Showing papers on "Energy management published in 2021"
TL;DR: A comprehensive overview of recent advances in the P1P energy system and an insightful discussion of the challenges that need to be addressed in order to establish P2P sharing as a viable energy management option in today’s electricity market are focused on.
236 citations
TL;DR: A comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting, and discusses the datasets used to train and test the differentDL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work.
Abstract: Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.
172 citations
TL;DR: In this paper, a detailed review of the planning, operation, and control of DC microgrids is presented, which explicitly helps readers understand existing developments on DC microgrid planning and operation, as well as identify the need for additional research in order to further contribute to the topic.
Abstract: In recent years, due to the wide utilization of direct current (DC) power sources, such as solar photovoltaic (PV), fuel cells, different DC loads, high-level integration of different energy storage systems such as batteries, supercapacitors, DC microgrids have been gaining more importance. Furthermore, unlike conventional AC systems, DC microgrids do not have issues such as synchronization, harmonics, reactive power control, and frequency control. However, the incorporation of different distributed generators, such as PV, wind, fuel cell, loads, and energy storage devices in the common DC bus complicates the control of DC bus voltage as well as the power-sharing. In order to ensure the secure and safe operation of DC microgrids, different control techniques, such as centralized, decentralized, distributed, multilevel, and hierarchical control, are presented. The optimal planning of DC microgrids has an impact on operation and control algorithms; thus, coordination among them is required. A detailed review of the planning, operation, and control of DC microgrids is missing in the existing literature. Thus, this article documents developments in the planning, operation, and control of DC microgrids covered in research in the past 15 years. DC microgrid planning, operation, and control challenges and opportunities are discussed. Different planning, control, and operation methods are well documented with their advantages and disadvantages to provide an excellent foundation for industry personnel and researchers. Power-sharing and energy management operation, control, and planning issues are summarized for both grid-connected and islanded DC microgrids. Also, key research areas in DC microgrid planning, operation, and control are identified to adopt cutting-edge technologies. This review explicitly helps readers understand existing developments on DC microgrid planning, operation, and control as well as identify the need for additional research in order to further contribute to the topic.
149 citations
TL;DR: A novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system is proposed.
Abstract: Energy management is critical to reducing the size and operating cost of hybrid energy systems, so as to expedite on-the-move electric energy technologies. This article proposes a novel knowledge-based, multiphysics-constrained energy management strategy for hybrid electric buses, with an emphasized consciousness of both thermal safety and degradation of onboard lithium-ion battery (LIB) system. Particularly, a multiconstrained least costly formulation is proposed by augmenting the overtemperature penalty and multistress-driven degradation cost of LIB into the existing indicators. Further, a soft actor-critic deep reinforcement learning strategy is innovatively exploited to make an intelligent balance over conflicting objectives and virtually optimize the power allocation with accelerated iterative convergence. The proposed strategy is tested under different road missions to validate its superiority over existing methods in terms of the converging effort, as well as the enforcement of LIB thermal safety and the reduction of overall driving cost.
147 citations
TL;DR: In this article, the authors investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through qualitative review, based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers.
Abstract: In recent years, smart cities have emerged with energy conservation systems for managing energy in cities as well as buildings. Although many studies on energy conservation systems of smart homes have already been conducted, energy management at the city level is still a challenge due to the various building types and complex infrastructure. Therefore, this paper investigated the research themes on smart homes and cities through a quantitative review and identified barriers to the progression of smart homes to sustainable smart cities through a qualitative review. Based on the results of the holistic framework of each domain (smart home and city) and the techno-functional barriers, this study suggests that the following innovative solutions be suitably applied to advanced energy conservation systems in sustainable smart cities: (i) construction of infrastructure for advanced energy conservation systems, and (ii) adoption of a new strategy for energy trading in distributed energy systems. Especially, to reflect consumer behavior and energy in sustainable smart cities, the following responses to future research challenges according to the “bottom-up approach (smart home level to smart city level)” are proposed: (i) development of real-time energy monitoring, diagnostics and controlling technologies; (ii) application of intelligent energy management technologies; and (iii) implementation of integrated energy network technologies at the city level. This paper is expected to play a leading role as a knowledge-based systematic guide for future research on the implementation of energy conservation systems in sustainable smart cities.
120 citations
TL;DR: The proposed C/GMRES algorithm shows great solving quality and real-time applicability in PEMS by comparing with sequence quadratic programming and genetic algorithms.
120 citations
TL;DR: In this article, the authors provide an overview of mid-to-high-temperature thermoelectrics, their application in modules, and the issues that need to be addressed to enable commercial implementation of state-of-the-art materials.
Abstract: Thermoelectric materials can be potentially employed in solid-state devices that harvest waste heat and convert it to electrical power, thereby improving the efficiency of fuel utilization. The spectacular increases in the efficiencies of these materials achieved over the past decade have raised expectations regarding the use of thermoelectric generators in various energy saving and energy management applications, especially at mid to high temperature (400–900 °C). However, several important issues that prevent successful thermoelectric generator commercialization remain unresolved, in good part because of the lack of a research roadmap. Thermoelectric materials can generate energy from a heat differential. This Review provides an overview of mid- to high-temperature thermoelectrics, their application in modules, and the issues that need to be addressed to enable commercial implementation of state-of-the-art materials.
119 citations
TL;DR: A stochastic energy management algorithm is proposed to address the participation of smart MGs in the electricity market, which minimizes the total cost and finds the optimal size of different components, including WT, PV unit, fuel cell, Electrolyzer, battery, and microturbine.
117 citations
TL;DR: Deep neural networks, Rpart regression tree and Random forest with variable reduction procedures were used to create prediction models of specific energy consumption of Croatian public sector buildings, and the most accurate model was produced by Random forest method.
110 citations
TL;DR: A tri-objective optimization framework for energy management of microgrids in the presence of smart homes and demand response (DR) program is presented and indicates that an increase in DR penetration reduces the PAR and operating costs and leads to a decrease in the customers’ comfort.
109 citations
04 May 2021
TL;DR: In this article, the authors present a review of the current research in the field of electric and hybrid electric vehicles (EV/HEV) and suggest challenges and scope of future research in this field.
Abstract: Electric and hybrid electric vehicles (EV/HEV) are promising solutions for fossil fuel conservation and pollution reduction for a safe environment and sustainable transportation. The design of these energy-efficient powertrains requires optimization of components, systems, and controls. Controls entail battery management, fuel consumption, driver performance demand emissions, and management strategy. The hardware optimization entails powertrain architecture, transmission type, power electronic converters, and energy storage systems. In this overview, all these factors are addressed and reviewed. Major challenges and future technologies for EV/HEV are also discussed. Published suggestions and recommendations are surveyed and evaluated in this review. The outcomes of detailed studies are presented in tabular form to compare the strengths and weaknesses of various methods. Furthermore, issues in the current research are discussed, and suggestions toward further advancement of the technology are offered. This article analyzes current research and suggests challenges and scope of future research in EV/HEV and can serve as a reference for those working in this field.
TL;DR: A novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs) and an energy storage system (ESS).
Abstract: Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users’ behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users’ demand uncertainties. To this aim, first, we formulate a min–max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min–max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints’ violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners —This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users’ payment and constraints’ violation rate considering the energy cost saving, the system technical limitations and the users’ comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand.
TL;DR: A review of energy systems for light-duty vehicles and highlights the main characteristics of electric and hybrid vehicles based on power train structure, environmental perspective, and cost is presented in this paper.
Abstract: Reduction in fossil fuel dependency has been an issue worldwide for several years. One of the solutions in the transportation sector to reduce the GHG, is the replacement of combustion engine vehicles with electric and hybrid vehicles. Furthermore, to make EVs competitive with ICEV, it is imperative to reduce the relatively high manufacturing cost, increase the range of those vehicles and find solutions to drastically reduce recharge times to a comparable ICEV refuelling time. Battery, Fuel Cell, and Super Capacitor are energy storage solutions implemented in electric vehicles, which possess different advantages and disadvantages. The combination of these Energy Storage Systems, rather than the sole use of one solution, has the potential to meet the required performance results, with regards to high energy density, lower energy consumption and a longer driving range of EVs, to replace ICEVs permanently. However, challenges such as energy management, size and cost of the energy storage systems, are essential concerns and need to be focused on for the production and adoption of EVs. Furthermore, limitations and requirements for changing power train configurations of conventional vehicles stimulate a market for biofuels and synthetic fuels, which also show potential to reduce greenhouse gas emission. This paper provides a review of energy systems for light-duty vehicles and highlights the main characteristics of electric and hybrid vehicles based on power train structure, environmental perspective, and cost. The review provides an overview of different solutions possible, which have the potential to significantly reduce GHG emissions in the transportation sector.
TL;DR: In this paper, the causal relationship between low-carbon energy transition and energy poverty was examined by using a novel nonparametric panel causality-inquantiles (PCIQ) method.
01 Jun 2021
TL;DR: A predictive energy management strategy considering travel route information is proposed to explore the energy-saving potential of plug-in hybrid electric vehicles, and using the real-world historical speed information for training can achieve higher prediction accuracy than using typical standard driving cycles.
Abstract: A predictive energy management strategy considering travel route information is proposed to explore the energy-saving potential of plug-in hybrid electric vehicles. The extreme learning machine is used as a short-term speed predictor, and the battery temperature is added as an optimization term to the cost function. By comparing the training data sets, it is found that using the real-world historical speed information for training can achieve higher prediction accuracy than using typical standard driving cycles. The speed predictor trained based on the data considering travel route information can further improve the prediction accuracy. The impact of battery temperature on the total cost is also analyzed. By adjusting the temperature weighting coefficient of the battery, a balance between economy and battery aging can be achieved. In addition, it is found that the ambient temperature also affects vehicular energy consumption. Finally, the proposed method is compared with PMP, MPC, and CD-CS methods, showing its effectiveness and practicability.
TL;DR: The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools.
Abstract: Since electricity plays a crucial role in countries’ industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models’ performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model’s performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.
TL;DR: Results from a critical review of selected real-world energy storage systems based on hydrogen indicate that the best solution from a technical viewpoint consists in hybrid systems where hydrogen is combined with short-term energy storage technologies like batteries and supercapacitors.
TL;DR: In this paper, the authors compared the advantages and disadvantages of three types of strategies (rule-based, optimization-based and learning-based strategies) for fuel cell electric vehicles and revealed the new technologies and DC/DC converters involved.
Abstract: With the development of technologies in recent decades and the imposition of international standards to reduce greenhouse gas emissions, car manufacturers have turned their attention to new technologies related to electric/hybrid vehicles and electric fuel cell vehicles. This paper focuses on electric fuel cell vehicles, which optimally combine the fuel cell system with hybrid energy storage systems, represented by batteries and ultracapacitors, to meet the dynamic power demand required by the electric motor and auxiliary systems. This paper compares the latest proposed topologies for fuel cell electric vehicles and reveals the new technologies and DC/DC converters involved to generate up-to-date information for researchers and developers interested in this specialized field. From a software point of view, the latest energy management strategies are analyzed and compared with the reference strategies, taking into account performance indicators such as energy efficiency, hydrogen consumption and degradation of the subsystems involved, which is the main challenge for car developers. The advantages and disadvantages of three types of strategies (rule-based strategies, optimization-based strategies and learning-based strategies) are discussed. Thus, future software developers can focus on new control algorithms in the area of artificial intelligence developed to meet the challenges posed by new technologies for autonomous vehicles.
TL;DR: A comprehensive review of DRL for SBEM from the perspective of system scale is provided and the existing unresolved issues are identified and possible future research directions are pointed out.
Abstract: Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy efficient and green buildings. However, it is a nontrivial task due to the following challenges. First, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Second, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Third, there are many spatially and temporally coupled operational constraints. Fourth, building energy optimization problems can not be solved in real time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this article provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.
TL;DR: This article performs a comprehensive review of how blockchain technology has been, and can be, deployed in energy applications, ranging from energy management to peer-to-peer trading to electric vehicle-related applications to carbon emissions trading, and others.
Abstract: As our fossil fuel reserves are rapidly depleting, there has been an increased focus to explore the utility of renewable energy (e.g., solar energy and wind energy) in replacing fossil fuel. One resulting trend is the energy market gradually shifting toward a distributed market, where renewable energy can be traded, partly evidenced by the number of blockchain-based solutions designed for the (distributed) energy sector. The interest in blockchain is also due to blockchain's underpinning characteristics such as anonymity, decentralized, and transparency. Therefore, in this article, we perform a comprehensive review of how blockchain technology has been, and can be, deployed in energy applications, ranging from energy management to peer-to-peer trading to electric vehicle-related applications to carbon emissions trading, and others. We also study the existing architectures and solutions, and existing and emerging security and privacy challenges, as well as exploring other potential applications of blockchain in the energy sector.
TL;DR: A machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing and a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation.
Abstract: Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.
TL;DR: Results demonstrate that the proposed SSA management strategy performed best compared with all other used strategies in terms of hydrogen fuel economy and overall efficiency.
TL;DR: A comprehensive evaluation and comparison of different hybrid systems of Proton Exchange Membrane Fuel Cell with battery and Solid Oxide Fuel cell with battery for mobility and other off-grid applications from perspectives of system configurations, technical specifications, energy management strategies, and experimental validation is presented.
Abstract: The global demand for fossil fuels in the transportation sector is increasing rapidly due to the continuous growth of internal combustion engine vehicles. This leads to severe environmental problems, including greenhouse gas emissions and air-quality deterioration. Thus, it is necessary to increase the use of renewable energy sources in the transportation sector as well as other off-grid applications. Battery and fuel cells are promising alternatives owing to high efficiency and low (even zero) local emissions. However, they are limited by either the low capacity or sluggish dynamic response. These shortcomings can be overcome by the hybridization of battery and fuel cells, which have been the focus of leading international automotive and shipbuilding companies. This paper presents a comprehensive evaluation and comparison of different hybrid systems of Proton Exchange Membrane Fuel Cell with battery and Solid Oxide Fuel Cell with battery for mobility and other off-grid applications from perspectives of system configurations, technical specifications, energy management strategies, and experimental validation. With the existing issues and corresponding solving strategies highlighted, the suggestions for designing high-performance fuel cell hybrid power systems are concluded accordingly. This review can serve as a reference and guide to advance the development of the fuel cell and battery hybrid power systems for mobility and off-grid applications.
TL;DR: In this paper, a blockchain-based predictive energy trading platform is proposed to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources in smart microgrids.
Abstract: It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.
TL;DR: The results show that an HEESS with appropriate sizing and enabling energy management can markedly reduce the battery degradation rate by about 40% only at an extra expense of 1/8 of the system cost compared with battery-only energy storage.
Abstract: Electrochemical energy storage systems are fundamental to renewable energy integration and electrified vehicle penetration. Hybrid electrochemical energy storage systems (HEESSs) are an attractive option because they often exhibit superior performance over the independent use of each constituent energy storage. This article provides an HEESS overview focusing on battery-supercapacitor hybrids, covering different aspects in smart grid and electrified vehicle applications. The primary goal of this paper is to summarize recent research progress and stimulate innovative thoughts for HEESS development. To this end, system configuration, DC/DC converter design and energy management strategy development are covered in great details. The state-of-the-art methods to approach these issues are surveyed; the relationship and technological details in between are also expounded. A case study is presented to demonstrate a framework of integrated sizing formulation and energy management strategy synthesis. The results show that an HEESS with appropriate sizing and enabling energy management can markedly reduce the battery degradation rate by about 40% only at an extra expense of 1/8 of the system cost compared with battery-only energy storage.
TL;DR: A real-time cost-minimization energy management strategy to mitigate the vehicle’s operating cost is proposed via model predictive control, wherein both hydrogen consumption and energy source degradations are incorporated in the multi-objective cost function.
TL;DR: A mixed integer linear programming model is suggested to solve the integrated operations planning and energy management problem for seaports with smart grid (e.g. port microgrid) considering uncertain renewable energy generation.
Abstract: The importance of energy efficiency and demand response management while harnessing renewable energy draws more attention from many industries in recent years. Seaports, as large scale end-users, aim to adopt energy management systems (EMS) since energy prices have increased over years and sustainable operations is a key target for greening the port industry. Many seaports start to install fully electrified equipment and use electricity as the source of energy because electricity consumption, instead of carbon-intensive energy sources, contributes to the climate change mitigation targets. In this study, a mixed integer linear programming model is suggested to solve the integrated operations planning and energy management problem for seaports with smart grid (e.g. port microgrid) considering uncertain renewable energy generation. The operations planning aims to determine the number of quay cranes (QCs) and yard equipment to assign to each ship for each one hour period. It also determines each ship’s berthing duration which affects the hourly energy consumption due to the cold ironing and the available reefer containers. These plans result in energy demand. Meanwhile, energy management matches energy demand and supply considering different energy pricing schemes and bidirectional energy trading between energy sources (e.g. utility grid, renewable energy sources) and energy storage systems. Results indicate that significant cost savings can be achieved with smart grid (port microgrid) compared to conventional settings. Deploying energy storage systems in port microgrid results in important cost savings. Energy consumption is dominated by QCs, cold-ironing and reefer containers. Finally ports which harness renewable energy obtain significant costs savings on total cost.
TL;DR: The lithium-ion battery's insight, overall synopsis and contribution, and further research directions to the EV system are provided, and the future scope of research is indicated.
Abstract: Renewable energy is in high demand for a balanced ecosystem. There are different types of energy storage systems available for long-term energy storage, lithium-ion battery is one of the most powerful and being a popular choice of storage. This review paper discusses various aspects of lithium-ion batteries based on a review of 420 published research papers at the initial stage through 101 published research articles that have been finally reviewed. This review paper focuses on several topics, including electrical vehicle (EV) systems, energy management systems, challenges and issues, and the conclusions and recommendations for future work. EV systems discuss all components that are included in producing the lithium-ion battery. The energy storage section contains the batteries, super capacitors, fuel cells, hybrid storage, power, temperature, and heat management. Energy management systems consider battery monitoring for current and voltage, battery charge-discharge control, estimation and protection, cell equalization. This paper's challenges and issues discuss some of the critical aspects of lithium-ion batteries, including temperature and safety, life-cycle and memory effects, environmental effects, and recycling processes. The conclusion and recommendation of this paper indicate the future scope of research. This review paper can provide the lithium-ion battery's insight, overall synopsis and contribution, and further research directions to the EV system.
TL;DR: Simulations are presented to investigate the impacts of DER sources, electric vehicles (EV), and energy storage system (ESS) on practicable architectures’ resilient operation and compare of control strategies, energy management strategies, and power quality issues associated with DER based microgrid.
Abstract: To accomplish feasible large-scale integration of distributed energy resources (DER) into the existing grid system, microgrid implementation has proven to be the most effective. This article reviews the vital aspects of DER based microgrid and presents simulations to investigate the impacts of DER sources, electric vehicles (EV), and energy storage system (ESS) on practicable architectures’ resilient operation. The focus is primarily on the concept and definition of microgrid, comparison of control strategies (primary, secondary, and tertiary strategies), energy management strategies, power quality (PQ) issues associated with DER based microgrid, and state-of-the-art entities such as ESS and EV's applications toward microgrid reliability. Following discussion on the different attributes of DER sources-based microgrid, simulations are performed to verify the results of the past works on the effects of solar, wind energy sources, ESS, and EVs on the microgrid frequency response. Additional simulations are conducted to assess the influences of DERs, ESS, EVs, and their operational strategies on the microgrid reliability aspects.
TL;DR: In this paper, the authors investigated and compared the various combinations of renewable energies (solar, wind) and storage technologies (battery, pumped hydro storage, hybrid storage) for an off-grid power supply system.