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21 pages, 3514 KiB

Open AccessArticle

Enhanced Coordination in the PV–HESS Microgrids Cluster: Introducing a New Distributed Event Consensus Algorithm

by Zaid Hamid Abdulabbas Al-Tameemi, Tek Tjing Lie, Ramon Zamora and Frede Blaabjerg

Energies 2024, 17(2), 293; https://doi.org/10.3390/en17020293 - 6 Jan 2024

Cited by 1 | Viewed by 906

Abstract

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, [...] Read more.

To ensure reliable power delivery to customers under potential disturbances, the coordination of a microgrid cluster (MGC) is essential. Various control strategies—centralized, decentralized, distributed, and hierarchical—have been explored in the literature to achieve this goal. The hierarchical control method, with three distinct levels, has proven effective in fostering coordination among microgrids (MGs) within the cluster. The third control level, utilizing a time-triggering consensus protocol, relies on a continuous and reliable communication network for data exchange among MGs, leading to resource-intensive operations and potential data congestion. Moreover, uncertainties introduced by renewable energy sources (RESs) can adversely impact cluster performance. In response to these challenges, this paper introduces a new distributed event-triggered consensus algorithm (DETC) to enhance the efficiency in handling the aforementioned issues. The proposed algorithm significantly reduces communication burdens, addressing resource usage concerns. The performance of this approach is evaluated through simulations of a cluster comprising four DC MGs, in each of which were PV and a hybrid Battery-Super capacitor in the MATLAB environment. The key findings indicate that the proposed DETC algorithm achieves commendable results in terms of voltage regulation, precise power sharing among sources, and a reduction in triggering instants. Based on these results, this method can be deemed as a good development in MGC management, providing a more efficient and reliable means of coordination, particularly in scenarios with dynamic loads and renewable energy integration. It is also a viable option for current microgrid systems, due to its ability to decrease communication loads while retaining excellent performance. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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17 pages, 1487 KiB

Open AccessArticle

Enhancing Sustainable Assessment of Electric Vehicles: A Comparative Study of the TOPSIS Technique with Interval Numbers for Uncertainty Management

by Aleksandra Kaczyńska, Piotr Sulikowski, Jarosław Wątróbski and Wojciech Sałabun

Energies 2023, 16(18), 6652; https://doi.org/10.3390/en16186652 - 16 Sep 2023

Cited by 1 | Viewed by 796

Abstract

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for [...] Read more.

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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15 pages, 6812 KiB

Open AccessArticle

Improving Photovoltaic MPPT Performance through PSO Dynamic Swarm Size Reduction

by Adel O. Baatiah, Ali M. Eltamaly and Majed A. Alotaibi

Energies 2023, 16(18), 6433; https://doi.org/10.3390/en16186433 - 5 Sep 2023

Cited by 2 | Viewed by 1281

Abstract

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) [...] Read more.

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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15 pages, 4995 KiB

Open AccessArticle

FRT Capability Enhancement of Offshore Wind Farm by DC Chopper

by Gilmanur Rashid and Mohd Hasan Ali

Energies 2023, 16(5), 2129; https://doi.org/10.3390/en16052129 - 22 Feb 2023

Cited by 1 | Viewed by 1300

Abstract

Offshore wind farms (OWF) are establishing their position to be the next strategy to expand the growth horizon of wind power production. For proper integration of OWFs into the existing grid, the voltage source converter (VSC)-based high voltage direct current (HVDC) transmission is [...] Read more.

Offshore wind farms (OWF) are establishing their position to be the next strategy to expand the growth horizon of wind power production. For proper integration of OWFs into the existing grid, the voltage source converter (VSC)-based high voltage direct current (HVDC) transmission is being vastly utilized. For the stable operation of the existing grid, these VSC-HVDC-connected OWFs need to abide by the fault ride through (FRT) grid codes. Though there are many proposed solutions to tackle the FRT problem of the onshore wind farms, all of them cannot be applied to the OWFs. The OWFs cannot respond to the onshore faults depending solely on local measurements. Additionally, there are very few techniques available for FRT capability enhancement of the doubly fed induction generator (DFIG)-based OWFs. One notable solution is the use of the DC chopper resistor across the HVDC line. No intelligent controller is yet to be reported for better control of the DC chopper resistor. To enhance the performance of the DC chopper resistor in enhancing the FRT capability of the DFIG-based OWF, a particle swarm optimization (PSO)-based nonlinear controller is proposed. Simulations carried out in the Matlab/Simulink environment reveal that the PSO-optimized nonlinear controller-based DC chopper is very effective in maintaining the FRT of the DFIG-based OWF systems. Additionally, the proposed method provides better FRT performance than that of the conventional controller-based DC chopper. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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13 pages, 6804 KiB

Open AccessFeature PaperArticle

The Potential for Rooftop Photovoltaic Systems in Nepal

by Ural Kafle, Timothy Anderson and Sunil Prasad Lohani

Energies 2023, 16(2), 747; https://doi.org/10.3390/en16020747 - 9 Jan 2023

Cited by 2 | Viewed by 2876

Abstract

Nepal possesses a good solar resource, and there has been increasing interest in the use of photovoltaic systems. About 1.1 million solar home systems, rated at nearly 30 MWp, have been installed across Nepal. With the introduction of net metering by the Nepal [...] Read more.

Nepal possesses a good solar resource, and there has been increasing interest in the use of photovoltaic systems. About 1.1 million solar home systems, rated at nearly 30 MWp, have been installed across Nepal. With the introduction of net metering by the Nepal Electricity Authority, an increase in rooftop photovoltaics (RPV) is expected. However, to inform any policy developments around increased electricity generation, and the uptake of RPV, there is a need to quantify the potential of such systems (a situation mirrored in many developing countries). To this end, this study utilized a hierarchical geospatial technique based on open-source data to estimate the potential output from RPV in several cities in Nepal (Kathmandu, Pokhara, Butwal, Nepalgunj, and Biratnagar). It was found that the potential theoretical output of RPV ranged from 637 GWh per annum, in Kathmandu, to 50 GWh per annum in Butwal. Moreover, the total RPV potential from urban households of Nepal was estimated to be in the order 6.5 TWh per annum. As such, the findings of this paper can be used to make informed policy decisions about the future of Nepal’s energy mix. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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31 pages, 8856 KiB

Open AccessArticle

Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid

by Asad Ali, Muhammad Salman Fakhar, Syed Abdul Rahman Kashif, Ghulam Abbas, Irfan Ahmad Khan, Akhtar Rasool and Nasim Ullah

Energies 2022, 15(23), 8933; https://doi.org/10.3390/en15238933 - 25 Nov 2022

Cited by 1 | Viewed by 1322

Abstract

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as [...] Read more.

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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14 pages, 5615 KiB

Open AccessArticle

Deep Reinforcement Learning for the Optimal Angle Control of Tracking Bifacial Photovoltaic Systems

by Shuto Tsuchida, Hirofumi Nonaka and Noboru Yamada

Energies 2022, 15(21), 8083; https://doi.org/10.3390/en15218083 - 31 Oct 2022

Cited by 1 | Viewed by 1294

Abstract

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the [...] Read more.

An optimal tilt-angle control based on artificial intelligence (AI control) for tracking bifacial photovoltaic (BPV) systems is developed in this study, and its effectiveness and characteristics are examined by simulating a virtual system over five years. Using deep reinforcement learning (deep RL), the algorithm autonomously learns the control strategy in real time from when the system starts to operate. Even with limited deep RL input variables, such as global horizontal irradiance, time, tilt angle, and power, the proposed AI control successfully learns and achieves a 4.0–9.2% higher electrical-energy yield in high-albedo cases (0.5 and 0.8) as compared to traditional sun-tracking control; however, the energy yield of AI control is slightly lower in low-albedo cases (0.2). AI control also demonstrates a superior performance when there are seasonal changes in albedo. Moreover, AI control is robust against long-term system degradation by manipulating the database used for reward setting. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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Review

Jump to: Research

25 pages, 2743 KiB

Open AccessReview

Artificial Intelligence and Machine Learning in Grid Connected Wind Turbine Control Systems: A Comprehensive Review

by Nathan Oaks Farrar, Mohd Hasan Ali and Dipankar Dasgupta

Energies 2023, 16(3), 1530; https://doi.org/10.3390/en16031530 - 3 Feb 2023

Cited by 10 | Viewed by 4248

Abstract

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and [...] Read more.

As grid-connected wind farms become more common in the modern power system, the question of how to maximize wind power generation while limiting downtime has been a common issue for researchers around the world. Due to the complexity of wind turbine systems and the difficulty to predict varying wind speeds, artificial intelligence (AI) and machine learning (ML) algorithms have become key components when developing controllers and control schemes. Although, in recent years, several review papers on these topics have been published, there are no comprehensive review papers that pertain to both AI and ML in wind turbine control systems available in the literature, especially with respect to the most recently published control techniques. To overcome the drawbacks of the existing literature, an in-depth overview of ML and AI in wind turbine systems is presented in this paper. This paper analyzes the following reviews: (i) why optimizing wind farm power generation is important; (ii) the challenges associated with designing an efficient control scheme for wind farms; (iii) a breakdown of the different types of AI and ML algorithms used in wind farm controllers and control schemes; (iv) AI and ML for wind speed prediction; (v) AI and ML for wind power prediction; (vi) AI and ML for mechanical component monitoring and fault detection; and (vii) AI and ML for electrical fault prevention and detection. This paper will offer researchers and engineers in the wind energy generation field a comprehensive review of the application of AI and ML in the control methodology of offshore and onshore wind farms so that more efficient and robust control schemes can be designed for future wind turbine controllers. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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58 pages, 2332 KiB

Open AccessReview

High-Performance and Parallel Computing Techniques Review: Applications, Challenges and Potentials to Support Net-Zero Transition of Future Grids

by Ahmed Al-Shafei, Hamidreza Zareipour and Yankai Cao

Energies 2022, 15(22), 8668; https://doi.org/10.3390/en15228668 - 18 Nov 2022

Cited by 2 | Viewed by 2230

Abstract

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind [...] Read more.

The transition towards net-zero emissions is inevitable for humanity’s future. Of all the sectors, electrical energy systems emit the most emissions. This urgently requires the witnessed accelerating technological landscape to transition towards an emission-free smart grid. It involves massive integration of intermittent wind and solar-powered resources into future power grids. Additionally, new paradigms such as large-scale integration of distributed resources into the grid, proliferation of Internet of Things (IoT) technologies, and electrification of different sectors are envisioned as essential enablers for a net-zero future. However, these changes will lead to unprecedented size, complexity and data of the planning and operation problems of future grids. It is thus important to discuss and consider High Performance Computing (HPC), parallel computing, and cloud computing prospects in any future electrical energy studies. This article recounts the dawn of parallel computation in power system studies, providing a thorough history and paradigm background for the reader, leading to the most impactful recent contributions. The reviews are split into Central Processing Unit (CPU) based, Graphical Processing Unit (GPU) based, and Cloud-based studies and smart grid applications. The state-of-the-art is also discussed, highlighting the issue of standardization and the future of the field. The reviewed papers are predominantly focused on classical imperishable electrical system problems. This indicates the need for further research on parallel and HPC approaches applied to future smarter grid challenges, particularly to the integration of renewable energy into the smart grid. Full article

(This article belongs to the Special Issue Renewable Energy System Technologies)

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