Thermal Power Generation Units Research Articles

Analysis of Deep Peak Shaving Methods for Thermal Power Generation Units Based on the Improved Energy Consumption Framework

Because thermal power producing units account for a large portion of the world's energy consumption, their energy consumption is a major concern. Reducing energy consumption and raising the overall efficiency of thermal power production units has become more and more important in recent years. Reducing energy consumption during peak hours is known as bottomless peak shaving, and it is one way to accomplish this. An enhanced framework for energy consumption is presented in this study to assess and examine deep peak shaving techniques for thermal power plants. The framework takes into consideration the various factors that affect energy consumption, such as fuel type, plant size, and external conditions. It also considers the different aspects of peak shaving, such as intensity and duration. Through the use of this framework, various deep peak shaving methods, such as thermal storage systems, load shifting, and demand response, are evaluated. The effectiveness of these methods is assessed based on their impact on energy consumption, cost, and environmental considerations. The framework also takes into account how feasible and useful it would be to apply these techniques in various kinds of thermal power generating units. The analysis's findings demonstrate that deep peak shaving techniques can dramatically lower energy use during peak hours, which can save money and possibly have positive effects on the environment. The best approach would rely on the unique qualities of every thermal power generating unit.

Switching Sudan’s Power Generation Units’ Fuel to Natural Gas Enhances the Efficiency, Durability and Lower the Cost

Sudan ranks among the top 20 Sub-Saharan countries facing electricity shortages, affecting approximately 20 million people as of 2020. Political and economic instability led to an increase in the electricity deficit, reaching over 62% in 2021, an 8% rise from 2019. Thermal power stations in Sudan, which contribute 37% of the electricity supply, encounter operational, maintenance, and fuel procurement challenges. They rely on various fuels, including liquefied petroleum gas (LPG), light diesel oil (LDO), and heavy fuel oil (HFO), with fuel procurement difficulties amid declining oil production. This study explores the potential of using natural gas instead of the conventional crude oil-based fuels listed above. Natural gas is known for its cost-effectiveness and lower greenhouse gas emissions, offering a cleaner alternative. The study involves the calculation of total gas volumes, flared gas, and operational costs for diesel and natural gas, with an evaluation of their compatibility with the operational requirements of thermal power generation units. The study reveals that upgrading existing thermal stations to a combined cycle system and transitioning to natural gas can reduce costs by 52% compared to diesel operations. The study also demonstrates that the annual gas requirement for operations is approximately 245 billion cubic feet, which is higher than the local production, indicating the need for importing liquified natural gas (LNG).

Effective mechanism for trading generation rights in the context of carbon emission rights

In the global context of intensive energy resource consumption and substantial greenhouse gas emissions, as the energy sector with the highest share of carbon emissions, the power generation industry is destined to play a central role in China's efforts to promote energy conservation and emission reduction. With the emergence of local carbon pilot projects and the implementation of a national carbon market, key emission units in the power industry, in addition to the electricity market business, also need to fulfill and pay carbon emission rights on schedule. The power generation rights are a market-based mechanism for clean energy units to replace conventional energy units in electricity generation, which can promote carbon emission reduction by incorporating the actual carbon emissions from conventional energy units. This paper establishes an optimized social welfare model that incorporates the interdependency between the thermal power generation unit market and the electric carbon market. It is based on the social welfare model of power generation rights and the initial allocation of carbon emission rights for the units, while considering the cost increase of excess emission under monthly forecasted power generation by the thermal power units. Taking a certain hydropower province as the simulation object, it was verified that the optimized model has a higher transaction rate of power generation rights, greater social welfare, and no reduction in unit revenue.

Study on Fracture Mechanism of Low-Pressure Blades of Steam Turbines in Power Plants

With the continuous improvement of steam turbine design technology and production level, steam turbine blade fracture accidents have dropped significantly, and blade fracture accidents are no longer the main reason for the forced shutdown of thermal power generation units. Due to the bad working conditions of turbine blades, blade designers still need help to accurately analyze their dynamic stress and evaluate their high cycle fatigue life. Up to now, blade fracture accidents still occur frequently. In this paper, a comprehensive analysis is made of the causes of the fracture of the low-pressure 16th stage blade of a 25 MW steam turbine in a thermal power plant. The fracture property of turbine moving blades is that small corrosion pits are formed first, and then the fatigue propagation fracture occurs. The main cause of corrosion is poor steam quality.

Microstructural and Performance Analysis of TP304H/T22 Dissimilar Steel Welded Joints.

In the power plant boiler industry, dissimilar steel welding is widely used in the connection of thermal power generation units. As an important component of the unit, research on the organizational properties of dissimilar steel welded joints has significant guidance for the life design of the joint. For the long-term service state of TP304H/T22 dissimilar steel welded joints, the microstructure's morphological evolution, the microhardness, and the tensile properties of tube samples were analyzed using tests and numerical simulations. The results show that the microstructure of each part of the welded joint was free of damaged features, such as a creep cavity and intergranular cracks. The microhardness of the weld was higher than that of the base metal. In the tensile test, the welded joints broke at the weld metal at room temperature and at the side of the TP304H base metal at a temperature of 550 °C. The tensile fracture morphology demonstrated a change from a ductile fracture to a hybrid fracture when the temperature rose. The fusion zone and base metal on the TP304H side were the stress concentration areas of the welded joint, which easily sprouted cracks. This study holds significant reference value in assessing the safety and reliability of dissimilar steel welded joints in superheater units.

Market clearing price-based energy management of grid-connected renewable energy hubs including flexible sources according to thermal, hydrogen, and compressed air storage systems

With the advancement of energy generation and storage technologies, it is expected that the environmentally-friendly integrated units of these elements will have a significant application in the power system so that the energy management of this unit can play a considerable role in improving the technical and economic status of energy networks, besides improving the technical and economic status of its power sources and storage facilities. Thus, the paper concerns the participation of flexible renewable energy hubs equipped with wind farms, bio-waste units, and hydrogen, thermal, and compressed air storage systems in the energy market based on the market clearing price model. Hubs are simultaneously present in both electrical and thermal networks. The bio-waste unit is equipped with combined heat and power technology, producing electrical and thermal energy. The proposed design is in the form of bi-level optimization. Its upper level formulates the maximization of the hub's expected profit, considering the operational constraints of the mentioned resources and storage devices and the hub's flexibility. The market clearing price strategy is included at the lower formulation level, considering minimizing the expected operation cost of electricity and thermal power generation units subject to the optimal power flow equations of electrical and thermal networks. The Karush-Kuhn-Tucker method obtains a single-level formulation for the design. The Unscented Transformation method is used to model uncertainties of load and renewable resources to achieve low computation time and accurate flexibility modeling. The obtained numerical results indicate the proposed design's ability to improve the operation and economic status of energy networks compared with optimal power flow studies (the hub-less network), along with optimal power scheduling of hubs in accordance with improving their flexibility and economic status. So, hydrogen, compressed air, and heat storage devices can reach 100 % flexibility conditions for hubs with wind farms and bio-waste units. Moreover, these storage devices lead to an 11.2 % enhancement in the economic status of the renewable hub. Optimal energy management of renewable hubs based on the storage system has led to a 27 % enhancement in energy network operation status compared to optimal power flow studies.

Machine learning approach to predict the biofuel production via biomass gasification and natural gas integrating to develop a low-carbon and environmental-friendly design: Thermodynamic-conceptual assessment

A hybrid energy cycle (HEC) based on biomass gasification can be suggested as an efficient, modern and low-carbon energy power plant. In the current article, a thermodynamic-conceptual design of a HEC based on biomass and solar energies has been developed in order to generate electric power, heat and hydrogen energy. The planned HEC consists of six main units: two electric energy production units, a heat recovery unit (HRU), a hydrogen energy generation cycle based on water electrolysis, a thermal power generation unit (based on LFR field), and a biofuel production unit (based on biomass gasification process). Conceptual analysis is based on the development of energy, exergy and exergoeconomic assessments. Besides that, the reduction rate of pollutant emission through the planned HEC compared to conventional power plants is presented. In the planned HEC, when hydrogen energy is not needed, excess hydrogen is feed into the combustion chamber to improve system performance and reduce the need for natural gas. Accordingly, the rate of polluting gases emitted from the cycle can be mitigated due to the reduction of fossil fuels consumption. Further, based on the machine learning technique (MLT), the level of biofuel produced from the mentioned process is estimated. In this regard, two algorithms (i.e., Support vector machine and Gaussian process regression) have been employed to develop the prediction model. The findings indicated that the considered HEC can produce about 10.2 MW of electricity, 153 kW of thermal power, and 71.8 kmol/h of hydrogen energy. In both training and testing sets, the Support vector machine model exhibits better behavior compared the two Gaussian process regression model. Based on machine learning technique, with increasing gasification pressure, the level of biofuel obtained from the process does not increase significantly.

Research on collaborative control and optimization of energy storage units under the high proportion of wind power infiltration

With the growing maturity of technology and strong support for national policies, the proportion of new energy power generation systems represented by wind power in the power network is increasing. Under the high proportion of wind power penetration in the power network, the power generation burden of thermal power generation unit is greatly reduced, and the running costs of the electricity grid system can be reduced while obtaining environmental value benefits. However, the stochastic and uncertain output of wind power poses a challenge to grid dispatch under a high proportion of wind power penetration. The energy storage power system responds quickly and can provide certain heat reserves and virtual rotational inertia for the power network, and the wind power generation system can effectively smooth the wind power output and reduce the wind power curtailment rate. This paper proposes an optimal allocation method that takes into account the operating cost of the energy storage plant, the operating cost of the thermal power generation group, the cost of wind turbine abandonment, the branch capacity constraint and the standby capacity constraint. A four-unit 14-node model is built to simulate the cooperative control of energy storage under the penetration of a high proportion of wind power in summer and winter, and verified the effectiveness of the strategy.

Prediction of Oxygen Content in Boiler Flue Gas Based on a Convolutional Neural Network

As one of the core pieces of equipment of the thermal power generation system, the economic and environmental performance of a boiler determines the energy efficiency of the thermal power generation unit. The oxygen content in boiler flue gas is an important parameter reflecting the combustion status of the furnace, and accurate prediction of flue gas oxygen content is of great significance for online boiler optimization. In order to solve the online prediction problem of the oxygen content in boiler flue gas, a CNN is applied to build a time series prediction model, which takes the time series samples within a fixed time window as the input of the model and uses several feature extraction modules containing convolutional, activation, and pooling layers for feature extraction and compression, and the model output is the oxygen content in boiler flue gas. Since the oxygen content in boiler flue gas is not only correlated with other variables but also influenced by its own historical trend, the input of the CNN model is improved, and an oxygen content in boiler flue gas time series prediction model (TS-CNN) is established, which takes the historical values of the boiler flue gas oxygen content as the input of the model. The comparison test results show that the R2 and RMSE of the TS-CNN model are 0.8929 and 0.1684, respectively. The prediction accuracy is higher than the CNN model, LSSVM model, and BPNN model by 18.6%, 31.2%, and 54.6%, respectively.

Combined Economic and Emission Power Dispatch Control Using Substantial Augmented Transformative Algorithm

The purpose of the Combined Economic Emission Dispatch (CEED) of electric power is to offer the most exceptional schedule for production units, which must run with both low fuel costs and emission levels concurrently, thereby meeting the lack of system equality and inequality constraints. Economic and emissions dispatching has become a primary and significant concern in power system networks. Consequences of using non-renewable fuels as input to exhaust power systems with toxic gas emissions and depleted resources for future generations. The optimal power allocation to generators serves as a solution to this problem. Emission dispatch reduces emissions while ignoring economic considerations. A collective strategy known as Combined Economic and Emission Dispatch is utilized to resolve the above-mentioned problems and investigate the trade-off relationship between fuel cost and emissions. Consequently, this work manages the Substantial Augmented Transformative Algorithm (SATA) to take care of the Combined Economic Emission Dispatch Problem (CEEDP) of warm units while fulfilling imperatives, for example, confines on generator limit, diminish the fuel cost, lessen the emission and decrease the force misfortune. SATA is a stochastic streamlining process that relies upon the development and knowledge of swarms. The goal is to minimize the total fuel cost of fossil-based thermal power generation units that generate and cause environmental pollution. The algorithm searches for solutions in the search space from the smallest to the largest in the case of forwarding search. The simulation of the proposed system is developed using MATLAB Simulink software. Simulation results show the effectiveness and practicability of this method in terms of economic and emission dispatching issues. The performance of the proposed system is compared with existing Artificial Bee Colony-Particle Swarm Optimization (ABC-PSO), Simulated Annealing (SA), and Differential Evolution (DE) methods. The fuel cost and gas emission of the proposed system are 128904 $/hr and 138094.4652$/hr.

Impact Of Carbon Price On The Cost Of Thermal Power Generation

The carbon market and electricity market play a decisive role in the optimal allocation of market resources, bringing challenges to the operation and development of power generation companies (especially thermal power). Based on the operation mechanism of the national carbon market, this paper constructs a cost assessment model for thermal power generation units in the context of carbon trading, with the aim of studying the impact of the carbon prices on the cost of thermal power generation both in the near and long term. The calculation results of the model show that the carbon price will have a small impact on the cost of electricity for thermal power companies and the electricity market in the near term. However, in the long term, as the carbon quota is tightened and the carbon price is raised, the carbon market will affect the quotation strategies of market players and market clearing price.

Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles

The growing number of electric vehicles (EVs) has resulted in increasing availability of battery storage capacities. The energy storage capacity of EVs is used to provide demand flexibility for the supply side. However, the different preferences of EV users will affect the charge and discharge decision of EVs. To overcome this problem, the concept of charging and discharging pressure is proposed to restrict the charging and discharging behavior of EVs. It is mainly dominated by the electricity price. Simultaneously, the charging and discharging time anxiety and state of charge (SoC) of EVs also affect the charging and discharging mode of EVs. This paper proposes a novel industrial microgrid (IMG) structure, which is mainly composed of power demand of industrial production, renewable energy sources (RES), energy storage systems (ESS), EVs and thermal power generation units. The aim of the proposed model is to minimize the operation cost of IMG and maximize the income of EV users. For the management of demand side, the strategy of time of use (ToU) price is adopted. In addition, considering the uncertainty of RES and industrial load, a robust optimization algorithm is proposed, and the operation of IMG under different uncertain scenarios is analyzed. Finally, the robust mixed integer quadratic programming (MIQP) of IMG is studied. The detailed simulation and comparison results verify the effectiveness of the proposed energy system under different charging and discharging pressures based on EVs.

The Strategies for Increasing Grid-Integrated Share of Renewable Energy with Energy Storage and Existing Coal Fired Power Generation in China

The growing share of renewable energies needs more flexible services to balance their intermittency and variance. The existing coal fired units and electrical energy storage (EES) systems may play an important role in delivering flexible services. The value of their flexibility services, along with the value of renewable energies, has to be analyzed from the perspective of the power system, in which the capacity costs and operation costs of renewable energy power units, EES systems, and thermal power generation units have to be taken into consideration. An optimal model is built to analyze the renewable energy integration and the flexibility services delivered by the EES systems and thermal power units in a power system. Taking the existing thermal power units and EES systems in North China Power Grid as an instance, the overall cost of the grid is examined for the penetration of renewable energies and flexible service provision. The results show that the growing shares of renewable energies are affected by their capacity credits and flexibility sources in the grid, and that the potential of thermal power units to provide flexible services will be reduced due to the replacement of renewable energies for thermal power generation. The results also indicate that the thermal units may be dispatched to have priority to delivering flexible services for the renewable energy integration, and that the curtailment of renewable energies may be regarded as one type of flexible service. According to these results, policy and strategy recommendations are put forward to weigh the role of existing coal-fired units and EES systems in providing flexible services, and to improve their compensation mechanism and their coordination.

Evaluation of TPGU using entropy - improved TOPSIS - GRA method in China.

China is still one of the countries dominated by thermal power generation. In order to generate more efficient, stable and clean power, it is necessary to evaluate thermal power generation units (TPGU). Firstly, a comprehensive evaluation index system for TPGU with 20 secondary indicators was established from four aspects: reliability indicators, economic indicators, technical supervision indicators, and major operating indicators. Secondly, the entropy weight method can be used to calculate the weight of each second-level index. Mahalanobis Distance improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is coupled with the Grey Relational Analysis (GRA), and the comprehensive evaluation values of 5 units (600MW) are respectively 0.4516, 0.5247, 0.3551, 0.5589 and 0.6168 from both vertical and horizontal dimensions. Finally, by comparing and analyzing this method with the above research methods, it is found that the results obtained by this method which re-establishes the coordinate system based on the data set are more accurate. In addition, this method can effectively evaluate the operation of TPGU, which is of great significance for cleaner production while generating electricity. In conclusion, some suggestions on clean production of TPGU are put forward, and the innovation points and limitations of this paper are pointed out.

People Resourcing Practices in Thermal Power Generating Units

A major challenge for business people in the field of globalisation is how to understand human resource management practices. It’s an obvious view that people are the assets of business entity irrespective of business. Managing people is not an easy task, since psychological factors speaks louder. Generally, the labour cum machine intensive industry should promptly set the people resourcing policies and practices. The thermal power generation units also need to frame the people resourcing practices in genuine. Accessing the opinion of employees about the people resourcing practices, in the select thermal power generation units of Tuticorin and satisfaction level of employees about the people resourcing practices are the core objectives. Analysis is done using Mann-Whitney test and Kruskal Wallis test. The researcher culminated that it is imperative for the thermal power generating units to realize the significance and intensity of people resourcing practices and ameliorate in all paths of work life.

Dispatch optimization of thermal power unit flexibility transformation under the deep peak shaving demand based on invasive weed optimization

The rapid development of new energy represented by wind power and photovoltaic power generation necessitates higher requirements for the flexibility of traditional thermal power generation units. The role of thermal power units will change from conventional main power to auxiliary power, not only because of the need for the survival and development of thermal power enterprises, but also because of the inevitable requirement for promoting the revolution of power energy production and consumption in China. The economy of thermal power unit flexibility transformation is an important constraint factor for decision-making in thermal power unit transformation. How to consider the economic factor of the thermal power unit transformation is a key problem for decision-making. In this paper, a nonlinear programming model is proposed to solve the problem. By introducing greedy strategy and improved reproduction strategy, an improved invasive weed optimization (IWO) algorithm is also proposed to solve the problem. Finally, a case study with three different scenario assumptions was conducted according to the most used transformation schemes of thermal power units in China, and the results were derived by using the proposed model and the improved IWO method. The comparative analysis of the results showed that the more the thermal power units participated in deep peak shaving, the greater the risk of the flexibility transformation of the thermal power units. In addition, under the condition of the same proportion of peak shaving, the risk of transformation without oil peak shaving was lower than that of oil peak shaving. Thus, the improved IWO algorithm can provide a stable and satisfactory solution. The research in this paper can provide a more scientific basis for decision-making on the flexibility transformation of the thermal power unit and can provide a reference for the selection of the flexibility transformation scheme of the thermal power unit.

Research of Turbine Oil Varnish in Generator Unit

In recent years, due to the fact that thermal power generating units have been deeply involved in the peak shaving work of the power grid, the frequent start and stop operations of the units and rapid load-carrying operations have made the operating conditions of the units very bad. In large-scale thermal power generation units, shutdown accidents caused by varnish failures are becoming more common. Take the serious varnish failure of Unit 3 in a domestic power plant as a case for analysis. The steam turbine oil after the varnish is formed is fully analyzed and tested and the metal ion content test is carried out. The changes in the physical indicators of the steam turbine oil during the formation of the varnish are studied to determine the influence of the steam turbine oil on the physical indicators of the steam turbine oil during the formation of the steam turbine oil. The operation and maintenance personnel can detect the varnish in advance through the daily sampling and analysis of the steam turbine oil, and timely take treatment measures to remove the varnish components in the oil to ensure the safe and stable operation of the unit. This article summarizes the reason for the formation of varnish and the detection method of varnish. It is of great significance to effectively discover and control the production of varnish during the operation of the generator set, to study the formation mechanism of varnish and to find an effective method to remove varnish. This article also introduces the commonly used paint film treatment methods for the convenience of electric power technicians.

An improved flower pollination solution for economic dispatch with valve point effect

<span>The economic dispatch is used to find the best optimal output of power generation at the lowest operating cost of each generator, to fulfill the requirements of the consumer. To get a practical solution, several constraints have to be considered, like transmission losses, the valve point effect, prohibited operating region, and emissions. In this research, the valve point effect is to be considered which increases the complexity of the problem due to its ripple effect on the fuel cost curve. Economic load dispatch problems are well-known optimization problems. Many classical and meta-heuristic techniques have been used to get better solutions. However, there is still room for improvement to get an optimal solution for the economic dispatch problem. In this paper, an Improved Flower Pollination Algorithm with dynamic switch probability and crossover operator is proposed to solve these complex optimization problems. The performance of our proposed technique is analyzed against fast evolutionary programming (FEP), modified fast evolutionary programming (MFEP), improved fast evolutionary programming (IFEP), artificial bee colony algorithm (ABC), modified particle swarm optimization (MPSO) and standard flower pollination algorithm (SFPA) using three generator units and thirteen thermal power generation units, by including the effects of valve point loading unit and without adding it. The proposed technique has outperformed other methods in terms of the lowest operating fuel cost.</span>

Toward a More Renewable Energy-Based LFC Under Random Packet Transmissions and Delays With Stochastic Generation and Demand

The load frequency control (LFC) aims to keep the frequency fluctuation of the power grids within certain specified limits, under various load disturbances. However, with the increased usage of renewable energy sources (RESs) in smart grids, it is essential to regulate the conventional power plants, based on renewable energy penetration levels. Moreover, with the decentralized nature of the control operation in smart grids, the communication network between the control center and actuator faces the challenge of random communication delays and packet drops in the form of cyberattacks. In this article, the conventional thermal power plant operations within an LFC have been modified using energy storage elements with an emphasis on maximizing the RES utilization while tackling the problems associated with cyber-physical systems, such as packet drops and random time delays. A filtered proportional&#x2013;integral&#x2013;derivative (PID) controller is tuned in the LFC using the particle swarm optimization (PSO) algorithm, including random time delays and cyberattacks modeled as random packet drops. The tuned PID control performance in the LFC scheme is tested with synthetic stochastic as well as real profiles of RES and load demands. The numerical analysis has been conducted on two-area LFC model with Monte Carlo simulations of stochastic demand and generation profiles. <i>Note to Practitioners</i>&#x2014;We are moving toward more renewable energy-based cyber-physical power grids, and it is becoming increasingly important to understand the limits of the balance between more renewable energy integration versus changing the prime-mover in thermal power generation units to meet uncertain load demands. This article proposes a new load frequency control (LFC) scheme employing a nonlinear dead-zone element between the control signals and the actuators (prime movers), which allows the utilization of the available renewable energy sources (RESs) to meet the load and then send a set-point change command in the thermal power generators if the RES does not meet the load. The robustness of the smart grid stability is also verified with the denial-of-service (DoS)-type cyberattack, which is represented in the form of high probability of packet drops and stochastic time delays in the communication channels between the control center and the generation units. It is also essential to understand how different stochastic profiles may affect the stability and performance of the tuned LFC loops, under random time delays and packet dropouts. These are quantified using the uncertainty bounds of grid frequency, its rate of change, control inputs, and power exchange between the two areas that are analyzed using Monte Carlo simulations with different types of nonstationary load and RES profiles and also using real data to show the effectiveness of the LFC scheme with communication constraints and the resulting imperfections.

Real-time optimal power flow solution for wind farm integrated power system using evolutionary programming algorithm

In order to satisfy the increasing demand for electricity, a huge burden is levied on the conventional coal-fired thermal power generation units, which affects the environment and human health adversely. To tackle this problem, the clean renewable sources of energy have gained more attention. With time, wind power is expected to contribute more significantly and should be used as much as possible. This has provided much focus to the wind farm integrated power system. But, the unsteady behavior of wind energy has made the operation of wind farm integrated power system more complex. Scenario generation methods are used for tackling the unsteady behavior of wind energy. A persistence method of scenario generation is proposed for solving real-time optimal power flow problem in wind farm integrated power system. The proposed persistence method performs better compared to other recent methods in the literature.