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In this paper, a novel model of an energy management system (EMS) for a microgrid (MG) under uncertain conditions is proposed. The MG consists of renewable photovoltaic and wind sources along with electric vehicle parking lots. Hence, the model incorporates the uncertainties of renewable DGs, parking lots, and also load. In this study, the MG operation cost and voltage stability index are considered objective functions. A novel combined algorithm (hMOPSO-HS) is proposed for microgrid energy management. The hMOPSO-HS algorithm is a combination of the mutant multiobjective particle swarm optimization (MOPSO) algorithm and the harmony search (HS) algorithm. The simulations are performed in two parts, with and without considering the uncertainty. The comparative analysis involves evaluating the optimization outcomes achieved by the hMOPSO-HS algorithm in contrast to various other metaheuristic algorithms for multiobjective optimization. The simulation findings validate the superior efficacy of the hMOPSO-HS algorithm compared to other approaches. Also, the simulation results showed that in the conditions of uncertainty, the operating cost is 6.1% higher and the microgrid stability index is 6.8% lower. Also, considering the uncertainty has caused the penalty for energy not supplied (ENS) and demand response program (DRP) costs to increase by 3% and 4%, respectively. [ABSTRACT FROM AUTHOR]

Summary: Electric and hybrid electric vehicle technology demonstrates a performance to replace the internal combustion engines (ICE) in the current scenario. It attracts attention with improved fuel or energy efficiency with lower emissions. The overall system performance depends on powertrains, types of energy sources, electro‐electronic interfaces, and energy management strategies (EMS). Significant issues of battery‐powered electric vehicles (EV) are effects on the range, battery life, EV performance, battery maintenance, and replacement cost. Hybrid power source system (HPSS) solves EV challenges to a large extent. A hybrid combination of battery and supercapacitor (SC) to power the EV enhances the overall performance and life of the vehicle. Learning and integrated‐based EMSs are gaining attention, with their ability in accurate and fast response in power handling among various sources. This paper analyses various DC‐DC converter topologies of HPSS and compares multiple EMS with recent developments. In the context of challenges involved in EVs and research gaps that are discussed in the paper, EMSs need to be enhanced. The EMSs must consider the inputs for varying driving behaviors, road traffic, load, and environmental conditions to assure the flexibility of EV among different users across the globe. This is achieved by the management of SC power available to support the vehicle during sudden power requirements and enabling it to recuperate braking energy to improve the energy efficiency throughout the trip. Lastly, recommending precise research directions to achieve the development and improvement of the EMS and power electronic interfaces. [ABSTRACT FROM AUTHOR]

Summary: Power systems face more uncertainty by increasing photovoltaic system installations on the roof of buildings. To optimally manage energy and available flexibility in a building, stochastic optimization is used to take an optimal decision under uncertainty and minimize the operational cost. In stochastic optimization, a scenario set is used as an input to represent the uncertainty in a random variable, PV energy generation in this case. In this paper, a data‐driven method is proposed to obtain the distribution of the random variable and later generate scenario sets representing the uncertainty on day‐ahead PV energy generation installed on the roof of a building. This method is only based on historical PV generation and it does not require any other external data such as weather forecasts. A machine learning‐based technique is applied to forecast the PV energy production following by generating a scenario set for day‐ahead decision‐making. Later, the day‐ahead PV‐battery system management problem is formulated as a two‐stage stochastic optimization while the generated scenario set is the input of this optimization. The proposed algorithm is tested in the day‐ahead scheduling of a PV‐battery system for a commercial building, informed by real‐life measurement data. The results show that the proposed algorithm is able to capture the uncertainty in PV system while providing a cost‐optimal and reliable solution to the application problem. Without using any weather data, the error of the proposed PV energy forecast method reaches to NRMSE = 11.89, while there is 5% reduction in the operational cost of the proposed two‐stage stochastic optimization. Moreover, the proposed algorithm is easy to implement in the energy management system of a building to manage PV‐battery system. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a new robust distributed approach for the energy management of rural multi‐microgrids communities in Chile. These rural communities are often geographically isolated and have a poor connection to the main electricity grid. Operating them as individual microgrids allows the community to connect to the grid or behave as a stand‐alone system (islanded). Connecting and coordinating the operation of the microgrids can improve the system's robustness to energy and demand uncertainty, as well as reducing the dependency on the main grid. However, this coordination can be challenging because the communication between the different microgrids can sometimes deteriorate, moreover, both load and local generation profiles are challenging to predict. For these reasons, in this paper, a new robust distributed hierarchical Energy Management System (EMS) for coordination of multiple microgrids (multi‐microgrids) is introduced, which uses robust distributed Model Predictive Control (MPC). Distributed optimisation permits improving reliability in the presence of poor or lack of communications between microgrids. To consider the uncertainty associated with renewable energy generation and load consumption, fuzzy prediction interval models are included in the MPC design. The proposed method is compared with robust centralised MPC and deterministic distributed MPC to assess its performance. Simulation results are presented using data representative of a rural community in coastal Chile applied to the robust MPC design. The outcomes of this work demonstrate that the proposed robust distributed EMS can improve the power supply, apply peak shaving, and can successfully operate when there is a loss of communication between microgrids. The use of decreasing fuzzy intervals allows an accurate prediction of both load and renewable energy generation, making the system robust to uncertainty in the available resources. Moreover, results show that in absence of communication, the power supply obtained under different scenarios with the distributed EMS is better than the equivalent centralised EMS. [ABSTRACT FROM AUTHOR]

Summary: The equivalent consumption minimization strategy is widely used in real‐time energy optimization for fuel cell electric vehicles due to its approximate optimality and good real‐time performance. However, equivalent factors have always been difficult to select in the research of this algorithm, and the existing methods have insufficient adaptability and robustness. In this paper, an equivalent factor predictor based on a neural network is proposed, which can predict the equivalent factor in real time by considering various operating conditions and vehicle states. In addition, we improve the real‐time performance of the equivalent consumption minimum strategy. A new method to quickly solve the continuous optimal solution of the algorithm by transforming the objective function into the quadratic function of the control variable is studied. Simulation and experimental results show that the designed equivalent consumption minimization strategy can ensure state of charge of the battery in a better range, and the energy saving effect is approximately 3% more than that of dynamic programming and 8% to 10% less than that of the traditional rule algorithm. Finally, the proposed method saves 2 to 3 orders of magnitude in computation time compared with the traditional equivalent consumption minimum strategy. [ABSTRACT FROM AUTHOR]

Summary: The installation of an energy storage system (ESS) is vital for the Micrgorid (MG) islanded operation to ensure the maintenance of demand–supply balance. Lithium‐ion batteries (LIBs) are among the most commonly used ESS technologies for grid‐based applications, which is used in this paper to constantly maintain the demand–supply balance in a residential MG. For this purpose, a novel frequency‐based energy management scheme is proposed. It uses an LIB ESS to handle the primary frequency control and energy management during peak‐load period, while the dispatchable distributed generators like microturbine and fuel cell supply the base load. The air‐conditioning TCLs consume a significant part of the residential load profile, especially during mid‐summer. Unlike TCLs that instantaneously use their generated cooling thermal energy to control indoor temperatures, the thermal energy storage systems (TESSs) can also store the thermal energy and use it later. In this paper, the TESSs are used instead of TCLs to reduce the power consumption of a residential Microgrid (MG) in islanded mode. By doing this, the required LIBESSs capacity is decreased 70% considerably than the case the TCLs are used for controlling the indoor temperatures. Also, replacing the ACs and EHPs with TESSs results in a 63.7% reduction in the total cost of electrical and thermal storage. [ABSTRACT FROM AUTHOR]

Summary: Computationally intelligent energy forecasting methods for appropriate energy management at the consumer/producer side have a positive impact on the preservation of energy and play a constructive role in tackling global climate change. The energy production and consumption are very high worldwide, demanding intelligent methods with real‐world implementation potentials for appropriate energy management. In this paper, we survey the existing intelligent load forecasting (ILF) systems, highlight their advantages and downsides, and briefly discuss the workflow of the employed literature. Furthermore, we debate on the existing load forecasting datasets and their features along with a brief overview of the challenges confronted by researchers using these datasets. Distinct from previous survey papers, we provide a detailed review of performance evaluation metrics and comparison of employed methods for energy load forecasting, thereby concluding the need of efficient, effective, and adoptable ILF methods functional in real‐world scenarios. Finally, we assess the employed techniques and deliver future research opportunities based on the derived conclusions from existing research works. This paper delivers the overall energy forecasting literature in a compact form with possible future insights for researchers working in ILF domain. [ABSTRACT FROM AUTHOR]

This paper proposes and analyses several configurations for hybridising concentrating solar power (CSP) plants with combined cycle gas turbines (CCGT). The objective is to increase the solar contribution to a large extent, much higher than those obtained in integrated solar combined cycles but maintaining synergies, which are usually lost when increasing the solar share. For that, two thermal energy management systems are introduced at different temperature levels. First, a configuration with only the low-temperature system is proposed. Then, an enhanced configuration with the low- and high-temperature systems is conceived. These configurations are compared to reference CSP and CCGT state-of-the-art plants. The analyses include different strategies of operation and two sizes for the thermal energy storage system. The results show that the first proposed configuration introduces some synergies but cannot improve the performance of the reference CSP and CCGT working separately, due to an issue with the solar dumping on days with high solar irradiation. The enhanced configuration overcomes this problem and maintains the synergies, leading to an improvement from both the thermodynamic and economic points of view, increasing the solar contribution and decreasing the levelized cost of energy over the reference plants. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a prediction‐based optimization strategy (POS) for the Energy Management System to balance the use of diesel generator (DG) and emergency battery (EB) in the microgrid. The POS is developed by combing two operating strategies, the "predictive analysis" and "optimal operation" in each scheduling period for the microgrid. Based on the predicted microgrid state and energy demand, a multi‐objective mixed‐integer nonlinear programming model (MOMINP) is constructed to minimize the fuel consumption and the regularization of battery charge/discharge subject to the practical constraints in the microgrid. This paper proposes a detailed scheme to deal with the multiple objectives and nonlinear constraints in the MOMINP, then the MOMINP is successfully converted into a mixed‐integer linear programming model (MILP). And an adjustment strategy is designed to obtain the near‐optimal solution of the MOMINP based on the optimal solution of the MILP solved by using the CPLEX Optimizer. Experimental results show that in a basic scheduling period, the working time of DG in the POS‐softmax regression strategy is shorter than the current operation, and the fuel consumption reduction ratio is about 15.3% with the same battery SoC value at the end of the scheduling. At the same time, the fuel consumption in the POS‐accurate prediction strategy can be reduced by up to 54.9% compared with the POS‐softmax regression strategy and can be reduced by 61.8% compared to the current operation. Based on the comparative analysis of the actual case data of a micro‐grid in 6 months, it can be seen that on average the POS works better than the current operation, with an approximately 23.6% decrease in the objective function and an additional 16.2% decrease with an accurate prediction. [ABSTRACT FROM AUTHOR]

Optimal management of sources, storage, and responsive loads in the energy network leads to improvement of economic and technical situation of the network. However, note that single-layer energy management requires to provide the network operator with more amounts of information. This results in the increase in the processing speed for the network operator. Also, coordination of sources, storage, and responsive loads in the form of an aggregator such as an energy hub can play an important role for them in promoting the economic situation and energy efficiency. So, this paper presents the participation of grid-connected energy hubs (EHs) in the day-ahead (DA) and real-time (RT) energy markets. Scheme has a two-stage optimization framework. In the first stage, participation of EHs in the hourly DA energy market is executed based on a two-layer energy management system that is modeled as bilevel problem. EH model in DA market is formulated in upper-level problem aimed at maximizing their profit subject to meeting the linearized constraints of the EH's operation. Lower-level problem deals with the operation of energy networks to minimize cost of energy purchase from the upstream network constrained to linearized optimal power flow equations. The second stage of participation of EHs in a 5 min RT market has an objective function to minimize the unbalance between the EHs' profits in the mentioned two markets. This stage is subject to the upper-level constraints and the lower-level formulation in the first stage. Robust optimization models the uncertainties of load, renewable power, energy price, and energy demand of mobile storage to achieve an optimal robust solution. Benders decomposition algorithm solves bilevel formulation in two-stage scheme. Numerical results confirm capabilities of the scheme in enhancing operation status of the networks and economic situation of EHs. Hence, the Benders decomposition algorithm is able to extract the optimal solution in shorter computation time. Two-layer energy management needs shorter computation time than the single-layer management. Simultaneous modeling of two real-time and day-ahead energy markets reduces the unbalance cost between these markets. The proposed scheme can enhance the operating and economic indicators of the network by up to around 18% and 26% compared to power flow studies of energy networks. [ABSTRACT FROM AUTHOR]

Nowadays, consumption of different energy carriers is increasing due to the division of community activities in various sectors such as residential, commercial, and industrial. The energy hub concept is used to meet the demands of different energy carriers in these sections. In this paper, a new method for energy management of a microgrid is presented in an intelligent network based on three types of commercial, residential, and industrial energy hubs. In the energy hubs, a wide range of components, including renewable and nonrenewable generation units, converters, storage devices, intelligent parking lots consisting of electric vehicles, P2G units, and cogeneration units, are used to supply electrical, heating, cooling, and natural gas energies. Some parameters like renewable generations, energy demands, and arriving and departure time of electric vehicles are considered to be uncertain, and a relevant method is applied to provide a near-realistic profile for them. In addition, a demand response paradigm has also been proposed for three types of electrical, heating, and cooling demands at the hub output side. Therefore, in this method, a mixed integer linear programming model is proposed based on benefit and reduction of emission caused by the activity of gas-burning units for short-term planning and obtaining an optimal solution for generation and sending loads in a distribution network containing energy hubs. To evaluate the performance of the proposed modeling and structure, the presented approach is applied on a modified 33-bus IEEE test network. According to the results of the energy management model, it is possible to significantly increase benefits and also obtain a smoother consumption pattern in consumption with financial incentives. [ABSTRACT FROM AUTHOR]

Summary: This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicle (EV) is considered as a critical requirement to the future development of transportation. However, the battery performance in terms of power density and energy density limits the use of EVs. An energy management system (EMS) of plug‐in hybrid electric vehicle (PHEV) is very critical to achieve successful transition from the conventional vehicle to the pure electric vehicle (PEV). This paper proposes a hybrid EMS for the series‐parallel PHEV utilising a rule‐based control strategy and genetic algorithm (GA)‐based optimisation technique to overcome the battery limitations. A mathematical model was developed and verified by conducting simulation studies using the vehicle model from ADVISOR database and the GA Optimization Toolbox (GAOT) in the Matlab Simulink environment. The simulation results show that the GA optimization successfully achieved the sub‐targets set in the fitness function. To show the effectiveness of the proposed technique, the results were compared with the simulation results of a single function of the designed rule‐based control strategy—the proposed EMS achieved a significant improvement in the hydrocarbon (HC) emission and NOx emission. [ABSTRACT FROM AUTHOR]

Summary: The significant increase in energy consumption by the growth of the population or by the use of new equipment has brought big challenges to the energy security as well as the environment. There is a need that consumers can track their daily use and understand consumption standards for better organizing themselves to obtain financial and energetic efficiency. With the improvement of smart networks technology for better energy supply, a smart meter is not just a simple measurement gadget anymore, but it has additional functions including smart equipment control, bidirectional communication that allows integration of users and networks, and other functionalities. Smart meters are the most fundamental components in smart power grids. Besides, the meters used with a management system can be utilized for monitoring and controlling home appliances and other gadgets according to the users' need. A solution of an integrated and single system should be more efficient and economical. Smart measurement systems allow monitoring the energy consumption of the final consumers while providing useful information about the energy quality. The information provided by these systems is used by the operators to enhance the energy supply, and different techniques can be also applied for this end, such as charge scheduling, management from the demand side, and non‐intrusive load monitoring. The Internet of Things (IoT) is becoming a great ally in the management of smart distribution and energy consumption in smart systems scenarios. To address these issues, this paper proposes and demonstrates a new smart energy meter following an IoT approach and its associated costs and benefits. The developed device incorporates several communication interfaces. In order to easily integrate with any monitoring software solution, the meter has a multi‐protocol connection. Finally, the provided solution is validated and demonstrated in real‐life environments and it is also under use. [ABSTRACT FROM AUTHOR]

Summary: Demand‐side management comprises a portfolio of actions on the consumers' side to ensure reliable power indices from the electrical system. The home energy management system (HEMS) is used to manage the consumption and production of energy in smart homes. However, the technology of HEMS architecture can be used for the detection and classification of power quality disturbances. This paper presents low‐voltage metering hardware that uses an ARM Cortex M4 and real‐time operating system to detect and classify power quality disturbances. In the context of HEMS, the proposed metering infrastructure can be used as a smart meter, which provides the service of power quality monitoring. For this type of application, there is a need to ensure that the development of this device has an acceptable cost, which is one of the reasons for the choice of an ARM microprocessor. However, managing a wide range of operations (data acquisition, data preprocessing, disturbance detection and classification, energy consumption, and data exchange) is a complex task and, consequently, requires the optimization of the embedded software. To overcome this difficulty, the use of a real‐time operating system provided by Texas Instruments (called TI‐RTOS) is proposed with the objective of managing operations at the hardware level. Thus, a methodology with low computational cost has been defined and embedded. The proposed approach uses a preprocessing stage to extract some features that are used as inputs to detect and classify disturbances. In this way, it was possible to evaluate and demonstrate the performance of the embedded algorithm when applied to synthetic and real power quality signals. Consequently, it is noted that the results are significant in the analysis of power quality in a smart grid scenario, as the smart meter offers low cost and high accuracy in both detecting (an accuracy rate above 90%) and classifying (an average accuracy rate above 94%) disturbances. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a new energy management strategy (EMS) for electric vehicles (EVs) with battery/supercapacitor hybrid energy storage systems (HESS). Firstly, the battery/supercapacitor HESS configuration and obtaining the load current from the driving cycle are comprehensively explained. Secondly, fixed and adaptive frequency‐based (AFB) EMS are discussed in detail. In the proposed method, the current demanded by the load is separated into low and high‐frequency components with the help of an adaptive low‐pass filter (A‐LPF). In addition, adaptive battery current (ABC) is generated according to the supercapacitor (SC) state of charge (SoC), and a double adaptive power allocation strategy is performed. The proposed method is compared with the AFB‐EMS for load currents, load powers, DC link voltages, filter cut‐off frequencies, battery, and SC SoCs on the UDDS driving cycle. When the obtained results were evaluated together, it was observed that the proposed method completed the driving cycle with a higher battery SoC and realized the adaptive cut‐off frequency in a narrower band. [ABSTRACT FROM AUTHOR]

Summary: In a power system, distributed energy resources, including renewables, are advantageous for reducing power transmission losses and environmental pollution. However, increasing penetration of distributed renewable energy sources is challenging as they may pose security issues to the grid and centralized control of quantum number of such sources is quite impossible. Microgrid based distribution system is facilitating integration of distributed renewable energy sources as they can supply the local loads and reduce burden on the grid. Moreover, distributed control mechanism of different microgrids enhances the functioning efficiency of the grid. However, intermittent and uncertain nature of renewables leads into demand‐supply imbalance in a microgrid. Networked microgrids is new concept where the energy sharing between the microgrids may encourage renewable integration and reduce demand‐supply imbalance. In order to manage such energy sharing among the different components in a microgrid or different microgrids in a networked structure, an appropriate energy management approach is necessitated. Transactive energy is an emerging technology for energy management in the distribution system, which uses market mechanisms for energy management. This paper discusses various types of market structures and market clearing approaches in transactive energy framework for energy management. Moreover, techno‐economic perspectives of utility in transactive energy trading, such as network constraints and network usage charges, are elaborated. Finally, this review details the challenges and future scope in the transactive energy management of microgrids and networked microgrids. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes the optimal design for fast electric vehicle charging stations for the capacity configuration of components and the power scheduling strategy. The proposed approach is the artificial transgender longicorn algorithm, which is a simple and powerful controller. The optimal capacity of the system components and the optimal power scheduling approach are determined. First, an electric vehicle charging simulation model is built considering the demand response, which dynamically modified the charging expectation based on the electricity price at the time of use. Second, based on the system design, a multi‐objective optimization model is proposed with minimal objectives for the electricity cost and polluting emissions. Finally, a scenario analysis is carried out and it is concluded that renewable energy supplies, connection to the utility grid and demand response may help improve the performance of the optimization objectives. By then, the performance of the proposed technique is implemented in the MATLAB platform and compared with several existing approaches. [ABSTRACT FROM AUTHOR]

Summary: Energy crisis and the global impetus to "go green" have encouraged the integration of renewable energy resources, plug‐in electric vehicles, and energy storage systems to the grid. The presence of more than one energy source in the grid necessitates the need for an efficient energy management system to guide the flow of energy. Moreover, the variability and volatile nature of renewable energy sources, uncertainties associated with plug‐in electric vehicles, the electricity price, and the time‐varying load bring new challenges to the power engineers to achieve demand‐supply balance for stable operation of the power system. The energy management system can effectively coordinate the energy sharing/trading among all available energy resources, and supply loads economically in all the conditions for the reliable, secure, and efficient operation of the power system. This paper reviews the framework, objectives, architecture, benefits, and challenges of the energy management system with a comprehensive analysis of different stakeholders and participants involved in it. The review paper gives a critical analysis of the distributed energy resources behavior and different programs such as demand response, demand‐side management, and power quality management implemented in the energy management system. Different uncertainty quantification methods are also summarized. This review paper also presents a comparative and critical analysis of the main optimization techniques used to achieve different energy management system objectives while satisfying multiple constraints. Thus, the review offers numerous recommendations for research and development of the cutting‐edge optimized energy management system applicable for homes, buildings, industries, electric vehicles, and the whole community. [ABSTRACT FROM AUTHOR]

Nowadays, a large number of smart devices in residential buildings is integrated with the evolution of internet of things (IoT), where it is necessary to effectively manage energy to meet the increase in demand. Therefore, in this paper, an optimal energy management strategy using the hybrid Gradient Boosting Decision Tree ‐Artificial Transgender Longicorn Algorithm (GBDT‐ALTA) is proposed for IoT‐enabled residential buildings. The main objective of the proposed approach is to minimize the electricity bill of customer, thereby Peak‐to‐Average Ratio (PAR) is reduced. Due to the scheduling process of residential electric devices, the proposed approach utilizes the waiting time threshold. Here, three types of appliances, such as shiftable electrical devices, thermostatically controlled electrical devices, and generally operated electrical devices are considered. Related to real‐time price signals (RTPS) in utilities, scheduling the shiftable electrical devices is processed. The cost minimization with less acceptable time of waiting is achieved by GBDT‐ATLA method, which considers the trade‐off between the electricity cost and waiting time. Moreover, the proposed approach maintains the stability of the grid, because the stability of grid depends on the PAR. The proposed method is carried out in MATLAB/Simulink site and the simulated results prove that the better performance of GBDT‐ATLA approach compared to the existing approaches, like Slime Mould Optimization (SMO), Chaos Game Optimization Algorithm (CGO), and Side‐Blotched Lizard Optimization Algorithm (SBLO). The efficiency of the proposed approach under 100, 200, 500, and 1000 trails are 99.7300%, 99.6513%, 99.8363%, and 99.7916% respectively. [ABSTRACT FROM AUTHOR]

Summary: In general, the load demand of a standard microgrid system, changes on an hourly basis. Keeping in line with the rise and fall of this load demand curve, utilities fix different prices at different hours, which is termed as time of usage‐based electricity pricing. Elastic and inelastic are the two types of load that comprise the hourly demand of the microgrid system. Demand‐side management (DSM) shifts the elastic loads from peak load hours to those hours when the utility charges less thereby, restructuring the entire demand model‐based on demand‐price elasticity. Considering that the elastic loads contribute about 5% to 20% of the total load consumed during an hour, this paper implements a novel hybrid CSAJAYA algorithm to minimize the overall cost of a microgrid system considering DSM strategy. The various cost components taken into consideration are fuel cost, penalized emission cost, the cost of operation and maintenance, the cost of depreciation, etc. Numerical results depict that 30% to 40% decrement in overall generation cost was realized when DSM‐based energy management microgrid system was performed using the novel hybrid algorithm when compared to those available in the literature. Measures of central tendencies analysis claim the superiority of the proposed optimization algorithm. [ABSTRACT FROM AUTHOR]

Summary: Residential building consumes a significant amount of energy. To address the issue, these structures have been supplied with renewable energy sources (RES), an energy storage system (ESS), and an electric vehicle (EV). In a home, a home energy management system (HEMS) has been implemented to schedule and regulate domestic appliances. Many studies in HEMS have been conducted in order to reduce the cost of power and the peak to average ratio (PAR). However, there is insufficient use of RES, ESS, EV, and excess domestic energy. As a result, this research presents a HEMS architecture that is integrated with RES and ESS and includes home‐to‐grid (H2G) power flow functionality. The RES electricity is initially utilized to power domestic appliances and charge ESS before being transferred back into the main grid to reap economic rewards. During the low‐price period, the ESS and EV are charged and discharged during the high‐price period. The paper built a multi‐objective optimization problem using the provided approach that integrates electricity cost and system PAR. The grey wolf optimization algorithm is used to tackle the multi‐objective optimization problem. The results clearly show that the proposed technique decreases expenditures by 45.80% and PAR by 28.44%, compared to the baseline timetable in 24 h. The H2G power flow feature allows the home to send back excess energy to the grid, cutting electricity expenditures by 70% and achieving additional economic benefits. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a novel control scheme on AC/DC ring bus islanded microgrid under unbalanced and non‐linear loads. The DC bus is integrated with the fuel cell, one wind turbine, and two batteries. The DC bus can be divided into two sub‐sections, where the first zone is supported by the fuel cell and battery 1, and the second zone is installed with the wind turbine and battery 2. The AC bus is also able to be sectionalized into two sub‐sections where the diesel generator and the unbalanced load are located on the first section and the non‐linear load with one wind turbine are placed on the other section. The DC bus is connected to the AC bus through two parallel three‐phase lines each one configured by three single‐phase inverters. The proposed control scheme in the ringed and separated modes presents several contributions including balancing the unbalanced loads, compensating the harmonics of non‐linear loads, improving the voltage stability and sag/swells, enabling partial operation under outages, and enhancing resilience. In the ring mode, wind turbine 1 and diesel generator inject balanced and linear P‐Q powers to the system as 39 kW/6 kVAr and 7 kW/9 kVAr, respectively. In the separated mode, the P‐Q powers of wind turbine 1 are 36 kW/9 kVAr and they are 15 kW/9 kVAr for diesel generators. In both modes, the unbalanced and non‐linear powers are supplied by the DC bus. The results verify that the developed control strategy efficiently deals with harmonic components, unbalanced currents and voltage issues. [ABSTRACT FROM AUTHOR]

Summary: The market of Lithium‐ion batteries has been growing strongly around the world for several years, especially the Nickel Manganese Cobalt Oxide (NMC) battery type which supplies almost all electric vehicles. This popularity requires optimal energy management and this needs precise modeling of the behavior of batteries for vehicular use. In this article, we present an overview of the existing electric battery models, including equivalent electrical circuits. A detailed study on the most used models is also presented. Subsequently, we detail the principle of the various characterization tests for extraction of the models parameters. Thus, we present the different parameters extracted for the models of NMC batteries of first and second order types. The accuracy of the two models was recorded by applying the dynamic discharge pulse validation test. The experimental results have shown better precision for the second order model which only exceeds by 1% of the first order model which is considered more suitable for embedded applications, hence, offering a compromise between ease of implementation and precision. [ABSTRACT FROM AUTHOR]

Summary: The Energy Management System (EMS) is a strong need for achieving cost‐effective, reliable, and pollution‐free operation of microgrids operating in isolated areas. Commonly, the EMS approaches use optimization and rolling‐horizon predictions. However, the inclusion of very short‐term predictions increases the complexity of the optimization, compromising its ability and reliability to operate online. In response to this problem, this paper proposes a rule‐based EMS (RB‐EMS) which uses neither optimization nor prediction horizons. Instead, the RB‐EMS evaluates historical data, and very short‐term predictions to calculate the setpoints of distributed energy resources, and schedulable loads in a cost‐effective, reliable, and sustainable manner. As a form of self‐regulation, the RB‐EMS introduces the Reliability Index to calculate the maximum state of charge of the energy storage system. Given the speed imposed by the very short‐term predictions, the RB‐EMS also introduces a start‐up manager for diesel units that present a dead time in their enlistment. The isolated ESUSCON‐HUATACONDO microgrid was used as a testbed for performance validation. The work shows a first scenario comparing the RB‐EMS against a rolling horizon EMS based on MILP optimization. A second scenario shows the energy management performance with 1 minute predictions. The better results obtained, and the computational simplicity of the RB‐EMS allows propose it to achieve faster and more reliable energy management in other isolated microgrids. [ABSTRACT FROM AUTHOR]

Summary: The expected significant growth in global electricity demand, followed by the adoption of green energy sources, led to the modernization of the energy distribution grids, such as the development of microgrids that can improve the reliability of the electric power system. Meanwhile, the increased integration of electric vehicles is expected to have a negative impact on power quality, normal operation, and investment costs of the microgrid. In this paper, a microgrid topology was studied for the islanded operation of a low‐voltage distribution network. The deployment of such energy management systems is essential to guarantee the safe and reliable operation of isolated microgrids. Hence, a decentralized energy management system, based on multi‐agent systems, was developed for the efficient charging of electric vehicles, by expanding a state‐of‐the‐art fuzzy logic controller‐based energy management strategy, in combination with a charging power controller, based on fuzzy cognitive maps. According to the authors' best knowledge, this is the first time that fuzzy cognitive maps theory is introduced in EVs' charging management problems. The performance of the proposed multi‐agent decentralized energy management system presented significant reduction in the investment costs of the microgrid, as compared with the use of a sole fuzzy logic controller‐based energy management strategy. The total cost of the microgrid for a 20‐year investment period is decreased by approximately 8.8%. Furthermore, the incorporation of the fuzzy cognitive maps‐based charging power controller leads to a significant amount of chargeable EVs. The mean chargeable EVs increased by 31%. Finally, the proposed energy management system reduces the peak load and load variances approximately 17% and 29%, respectively, without shifting and delaying the charging of the EVs. Hence, the proposed novel charging management system offers an intelligence approach for islanding distribution grids including the high penetration of electric vehicles by presenting operational and financial benefits. [ABSTRACT FROM AUTHOR]

Summary: Electric vehicles (EVs) and smart grids are gradually revolutionising the transportation sector and electricity sector respectively. In contrast to unplanned charging/discharging, smart use of EV in home energy management system (HEMS) can ensure economic benefit to the EV owner. Therefore, this paper has proposed a new energy pricing controlled EV charging/discharging strategy in HEMS to acquire maximum financial benefit. EV is scheduled to be charged/discharged according to the price of electricity during peak and off‐peak hours. In addition, two different types of EV operation modes, ie, grid‐to‐vehicle (G2V) in off‐peak time and vehicle‐to‐home (V2H) in on‐peak time are considered to determine comparative economic benefit of planned EV charging/discharging. The real load profile of a house in Melbourne and associated electricity pricing is selected for the case study to determine the economic gain. The simulation results illustrate that EV participating in V2H contributes approximately 11.6% reduction in monthly electricity costs compared with G2V operation mode. Although the facility of selling EV energy to the grid is not available currently, the pricing controlled EV charging/discharging presented in the paper can be used if such facility becomes available in the future. [ABSTRACT FROM AUTHOR]

Summary: Depletion of earth's petroleum resources, greenhouse gas emissions, and global warming issues are caused by the conventional vehicles around the globe. In recent years, automotive industries focus on emerging alternative energy sources to mitigate the relying on fossil fuels so as to reduce global harmful emissions. Researchers have focused on the different aspects of hybrid and battery electric vehicles, such as energy management, regenerative braking control, and architecture of power electronics. This paper emphasizes on review of various energy management systems (EMSs) based on fuel cell hybrid electric vehicles (FCHEV) in combination with two secondary energy storage systems like batteries and ultracapacitors to provide high‐performance energy storage system. The performance of the FC–battery–ultracapacitor with various types of energy management schemes and experimental investigations is reported in this paper. This paper provides various braking control schemes to alleviate the hydrogen utilization of an FCHEV in connection with batteries and ultracapacitors and furthermore gives thorough investigation of FCHEV on their energy utilization, configuration, and EMSs developed by different analysts. This study focuses on energy allocation schemes, experimental approaches, recovery of regeneration, and EMS for next generation hybrid electric vehicles. [ABSTRACT FROM AUTHOR]

Optimal operation of micro grids (MGs) with high penetration of renewable energy sources (RESs) has become inflexible as a consequence of the uncertainty and intermittency nature of RESs. Energy management along with battery energy storage system (BESS) is thus essential to minimize these uncertainties of MGs and to ensure continuity of energy supply and maintain the reliability of the system. In this paper, a dual-stage MG optimal energy management approach considering the BESS model and other dispatchable resources is introduced to minimize the operating cost. The proposed approach comprises of the schedule and dispatch stages. The mixed-integer linear programming is applied to solve the dual-stage energy management problem and the performance of the suggested technique is compared with the particle swarm optimization technique. The obtained results prove that the incorporation of the suggested BESS plant can significantly reduce the operating costs up to 7% and 6% when committed to supplying power in the scheduling and dispatch stages. The results also show that power loss has a considerable impact on MG energy management. The optimal simulation results confirm the effectiveness of the proposed microgrid energy resources optimization strategy. [ABSTRACT FROM AUTHOR]

Summary: Supercapacitors (SCs), or ultracapacitors, due to their attractive features, such as, high power density, long life cycle, etc., have received much attention from the transportation sector. SCs can be used as an additional energy storage system (ESS) in combination with lithium‐ion batteries to enhance the performance of electric vehicles (EVs) in dynamic states, including acceleration and regenerative braking modes of operation. Online accurate estimation of SCs' state of health (SoH) and state of energy (SoE) is essential for an efficient energy management and real‐time condition monitoring in EV applications. The accuracy of the estimation of the SoE and SoH is based on the model's efficiency, which ensures that in order to minimize the impact of aging, model parameters should be defined in real time. Nevertheless, because the SC model is obviously nonlinear and broad in scale, online identification of the parameters estimation is usually difficult. In this paper, a generalized SC model of high accuracy and good robustness is proposed. The classification of the estimation methodologies for SoH and SoE of SC will be very helpful in choosing the appropriate method for the development of reliable and secure ESS and an energy management strategy for EVs. [ABSTRACT FROM AUTHOR]

Summary: This paper deals with an experimental and numerical study of the effect of PCM incorporation into a building's wall on indoor air temperature. The macro‐encapsulated PCM was integrated into the vertical wall of a laboratory test cell. Another test cell that lacks the PCM was considered the reference. Measurement of the air temperature inside the test cells shows the PCM role to delay heat transfer through the wall as well as to decrease the indoor air temperature peak. Furthermore, a numerical model was built and experimentally calibrated and validated. Numerical simulation results show that many factors should be studied before the incorporation of the PCM, mainly its position, type and layer thickness. Compared to insulation materials used in building, PCM can be used as smart insulation material due to its low thermal conductivity and as a phase shifter leading to a passive temperature control. [ABSTRACT FROM AUTHOR]

Summary: The decentralization of energy supply and the fluctuating feed‐in from renewable energies lead to an increasing need to generate electricity and heat locally and efficiently. Therefore, different energy systems are used together at building level. Consequently, energy management systems (EMSs) in the building sector and households are becoming increasingly complex and are facing new challenges. To face these challenges, EMSs are required to enable efficient and combined operation of multiple energy systems and components. The increasing research into EMS is leading to a growing field of research with different characteristics, objectives, and algorithms. However, the resulting diversification of the research topic is accompanied by a lack of transparency. Therefore, this article is the first to provide a comprehensive quantitative evaluation of the state of research. For this purpose, 98 relevant publications are quantitatively evaluated. This article focuses on methods, model characteristics, optimization objectives, price structures, observation horizon, and components and devices used of EMS. The detailed evaluation of these topics creates a high level of transparency in EMS research to provide precise insights into current research priorities and potential weaknesses, to allow researchers to classify their own work and to identify potential research topics. Highlights: For the first time, a quantitative study on EMS research is presentedThe evaluation of 98 relevant publications provides detailed insightsInsights about methods, characteristics, objectives, prices, horizons, and componentsDetailed graphical evaluations for each sub‐area of the study [ABSTRACT FROM AUTHOR]

Summary: Mass roll‐out of plug‐in hybrid electric vehicles (PHEVs) and significant penetration of renewable energy sources in distribution system play a major role in delivering low carbon environment. However, placing and utilizing these units randomly result in overloading, increased power loss, and reduced voltage profile. This paper responds to these technical challenges by using a strategic placement method for locating the distributed generation (DG) and the charging station (CS) of PHEVs in a multi‐zone distribution system. For simultaneously scheduling of these units in each zone, the smart energy management framework is proposed in this paper. Apart from usual energy management constraints, this paper also incorporates the real‐time constraints involving the capacity of PHEV batteries, the mobility pattern, and the power level of the charging infrastructure. The simulation studies are carried out for each hour of a day. To cope with this time constraint execution, particle swarm optimization algorithm‐based approach is used. The proposed framework is tested in IEEE 33 and IEEE 69 bus radial distribution system. The obtained results imply that the presented energy management framework provides maximum profits for the vehicle owner, and meanwhile it fulfills preferences of the user in each zone simultaneously. [ABSTRACT FROM AUTHOR]

Summary: This study uses a new maximum power point tracking (MPPT) algorithm for Thermoelectric Generator (TEG) devices. The MPPT algorithm appears as an essential solution due to the nature and the variation characteristics of the TEG devices under certain conditions. In this paper, the power differentials‐maximum power point tracking (PD‐MPPT) algorithm is proposed to control the boost converter by measuring the output power of TEG devices at both the start and finishing points of the power curve along with making a comparison of these two measured power points. The priority is given to the highest power point until the maximum power point is achieved, and Kalman Filter has been applied to eliminate the oscillation generated from the TEG system. This algorithm does not require any extra circuit to measure the short‐circuit current or the open‐circuit voltage because there is no disconnection between the TEG and the load. The hardware implementation of the power differentials algorithm is demonstrated under steady‐state conditions. Moreover, the PD‐MPPT is an effective and applicable algorithm applied to grab the maximum power point from the Photovoltaics PVs and TEGs systems. The practical experiment is conducted using the "STM32f429" microcontroller to implement the algorithm. During the experiments, the change in the duty cycles is observed. The experimental results show that the PD‐MPPT algorithm performs better under a steady‐state and has the ability to track the maximum power point accurately. [ABSTRACT FROM AUTHOR]

Summary: As the use of equipment in office buildings increases, accurate equipment usage detection is valuable for the reduction of energy consumption and carbon emission. Using the collected equipment usage information, building energy management system (BEMS) can automatically adjust the operation of heating, ventilation, and air‐conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real time. Previous studies highlighted that the use of conventional control strategies in office buildings such as "static" operation schedules could cause large energy waste in particular during unoccupied hours. Common equipment load detection techniques, such as power meters and survey, are unable to provide comprehensive (location, count and heat emission) and real‐time equipment information necessary for demand‐driven strategies. Based on a deep learning‐based equipment load detection approach which employs artificial intelligence‐enabled cameras, the detection performance of the proposed approach is likely going to be affected by environmental conditions, such as the positions of cameras and lighting level, within the monitored spaces. Improper detection conditions can result in incorrect detection and lead to inaccurate building energy demand estimation. This paper aims to investigate the influences of illumination conditions on the detection accuracy of the vision‐based equipment load detection approach. The work will be using AI cameras to detect equipment information under different lighting levels, employing deep learning method to analyse and generate the real‐time equipment usage profiles for offices which can be inputted to the BEMS to increase the efficiency of HVAC systems. The results showed that as compared with the conventionally‐scheduled HVAC systems, the HVAC system with the use of equipment usage profiles conducted by the proposed approach can achieve up to 15% reduction of energy consumption. The finding indicates that adequate illumination level contributes to the decrease of building energy demand by achieving an effective deep learning approach. [ABSTRACT FROM AUTHOR]

Summary: Microgrids (MGs) are known as suitable options to accommodatethe high penetration of renewable energies, like solar and wind. MGs have provided the requirements of controlling and adjusting these sources. In addition, batteries are becoming indispensable components of MGs because of their capabilities in addressing the renewable energies' power output intermittency. In MGs, the problem of smart energymanagement along with battery sizing has been introduced as the necessity to ensure the efficient use of renewable sources and decrease traditional fossil‐fuel‐based generation technology penetration level in power systems. Accordingly, a novel method is presented in this paper to effectively address the above‐mentioned requirements, utilizing the modified shuffled frog leaping algorithm (MSFLA), applied to different case studies. The results, obtained from the numerical simulation, are compared to several well‐established optimization approaches to verify the performance of MSFLA. In terms of computational efficiency and quality of the obtained solution, the MSFLA has demonstrated promising outcomes, together with superior performance in comparison with other algorithms. The results show that the presented framework, including the battery sizing, would be very beneficial to minimize the operating cost of MGs. [ABSTRACT FROM AUTHOR]

Summary: A smart grid is an electricity network, which deals with electronic power conditioning and control of production, transmission, and distribution of electrical power by employing digital communication technologies to monitor and manage local changes in electricity usage. In the traditional power grid, energy consumers remain oblivious to their power consumption patterns, resulting in wasted energy as well as money. This issue is severely pronounced in the developing countries where there is a huge gap between demand and supply, resulting in frequent power outages and load‐shedding. For electrical energy savings, the smart grid employs demand side management (DSM), which refers to adaptation in consumer's demand for energy through various approaches such as financial incentives and awareness. The DSM in future smart grid must exploit automated energy management systems (EMS) built upon the state‐of‐the‐art technologies such as the internet of things (IoT) and cloud and/or fog computing. In this paper, we present the architecture framework, design, and implementation of an IoT and cloud computing‐based EMS, which generates load profile of consumer to be accessed remotely by utility company or by the consumer. The consumers' load profiles enable utility companies to regulate and disseminate their incentives and incite the consumers to adapt their energy consumption. Our designed EMS is implemented on a Project Circuit Board (PCB) to be easily installed at the consumer premises where it performs the following tasks: (a) monitors energy consumption of electrical appliances by means of our designed current and voltage sensors, (b) uploads sensed data to Google Firebase cloud over many‐to‐many IoT communication protocol Message Queuing Telemetry Transport (MQTT) where consumer's load profile is generated, which can be accessed via a web portal. These load profiles serve as input for implementing the various DSM approaches. Our results demonstrate generated load profiles of consumer load in terms of current, voltage, energy, and power accessible via a web portal. [ABSTRACT FROM AUTHOR]

Summary: Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES‐integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi‐objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi‐objective optimisation problem is converted into a single‐objective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%. [ABSTRACT FROM AUTHOR]

Summary: The universal adaptive equivalent consumption minimization strategy (A‐ECMS) has the potential of being implemented in real‐time for plug‐in hybrid electric vehicles (PHEVs). However, the imprecise prediction of a long‐term future driving cycle and biggish computation burdens remain the barriers for further real vehicle application. Thus, it is of great significance to develop a real‐time optimal energy management strategy for PHEVs by weakening the influence of future driving cycle to the control accuracy and improving its computation efficiency. In this paper, a novel real‐time energy management strategy for PHEVs based on equivalence factor (EF) dynamic optimization method is proposed. Firstly, a novel proportional plus integral adaption law for calculating the dynamic optimal EF is established for A‐ECMS using only instantaneous information of current vehicle speed and battery state of charge. Second, three key coefficients are obtained and converted into a three‐dimensional look up tables, so as to determine the dynamic optimal EF. Finally, the method of fast searching the optimal engine torque is proposed, which significantly enhances the computational efficiency. Compared with A‐ECMS, the computational time of A‐ECMS2 is decreased near 94.8% and the deviation of fuel consumption is controlled within 4.4%. Both the numerical results and hardware‐in‐loop results prove that the proposed novel energy management strategy A‐ECMS2 has better real‐time performance and less computing burden than the general A‐ECMS. [ABSTRACT FROM AUTHOR]

Summary: The optimal energy management problem of microgrids is an agenda item for the energy field. Especially industrial microgrids, whose energy demand is high and uninterruptible, need to be managed by a holistic view of optimization. Technical researches in this field are to be enriched with the literature review that looks through various angles. In the scope of this paper, the research literature is reviewed deeply and analyzed with the aim of presenting the gaps. Articles written in English, in the period of 2010 to 2019 have been revised thoroughly. By eliminating the search results, 105 relevant articles have been determined. Objective(s), criteria for analysis, processes included, power source(s), storage system, other system components and methodology of each article have been studied. The literature has been summarized, it has been seen that solar, wind, and diesel sources are used in about 82%, 55%, and 16% of articles, respectively. Then, they have been clustered using self‐organizing maps. Nine clusters have been obtained which separate the articles clearly. Especially, three of them which have fewer data have been analyzed. Clustering results have been interpreted and utilized to show the research openings. Achievements of this study will lead the scientific research that will fill in the gaps. [ABSTRACT FROM AUTHOR]

Summary: This paper considers the co‐optimization operation problem of the multiple micro energy grids (MEGs), and proposes a novel concept of the flexible boundary. Then, a three‐level co‐optimization model of reconfiguration, dispatching, and reserve is designed considering different time scales. Furthermore, an improved ant colony algorithm based on the information entropy and the chaotic search is proposed. The Shenzhen international low‐carbon park in China is selected as an example. The results show that: (a) The capacity configuration scheme considering the flexible boundary conditions could decrease the reserve capacity of power, heating, and cooling from the upper energy grid, namely, 75%, 50%, and 75%, and delay 537 × 104¥ the investment cost of the new capacity by utilizing the complementary characteristics between different MEGs. (b) The proposed three‐level co‐optimization model could establish the multi‐level optimal cooperative operation schemes of multi‐MEGs and decrease the reserve power from the upper energy grid and incentive‐based demand response. Among them, UPG provides MEG 1, MEG 2, and MEG 3 with cooling load reserve capacity reduction of 53.95, 85.44, and 38.3 kw h. The positive reserve provided by IBDR for MEG 1, MEG 2, and MEG 3 decreased by 30.53, 20.21, and 52 kW h. (c) The proposed improved ant colony algorithm can consider the operational characteristics of each energy entity in different stages of multi‐MEGs and obtain the global optimal operation scheme. Compared the traditional ant colony algorithm, the average convergence times decrease by 18% and the optimization degree of the objective value increase by 12.38%. (d) The sensitivity analysis shows the conditional value at risk method can formulate the optimal dispatching schemes of multi‐MEGs according to the risk attitudes of decision makers. When confidence degree is between 0.3 and 0.8, the decision‐makers have a high‐risk sensitivity and are more willing to avoid uncertain risks of wind power plant and photovoltaic power. Overall, the proposed model and solution algorithm could achieve the optimal operation strategy of the micro energy grids. [ABSTRACT FROM AUTHOR]

SUMMARY Economic, environmental and social pressures have increased the need for business organisations to control and manage their energy performance on a continual basis. Responding to these pressures follows a learning curve that is influenced by changing drivers and barriers. Consequently, different energy management factors have different development priorities over time. This paper explores the development priority of one factor, namely, energy performance measurement, in the energy-intensive industrial sector, which is the most advanced industrial sector in its energy management learning curve. In addition, the paper identifies the research and development needs of energy performance measurement that are required to further improve energy performance. The results are based on interviews carried out with managers and operators in three energy-intensive industrial sectors in Finland. Energy performance measurement is found to be the third development priority in energy management, behind resource and commitment issues. This represents a paradox as resources and commitment are prerequisites for energy performance measurement to be developed, whereas energy performance measurement influences the very same issues by enforcing changed behaviour. Several deficiencies are identified in energy performance measurement in the temporal, systemic and organisational dimensions. Research should be continued towards the implementation of energy performance measurement as a process, the integration of energy performance metrics into overall management and the development of metrics for different industrial sectors, companies and operating cultures. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Energy management strategy (EMS) is a way to reduce the energy consumption of hybrid power systems. This article proposes a unique deep reinforcement learning- (DRL-) based EMS for plug-in hybrid electric heavy-duty trucks (PHETs), combining driving cycle pattern recognition (DPR) and deep transfer learning (DTL). The proposed EMS can cope well with the complex usage scenarios of PHETs and the difficulty of generating EMS. While ensuring the minimum overall driving cost, the strategy can improve the convergence speed of the DRL method and the generalizability under segmented usage scenarios. Firstly, representative driving cycles that reflect different usage scenarios are constructed based on a naturalistic data-driven method. Secondly, a plug-in hybrid electric heavy-duty truck (PHET) driving pattern recognizer based on a learning vector quantization neural network (LVQ) is built. Thirdly, the deep deterministic policy gradient (DDPG) algorithm is innovatively combined with the DTL algorithm. The pretrained neural network in the corresponding usage scenarios is transferred to the natural driving cycles based on DTL. Moreover, the proposed EMS gives an emphasized consciousness on the battery degradation cost. Finally, the strategy is tested under natural driving cycles in different usage scenarios and proven through comparison with the current state-of-the-art techniques, deep reinforcement learning-based strategy, and dynamic programming (DP). The results show that the proposed strategy outperforms existing cutting-edge deep reinforcement learning techniques in terms of convergence speed, battery life extension, fuel consumption, and overall driving cost reduction. The proposed control strategy can improve the convergence speed by nearly 50%, while effectively extending the battery life and reducing the overall driving cost compared to the existing state-of-the-art strategies. The battery degradation rate is reduced by 48.46%, 57.95%, and 36.99%, and the driving cost is reduced by 17.76%, 8.51%, and 7.12%, respectively, under each usage scenario. [ABSTRACT FROM AUTHOR]

Summary: Proton exchange membrane fuel cell (PEMFC) electric vehicle is an effective solution for improving fuel efficiency and onboard emissions, taking advantage of the high energy density and short refuelling time. However, the higher cost and short life of the PEMFC system and battery in an electric vehicle prohibit the fuel cell electric vehicle (FCEV) from becoming the mainstream transportation solution. The fuel efficiency‐oriented energy management strategy (EMS) cannot guarantee the improvement of total operating costs. This paper proposes an EMS to minimize the overall operation costs of FCEVs, including the cost of hydrogen fuel, as well as the cost associated with the degradations of the PEMFC system and battery energy storage system (ESS). Based on the PEMFC and battery performance degradation models, their remaining useful life (RUL) models are introduced. The control parameters of the EMS are then optimized using a meta‐model based global optimization algorithm. This study presents a new optimal control method for a large mining truck operating on a real closed‐road operation cycle, using the combined energy efficiency and performance degradation cost measures of the PEMFC system and lithium‐ion battery ESS. Simulation results showed that the proposed EMS could improve the total operating costs and the life of the FCEV. [ABSTRACT FROM AUTHOR]

In this board sense, this special issue takes an opportunity to put forth forward few notable researches in the area of the green energy. Specifically the topics covered are follows Green Energy Fuel cells Solar energy Renewable energy Alternative fuels Biodiesel Nanoparticles and Characterization Waste to energy This special issue covers the selected research papers from IEEES-11 conference and some invited papers. In last few decades, the environmental friendly energy systems gained profound interest among different regions of interest. [Extracted from the article]

A distributed energy resource (DER) system uses renewable generation and energy storage to provide power and ancillary services directly to users in the distribution system, and benefits the power grid by offering a solution to the energy crisis and mitigating the waste pollution to the environment. A key challenge in implementing a DER system is how to ensure the standardization of its communication network. The goal of this paper is to study and propose a common approach to standardize the communication network in the DER system using IEC 61850 and includes a design and implementation of the communication architecture. Two prototypical testbeds for the DER system have been developed in the SMERC laboratory and at KIER, with the aim to demonstrate a case study using the proposed approach. The testbeds contain various DERs such as photovoltaics, energy management systems, and electric vehicles. The procedure of integrating the IEC 61850 standard includes the system architecture configuration, data set design, test file write-up, and deployment of the gateway/client for communication. In the data set design, the specification of logical nodes, data object names, data attributes and common data classes are explained in detail. The research results and data collected from the current testbeds indicate a quality integration of the IEC 61850 standard with stable power flow and unified communication architecture in the DER system. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Summary: This paper proposes a stochastic scheduling model to determine optimal operation of generation and storage units of a virtual power plant (VPP) for participating in a joint energy and regulation service (RS) market under uncertainty. Beside electricity, the VPP provides required RSs according to the probability of delivery request in the electricity market. A new model for providing RS is introduced in which the dispatchable generation units are financially compensated with their readiness declarations and will be charged/paid for their real‐time down/up regulations. Besides, the VPP sets up incentive price‐quantity curves to benefit from the potential of demand side management in both energy and RS market. Within the model presented here, the VPP consists of two types of generation units: wind turbine and standby diesel generator; the latter is modeled by considering CO2‐emission penalty costs. The given uncertainties are divided into two parts. Firstly, the uncertainties from the energy market price are simulated using information gap decision theory to evaluate the risk‐based resource scheduling for both risk‐taker and risk‐averse VPP. Other uncertainties affecting decision making such as wind turbine generation, load, regulation up/down calling probabilities, and regulation market prices are modeled via scenario trees. Three typical case studies are implemented to validate the performance and effectiveness of the proposed scheduling approach. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (Npv) and ESU batteries (Nbat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed Npv and Nbat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station. Methodology for modeling of PV‐ESU grid‐based EV charging system is described.Main features of existing popular system sizing techniques are compared.An energy management algorithm (EMA) is developed to control the charging system.Optimum sizes of PV modules and ESU batteries are determined by means of PSO.Proposed system is benchmarked against the standard grid charging system. [ABSTRACT FROM AUTHOR]

Summary: This study investigates the return on investment (ROI) of building energy management system (BEMS) through reviewing research works published from 1977 to 2017. The ROI is quantitatively evaluated by payback periods of BEMS cases. The results indicated that the BEMS payback periods decreased from 5.4 to 0.7 years for commercial buildings. For compound buildings, the BEMS payback periods were shortened from 3.7 to 1.8 years. For residential or office buildings, the BEMS payback periods could not be effectively shortened. Therefore, the BEMS for the commercial and compound buildings had the better energy savings and ROI. An ROI model, based on four characteristic parameters of buildings and four major BEMS technologies, is built. The calculating results indicate that the most effective technology to increase the ROI of BEMS is the scheduling control technology developed for the heating, ventilation, air‐conditioning, and refrigeration system of buildings. The future development of BEMS should focus on (1) scheduling control, (2) collecting the managing information through Internet, and (3) combining the mobile communicating devices for interacting with the occupants in the building. Therefore, using BEMS would be a worthwhile choice for most commercial buildings. The paper investigated return on investment (ROI) of building energy management system (BEMS) for four types of buildings (commercial, compound, residential, and office buildings), and three types of systems (HVAC & R equipment, artificial lighting system, motors and other equipment). An ROI model is set up by using a first‐order ordinary differential equation (ODE) model, including four major BEMS technologies, such as scheduling control, system optimization, occupant detection control, and variable speed control. [ABSTRACT FROM AUTHOR]