Research

16 pages, 611 KiB

Open AccessArticle

Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm

by Daniel Sanin-Villa, Oscar Danilo Montoya, Walter Gil-González, Luis Fernando Grisales-Noreña and Alberto-Jesus Perea-Moreno

Energies 2023, 16(11), 4304; https://doi.org/10.3390/en16114304 - 24 May 2023

Cited by 1 | Viewed by 1008

Abstract

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in [...] Read more.

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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24 pages, 2889 KiB

Open AccessArticle

Blockchain-Based Services Implemented in a Microservices Architecture Using a Trusted Platform Module Applied to Electric Vehicle Charging Stations

by Antonio J. Cabrera-Gutiérrez, Encarnación Castillo, Antonio Escobar-Molero, Juan Cruz-Cozar, Diego P. Morales and Luis Parrilla

Energies 2023, 16(11), 4285; https://doi.org/10.3390/en16114285 - 24 May 2023

Viewed by 1267

Abstract

Microservice architectures exploit container-based virtualized services, which rarely use hardware-based cryptography. A trusted platform module (TPM) offers a hardware root for trust in services that makes use of cryptographic operations. The virtualization of this hardware module offers high usability for other types of [...] Read more.

Microservice architectures exploit container-based virtualized services, which rarely use hardware-based cryptography. A trusted platform module (TPM) offers a hardware root for trust in services that makes use of cryptographic operations. The virtualization of this hardware module offers high usability for other types of service that require TPM functionalities. This paper proposes the design of TPM virtualization in a container. To ensure integrity, different mechanisms, such as attestation and sealing, have been developed for the binaries and libraries stored in the container volumes. Through a REST API, the container offers the functionalities of a TPM, such as key generation and signing. To prevent unauthorized access to the container, this article proposes an authentication mechanism based on tokens issued by the Cognito Amazon Web Service. As a proof of concept and applicability in industry, a use case for electric vehicle charging stations using a microservice-based architecture is proposed. Using the EOS.IO blockchain to maintain a copy of the data, the virtualized TPM microservice provides the cryptographic operations necessary for blockchain transactions. Through a two-factor authentication mechanism, users can access the data. This scenario shows the potential of using blockchain technologies in microservice-based architectures, where microservices such as the virtualized TPM fill a security gap in these architectures. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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25 pages, 910 KiB

Open AccessArticle

Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies

by Mark Kipngetich Kiptoo, Oludamilare Bode Adewuyi, Harun Or Rashid Howlader, Akito Nakadomari and Tomonobu Senjyu

Energies 2023, 16(10), 4147; https://doi.org/10.3390/en16104147 - 17 May 2023

Cited by 4 | Viewed by 1539

Abstract

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind [...] Read more.

A bi-objective joint optimization planning approach that combines component sizing and short-term operational planning into a single model with demand response strategies to realize a techno-economically feasible renewable energy-based microgrid is discussed in this paper. The system model includes a photovoltaic system, wind turbine, and battery. An enhanced demand response program with dynamic pricing devised based on instantaneous imbalances between surplus, deficit, and the battery’s power capacity is developed. A quantitative metric for assessing energy storage performance is also proposed and utilized. Emergency, critical peak pricing, and power capacity-based dynamic pricing (PCDP) demand response programs (DRPs) are comparatively analyzed to determine the most cost-effective planning approach. Four simulation scenarios to determine the most techno-economic planning approach are formulated and solved using a mixed-integer linear programming algorithm optimization solver with the epsilon constraint method in Matlab. The objective function is to minimize the total annualized costs (TACs) while satisfying the reliability criterion regarding the loss of power supply probability and energy storage dependency. The results show that including the DRP resulted in a significant reduction in TACs and system component capacities. The cost-benefit of incorporating PCDP DRP strategies in the planning model increases the overall system flexibility. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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23 pages, 12149 KiB

Open AccessArticle

Optimal Power Scheduling and Techno-Economic Analysis of a Residential Microgrid for a Remotely Located Area: A Case Study for the Sahara Desert of Niger

by Issoufou Tahirou Halidou, Harun Or Rashid Howlader, Mahmoud M. Gamil, M. H. Elkholy and Tomonobu Senjyu

Energies 2023, 16(8), 3471; https://doi.org/10.3390/en16083471 - 15 Apr 2023

Cited by 5 | Viewed by 2188

Abstract

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy [...] Read more.

The growing demand for electricity and the reconstruction of poor areas in Africa require an effective and reliable energy supply system. The construction of reliable, clean, and inexpensive microgrids, whether isolated or connected to the main grid, has great importance in solving energy supply problems in remote desert areas. It is a complex interaction between the level of reliability, economical operation, and reduced emissions. This paper investigates the establishment of an efficient and cost-effective microgrid in a remote area located in the Djado Plateau, which lies in the Sahara Ténéré desert in northeastern Niger. Three cases are presented and compared to find the best one in terms of low costs. In case 1, the residential area is supplied by PVs and a battery energy storage system (BESS), while in the second case, PVs, a BESS, and a diesel generator (DG) are utilized to supply the load. In the third case, the grid will take on load-feeding responsibilities alongside PVs, a BESS, and a DG (used only in scenario 1 during the 2 h grid outage). The central objective is to lower the cost of the proposed microgrid. Among the three cases, case 3, scenario 2 has the lowest LCC, but implementing it is difficult because of the nature of the site. The results show that case 2 is the best in terms of total life cycle cost (LCC) and no grid dependency, as the annual total LCC reaches about $2,362,997. In this second case, the LCC is 11.19% lower compared to the first case and 5.664% lower compared to the third case, scenario 1. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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24 pages, 5085 KiB

Open AccessArticle

Frequency Regulation Strategy of Two-Area Microgrid System with Electric Vehicle Support Using Novel Fuzzy-Based Dual-Stage Controller and Modified Dragonfly Algorithm

by Balvender Singh, Adam Slowik, Shree Krishan Bishnoi and Mandeep Sharma

Energies 2023, 16(8), 3407; https://doi.org/10.3390/en16083407 - 12 Apr 2023

Cited by 5 | Viewed by 1467

Abstract

Energy in microgrids (MGs) can now be generated from a variety of renewable sources, but their effective and sustainable use is dependent on electrical energy storage (EES) systems. Consequently, the expansion of MGs is greatly reliant on EES systems. The high infiltration of [...] Read more.

Energy in microgrids (MGs) can now be generated from a variety of renewable sources, but their effective and sustainable use is dependent on electrical energy storage (EES) systems. Consequently, the expansion of MGs is greatly reliant on EES systems. The high infiltration of electric vehicles (EVs) causes some problems for the smooth functioning of the electric power system. However, EVs are also able to offer ancillary services, such as energy storage, to power systems. The research presented in this paper aims to develop a novel frequency regulation (FR) approach for biogas diesel engines (wind), the organic Rankine cycle (ORC), and solar-based two-area islanded microgrids with EVs in both areas. This article discusses the introduction of a fuzzy logic controller (FLC) for FR with scaled factors configured as proportional integral (PI) and proportional derivative with filter (PDF), i.e., a FLC-SF-PI-PDF controller. A recently created modified dragonfly algorithm is used to determine the best values for the controller parameters. To justify the effectiveness of the proposed controller with the presence of EVs, the execution of the proposed controller is associated with and without the presence of EVs. This research also looks at the different uncertain conditions, non-linearities, and eigenvalue stability analysis to validate the supremacy of the proposed approach. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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17 pages, 481 KiB

Open AccessArticle

Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico

by Anne Carolina Rodrigues Klaar, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho

Energies 2023, 16(7), 3184; https://doi.org/10.3390/en16073184 - 31 Mar 2023

Cited by 16 | Viewed by 1370

Abstract

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation [...] Read more.

The energy price influences the interest in investment, which leads to economic development. An estimate of the future energy price can support the planning of industrial expansions and provide information to avoid times of recession. This paper evaluates adaptive boosting (AdaBoost), bootstrap aggregation (bagging), gradient boosting, histogram-based gradient boosting, and random forest ensemble learning models for forecasting energy prices in Latin America, especially in a case study about Mexico. Seasonal decomposition of the time series is used to reduce unrepresentative variations. The Optuna using tree-structured Parzen estimator, optimizes the structure of the ensembles through a voter by combining several ensemble frameworks; thus an optimized hybrid ensemble learning method is proposed. The results show that the proposed method has a higher performance than the state-of-the-art ensemble learning methods, with a mean squared error of 3.37 × 10

^{- 9}

in the testing phase. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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18 pages, 1067 KiB

Open AccessArticle

Aggregating Prophet and Seasonal Trend Decomposition for Time Series Forecasting of Italian Electricity Spot Prices

by Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani and Leandro dos Santos Coelho

Energies 2023, 16(3), 1371; https://doi.org/10.3390/en16031371 - 29 Jan 2023

Cited by 30 | Viewed by 3234

Abstract

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter [...] Read more.

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoothing) and Facebook Prophet methodologies to perform a more accurate and resilient time series analysis of Italian electricity spot prices. This can assist in enhancing projections and better understanding the variables driving the data, while also including additional information such as holidays and special events. The combination of approaches improves forecast accuracy while lowering the mean absolute percentage error (MAPE) performance metric by 18% compared to the baseline model. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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19 pages, 3418 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Influence of Increasing Renewable Power Penetration on the Long-Term Iberian Electricity Market Prices

by Pedro Leal, Rui Castro and Fernando Lopes

Energies 2023, 16(3), 1054; https://doi.org/10.3390/en16031054 - 18 Jan 2023

Viewed by 1460

Abstract

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease [...] Read more.

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penetration of renewables into MIBEL of up to 80% in 2030, showed that electricity prices are expected to decrease by around 50% in 2030 when compared to 2019, and there will be a new pattern of electricity prices throughout the year due to the uneven distribution of renewable electricity. The study’s findings are relevant for ongoing research on the unique challenges of energy markets with high levels of renewable generation. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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18 pages, 3750 KiB

Open AccessEditor’s ChoiceArticle

Flexibility Quantification and the Potential for Its Usage in the Case of Electric Bus Depots with Unidirectional Charging

by Amra Jahic, Felix Heider, Maik Plenz and Detlef Schulz

Energies 2022, 15(10), 3639; https://doi.org/10.3390/en15103639 - 16 May 2022

Cited by 1 | Viewed by 1415

Abstract

One of the crucial steps for a successful integration of electric bus fleets into the existing electric power systems is the active and intelligent usage of their flexibility. This is important not only for reducing the eventual negative effects on the power grid [...] Read more.

One of the crucial steps for a successful integration of electric bus fleets into the existing electric power systems is the active and intelligent usage of their flexibility. This is important not only for reducing the eventual negative effects on the power grid but also for reducing energy and infrastructure costs. The first step in the optimal usage of flexibility is its quantification, which allows the maximum provision of flexibility without any negative effects for the fleet operation. This paper explores the available flexibility of large-scale electric bus fleets with a concept of centralized and unidirectional depot charging. An assessment of available positive and negative flexibility was conducted based on the data from two real bus depots in the city of Hamburg, Germany. The analysis shows the biggest flexibility potential was in the period from 16:00 h to 24:00 h, and the smallest one was in the periods from 08:00 h to 16:00 h, as well as from 02:00 h to 08:00 h. The paper also gives an overview of the possible markets for flexibility commercialization in Germany, which can provide an additional economic benefit for the fleet operators. A further analysis of the impact of parameters such as the timeline (working day or weekend), charging concept, ambient temperature, and electrical preconditioning provides an additional understanding of available flexibility. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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24 pages, 7707 KiB

Open AccessArticle

Economic Operation of Utility-Connected Microgrids in a Fast and Flexible Framework Considering Non-Dispatchable Energy Sources

by Rasoul Akbari, Seyede Zahra Tajalli, Abdollah Kavousi-Fard and Afshin Izadian

Energies 2022, 15(8), 2894; https://doi.org/10.3390/en15082894 - 14 Apr 2022

Cited by 4 | Viewed by 1515

Abstract

This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the [...] Read more.

This paper introduces a modified consensus-based real-time optimization framework for utility-connected and islanded microgrids scheduling in normal conditions and under cyberattacks. The exchange of power with the utility is modeled, and the operation of the microgrid energy resources is optimized to minimize the total energy cost. This framework tracks both generation and load variations to decide optimal power generations and the exchange of power with the utility. A linear cost function is defined for the utility where the rates are updated at every time interval. In addition, a realistic approach is taken to limit the power generation from renewable energy sources, including photovoltaics (PVs), wind turbines (WTs), and dispatchable distributed generators (DDGs). The maximum output power of DDGs is limited to their ramp rates. Besides this, a specific cloud-fog architecture is suggested to make the real-time operation and monitoring of the proposed method feasible for utility-connected and islanded microgrids. The cloud-fog-based framework is flexible in applying demand response (DR) programs for more efficiency of the power operation. The algorithm’s performance is examined on the 14 bus IEEE network and is compared with optimal results. Three operating scenarios are considered to model the load as light and heavy, and after denial of service (DoS) attack to indicate the algorithm’s feasibility, robustness, and proficiency. In addition, the uncertainty of the system is analyzed using the unscented transformation (UT) method. The simulation results demonstrate a robust, rapid converging rate and the capability to track the load variations due to the probable responsive loads (considering DR programs) or natural alters of load demand. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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19 pages, 25536 KiB

Open AccessArticle

Efficient Energy Distribution for Smart Household Applications

by Md Masud Rana, Akhlaqur Rahman, Moslem Uddin, Md Rasel Sarkar, SK. A. Shezan, C M F S Reza, Md. Fatin Ishraque and Mohammad Belayet Hossain

Energies 2022, 15(6), 2100; https://doi.org/10.3390/en15062100 - 13 Mar 2022

Cited by 5 | Viewed by 1968

Abstract

Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from [...] Read more.

Energy distribution technique is an essential obligation of an intelligent household system to assure optimal and economical operation. This paper considers a small-scale household system detached from the power grids consisting of some electrical components in day-to-day life. Optimal power distribution generated from a photovoltaic system is vital for ensuring economic and uninterrupted power flow. This paper presents an optimal energy distribution technique for a small-scale smart household system to ensure uninterrupted and economical operation. A photovoltaic (PV) system is considered as the primary generation system, and a battery energy storage system (BESS) is viewed as a backup power supply source. The actual load and PV generation data are used to validate the proposed technique collected from the test household system. Two different load profiles and photovoltaic power generation scenarios, namely summer and winter scenarios, are considered for case studies in this research. An actual test household system is designed in MATLAB/Simulink software for analyzing the proposed technique. The result reveals the effectiveness of the proposed technique, which can distribute the generated power and utilize the BESS unit to ensure the optimal operation. An economic analysis is conducted for the household system to determine the economic feasibility. The capital investment of the system can be returned within around 5.67 years, and the net profit of the system is 2.53 times more than the total capital investment of the system. The proposed technique can ensure economical operation, reducing the overall operating cost and ensuring an environment-friendly power system. The developed strategy can be implemented in a small-scale detached interconnected smart household system for practical operation to distribute the generated energy optimally and economically. Full article

(This article belongs to the Special Issue Coherent Security Planning for Power Systems)

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