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Energies
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Smart Energy Systems: Learning Methods for Control and Optimization
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Smart Energy Systems: Learning Methods for Control and Optimization

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 5562

Image courtesy of Roman from Pixabay

Special Issue Editors

Prof. Dr. Ramiro Barbosa

E-Mail Website
Guest Editor
Institute of Engineering of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Interests: control; simulation; optimization; fractional calculus; evolutionary algorithms; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Faria

E-Mail Website
Guest Editor
GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, LASI—Intelligent Systems Associate Laboratory, Polytechnic of Porto, 4200-072 Porto, Portugal
Interests: demand response; electricity markets; energy communities; renewable energy integration; real-time simulation; smart grids
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the application of learning methods for the control and optimization of smart energy systems, which incorporate a wide range of technologies such as renewable energy sources, distributed energy resources, smart grids, smart energy infrastructures, energy storage systems, electric vehicles, and demand response. Through the integration of these technologies, the system can balance energy supply and demand, reduce energy waste, and increase energy efficiency, moving to future renewable and sustainable energy solutions. The potential authors are encouraged to contribute to all aspects related to smart energy and sustainable energy systems. The covering of relevant up-to-date learning methods of machine learning, deep learning, reinforcement learning, and evolutionary algorithms will bring new outcomes in the development of smart energy systems control and optimization.

Prof. Dr. Ramiro Barbosa
Dr. Pedro Faria
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart energy systems
  • smart grids
  • energy systems
  • building energy management systems
  • renewable energy and smart applications
  • demand response
  • artificial intelligence
  • energy forecasting
  • smart management of complex energy systems
  • multi-agent control
  • metaheuristics
  • evolutionary algorithms
  • neural networks
  • machine learning
  • deep learning
  • reinforcement learning

Published Papers (7 papers)

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Research

19 pages, 7621 KiB  
Article
Design, Detection, and Countermeasure of Frequency Spectrum Attack and Its Impact on Long Short-Term Memory Load Forecasting and Microgrid Energy Management
by Amirhossein Nazeri, Roghieh Biroon, Pierluigi Pisu and David Schoenwald
Energies 2024, 17(4), 868; https://doi.org/10.3390/en17040868 - 13 Feb 2024
Viewed by 472
Abstract
This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in [...] Read more.
This paper introduces a frequency-domain false data injection attack called Frequency Spectrum Attack (FSA) and explores its effects on load forecasting and the energy management system (EMS) in a microgrid. The FSA analyzes time-series signals in the frequency domain to identify patterns in their frequency spectrum. It learns the distribution of dominant frequencies in a dataset of healthy signals. Subsequently, it manipulates the amplitudes of dominant frequencies within this healthy distribution, ensuring a stealthy attack against statistical analysis of the signal spectrum. We evaluated the performance of FSA on LSTM, a state-of-the-art network for load forecasting. The results show that FSA can triple the Mean Absolute Error (MAE) of predictions compared to the normal case and increase it by 70% compared to noise injection attacks. Furthermore, FSA indirectly enhances battery utilization in the EMS by 45%. We then proposed a detection method that combines statistical analysis and machine-learning-based classification techniques with features. The model effectively distinguishes FSA from healthy and noisy signals, achieving an accuracy of 98.7% and an F1-score of 98.1% on a load dataset, covering healthy, FSA, and noisy load data. Finally, a countermeasure was introduced based on the statistical analysis of the frequency spectrum of healthy signals to mitigate the impact of FSA. This countermeasure successfully reduces the MAE of the attacked model from 0.135 to 0.053, validating its effectiveness in mitigating FSA. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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26 pages, 2375 KiB  
Article
Dynamic Knowledge Management in an Agent-Based Extended Green Cloud Simulator
by Zofia Wrona, Maria Ganzha, Marcin Paprzycki and Stanisław Krzyżanowski
Energies 2024, 17(4), 780; https://doi.org/10.3390/en17040780 - 06 Feb 2024
Viewed by 706
Abstract
Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” [...] Read more.
Cloud infrastructures operate in highly dynamic environments, and today, energy-focused optimization become crucial. Moreover, the concept of extended cloud infrastructure, which, among others, uses green energy, started to gain traction. This introduces a new level of dynamicity to the ecosystem, as “processing components” may “disappear” and “come back”, specifically in scenarios where the lack/return of green energy leads to shutting down/booting back servers at a given location. Considered use cases may involve introducing new types of resources (e.g., adding containers with server racks with “next-generation processors”). All such situations require the dynamic adaptation of “system knowledge”, i.e., runtime system adaptation. In this context, an agent-based digital twin of the extended green cloud infrastructure is proposed. Here, knowledge management is facilitated with an explainable Rule-Based Expert System, combined with Expression Languages. The tests were run using Extended Green Cloud Simulator, which allows the modelling of cloud infrastructures powered (partially) by renewable energy sources. Specifically, the work describes scenarios in which: (1) a new hardware resource is introduced in the system; (2) the system component changes its resource; and (3) system user changes energy-related preferences. The case study demonstrates how rules can facilitate control of energy efficiency with an example of an adaptable compromise between pricing and energy consumption. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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12 pages, 6203 KiB  
Article
Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations
by Michał Pikus and Jarosław Wąs
Energies 2024, 17(3), 628; https://doi.org/10.3390/en17030628 - 28 Jan 2024
Viewed by 823
Abstract
In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, [...] Read more.
In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the power system operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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21 pages, 664 KiB  
Article
An Applied Framework for Smarter Buildings Exploiting a Self-Adapted Advantage Weighted Actor-Critic
by Ioannis Papaioannou, Asimina Dimara, Christos Korkas, Iakovos Michailidis, Alexios Papaioannou, Christos-Nikolaos Anagnostopoulos, Elias Kosmatopoulos, Stelios Krinidis and Dimitrios Tzovaras
Energies 2024, 17(3), 616; https://doi.org/10.3390/en17030616 - 27 Jan 2024
Viewed by 637
Abstract
Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this [...] Read more.
Smart buildings are rapidly becoming more prevalent, aiming to create energy-efficient and comfortable living spaces. Nevertheless, the design of a smart building is a multifaceted approach that faces numerous challenges, with the primary one being the algorithm needed for energy management. In this paper, the design of a smart building, with a particular emphasis on the algorithm for controlling the indoor environment, is addressed. The implementation and evaluation of the Advantage-Weighted Actor-Critic algorithm is examined in a four-unit residential simulated building. Moreover, a novel self-adapted Advantage-Weighted Actor-Critic algorithm is proposed, tested, and evaluated in both the simulated and real building. The results underscore the effectiveness of the proposed control strategy compared to Rule-Based Controllers, Deep Deterministic Policy Gradient, and Advantage-Weighted Actor-Critic. Experimental results demonstrate a 34.91% improvement compared to the Deep Deterministic Policy Gradient and a 2.50% increase compared to the best Advantage-Weighted Actor-Critic method in the first epoch during a real-life scenario. These findings solidify the Self-Adapted Advantage-Weighted Actor-Critic algorithm’s efficacy, positioning it as a promising and advanced solution in the realm of smart building optimization. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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13 pages, 1189 KiB  
Article
Integrating Statistical Simulation and Optimization for Redundancy Allocation in Smart Grid Infrastructure
by Bahram Alidaee, Haibo Wang, Jun Huang and Lutfu S. Sua
Energies 2024, 17(1), 225; https://doi.org/10.3390/en17010225 - 31 Dec 2023
Viewed by 611
Abstract
It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem is presented. First, statistical simulation methods to [...] Read more.
It is a critical issue to allocate redundancy to critical smart grid infrastructure for disaster recovery planning. In this study, a framework to combine statistical prediction methods and optimization models for the optimal redundancy allocation problem is presented. First, statistical simulation methods to identify critical nodes of very large-scale smart grid infrastructure based on the topological features of embedding networks are developed, and then a linear integer programming model based on generalized assignment problem (GAP) for the redundancy allocation of critical nodes in smart grid infrastructure is presented. This paper aims to contribute to the field by employing a general redundancy allocation problem (GRAP) model from high-order nonlinear to linear model transformation. The model is specifically implemented in the context of smart grid infrastructure. The innovative linear integer programming model proposed in this paper capitalizes on the logarithmic multiplication property to reframe the inherently nonlinear resource allocation problem (RAP) into a linearly separable function. This reformulation markedly streamlines the problem, enhancing its suitability for efficient and effective solutions. The findings demonstrate that the combined approach of statistical simulation and optimization effectively addresses the size limitations inherent in a sole optimization approach. Notably, the optimal solutions for redundancy allocation in large grid systems highlight that the cost of redundancy is only a fraction of the economic losses incurred due to weather-related outages. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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13 pages, 1899 KiB  
Article
Comparison Analysis for Electricity Consumption Prediction of Multiple Campus Buildings Using Deep Recurrent Neural Networks
by Donghun Lee, Jongeun Kim, Suhee Kim and Kwanho Kim
Energies 2023, 16(24), 8038; https://doi.org/10.3390/en16248038 - 13 Dec 2023
Viewed by 616
Abstract
As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due [...] Read more.
As the scale of electricity consumption grows, the peak electricity consumption prediction of campus buildings is essential for effective building energy system management. The selection of an appropriate model is of paramount importance to accurately predict peak electricity consumption of campus buildings due to the substantial variations in electricity consumption trends and characteristics among campus buildings. In this paper, we proposed eight deep recurrent neural networks and compared their performance in predicting peak electricity consumption for each campus building to select the best model. Furthermore, we applied an attention approach capable of capturing long sequence patterns and controlling the importance level of input states. The test cases involve three campus buildings in Incheon City, South Korea: an office building, a nature science building, and a general education building, each with different scales and trends of electricity consumption. The experiment results demonstrate the importance of accurate model selection to enhance building energy efficiency, as no single model’s performance dominates across all buildings. Moreover, we observe that the attention approach effectively improves the prediction performance of peak electricity consumption. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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26 pages, 4768 KiB  
Article
Smart Energy Planning in the Midst of a Technological and Political Change towards a 100% Renewable System in Mexico by 2050
by Daniel Icaza-Alvarez, Nestor Daniel Galan-Hernandez, Eber Enrique Orozco-Guillen and Francisco Jurado
Energies 2023, 16(20), 7121; https://doi.org/10.3390/en16207121 - 17 Oct 2023
Cited by 1 | Viewed by 1030
Abstract
This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in [...] Read more.
This study presents a 100% renewable and diversified system taking advantage of the available energy potential of renewable energies in Mexico with a view to a planned energy transition in cooperation with the environment. The processes of change that are experienced worldwide in favor of the planet make us reflect and propose alternatives that break traditional schemes in the production of energy (for which reason Mexico cannot deviate from its current model). It is here that this research becomes a transcendental and important reference for decision-making and the transformation of the energy sector in Mexico. The current electrical system relies on fossil fuels that need to be replaced by renewable energy sources (and it is necessary to satisfy growing demands in the long term). The methodological process is carried out with the use of the 100% renewable energy market design tool EnergyPLAN, which puts the concept of intelligent energy into practice by 2050. Finally, after analyzing the results, it is concluded that a good energy mix for 2050 is 30% solar photovoltaic, 25% wind, 14.5% hydraulic, 13.8% CSP plants, and 16.7% other technologies. Surpluses may be sold to the United States and Central America through interconnection points. Full article
(This article belongs to the Special Issue Smart Energy Systems: Learning Methods for Control and Optimization)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Short-Term Forecast of Photovoltaic Solar Energy Production using LSTM
Authors: Filipe D. Campos; Tiago C. Sousa; Ramiro S. Barbosa
Affiliation: Institute of Engineering - Polytechnic of Porto (ISEP/IPP), Dept. of Electrical Engineering, Porto, Portugal
Abstract: In recent times, renewable energies have gained considerable vitality due to their inexhaustible resources and the detrimental effects of fossil fuels, such as the impact of greenhouse gases on the planet. This article aims to be a supportive tool for the development of research in the field of Artificial Intelligence (AI) as it presents a solution for predicting photovoltaic energy production. The basis for the AI models is provided from two datasets, one for generated electrical power and another for meteorological data, related to the year 2017, freely available on the Energias de Portugal (EDP) Open Project website. The AI models implemented rely on Long Short-Term Memory (LSTM) neural networks, providing a forecast value for electrical energy with a 60 minute horizon based on meteorological variables. The performance of the models is evaluated using performance indicators MAE, RMSE, and R2, where favorable results were obtained, with particular emphasis on forecasts for the spring and summer seasons.

Title: Development of an energy management system for the hybridization of high-power EV charging stations
Authors: Claudio GALLI; Alessandro Bianchini; Francesco Balduzzi; Stefano ROSSI; Luca COLLEGIANI; Michele PINZI; Daniele FARRUGGIA; Giovanni Ferrara
Affiliation: 1) University of Florence, Florence, Italy; 2) PRAMAC, PR Industrial SRL, Siena, Italy
Abstract: Enlarging electric vehicle fleets ask for efficient and sustainable charging. As many grids lack capacity, localized energy production needs to be explored. The study analyses a hybrid generator connected to the Italian grid and integrating a LFP battery and a small PV installation, powering multiple charging points and other loads for 450kW. An energy management system was developed to optimize components production scheduling. After 10 years of operation the energy cost obtained with the EMS was 30.8 c€/kWh (vs. ideal cost of 30.2 c€/kWh), outperforming Grid and Gas-Only scenarios, highlighting the potential of hybrid technologies for high-power EV charging stations.

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