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The synergistic effect of pollution and carbon reduction can alleviate the dual pressure of improving environmental quality and reducing greenhouse gas emissions in China. The carbon emission trading scheme (CETS) is a crucial market-based tool for carbon emission reduction, and understanding its synergistic impact on air pollution control is essential. Based on data from 30 provincial panels in China spanning from 2007 to 2021, we employ the difference-in-differences (DID) method to analyze the synergistic effects of the carbon emission trading plan in the power industry and its influence mechanisms are examined. We observe that the CETS significantly enhances both pollution and carbon reduction in China's power sector, particularly demonstrating effective synergy in reducing CO2, SO2, and PM2.5 emissions. Furthermore, mechanism analysis reveals that the CETS achieves joint emission reductions by lowering energy consumption, influencing the power industry's generation structure, promoting technological innovation among enterprises, and thereby realizing synergistic pollution and carbon reduction effects in China's power sector. Heterogeneity analysis shows that regions with limited power facility, low electricity generation, and small economic scale exhibit the most pronounced synergistic benefits from pollution and carbon reduction efforts. [ABSTRACT FROM AUTHOR]

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.)

The carbon emissions of the power industry account for over 50% of China's total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China's energy transition, economic development, and the power sector's energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China's energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector's generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended. [ABSTRACT FROM AUTHOR]

Sierra Leone is an electricity-poor country with one of the lowest electricity consumption per capita rates across sub-Saharan Africa. Yet, with ambitious targets to transform and stimulate its economy in the coming decades, energy demand forecasting becomes an integral component of successful energy planning. Through applying the MAED-D (version 2.0.0) demand software, this research study aims to generate Sierra Leone's electricity demand forecasts from 2023 to 2050. Three novel scenarios (baseline-, high-, and low-demand) are developed based on socio-economic and technical parameters. The baseline scenario considers the current electricity sector as business-as-usual; the high-demand scenario examines an ambitious development future with increased economic diversification and mechanisation, and the low-demand scenario examines more reserved future development. The modelled scenario results project an increase in electricity demand ranging from 7.32 PJ and 12.23 PJ to 5.53 PJ for the baseline-, high-, and low-demand scenarios, respectively, by 2050. This paper provides a base set of best-available data needed to produce an electricity demand model for Sierra Leone which can be used as a capacity-building tool for in-country energy planning alongside further integration into data modelling pipelines. [ABSTRACT FROM AUTHOR]

The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China's national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries. [ABSTRACT FROM AUTHOR]

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. [ABSTRACT FROM AUTHOR]

In modern scientific and political circles, the smart energy system is considered as an integral component of Industry 4.0, since in a broad sense it serves the purpose of increasing production efficiency due to the introduction of artificial intelligence technologies. However, currently there is no single, established vision regarding the meaning of the term "smart energy system". As a rule, it is viewed through the prism of separate, often unrelated technological processes, which complicates the development of consistent theoretical provisions and practical recommendations for the development of Industry 4.0. The purpose of the article is the systematization of scientific views on certain aspects of the application of digital technologies in the field of energy and the development of a holistic conceptual vision of a smart energy system. The key elements of the smart energy system consist of smart energy generation, as a set of digitalization measures for the control system of energy generating facilities, aimed at increasing the specific production of electricity, as well as adjusting the load within the nodes of electricity production from renewable sources with the aim of stabilizing the energy flow; smart synchronization, which is designed to distribute the load among the energy sectors on the basis of an intelligent assessment of system needs and capabilities; smart distribution, which optimizes the operation of distribution networks based on data analysis of temporal and spatial trends in the electricity transmission system; and smart consumption, which involves the implementation of technological measures that create conditions for economical consumption of electricity. The holistic vision of the "smart energy system" concept, which provides for the comprehensive implementation of measures provided for by the micro-concepts of smart energy generation, smart synchronization, smart distribution and smart consumption, is substantiated. It is argued that the implementation of the "smart energy system" concept will ensure a synergistic effect of economical utilization of energy resources. [ABSTRACT FROM AUTHOR]

To achieve sustainability and fulfill sustainable development goals, the digitalization of the power sector is vital. This study aims to examine how blockchain can be integrated into and enrich smart grids. In total, 10 research questions are explored. Scopus and Web of Science (WoS) were used to identify documents related to the topic. The study involves the analysis of 1041 scientific documents over the period 2015–2022. The related studies are analyzed from different dimensions including descriptive statistics, identification of the most common keywords and most widely used outlets, examination of the annual scientific production, the analysis of the most impactful and productive authors, countries, and affiliations. The advancement of the research focus and the most popular topics are also examined. Additionally, the results are analyzed, the main findings are discussed, open issues and challenges are presented, and suggestions for new research directions are provided. Based on the results, it was evident that blockchain plays a vital role in securing smart grids and realizing power sector digitalization, as well as in achieving sustainability and successfully meeting sustainable development goals. [ABSTRACT FROM AUTHOR]

The collaborative development of the electricity and carbon markets can reduce transaction costs, stimulate energy conservation and emission reductions, and accelerate the social transition to low carbon. In this paper, we present a comprehensive review of the current state and policy challenges of electricity-carbon trading and present an in-depth analysis of key research directions and technical details of electricity-carbon collaboration in the context of large-scale access to renewable energy. Then, by constructing a multi-agent behavioral decision-making model, we refine the decision-making mechanism of electricity-carbon synergy, simulated market clearing outcomes under different mechanisms, and proposed policies and incentive mechanisms to promote electricity-carbon trading. Furthermore, leveraging the massive data in energy systems, we design a carbon measurement method and carbon emission flow tracking method for low-carbon energy companies, achieving real-time carbon accounting for the power system. We establish a collaborative simulation model of the electricity and carbon markets, enabling an accurate prediction of the day-ahead carbon emission intensity. This study provides a new research framework and complete technical route for electricity-carbon coordination, which can further promote the low-carbon transformation of the energy system.

To achieve sustainability and fulfill sustainable development goals, the digitalization of the power sector is vital. This study aims to examine how blockchain can be integrated into and enrich smart grids. In total, 10 research questions are explored. Scopus and Web of Science (WoS) were used to identify documents related to the topic. The study involves the analysis of 1041 scientific documents over the period 2015–2022. The related studies are analyzed from different dimensions including descriptive statistics, identification of the most common keywords and most widely used outlets, examination of the annual scientific production, the analysis of the most impactful and productive authors, countries, and affiliations. The advancement of the research focus and the most popular topics are also examined. Additionally, the results are analyzed, the main findings are discussed, open issues and challenges are presented, and suggestions for new research directions are provided. Based on the results, it was evident that blockchain plays a vital role in securing smart grids and realizing power sector digitalization, as well as in achieving sustainability and successfully meeting sustainable development goals.

To measure and reduce carbon emissions in the urban power sector, a method for measuring carbon emissions in the urban power sector and a carbon reduction path are proposed. First, a carbon emission measurement model for the urban power sector is established based on the data of local power generation and net inward power. Then, carbon reduction measures for the urban power sector are proposed from the generation side, grid side, load side and energy storage side. After that, an evaluation model for the effect of the carbon reduction measures is established. Finally, taking a typical city F in the Pearl River Delta as an example, the proposed carbon emission calculation model is used to calculate the carbon emissions of power sector of the city, and the effectiveness of carbon reduction in 2030 carbon peak scenario of the city is evaluated based on the carbon reduction measures. The results show that the proposed model can accurately measure the carbon emissions of the urban power sector, and by utilizing carbon reduction measures, carbon emissions of the city can be reduced by at least 10.6 million tons in 2030.

Coal and natural gas have dominated ASEAN’s power sector in the past and contributed to 77% of power generation in 2019. To achieve net-zero by 2050, ASEAN countries need to decarbonize their power sector in the next three decades. This paper performs a technology screening exercise for ten decarbonization technologies for the power sector based on a holistic approach of sustainability, security, affordability, reliability, technology readiness and technology impact. This exercise has resulted in country-specific ranking of technologies which can inform policymakers of ASEAN countries. Overall speaking, simultaneous utilization of both fossil and non-fossil technologies is needed rather than waiting for certain technologies to mature. On the fossil side, switching from coal to gas for power generation and implementing carbon capture and storage (CCS) in coal- and gas-fired power plants have the highest decarbonization potential. Therefore, eleven first-mover CCS projects are proposed with the potential to mitigate up to 22% of energy-related CO2 emission in ASEAN in 2019. On the renewable side, hydropower has the highest potential but sustainability issues due to overdamming of the Mekong River needs to be addressed. Further addition of capacity will likely come from run-of-the-river hydropower plants. Within ASEAN, there is medium to high potential for further usage of agricultural resides as biomass for power generation, especially in Indonesia,​ Thailand, Malaysia and the Philippines. Solar photovoltaic (PV) has medium potential in ASEAN countries with high land mass but more research and development (R&D) is needed in grid improvement and energy storage technologies. Within ASEAN, wind energy suffers from a low average wind speed but there are exceptions. More R&D is needed in the deign of low wind speed wind turbines. Geothermal resources are found only in Indonesia and the Philippines and are under-utilized. Further implementation will require favorable government polices to incentivize private investment and streamlining permitting, among others. Energy policies that promote rapid decarbonization of ASEAN’s power sector based on our findings are also discussed.

Carbon dioxide is an important component and one of the contributors to greenhouse gas emissions. Taiwan has set new goals to reduce emissions by 23%-25% in 2030. However, electricity production in 2021 is still contributed by coal-fired power plants. This research explores potential carbon pricing strategies to decarbonize coal-fired power plants, which are carbon fees and carbon emission trading. This research uses a system dynamics framework for causal loop diagram development. This research creates a causal loop diagram to describe the carbon pricing strategy's impact on reducing carbon emissions. [ABSTRACT FROM AUTHOR]

Canadian provinces are required to regulate their power sectors using carbon pricing systems that meet national minimum stringency standards, which are set by the federal government. A diverse set of systems has emerged as a result. However, there has been limited assessment of how different pricing mechanisms impact the evolution of Canada's electricity system. To address this gap, we use an electricity system planning model called COPPER and a scenario-based approach to assess if, and to what extent, different policy regimes impact power sector greenhouse gas emissions and costs. Our results show that carbon pricing systems currently in place lead to significant carbon reductions over the long term, provided that free emissions allocations are reduced. However, the cost-optimal pathway for the power sector differs across provinces depending on the carbon pricing mechanism. Some provinces achieve least-cost emissions reductions by switching from high-carbon technologies to renewables, while others are better served by replacing high-carbon technologies with low-carbon fossil fuel alternatives. Further, provinces that implement cap-and-trade systems may affect the transitions of interconnected jurisdictions. Power sector climate policy design should reflect the heterogeneity of available assets, resources, and neighbouring approaches. [ABSTRACT FROM AUTHOR]

Offshore wind energy is becoming an increasingly important element in the decarbonisation of energy systems. This study aims to estimate the technical potential for offshore wind energy in the Polish exclusive economic zone (EZZ) of the Baltic Sea. Such estimates are necessary to plan the expansion of energy infrastructure. This paper proposes a multi-criteria analysis for the estimation of offshore wind potential and site selection using QGIS software (QGIS Desktop 3.28.6). The criteria include wind velocity, water depth, distance from nature conservation areas, distance from submarine cables and gas pipelines, distance from shipping routes, distance from fishing areas, distance from shipwrecks, and distance from the coastline. The best sites are identified through the Analytic Hierarchy Process (AHP). The results show that the total suitable marine area of 16,218.67 square kilometres can be divided into three parts, in which the highly suitable area for offshore wind farms accounts for 17.55%, the moderately suitable area accounts for 48.56%, and the marginally suitable area accounts for 10.26%. Two different wind turbine placement configurations are being considered, in which wind turbines with a nominal capacity of 15 MW are laid out: 2.5 km and 2 km apart from each other. The corresponding overall electrical capacities only in highly suitable areas equal ca. 9 GW and 15 GW, respectively. If this area is extended to also include the moderately suitable parts, these values increase to ca. 31 GW and 50 GW. The results indicate that the technical potential of offshore wind in the Polish EEZ is significant and should be taken into account when designing the future electricity generation mix. [ABSTRACT FROM AUTHOR]

Evaluating the effect of the carbon emissions trading scheme (ETS) on the decarbonization of the power sector is a basic task in the construction of China’s ETS. We use the energy flow relationship between the power and heat sectors to develop a more accurate accounting framework for CO2 emissions in the power sector and employ the difference-in-differences method to evaluate the impact of China’s ETS on CO2 emissions in the power sector and its dynamic effect. On this basis, we explore the mechanisms by which the ETS facilitates the decarbonization of the power sector, as well as its heterogeneous impacts. We find that the ETS has significantly promoted the decarbonization of the power sector, reducing its CO2 emissions in the pilot provinces by approximately 24.53 Mt compared to the non-pilot provinces. The decarbonization effect of the ETS was continuous and strengthened. In addition, the ETS promotes decarbonization of the power sector through technological advances in power generation and electricity utilization. Finally, the ETS’s decarbonization effect on the power sector is influenced by the heterogeneity of regions, carbon prices and quota allocation methods. Our findings have beneficial implications for the construction of carbon markets in the context of carbon neutralisation and carbon peaking. [ABSTRACT FROM AUTHOR]

The paper deals with the problems of maintaining Ukraine's grid reliability under high renewable electricity penetration and improving state policy measures to accelerate sustainable energy transition in the post-war period. Promoting renewable energy in Ukraine increased renewable electricity share in the country's electricity mix to 13.8% in 2021. However, such growth became critical for Ukraine's electric power system. Due to its inflexibility, the lack of balancing and energy storage capacities, adding significant amounts of renewable electricity to the grid created severe problems. Further renewable energy development required serious reforming policy measures, but the Russian invasion prevented introducing relevant legislative changes. At the same time, the war had an unprecedented impact on the Ukrainian energy sector, resulting in the damage of more than 50% of the energy infrastructure. Considering global trends towards decarbonisation and achieving climate neutrality, reconstructing Ukraine's electric power sector will be based on the large-scale involvement of renewable energy technologies. Given this, based on the study of the trends in renewable energy development in the pre-war and war-time periods and the analysis of the shortcomings of the state energy policy, the paper examines the required measures for fostering resilient reconstruction of Ukraine's electric power sector. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiao Tong University (1006-2467) is the property of Journal of Shanghai Jiao Tong University 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.)

The share of variable renewable energy (VRE) is forecasted to increase in the energy sector to meet decarbonization targets and/or reduce their dependence on fossil fuels. The modeling of future power system scenarios is crucial to assess the role of different flexibility options, including low-carbon technologies. The data presented here support the research article “The role of energy storage in Great Britain's future power system: focus on hydrogen and biomass”. These data include updated parameters, inputs, equations, biomass resource potential and biomass demand to balance bio-power and bio-hydrogen requirements. The Future Renewable Energy Performance into the Power System Model (FEPPS), a rule-based model that includes flexibility and stability constraints, has been used, and the hourly results of future scenarios by 2030 and 2040 are provided. Researchers, policymakers, and investors could use this paper as these data provide insights into the role of different technologies (including hydrogen and biomass) in power generation, system flexibility, decarbonization and costs.

This comprehensive review article illuminates Morocco's proactive response to climate change challenges over the past decades. Faced with drought episodes akin to other Mediterranean countries, Morocco contends with significant water stress, boasting limited water resources estimated at 20 billion cubic meters and an annual per capita consumption of 700 m3. The country experiences a temperature surge of 0.3–2.5 °C, coupled with decreased precipitation, heightening the impact of climate change on water resources. Morocco plans a 40 % reduction in emissions by 2030 (18.3 % unconditional, 27.2 % conditional on international aid) and an impressive 77 % decrease in emissions by 2050 compared to Business as Usual levels. The study offers a comprehensive, groundbreaking analysis, representing the first comprehensive review of Morocco's low-carbon strategy and energy initiatives. It meticulously examines 155 research papers through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, categorizing solutions into engineering and managerial domains. Covering power, transport, building, industry, and agriculture, the article provides a taxonomy of engineering solutions, including renewable energy, energy auditing, efficiency, sufficiency, and digitalization of management. Simultaneously, managerial solutions, ranging from education to subsidies and policy reforms, are detailed. These solutions contribute to Morocco's standing as a leader in climate action and clean energy transition. Despite these significant contributions, the article acknowledges potential limitations, including reliance on available literature and regional variations in solution applicability. Nonetheless, it serves as a foundational resource for understanding Morocco's endeavors in addressing climate change and promoting sustainable energy practices.

Abstract This study uses TIMES model to assess Indonesia’s power sector’s carbon price impact from 2020 to 2050 and the price needed by 2030 to meet the Paris Agreement NDC target. Four scenarios are used to model the impact of carbon price up to 2050: no carbon price, Indonesia’s current price of USD 2.02/tCO2e, ICPF middle- and high-income countries, USD 50/tCO2e and USD 75/tCO2e. Four price scenarios—10, 25, 35, and 150 USD/tCO2e —are added to better understand the carbon price’s effects. As carbon prices rise, installed capacity and power generation will shift to lower-carbon technology. Ultracritical coal, gas-fired, solar, geothermal, and hydropower plants will replace subcritical coal. Investment, fixed, and variable costs would exceed BaU with a higher carbon price. 2.02 to 25 USD/tCO2e can start the coal-to-gas switch but not significantly change the generation profile. The generation will change significantly above 35 USD/tCO2e. Carbon emissions peak lower with rising carbon prices. USD 25 carbon price reduces emissions significantly; a carbon price below that is costly and ineffective. Indonesian Law No. 16 of 2016 ratified the Paris Agreement NDC, committing Indonesia to reduce greenhouse gas emissions by 29% by 2030 or 41% with international assistance. Energy sector emissions need to decrease by 11% for a 29 percent reduction and 14% for a 41 percent reduction. A 29% reduction requires USD 39.65/tCO2e carbon price, while a 41% reduction requires USD 43.78/tCO2e. These prices are still within the reasonable ICPF price limit for Indonesia to approach the middle-income country price floor.

The power sector is the largest emitter of carbon dioxide in China, and the coal-based energy structure presents an additional challenge to its decarbonization. Several studies have been conducted to find cost-optimal pathways for the low-carbon transition of the power sector or individual power plants, but few studies have taken power generation companies as a research topic. In this paper we addressed the issue by building an optimization model that can characterize power generation companies, and selected a big company as a case to study how economic and technological factors drive the company to adjust its plant allocation and affect its profitability. We find that the transformation trajectory of the company is similar to that of the power sector as a whole. The share of coal power plants will remain stable at 53% during this decade and then decline rapidly after 2030. At the same time, the proportion of renewables will rise rapidly from 35% in 2020 to 90% by 2060. The total cost of achieving the carbon neutrality target is approximately RMB 4.6 trillion during 40 years, and the annual cost is approximately RMB 114.9 billion, which is equivalent to about 40% of its total revenue in 2021. Analysis of different scenarios suggests that achieving carbon neutrality 5 to 10 years ahead of time is financially and technologically feasible. Energy storage will play a significant role whereas CCS will have little impact on this company.

The carbon emissions of the power industry account for over 50% of China’s total carbon emissions, so achieving carbon peak and carbon neutrality in the power sector is crucial. This study aims to simulate the impacts of three energy policies—carbon constraints, the development of a high proportion of renewable energy, and carbon trading—on China’s energy transition, economic development, and the power sector’s energy mix. Through the construction of a dynamic computable general equilibrium (CGE) model for China and its integration with the SWITCH-China electricity model, the impact of diverse energy policies on China’s energy transition, economic progress, and the power mix within the electricity industry has been simulated. The integration of the SWITCH-China model can address the limitations of the CGE model in providing a detailed understanding of the specific intricacies of the electricity sector. The results indicate that increasing the stringency of carbon restrictions compels a reduction in fossil energy use, controlling the output of coal-fired power units, and thereby reducing carbon emissions. The development of a high proportion of renewable energy enhances the cleanliness of the power sector’s generation structure, further promoting the national energy transition. Implementing a carbon trading policy, where the entire industry shares the burden of carbon reduction costs, can effectively mitigate the economic losses of the power sector. Finally, the policies to further enhance the implementation of carbon trading policies, strengthen effective governmental regulation, and escalate the deployment of renewable energy sources are recommended.

The power sector is one of the major CO2-emitting industries in China. It is also the first key emissions control industry included in China’s national carbon trading market established in 2021. Therefore, based on the data of 30 provinces and cities in China from 2015 to 2022, this study analyzes the impact of the national carbon market policy on the carbon emissions of the power industry based on the generalized Difference-in-Difference (DID) model. Based on the method of text analysis, this paper constructs the carbon market policy intensity index of each region and incorporates it into the DID model. Empirical analysis finds that the national carbon market policy can significantly inhibit the carbon emissions of the power industry. However, the heterogeneity analysis of 30 provinces and cities shows that the national carbon market policy has a significant impact on the regions that already have a carbon pilot, but not on the non-pilot regions. Therefore, this paper demonstrates the effectiveness of the national carbon market policy in the power industry emissions control, and also exposes the heterogeneity between regions, providing an important empirical basis for the inclusion of other energy-intensive industries.

The realization of sustainable development and sustainable development goals achievement are essential. Hence, the power sector digitalization is imminent. This bibliometric and mapping study aims to explore the use of artificial intelligence in smart grids and how the topic has evolved over the years. In total, ten research questions are set to be explored. The analysis includes 1,926 articles that were identified and retrieved from Scopus and Web of Science (WoS) over the period 2005 to 2022. The analysis includes the descriptive statistics of the related studies and the annual scientific production, the identification of the most relevant and impactful authors, articles, outlets, affiliations, and countries, and the examination of the most commonly used keywords. The most popular topics and the advancement of the research focus are also explored. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. The significant role of artificial intelligence in the realization of smart grids and the digitalization of the power sector to enable sustainable development and the achievement of sustainable development goals was evident.

This study aims to analyze the impact of three demographic variables (gender, age, and work experience) on the commitment levels of employees in the Indian thermal power sector. 379 responses were collected through convenience sampling from the executives of NTPC Ltd, a major power utility in India. The facets of organizational commitment (affective commitment, normative commitment, and continuance commitment) were assessed using a standard scale. The quantitative research was done using statistical instruments of one-way ANOVA and independent sample t-test. The findings revealed a positive correlation between the three components of organizational commitment: affective, continuance, and normative. Further, the results highlighted that age plays an essential role in organizational commitment levels; it is found that commitment increases with the age of employees (F = 5.781, p < 0.01). However, gender is not making any variation in organizational commitment (F = –0.502, p < 0.01). Therefore, male and female employees enjoy equal commitment levels toward their work. The study further indicated that work experience significantly influences organizational commitment (F = 4.800, p < 0.01), and it is found to be greater for more experienced employees.

This paper analyzes the trend of power generation structure and carbon emission changes in the power industry and decomposes and analyzes the influencing factors of carbon emission in the power industry by using the LMDI decomposition method. Combined with the analysis of the influencing factors of carbon emissions in the power industry from 2016 to 2022, the carbon emissions of the power industry in the Yellow River Basin are simulated by the scenario analysis method. Four simulation scenarios were constructed based on the economic scale, industrial structure, industrial electricity consumption intensity, thermal power fuel conversion rate, and power supply structure. The IPSO-LSTM model for carbon emission prediction was created after optimizing the LSTM neural network prediction model. Combining the scenario analysis method to set the amount of changes in the high carbon, baseline, and low carbon scenarios of the influencing factors, the carbon emissions from the power sector in different scenarios are predicted for the years 2025-2035. From 2025 to 2035, the carbon emissions from the power sector in the three scenarios, except for the energy transition scenario, show a trend of increasing, then decreasing, and then increasing over the study period. The energy transition scenario shows a pattern of increasing and decreasing carbon emissions from the power sector.

Electrification scenarios dominate most plans to decarbonize the global economy and slow down the unfolding of climate change. In this work, we evaluate from a primary power perspective the impacts of electrifying the power, transport, residential and commercial sectors of the economy. We also investigate the electrification of industrial intense heat processes. Our analysis shows that, in terms of primary power, electrification can result in significant savings of up to 28% of final power use. However, actual savings depend on the sources of electricity used. For intense heat processes, these savings are very sensitive to the electricity sources, and losses of over 70% of primary power can occur during the conversion of heat to electricity and back to heat. Overall, this study highlights the potential benefits and limitations of electrification as a tool for reducing primary power consumption and transitioning to a more sustainable energy system. [ABSTRACT FROM AUTHOR]

This study uses TIMES model to assess Indonesia's power sector's carbon price impact from 2020 to 2050 and the price needed by 2030 to meet the Paris Agreement NDC target. Four scenarios are used to model the impact of carbon price up to 2050: no carbon price, Indonesia's current price of USD 2.02/tCO2e, ICPF middle- and high-income countries, USD 50/tCO2e and USD 75/tCO2e. Four price scenarios—10, 25, 35, and 150 USD/tCO2e —are added to better understand the carbon price's effects. As carbon prices rise, installed capacity and power generation will shift to lower-carbon technology. Ultracritical coal, gas-fired, solar, geothermal, and hydropower plants will replace subcritical coal. Investment, fixed, and variable costs would exceed BaU with a higher carbon price. 2.02 to 25 USD/tCO2e can start the coal-to-gas switch but not significantly change the generation profile. The generation will change significantly above 35 USD/tCO2e. Carbon emissions peak lower with rising carbon prices. USD 25 carbon price reduces emissions significantly; a carbon price below that is costly and ineffective. Indonesian Law No. 16 of 2016 ratified the Paris Agreement NDC, committing Indonesia to reduce greenhouse gas emissions by 29% by 2030 or 41% with international assistance. Energy sector emissions need to decrease by 11% for a 29 percent reduction and 14% for a 41 percent reduction. A 29% reduction requires USD 39.65/tCO2e carbon price, while a 41% reduction requires USD 43.78/tCO2e. These prices are still within the reasonable ICPF price limit for Indonesia to approach the middle-income country price floor. [ABSTRACT FROM AUTHOR]

The objective of this study was to determine whether a PESTLE analysis and sector factors can be incorporated as building blocks in the conceptual development of a PMMM for the South African power sector. A PESTLE analysis studies the key external factors (political, economic, sociological, technological, legal and environmental) that influence an organisation. A detailed literature review was conducted on the topic and a research design was developed followed with a case study. The research establishes the characteristics of the South African power sector through a literature review and a PESTLE analysis. This study contributes to the discussion on constructing a conceptual PMMM for the South African power sector. In 2018 African countries created the largest global continental market to boost trade in Africa by signing the Continental Free Trade Agreement. At that time, Africa had the lowest intra-trade figure compared to its Asian and European counterparts. However, Pasara (2020) highlights some of the challenges of African economic integration, including process and systems. The scope of the study focuses on the South African power sector because it is the largest producer of electricity in South Africa. The analysis of this study provides insight into practitioners and professionals in the literature that exists on the sector factors and provides the characteristics of the South African power sector. It also provides a foundation for understanding the building blocks for the PMMM development. The contribution includes an analysis to determine whether a PESTLE analysis and power sector factors should be incorporated into the PMMM measurement for the South African power sector. [ABSTRACT FROM AUTHOR]

Green transition creates change not only for the real economy, but also for widely perceived finance, exposing them both to risks and opportunities. This is of fundamental significance for enterprises and financial institutions, which need to adapt. Risk increases in areas subjected to regulatory and market pressure, forcing changes in market strategies or production methods. Regulations may present opportunities as well, and those may include clean technologies, in particular technologies aiming to reduce greenhouse gas emissions, the development of renewable energy sources, a digital revolution on the energy market, and introducing new elements into the financial market. Taking these trends into account is essential to economic success. Underestimating the progressing climate change, and the intensive human activity, has made climate protection and the need to transform the economy a priority. Consequently, the implementation of green transition requires appropriate funds for the financing of green projects. Considering the above trends in the global economy, the authors strive to present the financial instruments available for supporting the development of green transition, taking into consideration, in particular, the power market. Hence, they discuss the financial instruments used for energy transition and the fight against climate change. The authors point to the importance of green finance, which as an element of the financial system, takes over the responsibility for providing the funds and tools both for financing climate goals, and for effective risk management by the entities involved in this process.

In recent times, the power sector has become a focal point of extensive scientific interest, driven by a convergence of factors, such as mounting global concerns surrounding climate change, the persistent increase in electricity prices within the wholesale energy market, and the surge in investments catalyzed by technological advancements across diverse sectors. These evolving challenges have necessitated the emergence of new imperatives aimed at effectively managing energy resources, ensuring grid stability, bolstering reliability, and making informed decisions. One area that has garnered particular attention is the accurate prediction of end-user electricity load, which has emerged as a critical facet in the pursuit of efficient energy management. To tackle this challenge, machine and deep learning models have emerged as popular and promising approaches, owing to their having remarkable effectiveness in handling complex time series data. In this paper, the development of an algorithmic model that leverages an automated process to provide highly accurate predictions of electricity load, specifically tailored for the island of Thira in Greece, is introduced. Through the implementation of an automated application, an array of deep learning forecasting models were meticulously crafted, encompassing the Multilayer Perceptron, Long Short-Term Memory (LSTM), One Dimensional Convolutional Neural Network (CNN-1D), hybrid CNN–LSTM, Temporal Convolutional Network (TCN), and an innovative hybrid model called the Convolutional LSTM Encoder–Decoder. Through evaluation of prediction accuracy, satisfactory performance across all the models considered was observed, with the proposed hybrid model showcasing the highest level of accuracy. These findings underscore the profound significance of employing deep learning techniques for precise forecasting of electricity demand, thereby offering valuable insights with which to tackle the multifaceted challenges encountered within the power sector. By adopting advanced forecasting methodologies, the electricity sector moves towards greater efficiency, resilience and sustainability. [ABSTRACT FROM AUTHOR]

China's electricity demand has grown rapidly over only two decades and is currently the largest in the world. This was largely owing to a framework of regulation in which governments regulated prices and quantities, and there was ample incentive for investment. As the growth in electricity demand has slowed and the use of renewable energy has been impeded by integration-related challenges, China has embarked on a new round of power sector reform, with a focus on market construction. Considering four years of market experiments at the provincial level, we review the backgrounds and goals of the reforms, as well as the current progress toward meeting the initial targets. Then, we discuss the key challenges to be overcome to achieve complete implementation of the reforms, with special emphasis on the system of dispatch. We recommend that China's reforms, in order to achieve the goal of market-based competition, must embrace short-term markets, a greater role for independent regulation, compatibility with renewable energy, and inter-provincial coordination.

The pandemic has put a toll on businesses across the globe, especially power generation as an essential service. The role of leadership is exemplary in driving job engagement in the power sector. The study aims to explore the impact of a leader’s inspirational motivation and individualized consideration on employee job engagement in the Indian power sector. This study is quantitative exploratory research. Using a validated questionnaire, 444 survey responses were taken from executives of major Indian electricity generation utilities. Inspirational motivation and individualized consideration were taken as independent variables, while employee engagement and its components were taken as dependent variables. Statistical methods of simple and multiple correlations were performed using SPSS version 25. According to the findings, the components of transformational leadership have a positive and significant impact on employee engagement (r = 0.463) and its facets of vigor (r = 0.425), dedication (r = 0.455), and absorption (r = 0.267), respectively. In addition, the finding outlines that transformational leaders propagate their high power and enthusiasm to their workers through inspirational motivation to increase their job engagement. The paper also highlights the role of a leader as a coach and mentor through individualized consideration to cater to followers’ need for growth and achievement. These findings expand the literature on leadership and employee engagement in the Indian power sector.

Industrial development with the growth, strengthening, stability, technical advancement, reliability, selection, and dynamic response of the power system is essential. Governments and companies invest billions of dollars in technologies to convert, harvest, rising demand, changing demand and supply patterns, efficiency, lack of analytics required for optimal energy planning, and store energy. In this scenario, artificial intelligence (AI) is starting to play a major role in the energy market. Recognizing the importance of AI, this study was conducted on seven different energetics systems and their variety of applications, including: i) electricity production; ii) power delivery; iii) electric distribution networks; iv) energy storage; v) energy saving, new energy materials, and devices; vi) energy efficiency and nanotechnology; and vii) energy policy, and economics. The main drivers are the four key techniques used in current AI technologies, including: i) fuzzy logic systems; ii) artificial neural networks; iii) genetic algorithms; and iv) expert systems. In developed countries, the power industry has started using AI to connect with smart meters, smart grids, and the Internet of Things devices. These AI technologies will lead to the improvement of efficiency, energy management, transparency, and the usage of renewable energies. In recent decades/years, new AI technology has brought significant improvements to how power system devices monitor data, communicate with the system, analyze input–output, and display data in unprecedented ways. New applications in the energy system become feasible when these new AI developments are incorporated into the energy industry. But on the contrary, much more investment is needed in global research into AI and data-driven models. In terms of power supply, AI can help utilities provide customers with renewable and affordable electricity from complex sources in a secure manner, while at the same time providing these customers with the opportunity to use their own energy more efficiently. Moreover, policy recommendations, research opportunities, and how industry 4.0 will improve sustainability have been briefly described.

This study evaluates optimal carbon‐neutral power supply strategy in China up to 2060 with a multi‐region high spatial and temporal dynamic optimal power expansion planning model. Furthermore, this study explores installable potential of hydrogen and fuel cell system and analyzes the influences of its capital cost reduction at different levels. The results show that the national installed capacity would rise to be over 9000 GW in 2060, in which wind and solar PV will take up around 61%; the intermittency of renewable power generation is mainly managed with hydrogen production, power output curtailment and charge and discharge of energy storage; hydrogen and fuel cell system would improve the capacity factor and reduce the curtailment rate of renewable power generation. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Understanding dynamics on institution for sustainable development of society is a topical issue. Researchers in the past have qualitatively analyzed factors, such as inertia, that influence institutional evolution. This study proposes a quantitative approach. Using algorithms, we establish a phylogenetic tree based on data from the power sector institution of OECD countries during 1985–2019. Our results are verified by historical events in power sector institutions, such as the global change to a privatized competitive market and institutional revolution of the United Kingdom in 1990. We propose two quantitative measurements based on this tree: institutional inertia (which resists change) and ecological pressure (which causes change). Both institutional inertia and ecological pressure alter the dynamics of institutional evolution to be either gradual or rapid, respectively. Specifically, inertia causes a pattern shift from phyletic gradualism to punctuated equilibrium due to its diverse effect on institutional evolution. In the phyletic gradualism pattern, it suppresses rapid institutional change; however, it becomes stronger endogenously as institutions evolve and shifts the pattern to punctuated equilibrium. Using the phylogenetic tree to simultaneously analyze gradual and rapid institutional change is novel. [ABSTRACT FROM AUTHOR]