Showing papers on "Electric power published in 2021"
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: An overview of “Smart Grids” with its features and its different aspects on power distribution industry has been presented and it is explained that how these technologies change and have more potential to evolve and strength the distribution system.
145 citations
01 Feb 2021
TL;DR: In-depth analysis of TEGs is presented, beginning with a comprehensive overview of their working principles such as the Seebeck effect, the Peltier effect,The Thomson effect and Joule heating with their applications, materials used, Figure of Merit, improvement techniques including different thermoelectric material arrangements and technologies used and substrate types.
Abstract: Nowadays humans are facing difficult issues, such as increasing power costs, environmental pollution and global warming. In order to reduce their consequences, scientists are concentrating on improving power generators focused on energy harvesting. Thermoelectric generators (TEGs) have demonstrated their capacity to transform thermal energy directly into electric power through the Seebeck effect. Due to the unique advantages they present, thermoelectric systems have emerged during the last decade as a promising alternative among other technologies for green power production. In this regard, thermoelectric device output prediction is important both for determining the future use of this new technology and for specifying the key design parameters of thermoelectric generators and systems. Moreover, TEGs are environmentally safe, work quietly as they do not include mechanical mechanisms or rotating elements and can be manufactured on a broad variety of substrates such as silicon, polymers and ceramics. In addition, TEGs are position-independent, have a long working life and are ideal for bulk and compact applications. Furthermore, Thermoelectric generators have been found as a viable solution for direct generation of electricity from waste heat in industrial processes. This paper presents in-depth analysis of TEGs, beginning with a comprehensive overview of their working principles such as the Seebeck effect, the Peltier effect, the Thomson effect and Joule heating with their applications, materials used, Figure of Merit, improvement techniques including different thermoelectric material arrangements and technologies used and substrate types. Moreover, performance simulation examples such as COMSOL Multiphysics and ANSYS-Computational Fluid Dynamics are investigated.
131 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 new hybrid forecast model for short-term electricity load and price prediction has been developed that consists of a deep learning algorithm with LSTM networks which improves the accuracy of predictions.
106 citations
TL;DR: An attention-based encoder-decoder network with Bayesian optimization is proposed to do the accurate short-term power load forecasting, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
Abstract: Short-term electrical load forecasting plays an important role in the safety, stability, and sustainability of the power production and scheduling process. An accurate prediction of power load can provide a reliable decision for power system management. To solve the limitation of the existing load forecasting methods in dealing with time-series data, causing the poor stability and non-ideal forecasting accuracy, this paper proposed an attention-based encoder-decoder network with Bayesian optimization to do the accurate short-term power load forecasting. Proposed model is based on an encoder-decoder architecture with a gated recurrent units (GRU) recurrent neural network with high robustness on time-series data modeling. The temporal attention layer focuses on the key features of input data that play a vital role in promoting the prediction accuracy for load forecasting. Finally, the Bayesian optimization method is used to confirm the model’s hyperparameters to achieve optimal predictions. The verification experiments of 24 h load forecasting with real power load data from American Electric Power (AEP) show that the proposed model outperforms other models in terms of prediction accuracy and algorithm stability, providing an effective approach for migrating time-serial power load prediction by deep-learning technology.
81 citations
TL;DR: An integrated risk assessment framework is proposed for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State, to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences.
Abstract: With the increasing penetration of electric vehicles (EVs), more and more interactions appear between the transportation system and the power system, which might provide new hazards and channels for the proliferation of failures across the boundaries of the individual systems. In this context, this paper proposes an integrated risk assessment framework for an electric power system, considering scenarios that involve the electrified transportation system enabled by EVs charging technology in New York (NY) State. Firstly, scenarios in the transportation network of NY State, e.g. of reduced capacity and incident, are generated by a Monte Carlo non-sequential algorithm. Then, the cell transmission model (CTM) is used to simulate the evolution of the traffic flows under such scenarios. This allows evaluating the spatial-temporal EV charging loads in different areas of the electrified transportation system of NY State. Correspondingly, the running parameters in the studied power system are updated by the alternative current (AC) power flow model. Finally, the risk for the power system coming from the transportation system scenarios is assessed within a probabilistic risk analysis framework. The proposed integrated risk assessment framework is able to model the propagation of the effects of scenarios in the transportation system onto the power system of NY State and quantify the consequences. A real test case is used to illustrate the proposed framework.
75 citations
TL;DR: In this paper, a novel solar PV and wind energy based system for capturing carbon dioxide as well as producing hydrogen, urea and power is proposed in a way that PEM electrolyzer is powered by the wind turbines for hydrogen production, which is further converted into ammonia and then synthesizes urea by capturing CO2 and additional power is supplied to the community.
61 citations
TL;DR: In this article, the authors proposed an optimal energy management strategy for a combined hydrogen, heat, and power MG with hydrogen fueling stations (HFSs) for hydrogen vehicles (HVs), electric vehicle parking lots (EVPLs) and fuel cell micro-CHP (FC-MCHP) units to meet power and heat requirements.
58 citations
TL;DR: In this article, the authors proposed a novel model and optimal dispatch for CHP with power-to-gas (P2G) and carbon capture system (CCS), which solved the problems of the carbon source required for P2G and the CHP's carbon emissions by the optimal dispatch in IES.
57 citations
TL;DR: This paper addresses the stochastic energy management in a microgrid considering RESs such as solar, wind and tidal sources in the presence of the demand response program and storage devices and applies augmented e -constraint approach to solve the problem.
TL;DR: Wang et al. as discussed by the authors proposed a simulation model based on system dynamics theory and scenario design method to improve sustainable development of Chinese power industry considering the integration impact of the green certificate market and the carbon emissions trading market.
TL;DR: In this paper, a simplified LSTM algorithm built over the architecture of Machine Learning methodology to forecast one day-ahead solar power generation is introduced, which can successfully capture intra-hour ramping on different weather scenarios.
Abstract: In recent years, exploration and exploitation of renewable energies are turning a new chapter toward the development of energy policy, technology and business ecosystem in all the countries. Distributed energy resources (DERs) are being largely interconnected to electrical power grids. This dispersed and intermittent generational mixes bring technical and economic challenges to the power systems in terms of operations, stability, reliability, interoperability and the policy making. In additional, DERs cause the significant impacts to the operation of traditional centralized generation power plants and the dispatch control centers. Under such circumstances, the accuracy of DERs power forecasting is one of the critical problems for TSO and DSO such as unit commitment, smooth fluctuations, peak load shifting, demand response, etc. In this paper, a simplified LSTM algorithm built over the architecture of Machine Learning methodology to forecast one day-ahead solar power generation is introduced. Through the machine learning processes of data processing, model fitting, cross validation, metrics evaluation and hyperparameters tuning, the result shows that the proposed simplified LSTM model outperform the MLP model. Moreover, the forecast of LSTM model can successfully capture intra-hour ramping on different weather scenarios. The average RMSE is 0.512 which is quite promising to inspire that the proposed methodology and architecture can best fit the short-term solar power forecasting applications.
TL;DR: Existing models are inadequate to address grids with a high percentage of renewables and ES; and there is a challenge in integrating short-term temporal changes in LEPSMs due to model complexity and computational cost, and a framework for long-term electrical power system modeling considering ES and low-carbon power generation is proposed.
TL;DR: In this paper, the authors provide considerations focused on thermal management of heat sources for the design of thermoelectric generators and methods to evaluate specific energy sources and prototypes are presented.
TL;DR: In this article, the authors proposed a hybrid energy cycle composed of a wind turbine, solar photovoltaic field (PV), an alkaline fuel cell (AFC), a Stirling engine and an electrolyzer.
TL;DR: This paper addresses different energy harvesting resources for the railway environment, diverse methods of energy harvesting and their advantages and drawbacks, and at the end, energy harvesting applications in the railway industry.
13 Apr 2021
TL;DR: It can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting.
Abstract: Accurate forecasts of ocean waves energy can not only reduce costs for investment, but it is also essential for the management and operation of electrical power. This paper presents an innovative approach based on long short-term memory (LSTM) to predict the power generation of an economical wave energy converter named “Searaser”. The data for analysis is provided by collecting the experimental data from another study and the exerted data from a numerical simulation of Searaser. The simulation is performed with Flow-3D software, which has high capability in analyzing fluid–solid interactions. The lack of relation between wind speed and output power in previous studies needs to be investigated in this field. Therefore, in this study, wind speed and output power are related with an LSTM method. Moreover, it can be inferred that the LSTM network is able to predict power in terms of height more accurately and faster than the numerical solution in a field of predicting. The network output figures show a great agreement, and the root mean square is 0.49 in the mean value related to the accuracy of the LSTM method. Furthermore, the mathematical relation between the generated power and wave height was introduced by curve fitting of the power function to the result of the LSTM method.
TL;DR: In this article, the authors explore the impacts of carbon removal technologies on electric sector investments, costs, and emissions using a detailed capacity planning and dispatch model with hourly resolution, and show that adding carbon removal to a mix of low-carbon generation technologies lowers the costs of deep decarbonization.
Abstract: Carbon dioxide removal technologies, such as bioenergy with carbon capture and direct air capture, are valuable for stringent climate targets. Previous work has examined implications of carbon removal, primarily bioenergy-based technologies using integrated assessment models, but not investigated the effects of a portfolio of removal options on power systems in detail. Here, we explore impacts of carbon removal technologies on electric sector investments, costs, and emissions using a detailed capacity planning and dispatch model with hourly resolution. We show that adding carbon removal to a mix of low-carbon generation technologies lowers the costs of deep decarbonization. Changes to system costs and investments from including carbon removal are larger as policy ambition increases, reducing the dependence on technologies like advanced nuclear and long-duration storage. Bioenergy with carbon capture is selected for net-zero electric sector emissions targets, but direct air capture deployment increases as biomass supply costs rise.
TL;DR: A comprehensive review of MG elements, the different RE resources that comprise a hybrid system, and the various types of control, operating strategies, and goals in an EMS is presented.
Abstract: As promising solutions to various social and environmental issues, the generation and integration of renewable energy (RE) into microgrids (MGs) has recently increased due to the rapidly growing consumption of electric power. However, such integration can affect the stability and security of power systems due to its complexity and intermittency. Therefore, an optimal control approach is essential to ensure the efficiency, reliability, and quality of the delivered power. In addition, effective planning of policies for integrating MGs can help promote MG operations. However, outages may render these strategies inefficient and place the power system at risk. MGs are considered an ideal candidate for distributed power systems, given their capability to restore these systems rapidly after a physical or cyber-attack and create reliable protection systems. The energy management system (EMS) in an MG can operate controllable distributed energy resources and loads in real-time to generate a suitable short-term schedule for achieving some objectives. This paper presents a comprehensive review of MG elements, the different RE resources that comprise a hybrid system, and the various types of control, operating strategies, and goals in an EMS. A detailed explanation of the primary, secondary, and tertiary levels of MGs is also presented. This paper aims to contribute to the policies and regulations adopted by certain countries, their protection schemes, transactive markets, and load restoration in MGs.
TL;DR: In this paper, a sensitivity analysis is carried out between 1 and 3 buildings, i.e., between 20 and 100kW of committed electrical power located in the island as well as the surface dedicated to PV array for local production.
TL;DR: An energy hub (EH) planning model considering renewable energy sources (RES) and energy storage system (ESS) integration is proposed in this paper, in which the risk is measured by Conditional Value-at-Risk (CVaR), and the effectiveness of reducing potential operation risk by introducing ESS and RES are verified.
TL;DR: In this paper, a blockchain-based renewable energy microgrid design problem was solved, where the bank finances a loan to the power company to establish renewable generation units and the power companies also offers a credit period for manufacturers to stimulate demand.
TL;DR: In this article, a multi-generation system including an absorption chiller, an organic flash cycle, a concentrated photovoltaic module, a reverse osmosis unit, and thermoelectric modules are integrated to produce cooling, heating, electrical power, and freshwater.
TL;DR: An optimal operation strategy for IPHS which utilizes HT for transportation and the proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the solutions are coordinated accordingly.
Abstract: The renewable energy-based hydrogen production can lead to the integrated electric power and hydrogen system (IPHS) and offer a pathway to a sustainable energy utilization. Hydrogen is mainly transported via hydrogen tube trailers (HTs), making the hydrogen energy system (HES) operation quite different from those of other energy technologies. This paper proposes an optimal IPHS operation strategy which utilizes HT for transportation. The proposed strategy coordinates hydrogen generation, transportation, and storage stages considering constrained operations of electric power system (EPS), transportation system, and variable renewable energy. The proposed solution method is based on the alternating direction method of multipliers (ADMM) in which HES and EPS constraints are managed individually and the respective solutions are coordinated accordingly. The case studies using the modified IEEE-RTS79 have verified the validity of the proposed IPHS model and its solution method and confirmed the necessity of considering HES in enhancing the EPS operation. The synergies between EPS and HES are studied via numerical examples and the impact of the flexibilities in hydrogen generation, transportation and demand are highlighted.
TL;DR: Several researchers and manufacturers are paying attention to technologies of waste heat recovery due to its effectiveness in the conversion of the energy from exhaust gas into electric power as discussed by the authors, and they are using these technologies to improve the efficiency of their products.
Abstract: Several researchers and manufacturers are paying attention to technologies of waste heat recovery due to its effectiveness in the conversion of the energy from exhaust gas into electric power. Ther...
TL;DR: In this paper, wheat straw, a biodegradable material, was utilized to fabricate singleelectrode mode triboelectric nanogenerators (TENGs) for converting mechanical energy with low frequency and small amplitude into electrical energy.
TL;DR: In this article, the authors improved the link design to extract a higher feed light power from the double-clad fiber output and employed a specially customized photovoltaic power converter that directly converted optical power into electric power.
Abstract: Simultaneous over 40-W electric power and optical data transmission using an optical fiber is demonstrated for optically powered remote antenna units in future mobile communication networks. In this article, to further increase the delivered electric power by power-over-fiber link using a double-clad fiber, we improve the link design to extract a higher feed light power from the double-clad fiber output. Furthermore, to increase the electric power for driving remote antenna units, we employ a specially customized photovoltaic power converter that directly converts optical power into electric power. The photovoltaic power converter can input a feed light with power of over 20 W and has a high optical-to-electrical conversion efficiency of over 50%. As a result, the combination of the improved power-over-fiber link design and the use of the photovoltaic power converter successfully achieves the electric power delivery of up to 43.7 W. This is the highest electric power delivery demonstration by power-over-fiber with optical data signals using a single optical fiber, to the best of the authors’ knowledge.
TL;DR: This article reviews the domain of soft magnetic materials suitable for handling large electrical power from grid frequency to high-frequency applications and elaborates the role of a transformer for one specific application.
TL;DR: This article focuses on how to determine the reference operation state of the flywheel, which depends on both future power load and the power split between the battery and flywheel.
Abstract: Integrated power system combines electrical power for both ship service and electric propulsion loads by forming a microgrid. In this article, a battery/flywheel hybrid energy storage system (HESS) is studied to mitigate load fluctuations in a shipboard microgrid. This article focuses on how to determine the reference operation state of the flywheel, which depends on both future power load and the power split between the battery and flywheel. Two control strategies are proposed—an optimization-based approach and a lookup-table-based approach. Case studies are performed in different sea conditions, and simulation results demonstrate that the proposed control strategies outperform baseline control strategies in terms of power fluctuation mitigation and HESS power-loss reduction. A comparison between the two proposed approaches is performed, where their performances are quantified, the advantages and disadvantages of each strategy are analyzed, and the cases where they are most applicable are highlighted.