Search Results (4,428)

Vietnam’s exports expanded dramatically from $14.5 billion in 2000 to $405 billion in 2024, elevating the country to the world’s 22nd largest exporter despite persistent global shocks. This paper introduces the application of the Causal Machine Learning Approach to Resilience Estimation (CLARE) to industry-level trade analysis, utilizing a comprehensive panel of 97 HS2 sectors from 2000 to 2024 (2425 observations) drawn from UN COMTRADE and WITS databases. We implement Double Machine Learning to estimate causal effects of the Global Financial Crisis (2008–2009) and COVID-19 pandemic (2020–2021) on export growth. Results reveal stark industry disparities: electrical machinery (HS85) exhibits exceptional resilience, fueled by 72% high-technology content and low product concentration, while knitted apparel (HS61) proves highly vulnerable. Fixed effect regressions substantiate core hypotheses: a 10-percentage-point increase in high-tech share elevates the resilience index by 0.031 points (approximately 4.1% relative to the sample mean); a one-standard-deviation reduction in product HHI (0.14 units) yields a 0.026-point gain (3.6% relative); and each additional FTA contributes 0.047 points (approximately 6.2% relative), with all estimates significant at conventional levels. Robustness encompassing alternative learners, detrended outcomes, and synthetic controls upholds findings. Policy recommendations center on accelerating high-tech global value chain integration—targeting semiconductors and electric vehicles—while optimizing CPTPP and EVFTA utilization (currently 35%) and mitigating US–China market concentration (45% of exports). These insights chart pathways for Vietnam’s Vision 2045 high-income ambition amid intensifying geopolitical and climate risks, providing a replicable framework for other export-reliant emerging economies. Full article

With the implementation of market-oriented electricity trading in an increasing number of countries, accurate electricity price forecasting can not only help participants in the electricity market to make more reasonable decisions but also enable regulators to have a more reliable regulatory basis. Therefore, it is necessary to propose an appropriate electricity price forecasting method. In view of the insufficiency of the traditional models in dealing with nonlinear and non-stationary data, to improve the detection ability of the model for hidden information in data and considering the high randomness of electricity price data, this paper proposes an electricity price forecasting method based on singular spectrum analysis (SSA) to decompose the original sequence and combines it with an extreme learning machine (ELM) optimized by the grey wolf optimizer (GWO). First, SSA is used to decompose the original sequence, and then the ELM is used to predict each subsequence and add them, in which the number of neurons in the hidden layer of each ELM is jointly optimized by the GWO. To verify the effectiveness of the SSA–GWO–ELM model, a total of 2106 days of electricity price data in Victoria, Australia, were selected for modeling. The results show that the prediction accuracy of the model proposed in this paper is significantly higher than that of the other comparison models, and the R² score is as high as 0.989, which is 0.017 higher than that of the suboptimal SSA–ELM. It can also maintain strong robustness and high prediction accuracy for heterogeneous data on power demand. SSA has the potential for real-time prediction, which can provide reliable data support for electricity market participants and supervisors. Full article

Electricity infrastructure is widely regarded as a fundamental prerequisite for supporting sustainable industrial development and driving corporate digital transformation under energy constraints. Taking the quasi-natural experiment of changes in electricity supply resulting from the cross-regional power transmission of the Three Gorges Project, and using data from China’s A-share listed manufacturing companies over the period 2000 to 2023, this paper constructs a multi-period difference-in-differences model to investigate whether improvements in electricity infrastructure promote corporate digital transformation and to examine their potential role in supporting sustainable economic development. The empirical results indicate that improvements in electricity infrastructure significantly enhance the level of corporate digital transformation. In the mechanism analysis, the alleviation of financing constraints and the increase in R&D investment play important mediating roles in the process through which electricity infrastructure affects corporate digital transformation. Further heterogeneity analysis reveals that the above effects are more pronounced in non-STAR Market enterprises, labor-intensive enterprises, asset-intensive enterprises, state-owned enterprises, and regions characterized by relatively lower levels of marketization. This study reveals the intrinsic relationship between electricity infrastructure and corporate digital transformation at the micro level, provides empirical evidence for understanding how energy infrastructure supports sustainable digital transformation and enhances long-term economic resilience, and offers policy implications for promoting the coordinated development of energy security and the digital economy. Full article

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts. Full article

The rapid integration of renewable energy is increasing the need for fast and sustained load-side frequency regulation, and public electric vehicle (EV) charging networks are promising providers. Their participation, however, is constrained by the volatile charging demand and strict service requirements, which make it difficult to balance regulation revenue with charging quality. This paper proposes a three-layer coordinated framework for multi-site charging networks participating in secondary frequency regulation, comprising market bidding, rolling planning, and fast-response tracking. At the market layer, baseline charging schedules are co-optimized with symmetric regulation capacity bids. At the planning layer, completion margin and progress protection constraints are introduced as tractable service safeguards that preserve charging continuity and deadline compliance. At the execution layer, coordinator-assisted distributed station-level tracking and charger-level urgency-aware allocation track automatic generation control (AGC) commands while correcting the charging progress in real time. The station-level problem is decomposed into local box-constrained subproblems coordinated by a scalar dual signal, enabling real-time AGC tracking with limited inter-station information exchange. Case studies on a reproducible simulated network with 20 stations and 600 chargers show that the proposed method improves ancillary service benefits while maintaining strong tracking performance and markedly improving the charging continuity, deadline compliance, and spatial load balance. Full article

Stationary energy storage (SES) is increasingly needed to integrate variable renewable generation and improve consumer self-consumption, but technology choices involve associated trade-offs between cost, efficiency, and life-cycle impacts. This study evaluates the role of second-life lithium-ion (Li-ion) batteries repurposed from electric vehicles for stationary applications, compared to lead-acid (Pb-acid) batteries and power-to-hydrogen-to-power (PtH₂P) systems. We develop an optimization-based sizing and dispatch framework using measured PV–load profiles and hourly market electricity prices, and evaluate performance per 1 MWh delivered to the load over a 10-year life cycle. Economic performance is quantified through discounted cash flows equal to levelized cost of storage (LCOS), while environmental performance is assessed through life-cycle metrics with explicit representation of recycling and second-life credits. In addition to global warming potential (GWP), the analysis considers additional resource and impact metrics, as well as key operational efficiency metrics, including bidirectional consumption efficiency, autonomy, and share of self-consumption/export of photovoltaic systems. Scenario and sensitivity analyses examine the impact of policy and financial parameters, in particular feed-in tariff remuneration and discount rate, on the comparative ranking of technologies. The results highlight how circular economy pathways, especially second-life distribution for Li-ion batteries and high end-of-life recovery for lead-acid batteries, have a significant impact on the life-cycle burden for delivered energy, while market-driven conditions for dispatching and export activities shape economic outcomes. Overall, the proposed workflow provides a transparent, circularity-aware basis for selecting stationary storage technologies associated with photovoltaic systems, under realistic operational constraints. Full article

The hydrogen fuel cell electric vehicles (FCEVs) are becoming a worldwide recognized eco-friendly choice which produces no tailpipe emissions while providing better energy efficiency than traditional internal combustion engine vehicles. The review delivers an in-depth evaluation of FCEVs through their assessment which focuses on their transportation and power generation functions. The research investigates hydrogen production methods together with storage and distribution systems and vehicle integration practices and performance enhancement techniques. The paper highlights major technical challenges such as high production costs, limited refueling infrastructure, storage inefficiencies, and fuel cell durability. The research uses battery electric and hybrid vehicle comparisons to assess FCEV market competitiveness. The life-cycle environmental impact assessment proves that using clean hydrogen sources and sustainable end-of-life strategies is essential for achieving FCEV operational capabilities. The review examines new electrochemistry materials science and hybridization solutions which have become essential methods for creating better efficiency and durability while decreasing costs. The study shows how policy regulations and collaborative programs fast-track hydrogen adoption through their impact on future hydrogen grid integration and renewable hydrogen production and circular economy methods. The review shows how experts from different fields reached their achievements while still facing challenges to improve FCEVs as fundamental components of environmentally friendly transportation systems and clean energy networks. Full article

Graphene-related materials (GRMs) possess exceptional electrical, mechanical, thermal, and surface properties, offering significant potential across broad sectors and applications in electronics, energy storage, composites, and environmental technologies. Despite extensive investment in academic research and translation, large-scale industrial adoption of GRMs remains slower than projected. This review systematically analyzes the global graphene manufacturing landscape using available data from 100 commercial producers, with a focused evaluation of manufacturing technology, types and forms of produced GRMs, raw material sources, product forms, industrial quality control and characterization practices. Graphite-based production routes, particularly graphene oxide (GO) and reduced graphene oxide (rGO), dominate in the market due to their scalability and cost advantages. However, substantial inconsistencies in the quality of produced GRMs, characterization and standardization depth, analytical evidence, and technical data sheets (TDSs) remain widespread. A SWOT (strengths, weaknesses, opportunities and threats) analysis of emerging graphene in the industry highlights technological maturity and expanding market demand but reveals critical weaknesses and challenges in quality, standardization and cost–performance alignment. Overall, quality of manufactured materials, quality control transparency, and standardization rather than material manufacturing limitations emerge as the primary barriers to the widespread commercial realization of graphene. Full article

To address the challenges faced by virtual power plants (VPPs) in uncertain market environments and complex distribution networks, including strong market coupling, difficulty in multi-resource coordination, and strict safety constraints, this paper proposes a Hierarchical Hybrid Intelligent Framework (H2IF). The proposed framework integrates a market-aware meta-game mechanism, a topology-embedded graph attention coordination method, and a risk-aware soft/hard constraint safety mechanism to achieve economically optimal dispatch of VPPs in complex dynamic scenarios. By explicitly modeling competitive market interactions, the proposed method enhances strategy robustness; by exploiting grid topology priors, it improves multi-agent coordination capability; and by combining differentiable projection with risk-constrained optimization, it jointly ensures operational safety and revenue stability. Simulation results on a modified IEEE 33-bus system demonstrate that H2IF outperforms mainstream deep reinforcement learning methods and rule-based dispatch strategies in overall performance. In the 24 × 300-step testing scenario, H2IF achieves an average single-episode operating cost of 38.23 k$, which is 28.9%, 40.4%, and 26.5% lower than those of MADDPG, SAC, and the rule-based method, respectively, while also yielding the lowest constraint violation level. Ablation studies further verify the effectiveness of each key module in improving profit, reducing operating costs, enhancing tracking performance, and strengthening safety. The results indicate that the proposed method enables coordinated optimization of economy, safety, and robustness for VPP dispatch under uncertain market and operating conditions. Full article

The development of a green hydrogen industry is a strategic priority for Russia’s energy transition, yet the dynamics of scaling up this nascent sector remain poorly understood. This study uses agent-based modeling (ABM) to simulate the co-evolution of Russia’s electricity, hydrogen, and electrolyzer sectors over 2024–2050. The model incorporates three types of heterogeneous agents (power producers, hydrogen producers, and electrolyzer manufacturers) operating under bounded rationality. Four scenarios are examined across 50 Monte Carlo runs each, varying the electrolyzer learning rate (10–25%), willingness to pay for green hydrogen (2–6 $/kg), and government support intensity. The results reveal an endogenous three-phase development pattern: Phase I (2024–2028) dominated by renewable capacity build-up reaching ~30 GW; Phase II (2029–2040) characterized by rapid electrolyzer deployment scaling to 14.5 GW; and Phase III (2041–2050) marked by stabilization at approximately 30 GW producing 1.12 Mt/year at 3.1 $/kg. Two critical thresholds are identified: renewable capacity exceeding 30–38 GW and low-cost electricity above 4–7 TWh/year. The electrolyzer learning rate emerges as the most influential parameter, while the pessimistic scenario confirms market failure without a green premium (WTP < 2 $/kg). Strategic investment losses of 2–6 billion USD are necessary catalysts for industry emergence. Russia’s 2030 production target (0.55 Mt) is found structurally infeasible under all scenarios. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

Electric flight accelerates innovation and demands digitalization. DIREKT develops digital twins across the lifecycle of (hybrid) electric propulsion systems to fuse data, cut costs, and shorten time-to-market. In this context we present a production-to-design feedback approach. A system is developed which compares the scanned manufactured part with the design to visualize manufacturing deviations to improve upcoming designs. The system is tested with three different additive manufacturing technologies and two parts from an urban air mobility electric propulsion system. Furthermore, the comparison data is stored in a knowledge base for machine-learning-driven deviation prediction later on. Full article

This paper investigates the long-run relationship and short-run price dynamics between the German electricity and natural gas markets to assess market efficiency, with a focus on the impact of the Russia–Ukraine conflict. Employing Johansen cointegration tests and a Vector Error Correction Model (VECM) on weekly data from 2018 to 2025, we find a stable long-run equilibrium between the two prices. The results show that while the electricity market exhibits a self-correcting mechanism, indicating a certain degree of efficiency, this efficiency significantly deteriorated following the conflict’s outbreak. The natural gas market lost its error-correction capability post-conflict, and momentum effects became pronounced, suggesting impaired price discovery and weakened market efficiency under severe geopolitical stress. The findings provide empirical evidence supporting the reform of marginal pricing models in Europe to enhance resilience against geopolitical shocks. Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article