Search Results (247)

Using waste biomass as a raw material for the combined production of electricity and heat offers corresponding energy, economic, environmental and resource efficiency benefits. The study examines both the performance of a system for combined energy production based on the Organic Rankine Cycle (ORC) utilizing wood biomass and the market interest in its deployment within Bulgaria. Its objective is to propose a technically and economically viable solution for the recovery of waste biomass through the combined production of electricity and heat while simultaneously assessing the readiness of industrial and municipal sectors to adopt such systems. The cogeneration plant incorporates an ORC module enhanced with three additional economizers that capture residual heat from flue gases. Operating on 2 t/h of biomass, the system delivers 1156 kW of electric power and 3660 kW of thermal energy, recovering an additional 2664 kW of heat. The overall energy efficiency reaches 85%, with projected annual revenues exceeding EUR 600,000 and a reduction in carbon dioxide emissions of over 5800 t/yr. These indicators can be achieved through optimal installation and operation. When operating at a reduced load, however, the specific fuel consumption increases and the overall efficiency of the installation decreases. The marketing survey results indicate that 75% of respondents express interest in adopting such technologies, contingent upon the availability of financial incentives. The strongest demand is observed for systems with capacities up to 1000 kW. However, significant barriers remain, including high initial investment costs and uneven access to raw materials. The findings confirm that the developed system offers a technologically robust, environmentally efficient and market-relevant solution, aligned with the goals of energy independence, sustainability and the transition to a low-carbon economy. Full article

Battery-swapping stations (BSSs) are pivotal for supplying energy to electric heavy-duty trucks. However, their operations face challenges in accurate demand forecasting for battery-swapping and fair revenue allocation. This study proposes an optimization strategy for the economic operation of BSSs that optimizes revenue allocation and load balancing to enhance financial viability and grid stability. First, factors including geographical environment, traffic conditions, and truck characteristics are incorporated to simulate swapping behaviors, supporting the construction of an accurate demand-forecasting model. Second, an optimization problem is formulated to maximize the weighted difference between BSS revenue and squared load deviations. An economic operations strategy is proposed based on an adaptive Shapley value. It enables precise evaluation of differentiated member contributions through dynamic adjustment of bias weights in revenue allocation for a strategy that aligns with the interests of multiple stakeholders and market dynamics. Simulation results validate the superior performance of the proposed algorithm in revenue maximization, peak shaving, and valley filling. Full article

Energy storage technologies are becoming increasingly important when it comes to maintaining the balance between electricity generation and consumption, especially with the increasing share of variable renewable energy sources (VRES). Pumped storage hydropower plants (PSHs) are currently the largest form of energy storage at the grid level. The aim of this study is to investigate the importance and prospects of using PSHs as part of the energy transition to decarbonize energy sources. A comparison was made between PSHs and battery energy storage systems (BESSs) in terms of technical, economic, and ecological aspects. To identify the key factors influencing the wider adoption of PSHs, a combined approach using SWOT analysis (which assesses strengths, weaknesses, opportunities, and threats) and the Analytical Hierarchy Process (AHP) as a decision support tool was applied. Regulatory and market uncertainties (13.54%) and financial inequality (12.77%) rank first and belong to the “Threats” group, with energy storage capacity (10.11%) as the most important factor from the “Strengths” group and increased demand for energy storage (9.01%) as the most important factor from the “Opportunities” group. Forecasts up to 2050 show that the capacity of PSHs must be doubled to enable the integration of 80% of VRES into the grids. The study concludes that PSHs play a key role in the energy transition, especially for long-term energy storage and grid stabilization, while BESSs offer complementary benefits for short-term storage and fast frequency regulation. Recommendations to policymakers include the development of clear, accelerated project approval procedures, financial incentives, and support for hybrid PSH systems to accelerate the energy transition and meet decarbonization targets. Full article

Globalization and the spread of technological innovations have made world markets and economies increasingly unified and conditioned by international trade, not only for sales markets but above all for the supply of raw materials necessary for the functioning of the production complex of each country. Alongside oil and gold, the main commodities traded include industrial metals, such as aluminum and copper, mineral products such as gas, electrical and electronic components, agricultural products, and precious metals. The conflict between Russia and Ukraine tested the unification of markets, given that these are countries with notable raw materials and are strongly dedicated to exports. This suggests that commodity prices were able to influence the stock markets, especially in the countries most closely linked to the two belligerents in terms of import-export. Given the importance of industrial metals in this period of energy transition, the aim of our study is to analyze whether Industrial Metals volatility affects G7 stock markets. To this end, the BEKK-GARCH model is used. The sample period spans from 3 January 2018 to 17 September 2024. The results show that lagged shocks and volatility significantly and positively influence the current conditional volatility of commodity and stock returns during all periods. In fact, past shocks inversely influence the current volatility of stock indices in periods when external events disrupt financial markets. The results show a non-linear and positive impact of commodity volatility on the implied volatility of the stock markets. The findings suggest that the war significantly affected stock prices and exacerbated volatility, so investors should diversify their portfolios to maximize returns and reduce risk differently in times of crisis, and a lack of diversification of raw materials is a risky factor for investors. Full article

This paper examines arbitrage opportunities available in one of the largest wholesale electricity markets in the world, the Midcontinent Independent System Operator (MISO) electricity exchange. While prior research suggests that market efficiency on the exchange has increased over time, this study reveals that historical pricing information can still be used to generate positive returns. I find that a trading rule based on prior spot and forward prices generates statistically and economically significant risk-adjusted returns across the entire MISO footprint. These returns may in part be explained by the relatively small number of financial traders in the market and the ability of generation owners to exercise market power. Full article

Battery energy storage systems (BESS) rely on accurate electricity price forecasts to maximize arbitrage profits in day-ahead markets. We examined whether specific forecasting models, ranging from statistical benchmarks to machine learning methods, consistently deliver superior financial outcomes for storage operators. Using real market data from the Italian day-ahead electricity market over 2020–2024, we compared univariate singular spectrum analysis (SSA), ARIMA, SARIMA, random forests, and a 30-day simple moving average under a unified trading framework. All models were evaluated based on their ability to generate arbitrage profits. Univariate SSA clearly outperformed all alternatives, achieving on average 98% of the theoretical maximum profit while maintaining the lowest forecast error. Among the other models, simpler approaches performed surprisingly well: they achieved comparable, if not superior, profit performance to more complex, hour-specific, or computationally intensive configurations. These results were robust to plausible variations in battery parameters and retraining schedules, suggesting that univariate SSA offers a uniquely effective forecasting solution for battery arbitrage and that simplicity can often be more effective than complexity in operational revenue terms. 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

Although the transition to electric vehicles (EVs) is well underway and expected to continue in global car markets, most vehicles on the world’s roads will be powered by internal combustion engine vehicles (ICEVs) and fossil fuels for the foreseeable future, possibly well past 2050. Thus, good environmental performance and effective emission control of ICE vehicles will continue to be of paramount importance if the world is to achieve the stated air and climate pollution reduction goals. In this study, we review 228 publications and identify four main issues confronting these objectives: (1) cheating by vehicle manufacturers, (2) tampering by vehicle owners, (3) malfunctioning emission control systems, and (4) inadequate in-service emission programs. With progressively more stringent vehicle emission and fuel quality standards being implemented in all major markets, engine designs and emission control systems have become increasingly complex and sophisticated, creating opportunities for cheating and tampering. This is not a new phenomenon, with the first cases reported in the 1970s and continuing to happen today. Cheating appears not to be restricted to specific manufacturers or vehicle types. Suspicious real-world emissions behavior suggests that the use of defeat devices may be widespread. Defeat devices are primarily a concern with diesel vehicles, where emission control deactivation in real-world driving can lower manufacturing costs, improve fuel economy, reduce engine noise, improve vehicle performance, and extend refill intervals for diesel exhaust fluid, if present. Despite the financial penalties, undesired global attention, damage to brand reputation, a temporary drop in sales and stock value, and forced recalls, cheating may continue. Private vehicle owners resort to tampering to (1) improve performance and fuel efficiency; (2) avoid operating costs, including repairs; (3) increase the resale value of the vehicle (i.e., odometer tampering); or (4) simply to rebel against established norms. Tampering and cheating in the commercial freight sector also mean undercutting law-abiding operators, gaining unfair economic advantage, and posing excess harm to the environment and public health. At the individual vehicle level, the impacts of cheating, tampering, or malfunctioning emission control systems can be substantial. The removal or deactivation of emission control systems increases emissions—for instance, typically 70% (NO_x and EGR), a factor of 3 or more (NO_x and SCR), and a factor of 25–100 (PM and DPF). Our analysis shows significant uncertainty and (geographic) variability regarding the occurrence of cheating and tampering by vehicle owners. The available evidence suggests that fleet-wide impacts of cheating and tampering on emissions are undeniable, substantial, and cannot be ignored. The presence of a relatively small fraction of high-emitters, due to either cheating, tampering, or malfunctioning, causes excess pollution that must be tackled by environmental authorities around the world, in particular in emerging economies, where millions of used ICE vehicles from the US and EU end up. Modernized in-service emission programs designed to efficiently identify and fix large faults are needed to ensure that the benefits of modern vehicle technologies are not lost. Effective programs should address malfunctions, engine problems, incorrect repairs, a lack of servicing and maintenance, poorly retrofitted fuel and emission control systems, the use of improper or low-quality fuels and tampering. Periodic Test and Repair (PTR) is a common in-service program. We estimate that PTR generally reduces emissions by 11% (8–14%), 11% (7–15%), and 4% (−1–10%) for carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO_x), respectively. This is based on the grand mean effect and the associated 95% confidence interval. PTR effectiveness could be significantly higher, but we find that it critically depends on various design factors, including (1) comprehensive fleet coverage, (2) a suitable test procedure, (3) compliance and enforcement, (4) proper technician training, (5) quality control and quality assurance, (6) periodic program evaluation, and (7) minimization of waivers and exemptions. Now that both particulate matter (PM, i.e., DPF) and NO_x (i.e., SCR) emission controls are common in all modern new diesel vehicles, and commonly the focus of cheating and tampering, robust measurement approaches for assessing in-use emissions performance are urgently needed to modernize PTR programs. To increase (cost) effectiveness, a modern approach could include screening methods, such as remote sensing and plume chasing. We conclude this study with recommendations and suggestions for future improvements and research, listing a range of potential solutions for the issues identified in new and in-service vehicles. Full article

The large-scale integration of renewable energy sources (RESs) has raised concerns regarding grid congestion in Japan. Battery energy storage systems (BESSs) can mitigate congestion by adjusting charging schedules; however, BESS owners basically prioritize market arbitrage, which may not be aligned with congestion mitigation. This paper proposes a charging incentive design to guide arbitrage-oriented BESS charging toward time periods that are effective for grid congestion mitigation. The system operator predicts congested hours and ensures that BESS owners can purchase electricity at the lowest daily market price. This design intends to shift the BESS charging time towards congestion periods. Because market prices tend to decline during congestion periods, the proposed method reduces the operator’s financial burden while encouraging congestion-mitigating charging behavior. Numerical simulations using a simplified Japanese east-side power system model demonstrate that the proposed method reduced the congestion mitigation costs by 3.86% and curtailed the RES output by 3.89%, compared to using no incentive method (current operation in Japan). Furthermore, additional payments to BESS owners accounted for only around 7% of the resulting cost savings, indicating that the proposed method achieved lower overall system operating costs. 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

The energy transition is not only a technological or market-driven process but also a discursive and institutional challenge. While conventional research emphasizes financial incentives and regulatory frameworks, the role of policy narrative intensity in shaping renewable energy innovation has received limited empirical attention. This study addresses this gap by analyzing 8837 provincial-level policy documents (2005–2023) from 31 regions across China. We construct a policy narrative intensity index using the PMC framework to systematically assess how institutional discourse influences the direction and intensity of renewable energy development. The results reveal that a 1% increase in policy narrative intensity corresponds to a 4.60% rise in renewable energy innovation, as measured by renewable electricity generation, with robustness confirmed through IV and IHS methods. Regional heterogeneity is also evident: executive-led regions such as Jiangxi, Shandong, and Fujian exhibit higher narrative strength and stronger renewable energy outcomes, while market-driven provinces like Shanghai and Guangdong show weaker narrative alignment. Mechanism testing demonstrates that policy narratives enhance renewable energy innovation by (1) strengthening environmental regulation enforcement (β = 0.37), (2) increasing green patent activity by 23.6%, and (3) raising public adoption of renewable energy by 17.2 percentage points. This study highlights the governing value of policy narratives as institutional public goods and reveals their crucial role in aligning administrative capacity, corporate innovation, and public engagement to drive energy transition. These insights contribute to the broader discourse on SDG7/SDG13-aligned sustainability governance. Full article

The accelerating energy transition, coupled with increasing market volatility and computational advances, has created an urgent need for sophisticated decision-making tools that can address the unique challenges of energy finance—a gap that reinforcement learning methodologies are uniquely positioned to fill. This paper provides a comprehensive review of the application of reinforcement learning (RL) in energy finance, with a particular focus on option value and risk management. Energy markets present unique challenges due to their complex price dynamics, seasonality patterns, regulatory constraints, and the physical nature of energy commodities. Traditional financial modeling approaches often struggle to capture these intricacies adequately. Reinforcement learning, with its ability to learn optimal decision policies through interaction with complex environments, has emerged as a promising alternative methodology. This review examines the theoretical foundations of RL in financial applications, surveys recent literature on RL implementations in energy markets, and critically analyzes the strengths and limitations of these approaches. We explore applications ranging from electricity price forecasting and optimal trading strategies to option valuation, including real options and products common in energy markets. The paper concludes by identifying current challenges and promising directions for future research in this rapidly evolving field. Full article

This study analyzes the economic impacts of integrating floating offshore wind farms with a Floating Production, Storage and Offloading (FPSO) unit to reduce carbon dioxide emissions. The idea is to replace the use of natural gas for power supply with an offshore wind farm, considering the effects of carbon pricing. Results show that wind integration reduces emissions by 23% to 76%, depending on the installed capacity. However, higher wind capacity increases total system costs, initial investment, electricity and operational expenses. The Brazilian carbon credit market adversely impacts existing FPSO units as a result of the compulsory carbon trading costs necessary to mitigate their emissions. In contrast, wind-integrated scenarios benefited from carbon pricing, improving financial indicators such as payback period and Return on Investment. Wind shares of 30% and 70% yielded the best financial results for carbon prices between 10 and 50 United States Dollars per ton, with higher penalties further improving viability. These findings elucidate the significance of carbon pricing in mitigating emissions and enhancing the economic feasibility of offshore wind farms within the context of the Brazilian national FPSO decarbonization strategy. Full article

This paper investigates the long-term impact of integrating emerging Small Modular Reactors (SMR) in modern power systems. A chronological simulation of the Greek day-ahead market and real-time balancing market with fine time granularity is conducted for a future 20-year period (2032–2051) under four SMR penetration scenarios using a specialized integrated market simulation software. Simulation results indicate that SMR units can be regarded as a promising electricity generation solution in the forthcoming energy transition landscape. The introduction of up to 3 GW of SMR capacity is projected to significantly decrease reliance on gas imports by up to 62%, reduce carbon emissions by up to 52%, and lower overall electricity costs for end-consumers by up to 21% as compared to a baseline scenario without SMRs. It is anticipated that SMR units are expected to leverage their operating advantages and generally achieve positive financial results when participating directly in the wholesale market. However, their economic viability is highly dependent on their initial capital expenditure and other operating cost components, which at present are highly uncertain. Full article

The COVID-19 pandemic and the Russia–Ukraine war have struck the world’s energy markets. This study analyzed how the recent unstable fossil fuel markets impacted the Japanese electricity contracts, classified as extra-high-, high-, and low-voltage contracts. Multiple structural break tests were conducted to endogenously determine breaks affecting electricity prices during January 2019 to November 2022. By incorporating the effects of these breaks in the autoregressive distributed lag (ARDL) model, the study analyzed the effects of natural gas, coal, and crude oil prices on the types of electricity contract prices. The results of the analyses indicated a surge in electricity prices for low- and high-voltage contracts driven by an increase in natural gas. The results imply the importance of providing proper financial support to mitigate the effects of soaring electricity prices and implementing policies to diversify the electricity generation mix in Japan. Full article