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Modelling and Simulation of Smart Energy Management Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 32415

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Prof. Dr. Ravinesh Deo

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School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia
Interests: metaheuristic algorithm; deep learning; artificial intelligence in renewable energy; smart electricity grids; energy loads or demand model; energy informatics or economics; green or cleaner energy solutions; energy generation; utilization; conversion; storage; transmission; management; and sustainability; sources such as mechanical; thermal; nuclear; chemical; electromagnetic; magnetic; electricity; solar; bio; hydro; wind; geothermal; tidal and ocean energy; fossil fuels and nuclear resources
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Dear Colleagues:

Artificial intelligence approaches are attractive for the modelling and simulation of energy systems. National electricity markets, energy utilities, climate–energy policy makers and electronic, electrical and mechatronic engineers employ optimizations to improve energy systems and possibly, develop ways to integrate renewable energies into the grid to provide both optimal energy security and also the environmentally-friendly and sustainable operation of the national energy market. Artificial intelligence algorithms are implemented in power management to utilize renewable energies such as solar, wind and hydropower.

We welcomes original and high quality submissions in the modelling and simulation of real energy systems that build a responsive management platform. It entails complex consumer markets including real-time prediction and management tools with smart adaptive modelling incorporated in energy management environments.

This Special Issue focuses on the modelling, analysis, design and the implementation of such systems with advanced algorithms, recent theoretical developments, novel applications, target case studies, extensive reviews and discussion on machine learning for energy forecasting, and renewable energies in grids and decision systems designed for a smart energy management platform with advancing big data techniques.

Prof. Dr. Ravinesh C Deo
Guest Editor

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Keywords

Energy Informatics
Energy Modelling and Simulation
Power Grid Systems
Renewable Energy Systems
Artificial Intelligence
Smart Energy Market
Energy Security, Climate Change & Sustainability
Machine Learning Applications

Published Papers (9 papers)

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Research

30 pages, 2518 KiB

Open AccessArticle

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

by Anh Ngoc-Lan Huynh, Ravinesh C. Deo, Duc-Anh An-Vo, Mumtaz Ali, Nawin Raj and Shahab Abdulla

Energies 2020, 13(14), 3517; https://doi.org/10.3390/en13143517 - 08 Jul 2020

Cited by 28 | Viewed by 2601

Abstract

This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy). Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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17 pages, 600 KiB

Open AccessArticle

Event-Based Simulation of a Decentralized Protection System Based on Secured GOOSE Messages

by Pablo Ledesma, Peyman Jafary, Sami Repo, Amelia Álvarez, Francisco Ramos, Davide Della Giustina and Alessio Dedè

Energies 2020, 13(12), 3250; https://doi.org/10.3390/en13123250 - 23 Jun 2020

Cited by 2 | Viewed by 2202

Abstract

A new simulation library is developed on OMNeT++ to model faults in distribution systems. The proposed library makes it possible to calculate the status of lines and busbars from the point of view of a protection system, enabling the modeling of overcurrents, power outages and fault passage indicators. The library is applied to model a decentralized protection system based on the exchange of IEC 61850 Generic Object Oriented Substation Events (GOOSE) messages between intelligent electronic devices responsible for the operation of circuit breakers and disconnectors. The time needed to secure and transmit GOOSE messages over the Internet is characterized and included in the model. Several studies are carried out to analyze the effect of different parameters, such as GOOSE retransmission times and failure rates of switching devices and communication channels, on the performance of the protection system. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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19 pages, 2243 KiB

Open AccessArticle

Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

by Mohanad S. Al-Musaylh, Ravinesh C. Deo and Yan Li

Energies 2020, 13(9), 2307; https://doi.org/10.3390/en13092307 - 06 May 2020

Cited by 20 | Viewed by 2252

Abstract

To support regional electricity markets, accurate and reliable energy demand (G) forecast models are vital stratagems for stakeholders in this sector. An online sequential extreme learning machine (OS-ELM) model integrated with a maximum overlap discrete wavelet transform (MODWT) algorithm was developed using daily G data obtained from three regional campuses (i.e., Toowoomba, Ipswich, and Springfield) at the University of Southern Queensland, Australia. In training the objective and benchmark models, the partial autocorrelation function (PACF) was first employed to select the most significant lagged input variables that captured historical fluctuations in the G time-series data. To address the challenges of non-stationarities associated with the model development datasets, a MODWT technique was adopted to decompose the potential model inputs into their wavelet and scaling coefficients before executing the OS-ELM model. The MODWT-PACF-OS-ELM (MPOE) performance was tested and compared with the non-wavelet equivalent based on the PACF-OS-ELM (POE) model using a range of statistical metrics, including, but not limited to, the mean absolute percentage error (MAPE%). For all of the three datasets, a significantly greater accuracy was achieved with the MPOE model relative to the POE model resulting in an MAPE = 4.31% vs. MAPE = 11.31%, respectively, for the case of the Toowoomba dataset, and a similarly high performance for the other two campuses. Therefore, considering the high efficacy of the proposed methodology, the study claims that the OS-ELM model performance can be improved quite significantly by integrating the model with the MODWT algorithm. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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37 pages, 4261 KiB

Open AccessArticle

e4clim 1.0: The Energy for a Climate Integrated Model: Description and Application to Italy

by Alexis Tantet, Marc Stéfanon, Philippe Drobinski, Jordi Badosa, Silvia Concettini, Anna Cretì, Claudia D’Ambrosio, Dimitri Thomopulos and Peter Tankov

Energies 2019, 12(22), 4299; https://doi.org/10.3390/en12224299 - 11 Nov 2019

Cited by 12 | Viewed by 4890

Abstract

We develop an open-source Python software integrating flexibility needs from Variable Renewable Energies (VREs) in the development of regional energy mixes. It provides a flexible and extensible tool to researchers/engineers, and for education/outreach. It aims at evaluating and optimizing energy deployment strategies with higher shares of VRE, assessing the impact of new technologies and of climate variability and conducting sensitivity studies. Specifically, to limit the algorithm’s complexity, we avoid solving a full-mix cost-minimization problem by taking the mean and variance of the renewable production–demand ratio as proxies to balance services. Second, observations of VRE technologies being typically too short or nonexistent, the hourly demand and production are estimated from climate time series and fitted to available observations. We illustrate e4clim’s potential with an optimal recommissioning-study of the 2015 Italian PV-wind mix testing different climate data sources and strategies and assessing the impact of climate variability and the robustness of the results. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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17 pages, 5446 KiB

Open AccessArticle

Analytical Study of Tri-Generation System Integrated with Thermal Management Using HT-PEMFC Stack

by Hyun Sung Kang and Yoon Hyuk Shin

Energies 2019, 12(16), 3145; https://doi.org/10.3390/en12163145 - 09 Aug 2019

Cited by 5 | Viewed by 3054

Abstract

Recently, extensive studies on power generation using clean energy have been conducted to reduce air pollution and global warming. In particular, as existing internal combustion engines lose favor to power generation through hydrogen fuel cells, the development of tri-generation technology using efficient and reliable fuel cells is gaining importance. This study proposes a tri-generation thermal management model that enables thermal control and waste heat utilization control of a high-temperature PEMFC stack that simultaneously satisfies combined cooling, heating, and power (CCHP) load. As the high-temperature PEMFC stack operates at 150 °C or more, a tri-generative system using such a stack requires a thermal management system that can maintain the operating temperature of the stack and utilize the stack waste heat. Thus, to apply the waste heat produced through the stack to heating (hot water) and absorption cooling, proper distribution control of the thermal management fluid (cooling fluid) of the stack is essential. For the thermal management fluid control design, system analysis modeling was performed to selectively design the heat exchange amount of each part utilizing the stack waste heat. In addition, a thermal management system based on thermal storage was constructed for complementary waste heat utilization and active stack cooling control. Through a coupled analysis of the stack thermal management model and the absorption cooling system model, this study compared changes in system performance by cooling cycle operation conditions. This study investigated into the appropriate operating conditions for cooling operation in a tri-generative system using a high-temperature PEMFC stack. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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39 pages, 2906 KiB

Open AccessArticle

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

by Sujan Ghimire, Ravinesh C Deo, Nawin Raj and Jianchun Mi

Energies 2019, 12(12), 2407; https://doi.org/10.3390/en12122407 - 22 Jun 2019

Cited by 70 | Viewed by 5519

Abstract

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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20 pages, 2214 KiB

Open AccessArticle

A New Hybrid Approach for Short-Term Electric Load Forecasting Applying Support Vector Machine with Ensemble Empirical Mode Decomposition and Whale Optimization

by Tongxiang Liu, Yu Jin and Yuyang Gao

Energies 2019, 12(8), 1520; https://doi.org/10.3390/en12081520 - 22 Apr 2019

Cited by 20 | Viewed by 2712

Abstract

Electrical power system forecasting has been a main focus for researchers who want to improve the effectiveness of a power station. Although some traditional models have been proved suitable for short-term electric load forecasting, its nature of ignoring the significance of parameter optimization and data preprocessing usually results in low forecasting accuracy. This paper proposes a short-term hybrid forecasting approach which consists of the three following modules: Data preprocessing, parameter optimization algorithm, and forecasting. This hybrid model overcomes the disadvantages of the conventional model and achieves high forecasting performance. To verify the forecasting effectiveness of the hybrid method, 30-minutes of electric load data from power stations in New South Wales and Queensland are used for conducting experiments. A comprehensive evaluation, including a Diebold-Mariano (DM) test and forecasting effectiveness, is applied to verify the ability of the hybrid approach. Experimental results indicated that the new hybrid method can perform accurate electric load forecasting, which can be regarded as a powerful assist in managing smart grids. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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23 pages, 7689 KiB

Open AccessArticle

Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm

by Mohammad Rezaie-Balf, Niloofar Maleki, Sungwon Kim, Ali Ashrafian, Fatemeh Babaie-Miri, Nam Won Kim, Il-Moon Chung and Sina Alaghmand

Energies 2019, 12(8), 1416; https://doi.org/10.3390/en12081416 - 12 Apr 2019

Cited by 50 | Viewed by 4023

Abstract

The precise forecasting of daily solar radiation (DSR) is receiving prominent attention among thriving solar energy studies. In this study, three standalone models, including gene expression programing (GEP), multivariate adaptive regression splines (MARS), and self-adaptive MARS (SaMARS), were evaluated to forecast DSR. A SaMARS model was classified as MARS model when using the crow search algorithm (CSA). In addition, to overcome the limitations of the standalone models, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) was employed to enhance the accuracy of DSR forecasting. Therefore, three hybrid models including CEEMDAN-GEP, CEEMDAN-MARS, and CEEMDAN-SaMARS were proposed to forecast DSR in Busan and Incheon stations in South Korea. The performance of proposed models were evaluated and affirmed that the accuracy of the CEEMDAN-SaMARS model (NSE = 0.878–0.883) outperformed CEEMDAN-MARS (NSE = 0.819–0.818), CEEMDAN-GEP (NSE = 0.873–0.789), SaMARS (NSE = 0.846–0.769), MARS (NSE = 0.819–0.758), and GEP (NSE = 0.814–0.755) models at both stations. Therefore, it can be concluded that the optimized CEEMDAN-SaMARS model significantly enhanced the accuracy of DSR forecasting compared to that of standalone models. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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24 pages, 3387 KiB

Open AccessArticle

Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

by Hai Tao, Isa Ebtehaj, Hossein Bonakdari, Salim Heddam, Cyril Voyant, Nadhir Al-Ansari, Ravinesh Deo and Zaher Mundher Yaseen

Energies 2019, 12(7), 1365; https://doi.org/10.3390/en12071365 - 09 Apr 2019

Cited by 17 | Viewed by 3377

Abstract

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m²]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions. Full article

(This article belongs to the Special Issue Modelling and Simulation of Smart Energy Management Systems)

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