Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Problems-based Learning in Higher Education Under Regular and COVID19 Pandemic Conditions: An Educational Experience

Offshore wind and waves will play an important role at helping the European Union (EU) meet its sustainability goals. Among other efforts, a clear commitment is needed in the area of High Education, a field in which the European Union is also making its contribution through a miriad of initiatives. Amongst these, it is worth highlighting the Renewable Energy in the Marine Environment (REM) master (https://www.master-remplus.eu/), an European Union funded Erasmus Mundus program that started in 2018. In this work, an educational experience corresponding to one subject in the REM Msc is described. The objective of this paper is to evaluate the performance and resilience of the Problems-Based Learning Methodology (PBL) and compare the educational outcomes obtained in standard conditions first, and then, under COVID-19 pandemic conditions. © 2023 IEEE.

Dual-stage day-ahead optimized performance of renewable-based microgrids

In distributed networks, wind turbine generators (WTGs) are to be optimally sized and positioned for cost-effective and efficient network service. Various meta-heuristic algorithms have been proposed to allocate WTGs within microgrids. However, the ability of these optimizers might not be guaranteed with uncertainty loads and wind generations. This paper presents novel meta-heuristic optimizers to mitigate extreme voltage drops and the total costs associated with WTGs allocation within microgrids. Arithmetic optimization algorithm (AOA), coronavirus herd immunity optimizer, and chimp optimization algorithm (ChOA) are proposed to manipulate these aspects. The trialed optimizers are developed and analyzed via Matlab, and fair comparison with the grey wolf optimization, particle swarm optimization, and the mature genetic algorithm are introduced. Numerical results for a large-scale 295-bus system (composed of IEEE 141-bus, IEEE 85-bus, IEEE 69-bus subsystems) results illustrate the AOA and the ChOA outperform the other optimizers in terms of satisfying the objective functions, convergence, and execution time. The voltage profile is substantially improved at all buses with the penetration of the WTG with satisfactory power losses through the transmission lines. Day-ahead is considered generic and efficient in terms of total costs. The AOA records costs of 16.575M$/year with a reduction of 31% compared to particle swarm optimization. © 2023 The Authors. IET Renewable Power Generation published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

Covid-19 Mppt Algorithm Application to Solar Energy Conversion System

Alternative energy alternatives to traditional energy sources like coal and fossil fuels include solar PV and wind energy conversion systems. The solar and wind energy conversion system's maximum power may be obtained by activating the converters. There are several MPPT (Maximum Power Point Tracking) regulating methods for solar and wind energy conversion systems. For solar PV energy conversion systems, this study suggests two MPPT controlling techniques: Covid-19 MPPT and FLC-based MPPT. The two MPPT methods that are suggested are put into practise using MATLAB. The first Covid-19 approach that has been developed combines aspects of hill climbing and progressive conductance methods. Calculate the direction of the perturbation for the PV modules' operation using the incremental conductance approach. The method of ascending hills is straightforward and involves fewer variables. When dI/dV equals the incremental conductance, the Maximum Power Point (MPP) is attained using the incremental conductance approach. In the hill climbing approach, the MPP is determined by comparing the power in the present and the past. Both incremental conductance and change of power are taken into account in the proposed Covid-19 MPPT regulating approach to obtain the MPP. With this hybrid approach, solar PV generates the most electricity possible under all conditions of temperature and irradiance. As a result, the planned Covid-19 technique moves forward as intended and swiftly reaches the MPP.Copyright © 2022, Anka Publishers. All rights reserved.

An Evaluation of ASEAN Renewable Energy Path to Carbon Neutrality

Maintaining our standard of living and keeping the economy running smoothly is heavily reliant on a consistent supply of energy. Renewable energy systems create abundant energy by utilizing resources such as the sun, wind, earth, and plants. The demand for renewable energy is increasing, despite power scarcity, pollution, and climate change posing challenges to long-term development in the Association of Southeast Asian Nations (ASEAN), which has seen significant social and economic growth in recent years. To achieve its 23% renewable energy (RE) target, ASEAN can develop solar photovoltaic (PV) electricity. Members of the ASEAN have established regulations and incentives to encourage individuals and businesses to use renewable energy in the future. This paper explores Southeast Asian countries' comprehensive fossil-free energy options, the region's renewable energy potential, current capacity, goals, and energy needs. Through the ASEAN Plan of Action for Energy Cooperation (APAEC) 2016–2025 and the ASEAN Declaration on Renewable Energy, ASEAN is committed to reducing its greenhouse gas emissions and promoting sustainable development aligning with the Paris Agreement's aim to limit global warming to well below 2 degrees Celsius above pre-industrial levels. Results show that decarbonizing the region's energy system is possible, but current policies and actions must be altered to reach that target level. Further research is necessary to optimize the ASEAN region's renewable resource technical potential and commercial viability with available technology.

Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation

The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.

Dynamics of Electricity Consumption in the Context of the COVID-19 Pandemic

This study attempts to study the impact of social and economic constraints, identification of new diseases, wind and solar energy consumption during the 2019 crisis on daily electricity demand by constructing multivariate correlation regression. The aim of the study is to determine the impact of the COVID-19 pandemic on the structure of electricity consumption by building regression models to analyse how various variables (detection of new diseases, wind and solar energy consumption) and social behaviour affect electricity demand. Tasks: to identify the main dates from the chronologies of COVID-19 in Russia, compare the electricity indicators by years, compare the data with the pre-pandemic period, study the share of generated electricity in the balance, conduct a correlation-regression analysis in order to identify the relationship between the detection of new cases of COVID-19 disease in the period from 03/30/2020 to 10/27/2021 and energy consumption, to study the impact of social activity on the level of consumption of renewable energy sources. This study identified links between new cases of coronavirus disease and energy consumption;wind energy consumption and general indicator;consumption of wind energy and solar with an indicator of morbidity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Could volatile cryptocurrency stimulate systemic risks in the energy sector? Evidence from novel connectedness models

By identifying the connectedness of seven indicators from January 1, 2019, to June 13, 2022, we choose an extended joint connectedness approach to a vector autoregression model with time-varying parameter (TVP-VAR) to analyze interlinkages between Crypto Volatility (CV) and Energy Volatility (EV). Our findings show that the COVID-19 outbreak seems to have an impact on the dynamic connectedness of the whole system, which peaks at about 60% toward the end of 2019. According to net total directional connectedness over a quantile, throughout the 2020–2022 timeframe, natural gas and crude oil are net shock transmitters, while the CV, clean energy, solar energy, and green bonds consistently receive all other indicators. Specifically, pairwise connectedness indicates that the CV appears to be a net transmitter of shocks to all energy indicators before the COVID-19 outbreak but acts as a net receiver of shocks from clean energy, wind energy, and green bonds in late 2020. The CV mostly has spillover effects on green bonds. The primary net transmitter of shocks to the Crypto market is crude oil. Our findings are critical in helping investors and authorities design the most effective policies to lessen the vulnerabilities of these indicators and reduce the spread of risk or uncertainty. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Quantile time-frequency connectedness between cryptocurrency volatility and renewable energy volatility during the COVID-19 pandemic and Ukraine-Russia conflicts

Our article employs a quantile vector autoregression (QVAR) to identify the connectedness of seven variables from April 1, 2019, to June 13, 2022, in order to examine the relationships between crypto volatility and energy volatility. Our findings reveal that the dynamic connectedness is approximately 25% in the short term and approximately 9% in the long term. The 50% quantile equates to the overall average connectedness of the entire period, according to dynamic net total directional connectedness over a quantile, which also indicates that connectedness is very intense for both highly positive changes (above the 80% quantile) and crypto and energy volatility (below the 20% quantile). With the exception of the early 2022 period when the Crypto Volatility Index transmits a net of shocks because of the Ukraine-Russia Conflict, dynamic net total directional connectedness implies that in the short term, the Crypto Volatility Index acts as a net shock receiver across time. While this indicator is a net shock receiver for long-term dynamics, wind energy is a net shock transmitter during the short term. Green bonds are a short-term net shock receiver. This role is valid in the long term. Clean energy and solar energy are the long-term net transmitters of shocks;nevertheless, the series is always and only momentarily a net receiver of shocks because of the short-term dynamics. Natural gas and crude oil play roles in both two quantiles. Dynamic net pairwise directional connectedness over a quantile suggests that uncertain events like the COVID-19 epidemic or Ukraine-Russia Conflict influence cryptocurrency volatility and renewable energy volatility. © 2022 Elsevier Ltd

Impacts of COVID-19 on dynamic return and volatility spillovers between rare earth metals and renewable energy stock markets

We examine the time-frequency co-movements and return and volatility spillovers between the rare earths and six major renewable energy stocks. We employ the wavelet analysis and the spillover index methodology from January 1, 2018 to May 15, 2020. We report that the COVID-19-triggered significant increase in co-movements and spillovers in returns and volatility between the rare earths and renewable energy returns and volatility. The rare earths act as net recipient of both return and volatility spillovers, while the clean energy stocks are net transmitters of return and volatility spillovers before and during the COVID-19 crisis. The solar and wind stocks are net transmitters/receivers of spillovers before/during the pandemic. The remaining markets shift from net spillover receivers to transmitters or vice versa;evidencing the effects of the pandemic. Our results show that cross-market hedge strategies may have their efficiency impaired during the periods of crises implying a necessity of portfolio rebalancing. © 2022 The Authors

Which is leading: Renewable or brown energy assets?

This study examines the relationship between crude oil, a proxy for brown energy, and several renewable energy stock sector indices (e.g., solar energy, wind energy, bioenergy, and geothermal energy) over various investment horizons. Using daily data from October 15, 2010, to February 23, 2022, we apply a combination of methods involving co-integration, wavelet coherency, and wavelet-based Granger causality. The results show that the relationship between crude oil and renewable energy indices is non-linear and somewhat multifaceted. Firstly, there are sectorial differences in the intensity of the relationships. Notably, the relationship intensity between the wind and crude oil is lower than that involving geothermal energy or bioenergy. Secondly, the relationship evolves with time. For example, the COVID-19 outbreak seems to have increased the relationship between crude oil and renewable energy markets, notably for solar, bioenergy, and geothermal. Thirdly, the relationship varies across scales. When controlling for the VIX (volatility index), a proxy of the sentiment of market participants, and EPU (economic policy uncertainty index), the relationship seems strong in the long term but weak in the short term. This result is confirmed using a Granger causality test on the wavelet-decomposed series. These findings have important implications for long-term investors, short-term speculators, and policymakers regarding the co-movement between brown and renewable energy markets. © 2022 Elsevier B.V.

Green financing and wind power energy generation: Empirical insights from China

The study tests the connection between green financing and wind power energy generation during the COVID-19 crisis. The study tested the relationship between the variables using the Kalman approach, Hansen technique and sensitivity analysis using matrix component factors. The findings revealed that wind power energy consumption had increased quickly in past times due to its input nature for biofuel production. However, the capability of onshore and offshore wind power production grew by 7% in COVID-19 with the role of green financing in the wind power sector. Moreover, green financing enhances the demands on wind generators and energy converters' usage and dependability by 26%. For this, a 39% increase in green financing is noticed by the research findings during the COVID-19 crisis period. Such robust study findings present the latest insights that green financing is an eminent and viable source of financing to enhance wind power energy generation. Following these, multiple research implications are also presented for the key stakeholders.

Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom

With rising electricity demand, heavy reliance on imports, and recent economic downturns due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the Russian invasion of Ukraine, Thailand is suffering severely from energy resilience risks. The government has therefore set a goal of decentralizing energy production through small-scale distributed renewable energy systems. To support their design and the planning process, we simulate multiple scenarios with wind turbines, photovoltaic systems, and battery storage for a model community in rural Nakhon Phanom, Thailand. Using the software NESSI4D, we evaluate and discuss their impact on energy resilience by considering environmental sustainability, economic attractiveness, and independence from the central power grid. To fill the gap of missing data on energy demand, we synthesize high-resolution load profiles from the Thailand Vietnam Socio-Economic Panel. We conclude that distributed photovoltaic systems with additional battery storage are only suitable to promote energy resilience if the government provides appropriate financial incentives. Considering temporal variations and local conditions, as well as a participatory decision-making process, are crucial for the long-term success of energy projects. Our advice to decision-makers is to design policies and regulatory support that are aligned with the preferences and needs of target communities.

Life-Cycle Assessment of Power-to-Liquid Kerosene Produced from Renewable Electricity and CO2 from Direct Air Capture in Germany

Decarbonization of the aviation sector is crucial to reaching the global climate targets. We quantified the environmental impacts of Power-to-Liquid kerosene produced via Fischer-Tropsch Synthesis from electricity and carbon dioxide from air as one broadly discussed alternative liquid jet fuel. We applied a life-cycle assessment considering a well-to-wake boundary for five impact categories including climate change and two inventory indicators. Three different electricity production mixes and four different kerosene production pathways in Germany were analyzed, including two Direct Air Capture technologies, and compared to fossil jet fuel. The environmental impacts of Power-to-Liquid kerosene varied significantly across the production pathways. E.g., when electricity from wind power was used, the reduction in CO2-eq. compared to fossil jet fuel varied between 27.6–46.2% (with non-CO2 effects) and between 52.6–88.9% (without non-CO2 effects). The reduction potential regarding CO2-eq. of the layout using low-temperature electrolysis and high-temperature Direct Air Capture was lower compared to the high-temperature electrolysis and low-temperature Direct Air Capture. Overall, the layout causing the lowest environmental impacts uses high-temperature electrolysis, low-temperature Direct Air Capture and electricity from wind power. This paper showed that PtL-kerosene produced with renewable energy could play an important role in decarbonizing the aviation sector.

Overview of Solar–Wind Hybrid Products: Prominent Challenges and Possible Solutions

Solar and wind power systems have been prime solutions to the challenges centered on reliable power supply, sustainability, and energy costs for several years. However, there are still various challenges in these renewable industries, especially regarding limited peak periods. Solar–wind hybrid technology introduced to mitigate these setbacks has significant drawbacks and suffers from low adoption rates in many geographies. Hence, it is essential to investigate the challenges faced with these technologies and analyze the viable solutions proposed. This work examined solar–wind hybrid plants’ economic and technical opportunities and challenges. In the present work, the pressing challenges solar–wind hybrids face were detailed through extensive case studies, the case study of enabling policies in India, and overproduction in Germany. Presently, the principal challenges of solar–wind hybrids are overproduction, enabling policies, and electricity storage. This review highlights specific, viable, proposed solutions to these problems. As already recorded in the literature, it was discovered that academic research in this space focuses majorly on the techno-economic and seemingly theoretical aspects of these hybrid systems. In contrast, reports and publications from original equipment manufacturers (OEMs) and engineering, procurement, and construction engineers (EPCs) are more rounded, featuring real-life application and implementation.

Preface: 19th International Conference on Emerging Nuclear Energy Systems (ICENES 2019)

This is an exclusively prepared special issue containing selected papers from well-established events, namely, International Conference on Emerging Nuclear Energy Systems (ICENES) and some invited papers to enrich and broaden the novelty of nuclear energy technologies and its applications. The 19th International Conference on Emerging Nuclear Energy Systems (ICENES 2019) is one of the international conference on scientific, engineering, education and other technical aspects of innovative nuclear reactor design, advanced nuclear technology, energy related technology and its applications.The conference was held in Holiday Inn, Bali, Indonesia (6-9 October 2019), organized by the Bandung Institute of Technology (ITB) and in cooperation with the International Atomic Energy Agency (IAEA). The participants come from several 14 countries and from many institutions from universities, governments, companies, society and some other organizations that shared their ideas and research results on emerging nuclear energy technologies and applications, which covered by keynote speakers, invited and contributed oral talks and poster presentations. Some selected presented paper in the conference have been elected as selected papers after reviewing process to be submitted to the Institute of Physics (IoP), Journal of Physics: Conference Series.Nuclear energy recently is recognized as secure, sustain and green energy source as an ultimate energy resource to secure the future of the mankind and its civilization. Hence, considerable research activities and international collaboration are continuing on innovative nuclear energy systems, reactor physics, radiations and its application, nuclear computational system, including fusion energy technology, fusion-fission hybrids systems, GEN-IV reactors technology, small and modular reactor (MSR) technology, space nuclear reactors, and power systems and accelerator-driven systems technologies. Some related topics are also covered related to nuclear power production;nuclear hydrogen production;hydrogen energy, energy efficiency, and management;solar energy;wind energy;hydrogen production and storage;renewable energy;fuel cells;bio-energy, etc.Finally, on behalf of the organizer and advisory board, we would like to express my sincere appreciation and gratitude to all of authors during the conference and publication processes for their valuable contributions and to the members of the committee, reviewers, and advisors for their excellent works in preparing and finalizing this document. We apologize for any inconveniences for this long process of publication due to our conditions and some restrictions as well as some difficulties during COVID19 pandemicList of Organizer, Editorial Board are available in this Pdf.

Wind Energy Policies in India and the Way Ahead

g-2022 Favourable policies and fiscal incentives like Accelerated Depreciation, Generation Based Incentives, RPOs, Wheeling and Banking polices have driven the India’s wind energy sector to become 4th largest wind energy producer in the world with an installed capacity of 40. 35 GW GB of as on 31 Mar 2022. Indian government has stated goal of 450GW of Renewable Energy by 2030 out which 140GW is of wind energy. Introduction of reverse auctions and subsequently COVID-19 has slowed down the sector and Wind Power Developers today face major challenges of land acquisition, power transmission and evacuation infrastructure. Offshore wind energy projects are yet to take off, however wind solar hybrid project auctions have become competitive. Through this paper an attempt has been made to highlight all the central and state government policies and fiscal incentives which have driven the growth of wind energy in India, barriers and challenges the sector faces and the way ahead. © 2022, Central Board of Irrigation and Power. All rights reserved.

Evaluation of priority strategies for the expansion of installed wind power capacity in Turkey using a fuzzy analytic network process analysis

Given its widespread potential and technological advancements, it is critical to use the greatest amount of wind power in reaching the 100% renewable energy target in power grids. SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is frequently used to evaluate alternative strategies in multi-criteria decision issues. This paper employs a hybrid Fuzzy Analytic Network Process (FANP) approach based on SWOT to assess the future development of wind power capacities in Turkey in light of the sectoral effects of the Covid-19 outbreak in 2020. To validate the proposed approach, the results are compared to the results of the SWOT-based Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and fuzzy AHP (FAHP). According to the findings, “Development of domestic and efficient technologies (ST1)" and “Sustaining support mechanisms in investments and technological research (SO2)" are the best alternative strategies in all analysis models. While the priority ranks of other alternative strategies are the same in AHP and FAHP hierarchical techniques, FANP differs from ANP when the linguistic assessment process is taken into account. This study proposes long-term strategies for increasing wind power capacity and sector sustainability, and it demonstrates that FANP could be an appropriate approach for prioritizing these strategies in current scenarios.

Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran

Selecting the best place for constructing a renewable power plant is a vital issue that can be considered a site-selection problem. Various factors are involved in selecting the best location for a renewable power plant. Therefore, it categorizes as a multi-criteria decision-making (MCDM) problem. In this study, the site selection of a wind power plant is investigated in a central province of Iran, Semnan. The main criteria for classifying various parts of the province were selected and pairwise compared using experts’ opinions in this field. Furthermore, multiple restrictions were applied according to local and constitutional rules and regulations. The Analytic Hierarchy Process (AHP) was used to weigh the criteria, and according to obtained weights, wind speed, and slope were the essential criteria. Moreover, a geographic information system (GIS) is used to apply the weighted criteria and restrictions. The province’s area is classified into nine classes according to the results. Based on the restrictions, 36.2% of the total area was unsuitable, mainly located in the north part of the province. Furthermore, 2.68% (2618 km2) and 4.98% (4857 km2) of the total area are the ninth and eightieth classes, respectively, which are the best locations for constructing a wind farm. The results show that, although the wind speed and slope are the most essential criteria, the distance from power facilities and communication routes has an extreme impact on the initial costs and final results. The results of this study are reliable and can help to develop the wind farm industry in the central part of Iran.