Power Generation Sources Research Articles

그리드포밍 인버터 자원 기반 전력계통의 블랙스타트 및 계통 동기화 영향 연구

The increasing use of inverter-based resources, while reducing synchronous generators, decreases the system"s inertia, leading to an increased risk of power outages. The shift from a centralized power generation structure to a decentralized structure can be effective for stable operation of inverter-based power generation sources and to mitigate the risks of power outages. The grid forming inverter can perform the same role as a synchronous generator and can contribute to energizing primary restorative transmission systems. Grid-forming inverters must meet several requirements to replace synchronous generators in power systems. One of the most important requirements is the ability to operate as AC voltage sources. This means that the grid-forming inverter must be able to provide a stable AC voltage to the grid, just like a synchronous generator. To address this, a black start study using inverter resources was conducted, and a pumping generator-IBR cooperative black start was simulated through cooperation with an existing black start resource. The paper proposed the grid-synchronization method of grid-forming inverter and emphasizes the need for independent and quick recovery of inverter resources, which must operate as AC voltage sources to replace synchronous generators and contribute the primary power system restoration. The effectiveness of the proposed method to increase the input capacity and speed by stably inputting through synchronization has been studied and were verified through PSCAD/EMTDC simulation.

A Novel Solar System of Electricity and Heat

Thermoelectric devices may have an essential role in the development of fuel-saving, environmentallyfriendly, and cost-effective energy sources for power generation based on the direct conversion of heat into electrical energy. A wide usage of thermoelectric energy systems already exhibits high reliability and long operation time in the space industry and gas pipe systems. The development and application of solar thermoelectric generators (TEGs) arelimited mainly by relatively low thermoelectric conversion efficiency. Forthe first time, we propose to use the direct energy conversion of solar energy by TEGs based on the high-performance multilayer thermoelectric modules with electric efficiency of ~15%. Solar energy was absorbed and converted to thermal energy, which is accumulated by a phase-change material (aluminum alloys at solidification temperature ~900 K). The heat flow from the accumulator through the thermoelectric convertor (generator) allows electrical power to be obtained and the exhaust energy to be used for household purposes (heating and hot water supply) or for the operation of a plant for thermal desalination of water.

Techno-economic assessment of biomass and coal co-fueled chemical looping combustion unit integrated with supercritical CO2 cycle and Organic Rankine cycle

This study evaluates the effect of co-combustion of woody biomass and coal on the techno-economic performance of a 300 MWth in-situ gasification chemical looping combustion power plant. The circulating fluidized bed reactor system was designed and a modified macroscopic model was applied to predict the fuel conversion and the CO2 capture efficiency. The supercritical CO2 cycle and the Organic Rankine cycle were integrated with the heat sources for power generation. The results showed that increasing biomass share in the input thermal power, in addition to enhancing the CO2 capture efficiency, improves the coal conversion. Moreover, negative CO2 emissions are achieved as the biomass share exceeds 50 MWth. The net electrical efficiency of the plant is about 43.89% and grows slightly for higher biomass shares. For biomass share of 150 MWth, the CO2 capture efficiency increases from 79.27% to 90.87%. Also, the levelized cost of electricity (LCOE) rises by 15.9%–108.262 $/MWh due to higher biomass price. Despite this increased fuel cost, the LCOE is still lower than that value for the coal-fired power plant with conventional CO2 capture technology. Biomass utilization becomes more cost-effective than coal if the carbon tax is above 30 $/tCO2.

Thermo-economic analysis of the performance of the combined system with evacuated tube collectors

Abstract Using combined cooling, heat and power systems can be an appropriate substitute for preventing emissions of pollutants and excessive consumption of fossil fuels. Utilizing renewable energy in these systems as a source of power generation can be an appropriate substitute for fossil-fuel-based systems. Therefore, in this paper, cogeneration cooling, heat and power systems based on gas-fired internal combustion engines with a solar-thermal system with evacuated tube collectors have been modelled and thermo-economic analysis has been done to compare fossil-fuel-based systems. The required rate of heat to supply the hot water is 50 kW. In the studied system, the internal combustion engine produces electrical energy. Then, the solar-thermal system with evacuated tube collectors and the gas-burning generator provide the thermal energy required by the studied building and the primary stimulus of the absorption chiller for cooling. In this study, two different scenarios are conducted in states considering simultaneous production systems and regardless of this environmental and thermo-economic analysis system. The results showed that the efficiency of the studied system was 60% in summer and 56% in winter.

Impacts of Renewable Energy Generation on Greenhouse Gas Emissions in Saudi Arabia: A Comprehensive Review

Over the last few years, the electric energy demand in the Kingdom of Saudi Arabia (KSA) has increased many folds due to several factors including increased population, industrialization, economic activities, and urbanization. The main source of electric power generation in KSA is the burning of petroleum products. Almost one third of greenhouse gas (GHG) emissions are contributed from the electric power generation sector, mainly, by burning diesel and natural gas. As a result, it makes it necessary to consider alternate forms of electricity generation in order to cut down emissions and to keep sustainable growth alive. The government has planned to diversify energy sources and suppliers. In recent years, energy generation from renewable sources including solar photovoltaic (PV), wind, concentrated solar power (CSP), biomass, geothermal, and tidal, has been given more importance. The ambitious Saudi Vision 2030 targets of 58.7 GW of power generation from renewable energy sources will cause a significant reduction in GHG emissions from the energy sector. This article systematically reviews the impact of renewable energy generation on GHG emissions. The detailed breakdown of GHG emission is discussed. Then, the status of renewable energy generation is investigated, focusing on the technical and economic potentials. The correlation of renewable energy generation and GHG emissions is then explained. The most distinguishing feature of this review is that it provides a comprehensive list of recommendations to reduce GHG emissions. The discussions and recommendations of this article will support decision makers, system planners, industry personnel, researchers, and academics to develop sustainable energy pathways for the Kingdom.

Application of Augmented Echo State Networks and Genetic Algorithm to Improve Short-Term Wind Speed Forecasting

The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable output power forecasts to set up the optimal operating point of the electrical system. In previous research, the authors developed an evolutionary approach algorithm called RCDESIGN to optimize the hyperparameters and topology of Echo State Networks (ESN), and applied the model in different time series forecasting, including wind speed. In this paper, RCDESIGN was modified in some aspects of the genetic algorithm, and now it optimizes an ESN with augmented states (ESN-AS) and has been called RCDESIGN-AS. The evolutionary algorithm allows the search for the best parameters and topology of the recurrent neural network to be performed simultaneously. In addition, RCDESIGN-AS has the important characteristic of requiring little computational effort and processing time since it is not necessary for the eigenvalues of the reservoir weight matrix to be reduced and also due to the fact that the augmented states make it possible to reduce the number of neurons in the reservoir. The method was applied for wind speed forecasting with a 24-h ahead horizon using real data of wind speed from five cities in the Northeast Region of Brazil. All results obtained with the proposed method overcame forecasting performed by the persistence method, obtaining prediction gains ranging from 60% to 80% in relation to this reference method. In some datasets, the proposed method also yielded better results than the traditional ESN, showing that RCDESIGN-AS can be a powerful tool for wind-speed forecasting and possibly for other types of time series.

Emerging Energy Sources for Sustainable Power Generation: An Overview

Electricity plays a big part in our daily life. Energy supply is a key factor in the modernization, automation, and expansion of the economy. Traditionally used energy sources are primarily used to create it. While traditional energy sourcesare rapidly running out, energy demand is rapidly increasing. A global problem is poor energy management as a result of scarce land resources, tight energy budgets, and weather-dependent renewable energy sources. Researchers that study energy are primarily concerned withthe depletion of conventional energy and the parallel development of practical alternative energy sources. Only 20% of the country's energy is used by homes; the remaining 80% is split across different economic sectors. When it comes to achieving sustainability in power generationfor sustainable development, the topic of renewable energy sources for generation cannot be overemphasized.The two main issues in the fight against climate change are without a doubt energy security and sustainability. Clean energy and a healthy environment are necessary for a world where people, plants, and animals can live in peace and health. Therefore, finding ways to incorporate renewable energy into the mix of energy production is necessary to improve energy sustainability and combat climate change. Todemonstrate their current condition and potential in the future, the literature on new energy sources is explored in this article. This analysis explains policies for renewable energy, potential assessments, comparative studies, energy optimization, sustainability, and the adaptation of strategic policies to promote the development of emerging renewable energy as a source of power generation to accelerate economic growth and preserve the environment.

Optimal design of PI/PD dual mode controller based on quasi opposition based learning for power system frequency control

Due to uncertain load deviations in the unified power system, it becomes more challenging to uphold the system frequency at a constant desired value. The secondary controller has a significant role in damping the oscillations of frequency and interline power at a faster rate. Here a novel PI/PD dual mode controller (DMC) is suggested to regulate the frequency of unified system comprising renewable and conventional sources of power generation. The various sources of generation instigated in the dual-area system are hydro, diesel, thermal, PV, wind and battery energy storage system (BESS). The gains of the PI/PD DMC are suitably obtained by the novel application of quasi opposition based Sine Cosine Algorithm (QOBSCA) algorithm. The dynamic outcomes of the model are examined with the application of abrupt load deviation of 0.02 per unit in control area 2. During this deviated loading condition QOBSCA algorithm is utilized to achieve the appropriate gains of PID and PI/PD dual mode controller in view of a time based fitness function denoted as integral time absolute error (ITAE). Considering few response evaluative indices like overshoots, settling time and undershoots the ascendency of the projected PI/PD dual mode controller is recognized over popular PID controller during the abnormal loading condition. Additionally, the efficacy of the mentioned PI/PD dual mode controller is verified by applying arbitrary load pattern in control area 2. The robustness of the mentioned control schemes is also evidenced by confirming the stable response during system parameter deviations.

Load-Frequency Control in a Two-Area Power System Using the Fuzzy-PID Method

Power systems are plant units connected to each other, with the electrical power flow constantly moving between them and the load. All systems must be implemented in such a way that they remain stable not only under normal conditions but also after the implementation of unwanted inputs or malfunctions and should be able to return to stable nominal conditions as soon as possible. The basic factors of stability control in a power system are the frequency of different areas and the amount of power flow between them. Now that the main goals of stability control in a power system have been stated, these indicators must be kept at their desired levels through the design and implementation of controllers. This paper first describes the mechanism of load frequency control (LFC). The parameters of several types of complementary controllers are then designed based on the proposed algorithm. The controllers designed on the simulated power system are finally applied, and the effect of each controller on faster damping of frequency fluctuations is discussed. In this study, a multi-area power system is simulated and the effect of disturbance in each area and the performance of the proposed controller in controlling the frequency of the whole network are investigated. The case studies show that the share of active and reactive power generation sources in each region have the greatest impact on frequency control, given that the proposed control coefficients are determined using fuzzy logic in the shortest possible time and the number of transient oscillations eliminated, resulting in a steady system.

Design and Fabrication of Mechanical Speed Breaker for Power Generation

In the recent period, the need for power is growing per day as the population grows. Now, almost every vehicle creates pollution and we are using its mechanical energy just for transportation purposes only instead of something else even, but now we have a solution to this problem with a technique that helps to overcome the pollution that is produced by “conventional source of power generation” as well as energy generate and also should be eco-friendly. Such power that will be generated by this method will be used for traffic lights as well as signals. The economy depends on energy and its approaches to development simultaneously which indicates more energy production. This research work clarifying that how we can generate power by linear motion is converted into rotary motion (using the rack-pinion method). It also shows a thorough analysis of power generating with the help of a speed breaker and emphasizes an idea which shows that with the help of a specially designed speed breaker power could easily be generated which having the aim of producing electrical power using the weight of vehicles and can be utilized for street light, signals, etc.

Pandemic restrictions in 2020 highlight the significance of non-road NOx sources in central London

Abstract. Fluxes of nitrogen oxides (NOx=NO+NO2) and carbon dioxide (CO2) were measured using eddy covariance at the British Telecommunications (BT) Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 73 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled, identifying that transport NOx emissions had reduced by >73 % since 2017. This was attributed in part to the success of air quality policy in central London but crucially due to the substantial reduction in congestion that resulted from pandemic-reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which, until now, has been primarily focused on the emissions from diesel exhausts.

Power Generation Sources and Carbon Dioxide Emissions in BRICS Countries: Static and Dynamic Panel Regression

Purpose: The threat of global warming has escalated as a result of industrialization, urbanization, population growth, and lifestyle changes in Brazil, Russia, India, China, and South Africa (BRICS). The amount of electricity generated by various sources is directly influenced by their respective carbon dioxide (CO2
 ) emissions. This study’s primary goal is to determine which sources are bad for the environment and which are not.
 Methodology: Examining the impact of different energy generation sources on CO2
 emissions using data from the BRICS. To analyze the data, pooled OLS and Generalized Method of Moments (GMM) are used, as well as Quantile Regression (QR).
 Findings: We found that coal and gas power generation had a positive and large influence on CO2
 emissions regardless of the method used. As compared to other emissions, coal-fired energy production has a more significant impact. In all regression models, hydroelectric and renewable energy generation can reduce CO2
 emissions.
 Originality: Identifying an empirical link between CO2
 emissions and energy production sources is the study’s most significant accomplishment. To obtain solid results, the paper used a combination of QR and GMM techniques. The conclusions presented in this article have important environmental policy consequences. CO2 emissions can be reduced by reducing the consumption of fossil fuels and promoting the development of alternative energy sources such as hydroelectric, wind, and solar power.

Spherical autoregressive models, with application to distributional and compositional time series

We introduce a new class of autoregressive models for spherical time series. The dimension of the spheres on which the observations of the time series are situated may be finite-dimensional or infinite-dimensional, where in the latter case we consider the Hilbert sphere. Spherical time series arise in various settings. We focus here on distributional and compositional time series. Applying a square root transformation to the densities of the observations of a distributional time series maps the distributional observations to the Hilbert sphere, equipped with the Fisher–Rao metric. Likewise, applying a square root transformation to the components of the observations of a compositional time series maps the compositional observations to a finite-dimensional sphere, equipped with the geodesic metric on spheres. The challenge in modeling such time series lies in the intrinsic non-linearity of spheres and Hilbert spheres, where conventional arithmetic operations such as addition or scalar multiplication are no longer available. To address this difficulty, we consider rotation operators to map observations on the sphere. Specifically, we introduce a class of skew-symmetric operators such that the associated exponential operators are rotation operators that for each given pair of points on the sphere map the first point of the pair to the second point of the pair. We exploit the fact that the space of skew-symmetric operators is Hilbertian to develop autoregressive modeling of geometric differences that correspond to rotations of spherical and distributional time series. Differences expressed in terms of rotations can be taken between the Fréchet mean and the observations or between consecutive observations of the time series. We derive theoretical properties of the ensuing autoregressive models and showcase these approaches with several motivating data. These include a time series of yearly observations of bivariate distributions of the minimum/maximum temperatures for a period of 120 days during each summer for the years 1990-2018 at Los Angeles (LAX) and John F. Kennedy (JFK) international airports. A second data application concerns a compositional time series with annual observations of compositions of energy sources for power generation in the U.S..

Study of aerodynamic performance of built-in variable wavelength traveling wave turbine

Turbines, as power sources for power generation, ships, etc. are capable of absorbing energy from high-temperature and high-pressure gas, converting thermal energy into mechanical energy. We propose a built-in variable wavelength traveling wave turbine that establishes a traveling wave mechanism in the rectangular flow channel. This device has the same function as a conventional turbine, extracting energy from incoming high-pressure gas, but it operates in a completely different way from axial flow and centripetal types. Numerical simulation is adopted to investigate the energy absorption characteristics and flow mechanism of the variable wavelength traveling wave turbine of two wave numbers. The traveling wave turbine is simplified to a two-dimensional traveling wave plate with analytical functions to control the motion parameters. The results show that influenced by variable wavelength, the motion waveform forms expansion type, equal wavelength type and contraction type. The curve of energy absorption rate of traveling wave turbine shows first increase and then decrease with the highest efficiency reaching 89.71%. The mass flow coefficients under two wave numbers correspond to the efficiency curves with approximately the same trend. There is the obvious distinction between pressure regions divided by the moving traveling wave plate, indicating that fluid energy is mainly transferred to traveling wave plate. The velocity varies prominently in X direction near the leading edge and trailing edge of the plate. Interestingly, as the variable wavelength changes, the vortex direction alters where the first formed vortex sheet shed the trailing part of the traveling wave plate. The area of vortex sheet varies significantly.

Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.

An extended approach to appraise electricity distribution and carbon footprint of bitcoin in a smart city.

A nation cannot sustain a highly productive and efficient population without smart cities. Due to their significant reliance on digital technologies, these cities require a high level of cybercrime protection. Cryptocurrencies have gained significant attention due to their secure and reliable infrastructure. The decentralised cryptocurrency operates in a trust-less environment known as the blockchain, where each network participant has a ledger copy of all transactions. Blockchain technology employs a proven consensus mechanism without requiring establishment of a central authority. But the consensus mechanism requires miner to solve a cryptographic problem by generating random hashes until one of them matches the desired one. This procedure is energy-intensive, and when thousands of miners repeat it to verify a single transaction, a substantial amount of electricity is consumed. Moreover, electricity produces a significant amount of carbon footprint. Patch methodology utilises the data of all hashes created per year and the efficiency of mining hardware over a 10-year period to calculate the Bitcoins energy consumption. Due to a large number of unknown and uncertain factors involved, it is difficult to precisely calculate a single value for electricity consumption and carbon footprint as reported by Patch methodology. The proposed method extends the Patch methodology by adding a maximum and minimum limit to the hardware efficiency as well as the sources of power generation, which can help refine estimates of electricity consumption and carbon emissions for a more accurate picture. Using the proposed methodology, it was estimated that Bitcoin consumed between 38.495 and 120.72 terawatt hours of electricity in 2021 and released between 2.12 and 45.37 million metric tonnes of carbon dioxide. To address the issue of excessive energy consumption and carbon emissions, a significant number of individual miners and mining pools are relocating to energy-intensive regions, such as aluminium mining sites that rely on hydroelectricity for energy generation.

An integrated Method for Evaluating the Energy-Saving and Emission Reduction of Thermal Power Plants with Interval-Valued Intuitionistic Fuzzy Numbers

With the continuous development of the domestic economy, the demand for electricity is also increasing, and the number of power plants is also increasing. Thermal power plants have become the main source of power generation in my country. According to statistics, thermal power generation accounts for more than half of the country's power generation. In the process of thermal power generation, it is also accompanied by a large amount of exhaust gas and pollutant emissions. The emission of sulfur dioxide (sulfur dioxide) from thermal power plants accounts for about half of the total national emissions, and the emissions of greenhouse gases such as carbon dioxide and nitrogen oxides also account for more than 30% of the total national emissions. and clean emissions have become an important task. Before energy conservation and emission reduction of thermal power plants, it is necessary to find out the current situation of thermal power plants and to find out the current energy conservation and emission reduction effects of thermal power plants. row for evaluation. The problems of evaluating the energy-saving and emission reduction of thermal power plants are classical multiple attribute decision making (MADM) problems. In this paper, firstly the definition of interval-valued intuitionistic fuzzy sets (IVIFSs), Heronian mean (HM) operator, Dombi operations are introduced and the interval-valued intuitionistic fuzzy Dombi power Heronian mean (IVIFDPHM) operator is proposed. Some desirable properties of this operator are established, such as commutativity, idempotency and monotonicity. Then, the IVIFDPHM operator is used to deal with the interval-valued intuitionistic fuzzy (IVIF) multiple attribute decision making (IVIF-MADM) problems. Finally, an illustrative example for evaluating the energy-saving and emission reduction of thermal power plants is given to verify the built approach.

Automation levels for nuclear reactor operations: A revised perspective

This work serves to propose updated levels of automation for nuclear reactor operations, as a result of considering long-term economic and commercial ambitions of the advanced reactor developer community. As in other fields such as road-going vehicles and aviation, reactor technologies can benefit from modern automation through the resulting reduction in operations and maintenance costs, while still maintaining the current industry standards regarding safety, resilience, reliability, overall performance, and the capacity for root-cause analysis. The current guidelines on automation levels, as published by the U.S. Nuclear Regulatory Commission in Section 9 of NUREG-0700, reflect outdated design principles that implicitly limit the potential of automation innovation for reactor operations, particularly in regard to advanced reactors intended to operate in remote locations or be used for off-grid applications. Motivated by the operational paradigms anticipated for future reactor designs, we propose a six-level approach that aligns with contemporary automation concepts as well as automation level definitions from other non-nuclear safety–critical industries. These levels build upon the current guidelines in order to enable next-generation nuclear reactor technologies to become increasingly economically competitive and commercially viable relative to competing power generation sources. Using a hypothetical heat removal reactor transient, we provide examples of how the human–machine interactions change at each level of automation, ranging from traditional operator control (Level 0) to operator-free unattended operations (Level 5)—the latter being one of the key attributes proposed at the Fission Battery Initiative led by Idaho National Laboratory. Finally, we critically examine the identified challenges, knowledge gaps, and enabling technologies to achieve advanced levels of automation.

Lifecycle carbon footprint comparison between internal combustion engine versus electric transit vehicle: A case study in the U.S.

Since transportation accounts for a large portion of a city's overall greenhouse gas emissions, cities are looking to lessen this effect by replacing more internal combustion engine vehicles (ICEVs) with electric vehicles (EVs). In this research, two vehicle models of comparable size from each category were compared using life cycle analysis (ICEV and EV). The ICEV is a Ford Transit van, while the EV is a Ford E-transit van, both of which are supposed to have a lifespan of 150,000 km for comparison. Carbon footprint for both vehicles is examined from cradle-to-grave: including raw material production, manufacturing, transportation, operation, and decommissioning. According to the findings, the carbon emissions produced per kilometer by an electric vehicle are considerably lower. The Ford transit van emits 469.1 gCO2-eq/km and the Ford E-transit van emits 363.2 gCO2eq/km during a vehicle's lifetime. The impact of electricity generating in each U.S. states on the carbon footprint of ICEVs and EVs showed that the use of renewable energy sources for power generation would result in a reduction in carbon dioxide emissions. Lifespan sensitivity analysis with 100000 km and 350000 km indicated that the longer life of a vehicle alters the efficiency balance in favor of an electric vehicle.

Geochemical Characteristics of Rare-Metal, Rare-Dispersed, and Rare-Earth Elements and Depositional Environments in the Shanxi Formation Coal, Huainan Coalfield, Anhui, China

Coal, being one of the major energy sources for power generation, contains several critical trace elements. There is a growing scarcity and expense of these critical elements as a result of the increased demand and limitation of mining sources. To explore the geochemical characteristics of the rare-metal, rare-dispersed (scattered), and rare-earth elements (TREs) in coal, 25 coal seam samples of the Shanxi Formation in the Huainan coalfield were collected. The major element oxides, minerals, and TREs were analyzed by X-ray fluorescence spectroscopy (XRF), X-ray diffraction (XRD), and inductively coupled plasma-mass spectrometry (ICP-MS). The results revealed that the coal of the Shanxi Formation had ultra-low moisture and low ash yield and was medium-high-volatility with low sulfur content and high calorific value. Concerning minerals, the coal was mainly composed of kaolinite, illite, quartz, calcite, dolomite, and pyrite. Compared with Chinese coal and world hard coal, rare-metal element Li and rare-dispersed element Se were enriched, whereas Ga and Ta were only slightly enriched. The average content of REYs was 51.34 μg/g, which is lower than the average content of REYs in Chinese coal. It has the enrichment characteristics of light REYs. In the vertical direction, the content of most TREs was higher in the roof and floor of the coal seam and the parting, indicating that the sedimentary microenvironment plays an important role in controlling the migration and enrichment of elements. The experimental results of sequential chemical extraction and correlation analysis showed that the TREs in the Shanxi Formation coal mainly exist in a residual and carbonate bound state, and occur in clay minerals and carbonate minerals. The enrichment of Se may be due to its high organic form ratio. The C-value, B content, w(Sr)/w(Ba), and REY geochemical parameters indicated that the Shanxi Formation Coal seam was developed in a transitional, semi-saline, deltaic sedimentary environment. With their development affected by seawater, REYs in coal are greatly supplied by terrigenous clastics. The complex sedimentary environment is an important reason for the varying occurrence states of TREs in the Shanxi Formation coals.