Hasil untuk "Renewable energy sources"

K. Ukoba, Kehinde Oladoke Olatunji, Eyitayo Adeoye et al.

The global transition toward sustainable energy sources has prompted a surge in the integration of renewable energy systems (RES) into existing power grids. To improve the efficiency, reliability, and economic viability of these systems, the synergistic application of artificial intelligence (AI) methods has emerged as a promising avenue. This study presents a comprehensive review of the current state of research at the intersection of renewable energy and AI, highlighting key methodologies, challenges, and achievements. It covers a spectrum of AI utilizations in optimizing different facets of RES, including resource assessment, energy forecasting, system monitoring, control strategies, and grid integration. Machine learning algorithms, neural networks, and optimization techniques are explored for their role in complex data sets, enhancing predictive capabilities, and dynamically adapting RES. Furthermore, the study discusses the challenges faced in the implementation of AI in RES, such as data variability, model interpretability, and real-time adaptability. The potential benefits of overcoming these challenges include increased energy yield, reduced operational costs, and improved grid stability. The review concludes with an exploration of prospects and emerging trends in the field. Anticipated advancements in AI, such as explainable AI, reinforcement learning, and edge computing, are discussed in the context of their potential impact on optimizing RES. Additionally, the paper envisions the integration of AI-driven solutions into smart grids, decentralized energy systems, and the development of autonomous energy management systems. This investigation provides important insights into the current landscape of AI applications in RES.

Yuehong Lu, Z. Khan, Manuel S. Alvarez‐Alvarado et al.

Meeting the rising energy demand and limiting its environmental impact are the two intertwined issues faced in the 21st century. Governments in different countries have been engaged in developing regulations and related policies to encourage environment friendly renewable energy generation along with conservation strategies and technological innovations. It is important to develop sustainable energy policies and provide relevant and suitable policy recommendations for end-users. This study presents a review on sustainable energy policy for promotion of renewable energy by introducing the development history of energy policy in five countries, i.e., the United States, Germany, the United Kingdom, Denmark and China. A survey of the articles aimed at promoting the development of sustainable energy policies and their modelling is carried out. It is observed that energy-efficiency standard is one of the most popular strategy for building energy saving, which is dynamic and renewed based on the current available technologies. Feed-in-tariff has been widely applied to encourage the application of renewable energy, which is demonstrated successfully in different countries. Building energy performance certification schemes should be enhanced in terms of reliable database system and information transparency to pave the way for future net-zero energy building and smart cities.

Arvind R. Singh, R. Kumar, Mohit Bajaj et al.

The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model’s superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system’s ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.

Y. E. García Vera, R. Dufo-López, J. Bernal-Agustín

Renewable energy sources have emerged as an alternative to meet the growing demand for energy, mitigate climate change, and contribute to sustainable development. The integration of these systems is carried out in a distributed manner via microgrid systems; this provides a set of technological solutions that allows information exchange between the consumers and the distributed generation centers, which implies that they need to be managed optimally. Energy management in microgrids is defined as an information and control system that provides the necessary functionality, which ensures that both the generation and distribution systems supply energy at minimal operational costs. This paper presents a literature review of energy management in microgrid systems using renewable energies, along with a comparative analysis of the different optimization objectives, constraints, solution approaches, and simulation tools applied to both the interconnected and isolated microgrids. To manage the intermittent nature of renewable energy, energy storage technology is considered to be an attractive option due to increased technological maturity, energy density, and capability of providing grid services such as frequency response. Finally, future directions on predictive modeling mainly for energy storage systems are also proposed.

Odunayo Adewunmi Adelekan, Bamidele Segun Ilugbusi, Olawale Adisa et al.

The global landscape of energy production is undergoing a profound transformation as nations grapple with the imperatives of sustainable development and climate change mitigation. This paper encapsulates a comprehensive review of Energy Transition Policies, specifically focusing on the global shift towards renewable energy sources. The paper delves into the myriad policy initiatives adopted by countries worldwide to transition from conventional fossil fuel-based energy systems to cleaner and more sustainable alternatives. The analysis explores the diverse motivations driving these energy transitions, ranging from environmental concerns and climate change imperatives to economic competitiveness and energy security. By examining the unique policy frameworks of different regions, the research provides valuable insights into the challenges and opportunities associated with the adoption of renewable energy sources. It investigates the regulatory mechanisms, financial incentives, and technological advancements that shape the trajectory of energy transition policies on a global scale. Furthermore, the paper sheds light on the impact of energy transition policies on various stakeholders, including governments, industries, and local communities. It examines the socio-economic implications of renewable energy integration, job creation, and the role of international collaborations in fostering sustainable energy practices. The study adopts a comparative approach, drawing on case studies from diverse geographical contexts to distill common trends and best practices in energy transition policy formulation and implementation. This paper provides a nuanced understanding of the complex interplay between policy frameworks and the global transition towards renewable energy. By synthesizing insights from different regions, it contributes to a holistic perspective on the challenges and opportunities inherent in shaping a sustainable and resilient energy future. The research is an invaluable resource for policymakers, researchers, and industry stakeholders seeking to navigate the intricate landscape of energy transition on a global scale. Keywords: Energy, Renewable Energy, Energy Innovation, Energy Transition, Review.

H. A. Behabtu, M. Messagie, T. Coosemans et al.

Renewable energy sources (RESs) such as wind and solar are frequently hit by fluctuations due to, for example, insufficient wind or sunshine. Energy storage technologies (ESTs) mitigate the problem by storing excess energy generated and then making it accessible on demand. While there are various EST studies, the literature remains isolated and dated. The comparison of the characteristics of ESTs and their potential applications is also short. This paper fills this gap. Using selected criteria, it identifies key ESTs and provides an updated review of the literature on ESTs and their application potential to the renewable energy sector. The critical review shows a high potential application for Li-ion batteries and most fit to mitigate the fluctuation of RESs in utility grid integration sector. However, for Li-ion batteries to be fully adopted in the RESs utility grid integration, their cost needs to be reduced.

A. Mohammadi, M. Mehrpooya

Abstract Fossil fuels cannot provide all the required energy in future due to their exhaustibility. Their utilization also leads to huge environmental problems. Therefore, it is vital to find a new inexhaustible and clean source of energy which could be easily stored and transported. Hydrogen produced by renewable energy sources has all the above mentioned advantages. There are many hydrogen production methods which could be coupled with renewable energy sources. Among them, electrolysis process is the most mature one. In this paper, a review is performed on different methods of coupling electrolyzer systems with different renewable energy sources, including solar energy, wind energy, geothermal energy, ocean thermal energy conversion system and hydroelectric energy. The focus of this study is on how these systems can be integrated and which one has the highest total efficiency. Finally, cost of hydrogen production by different energy sources is compared.

Ardavan Babaei, Erfan Babaee Tirkolaee, Esra Boz

Pratibha Rani, A. Mishra, A. Mardani et al.

Abstract In recent years, the selection of appropriate renewable energy sources is an extremely significant issue that affects the environmental development and economic growth. To tackle the concern, various authors have concentrated on preferring desirable energy source(s) adopting decision-making approaches under different fuzzy sets methods. In this regard, in the present study, a new divergence measure is proposed for ranking and choosing the renewable energy sources in multi-criteria decision-making problems based on fuzzy TOPSIS, and it is compared to some existing methods. Then, a set of experts related to renewable energy sources is selected to evaluate possible alternatives amongst conflicting criteria. Moreover, the fuzzy decision matrix and criteria weights are measured using linguistic values that are transformed into fuzzy values. Furthermore, the weight of each energy source decision expert is evaluated by the proposed method. Next, the importance of criteria is computed by an extensive maximizing deviation method inspired by fuzzy divergence measure. Finally, the problem of choosing a renewable energy source is considered to show the thorough execution process of the introduced method. The proposed method’s strength lies in its capability of providing effective solutions where there is a shortage of quantitative information.

Longjian Piao, Laurens de Vries, Mathijs de Weerdt et al.

Future energy markets for low voltage AC and DC distribution systems will facilitate prosumer participation in the market. To comply with market regulations and grid constraints, a tailored market design reflecting (DC) operational requirements is needed. Our previous work identified a locational energy market design. However, its real-life implementation faces challenges due to uncertainties in system operation, prosumer preferences, and bidding strategies. This article tests the market design under uncertain scenarios. To this end, we develop an agent-based model that simulates typical electric vehicle user preferences and bidding strategies, influenced by varying degrees of range anxiety. The market design is tested in challenging scenarios with a high share of solar panels and electric vehicles, modelled using the high-resolution Pecan Street database. Simulations indicate that the proposed market design maintains both economic efficiency and system reliability under real-life uncertainties. This in turn indicates the practical feasibility of locational energy markets in helping to integrate renewable generation sources and bidirectional power flows.

Yundie Guan, Xiangyu Zhang, Zheming Liang et al.

Abstract The intermittency and volatility of renewable energy have been major challenges in modern power systems. This paper proposes a self‐adaptive energy management strategy based on deep reinforcement learning (DRL) to integrate renewable energy sources into a system comprising compressed air energy storage, battery energy storage systems, and solid oxide fuel cells. However, the basic deep deterministic policy gradient algorithm lacks sensitivity to environmental changes, particularly when there is a mismatch in module capacity within the system. This limitation may affect the proper selection of the charging and discharging actions for the hybrid energy storage system. Thus, some modifications are dedicated to the careful replay buffer design in the basic algorithm, improving the ability to identify subtle changes in the reward function. The proposed method is also compared with other DRL methods to validate the feasibility and effectiveness. The simulation results demonstrate the compatibility of the improved algorithm with the proposed energy management strategy and better performance in terms of economic benefits.

Guglielmo D’Amico, Giovanni Masala, Filippo Petroni

Abstract In this paper, we propose some new measures of wind power variability based on reliability indexes. The measures are computed for continuous-time Markov models of wind power generation and for different wind parks. This allows us to compare the differences in the measurement of the variability of wind power due to the different geographical positions of the wind plants. The results have direct practical relevance and utility in the real-world wind energy sector, as they contribute to a more in-depth understanding of the variability risk of wind power generation plants.

Evangelia Karasmanaki, Garyfallos Arabatzis, Georgios Tsantopoulos

By investing in renewable energy sources (RES), citizens can participate actively in energy transition. The problem, however, is that citizen investment decisions are highly complex, while most strategies for capital mobilization rely on generic incentives or broad campaigns. To provide a new approach to mobilizing citizen capital, this study considers perceived barriers, as it is important to address aspects that disincline citizens from investing, and their preferred information sources, because attitudes are shaped and actions are empowered or disempowered through these channels. Drawing on a representative sample of Greek citizens, we used k-means clustering to segment citizens; the first cluster was inhibited to invest by loaning conditions, highlighting the need for banks to offer better terms for loans, while the second cluster was inhibited by a wide array of technical, economic, and systemic concerns requiring different stakeholders to address the barriers underlying these concerns. The third cluster was inhibited by barriers related to the technology of renewables and the availability of experts for installing and maintaining the systems, indicating the need to address such. Results also showed that several information sources can have a negative effect, suggesting that there should be policy intervention to enhance the accuracy of information.

N. Mir, Y. Biçer

There is an increasing demand for clean water as the population of the earth is exponentially increasing. Many countries are facing water shortage problems, which are bound to become more prevalent in upcoming years. Therefore, it is necessary to investigate sustainable methods to produce clean water for drinking, irrigation, agriculture and domestic use. Electrodialysis uses electricity and specialized membranes to separate ionic substances from water. This practice can be used for desalination and wastewater treatment. To make the process more sustainable, electrodialysis can be coupled with renewable sources of energy such as solar and wind power. Photo-electrodialysis and photovoltaic-electrodialysis are two methods commonly used to couple solar energy with the electrodialysis process. However, these processes are dependent on the availability of sunlight and wind as weather conditions and the positioning of the sun vary by time. Electrodialysis is more favourable for brackish water desalination instead of seawater desalination as it has a lower energy requirement. Desalinating brackish water (1000-5000 ppm) has an energy requirement in the range of 0.4-4 kWh/m3. This review paper summarizes the fundamental concepts of electrodialysis technology and its integration with renewable energy sources such as photo electrodialysis, photovoltaic assisted electrodialysis, reversible electrodialysis/electrodialysis and wind energy-driven electrodialysis. Some aspects that have been considered are the freshwater capacity, specific energy and costs of the hybrid systems.

K. Maciejowska

The literature on renewable energy sources indicates that an increase of the intermittent wind and solar generation affects significantly the distribution of electricity prices. In this article, the influence of two types of renewable energy sources (wind and solar photo voltaic) on the level and variability of German electricity spot prices is analyzed. The quantile regression models are built to estimate the merit order effect for different quantiles of electricity prices. The results indicate that both types of renewable generations have a similar, negative impact on the price level, approximated by the price median. When the price volatility, measured by the inter-quantile range (IQR), is considered, the outcomes show that wind and solar influence prices differently. Conditional on the level of the total demand, the wind generation would either increase (when the demand is low) or decrease (when the demand is high) the IQR. Meanwhile, the increase of solar power stabilizes the price variance for moderate demand level. Thus, policy supporting the development and integration of RES should search for a balance between the wind and solar power.

R. Marks-Bielska, S. Bielski, Katarzyna Pik et al.

Exploitation of renewable energy sources for power generation has been more and more important in recent years. This results from the economic issues and the measures taken to ensure the energy security. The aim of this research was to determine the significance of renewable energy sources (RES) in the energy mix of Poland, and to elicit the opinions and the level of knowledge of the society on the use and development of non-conventional energy. The article also presents advantages and disadvantages of types of RES, the obstacles which hinder the progress of green energy in Poland, and the proposed measures to expand the share of the RES in the overall energy mix. The research relied on the statistical data gathered by Statistics Poland. The opinions of respondents were elicited through a diagnostic survey based on a questionnaire. The research has demonstrated that the respondents support the development of RES in Poland. However, they also maintain that the purchase and installation of devices for the production of non-conventional energy are too expensive. The respondents believe that in order to increase the number of green power plants, subsidies to encourage RES investment and tax reliefs related to this investment should be bigger.

Erik Jonasson, Christoffer Fjellstedt, Irina Temiz

Vallem V. V. S. N. Murty, Ashwani Kumar

Microgrids with hybrid renewable energy sources are increasing and it is a promising solution to electrify remote areas where distribution network expansion is not feasible or not economical. Standalone microgrids with environment-friendly hybrid energy sources is a cost-effective solution that ensures system reliability and energy security. This paper determines the optimal capacity, energy dispatching and techno-economic benefits of standalone microgrid in remote area in Tamilnadu, India. Microgrids with hybrid energy sources comprising photovoltaic (PV), wind turbine (WT), battery energy storage system (BESS) and diesel generator (DG) are considered in this paper. Various case studies are implemented with hybrid energy sources and for each case study a comparative analysis of techno-economic benefits is demonstrated. Eight different configurations of hybrid energy sources are modeled with renewable fractions of 50%, 60%, 65%, and 100%, respectively. The optimization analysis is carried out using Hybrid Optimization Model for Electric Renewable (HOMER) software. Impact of demand response is also demonstrated on energy dispatching and techno-economic benefits. Simulation results are obtained for the optimal capacity of PV, WT, DG, converter, and BESS, charging/discharging pattern, state of charge (SOC), net present cost (NPC), cost of energy (COE), initial cost, operation cost, fuel cost, greenhouse gas emission penalty and payback period considering seasonal load variation. It is observed that PV+BESS is the most economical configuration. COE in standalone microgrid is higher than the conventional grid price. The results show that CO 2 emissions in hybrid PV+WT+DG+BESS are reduced by about 68% compared with the traditional isolated distribution system with DG.

M. K. Mehta, M. Zaaijer, D. von Terzi

<p>Traditionally, wind turbine and wind farm designs have been optimized to minimize the cost of energy. Such a design would make sense when bidding in price-based auctions. However, in a future with a high share of renewables and zero subsidies, the wind farm developer is exposed to the volatility of market prices, where the price paid per kilowatt-hour of energy would not be constant anymore. The developer might then have to maximize the revenue earned by participating in different energy, capacity, or ancillary services markets. In such a scenario, a turbine designed for maximizing its market value could be more profitable for the developer compared to a turbine designed for minimizing the levelized cost of electricity (LCoE). This study is in line with this paradigm shift in the field of turbine and farm design. It is a continuation of a previous study conducted by the same authors <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx31">Mehta et al.</a>, <a href="#bib1.bibx31">2024</a>)</span>, which explicitly focused on the drivers of turbine sizing with respect to LCoE. The goal of this study is to optimize the design for a new set of objective functions and analyze how various day-ahead market conditions and objectives drive turbine design. A simplified market model that can generate hourly day-ahead market prices is developed and coupled with a wind-farm-level multidisciplinary design analysis and optimization (MDAO) framework to evaluate key economic indicators of the wind farm. The results show how the optimum turbine design is driven by both the choice of the economic metric and the market scenario. However, an LCoE-optimized design is found to perform well with respect to profitability-based economic metrics like modified internal rate of return (MIRR) or profitability index (PI), indicating a limited need to redesign turbines for a specific day-ahead market scenario.</p>

André F. P. Ribeiro, Claudia Muscari

Abstract Simulating entire wind farms with an actuator line model requires significant computational effort, especially if one is interested in wake dynamics and wants to resolve the tip vortices. A need to explore unconventional approaches for this kind of simulation emerges. In this work, the actuator line method is implemented within a lattice‐Boltzmann flow solver, combined with a sliding mesh approach. Lattice‐Boltzmann solvers have advantages in terms of performance and low dissipation, while the sliding mesh allows for local refinement of the blade and tip vortices. This methodology is validated on a well‐documented case, the NREL Phase VI rotor, and the local refinement is demonstrated on the NREL 5 MW rotor. Results show good agreement with reference Navier–Stokes simulations. Advantages and limitations of the sliding mesh approach are identified.