Energies

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26 pages, 3550 KiB

Open AccessArticle

Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

by Izabela Rojek, Dariusz Mikołajewski, Adam Mroziński and Marek Macko

Energies 2023, 16(18), 6613; https://doi.org/10.3390/en16186613 - 14 Sep 2023

Cited by 6 | Viewed by 1752

Abstract

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing [...] Read more.

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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18 pages, 5737 KiB

Open AccessArticle

Machine Learning Estimation of Battery Efficiency and Related Key Performance Indicators in Smart Energy Systems

by Joaquín Luque, Benedikt Tepe, Diego Larios, Carlos León and Holger Hesse

Energies 2023, 16(14), 5548; https://doi.org/10.3390/en16145548 - 22 Jul 2023

Viewed by 1125

Abstract

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these [...] Read more.

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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15 pages, 5793 KiB

Open AccessArticle

Machine-Learning-Based Prediction of HVAC-Driven Load Flexibility in Warehouses

by Farzad Dadras Javan, Italo Aldo Campodonico Avendano, Behzad Najafi, Amin Moazami and Fabio Rinaldi

Energies 2023, 16(14), 5407; https://doi.org/10.3390/en16145407 - 16 Jul 2023

Cited by 1 | Viewed by 1102

Abstract

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office [...] Read more.

This paper introduces a methodology for predicting a warehouse’s reduced load while offering flexibility. Physics-based energy simulations are first performed to model flexibility events, which involve adjusting cooling setpoints with controlled temperature increases to reduce the cooling load. The warehouse building encompasses office and storage spaces, and three cooling scenarios are implemented, i.e., exclusive storage area cooling, exclusive office area cooling, and cooling in both spaces, to expand the study’s potential applications. Next, the simulation data are utilized for training machine learning (ML)-based pipelines, predicting five subsequent hourly energy consumption values an hour before the setpoint adjustments, providing time to plan participation in demand response programs or prepare for charging electric vehicles. For each scenario, the performance of an Artificial Neural Network (ANN) and a tree-based ML algorithm are compared. Moreover, an expanding window scheme is utilized, gradually incorporating new data and emulating online learning. The results indicate the superior performance of the tree-based algorithm, with an average error of less than 3.5% across all cases and a maximum hourly error of 7%. The achieved accuracy confirms the method’s reliability even in dynamic scenarios where the integrated load of storage space and offices needs to be predicted. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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29 pages, 7343 KiB

Open AccessArticle

A Holistic Approach to Power Systems Using Innovative Machine Learning and System Dynamics

by Bibi Ibrahim, Luis Rabelo, Alfonso T. Sarmiento and Edgar Gutierrez-Franco

Energies 2023, 16(13), 5225; https://doi.org/10.3390/en16135225 - 07 Jul 2023

Cited by 1 | Viewed by 1334

Abstract

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, [...] Read more.

The digital revolution requires greater reliability from electric power systems. However, predicting the growth of electricity demand is challenging as there is still much uncertainty in terms of demographics, industry changes, and irregular consumption patterns. Machine learning has emerged as a powerful tool, particularly with the latest developments in deep learning. Such tools can predict electricity demand and, thus, contribute to better decision-making by energy managers. However, it is important to recognize that there are no efficient methods for forecasting peak demand growth. In addition, features that add complexity, such as climate change and economic growth, take time to model. Therefore, these new tools can be integrated with other proven tools that can be used to model specific system structures, such as system dynamics. This research proposes a unique framework to support decision-makers in dealing with daily activities while attentively tracking monthly peak demand. This approach integrates advances in machine learning and system dynamics. This integration has the potential to contribute to more precise forecasts, which can help to develop strategies that can deal with supply and demand variations. A real-world case study was used to comprehend the needs of the environment and the effects of COVID-19 on power systems; it also helps to demonstrate the use of leading-edge tools, such as convolutional neural networks (CNNs), to predict electricity demand. Three well-known CNN variants were studied: a multichannel CNN, CNN-LSTM, and a multi-head CNN. This study found that the multichannel CNN outperformed all the models, with an R² of 0.92 and a MAPE value of 1.62% for predicting the month-ahead peak demand. The multichannel CNN consists of one main model that processes four input features as a separate channel, resulting in one feature map. Furthermore, a system dynamics model was introduced to model the energy sector’s dynamic behavior (i.e., residential, commercial, and government demands, etc.). The calibrated model reproduced the historical data curve fairly well between 2005 and 2017, with an R² value of 0.94 and a MAPE value of 4.8%. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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21 pages, 3386 KiB

Open AccessArticle

Intelligent Micro-Cogeneration Systems for Residential Grids: A Sustainable Solution for Efficient Energy Management

by Daniel Cardoso, Daniel Nunes, João Faria, Paulo Fael and Pedro D. Gaspar

Energies 2023, 16(13), 5215; https://doi.org/10.3390/en16135215 - 06 Jul 2023

Cited by 1 | Viewed by 953

Abstract

This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility [...] Read more.

This paper presents an optimization approach for Micro-cogeneration systems with internal combustion engines integrated into residential grids, addressing power demand failures caused by intermittent renewable energy sources. The proposed method leverages machine learning techniques, control strategies, and grid data to improve system flexibility and efficiency in meeting electricity and domestic hot water demands. Historical residential grid data were analysed to develop a machine learning-based demand prediction model for electricity and hot water. Thermal energy storage was integrated into the Micro-cogeneration system to enhance flexibility. An optimization model was created, considering efficiency, emissions, and cost while adapting to real-time demand changes. A control strategy was designed for the flexible operation of the Micro-cogeneration system, addressing excess thermal energy storage and resource allocation. The proposed solution’s effectiveness was validated through simulations, with results demonstrating the Micro-cogeneration system’s ability to efficiently address high electricity and hot water demand periods while mitigating power demand failures from renewable energy sources. The research presents a novel approach with the potential to significantly improve grid resilience, energy efficiency, and renewable energy integration in residential grids, contributing to more sustainable and reliable energy systems. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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22 pages, 5829 KiB

Open AccessFeature PaperArticle

Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models

by Kristin Wode, Tom Strube, Eva Schischke, Markus Hadam, Sarah Pabst and Annedore Mittreiter

Energies 2023, 16(3), 1525; https://doi.org/10.3390/en16031525 - 03 Feb 2023

Viewed by 1245

Abstract

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences [...] Read more.

Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in the case of PHES, the dependency of the charging and discharging efficiency on the power is significant, but no further influencing factor has to be considered for accurate modeling (0.946 ≤ R² ≤ 0.988) of the efficiency. It is concluded that the presented toolchain is suitable for other storage technologies as well, including the analysis of aging behavior. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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28 pages, 3027 KiB

Open AccessArticle

A Machine Learning-Based Method for Identifying Critical Distance Relays for Transient Stability Studies

by Ramin Vakili and Mojdeh Khorsand

Energies 2022, 15(23), 8841; https://doi.org/10.3390/en15238841 - 23 Nov 2022

Cited by 2 | Viewed by 1262

Abstract

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system [...] Read more.

Protective relays play a crucial role in defining the dynamic responses of power systems during and after faults. Therefore, modeling protective relays in stability studies is crucial for enhancing the accuracy of these studies. Modeling all the relays in a bulk power system is a challenging task due to the limitations of stability software and the difficulties of keeping track of the changes in the setting information of these relays. Distance relays are one of the most important protective relays that are not properly modeled in current practices of stability studies. Hence, using the Random Forest algorithm, a fast machine learning-based method is developed in this paper that identifies the distance relays required to be modeled in stability studies of a contingency, referred to as critical distance relays (CDRs). GE positive sequence load flow analysis (PSLF) software is used to perform stability studies. The method is tested using 2018 summer peak load data of Western Electricity Coordinating Council (WECC) for various system conditions. The results illustrate the great performance of the method in identifying the CDRs. They also show that to conduct accurate stability studies, only modeling the CDRs suffices, and there is no need for modeling all the distance relays. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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21 pages, 3844 KiB

Open AccessArticle

Critical Reliability Improvement Using Q-Learning-Based Energy Management System for Microgrids

by Lizon Maharjan, Mark Ditsworth and Babak Fahimi

Energies 2022, 15(23), 8779; https://doi.org/10.3390/en15238779 - 22 Nov 2022

Cited by 1 | Viewed by 954

Abstract

This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions [...] Read more.

This paper presents a power distribution system that prioritizes the reliability of power to critical loads within a community. The proposed system utilizes reinforcement learning methods (Q-learning) to train multi-port power electronic interface (MPEI) systems within a community of microgrids. The primary contributions of this article are to present a system where Q-learning is successfully integrated with MPEI to reduce the impact of power contingencies on critical loads and to explore the effectiveness of the subsequent system. The feasibility of the proposed method has been proven through simulation and experiments. It has been demonstrated that the proposed method can effectively improve the reliability of the local power system—for a case study where 20% of the total loads are classified as critical loads, the system average interruption duration index (SAIDI) has been improved by 75% compared to traditional microgrids with no load schedule. Full article

(This article belongs to the Special Issue Machine Learning and Data Based Optimization for Smart Energy Systems)

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