Asymmetric effects and volatility transmission from metals markets to solar energy stocks: Evidence from DCC, ADCC, and quantile regression approach

Detrimental environmental repercussions have recently given rise to an interest in green investments. Although solar energy stocks are appealing assets for ethical investors, little is known about their dynamic correlations and linkages with metal (silicon, lithium, and rare earth) markets, particularly during economic events which is essential for hedging effectiveness and asset allocation. This study investigates the nexus between metal markets, oil price volatility (OVX), market sentiments (VIX), and solar energy markets using DCC, ADCC models, and the quantile regression approach. The results show both symmetric and asymmetric shock spillover between metals markets, VIX, OVX, and solar energy markets which are more prominent during COVID-19 pandemic, US-China trade frictions, and Russian invasion of Ukraine. For portfolio management, the hedging effectiveness of lithium stocks is highest, followed by silicon and rare earth metals. However, the hedge ratios are time-varying, and the variability is highest during US-China trade frictions. The quantile regression estimates reveal that lithium market is the most persistent determinant of solar energy stocks followed by silicon market even after segregating the periods into Paris Agreement and COVID-19 pandemic. Thus, lithium and silicon are driving markets of solar energy markets and can be a cause of omitted variable bias if stay unobserved. Nonetheless, there is little influence of VIX, rare earth metals, and OVX on solar energy stocks. Lastly, the estimations of threshold regression suggest that market sentiments change the association between metal markets and solar energy markets after the VIX reaches a certain threshold level. © 2023

How do carbon, stock, and renewable energy markets interact: Evidence from Europe

In a low-carbon context, the connectedness among carbon, stock, and renewable energy markets has been strengthening. This study examines the effect of Brexit, the launch of the European Green Deal and the COVID-19 pandemic on the connectedness among carbon, stock, and renewable energy markets by employing Time Varying Parameter -Vector Auto Regression (TVP-VAR). First, equal interval impulse response analysis shows that in the short term, the renewable energy market suffers from a positive shock from the carbon market and this shock gradually decreases from the initial 1.6×10−3. In the long run, the connectivity between the carbon market and the stock market, and between the carbon market and the renewable energy market is almost 0. Second, we can conclude that the positive connectivity between stock market to carbon market and renewable energy market to carbon market is enhanced by COVID-19 in the short term, with values of 7.5×10−3 and 3.6×10−3 respectively. Finally, renewable energy market received a greater negative impact from the carbon market during COVID-19 than during the release of the European Green Deal, while Brexit allowed positive carbon price spillover to renewable energy price. © 2023 Elsevier Ltd

Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach

During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.

Electricity price estimation using deep learning approaches: An empirical study on Turkish markets in normal and Covid-19 periods

This study aims to estimate the prices in the next 24 h with deep learning methods in the Turkish electricity market. The model is based on hourly data for the period 2017–2021 using electricity prices. The model's Root Mean Square Error (RMSE) value is 3.14, and the explanatory power R2 is 0.94. Since this model also considers the subgroups in the database, it can make price predictions for the pandemic period. To test the robustness and consistency of the model, twelve RNN-based models were re-estimated with the same data set. Although all models successfully predict the prices, The TEDSE Model performs better than the others. This study will be especially beneficial to electricity market players and policymakers. In further studies, the TEDSE model can be used for price prediction in intraday energy markets. This study's most important contribution is methodology innovation, using the Transformer Encoder-Decoder with Self-Attention (TEDSE) model for the first time to estimate electricity prices. © 2023 Elsevier Ltd

Impacts of COVID-19 Pandemic on Italian Electricity Demand and Markets

In this paper, the short-, medium-, and long-term effects of the COVID-19 pandemic on the Italian power system, particularly electricity consumption behavior and electricity market prices, are investigated by defining various metrics. The investigation reveals that COVID-19 lockdown caused a drop in load consumption and, consequently, a decrement in day-ahead market prices and an increase in ancillary service prices. © 2015 CSEE.

Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development

This study employs the time-varying vector parameter autoregression model and Diebold-Yilmaz (2012, 2014) spillover approach to explore the static, net, dynamic and directional spillover effects between China's traditional energy and emerging green markets and the impact of the COVID-19 outbreak on spillover effects. Spillover networks are constructed to observe structural changes in the directional spillover of each target financial market before and after the pandemic's outbreak. Changes in hedging indicators of portfolios composed of two types of markets before and after the outbreak of COVID-19 are compared to provide directional guidance for investors to choose portfolios in the post-pandemic era. We found that the outbreak of the pandemic had a considerable impact on the volatility of various spillover effects of the studied markets. The total spillover level of the system increased rapidly by 18% in the early stages of the pandemic. Green bond was the largest net recipient of volatility spillovers in the whole system, followed by crude oil, while new energy was the largest net contributor of volatility spillovers in the whole system, followed by clean energy. After the outbreak, the hedging effectiveness of portfolios with long positions in traditional energy markets and short positions in emerging green markets improved significantly. In particular, a portfolio with long positions in the crude oil market and short positions in the green bond market is the best risk-hedging portfolio. © 2023 Elsevier Ltd

Dynamic spillovers and asymmetric connectedness between fossil energy and green financial markets: Evidence from China

With the purpose of risk management for fossil energy investors, this paper examines the dynamic spillover effect and asymmetric connectedness between fossil energy, green financial and major traditional financial markets in China. By employing the spillover index model of Diebold and Yilmaz, a weak correlation between green financial and fossil energy markets is verified, and the market connectedness remains relatively calm despite the COVID-19 pandemic outbreak. Specifically, green bonds receives fewer shocks from crude oil than coal, green stocks receive fewer shocks from coal than crude oil. In addition, rather than the safe-haven characteristics presented by gold, this paper further proves that green bonds also have the potential to act as safe-haven assets, due to the fact that the connectedness between green bonds and energy markets is at low levels. Finally, the magnitude of return spillovers between markets would vary significantly during different periods. The results obtained in this paper have practical implications for both investors and policymakers. Copyright © 2022 Deng, Guan, Zheng, Xing and Liu.

Market Conditions and their Effect on Market Clearing Price Forecasting

High volatility in deregulated electricity markets is that characteristic that exposes its participants to higher risks. Volatility is due to many, and most of the times unpredictable, factors, ranging from fuel prices, production from renewable energy sources, electricity demand to Covid-19 and energy crisis. This article describes the initial stages of the work that combines signalling with market conditions in order to analyse the factors affecting the clearing prices of the day ahead market in view of enhancing the forecasting of these prices. The proposed forecasting methodology is based on the extreme learning machine (ELM) and it is tested on the German and Finnish markets. © 2022 IEEE.

Dynamic risk management in European energy portfolios: Evolution of the role of clean and carbon markets

This paper examines the potential of clean energy stocks and emission permits to reduce downside risk when combining them in a portfolio with dirty energy assets. We propose a strategy for building portfolios that are well diversified between equity energy and carbon markets that takes into account their dynamic price relationship. The asset allocation proposed is framed in a volatility-timing context, which reacts to changing market conditions, holding different weights at different times. To achieve this objective, we use multivariate GARCH models, specifically the Asymmetric Dynamic Conditional Correlations family, which allow us to obtain good estimations of the conditional covariance matrices of the daily asset returns. To determine the weights of the optimum minimum-risk portfolio, we use a method based on Engle and Colacito (2006) to compare the portfolio volatilities obtained with different models. The analysed period runs from January 19, 2010, to April 4, 2022, which, on the one hand, includes more than twelve years of the EU Emissions Trading System (EU ETS) beyond the Phase I pilot;and, on the other, considers the latest crisis episodes (Sovereign debt crisis, Brexit COVID-19, and the recent Russo–Ukrainian war). Our findings show that investing in clean energy companies is now valuable not only because of its contribution to a sustainable energy transition to renewable sources, but also due to its attractiveness from a financial point of view. This fact provides a ray of hope in terms of the climate emergency and avoiding the current geopolitical conflicts principally caused by certain countries’ energy dependence because their energy mix is still heavily overpowered by fossil fuels. The results of this research should encourage investors to decarbonise their equity portfolios, thus promoting the needed alignment of the financial system with the requirements of the energy transition. © 2022 The Authors

How Did the COVID-19 Crisis Affect the Efficiency of European Intraday Electricity Markets?

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models is more accurate than others. The focus is on the German intraday market for electricity. We want to find out whether the COVID-19 crisis has an influence on the price development. This paper includes a comprehensive review between Germany, France and Norway (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyse the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions (i) we reviewed the market data linked to COVID-19. We expected a higher volatility in the lockdowns than before and therefore decrease in efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer the question (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis we use data visualization and statistical models as well as sample and out-of-sample data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Is COVID-19 pandemic a “Black Swan” event? The impact of the pandemic on the Energy Market

The recent coronavirus disease (COVID-19) pandemic outbreak affected our society greatly, offering a chance to rebuild and rethink our way of living. Energy, as a driving factor of everyday life faced an unprecedented shock. How big was this shock for both the economic and political levels? No consolidated study exists where both aspects are considered. To rethink our way of living, we should reconsider the energy policies strategies for the upcoming years. To date, such an impact has not yet been quantified using price forecasting mathematical models. We have therefore developed a methodology, to quantify the impact of COVID-19 pandemic on European energy market. This paper is addressing the following question “Is the COVID-19 pandemic a Black Swan event?”Evidently COVID-19 had significant consequences on the European energy market. Stocks suffered historical minimum prices duringthis year, with a greater impact on coal technologies than renewable ones. Moreover, stock prices return is showing unexpected fluctuations, hence resulting in incorrectly predicted price forecasts. Despite the initial shock, the energy market is returning to pre-crisis levels, with the renewable technologies leading the comeback. Based on our findings and methods, we conclude that COVID-19 pandemic was not a Black Swan event. We foresee to extend our methodology beyond European energy market and the short-term effects of the pandemic with possible application on the impact of policy makers on energy models. © ECOS 2021 - 34th International Conference on Efficency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.

Impact of the COVID-19 Pandemic Crisis on the Efficiency of European Intraday Electricity Markets

Our goal is to examine the efficiency of different intraday electricity markets and if any of their price prediction models are more accurate than others. This paper includes a comprehensive review of Germany, France, and Norway’s (NOR1) day-ahead and intraday electricity market prices. These markets represent different energy mixes which would allow us to analyze the impact of the energy mix on the efficiencies of these markets. To draw conclusions about extreme market conditions, (i) we reviewed the market data linked to COVID-19. We expected higher volatility in the lockdowns than before and therefore decrease in the efficiency of the prediction models. With our analysis, (ii) we want to draw conclusions as to whether a mix based mainly on renewable energies such as that in Norway implies lower volatilities even in times of crisis. This would answer (iii) whether a market with an energy mix like Norway is more efficient in highly volatile phases. For the analysis, we use data visualization and statistical models as well as sample and out-of-sample data. Our finding was that while the different price and volatility levels occurred, the direction of the market was similar. We could find evidence that our expectations (i–iii) were met. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Experiences on Incorporating Market Experiments into Energy System Education

This paper summarizes an experience on integrating electricity market experiments into a senior-level course in Columbia University: Energy System Economics and Optimization, which attracted a diverse student body covering various engineering programs as well as public policies and business school. In accordance with the virtual instruction method due to Covid-19, these experiments are assigned to students in the form of online quizzes. In these experiments, students role play electricity market participants by submitting bids or estimating future scenarios spanning topics including centralized market clearing, demand serving contracting, and wind power contracting. These experiments where performed using down-scaled real data from New York Independent System Operator, and the instructor will perform market clearing and analyze the results with the students. Experiences from this course show that these economic experiments attract students'attention and study interests especially at the beginning of the course, but also show rooms for improvements especially on better engaging student in more complex market settings. © 2021 IEEE.

The Significance of Dissemination Activities at Mediterranean University in the frame of project 'Electrical Energy Markets and Engineering Education (ELEMEND)'

Engineering and science curricula are revamped to address sustainability as a cross cutting horizontal issue in order to have an impact on how students appreciate, understand and think critically about complex environmental, social and economic problems. Key to maximizing impact is the dissemination of a novel practice as well as its outcomes and outputs. The target of the ERASMUS+ project 'Electrical Energy Markets and Engineering Education - ELEMEND' is the capacity building of academic and teaching staff, students, electro engineering staff, employers as well as the general public. ELEMEND also aspires to create a favorable environment for energy related business and to modify the electricity user's behavior in the Western Balkan Countries in line with technological developments in smart grids. In order to ensure the sustainability of the ELEMEND results, a Dissemination and Exploitation Plan was elaborated by each of partner university taking into account all relevant stakeholders and key actors. In particular, we examine how the dissemination plan of Mediterranean University (MU), a partner university of the ELEMEND project, sets the targets to be achieved through the dissemination activities which make the project's objective and results visible and usable to all potential stakeholders. We focus on the dissemination activities of the Mediterranean University, we analyze applied methods and forms, we provide examples of how Mediterranean University pursued dissemination targets under Covid-19 conditions. © 2021 IEEE.

Oil-Price Based Long-Term Hourly System Marginal Electricity Price Scenario Generation

We synthesize scenarios of hourly electricity price, which is known as the system marginal price (SMP), for thirty-years based on the oil price. Hourly SMP scenarios are very important when planning new generators because the revenue and cost of new capacity margins are determined based on the SMP. Because the SMP contains both short-term and long-term periodic patterns, designing a single model based on these patterns to predict the SMP is difficult. Although oil price affects SMP, they can not be directly used in the forecasting model because the resolution of SMP is at hourly intervals, but that of oil price is at yearly intervals. To overcome these problems, we decompose the SMP into annual, monthly, and daily components, and the components are predicted based on different models. The model for the annual component (AC) is designed to predict the long-term trend based on fuel price scenarios. The model for the monthly component (MC) is designed to predict the seasonal trends based on the long short term memory (LSTM) model. The model for the daily component (DC) is designed to predict the daily SMP fluctuation. Finally, we synthesize SMP scenarios by aggregating three components. We make three types of SMP scenarios (high, reference, and low), and the performance of the scenarios is tested using previous data for two years on the basis of mean absolute error (MAE). Due to the global COVID-19 pandemic, the low type of SMP scenario is most accurate. We also verify that the reliability of long-term scenarios can be secured by using oil price while maintaining monthly and daily patterns. Author

Impact of COVID-19 on Electric Energy Consumption

COVID-19 has been a source of great disruption to governments and people ali over the world. Health care systems have been overwhelmed, already destabilized financial markets further jeopardized, with oil and energy markets plunging into crises and economic activities coming to severe halts. To keep people connected while working from home and upholding life-saving facilities in hospitals and around industrial regimes, reliable affordable electricity is imminent. As an immediate consequence of the pandemic, much pressure has befallen countries with low-energy reserves, where the energy sectors involved had already been staggering and facing major challenges ahead of COVID-19. Jordan's, being of these impacted countries, peak demand on electricity decreased by 17.5% by early April 2020 compared with the mean 2019 levels. Based on the ARMA and ARIMA models obtained, the impact on 2020 electricity demand in Jordan depends on the durations and levels of the confinement/s, with a low estimate of 2.76% if pre pandemic conditions return by mid-September (2020), and a high estimate of 7% if some restrictions remain in effect worldwide until the end of 2020. This article reports the short-term impact of the COVID-19 pandemic on the energy sector in Jordan, with Jordan being a typical example of many such countries worldwide. This will help leverage contingencies and good planning that would help the energy sectors worldwide remain within some sustainable regime/s. © 2021 IEEE.

Machine Learning Based Prediction and Forecasting of Electricity Price during COVID-19

During COVID-19 impact especially on energy markets, reliable electricity pricing has now become unpredictable and it becomes a challenging task to get prepared for the future price forecasting. The pandemic has mostly affected energy markets and efficient operation of the restructured electricity market effectively all over the world. In this work, the analysis of electricity price and forecasting is carried out on the wholesale market of United States namely MISO electricity market. Due to uncertainty of demand occurring during the pandemic period, the market price data is analyzed. And, using statistical learning and deep learning method day ahead price is forecasted which would prepare the electricity market to operate in an efficient manner to face such pandemics in the future. In this study, three methods are proposed namely Auto Regressive Integrated Moving Average (ARIMA), decision-tree-based ensemble Machine Learning algorithm namely Extreme Gradient Boosting (XGboost) and Recurrent Neural Network (RNN) for forecasting the electricity price. Depending upon the electricity price data attributes, the electricity price of MISO electricity market is predicted and forecasted. The performance of the methods to predict and forecast the electricity price is compared based on the processing speed and error. © 2021 IEEE.

Improving load forecast in energy markets during COVID-19

The abrupt outbreak of the COVID-19 pandemic was the most significant event in 2020, which had profound and lasting impacts across the world. Studies on energy markets observed a decline in energy demand and changes in energy consumption behaviors during COVID-19. However, as an essential part of system operation, how the load forecasting performs amid COVID-19 is not well understood. This paper aims to bridge the research gap by systematically evaluating models and features that can be used to improve the load forecasting performance amid COVID-19. Using real-world data from the New York Independent System Operator, our analysis employs three deep learning models and adopts both novel COVID-related features as well as classical weather-related features. We also propose simulating the stay-at-home situation with pre-stay-at-home weekend data and demonstrate its effectiveness in improving load forecasting accuracy during COVID-19. © 2021 ACM.