Search

In the article, the current state of research on the application of planned and cost-accounting (evaluative) sanctions as a form of economic and legal responsibility is analysed in general and specifically in the energy sector. The specificity of applying this form of economic and legal responsibility in the energy sector, existing problematic issues of its application, and proposals for their resolution are investigated. The law enforcement practice of the Supreme Court, which has developed in applying this form of economic and legal responsibility, is analysed. The role of the permanent central executive body with a special status -- the National Commission for State Regulation of Energy and Utilities -- in regulating the application of planned and cost-accounting (evaluative) sanctions to participants in the ener gy markets is studied. Ways to improve the regulatory framework for planned and cost-accounting (evaluative) sanctions in the energy sector are proposed, along with ways to enhance the regulatory framework to minimize similar violations by energy market participants in the future and to foster a high culture of compliance with current legislation norms. The research utilized the following materials and methods: 1) regulatory acts that define the grounds for applying such a form of economic and legal responsibility as operational-economic sanctions; 2) works by domestic and foreign authors conducting their scientific and practical research in the field of economic and legal responsibility; 3) the practice of domestic courts. During the research, methods of theoretical generalization and grouping, formalization, analysis and synthesis, and logical generalization of results (formulation o f conclusions) were used. The scientific article reveals the main patterns in applying such a form of responsibility as planned and cost-accounting (evaluative) sanctions in general and in the energy sector, the grounds, and main features of applying planned and cost-accounting (evaluative) sanctions in the energy sector, and examples of applying such sanctions to business entities -- part icipants in the energy markets. [ABSTRACT FROM AUTHOR]

We examine the impact of physical climate risks on energy markets in China, distinguishing between traditional energy and new energy stock markets, and the energy commodity market, utilizing a time-varying parameter vector autoregressive model with stochastic volatility (TVP-SV-VAR). Specifically, we investigate the dynamic effects of five specific subtypes of physical climate risks, namely waterlogging by rain, drought, typhoon, cryogenic freezing, and high temperature, on WTI oil prices and coal prices. The findings reveal that these physical climate risks exhibit time-varying similar effects on the returns of traditional energy and new energy stocks, but heterogeneous effects on the returns of WTI oil prices and coal prices. Finally, we categorize and examine the impact of both acute and chronic physical risks on the energy commodity market. [ABSTRACT FROM AUTHOR]

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators. [ABSTRACT FROM AUTHOR]

Abstract A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy.

Taking six representative futures in the international energy and agricultural markets as the research objects, we use multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations within and between these markets before and after the outbreak of the Russia–Ukraine conflict in this paper. The empirical results show that both the auto-correlations and cross-correlations have obvious multifractal features. It is confirmed that the multifractal strength and market risks of the international energy markets have weakened, while those of the international agricultural markets have enhanced after the Russia–Ukraine conflict broke out. In addition, the Russia–Ukraine conflict has intensified the strength of the multifractality and the degree of fluctuation complexity between these two classes of international markets. Further, the intrinsic multifractal natures of cross-correlations are tested, and the apparent and intrinsic multifractality before and after the conflict are revealed. Finally, some policy suggestions are put forward based on the empirical results. [ABSTRACT FROM AUTHOR]

This paper investigates the nonlinear risk transmission from the oil and natural gas markets to the foreign exchange markets of five energy importers and one major energy exporter. We separate conditional volatility into the transitory (short-term) and permanent (long-term) parts, and then these volatilities are embedded in an elaborate robust linear quantile regression model. We find that the risk spillover effect is relatively low in Central and Eastern European countries (CEECs) probably because they pursue a managed float exchange rate regime. On the other hand, this effect is higher for Turkey and Russia, which is especially true for the effect from oil to the rouble at the highest quantile. This happens because Russia receives the largest amount of foreign currency from oil exports. The results indicate that the short-term risk spillover effect is notably stronger than the long-term one, which means that the exchange rate volatility is mainly determined by market sentiment. The rolling regression results coincide very well with the estimated quantile parameters. [ABSTRACT FROM AUTHOR]

This study presents a novel comparative analysis on global energy scenarios in terms of main characteristics, scenario building frameworks and time frames. The study analyzes each energy scenario's outputs and outlooks by 2040 such as gross domestic product growth, technology developments, primary energy demand by sector, energy supply by fuel, energy intensity and carbon emissions. Based on the foresight semiquantitative method, global energy driving forces which play significant roles in reshaping the world energy future by 2040 have been identified. The study applies mathematical principles to quantify the rational judgments of an expert panel in social, technological, economic, environmental and political framework through cross-impact analysis. Among the 10 global energy driving forces by 2040, population growth rate and climate change are identified as social and environmental driving forces, respectively. Investment in infrastructures, financial shocks, consumer behavior and energy efficiency are marked as economic driving forces, while global governance and geopolitical relationships are identified as the global energy market's political driving force. And finally, technological development in renewable energies as well as in oil and gas upstream sector is recognized as a technological driving force. [ABSTRACT FROM AUTHOR]

Economists have widely endorsed pricing CO 2 emissions to internalize climate change-related externalities. Doing so would significantly affect coal, the most carbon-intensive energy source. However, U.S. coal markets exhibit an additional distortion: the railroads that transport coal to power plants can exert market power. This article estimates how coal-by-rail markups respond to changes in coal demand. I identify markups in a major intermediate goods market using both reduced-form and structural methods. I find that rail carriers reduce coal markups when downstream power plant demand changes due to a drop in the price of natural gas (a competing fuel). My results imply that decreases in coal markups have increased recent U.S. climate damages by $11.9 billion, compared to a counterfactual where markups did not change. Incomplete pass-through would likely erode the environmental benefits of an incremental carbon tax, shifting the tax burden towards upstream railroads. Still, a non-trivial tax would likely increase welfare. [ABSTRACT FROM AUTHOR]

This work analyses the trading of strategic merchant hydrogen technologies in energy and ancillary services markets. The hydrogen firms trade in two markets: (1) a joint hydrogen and energy/reserves day-ahead market and (2) the balancing settlements market. We contrast the co-optimised markets with trading in an energy-only market. Trading both energy and ancillary services leads hydrogen firms to produce and use more hydrogen, leading to less reliance on fossil fuels and an increase in the revenue streams of the electrolysis-based firms. The problem is formulated as a stochastic multi-leader-multi-follower model. Each leader firm solves a bi-level Stackelberg problem. The upper-level is the Nash game among strategic firms. The lower-level is an instance of a Generalised Nash Equilibrium of the followers. • Strategic hydrogen firms modelled in an energy market. • Stackelberg equilibrium found via diagonalisation. • Hydrogen providing reserves boosts hydrogen usage. • Ancillary services shift from SMR to electrolysis. [ABSTRACT FROM AUTHOR]

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

Abstract The wide deployment of distributed energy resources, combined with a more proactive demand‐side management, is boosting the emergence of the peer‐to‐peer market. In the present study, an innovative peer‐to‐peer energy trading model is introduced, enabling a group of price‐setting prosumers to engage in direct negotiations via a straightforward best‐response approach. A Nash equilibrium problem (NEP) is initially formulated and a sufficient condition for the unique solution of the NEP is derived. Afterward, an asynchronous and convergence‐fast solving method is employed to determine the trading quantity and price. The efficiency and resilience of the presented method are demonstrated through a comprehensive case study.

This article aims to show the areas in which technological changes will take place to bring about energy security, and the prerequisites and costs. It has been assumed that technological progress will contribute to the development of energy markets and increase the level of energy security. Changes in energy markets are a necessity. Energy is a source of economic competitiveness. The ability to produce cheap energy and in quantities adequate to needs is a social and economic development factor. The opportunities for change will be the need for new energy sources, instability in the energy markets, as well as technological progress. New technological solutions allow for better energy demand and supply management and preparing forecasts for increases/decreases in the energy demand. The threats are high costs of introducing new technologies and the necessity of implementing many investments at the same time, which require changes in entire systems. Investments require a long period of time and anticipation of trends.

An attempt has been made in the study, to examine the correlation between Twitter Sentiments and Stock Indices Returns, with reference to Nifty Energy Indices. This study used daily time series data, for a period of five years from 01.01.2018 to 31.12.2022. The study found positive relationship between variables of twitter positive, negative sentiment and nifty energy indices but negative relationship was found between neutral sentiment and nifty energy indices. The findings of the study would be useful to the investors and other participants of stock market, understanding the influence of Twitter sentiment on the energy indices returns.

As energy markets become more decentralised, energy trading platforms are emerging as useful tools to facilitate the coordination of energy consumption and generation, encouraging a more efficient use of renewable energy by residential producers and consumers. By exploring three different European countries, we study the effectiveness of both monetary and non-monetary incentives in fostering energy trade via trading platforms. We use an incentivized survey experiment to evaluate the drivers of consumers' and prosumers' willingness to participate in an energy trading platform. We find that the monetary incentive is not necessarily the main reason why people would choose to trade their energy, but other dimensions, such as environmental concerns and independence from the national grid, play an important role. [ABSTRACT FROM AUTHOR]

Substantial R &D efforts are currently directed towards the development of combined heat and power (CHP) systems that automatically and seamlessly connect to the power grid. In this paper we develop a real options model to assess the impact that the operational flexibility characterizing such systems will have on the optimal timing and capacity associated with investments in CHP plants. We take the viewpoint of a manufacturer operating in an energy-intensive industry who contemplates investing in CHP. We discuss and compare investments in two types of CHP systems: a standard one that is operationally rigid and a technologically advanced one that is operationally flexible. The interaction between temporal and operational flexibility under uncertainty and irreversibility is central to our analysis. We show that operational flexibility guarantees earlier investment but has an ambiguous effect in terms of capacity. In particular, when operational flexibility is very valuable the potential investor is opting for investing in a plant with larger productive capacity. The potential investor chooses a smaller CHP unit if otherwise. A numerical exercise calibrated using data from the Italian pulp and paper and electricity industries complements our theoretical analysis. [ABSTRACT FROM AUTHOR]

Copyright of Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji is the property of Gazi University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Energy transition towards a local resilient energy supply is necessary for energy security. Climate change and the threat of economic collapse are reasons to force society to become less dependent on fossil fuel. Small-scale solutions are expected to be more sustainable, as large-scale integrated networks are featured by complexity and difficult-to-notice vulnerabilities, creating system risks. This paper presents the results of empirical research among a sample of Polish business students (N = 205) on the importance of worldviews for the choice of the energy transition scenario (local solutions vs. large-scale solutions). Worldviews are represented here by the Dominant Social Paradigm (defined for the purpose of this study as the belief that liberal democracy, free markets, and technological development are to solve all type of problems). This study addresses two research questions: (1) Is there a difference in preference for the energy transition scenarios? (2) Does adherence to the Dominant Social Paradigm determine the choice of the scenario for energy transition? The results present a preference for non-cooperative solutions (individual household solutions and large-scale solutions), while no significant relation to the worldviews represented by the Dominant Social Paradigm has been found. The results suggest that preferences for individual and large-scale solutions may be influenced more by institutional factors than by personal worldviews. A policy implication is that a change in the socio-political institutions and strengthening local governance may be a prerequisite for a sustainable energy transition. [ABSTRACT FROM AUTHOR]

Large economies are trying to shift their energy procurement from fossil dependence to renewable sources. In 2022 global commodity markets saw rapid price increases due to Russia's invasion of Ukraine and subsequent economic barriers on Russian commodity exports. Australia was not immune to these commodity price rises. Throughout the last decade Australia has become increasingly exposed to global energy prices through its commodity exports of LNG and thermal coal. Over this same decade Australia also experienced energy and climate policy discontinuity, and therefore policy uncertainty. These factors combined with a continued reliance on aging thermal plant resulted in a perfect storm of events which saw Australian electricity and gas prices reach all-time highs across the east-coast. The outcome resulted in long-term government intervention in markets to stabilise prices. This article will argue domestic factors helped fuel record high electricity and gas prices but have since subsided or are in the process of being mitigated. As a result, policymakers should reconsider how interaction occurs with electricity and gas markets to stabilise prices. Another decade of energy and climate policy discontinuity would not be a helpful development for the desired energy transition. A survey of market participants exemplifies concern that this outcome is materialising. [ABSTRACT FROM AUTHOR]

This paper studies the volatility dynamics of futures contracts on crude oil, natural gas, and gasoline. An appropriate Bayesian model comparison exercise between seven stochastic volatility (SV) models is estimated using daily prices for our futures contracts between 2005 and 2023. Moreover, to assess the impacts of COVID‐19 and the Russia–Ukraine conflict on volatility, we analyze these two subsamples. Overall, we find that: (i) the Bayes factor shows that the SV model with t $t$‐distributed innovations outperforms the competing models; (ii) crude oil contracts with different expiry dates may require the introduction of leverage effects; (iii) the t $t$‐distributed innovations remain the appropriate model for the COVID‐19 subsample, while jumps are needed in the conflict period; and (iv) other Bayesian criteria more appropriate to short‐term predictive ability—such as the conditional and the observed‐date deviance information criterion—suggest other rank order to model our futures contracts, despite the agreements for the best models. [ABSTRACT FROM AUTHOR]

This paper presents a model that explores the strategic bidding equilibrium within a microgrid and its interactions with other strategic and non-strategic rivals in a joint energy and balancing market. The model utilizes a tri-level mathematical program with equilibrium constraints (MPEC) to represent the behavior of each strategic producer. Upper level maximizes the profit of each strategic producer, including microgrid and another strategic rival. The first lower-level problem involves maximizing social welfare through the day-ahead market clearing process, while the second lower-level problem deals with the balancing market clearing process. These objectives form the core of the proposed model. To simplify the tri-level problem, duality theory and the Karush-Kuhn-Tucker (KKT) optimally conditions are employed to transform it into a mixed integer linear programming (MILP) problem. By simultaneously solving all MPECs, an equilibrium problem with equilibrium constraints (EPEC) is formulated. The resulting EPEC is then addressed using a diagonalization algorithm and game theory to obtain a market Nash equilibrium, which constitutes the secondary objective of this paper. The effectiveness of the model is evaluated using the 6-bus test system as a case study. The results indicate that, at the equilibrium point, both the microgrid and rivals experience reduced profits compared to the initial state.

The energy markets are experiencing an enhanced volatility and unpredictability due to the growing integration of renewable energy sources in the grid and to the unstable geopolitical situation that is developing worldwide. Energy traders are therefore raising concerns on how to achieve solutions that not only ensure stability in terms of energy needs, both on the supply and demand side, but also enable profits within these markets. To cope with the complexity of this emerging scenario, tools that support traders in their decisions, such as algorithmic trading strategies, are attracting always more and more attention. In particular, evolutionary algorithms have emerged as an effective tool for developing robust and innovative trading strategies. Indeed, their flexibility and adaptability allow for the inclusion of various performance metrics. This article employs a recently issued evolutionary algorithm, called social network optimization, to identify the optimal closing criteria of already opened positions in an energy commodity market. More specifically, the proposed trading strategy is based on five self-defined parameters, which determine a profitable solution over nearly six years of available data. In particular, the overall average positive return achieved and the maximum monthly yield of 1.9% highlight the adaptability and robustness of the developed algorithmic trading strategy. Therefore, the results suggest the potentialities of developing and upgrading novel trading strategies by exploiting evolutionary computation techniques in the actual complex energy markets.

As renewable energy sources become more prevalent, effective grid balancing becomes crucial due to their inherent uncertainty. Battery Energy Storage Systems (BESS) can enhance grid reliability and efficiency by complementing these variable sources. However, to encourage investments in BESS, market participation must be economically viable for owners. Energy arbitrage is one of the main revenue streams for BESS allowing them to buy electricity when prices are low and sell it when they become higher, thus optimizing the revenues. However, in energy markets such as the Day-Ahead market (DA), the BESS owners submit their bids/offers one day before delivery, without perfect foresight of the future rates. This uncertainty poses a challenge that limits the energy provision capabilities and can incur a loss of profit due to the imperfect price forecast. Tailored strategies are then needed to mitigate those uncertainties and minimize the profit loss. This article proposes different operational planning strategies for a BESS participating in DA. Specific interest is attached to the explainability of the proposed methods to assure high profits while reducing the model’s complexity and computational time. The proposed strategies include 1) price forecast and scenario generation, using Geometric Brownian Motion (GBM) based either on a single-point forecast or historical data; 2) optimization process; and 3) choice of a single BESS bidding and operating schedule that is ultimately applied in real-time. Two baselines are introduced, one relying on a back-casting method, and a second based on traditional stochastic optimization. Several studies have neglected to thoroughly assess the bidding strategies by evaluating the profit against the actual prices. Hence, this study assesses the performance of the proposed methods and the baselines relative to the profit obtained in an ideal scenario with a perfect forecast in the French market over 2021.

The offering strategy of energy storage in energy and frequency response (FR) markets needs to account for country-specific market regulations around FR products as well as FR utilization factors, which are highly uncertain. To this end, a novel optimal offering model is proposed for stand-alone price-taking storage participants, which accounts for recent FR market design developments in the UK, namely the trade of FR products in time blocks, and the mutual exclusivity among the multiple FR products. The model consists of a day-ahead stage, devising optimal offers under uncertainty, and a real-time stage, representing the storage operation after uncertainty is materialized. Furthermore, a concrete methodological framework is developed for comparing different approaches around the anticipation of uncertain FR utilization factors (determinis- one based on expected values, deterministic one based on worst-case values, stochastic one, and robust one), by providing four alternative formulations for the real-time stage of the proposed offering model, and carrying out an out-of-sample validation of the four model instances. Finally, case studies employing real data from UK energy and FR markets compare these four instances against achieved profits, FR delivery violations, and computational scalability.

Using a time-dependent copulas model, this study attempts to investigate the risk spillover from global energy markets to Pakistan’s agricultural commodities market. Additionally, it examines the structure of dependency between the agricultural and energy markets. The results show commodities such as wheat has less volatile behavior and weak lower tail dependence in energyagricultural commodity pairs. The world energy volatility index is more volatile as compared to Pakistan’s agricultural commodities markets. Wheat is the least volatile commodity while palmolien is most volatile. The normal copula is best fitted for all energy and commodity pairs at 1% level of significance. Student-t copula is the second best-fitted model, providing significant results for all parameters. Normal copula and student-t copula highlight symmetric dependence among world energy and agricultural commodity markets. However, “student-t copula” has tail dependence and normal copula has no tail dependence. The other copula models have mixed effects in terms of fitness and parameters significance. SJC copula is the least fit copula model for all pairs in this study. The results have inferences for investors with respect to portfolio diversification.

Energy communities are a transformative force in the electricity markets and ancillary services, reshaping the energy landscape through collective action. This paper explores the successful strategies adopted by these communities, highlighting real-world cases where they have participated directly in the market, or through aggregators, or sold their energy to retailers, which is of paramount importance because it serves as a foundation for those countries that wish to implement these entities as part of their decarbonization plan. It also serves as a model for the development of future citizen initiatives that aim to turn citizens into active users of the electricity system. The paper examines collaborative dynamics within the energy sector, highlighting how these communities optimize resource sharing and contribute to a more resilient and sustainable energy system. The study emphasizes the potential of energy communities in driving innovation and fostering a participatory approach to energy management. The results show that some pilot projects are being developed and several electricity cooperatives, one of the most common forms of energy communities, are participating in energy trading with their members and other entities. More efforts are also needed for energy communities to participate more directly in the market and/or through aggregators.

Wind-power generators around the world face two risks, one due to changes in wind intensity impacting energy production, and the second due to changes in electricity retail prices. To hedge these risks simultaneously, the quanto option is an ideal financial tool. The natural logarithm of electricity prices of the study will be modeled with a variance gamma (VG) and normal inverse Gaussian (NIG) processes, while wind speed and power series will be modeled with an Ornstein–Uhlenbeck (OU) process. Since the risk from changing wind-power production and spot prices is highly correlated, we must model this correlation as well. This is reproduced by replacing the small jumps of the Lévy process with a Brownian component and correlating it with wind power and speed OU processes. Then, we will study the income of the wind-energy company from a stochastic point of view, and finally, we will price the quanto option of European put style for the wind-energy producer. We will compare quanto option prices obtained from the VG process and NIG process. The novelty brought into this study is the use of a new dataset in a new geographic location and a new Lévy process, VG, apart from NIG. [ABSTRACT FROM AUTHOR]

In this paper, we examine the presence of spillover among the regional energy markets in Asia, Europe, North America, Pacific region, and South America from January 2, 1995 to May 27, 2021. We report heterogeneous results for spillover between the regional energy markets. Our work separately highlights the presence of negative and positive returns spillover across sampled energy markets. We also report a significant increase in spillover during different crisis periods, supporting the contagion phenomena. Finally, we also report the asymmetry in short- and long-run spillover and negative and positive returns during normal and crisis periods. Our results carry implications for investments in the regional energy markets under different market conditions and investment periods. [ABSTRACT FROM AUTHOR]

Deploying renewable hydrogen presents a significant challenge in accessing off-takers who are willing to make long-term investments. To address this challenge, current projects focus on large-scale deployment to replace the demand for non-renewable hydrogen, particularly in ammonia synthesis for fertiliser production plants. The traditional process, involving Steam Methane Reformers (SMR) connected to Haber-Bosch synthesis, could potentially transition towards decarbonisation by gradually integrating water electrolysis. However, the coexistence of these processes poses limitations in accommodating the integration of renewable hydrogen, thereby creating operational challenges for industrial hubs. To tackle this issue, this paper proposes an optimal dispatch model for producing green hydrogen and ammonia while considering the coexistence of different processes. Furthermore, the objective is to analyse external factors that could determine the appropriate regulatory and pricing framework to facilitate the phase-out of SMR in favour of renewable hydrogen production. The paper presents a case study based in Spain, utilising data from 2018, 2022 and 2030 perspectives on the country's renewable resources, gas and electricity wholesale markets, pricing ranges, and regulatory constraints to validate the model. The findings indicate that carbon emissions taxation and the availability and pricing of Power Purchase Agreements (PPAs) will play crucial roles in this transition - the carbon emission price required for total phasing out SMR with water electrolysis would be around 550 EUR/ton CO 2. • Addressing the co-existence of steam methane reformers and electrolysers. • Novel power dispatch model addressing the flexibility of co-existential pathways. • PPA availability and pricing and ETS taxation as key enablers of renewable hydrogen. • Carbon intensity constraints heavily impact the project's sizing and feasibility. • Techno-environmental study of the carbon intensity of ammonia production. [ABSTRACT FROM AUTHOR]

This paper presents a novel energy management strategy (EMS) to control a wind-hydrogen microgrid which includes a wind turbine paired with a hydrogen-based energy storage system (HESS), i.e., hydrogen production, storage and re-electrification facilities, and a local load. This complies with the mini-grid use case as per the IEA-HIA Task 24 Final Report, where three different use cases and configurations of wind farms paired with HESS are proposed in order to promote the integration of wind energy into the grid. Hydrogen production surpluses by wind generation are stored and used to provide a demand-side management solution for energy supply to the local and contractual loads, both in the grid-islanded and connected modes, with corresponding different control objectives. The EMS is based on a hierarchical model predictive control (MPC) in which long-term and short-term operations are addressed. The long-term operations are managed by a high-level MPC, in which power production by wind generation and load demand forecasts are considered in combination with day-ahead market participation. Accordingly, the hydrogen production and re-electrification are scheduled so as to jointly track the load demand, maximize the revenue through electricity market participation and minimize the HESS operating costs. Instead, the management of the short-term operations is entrusted to a low-level MPC, which compensates for any deviations of the actual conditions from the forecasts and refines the power production so as to address the real-time market participation and the short time-scale equipment dynamics and constraints. Both levels also take into account operation requirements and devices' operating ranges through appropriate constraints. The mathematical modeling relies on the mixed-logic dynamic (MLD) framework so that the various logic states and corresponding continuous dynamics of the HESS are considered. This results in a mixed-integer linear program which is solved numerically. The effectiveness of the controller is analyzed by simulations which are carried out using wind forecasts and spot prices of a wind farm in center-south of Italy. • Design of control algorithms for islanded and grid-connected microgrids. • Development of hierarchical MPCs to target long-term and short-term operations. • Tracking the load demand and maximizing the revenue via electricity markets. • Minimization of the HESS operational costs. [ABSTRACT FROM AUTHOR]

Copyright of Current Social & Economic Processes / Jelenkori Társadalmi és Gazdasági Folyamatok is the property of University of Szeged, Faculty of Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brazil's significant commodity production is internationally recognized, yet the absence of a mature futures market exposes it to price risks and international shocks. This study explores the dynamic connectedness between commodity futures and the Brazilian cash markets, using a time‐varying parameter vector autoregressive model. We also assess COVID‐19's impact on this connectedness. We find a significant influence of oil prices on Brazilian ethanol prices, and particularly emphasize the Heating Oil spillover effect on ethanol in the post‐COVID‐19 era. We also note the ascension of Brazilian soybean spot markets' international significance since 2017, amplifying their role in global grain price discovery. Finally, by computing hedge ratios and effectiveness between commodity futures contracts and Brazilian spot prices, our study reveals soybean cash price as the most effective hedge. These insights deepen comprehension of connectedness within Brazilian commodity markets, thereby guiding investors and policymakers in strategic energy policy decisions. [ABSTRACT FROM AUTHOR]

We propose a new methodology for pricing options on flow forwards by applying infinite-dimensional neural networks. We recast the pricing problem as an optimisation problem in a Hilbert space of real-valued functions on the positive real line, which is the state space for the term structure dynamics. This optimisation problem is solved by using a feedforward neural network architecture designed for approximating continuous functions on the state space. The proposed neural network is built upon the basis of the Hilbert space. We provide case studies that show its numerical efficiency, with superior performance over that of a classical neural network trained on sampling the term structure curves. [ABSTRACT FROM AUTHOR]

Copyright of EMERG: Energy. Environment. Efficiency. Resources. Globalization is the property of Romanian National Committee of World Energy Council and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Electric Power is the property of Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper investigates the Markov-Switching GARCH and Single-Regime (SR) GARCH models for the extreme-risk prediction of the global energy markets. Using daily data from Jan. 2020 to July. 2022, we find the MS-GARCH-types models are appropriate for both developed and emerging energy markets because they efficiently measure the extreme risk of energy commodities in various cases. Meanwhile, the regime-switching model's capture-dynamic structures in the financial markets and this model is only better than single-regime models in terms of long position risk predicting, rather than short position risk forecasting. That is, on the downside risk predicting, it just outperforms the single regime. Through competitive models, this study examines the risk forecast of energy commodities in different conditions. The findings have strong implications for investors and policymakers in selecting the appropriate model to predict the extreme risk of energy commodities when facing asset allocation, portfolio selection, and risk management. [ABSTRACT FROM AUTHOR]

The UK is, like its peers in the EU, just under 80% dependent on fossil fuels. This figure has come down just over 10% since the 1970s, a period when the UK had major energy-intensive industries, most of which are now gone. The government is committed to achieving net zero for the electricity sector by 2035 on the pathway to net zero for the whole economy by 2050.. The Labour opposition has set 2030 as its electricity target. Setting out the scale of this challenge, this paper demonstrates how implausible the 2035 target is. On present policies there is little prospect that the 2035 target will be met (and virtually none for the Labour 2030 target). This paper reviews the multiple current policies and the capacity objectives and explores what would have to happen to meet the target. It sets out some of the consequences of failure to deliver, and the expectation of that failure. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the effects and risk spillover from the global crude oil market on Tehran Stock Exchange Oil Group. For this purpose, we used a combination of copula models and switching models in this research. First, we will examine marginal models and examine Heston switching and Markov switching models in this market. Then we create the multivariate distribution function using Clayton's copula. The data analyzed in this research are related to the global crude oil markets and the Tehran Stock Exchange Oil Group from December 2011 to January 2023. This time period was chosen due to the examination of different regimes in the above markets and also the selection of the appropriate marginal model for these markets. The results show the crude oil market has influenced on Tehran Stock Exchange and also the Tehran Stock Exchange Oil Group indices. Volatility in this global market cause turbulence in the Tehran stock market and this market is affected by the global crude oil market. This is due to the influence of the global crude oil market on total prices in these markets. Heston switching model and its combination with copula models including Clayton copula can bring good results. This is confirmed by comparing this model with other models such as copula Markov switching models.

Abstract The energy hub applications have been investigated from different perspectives in recent years due to their benefits such as high flexibility and reliability. This paper proposes a two‐stage stochastic programming to enable the participation of a virtual energy hub integrated with parking lots in the electrical and thermal energy markets under the uncertainty of renewable resources, load demands and energy prices. In such modeling, the first stage optimizes the offers of the virtual hub in the day‐ahead markets before realizing uncertain parameters and the second stage minimizes the real‐time imbalanced cost and determines the operation of components considering actual data. In order to evaluate the risk of decisions, the conditional value‐at‐risk metric is utilized and the results are discussed in the risk‐neutral and risk‐averse strategies for a sample day of summer and winter. In the following, a time‐based program is implemented to indicate the influence of responsive loads and a sensitivity analysis is conducted to assess the effectiveness of the flexible units. The simulations approve that although the risk‐averse optimization significantly decreases the profit, the initiative against fluctuations enhances. The outcomes also demonstrate the notable role of flexible units and loads for energy trading in the electrical and thermal markets.

This review paper thoroughly examines the role of technical analysis in energy and financial markets with a primary focus on its application, effectiveness, and comparative analysis with fundamental analysis. The discussion encompasses fundamental principles, investment strategies, and emerging trends in technical analysis, underscoring their critical relevance for traders, investors, and analysts operating within these markets. Through the analysis of historical price data, technical analysis serves as a crucial tool for recognizing market trends, determining trade timing, and managing risk effectively. Given the complex nature of energy and financial markets, where many factors influence prices, the significance of technical analysis is particularly pronounced. This review aims to provide practical insights and serve as a roadmap for future research in the realm of technical analysis within energy and financial markets. This review contributes to the ongoing discourse and advancement of knowledge in this crucial field by synthesizing existing perspectives and proposing avenues for further exploration.

With heightened concern over carbon neutrality and increased energy market fluctuations against the backdrop of increasing global uncertainty, it becomes imperative to thoroughly investigate the information transmission and risk contagion between carbon and energy markets. This paper empirically explores the return and volatility connectedness among carbon and energy markets (electricity, natural gas, crude oil, and coal) from April 2008 to September 2021 by employing the time-frequency domain connectedness approaches. This paper indicates: i) the return connectedness exceeds volatility connectedness, and extreme events can intensify the dynamic changes; ii) the return connectedness is predominantly concentrated at high-frequency, while the volatility connectedness is concentrated at medium- and low-frequency. Net volatility connectedness maintains a consistent direction across all markets in the time-frequency domain. In contrast, net return connectedness exhibits an opposing direction at high-frequency and medium- and low-frequency. iii) the natural gas and coal markets predominantly act as net transmitters in both return and volatility connectedness, while the electricity, crude oil, and carbon markets operate as net receivers. Moreover, during the COVID-19 pandemic, the carbon market emerged as an information receiver in time-frequency domains and acted as a risk transmitter, exporting risk, particularly to the electricity market. These conclusions help investors, high-carbon enterprises, and policymakers to comprehensively understand the carbon-energy relationships, thereby supporting sustainable energy development and low-carbon economic goals. Simultaneously, this paper provides insights for enhancing carbon markets in emerging markets like China.