Electricity Generation Sector Research Articles

A dynamic material flow analysis of lithium-ion battery metals for electric vehicles and grid storage in the UK: Assessing the impact of shared mobility and end-of-life strategies

Limiting human-induced climate change represents a critical challenge for the future, and due to their disproportionate contribution to the problem, the energy and transport sectors are attracting the most attention in terms of emission reduction roadmaps and targets. Energy storage, particularly electrochemical storage, is poised to be a cornerstone in allowing those sectors to become more sustainable. This study presents the results of an integrated dynamic material flow analysis of the cumulative demand for lithium-ion battery metals (Li, Co, Ni and Mn) by the light duty vehicle and electricity generation sectors in the UK over the next three decades. Results have shown that recycling of end-of-life electric vehicle battery packs is very effective in “closing the loop”, and would enable driving the demand for all four metals back down to present levels by 2050, despite having achieved by then a complete shift to 100% electric vehicles. Additionally, repurposing end-of-life vehicle batteries for grid storage (with over 50 GWh of grid storage capacity expected to be in place by 2050) has been found to enable reducing purpose-built grid storage batteries to zero. Finally, an additional scenario analysis has indicated that a widespread behavioural shift from conventional vehicle ownership to shared mobility could even drive the demand for virgin battery metals into negative territory by 2040.

Deep reinforcement learning optimization framework for a power generation plant considering performance and environmental issues

In the electric power generation sector, striking a balance between maximum power production and acceptable emission limits is a challenging task that requires sophisticated techniques. With traditional methods, this is an extremely complex issue due to the large number of process variables that are involved. In this paper, a deep reinforcement learning optimization framework (DRLOF) is proposed to determine the optimal operating conditions for a commercial circulating fluidized bed (CFB) power plant that strikes a good balance between performance and environmental issues. The DRLOF included the CFB as an environment created from over 1.5 years of plant data with a 1 min sampling time which interacted with an advantage actor-critic (A2C) agent of two architectures named ‘separate-A2CN’ and ‘shared-A2CN’. The framework was optimized by maximizing electric power generation within the constraints of the plant’s capacity and environmental emission standards, taking into consideration the cost of operations. After training, the framework of the separate-A2CN architecture achieved a 1.97% increase in electricity generation and 1.59% emission reduction for NOx at 14.3 times lower computational cost. Furthermore, we demonstrated the framework’s flexibility, adaptability and lower computational burden by carrying out different test scenarios which demonstrated the effectiveness of the DRLOF. The findings of this study are not limited to the CFB power plant but can be extended to other chemical processes and industries. This approach minimizes the need for costly experiments, online optimization challenges and associated customizations. • Multi-objective optimization of CFB power plant with deep reinforcement learning. • Objective formulation considered power, fuel, reagent and environmental standards. • Two main reinforcement learning architectures were analysed for better CFB results. • Improved performance of CFB by 1.97% power increase and 1.59% emission reduction. • The framework’s generality, adaptability and computational efficiency were tested.

Microcontroller Based Solar Tracking System Design and Grid Connected Photovoltaic Systems

In the era of ever-rising demands of electrical energy, the concerns related to scarcity and environmental damages associated with the application of fossil fuels have increased. Renewable energy resources have proved to be a blessing. With their world-class ability to produce zero greenhouse emissions and diversifying the energy supply in turn reducing the dependency on fossil fuels, renewable energy resources have been gaining quite a lot of popularity in the electricity generation sector. Aiming to create a more well developed, efficient, and reliable system, this paper focuses on the concept of employing the best possible application of the most abundant source of energy - Solar Energy. The system is based on mechanically tracking the position of the Sun and orienting the Solar Panel with respect to the Sun’s radiation for achieving the maximum output by incorporating the application of Microcontroller. The proposed system focuses on the design and simulation of a Microcontroller-based solar tracking system on Proteus 8 professional software. The paper also incorporates the concept of ‘Grid-connected Photovoltaic systems’ as a dominant phenomenon and advancement for increasing the efficiency of the solar tracking system.

Methanol economy and net zero emissions: critical analysis of catalytic processes, reactors and technologies

The overuse of fossil fuels has led to the disruption of the balance of the carbon cycle: the transportation and electricity generation sectors are the greatest contributors. The methanol economy can replace the fossil fuel economy using CO2and green hydrogen as sources.

Dynamic Input Output Analysis: Carbon, Water and Ecological Hierarchy

As the main component of greenhouse gases, excessive carbon emissions are responsible for global climate change and anomalies. Carbon emission metabolism is essential for alleviating climate changes globally. Here, a dynamic input-output model is developed for analyzing carbon emission metabolism and ecological hierarchy. Direct and indirect carbon emissions are evaluated, and mutual relationships between various sectors are explored. The innovation of this research is summarized as follows: firstly, for carbon emission, the competition relationships reveal good interactions in terms of emission reduction while mutualism relationship provides effective pathways to mitigate carbon emissions between pairwise sectors simultaneously. Moreover, analysis of direct and indirect carbon emissions could help to extract embodied sectors with large emissions and identify whether the sectors should adjust their production structure or upgrade the clean combustion technology. In addition, ecological hierarchy analysis would help to identify key sectors lacking driving or pulling force during study period. It provides a new insight into the potential sectors which play an important role in the emission reduction. Results show that carbon emissions mainly concentrated in electricity-generation, manufacturing, and transportation sectors. In Guangdong province, upgrading the clean combustion technology in electric power generation and energy extraction sectors would drive other sectors to cut emissions and adjusting the production structure of the construction sector also contribute to achieve this goal. Tertiary industry is not sufficiently promoting Guangdong’s economic development, and therefore plays a restrictive role in the current economy. Results could help decision makers to develop emission mitigation policies.

Large-scale Battery Storage, Short-term Market Outcomes, and Arbitrage

The expansion of the share of renewable energy in the portfolio mix of the electricity generation sector has accelerated the development and integration of large-scale battery storage facilities. We document charging and discharging patterns in the California market and show how battery activity correlates with load and real-time prices. We further propose an instrumental variable strategy to estimate the short-term impact of prices on battery discharge. The empirical findings are consistent with the optimal solution of an arbitrage maximizer, indicating that battery owners respond to price incentives. In addition, we provide evidence that battery deployment in the years 2018 and 2019 lowered average wholesale equilibrium prices in California by about 0.8%, impacting private returns of battery investment.

Electricity Generation in Saudi Arabia: Tracing Opportunities and Challenges to Reducing Greenhouse Gas Emissions

The electrical energy demand of the Kingdom of Saudi Arabia (KSA) has been observing a surging growth due to high population growth and urbanization rate. The generation mostly comes from the burning of petroleum products. The electricity sector emits more than one-third of the national greenhouse gas (GHG) of the country. Thus, it is essential to reduce the GHG from the electricity generation sector to maintain sustainability. This article mainly concentrates on the existing electricity generation and focuses on the Kingdom’s GHG emissions mitigation initiatives. Although the use of natural gas and the number of cogeneration plants have been increased in electricity generation, the country has also been focusing on other initiatives, including the adaptation of combined cycle power plants, carbon capture, utilization, and sequestration (CCUS), renewable and alternative energy sources, and various utility-scale energy efficiency measures. The ambitious and inspirational renewable energy (RE) targets of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Saudi Vision 2030</i> will significantly facilitate GHG emission avoidance, which will support the Kingdom to meet its Nationally Determined Contributions. This article recommends various mitigation measures, including the usage of the efficient internal combustion engine, poly-generation, combined heat and power (CHP) systems, deployment of renewable resources in the distribution and microgrid systems, bio-mass engine, and ocean thermal energy conversion plants. The decision-makers and the researchers will find the discussions and recommendations helpful in determining suitable and appropriate mitigation initiatives and technologies.

Dynamic Data Envelopment Analysis Model Involving Undesirable Outputs in the Electricity Power Generation Sector: The Case of Latin America and the Caribbean Countries

Studying the evolution of the efficiency of the electricity generation sector is a relevant task for policy makers, and requires the undesirable outputs derived from the activity to be considered in the evaluation. In this work, we propose a dynamic slack-based Data Envelopment Analysis (DEA) model that incorporates the assumption of weak disposability between the generation of electricity from fossil sources and the CO2 emissions caused by the sector to measure the technical efficiency of 24 Latin American and Caribbean countries in the period 2000–2016. The results show that, of the total number of countries studied, four are efficient overall, and four groupings of countries in relation to the levels of efficiency achieved are also identified. These results are important given that less-efficient countries can, through learning, increase their efficiency in electricity generation or emulate the future strategies proposed by the most efficient countries.

GTAP- Power Database: Version 10

This paper documents changes introduced to version 10 of the Global Trade Analysis Project (GTAP) Power (GTAP-Power 10) Data Base construction process relative to the GTAP-Power build stream developed in Peters (2016). First, in Peters (2016) output of the electricity and heat generation sector was split into different technologies using electricity generation data only. We use heat and electricity generation volumes to provide a more representative sectoral split and achieve a better concordance with GTAP 10 Data Base sectoral definitions. Second, we introduce data on country and year-specific shares of transmission and distribution costs in electricity price for 80 countries. In the GTAP-Power 9 Data Base this cost share was assumed to be uniform across all countries and regions. Finally, for every reference year, we update the levelized cost of electricity generation. We first compare GTAP-Power 9 Data Base construction results with and without corresponding changes. We then construct the GTAP-Power 10 Data Base and showcase how it can be used to estimate carbon dioxide emissions embodied in final consumption of electricity generated by different technologies.

Climate co-benefits of air quality and clean energy policy in India

Sustainable development goals connect policies addressing air quality and energy efficiency with complementary benefits for climate mitigation. However, a typically fragmented approach across these domains hinders effectiveness in addressing short-lived climate forcers (SLCFs)—including methane, carbon monoxide, non-methane volatile organic compounds and black carbon—to supplement CO2 mitigation. Here, to support policy coordination in India, we assess climate co-benefits of air quality and clean energy policies, using multiple metrics (global warming and temperature change potentials). We estimate an emission reduction potential of −0.1 to −1.8 GtCO2e yr−1 in 2030. The largest benefits accrue from residential clean energy policy (biomass cooking) and air pollution regulation (curbing brick production and agricultural residue burning emissions), which cut black carbon. In the next 1–2 decades (using global warming potential—GWP20), emission reduction potentials of warming SLCFs exceed those of CO2, which is not evident on longer timescales. Concurrently, policies in the electricity generation and transport sectors reduce cooling SLCFs (SO2 and NOx), potentially unmasking 0.1–2.4 GtCO2e yr−1. Integrating these interventions into national climate policies can strengthen both climate action and sustainability. The crucial impact of black carbon suggests that it should be included in the international climate accord. Climate-oriented goals in air quality and clean energy policies can substantially reduce greenhouse gas emissions, particularly if shifting to biomass cooking and addressing emissions from brick production and agricultural residue burning.

Structural and technological determinants of carbon intensity reduction of China's electricity generation.

Electricity generation is the largest sector with decarbonization potential for China and the world. Based on the new emission factors, this paper aims to identify the structural and technological determinants of provincial carbon intensity in the electricity generation sector (CIE) using the multiplicative LMDI-II method. Results demonstrate that (1) China's overall CIE decreases by 7.3% in 2001-2015, and the research period can be divided into four stages according to CIE changes (i.e., rapid growth, rapid decline, slow growth, and transition). The CIE in the 12th FYP estimated in this paper, 24.9% lower than that using the emission factors from IPCC, is closer to China's actual situation. (2) There exists huge heterogeneity in the determinants of provincial CIE changes in four stages. CIE growth in the Northwest and Northeast is caused by the coal-dominated energy structure. CIE growth in the Southwest is attributed to the electricity structure effect, while that of the Coast region is caused by the geographic distribution effect. The electricity efficiency effect is attributed to the CIE growth for these regions and the Southwest should also place focus on the electricity trade effect. The impact of electricity trade-related factors depends on the region being a net exporter or importer of electricity. (3) To achieve carbon intensity reduction targets, 30 provinces are categorized into four types based on various combinations of structural and technological determinants. The findings provide insights into capturing future emission-mitigating focus as well as defining the emission-mitigating responsibilities between electricity exporters and importers in China.

Critical transmission paths and nodes of carbon emissions in electricity supply chain

As the largest contributor to production-based emissions, electricity generation sector has led to huge carbon emission transmissions. This is the first attempt to explore the latest features of carbon emission transmissions from electricity sector to the final domestic consumption of China in 2002–2015, combining MRIO-based Structural Path Analysis and transmission-based emission method. Results show that: (1) Although inter-provincial transmissions are increasing significantly, emission transmissions within intra-provincial trading are dominated. (2) 30 provinces are classified into two types, i.e., consumption centers and production centers. Both the inter-provincial transmission paths in consumption centers and production centers show the grid-level agglomeration and provincial heterogeneity. The inflow paths in consumption centers are mainly sourced from the production of Eastern China and South China, while the outflow paths for production centers are caused by the consumption in Central China, Guangdong and Jiangsu. Inter-provincial linkages are intensified and perform the feature of territorial propinquity. (3) Both intra-grid and inter-grid transmission nodes show an agglomeration trend of “electricity sector < intermediate sectors < electricity sector < consumption”. These intermediate sectors include manufacture sectors, energy-intensive sectors and service sector. This paper provides policy implications on promoting low-carbon electricity cooperation across provinces and managing intermediate transmissions along supply chain.

Patent Citation Network Analysis

The synergistic network analysis framework incorporates the fast community detection strategy and eigenvector centrality for the patent family citation networks in the electricity generation sector. When the radical fundamental technologies have the great central influences in the electricity generation sector patents, the isolating incremental innovations are the subsequent inheriting technologies without the sufficient industry exposure. The general simplified family citation network summarizes the complex structure in the potential consecutive outcomes and stable technological innovations. There is the sharp cut-off between the lower centrality patent family and higher centrality patent family for the compound transitional influences in the patent family citation networks.

When is the sun going to shine for the Brazilian energy sector? A story of how modelling affects solar electricity

The energy sector has a vital role in climate change mitigation. In Brazil, the electricity generation sector is strongly dependent on hydropower generation, but thermoelectricity contribution has increased leading to a more carbon-intensive electricity mix. This trend will be aggravated with the expected significant increase in the electricity demand until 2050 and with the potential limitation of hydropower generation expansion due to environmental constraints. The extent of the role of solar power to overcome these challenges in the future has been hidden by energy modelling assumptions. A review on how solar energy has been considered in different models, was performed. The research further assessed the relevance of assumptions by developing an optimization model to explore the cost-effectiveness of solar electricity large-scale deployment up to 2050. The work found that most published solar energy assessments based on long-term energy modelling for Brazil currently are not updated regarding several assumptions, leading to models results where solar appears with a marginal role. The energy modeling exercise shows solar PV is cost-effective for Brazil, providing more than 36% of total electricity in 2050. This is a different conclusion from the energy and power modeling exercises conducted until now, where solar deployment has been underestimated.

Evaluation of CO2 emission from Egypt's future power plants.

Energy-related CO2 emissions increased to a global peak of 33 Gt in 2019, resulting in an unprecedented level of “Global Warming”. Egypt emitted 250 million tons of CO2 in 2018, thereby ranking 27th among the countries of the world in terms of energy-related CO2 emissions. Approximately 40% of CO2 emissions in Egypt orginate from the electricity generation sector, which is predominately dependent on oil and gas (90%), followed by renewables (10%; solar, wind and hydroelectric). To achieve its development goals, Egypt plans to build new power plants with a total generating capacity of 30 gigawatts (GW). Added to the current generating capacity of Egypt’s power plants (60 GW), the new power plants will enable electricity generating capacities to reach approximately 90 GW by 2030. Egypt has three scenarios to achieve this goal. In the first scenario, a diversified energy mix scenario, dependence on oil and gas will be decreased in favor of a more diversified energy mix of coal, nuclear power, in addition to renewables and hydro. The second scenario, a fossil fuel-based scenario, is based on recent discoveries of proven natural gas reserves, possibly shifting Egypt’s vision towards more dependence on natural gas, as well as renewables, nuclear and hydro. These two scenarios might lead to increased amounts of released CO2 into the atmosphere. Here we suggest a third scenario, the environmentally friendly scenario or the green scenario, in which more dependence is placed on renewables, hydro and nuclear power, in addition to natural gas, with no coal in the suggested energy mix. In this article, we analyze CO2 emissions derived from electricity generation under these three futuristic scenarios. The results of our comparison show that building new power plants will lead to CO2 emissions of 307, 330 and 128 million tons (Mt), respectively, according to the first, second and third scenario, respectively, compared to the current 100 Mt of emissions. These results clearly demonstrate that the third (green) scenario is the only scenario that allows the country to build new power plants to achieve its goals of development while only slightly increasing the amount of CO2 emissions. In addition, this scenario may be incorporated into the Nationally Determined Contributions ratified by Egypt in the Paris Agreement to limit global warming.

Carbon emission pinch analysis (CEPA) for planning the decarbonization of the UK power sector

Abstract Carbon emissions pinch analysis (CEPA) was carried out for the planning of deep cuts in the carbon dioxide (CO2) emissions of the electric power generation sector of the United Kingdom (UK). The graphical CEPA technique is used to examine alternatives by which the UK can achieve or approach net zero emissions by 2050. The analysis considers constraints such as the condition of the current fleet of power plants, available renewable energy resources, and technological transitions such as carbon capture and storage (CCS) and potential entry of negative emissions technologies (NETs). Different scenarios are proposed for the planning of years 2030 and 2050, based on realistic assumptions and extension of various potential renewables. For the planning of year 2030, compared to the governmental forecasts for the same year, biomass from the UK is to be increased by 63%, while the remaining electricity generation is to be obtained from wind and solar power, which are to be increased by 25% respectively (scenario SC4-RE-WSB). For year 2050, natural gas is to be increased by six folds from the 2030 scenario, and other renewables have their respective increased generation, i.e. 50% wind generation, 80% solar generation, 50% biomass import from EU, and 60% of UK biomass. Furthermore, 51% of power from biomass plants are to be installed with CCS, making it a NET with negative emission factor. Doing this achieved the zero emission target for the UK power generation sector. Results showed that an electricity generation mix having net zero emissions for the UK is possible if sustainably grown biomass is used coupled with CCS technologies.

Can energy conservation and substitution mitigate CO2 emissions in electricity generation? Evidence from Middle East and North Africa

This study objectively investigates the influence of energy conservation and substitution strategies in mitigating CO2 emissions in the electricity generation sector in Middle East and North Africa (MENA) region. The paper specifically explores if climate action implementation may negatively affect energy consumption on economic growth ambition and whether technical change in factor and inter-fuel substitution support in achieving sustainable development goals (SDGs) 7, 8 &13. This paper conducts the study under the life cycle analysis (LCA) procedure. We also apply stochastic frontier Translog production and ridge regression techniques with data for 2000–2015. The results currently show that renewable energy intensity falls below the breakeven level. Meanwhile, nonrenewable energy intensity is much higher. Furthermore, production factors and inter-fuel technological change show that capital-energy and renewable-nonrenewable energy are perfect substitutes under the electricity sector. Moreover, the findings reveal low energy efficiency of 40 percent with poor carbon performance. We, therefore, conclude that MENA region may not achieve cleaner, affordable energy and climate action concurrently with economic growth under the current production technologies by the fiscal year 2030. We further conclude that economic growth is presently inversely related to energy efficiency but positively with energy intensity. This suggests that MENA region economic growth drive is highly linked with nonrenewable consumption and also not supporting energy conservation strategies. Additionally, we conclude that energy efficiency improvement and reduction in energy intensity may not be achieved as the governments keep subsidizing electricity in the region. Besides, the more policymakers pursue economic growth through energy intensity indicators, the more energy is consumed through the energy rebound effects. Based on these findings and conclusions, we recommend insightful strategies under the electricity sector. This study adds new body of knowledge by extending Translog production to estimate energy intensity & efficiency under the LCA procedure. Scholars also contribute to data and variable modeling under the SDGs in discovering CO2 emission mitigation in the electricity sector which could be replicated in other countries or regions.

What did infrastructure ever do for us?

What did infrastructure ever do for us?

A novel approach for analyzing the effectiveness of the R&D capital for resource conservation: Comparative study on Germany and UK electricity sectors

Limited access to energy resources and increasing demand for mitigation of the environmental impacts have necessitated higher production efficiencies. Therefore, the promotion of R&D activities has been one of the main parts of national energy policies. In this research work, two methods are used for assessing the effectiveness of the R&D capital on resource conservation. First, by developing the resource-included knowledge production function of the energy sector and second, through direct assessment of the functionality of resource productivity from R&D capital. Based on official data provided by national statistical centers, a comparative analysis has been conducted to examine the effectiveness of R&D in the electricity generation sectors in the United Kingdom and Germany. In these analyses, non-renewable exergy cost is used as an index of resource depletion and environmental burden. It is shown that, due to non-economic energy transition policies, the inclusion of the R&D investment does not result in statistically meaningful elasticity of production for the R&D capital. However, according to the second method, it is shown that the R&D investment in the United Kingdom has been more effective in resource conservation than in Germany. Possible reasons for such differences are discussed and recommendations for further investigations are presented.

Multi-region and multi-sector comparisons and analysis of industrial carbon productivity in China

In the context of global warming, increasing carbon productivity is an important way to balance environmental goals with economic growth and development. In this study, we measure the provincial industrial carbon productivity (ICP) in China. Employing spatial production-theoretical decomposition analysis and data from the industrial sector in each province of China, we investigate the regional disparities in ICP and the driving factors at the provincial and sectoral levels. The results indicate that the ICP discrepancies across different regions are obvious: the eastern region had the highest ICP, followed by the northeastern, central, and western regions. The capital-energy substitution effect and CO2 emission performance were two principal contributors to increasing the regional disparities for most provinces. By contrast, the labor-energy substitution effect and energy consumption structure remained relatively backward and resulted in lower ICP than the average level in most provinces. Furthermore, 12 key industrial subsectors, including electricity generation sector, five energy-intensive manufacturing sectors and six nonenergy intensive manufacturing sectors, in 13 provinces (including Hebei, Liaoning, Heilongjiang, Anhui, and all the other western provinces) were identified as the main drivers of the lower than average ICP in these 13 provinces. For the12 industrial sectors in the 13 provinces, industrial structure, and CO2 emission performance were the main causes of their backward carbon productivity. Based on the findings of this study, several relevant suggestions for policymakers are provided.