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In the electricity market, the issue of contract negotiation prices between generators/traders and buyers is of particular relevance, as an accurate contract modeling leads to increased financial returns and business sustainability for the various participating agents, encouraging investments in specialized sectors for price forecasting and risk analysis. This paper presents a methodology applied in experiments on energy forward curve scenarios using a set of techniques, including Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), and Feature Engineering to generate a 10-year projection of the Conventional Long-Term Price. The model validation proved to be effective, with errors of only 4.5% by Root Mean Square Error (RMSE) and slightly less than 2% by Mean Absolute Error (MAE), for a time series spanning from 7 January 2012 to 31 August 2024, in the Brazilian energy market. Full article

In recent years, the rapid advancement of digital technologies and the growing demand for sustainability have driven unprecedented transformations in the automotive industry, particularly toward electric vehicles (EVs) and renewable energy. The EV supply chain, a complex global network, has become increasingly vulnerable to globalization and frequent “black swan” events. The purpose of this study, grounded in organizational information processing theory, aims to systematically examine the role of digital capability in strengthening supply chain resilience (SCR) through improved risk management effectiveness. Specifically, it explores the multidimensional nature of digital capability, clarifies its distinct impact on SCR, and addresses existing research gaps in this domain. To achieve this, this study develops a theoretical framework and validates it using survey data collected from 249 EV supply chain enterprises in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) is employed to empirically test the proposed relationships. The findings provide valuable theoretical insights and actionable guidance for EV manufacturers seeking to leverage digital transformation to mitigate risks effectively and enhance supply chain resilience. However, as the study focuses on Chinese EV supply chain enterprises, caution is needed when generalizing the findings to other regions. Future research could extend this investigation to different markets, such as to Europe and the United States, to explore potential variations. Full article

Electric vehicles (EVs) are a recognized solution for lowering greenhouse gas emissions and decreasing oil dependency, especially in Indonesia. Existing studies have explored the economic impact, challenges, and opportunities of EV adoption separately, lacking a holistic analysis. This study addresses this gap by providing a comprehensive assessment of the economic implications, challenges, and opportunities of EV adoption in Indonesia through a systematic literature review of 65 peer-reviewed articles, industry reports, and reputable publications from 2016 to 2024. The document analysis involved keyword-based literature selection, content analysis of economic metrics, and synthesis into key thematic areas. The findings reveal that EV sales in Indonesia have been rising annually, influenced by cost, driving range, environmental impact, technological features, charging infrastructure, battery, and government policies and incentives. EV adoption has positively impacted Indonesia’s GDP, attracted Foreign Direct Investment (FDI), created jobs, and reduced fuel consumption and imports. However, several challenges persist, including high EV costs, inadequate charging infrastructure, societal readiness, battery replacement costs and waste management, and limited model variety. Despite these challenges, opportunities exist in the form of market growth, FDI from nickel resources, energy security, job creation, and industrial expansion. Recommendations for creating a conducive EV ecosystem are provided for relevant stakeholders. Full article

The transition towards renewable energy has increased the importance of virtual power plants (VPPs) in integrating distributed energy resources (DERs). However, questions remain regarding the most appropriate auction mechanisms (pay-as-bid (PAB) versus pay-as-clear (PAC)) and imbalance penalty structures, which significantly influence VPP bidding strategies and market operations. This study employs a three-stage stochastic programming model to evaluate VPP bidding behaviors under these auction mechanisms while also considering the effects of imbalance penalty structures. By simulating various market scenarios, the results reveal that PAC markets offer higher VPP revenues due to settlement at the market-clearing price; they also exhibit greater volatility and elevated imbalance penalties. For instance, power deviations in PAC markets were 52.60% higher than in PAB markets under specific penalty structures, and imbalance penalty cost ranges differed by up to 82.32%. In contrast, PAB markets foster stable, stepwise bidding strategies that minimize imbalance penalties and improve renewable energy utilization, particularly during high- and moderate-generation periods. The findings emphasize the advantages of the PAB mechanism in electricity markets with substantial renewable energy integration, providing significant insights for the design of auction mechanisms that facilitate reliable and sustainable market operations. Full article

Microalgae have emerged as a sustainable and efficient source of protein, offering a promising alternative to conventional animal and plant-based proteins. Species such as Arthrospira platensis and Chlorella vulgaris contain protein levels ranging from 50% to 70% of their dry weight, along with a well-balanced amino acid profile rich in essential amino acids such as lysine and leucine. Their cultivation avoids competition for arable land, aligning with global sustainability goals. However, the efficient extraction of proteins is challenged by their rigid cell walls, necessitating the development of optimized methods such as bead milling, ultrasonication, enzymatic treatments, and pulsed electric fields. These techniques preserve functionality while achieving yields of up to 96%. Nutritional analyses reveal species-dependent digestibility, ranging from 70 to 90%, with Spirulina platensis achieving the highest rates due to low cellulose content. Functionally, microalgal proteins exhibit emulsifying, water-holding, and gel-forming properties, enabling applications in baking, dairy, and meat analogs. Bioactive peptides derived from these proteins exhibit antioxidant, antimicrobial (inhibiting E. coli and S. aureus), anti-inflammatory (reducing TNF-α and IL-6), and antiviral activities (e.g., Dengue virus inhibition). Despite their potential, commercialization faces challenges, including regulatory heterogeneity, high production costs, and consumer acceptance barriers linked to eating habits or sensory attributes. Current market products like Spirulina-enriched snacks and Chlorella tablets highlight progress, but food safety standards and scalable cost-effective extraction technologies remain critical for broader adoption. This review underscores microalgae’s dual role as a nutritional powerhouse and a source of multifunctional bioactives, positioning them at the forefront of sustainable food and pharmaceutical innovation. Full article

The increasing penetration of photovoltaic (PV) systems and the need for reliable backup power solutions have led to the development of hybrid uninterruptible power supply (UPS) systems. These systems integrate PV energy storage with battery backup and grid power to optimize real-time energy management. This paper proposes an advanced energy management strategy and an artificial neural network (ANN)-based control method for PV-integrated hybrid UPS systems. The proposed strategy dynamically determines the optimal power-sharing ratio between battery storage and the grid based on real-time economic parameters, load demand, and battery state of charge (SoC). A centralized ANN-based controller ensures precise control of the LLC converter and rectifier, achieving stable and efficient power distribution. Additionally, a genetic algorithm is implemented to optimize the power sharing ratio, minimizing the LCOE under varying load and electricity pricing conditions. The proposed approach is validated through simulations, demonstrating significant improvements in cost-effectiveness, system stability, and dynamic adaptability compared to conventional control methods. These findings suggest that integrating ANN-based control with optimized energy management can enhance the efficiency and sustainability of hybrid UPS systems, particularly in fluctuating energy markets. Full article

The global energy sector has been profoundly reshaped by the COVID-19 pandemic, triggering diverse reactions in energy demand patterns, accelerating the transition toward renewable energy sources, and amplifying concerns over global energy security and the digital safety of energy infrastructure. Five years after the pandemic’s onset, this study provides a taxonomy-based lesson-learned analysis, offering a comprehensive examination of the pandemic’s enduring effects on energy systems. It employs a detailed analytical framework to map short-, medium-, and long-term transformations across various energy-related sectors. Specifically, the study investigates significant shifts in the global energy landscape, including the electric and conventional vehicle markets, the upstream energy industry (oil, coal, and natural gas), conventional and renewable energy generation, aerial transportation, and the broader implications for global and continental energy security. Additionally, it highlights the growing importance of cybersecurity in the context of digital evolution and remote operations, which became critical during the pandemic. The study is structured to dissect the initial shock to energy supply and demand, the environmental consequences of reduced fossil fuel consumption, and the subsequent pivot toward sustainable recovery pathways. It also evaluates the strategic actions and policy measures implemented globally, providing a comparative analysis of recovery efforts and the evolving patterns of energy consumption. In the face of a global reduction in energy demand, the analysis reveals both spatial and temporal disparities, underscoring the complexity of the pandemic’s impact on the energy sector. Drawing on the lessons of COVID-19, this work emphasizes the need for flexible, forward-thinking strategies and deeper international collaboration to build energy systems that are both resilient and sustainable in the face of uncertainties. Full article

This research identified the optimal scenarios to produce three bioenergy outputs: dual generation (electricity and heat), electricity, and heat in two regions located in the northern part of Costa Rica. Two biomass conversion technologies—boilers and gasification—with 2, 5, and 10 MW production capacities were assessed to ascertain the most suitable technology-capacity pairing for each bioproduct. To this end, a comprehensive financial model was developed to maximize the net present value. Following this, the equilibrium point for biomass supply and demand was ascertained, alongside estimations of the associated costs and energy utility. The findings indicated that the three bioenergy products could be completed within the local energy market at prices below 0.14 USD/kWh, with maximum supply distances of 90 km. The boiler and turbine technology proved most suitable for dual and electricity generation, with capacities ranging between 2 MW and 5 MW, where differentiation was influenced by biomass transportation. Furthermore, heat generation demonstrated financial viability at a capacity of 2 MW. In the evaluation of supply-demand break-even points, a maximum benefit of 26% was observed, with dual production yielding the highest benefits and heat production being the least favorable option due to the costs linked to biomass transportation and the low efficiency of energy transformation. Full article

The compound energy system is an important component of zero-carbon buildings. Due to the complex form of the system and the difficult-to-capture characteristics of thermo-electric coupling interactions, the operation control of the zero-carbon building’s energy system is difficult in practical engineering. Therefore, it is necessary to carry out relevant optimization methods. This paper investigated the current research status of the control and scheduling of compound energy systems in zero-carbon buildings at home and abroad, selected a typical zero-carbon building as the research object, analyzed its energy system’s operational data, and proposed an operation scheduling algorithm based on day-ahead flexible programming and intraday rolling optimization. The multi-energy flow control algorithm model was developed to optimize the operation strategy of heat pump, photovoltaic, and energy storage systems. Then, the paper applied the algorithm model to a typical zero-carbon building project, and verified the actual effect of the method through the actual operational data. After applying the method in this paper, the self-absorption rate of photovoltaic power generation in the building increased by 7.13%. The research results provide a theoretical model and data support for the operation control of the zero-carbon building’s compound energy system, and could promote the market application of the compound energy system. Full article

HTs account for less than 7% of the automotive market in China, yet they contribute to more than 40% of the total carbon emissions from vehicles, with nitrogen oxide and particulate matter emissions exceeding 50% of the total vehicular emissions. BS for HTs has emerged as a crucial approach to reducing carbon emissions.As the number of BSHTs increases, the construction and operation of BSSs have become a pressing issue. This study focuses on the optimal charging strategy for BSSs by considering factors such as charging modes, charging durations, and real-time electricity prices. An optimal charging model, BS-CDE, is developed to formulate the operational cost problem of BSSs as a MOOP. By enhancing the traditional NSGA-II algorithm in aspects such as operators and parameter adjustments, the model is solved to obtain the optimal charging strategy, thereby reducing the operational costs of BSSs. Simulation results demonstrate that the proposed model effectively simulates the actual charging and battery-swapping processes for HTs. The results provide valuable guidance for the initial battery configuration and charging strategies of BSSs. Compared with traditional methods, the proposed model incorporates the actual operational scenarios of BSHTs while addressing multiple objectives during the charging process. Experimental results demonstrate that the proposed algorithm outperforms traditional methods, improving the HV and Sp metrics by 6.2% and 13.9%, respectively. Full article

Energy transformation is essential for reducing electricity production costs and building a competitive advantage for each country. Its success relies on balancing environmental goals with the need to maintain secure energy supplies, keep prices at an acceptable level for consumers, and ensure the economy’s competitiveness. Although the literature presents various investment constraints for onshore wind farms, little is known about the regulations that were supposed to protect the natural environment, and in practice, they turned out to be legal constraints on the development of onshore wind farms. This research aims to eliminate this research gap, and identify the legal limitations hindering the development of onshore wind farms, using Poland as a case study. It was examined whether legal provisions aimed at ensuring sustainable development could negatively impact the growth of onshore wind farms. The systematic literature study was supplemented by reviewing documents (available in the Polish Parliament and the Government Legislation Centre) relating to the location policy for onshore wind farms. The findings reveal that unfavourable legal solutions introduced in Poland over nearly a decade have severely obstructed the growth of onshore wind energy. This has led to harmful and measurable effects on society and the economy. Therefore, it is suggested that the creation of energy market regulations should be subject to greater stakeholder oversight. This study fits into the research field on legal barriers, classified as any negative phenomena and processes that do not contribute to achieving assumed goals. Full article

This paper explores the current state of energy decentralization in Oman, emphasizing its importance for the country’s energy sector. The primary focus is on the electricity market, examining how decentralization is evolving within this context. The analysis evaluates Oman’s regulatory framework to determine its suitability for fostering decentralization and highlights the role of emerging tools and technologies in this transition. This paper reviews the progress made in deploying these innovations, identifies specific regulatory challenges, and provides recommendations to create a more supportive regulatory environment. Additionally, it delves into data protection concerns arising from technologies like smart grids, which collect personal information. By assessing existing data protection regulations, this study identifies gaps and suggests improvements. The methodology involves a textual analysis of the academic literature, offering a comprehensive understanding of the regulatory and technological landscape shaping energy decentralization in Oman. Full article

As the coverage of China’s carbon emissions trading market expands from the power industry to the cement, steel, and electrolytic aluminum industries, the measurement and verification of carbon emissions of Chinese enterprises become increasingly important. This paper draws on the IPCC inventory compilation method and constructs an electric carbon model at the enterprise level in terms of energy consumption and production process; at the same time, it collects microdata from a total of 44 enterprises in three industries, namely, electrolytic aluminum, cement, and ferroalloy, in Qinghai Province. Based on the constructed electric carbon model, high-frequency measurement of enterprise carbon emissions was conducted. In order to verify the validity of the results, this paper examines the results from the perspectives of internal logic and external standards. The examination shows that the carbon model constructed in this paper has advantages such as cost-effectiveness, high measurement frequency, and accuracy, and it is suitable for third-party verification organizations or relevant management departments to use in the wide-scale measurement and verification of carbon emissions of enterprises. Full article

Vehicle-grid integration (VGI) is critical for the future of electric power systems, with decarbonization targets anticipating millions of electric vehicles (EVs) by 2030. As EV adoption grows, charging demand—particularly during peak hours in cities—may place significant pressure on the electrical grid. Charging at high power, especially during the evening when most EVs are parked in residential areas, can lead to grid instability and increased costs. One promising solution is to leverage long-duration, low-power charging, which can align with typical user behavior and improve grid compatibility. This paper delves into how public slow charging stations (<7.4 kW) in metropolitan residential areas can alleviate grid pressures while fostering a host of additional benefits. We show that, with respect to a reference (22 kW infrastructure), such stations can increase EV user satisfaction by up to 20%, decrease grid costs by 40% owing to a peak load reduction of 10 to 55%, and provide six times the flexibility for energy markets. Cities can overcome the limitation of private garage scarcity with this charging approach, thus fostering the transition to EVs. Full article

The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO₂ emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must compete against other marine energy renewables such as offshore wind. Here, we conduct a real option valuation that includes the uncertain market price of wholesale electricity and managerial flexibility expressed in determining future optimal decisions. We demonstrate the probability that the project’s embedded compound real option value can turn a negative net present value wave energy project to a positive expected value. This change in investment decision uses decision tree analysis, where real options are developed as decision nodes, and models the uncertainty as a risk-neutral stochastic process using chance nodes. We also show how our results are analogous to a financial out-of-the-money call option. Our results highlight the distribution of outcomes and the benefit of a staged long-term investment in wave energy systems to better understand and manage project risk, recognizing that these probabilistic results are subject to the ongoing evolution of wholesale electricity prices and the stochastic process models used here to capture their future dynamics. Lastly, we show that the near-term optimal decision is to continue to fund ongoing development of a reference architecture to a higher technology readiness level to maintain the long-term option to deploy such a renewable energy system through private investment or private–public partnerships. Full article