and model-free nature. However, the time-consuming convergence of deep reinforcement learning poses a challenge. To address this, offline pre-training of deep reinforcement learning controllers using physics-based simulation environments has been commonly employed. However, developing these models requires significant effort and expertise. Alternatively, data-driven models offer a promising solution by emulating building dynamics, but they struggle to predict previously unseen patterns. Therefore, this paper introduces a strategy to effectively train and deploy a deep reinforcement learning controller by means of long short-term memory neural networks. The experiments were carried out using an EnergyPlus simulation environment as a proxy of a real building. An automatic and recursive procedure is designed to determine the minimum amount of historical data required to train a robust data-driven model which mimics building dynamics. The trained deep reinforcement learning agent meets safety requirements in the simulation environment after two and a half months of training. Additionally, it reduces indoor temperature violations by 80% while consuming the same amount of energy as a baseline rule-based controller. • Data-driven models successfully emulate building dynamics for agent training. • Reinforcement learning adoption can be scaled up through the proposed approach. • Safety checks were introduced to supervise reinforcement learning operation. • The proposed approach was designed to be effective with few input data available. [ABSTRACT FROM AUTHOR]

• Real-time multi-objective energy management strategy based on MPC. • A vehicle speed prediction method that balances accuracy and efficiency is proposed. • A data fusion technique is used to optimize energy consumption and battery ageing. • The proposed strategy is compared with common strategies and analyzed in depth. The real-time control of energy management strategy (EMS) is becoming increasingly challenging as the complexity of the model and control strategy increases. To address this issue while ensuring the accuracy of the EMS, a multi-objective real-time EMS based on model predictive control (MPC) that considers battery life is proposed in this paper. Firstly, to balance the accuracy and efficiency of the prediction module, a kernel extreme learning machine based on the whale optimization algorithm is proposed as a short-term speed prediction model. Secondly, an adaptive state of charge (SOC) trajectory planning method is established to plan MPC reference trajectory. Next, to optimize fuel efficiency, electrical energy consumption, and battery aging in real-time, a multi-objective real-time MPC (MOR-MPC) algorithm is proposed. Finally, the effectiveness, real-time performance, and robustness of the proposed strategy are verified. Simulation results demonstrate that the total cost of the strategy is reduced by 6.15% compared to the equivalent consumption minimization strategy (ECMS), with 98.17% of dynamic programming (DP) performance achieved. Real-time performance is improved by 97.89% compared to DP-MPC. Hardware-in-the-loop (HIL) testing is also carried out to evaluate the proposed strategy. [ABSTRACT FROM AUTHOR]

Community-Based Residential load and EV charging infrastructure, Transactive Energy Management System, Solar PV, Cost, Energy and Emission Analysis, HOMER Software. [Display omitted] • Proposing a Transactive Energy Management System (TEMS) in a community-based residential system. • Performing feasibility analysis of the proposed TEMS with V2H concept using HOMER. • Three scenarios of on-grid/off-grid solar PV for house & public loads, and EV loads with V2H concept. • Carrying out an economic analysis for three scenarios with and without TEMS. In this world, with enhancing environmental issues of greenhouses and fossil fuels-based systems in societies and assumptions of sustainable solutions includes Renewable Energy Technologies (RET) and Electric Vehicles (EV) are increased in the residential and commercial buildings. Moreover, Renewable Energy Resources (RES) have an exceptional milestone towards the world to supply energy from the energy market to the consumers without any scarcity and the EV charging facilities among the residential building is the most eminent research domain in the developing countries. This paper investigates the feasibility of solar energy for around 320 buildings include residential load with EV charging is considered for the selected location Vistara Residential Enclave at Madurai. Additionally, the optimal solution is performed using HOMER software for the proposed solar Photo Voltaic (PV), Battery Energy Storage System (BESS), grid extension, V2H process for increase the efficient energy management. Instead of, three scenarios are considered for analyse the technical, economic and environmental basis. Moreover, in the first one, off-grid/battery support system for only residential load using solar energy. Then, the second scenario is evaluated with on-grid/battery backup system by solar PV for household loads only. Finally, in the third scenario, on-grid/battery system for residential load as well as EV charging with considered V2H performance. Meanwhile, the multiple houses are confined to a residential site in the consideration of overall load system as well as divided into five zones. Moreover, the following these three scenarios cost and electrical parameters are predicted as with and without Transactive Energy Management System (TEMS) to overcome the peak load challenges. Therefore, the consequences of this study proposed that 36.90 % of Net Present Cost (NPC) is maximized and 51.87 % reduced Operating and Maintenance cost (O&M) between presence and absence of TEMS shows that overall system load with TEMS towards the localized grid is the best effective reliable system in an effective manner than five divided zones without TEMS. [ABSTRACT FROM AUTHOR]

• Energy management method of electric vehicles with triple-source hybrid powertrain. • Vehicle fuel economy and battery longevity are optimized at the same time. • An adaptive constraint update algorithm is proposed for power distribution issue. • A HIL platform is used to experimentally validate the presented adaptive EMS. • It is proved that the total economy of PHEV can be improved by the proposed approach. The adoption of hybrid powertrain technology brings a bright prospective to improve the economy and environmental friendliness of traditional oil-fueled automotive and solve the range anxiety problem of battery electric vehicle. However, the concern of the battery aging cost is the main reason that keeps plug-in hybrid electric vehicles (PHEV) from being popular. To improve the total economy of PHEV, this paper proposes a win-win energy management strategy (EMS) for Engine-Battery-Supercapacitor hybrid powertrains to reduce energy consumption and battery degradation cost at the same time. First of all, a novel hierarchical optimization energy management framework is developed, where the power of internal combustion engine (ICE), battery and super capacitor (SC) can be gradationally scheduled. Then, an adaptive constraint updating rule is developed to improve vehicle efficiency and mitigate battery aging costs. Additionally, a control-oriented cost analyzing model is established to evaluate the total economy of PHEV. The quantified operation cost is further designed as a feedback signal to improve the performance of the power distribution algorithm. The performance of the proposed method is verified by Hardware-in-the-loop experiment. The results indicate that the developed EMS method coordinates the operation of ICE, driving motor (DM) and energy storage system effectively with the fuel cost and battery aging cost reduced by 6.1% and 28.6% respectively compared to traditional PHEV. Overall, the introduction of SC and the hierarchical energy management strategy improve the total economy of PHEV effectively. The results from this paper justify the effectiveness and economic performance of the proposed method as compared to conventional ones, which will further encourage the promotion of PHEVs. [ABSTRACT FROM AUTHOR]