Energies

24 pages, 7681 KiB

Open AccessArticle

Estimation of the Optimum Tilt Angle of Solar PV Panels to Maximize Incident Solar Radiation in Libya

by Alhassan Ali Teyabeen and Faisal Mohamed

Energies 2024, 17(23), 5891; https://doi.org/10.3390/en17235891 (registering DOI) - 23 Nov 2024

The most significant factor affecting the performance of a solar photovoltaic (PV) system is its tilt angle. It determines the amount of incident solar energy at the panel surface. In this paper, the optimum tilt angle of solar PV panels is estimated based [...] Read more.

The most significant factor affecting the performance of a solar photovoltaic (PV) system is its tilt angle. It determines the amount of incident solar energy at the panel surface. In this paper, the optimum tilt angle of solar PV panels is estimated based on measured data recorded in twelve major cities in Libya by changing the panel’s tilt angle from

0^{\circ}

up to

90^{\circ}

in steps of

1^{\circ}

and searching for the corresponding maximum daily total solar radiation. A non-linear regression technique was applied to establish six empirical models to determine the optimum tilt angle in Libya. The accuracy of the models was evaluated using statistical criteria such as Taylor diagrams, root mean square error, mean bias error, and correlation coefficient. The results demonstrated that the monthly optimum tilt angle increased during the winter and decreased during the summer varying from

0^{\circ}

to

59^{\circ}

. In addition, both third-order polynomial and Fourier models presented the best efficiency in estimating the optimum tilt angle with a correlation coefficient of 0.9943. The percent gain in average yearly solar energy received at the monthly optimum tilt angle varies from 12.43% to 17.24% for all studied sites compared to the horizontal surface. Full article

(This article belongs to the Special Issue Energy Performance of Photovoltaic Systems)

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23 pages, 5834 KiB

Open AccessArticle

Sensitivities of Geometric Parameters and Inlet Conditions on the Flow-Heat Characteristics of the Precooler in SABRE

by Weiwei Cui, Zongming Li, Xiaorong Xiang, Guoli Pang and Laishun Yang

Energies 2024, 17(23), 5890; https://doi.org/10.3390/en17235890 (registering DOI) - 23 Nov 2024

Abstract

Abstract: A physical model for the precooler in a SABRE engine was established, and both the influencing degrees and physical mechanisms of inlet conditions and geometric parameters on the flow-heat transfer characteristics of the precooler unit were explored through parametric analysis and sensitivity [...] Read more.

Abstract: A physical model for the precooler in a SABRE engine was established, and both the influencing degrees and physical mechanisms of inlet conditions and geometric parameters on the flow-heat transfer characteristics of the precooler unit were explored through parametric analysis and sensitivity analysis, respectively. The results demonstrated that the air massflow rate exerted the greatest influence on the total pressure loss coefficient of the precooler unit due to its significant impact on airflow velocity and thermal loading. The inlet total temperature had a dominant effect on heat transfer rate, owing to its substantial influence on air viscosity and thermal loading. Furthermore, both alone and in combination with air massflow rate, inlet total pressure exhibited a greater impact on total pressure loss of precooler unit compared to inlet total temperature and its coupling effects with air massflow rate. In contrast, both air massflow rate and its coupling effects with inlet total temperature significantly affected the heat transfer rate. On the other hand, tube spacing had the most significant impact on both the total pressure loss coefficient and heat transfer rate due to its substantial influence on air throughflow area and acceleration between microtubes. Moreover, the number of tube rows played a predominant role in determining the heat transfer rate of the precooler unit as it caused significant changes in the contact area between the hot air and the microtubes, as well as airflow velocity. However, only two geometric interaction terms (corresponding to interactions between the number of tube rows and tube spacing, as well as between tube spacing and tube diameter) significantly affected the total pressure loss coefficient, and no interaction term was found to be significant for influencing the heat transfer rate. Eventually, two optimal schemes involving inlet conditions and geometric parameters were established to enhance the flow-heat transfer characteristics of the precooler unit. Full article

(This article belongs to the Collection Advances in Heat Transfer Enhancement)

25 pages, 8176 KiB

Open AccessArticle

Study on the Resonance Characteristics and Active Damping Suppression Strategies of Multi-Inverter Grid-Connected Systems Under Weak Grid Conditions

by Tianhao Hou, Yunhao Jiang and Zishuo Cai

Energies 2024, 17(23), 5889; https://doi.org/10.3390/en17235889 (registering DOI) - 23 Nov 2024

Abstract

When a multi-inverter grid-connected system is influenced by the parasitic parameters of LCL-type inverters and the impedance of the connected system’s lines, its resonance characteristics become more complex and difficult to predict. For LCL-type multi-inverter grid-connected systems, a mathematical model that considers the [...] Read more.

When a multi-inverter grid-connected system is influenced by the parasitic parameters of LCL-type inverters and the impedance of the connected system’s lines, its resonance characteristics become more complex and difficult to predict. For LCL-type multi-inverter grid-connected systems, a mathematical model that considers the effects of parasitic parameters and line impedance has been established, leading to the derivation of the system’s Norton equivalent circuit and a general expression for the inverter output current. The resonance characteristics of multi-inverter grid-connected systems composed of inverters with the same and different parameters were analyzed under the influence of parasitic parameters and line impedance. To suppress the resonance in multi-inverter grid-connected systems and address the issue of traditional PI control not meeting grid requirements for LCL-type grid-connected inverters, a strategy combining superhelical sliding mode control with active damping was adopted. To verify the practical performance of the adopted resonance suppression strategy in complex environments, a grid-connected system model containing two LCL-type inverters was constructed using the MATLAB/Simulink software platform, followed by simulation analysis. The simulation results strongly confirm the feasibility and effectiveness of the adopted resonance suppression strategy, considering the effects of parasitic parameters and line impedance. This strategy demonstrates significant suppression effects in addressing resonance issues caused by parasitic parameters and line impedance, effectively enhancing the quality of grid-connected electrical energy. Full article

(This article belongs to the Section F: Electrical Engineering)

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23 pages, 820 KiB

Open AccessArticle

Navigating and Overcoming Barriers to Digital Energy Transition for Carbon Neutrality in China

by Shuao Sun and Sheeraz Ahmed

Energies 2024, 17(23), 5888; https://doi.org/10.3390/en17235888 (registering DOI) - 23 Nov 2024

Abstract

As China strives for carbon neutrality, the transition to digital energy systems presents both significant opportunities and formidable challenges. This study investigates the key barriers hindering this transition and the urgent need for effective strategies to address them, raising the critical research question: [...] Read more.

As China strives for carbon neutrality, the transition to digital energy systems presents both significant opportunities and formidable challenges. This study investigates the key barriers hindering this transition and the urgent need for effective strategies to address them, raising the critical research question: What are the main obstacles to digital energy adoption in China, and how can these challenges be overcome? In this study, the fuzzy AHP method has been utilized to prioritize barriers and fuzzy WASPAS to evaluate the strategies. Using fuzzy AHP, we found that stakeholder and governance barriers are the most critical, emphasizing issues like misalignment among stakeholders and governance challenges. Following this, financial constraints and technological limitations emerged as other significant barriers, highlighting the need for improved financing mechanisms and robust infrastructure. Through fuzzy WASPAS analysis, the top strategies identified are enhancing public awareness and capacity-building programs, strengthening governance and anti-corruption measures, and increasing investment in green finance. The results emphasize the importance of tackling governance and financial issues alongside technological advancements. Policy implications and recommendations are provided to guide China’s digital energy transition, with suggestions for future research focused on broader regional comparisons and the integration of emerging technologies. Full article

(This article belongs to the Special Issue Energy Markets and Energy Economy)

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27 pages, 1058 KiB

Open AccessArticle

Optimal Coordination of Directional Overcurrent Relays in Microgrids Considering European and North American Curves

by León F. Serna-Montoya, Sergio D. Saldarriaga-Zuluaga, Jesús M. López-Lezama and Nicolás Muñoz-Galeano

Energies 2024, 17(23), 5887; https://doi.org/10.3390/en17235887 (registering DOI) - 23 Nov 2024

Abstract

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs [...] Read more.

Protecting AC microgrids (MGs) is a challenging task due to their dual operating modes—grid-connected and islanded—which cause sudden variations in fault currents. Traditional protection methods may no longer ensure network security. This paper presents a novel approach to protection coordination in AC MGs using non-standard features of directional over-current relays (DOCRs). Three key optimization variables are considered: Time Multiplier Setting (TMS), the plug setting multiplier’s (PSM) maximum limit, and the standard characteristic curve (SCC). The proposed model is formulated as a mixed-integer nonlinear programming problem and solved using four metaheuristic techniques: the genetic algorithm (GA), Imperialist Competitive Algorithm (ICA), Harmonic Search (HS), and Firefly Algorithm (FA). Tests on a benchmark IEC MG with distributed generation and various operating modes demonstrate that this approach reduces coordination times compared to existing methods. This paper’s main contributions are threefold: (1) introducing a methodology for assessing the optimal performance of different standard curves in MG protection; (2) utilizing non-standard characteristics for optimal coordination of DOCRs; and (3) enabling the selection of curves from both North American and European standards. This approach improves trip time performance across multiple operating modes and topologies, enhancing the reliability and efficiency of MG protection systems. Full article

(This article belongs to the Section F3: Power Electronics)

23 pages, 7332 KiB

Open AccessArticle

Environmental Impact of Electrification on Local Public Transport: Preliminary Study

by Daniele Martini, Pietro Bezzini and Michela Longo

Energies 2024, 17(23), 5886; https://doi.org/10.3390/en17235886 (registering DOI) - 23 Nov 2024

Abstract

The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares [...] Read more.

The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares emissions from a diesel bus and an electric bus on a specific route in a small town in central Italy. Key findings reveal that electric buses significantly reduce local exhaust emissions but are not entirely emission-free, considering the full life cycle, including electricity generation. The Well-to-Wheel analysis shows lower

{CO}_{2}

emissions for the electric bus compared with the diesel bus, with a substantial part of the emissions occurring at power generation facilities. Non-exhaust emissions, especially Total Suspended Particles, are similar for both bus types. This study highlights the advantages of adopting electric buses in urban areas to decrease local air pollution and greenhouse gas emissions. However, it also underscores the importance of cleaner electricity generation methods to fully leverage the environmental benefits of electric vehicles. The findings provide valuable insights for decision makers and urban planners in developing sustainable urban transportation systems. Full article

(This article belongs to the Special Issue Advancements in Smart Electric Mobility Systems: Integration of Renewable Energy and Energy Storage)

15 pages, 3301 KiB

Open AccessArticle

Analysis of the Energy and Emission Performance of an Automatic Biomass Boiler in the Context of Efficient Heat Management

by Adam Nocoń, Artur Jachimowski, Wacław Koniuch, Grzegorz Pełka, Wojciech Luboń, Paweł Kubarek, Marta Jach-Nocoń and Dominika Dawiec

Energies 2024, 17(23), 5885; https://doi.org/10.3390/en17235885 (registering DOI) - 23 Nov 2024

Abstract

This paper presents the results of an examination of an automatic biomass boiler identifying its strengths and weaknesses and computing its seasonal energy and emission parameters. The boiler was found to meet the energy and emission requirements for distribution in Poland. The boiler [...] Read more.

This paper presents the results of an examination of an automatic biomass boiler identifying its strengths and weaknesses and computing its seasonal energy and emission parameters. The boiler was found to meet the energy and emission requirements for distribution in Poland. The boiler is characterised by good heating efficiency and low dust and carbon monoxide emissions. The aim of this paper is to provide and analyse these parameters, and by doing so classify it in the context of its competitors. The average heating output is 26.86 kW and the thermal efficiency is 87.97%. Carbon monoxide emissions are very low (22.71 mg/m³). However, nitrogen oxide emissions (187.6 mg/m³) can be a problem. Filters made out of metalworking waste, i.e., machining shavings, significantly improve the boiler performance, contributing to an increased heat output and efficiency and reduced dust emissions. Compared with other solutions available in the market, the boiler compares favourably in terms of dust and carbon monoxide emissions and is also characterised by similar efficiency, especially with the filters in place. Regarding the context of thermal energy management, the appliance under investigation demonstrates not only favourable energy and emission parameters, but also the potential for the efficient use of thermal energy, which can bring additional economic and environmental benefits. Full article

(This article belongs to the Special Issue Bio-Energy and Its Sustainable Utilization)

21 pages, 692 KiB

Open AccessArticle

Optimal Operation of Generation Company’s Participating in Multiple Markets with Allocation and Exchange of Energy-Consuming Rights and Carbon Credits

by Hanyu Yang, Mengru Ding, Muyao Li, Shilei Wu, Ye Zhang and Xun Dou

Energies 2024, 17(23), 5884; https://doi.org/10.3390/en17235884 (registering DOI) - 23 Nov 2024

Viewed by 5

Abstract

The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the [...] Read more.

The proposal of the energy-consuming right (ECR) market may lead to generation companies (GenCos) facing the risk of being overcharged due to the inaccurate calculation of carbon emission reduction, since it claims the same credit as the carbon market does. To estimate the carbon emission reduction accurately for the GenCos that participate in electricity, carbon, and ECR markets simultaneously, this paper proposes a market framework where a flexible exchange mechanism between the ECR and carbon markets is specially considered. To investigate the influence of the allocation and exchange of ECR and carbon credits on the behavior of GenCos that participate in multi-type markets, a bi-level model based on the leader–follower game theory is proposed. In the upper level of the proposed model, a decision problem for maximizing the profit of GenCos is developed, which is especially constrained to the primary allocation of ECR and carbon credits. While the multi-type market clearing model and an exchange mechanism between the ECR and carbon credits are proposed in the lower level of the model. The bi-level problem is converted into the mathematical program with equilibrium constraints (MPECs) through the Karush–Kuhn–Tucker (KKT) condition to solve. The results illustrate that the interaction between the ECR market and the carbon market can improve the energy efficiency and reduce the carbon emissions of GenCos. Full article

(This article belongs to the Topic Energy and Environmental Situation Awareness)

19 pages, 3137 KiB

Open AccessArticle

Smart Transformer-Assisted Frequency Control Mechanism for RES Penetrated Power Systems Considering Metaheuristic-Based Secondary Controller

by Chakka Bapi Ayyappa Raju, Sanjoy Debbarma and Rayapudi Srinivasa Rao

Energies 2024, 17(23), 5883; https://doi.org/10.3390/en17235883 (registering DOI) - 23 Nov 2024

Viewed by 2

Abstract

Abstract: The gradual replacement of conventional generators with variable renewable energy sources (RES) will reduce their online frequency regulation (FR) resources and degrade their overall frequency control capabilities. Although various inertia emulation methods exist, shaping load consumption is considered a more effective strategy [...] Read more.

Abstract: The gradual replacement of conventional generators with variable renewable energy sources (RES) will reduce their online frequency regulation (FR) resources and degrade their overall frequency control capabilities. Although various inertia emulation methods exist, shaping load consumption is considered a more effective strategy during emergency conditions than under-frequency load shedding. Managing loads following frequency excursions can support grid stability owing to rapid power response. In this context, a Smart Transformer (ST)-based FR framework for a RES-penetrated power system is studied in this paper. The ST, with its distinctive features, effectively shapes the load profile through online load sensitivity identification-based control, aiding in the stabilization of grid frequency. This paper also proposes a tilt integral second-order double derivative (TIDD²) controller for a secondary loop whose parameters are optimized using the Learner Performance-based Behavior (LPB) algorithm. A thorough investigation reveals that the response from ST controlling the voltage-dependent load in the presence of TIDD² controllers can greatly enhance system performance by damping oscillations and peak deviations. In addition, the performance of Proportional–Integral–Derivative and TIDD²considering ST in the primary loop is compared to delineate the robustness of the LPB-based TIDD²controller. It is found that the proposed control scheme offers greater controllability and flexibility, enhancing the system’s dynamic performance. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

15 pages, 1457 KiB

Open AccessArticle

Study on the Co-Combustion Behavior of Municipal Sludge and Bagasse: Evaluation of Ultrasonic Pretreatment

by Shiwen Fang, Lifa Zhang, Shu Chen, Ziyuan Xie, Lanke Wang, Luyou Chen, Wei Liang and Pengfei Lei

Energies 2024, 17(23), 5882; https://doi.org/10.3390/en17235882 (registering DOI) - 23 Nov 2024

Viewed by 68

Abstract

Currently, the production of sludge in China is on the rise annually, and the co-combustion of sludge with biomass for power and heat generation represents a viable method for the bulk treatment of sludge. In this study, we examined the combustion characteristics of [...] Read more.

Currently, the production of sludge in China is on the rise annually, and the co-combustion of sludge with biomass for power and heat generation represents a viable method for the bulk treatment of sludge. In this study, we examined the combustion characteristics of municipal sludge (MS), bagasse (BA), and their blends using thermogravimetric analysis. Orthogonal experiments were conducted to assess the impact of ultrasonic pretreatment on the co-combustion properties of MS and BA. Prior to ultrasonic pretreatment, the combustion of BA was characterized by three distinct stages, while MS exhibited two stages. At a 30% MS ratio, the promotional interaction between BA and MS was most pronounced. Following ultrasonic pretreatment, the combustion of BA was simplified to two stages. With a 10% MS mass ratio, ultrasonic pretreatment enhanced the comprehensive combustion characteristic index, thereby improving the combustion performance of the mixture. The activation energy increased post-pretreatment, particularly when the MS content was 50%. Under the conditions of 45 kHz frequency, 500 W power, 3 h duration, and a 10% MS blending ratio, the mixture displayed reduced mass residue, elevated reaction rates, and superior combustion efficiency. This research aims to introduce a novel approach to the harmless disposal, volume reduction, and resourceful utilization of sludge. Full article

(This article belongs to the Section I2: Energy and Combustion Science)

20 pages, 3512 KiB

Open AccessArticle

Multi-Level Decomposition and Interpretability-Enhanced Air Conditioning Load Forecasting Study

by Xinting Yang, Ling Zhang, Hong Zhao, Wenhua Zhang, Chuan Long, Gang Wu, Junhao Zhao and Xiaodong Shen

Energies 2024, 17(23), 5881; https://doi.org/10.3390/en17235881 (registering DOI) - 23 Nov 2024

Viewed by 65

Abstract

This study seeks to improve the accuracy of air conditioning load forecasting to address the challenges of load management in power systems during high-temperature periods in the summer. Given the limitations of traditional forecasting models in capturing different frequency components and noise within [...] Read more.

This study seeks to improve the accuracy of air conditioning load forecasting to address the challenges of load management in power systems during high-temperature periods in the summer. Given the limitations of traditional forecasting models in capturing different frequency components and noise within complex load sequences, this paper proposes a multi-level decomposition forecasting model using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sample entropy (SE), variational mode decomposition (VMD), and long short-term memory (LSTM). First, CEEMDAN is used for the preliminary decomposition of the raw air-conditioning load series, with modal components aggregated by sample entropy to generate high-, medium-, and low-frequency subsequences. VMD then performs a secondary decomposition on the high-frequency subsequence to reduce its complexity, while LSTM is applied to each subsequence for prediction. The final prediction result of the air-conditioning load is obtained through reconstruction. To validate model performance, this paper uses air-conditioning load data from Nanchong City and Sichuan Province, for experimental analysis. Results show that the proposed method significantly outperforms the LSTM model without decomposition and other benchmark models in prediction accuracy, with the Root Mean Square Error (RMSE) reductions ranging from 40.26% to 74.18% and the Modified Mean Absolute Percentage Error (MMAPE) reductions from 37.75% to 73.41%. By employing the SHAP (Shapley additive explanations) method for both global and local interpretability, the model reveals the influence of key factors, such as historical load and temperature, on load forecasting. The decomposition and aggregation approach introduced in this paper substantially enhances forecasting accuracy, providing a scientific foundation for power system load management and dispatch. Full article

(This article belongs to the Topic Short-Term Load Forecasting)

35 pages, 1675 KiB

Open AccessReview

Integrating Social Aspects into Energy System Modelling Through the Lens of Public Perspectives: A Review

by Riasad Amin, Deepika Mathur, David Ompong and Kerstin K. Zander

Energies 2024, 17(23), 5880; https://doi.org/10.3390/en17235880 (registering DOI) - 23 Nov 2024

Viewed by 77

Abstract

The energy system model (ESM) predominantly emphasises techno-economic factors and often overlooks the essential social dimensions that are crucial for successful energy transitions. This review investigates the integration of social aspects into energy system models (ESMs) and explores approaches for incorporating public perspectives. [...] Read more.

The energy system model (ESM) predominantly emphasises techno-economic factors and often overlooks the essential social dimensions that are crucial for successful energy transitions. This review investigates the integration of social aspects into energy system models (ESMs) and explores approaches for incorporating public perspectives. Through a systematic literature review utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses approach (PRISMA), 79 relevant publications were identified. The analysis revealed that while some studies considered socio-economic and socio-environmental elements, these efforts are frequently insufficient to fully comprehend social dynamics. The review highlights the significance of public engagement in ESMs, with 29 studies demonstrating some form of public participation. However, current engagement strategies are often limited to information sharing and consultation, with minimal strong collaboration. This study advocates for the implementation of transparent collaborative approaches in ESMs, including co-creation, active participation, and iterative processes, to enhance the comprehensiveness and societal relevance of models. It also addresses challenges, such as time constraints and the conversion of qualitative inputs into quantitative parameters. The review concludes by calling for further research to develop transparent, iterative frameworks for collaborative approaches in ESMs, emphasising the inclusion of vulnerable population perspectives to ensure equitable energy transitions and more effective, socially acceptable outcomes. Full article

(This article belongs to the Section C: Energy Economics and Policy)

28 pages, 1407 KiB

Open AccessArticle

Methodology for Optimal Design of Active Fluid Film Bearings Considering Their Power Losses, Stability and Controllability: Theory and Experiment

by Denis Shutin, Alexander Fetisov, Maksim Litovchenko, Aleksey Rodichev, Yuri Kazakov and Leonid Savin

Energies 2024, 17(23), 5879; https://doi.org/10.3390/en17235879 (registering DOI) - 23 Nov 2024

Viewed by 72

Abstract

This study addresses the problem of the automated synthesis of active fluid film bearings optimized for their adjustable design for new generations of turbomachines. The developed methodology proposes a criterion describing the ability of a bearing’s mechanical design to effectively implement control actions [...] Read more.

This study addresses the problem of the automated synthesis of active fluid film bearings optimized for their adjustable design for new generations of turbomachines. The developed methodology proposes a criterion describing the ability of a bearing’s mechanical design to effectively implement control actions along with its energy efficiency and stability properties considered in a solved multi-objective optimization problem. The design process of actively lubricated journal bearings was investigated in the context of the proposed approach. A multi-objective optimization problem was solved with heuristic algorithms. An analysis of the results obtained with the MOGA and MOPSO algorithm revealed their shortcomings emerging in such problems. The MOPSO algorithm was improved to expand the range and uniformity of the distribution of solutions in the resulting Pareto set and to speed up calculations. Four bearing configurations with significantly different properties were selected from the obtained set of solutions, manufactured and experimentally tested, showing the good agreement between the actual parameters and those set during the design procedure. The results substantiate the applicability of the proposed theoretical and computational tools for designing active fluid film bearings with pre-specified properties to meet the comprehensive requirements of the energy efficiency, reliability and service life of turbomachines. Full article

(This article belongs to the Special Issue Flow Control and Optimization in Power Systems)

15 pages, 851 KiB

Open AccessArticle

Electrochemical Storage and Flexibility in Transfer Capacities: Strategies and Uses for Vulnerable Power Grids

by Gustavo Adolfo Gómez-Ramírez, Luis García-Santander, José Rodrigo Rojas-Morales, Markel Lazkano-Zubiaga and Carlos Meza

Energies 2024, 17(23), 5878; https://doi.org/10.3390/en17235878 (registering DOI) - 23 Nov 2024

Viewed by 103

Abstract

The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of [...] Read more.

The integration of renewable energy sources into electrical power systems presents enormous challenges in technical terms, especially with energy storage. Battery electrochemical storage systems (BESSs) are becoming a crucial solution for reducing the intermittency of renewable energy supply and enhance the stability of power networks. Nonetheless, its extensive implementation confronts constraints, including expense, life expectancy, and energy efficiency. Simultaneously, these technologies present prospects for improved energy management, increase the hosting capacity of renewable energy, and diminish reliance on fossil fuels. This paper investigates the obstacles of integrating electrochemical storage into electrical power systems, explores solutions to use its promise for creating more resilient and sustainable grids, and presents a method for the size estimation and strategic allocation of electrochemical energy storage systems (EESSs). The aim is to improve grid voltage profiles, manage demand response, increase the adoption of renewable energy resources, enhance power transfer among various areas, and subsequently improve the stability of a power system during large disturbances. The methodology utilizes a multi-stage optimization process based on economic considerations supported by dynamic simulation. This methodology was tested employing a validated dynamic model of the Interconnected Electrical System of the Central American Countries (SIEPAC). The system experienced multiple significant blackouts in recent years, primarily due to the increasing amount of renewable energy generation without adequate inertial support and limited power transfer capabilities among countries. Based on the results of using the technique, EESSs can effectively lower the risk of instability caused by an imbalance between power generation and demand during extreme situations, as seen in past event reports. Based on economical constraints, it has been determined that the cost of installing EESSs for the SIEPAC, which amounts to 1200 MWh/200 MW, is 140.91 USD/MWh. Full article

(This article belongs to the Special Issue Challenges and Opportunities for Renewable Energy)

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Methodological framework for improving the flexibility of transfer capabilities among various areas. Full article ">Figure 2
Interconnection voltage behaviour without electrochemical storage. Full article ">Figure 3
Interconnection power behaviour without electrochemical storage. Full article ">Figure 4
Seven states sequence of the collapse explained in case study. Full article ">Figure 5
Interconnection frequency behaviour without electrochemical storage. Full article ">Figure 6
Siting and sizing for electrochemical storage in Central American power system according to <a href="#energies-17-05878-t001" class="html-table">Table 1</a>. Full article ">Figure 7
Interconnection frequency behaviour with electrochemical storage. Full article ">Figure 8
Interconnection power behaviour with electrochemical storage. Full article ">Figure 9
Sequence of power system states shown in case study and proposed solution. Full article ">

24 pages, 3781 KiB

Open AccessArticle

Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework

by Dong Ha Choi, Wei Li and Albert Y. Zomaya

Energies 2024, 17(23), 5877; https://doi.org/10.3390/en17235877 (registering DOI) - 23 Nov 2024

Viewed by 157

Abstract

This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain. By incorporating time-related attributes as conditioning information, our method ensures the [...] Read more.

This paper presents a novel framework that integrates Conditional Generative Adversarial Networks (CGANs) and TimeGAN to generate synthetic Building-Integrated Photovoltaic (BIPV) power data, addressing the challenge of data scarcity in this domain. By incorporating time-related attributes as conditioning information, our method ensures the preservation of chronological order and enhances data fidelity. A tailored learning scheme is implemented to capture the unique characteristics of solar power generation, particularly during sunrise and sunset. Comprehensive evaluations demonstrate the framework’s effectiveness in generating high-quality synthetic data, evidenced by a 79.58% improvement in the discriminative score and a 13.46% improvement in the predictive score compared to TimeGAN. Moreover, integrating the synthetic data into forecasting models resulted in up to 23.56% improvement in mean absolute error (MAE) for BIPV power generation predictions. These results highlight the potential of our framework to enhance prediction accuracy and optimize data utilization in renewable energy applications. Full article

(This article belongs to the Section A2: Solar Energy and Photovoltaic Systems)

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17 pages, 932 KiB

Open AccessArticle

Exploring the Preference for Discrete over Continuous Reinforcement Learning in Energy Storage Arbitrage

by Jaeik Jeong, Tai-Yeon Ku and Wan-Ki Park

Energies 2024, 17(23), 5876; https://doi.org/10.3390/en17235876 - 22 Nov 2024

Viewed by 282

Abstract

In recent research addressing energy arbitrage with energy storage systems (ESSs), discrete reinforcement learning (RL) has often been employed, while the underlying reasons for this preference have not been explicitly clarified. This paper aims to elucidate why discrete RL tends to be [...] Read more.

In recent research addressing energy arbitrage with energy storage systems (ESSs), discrete reinforcement learning (RL) has often been employed, while the underlying reasons for this preference have not been explicitly clarified. This paper aims to elucidate why discrete RL tends to be more suitable than continuous RL for energy arbitrage problems. When using continuous RL, the charging and discharging actions determined by the agent often exceed the physical limits of the ESS, necessitating clipping to the boundary values. This introduces a critical issue where the learned actions become stuck at the state of charge (SoC) boundaries, hindering effective learning. Although recent advancements in constrained RL offer potential solutions, their application often results in overly conservative policies, preventing the full utilization of ESS capabilities. In contrast, discrete RL, while lacking in granular control, successfully avoids these two key challenges, as demonstrated by simulation results showing superior performance. Additionally, it was found that, due to its characteristics, discrete RL more easily drives the ESS towards fully charged or fully discharged states, thereby increasing the utilization of the storage system. Our findings provide a solid justification for the prevalent use of discrete RL in recent studies involving energy arbitrage with ESSs, offering new insights into the strategic selection of RL methods in this domain. Looking ahead, improving performance will require further advancements in continuous RL methods. This study provides valuable direction for future research in continuous RL, highlighting the challenges and potential strategies to overcome them to fully exploit ESS capabilities. Full article

(This article belongs to the Special Issue Tiny Machine Learning for Energy Applications)

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14 pages, 1430 KiB

Open AccessArticle

Combustion of Pelletized Coffee Residues for Bioenergy Valorization Within a Circular Economy Vision

by Vincenzo Civitarese, Angelo Del Giudice, Andrea Acampora, Elisa Fischetti, Thomas Gasperini, Carmine De Francesco, Giuseppe Toscano and Antonio Scarfone

Energies 2024, 17(23), 5875; https://doi.org/10.3390/en17235875 - 22 Nov 2024

Viewed by 277

Abstract

Coffee is one of the most widely consumed beverages in the world; the European Union alone consumes about 2.5 million tons of coffee per year. Yearly, millions of tons of coffee residues are generated, becoming an attractive material for circular economy flows. This [...] Read more.

Coffee is one of the most widely consumed beverages in the world; the European Union alone consumes about 2.5 million tons of coffee per year. Yearly, millions of tons of coffee residues are generated, becoming an attractive material for circular economy flows. This study explores the potential of utilizing pelletized coffee residues as sustainable bioenergy sources within the framework of a circular economy. The coffee residues, obtained from damaged capsules and pods from factories, were utilized in pure form or blended with sawdust at different percentages, then analyzed with respect to their physical and thermochemical parameters. The results indicate that unblended coffee residues exhibit favorable combustion properties with respect to heating value (18.84 MJ kg⁻¹), but also high concentrations of N (4.14%) compared to the conventional pellets obtained from other agricultural residues. The blending with woody material negatively affects both durability and bulk density, but simultaneously promotes a reduction in ash content (3.09%) and N content (1.94%). In general, this study confirmed the findings of previous scientific reports, highlighting that at least 50% blending with low-nitrogen biomasses is necessary to reach the marketability of the product. In addition, this study highlighted the criticality in terms of durability that these mixtures confer to the final product, emphasizing that future research should focus on optimizing the combination of these factors to improve the properties of the pellet. Full article

(This article belongs to the Special Issue In-Depth Investigations in Bioenergy)

23 pages, 5828 KiB

Open AccessArticle

Mapping Solar Global Radiation and Beam Radiation in Taiwan

by Tsung-En Hsieh and Keh-Chin Chang

Energies 2024, 17(23), 5874; https://doi.org/10.3390/en17235874 - 22 Nov 2024

Viewed by 251

Abstract

Data for solar radiation resources play a pivotal role in assessing the energy yield capability of solar applications. A nationwide database for the typical meteorological year from the 30 weather stations of the Central Weather Bureau (CWB) in Taiwan is used to determine [...] Read more.

Data for solar radiation resources play a pivotal role in assessing the energy yield capability of solar applications. A nationwide database for the typical meteorological year from the 30 weather stations of the Central Weather Bureau (CWB) in Taiwan is used to determine the spatial distribution of global radiation over the terrain of Taiwan. There is no available beam radiation information in daily reports from all CWB stations. Information on the diffuse fraction for all CWB stations is estimated using three available correlation models that account for topographical and geographical effects in Taiwan. The databases for beam radiation are generated using these estimated diffuse fractions. The mappings of global and beam radiation on the Taiwanese mainland are performed with databases from 24 CWB stations using the residual kriging method. There are no mappings of the remote islands, where six CWB stations are located. The databases for global and beam radiation for these six CWB stations are applied to nearby remote islands. The effects of topography and geography on the distributions of global and beam radiation are discussed. The spatial distributions of solar radiation presented are good scientific references for assessing the performances of solar energy systems in Taiwan. Full article

(This article belongs to the Special Issue Modern Technologies for Renewable Energy Development and Utilization: 3rd Edition)

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25 pages, 823 KiB

Open AccessReview

Recovery of Biogas and Other Valuable Bioproducts from Livestock Blood Waste: A Review

by Katarzyna Bułkowska and Magdalena Zielińska

Energies 2024, 17(23), 5873; https://doi.org/10.3390/en17235873 - 22 Nov 2024

Viewed by 219

Abstract

The anaerobic digestion (AD) of livestock blood represents a sustainable solution for the management of waste generated by the meat processing industry while simultaneously generating renewable energy. The improper treatment of livestock blood, which is rich in organic matter and nutrients, can result [...] Read more.

The anaerobic digestion (AD) of livestock blood represents a sustainable solution for the management of waste generated by the meat processing industry while simultaneously generating renewable energy. The improper treatment of livestock blood, which is rich in organic matter and nutrients, can result in environmental risks such as water pollution, soil degradation, and greenhouse gas emissions. This review examines a range of AD strategies, with a particular focus on technological advances in reactor design, pretreatment, and co-digestion, with the aim of optimizing process efficiency. While the high protein content of blood has the potential to enhance biogas production, challenges such as ammonia inhibition and process instability must be addressed. Innovations such as bio-carriers, thermal pretreatment, and co-digestion with carbon-rich substrates have demonstrated efficacy in addressing these challenges, resulting in stable operation and enhanced methane yields. The advancement of AD technologies is intended to mitigate the environmental impact of livestock blood waste and facilitate the development of a circular bioeconomy. Furthermore, the possibility of utilizing slaughterhouse blood for the recovery of valuable products, including proteins, heme iron, and bioactive peptides, was evaluated with a view to their potential applications in the pharmaceutical and food industries. Furthermore, the potential of utilizing protein-rich blood as a substrate for mixed culture fermentation in volatile fatty acid (VFA) biorefineries was explored, illustrating its viability in biotechnological applications. Full article

(This article belongs to the Special Issue Sustainable Biomass Energy Production and Utilization)

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16 pages, 4052 KiB

Open AccessArticle

Integration of Water Transfers in Hydropower Operation Planning

by Roberto Asano, Jr., Fabiana de Oliveira Ferreira, Jacyro Gramulia, Jr. and Patrícia Teixeira Leite Asano

Energies 2024, 17(23), 5872; https://doi.org/10.3390/en17235872 - 22 Nov 2024

Viewed by 152

Abstract

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, [...] Read more.

The rising demand for clean energy production due to climate change emphasizes the importance of optimizing water resources, particularly in countries with significant hydropower potential. Existing models for the Operational Planning of Hydropower Systems (HPSOP) typically focus on the natural flows of rivers, often overlooking the potential of water transfers between rivers and basins. To address this gap, this article employs an improved mathematical model of hydropower production, considering the adjustment of the water transfer in the operation schedule as an additional optimization variable. A customized meta-heuristic, named the Evolutionary Socio-Bio Inspired Technique (ESBIT), has been tailored to integrate water transfer mechanisms into the operational planning model. The proposed model was validated through a case study at the Henry Borden Complex in São Paulo, Brazil, using real power plant parameters and inflow data from the Brazilian system. The results obtained from the test case, both with and without water transfer, demonstrate that the proposed methodology effectively captures the operational characteristics of a system that allows water transfers between rivers or basins to optimize the available water resources and system costs. Full article

(This article belongs to the Section A3: Wind, Wave and Tidal Energy)

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22 pages, 859 KiB

Open AccessArticle

Intelligent Energy Management Systems in Industry 5.0: Cybersecurity Applications in Examples

by Barbara Wyrzykowska, Hubert Szczepaniuk, Edyta Karolina Szczepaniuk, Anna Rytko and Marzena Kacprzak

Energies 2024, 17(23), 5871; https://doi.org/10.3390/en17235871 - 22 Nov 2024

Viewed by 236

Abstract

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and [...] Read more.

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0. Full article

(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)

20 pages, 4057 KiB

Open AccessArticle

Cybersecurity in Smart Grids: Detecting False Data Injection Attacks Utilizing Supervised Machine Learning Techniques

by Anwer Shees, Mohd Tariq and Arif I. Sarwat

Energies 2024, 17(23), 5870; https://doi.org/10.3390/en17235870 - 22 Nov 2024

Viewed by 298

Abstract

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which [...] Read more.

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively. Full article

(This article belongs to the Section A1: Smart Grids and Microgrids)

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34 pages, 651 KiB

Open AccessReview

How Can We Achieve a Long-Term Effect of Serious Energy Games on the Change in Residential Electricity Demand?

by Anton Belinskiy, Ioannis Lampropoulos, Hossein Nasrollahi, Jan Dirk Fijnheer, Remco C. Veltkamp and Wilfried van Sark

Energies 2024, 17(23), 5869; https://doi.org/10.3390/en17235869 - 22 Nov 2024

Viewed by 299

Abstract

As global energy concerns escalate, there is a growing need for effective strategies to promote sustainable energy practices among individuals and communities. Gamification, the integration of game-design elements in non-game contexts, emerges as a promising tool to enhance user engagement and foster sustainable [...] Read more.

As global energy concerns escalate, there is a growing need for effective strategies to promote sustainable energy practices among individuals and communities. Gamification, the integration of game-design elements in non-game contexts, emerges as a promising tool to enhance user engagement and foster sustainable behaviour in energy management. In this review, we examine the theoretical aspects of gamification and its application in energy management in users’ households, highlighting its potential to transform repetitive or even monotonous tasks into engaging activities, focusing on studies that measure a long-term effect. We delve into various gamified elements adopted in long-term studies, such as feedback, social interactions, point systems, leader boards, narrative-driven challenges, etc., to understand their effect on user motivation and behavioural changes. From our set of studies, we found out that strong social game elements contribute the most to the long-term behaviour change of energy usage. One more condition of behaviour change is strong positive user satisfaction: the game should be engaging. We highlight the possible limitations of gamification in an energy management situation, a strong need for better practices of design and evaluation, and innovative approaches (such as DSM; Demand Side Management) in gamification for long-term engagement in household energy management. Full article

(This article belongs to the Topic Building Energy and Environment, 2nd Volume)

21 pages, 3427 KiB

Open AccessArticle

Electrical Model Analysis for Bifacial PV Modules Using Real Performance Data in Laboratory

by Valentina González Becerra, Patricio Valdivia-Lefort, Rodrigo Barraza and Jesús García García

Energies 2024, 17(23), 5868; https://doi.org/10.3390/en17235868 - 22 Nov 2024

Viewed by 298

Abstract

The new PV technologies, such as bifacial modules, bring the challenge of analyzing the response of numerical models and their fit to actual measurements. Thus, this study explores various models available in the literature for simulating the IV curve behavior of bifacial photovoltaic [...] Read more.

The new PV technologies, such as bifacial modules, bring the challenge of analyzing the response of numerical models and their fit to actual measurements. Thus, this study explores various models available in the literature for simulating the IV curve behavior of bifacial photovoltaic modules. The analysis contains traditional models, such as single and double-diode models, and empirical or analytical methodologies. Therefore, this paper proposes and implements a model performance assessment framework. This framework aims to establish a common basis for comparison and verify the applicability of each model by contrasting it with experimental data under controlled conditions of irradiance and temperature. The study utilizes bifacial modules of PERC+, HJT, and n-PERT technologies, tracing IV curves using a high-precision A+A+A+ solar simulator and conducting two sets of laboratory illumination measurements: single-sided and double-sided. In the first case, each face of the module is illuminated separately, while in the latter, the incident frontal illuminating light is reflected on a reflective surface. Experimental data obtained from these measurements are used to evaluate three different approximations for bifacial IV curve models in the case of double-sided illumination. The employed model for single-sided illumination is a single-diode model. The evaluation of various models revealed that shadowing from frames and junction boxes contributes to an increase in the error of modeled IV curves. However, among the three evaluated bifacial electrical models, one exhibited superior performance, with current errors approaching approximately

20 %

. To mitigate this discrepancy, a proposed methodology highlighted the significance of accurately estimating

I_{o}

, suggesting its potential to reduce errors. This research provides a foundation for comparing electrical models to identify their strengths and limitations, paving the way for the development of more accurate modeling approaches tailored to bifacial modules. The insights gained from this study are crucial for enhancing the precision of IV curve predictions under various illumination conditions, which is essential for optimizing bifacial module performance in real-world applications. Full article

(This article belongs to the Special Issue Recent Advances in Solar Thermal Technologies and Solar Energy Application)

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31 pages, 365 KiB

Open AccessArticle

Pathways to Carbon Neutrality: Integrating Energy Strategies, Policy, and Public Perception in the Face of Climate Change—A Global Perspective

by Artur Pawłowski and Paweł Rydzewski

Energies 2024, 17(23), 5867; https://doi.org/10.3390/en17235867 - 22 Nov 2024

Viewed by 287

Abstract

The significance of the research presented in this article lies in its comprehensive view of global efforts to achieve carbon neutrality, integrating technological, policy, and social aspects. The urgent need to address climate change requires the development of renewable energy sources, technological innovations, [...] Read more.

The significance of the research presented in this article lies in its comprehensive view of global efforts to achieve carbon neutrality, integrating technological, policy, and social aspects. The urgent need to address climate change requires the development of renewable energy sources, technological innovations, and public engagement in shaping climate policies. This article presents an interdisciplinary approach, combining scientific data on carbon dioxide emissions with public opinion research. It highlights the need for global cooperation, emphasizing that the responsibility for achieving carbon neutrality extends beyond governments to entire societies. The analysis includes empirical findings from the International Social Survey Programme (ISSP), providing insights into public perceptions of climate change and their influence on climate policies. The key findings of the research indicate that accelerating the development of renewable energy sources, such as solar and wind energy, and implementing carbon capture and storage (CCS) technologies are crucial. Public opinion plays a vital role in the success of these initiatives. The article demonstrates that international cooperation, particularly in supporting developing countries, is essential to overcoming barriers to the adoption of renewable energy. Ultimately, achieving carbon neutrality by 2050 is possible, but it requires not only technological innovations but also public support and coherent climate policies at both global and regional levels. Full article

(This article belongs to the Special Issue Green, Low-Carbon and Sustainable Development)

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20 pages, 1959 KiB

Open AccessArticle

Predictive Analytics for Energy Efficiency: Leveraging Machine Learning to Optimize Household Energy Consumption

by Piotr Powroźnik and Paweł Szcześniak

Energies 2024, 17(23), 5866; https://doi.org/10.3390/en17235866 - 22 Nov 2024

Viewed by 171

Abstract

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home [...] Read more.

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented. Full article

(This article belongs to the Special Issue Novel Energy Management Approaches in Microgrid Systems)

15 pages, 11340 KiB

Open AccessArticle

The Estimation of Power Losses in Composite Cores Excited by Harmonic Flux Density Waveforms

by Mariusz Najgebauer, Damian Gzieł, Jarosław Kalinowski and Branko Koprivica

Energies 2024, 17(23), 5865; https://doi.org/10.3390/en17235865 - 22 Nov 2024

Viewed by 179

Abstract

This paper presents a study of the empirical approach to the estimation of power losses in composite cores excited by harmonic flux density waveforms. Nowadays, magnetic cores operating in power electronic devices are excited by distorted (e.g., harmonic) flux density waveforms. Magnetic material [...] Read more.

This paper presents a study of the empirical approach to the estimation of power losses in composite cores excited by harmonic flux density waveforms. Nowadays, magnetic cores operating in power electronic devices are excited by distorted (e.g., harmonic) flux density waveforms. Magnetic material properties are mostly determined for sinusoidal waveforms of the magnetic flux density. However, these properties can vary significantly in the case of harmonic excitations, which affects the devices’ efficiency. The effect of harmonic flux density waveform parameters (amplitude ratio and phase angle) on the level of power losses in soft magnetic composites is analyzed. A simple, empirical model of harmonic losses based on standardized measurements, i.e., carried out for sinusoidal flux density waveforms, is modified and validated. It is revealed that the empirical approach to estimating harmonic losses can be applied to magnetic composites with satisfactory modelling results. Full article

(This article belongs to the Section F: Electrical Engineering)

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16 pages, 908 KiB

Open AccessArticle

Power System Transient Stability Assessment Based on Intelligent Enhanced Transient Energy Function Method

by Tianxiao Mo, Jun Liu, Jiacheng Liu, Guangyao Wang, Yuting Li and Kaiwei Lin

Energies 2024, 17(23), 5864; https://doi.org/10.3390/en17235864 - 22 Nov 2024

Viewed by 155

Abstract

The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria [...] Read more.

The development of power systems puts forward higher requirements for transient stability evaluations of power systems. The accuracy and timeliness of transient stability assessment are of great significance to the safe and stable operation of power systems. Traditional mechanistic judgment methods and criteria have strong interpretability, but they also have great limitations. They are still difficult to apply to complex power systems and are in urgent need of improvement. Artificial intelligence methods have high accuracy in stability judgment, but they have problems such as poor interpretability, and their stability judgment results are often difficult to explain. Based on the transient stability judgment mechanism of the response-driven transient energy function, this paper proposes a transient energy function stability judgment method based on a two-machine equivalent model and enhanced by a convolutional neural network. Firstly, the ST-kmeans method is used to cluster the generator sets, and the S-transformation is performed on the power angle changes of the generator sets to extract features. Then, the principal component analysis method is used to reduce the dimension of the feature data. Based on the k-means clustering method, the IEEE-39 node system generator synchronization units are grouped according to the power angle change trend of each generator after the fault. On the basis of the above methods, a two-machine equivalent model of the IEEE-39 node system is established, and the transient energy function of the two-machine system is derived. Based on the convolutional neural network, the critical energy is enhanced, and the fixed critical energy threshold is replaced by the corrected critical energy. The example results show that the transient stability prediction framework proposed in this paper can improve the scope of the application of mechanism discrimination and enhance the interpretability of the results of the intelligent method. Full article

(This article belongs to the Special Issue Power System Operation and Control Technology)

26 pages, 1224 KiB

Open AccessReview

Recent Advances in Wastewater Electrocoagulation Technologies: Beyond Chemical Coagulation

by Sangyeol Jo, Rahul Kadam, Heewon Jang, Dongyun Seo and Jungyu Park

Energies 2024, 17(23), 5863; https://doi.org/10.3390/en17235863 - 22 Nov 2024

Viewed by 185

Abstract

This review provides a comprehensive analysis of the recent research trends and application cases of chemical coagulation (CC) and electrocoagulation (EC), which play a crucial role in wastewater treatment. In particular, the principles and process performances of the EC technologies are comparably reviewed [...] Read more.

This review provides a comprehensive analysis of the recent research trends and application cases of chemical coagulation (CC) and electrocoagulation (EC), which play a crucial role in wastewater treatment. In particular, the principles and process performances of the EC technologies are comparably reviewed with traditional CC technologies. EC offers the advantage of reducing the use of chemical agents and minimizing sludge generation compared to CC. Moreover, recent research cases have demonstrated its effectiveness in removing pollutants from wastewater. With increasing water consumption due to industrial development, the application of coagulation processes in wastewater and sludge treatment is expected to expand to minimize environmental impact. This review provides insights into the current status and future development direction of CC and EC technologies and can serve as foundational information for more efficient and environmentally friendly coagulation systems. Full article

(This article belongs to the Special Issue Advances in Wastewater Treatment 2024)

41 pages, 4833 KiB

Open AccessReview

Building the Future: Integrating Phase Change Materials in Network of Nanogrids (NoN)

by Ali Kalair, Elmira Jamei, Mehdi Seyedmahmoudian, Saad Mekhilef and Naeem Abas

Energies 2024, 17(23), 5862; https://doi.org/10.3390/en17235862 - 22 Nov 2024

Viewed by 162

Abstract

Buildings consume 10% of global energy and 50% of global electricity for heating and cooling. Transitioning to energy-efficient buildings is essential to address the global energy challenge and meet sustainable development goals (SDGs) to limit global temperature rise below 1.5

^{\circ}

C. The [...] Read more.

Buildings consume 10% of global energy and 50% of global electricity for heating and cooling. Transitioning to energy-efficient buildings is essential to address the global energy challenge and meet sustainable development goals (SDGs) to limit global temperature rise below 1.5

^{\circ}

C. The shift from traditional to smart grids has led to the development of micro, milli, and nanogrids, which share energy resources symbiotically and balance heating/cooling demands dealing with acute doldrums (dunkelflaute). This scoping review explores the methods by which phase change materials (PCMs) can be used in residential buildings to form a nanogrid. This review examines the components and concepts that promote the seamless integration of PCMs in residential houses. It also discusses the key challenges (e.g., scalability, stability, and economic feasibility in high summer temperatures), proposing the community-scale network of nanogrids (NoN) and the potential of thermochromic and photochromic materials. The findings of this review highlight the importance of latent heat storage methods and ingenious grid architectures such as nanogrids to construct resilient and sustainable houses in the future and thereby offer practical insights for policymakers and industries in the energy sector. Full article

(This article belongs to the Special Issue Phase Change Materials for Building Energy Applications)

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Energies, Volume 17, Issue 23 (December-1 2024) – 82 articles

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