Search Results (170)

The BRICS countries have been increasingly prioritizing electricity transition as a crucial step towards achieving sustainable growth, energy security, and mitigating climate change. As major emerging economies, the BRICS nations will play a significant role in the global energy landscape since their transition to renewable energy sources holds a significant implication for global energy markets and environmental sustainability. This study investigates the impact of the renewable energy transition on economic growth in BRICS nations from 1990 to 2023, employing a panel NARDL, DOLS, and FMOLS models. This study investigates the relationship between disaggregated renewable energy sources and economic growth. The findings show that renewable energy’s impact on economic growth varies across countries and depends on the type of renewable energy source. Specifically, hydropower, and wind power are found to have significant positive impacts on economic growth in some BRICS countries, while other renewables and trade openness have insignificant impacts. To foster economic growth and the expansion of renewable energy, it is essential for policymakers to focus on investments in hydropower and wind energy. Furthermore, they should encourage trade liberalization, as well as nuclear power development, and enhance regional collaboration. This study offers significant contributions to the current body of literature on the renewable energy–economic growth nexus, supplying crucial insights for both policymakers and researchers. Full article

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

Electric passenger vehicles are set to dominate the European car market, driven by EU climate policies and the 2035 ban on internal combustion engine production. This study assesses the sustainability of this transition, focusing on global warming potential and Critical Raw Material (CRM) extraction throughout its life cycle. The intensive use of CRMs raises environmental, economic, social, and geopolitical concerns. These materials are scarce and are concentrated in a few politically sensitive regions, leaving the EU highly dependent on external suppliers. The extraction, transport, and refining of CRMs and battery production are high-emission processes that contribute to climate change and pose risks to ecosystems and human health. A Life Cycle Assessment (LCA) was conducted, using OpenLCA software and the Ecoinvent 3.10 database, comparing a Peugeot 308 in its diesel and electric versions. This study adopts a cradle-to-grave approach, analyzing three phases: production, utilization, and end-of-life treatment. Key indicators included Global Warming Potential (GWP100) and Abiotic Resource Depletion Potential (ADP) to assess CO₂ emissions and mineral resource consumption. Technological advancements could mitigate mineral depletion concerns. Li-ion battery recycling is still underdeveloped, but has high recovery potential, with the sector expected to expand significantly. Moreover, repurposing used Li-ion batteries for stationary energy storage in renewable energy systems can extend their lifespan by over a decade, decreasing the demand for new batteries. Such innovations underscore the potential for a more sustainable electric vehicle industry. Full article

Agent-based models are a flexible and scalable modeling approach employed to study and describe the evolution of complex systems in different fields, such as social sciences, engineering, and economics. In the latter, they have been largely employed to model financial markets with a bottom-up approach, with the aim of understanding the price formation mechanism and to generate market scenarios. In the last few years, they have found application in the analysis of energy markets, which have experienced profound transformations driven by the introduction of energy policies to ease the penetration of renewable energy sources and the integration of electric vehicles and by the current unstable geopolitical situation. This review provides a comprehensive overview of the application of agent-based models in energy commodity markets by defining their characteristics and highlighting the different possible applications and the open-source tools available. In addition, it explores the possible integration of agent-based models with machine learning techniques, which makes them adaptable and flexible to the current market conditions, enabling the development of dynamic simulations without fixed rules and policies. The main findings reveal that while agent-based models significantly enhance the understanding of energy market mechanisms, enabling better profit optimization and technical constraint coherence for traders, scaling these models to highly complex systems with a large number of agents remains a key limitation. Full article

With rising urgency around carbon emissions and climate change, electrification has emerged as a central focus in traditionally combustion-reliant industries. With increasing regulatory restrictions on automotive and smaller off-highway markets (<25 hp), the heavy equipment industry faces growing pressures to adopt hybrid and fully electric solutions. Current literature primarily addresses technical electrification challenges, leaving a gap in understanding industry perspectives. This study explores trends, challenges, and expectations of electrification from industry representatives’ viewpoints, using data from 84 surveys conducted at the CONEXPO/CONAGG trade show and sentiment analysis of 100 interview notes gathered through an NSF Innovation Corps workshop. Results indicate substantial uncertainty toward electrification, with key limitations including power-to-weight ratios, high costs, maintenance, leakage concerns, and reliability of electronic components. The majority (77%) preferred traditional hydraulic systems due to familiarity and reliability, though concerns over maintenance and environmental impact remain prevalent. Participants anticipate a gradual industry transition, projecting widespread adoption of hybrid solutions in 10–15 years and longer timelines for fully electric systems. Effective adoption of greener technologies is likely through industry-wide standards and financial incentives. This study emphasizes the industry’s cautious yet gradually increasing openness to electrification amidst persistent technological and economic challenges. Full article

In the automotive industry, battery electric vehicles (BEVs) represent the future of individual mobility. To establish a long-term market presence, innovative vehicle and powertrain concepts are essential, and therefore, identifying the most promising concepts is crucial to determine where to focus research and development further. Academia plays a significant role in this identification process; however, researchers often face restricted access to data from the industry, and identifying different technological approaches is often connected to significant costs. We present a comprehensive study of the Porsche Taycan Performance Battery Plus, which integrates two technological advancements: the first series-production implementation of a two-speed transmission in an electric vehicle allowing for high acceleration while reaching high top speeds and a 800 V battery system architecture providing more efficient charging capabilities. This study details vehicle dynamics, electric powertrain efficiencies, their impact on vehicle level, and the two technological advancements. This work aims to provide researchers access to vehicle dynamometer and real-world data from one of the most advanced and innovative battery electric sports cars. This allows for further analysis of cutting-edge technologies that have yet to reach the mass market. In addition to providing researchers with this study’s results, all data utilized in this study will be made available as open-access, enabling individual use of test data for parameter identification and the development of simulation models. Full article

The growing investment in variable renewable energy sources is changing how electricity markets operate. In Europe, players rely on forecasts to participate in day-ahead markets closing between 12 and 37 h ahead of real-time operation. Usually, transmission system operators use a symmetrical procurement of up and down secondary power reserves based on the expected demand. This work uses machine learning techniques that dynamically compute it using the day-ahead programmed and expected dispatches of variable renewable energy sources, demand, and other technologies. Specifically, the methodology incorporates neural networks, such as Long Short-Term Memory (LSTM) or Convolutional neural network (CNN) models, to improve forecasting accuracy by capturing temporal dependencies and nonlinear patterns in the data. This study uses operational open data from the Spanish operator from 2014 to 2023 for training. Benchmark and test data are from the year 2024. Different machine learning architectures have been tested, but a Fully Connected Neural Network (FCNN) has the best results. The proposed methodology improves the usage of the up and down secondary reserved power by almost 22% and 11%, respectively. Full article

In the context of growing challenges associated with pollution prevention and control, developing more efficient technologies and precise policy measures to address the bottleneck period is imperative. This study utilized daily electricity consumption data from nine prefecture-level cities in Fujian Province from January 2019 to June 2024 to develop a high-quality development index (HQDI) and empirically investigate how HQDI affects the decoupling of economic growth from pollution emissions. Results suggest that HQDI can significantly promote decoupling, with innovation, openness, and sharing playing positive roles, while brown industries’ development and the electricity capacity installation of small and micro-enterprises hinder these processes. Moreover, extreme high temperatures exert a significant negative impact on decoupling, whereas increased market concentration fosters decoupling. Policy recommendations include prioritizing innovation, green technologies, and energy efficiency (particularly for SMEs); addressing climate resilience; and expanding HQDI to include factors like digital technologies for sustainable growth in Fujian and similar regions. Full article

Advances in electric vehicles and charging infrastructure technology have given the electrification of road traffic a positive momentum. Nowadays, it is becoming more and more evident that the related energy and financial processes of the current e-mobility ecosystem are reaching their limits. This leads to usability losses for end users as well as administrative and non-causation-based financial burdens on various energy system participants. In this article, use cases are inferred from the literature, the aforementioned challenges are discussed in more detail, and strategies for addressing them are presented. Furthermore, the information system architecture of the BANULA project, with its core elements of open communication standards, virtual balancing areas, and blockchain components, is explained. BANULA addresses the aforementioned challenges by holistically considering the needs of all participants. A special focus of the project is implementing and investigating the concept of virtual balancing areas. This concept has been available since 2020 but has not been implemented in the market yet. To the best of the authors’ knowledge, BANULA is the first project that utilizes current legislation to transfer charging infrastructure to virtual balancing areas in conjunction with distributed ledger technology to support related processes. In the first step, the BANULA implementation prototype targets the German e-mobility ecosystem, but applicability to other states in the European Union is planned. Using an independent framework, the BANULA architecture and its prototypical implementation are evaluated. The authors show that the unique combination of virtual balancing areas and the related processes, enhanced through distributed ledger technology, has the potential to contribute to a user-centered, trustworthy, and grid-supportive e-mobility ecosystem. Full article

This study examines integrating large-scale photovoltaic (PV) systems into the power grid to achieve a 30% PV share, addressing operational and economic challenges such as backup generation, storage, and grid stability. Applying an electricity dispatch model to the test case of Israel, it highlights significant impacts on fuel consumption, cost, and carbon emissions. Key findings include an 8% drop in the capacity factor of natural gas combined cycle (NGCC) plants, leading to increased starts, stops, and higher fuel consumption. Annual power generation will grow from 81 to 104 TWh, with PV generation increasing from 8.1 to 31.1 TWh. Open cycle gas turbine (OCGT) output will grow from 2.4 to 10.2 TWh, increasing OCGT’s market share from 3% to 10%. NGCC operations’ intermittency will double annual starts from 3721 to 7793, causing a 1.1% efficiency drop and a 2% rise in natural gas consumption. 3.45 GWh of Li-ion battery capacity will be needed. The LCoE is expected to increase from 6.6 to 7.0 c$/kWh without a carbon tax and from 8.7 to 8.8 c$/kWh with a $40/t carbon tax. Annual emissions will rise from 41.8 to 46.5 Mt. This study provides insights for sunny Mediterranean countries with similar renewable energy goals. Full article

The 2050 global ambition for a carbon-neutral society is increasing the penetration of the most competitive variable renewable technologies, onshore wind and solar PV. These technologies are known for their near-zero marginal costs but highly variable time-dependent generation. Power systems with major penetrations of variable generation need high balancing flexibility to guarantee their stability by maintaining the equilibrium between demand and supply. This work presents the open-access Multi-agent Trading of Renewable Energy Sources (RES.Trade) system, which includes different market designs of the imbalance settlement and the secondary and tertiary reserves. A new imbalance settlement is also proposed in this work. The main features of RES.Trade are demonstrated using two case studies and projected 2030 scenarios: the first analysed four imbalance settlement mechanisms in Portugal, achieving a 43% reduction in penalties using the new method; the second case study assesses the impact of five procurement mechanisms of secondary power reserves in the Spanish power system, resulting in a cost reduction by 34% in the case of dynamic reserves. Full article

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed. Full article

With the increasing market demands for wearable and portable electronic devices, binary metal oxides (BMOs) with a remarkable capacity and good structure stability have been considered as a promising candidate for fabricating coplanar micro-supercapacitors (CMSCs), serving as the power source. However, the current fabrication methods for BMO microelectrodes are complex, which greatly hinder their further development and application in BMO CMSCs. Herein, the one-step fabrication of 2.5D CuMoO_x-based CMSCs (CuMoCMSCs) has been realized by numerically controlled electric discharge machining (NCEDM) for the first time. In addition, the controllable capacity of CuMoCMSCs has been achieved by adjusting the NCEDM-machining voltage. The CuMoCMSCs machined by a machining voltage of 60 V (CuMoCMSCs60) showed the best performance. The fabricated CuMoCMSCs60 with binary metal oxides could operate at an ultra-high scanning rate of 10 V s⁻¹, and gained a capacity of 40.3 mF cm⁻² (1.1 mA cm⁻²), which is more than 4 times higher than that of MoO_x-based CMSCs (MoCMSCs60) with a single metal oxide. This is because CuMoO_x BMOs materials overcome the poor electroconductivity problem of the MoO_x single metal oxide. This one-step and numerically controlled fabrication technique developed in this research opens a new vision for preparing BMO materials, BMO microelectrodes, and BMO microdevices in an environmental, automatic, and intelligent way. Full article

Amidst the swift expansion of the electric vehicle industry, the imperative for alternative battery technologies that balance economic feasibility with sustainability has reached unprecedented importance. Herein, we utilized Perovskite-based oxide compounds barium titanate (BaTiO₃) and strontium titanate (SrTiO₃) nanoparticles as anode materials for lithium-ion batteries from straightforward and standard carbonate-based electrolyte with 10% fluoroethylene carbonate (FEC) additive [1M LiPF₆ (1:1 EC: DEC) + 10% FEC]. SrTiO₃ and BaTiO₃ electrodes can deliver a high specific capacity of 80 mA h g⁻¹ at a safe and low average working potential of ≈0.6 V vs. Li/Li⁺ with excellent high-rate performance with specific capacity of ~90 mA h g⁻¹ at low current density of 20 mA g⁻¹ and specific capacity of ~80 mA h g⁻¹ for over 500 cycles at high current density of 100 mA g⁻¹. Our findings pave the way for the direct utilization of perovskite-type materials as anode materials in Li-ion batteries due to their promising potential for Li⁺ ion storage. This investigation addresses the escalating market demands in a sustainable manner and opens avenues for the investigation of diverse perovskite oxides as advanced anodes for next-generation metal-ion batteries. Full article

This paper reviews recent works related to applications of reinforcement learning in power system optimal control problems. Based on an extensive analysis of works in the recent literature, we attempt to better understand the gap between reinforcement learning methods that rely on complete or incomplete information about the model dynamics and data-driven reinforcement learning approaches. More specifically we ask how such models change based on the application or the algorithm, what the currently open theoretical and numerical challenges are in each of the leading applications, and which reinforcement-based control strategies will rise in the following years. The reviewed research works are divided into “model-based” methods and “model-free” methods in order to highlight the current developments and trends within each of these two groups. The optimal control problems reviewed are energy markets, grid stability and control, energy management in buildings, electrical vehicles, and energy storage. Full article