Power Generation Plants Research Articles

Air Excess Coefficient and Irreversibility Value Analysis of Gas Turbines

Gas turbines used in natural gas cycle power plants have been researched in terms of energy efficiency. Gas turbines used in power generation plants have a very complex structure because they contain many components. In order to determine the thermodynamic performance of this system, the efficiency of each component and the increase in entropy of each component were calculated using exergy analysis. Irreversibility was chosen as the main parameter and used for thermodynamic optimization of production. The system consists of many components. An increase in the irreversibility of a single component causes an increase in the irreversibility of other components. The effect of gas turbine pressure ratio and excess air percentage on fuel consumption, fuel exergy, combustion chamber exergy change and irreversibility change was investigated. It was observed that the increase in compressor pressure ratio increased irreversibility. Maximum irreversibility was observed in the combustion chamber. Irreversibility was compared with the excess air used in the combustion chamber. The air excess coefficient was examined as percentages of 100%, 200%, and 300%, respectively. The compression pressure ratio was examined for air excess coefficient percentage values from 3 to 40.

Ensemble learning and deep learning-based defect detection in power generation plants

Abstract One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.

Qualification Of Inspection Systems In Nuclear Power Plants. Looking For Excellence

Inspection reliability is an area of growing interest. In particular, in certain industrial sectors involving critical structures, such as nuclear power plants for energy generation, reliability of Non-Destructive Examinations (NDE) has received special attention since more than 40 years. Many initiatives and projects (PISC, EPRI NDE, ENIQ, etc.) developed and generated new findings and results. In addition, the inspection systems are regularly optimized for improving the NDE reliability as much as possible. In the early 1990s, the European Network for Inspection Qualification (ENIQ) continued the PISC NDE reliability research and, few years later, issued the European Methodology for Qualification of Non-Destructive Testing. The methodology considers the NDE system follows the modular model of reliability as well as complementary tasks to address human factors issues. Then, every country develops his own methodology based on the existing legal requirements and the ENIQ Methodology. This is the case of Spain, in which the CSN (the Nuclear Safety Body) requires the qualification of inspection systems before their application in the Nuclear Power Plants (NPP). This paper describes the main characteristics of the Spanish Methodology as well as examples to facilitate the understanding of the ideas behind the approach that currently applied in the Spanish NPPs since the mid-2000s.

Assessment of Power Generation Plant Performance by Energy Sources Rating Environmental Impact and Operating Cost

Assessment of Power Generation Plant Performance by Energy Sources Rating Environmental Impact and Operating Cost

Regional Operation of Electricity-Hythane Integrated Energy System Considering Coupled Energy and Carbon Trading

The deepening implementation of the energy and carbon market imposes trading requirements on multiple regional integrated energy system participants, including power generation plants, industrial users, and carbon capture, utilization, and storage (CCUS) facilities. Their diverse roles in different markets strengthen the interconnections among these subsystems. On the other hand, the operation of CCUS, containing carbon capture (CS), power-to-hydrogen (P2H), and power-to-gas (P2G), results in the coupling of regional carbon reduction costs with the operation of electricity and hythane networks. In this paper, we propose a regional economic dispatching model of an integrated energy system. The markets are organized in a centralized form, and their clearing conditions are considered. CCUS is designed to inject hydrogen or natural gas into hythane networks, operating more flexibly. A generalized Nash game is applied to analyze the multiple trading equilibria of different entities. Simulations are carried out to derive a different market equilibrium regarding network scales, seasonal load shifts, and the ownership of CCUS. Simulation results in a 39-bus/20-node coupled network indicate that the regional average carbon prices fluctuate from ¥1078.82 to ¥1519.03, and the organization of independent CCUS is more preferred under the proposed market structure.

Design and simulation of a photovoltaic plant for maximum use of the solar resource in the Toluviejo municipality of Colombia

Currently, there are various technologies for constructing photovoltaic plants including monofacial and bifacial photovoltaic panels, centralized and string-type inverters, and fixed mounting structures with light followers, to build electrical energy generation systems. This work reviewed the advantages and disadvantages between these technologies, and the implementation of a photovoltaic plant in Toluviejo, Colombia was proposed. To justify and confirm the design, simulations were carried out with the PVsyst software, resulting in the bifacial panels, centralized inverters, and structures with light followers, the most effective solution, and with the highest production for the proposed photovoltaic power generation plant.

Optimizing building energy efficiency with a combined cooling, heating, and power (CCHP) system driven by boiler waste heat recovery

In light of increasing challenges related to fossil fuel consumption, global warming, and rising energy costs, optimizing energy systems has become essential. This study introduces a multi-purpose system designed to capture and utilize waste heat from boiler flue gases at the Tous power plant for simultaneous power generation, cooling, and heating. The system integrates an Organic Rankine Cycle (ORC) for power generation and an Absorption Refrigeration Cycle (ARC) for cooling, with the added function of recovering waste heat to preheat natural gas for the boiler and power plant. The system's performance was rigorously analyzed through energy, exergy, and exergoeconomic assessments. To determine the optimal operating conditions, a multi-objective optimization was conducted using the TOPSIS technique, focusing on critical parameters such as flue gas exit temperature, natural gas inlet temperature, and turbine inlet pressures. The results identified optimal conditions with a flue gas exit temperature of 532.5 K, a natural gas inlet temperature of 285 K, and turbine inlet pressures of 2.54 MPa and 14.62 MPa for the first and second turbines, respectively. Under these conditions, the system achieved an exergy efficiency of 41.1 %, cooling capacity of 133.1 kW, power generation of 210.4 kW, heat transfer to natural gas of 979.6 kW, and operational costs of 8.53 $/h. This study highlights the potential of the proposed system to significantly enhance energy efficiency and reduce operational costs in practical applications, offering a sustainable solution for energy management.

Considering Embodied Greenhouse Emissions of Nuclear and Renewable Power Plants for Electrolytic Hydrogen and Its Use for Synthetic Ammonia, Methanol, Fischer-Tropsch Fuel Production.

Recent concerns surrounding climate change and the contribution of fossil fuels to greenhouse gas (GHG) emissions have sparked interest and advancements in renewable energy sources including wind, solar, and hydroelectricity. These energy sources, often referred to as "clean energy", generate no operational onsite GHG emissions. They also offer the potential for clean hydrogen production through water electrolysis, presenting a viable solution to create an environmentally friendly alternative energy carrier with the potential to decarbonize industrial processes reliant on hydrogen. To conduct a full life cycle analysis, it is crucial to account for the embodied emissions associated with renewable and nuclear power generation plants as they can significantly impact the GHG emissions linked to hydrogen production and its derived products. In this work, we conducted a comprehensive analysis of the embodied emissions associated with solar photovoltaic (PV), wind, hydro, and nuclear electricity. We investigated the implications of including plant-embodied emissions in the overall emission estimates of electrolysis hydrogen production and subsequently on the production of synthetic ammonia, methanol, and Fischer-Tropsch (FT) fuels. Results show that average embodied GHG emissions of solar PV, wind, hydro, and nuclear electricity generation in the United States (U.S.) were estimated to be 37, 9.8, 7.2, and 0.3 g CO2 e/kWh, respectively. Life cycle GHG emissions of electrolytic hydrogen produced from solar PV, wind, and hydroelectricity were estimated as 2.1, 0.6, and 0.4 kg of CO2 e/kg of H2, respectively, in contrast to the zero-emissions often used when the embodied emissions in their construction were excluded. Average life cycle emission estimates (CO2 e/kg) of synthetic ammonia, methanol, and FT-fuel from solar PV electricity are increased by 5.5, 16, and 49 times, respectively, compared to the case when embodied emissions are excluded. This change also depends on the local irradiance for solar power, which can result in a further increase of GHG emissions by 35-41% in areas of low irradiance or reduce GHG emissions by 21-25% in areas with higher irradiance.

Design, multi-aspect investigation and economic advantages of an innovative CCHP system using geothermal energy, CO2 recovery using a cryogenic process, and methanation process with zero CO2 footprint

The significance of devoting attention to environmental pollution control strategies is widely recognized as an effective means of mitigating the harmful environmental effects caused by fossil fuels in industrial and power plant sectors. Carbon dioxide (CO2) capture and recovery technologies have opened up the possibility of utilizing this pollutant gas. Hence, the current study suggests a methodology for the CO2 hydrogenation of the flue gas leaving a power plant. This approach facilitates methane generation via a methanation reactor, subsequently is utilized as fuel for a power plant. For this purpose, a cryogenic method using liquefied natural gas cold energy facilitates CO2 recovery. Moreover, the whole system utilizes geothermal energy to launch a power plant for power generation and supply the power demands of a hydrogen production unit, relying on a water electrolysis process. The hydrogen generated is employed for CO2 hydrogenation, while the oxygen produced is utilized for the combustion reaction. Heating provider units and an absorption chiller are also included in the design. The system is modeled utilizing Aspen HYSYS software. This study incorporates a sensitivity analysis alongside energy, exergy, environmental, and economic assessments. The energy and exergy efficiencies, as determined by the thermodynamic analysis, are 30.87% and 48.61%, respectively. Additionally, according to the economic study, the levelized energy cost amounts to 16.65 $/MWh, demonstrating a substantial reduction of 87.6% compared to the power generation mode. One notable merit of the suggested system lies in its zero CO2 footprint framework, showing a noteworthy supremacy when compared with previous studies.

Impact of Soybean Biodiesel Blends with Mixed Graphene Nanoparticles on Compression Ignition Engine Performance and Emission: An Experimental and ANN Analysis

The extensive use of fuels in power generation plants, industries, and transportation has led to a scarcity of fossil fuels and has contributed to global warming. This has prompted researchers to focus on improving internal combustion engine performance, as the transportation system accounts for 50% of global fuel consumption. Soybean biodiesel plays a vital role in reducing emissions and fuel consumption in engines. In the current work, a blend of soybean oil is used as a biodiesel (B20, B40, and B60), with and without the addition of graphene nanoplatelets (GNPs) examined on a constant-speed compression ignition (CI) engine with respect to pure diesel. The blends are denoted as B20GNP10, B20GNP20, B20GNP40, B20GNP60, B20GNP80, and B20GNP100, according to their proposition of biodiesel and graphene nanoplatelets. Similar blends were prepared for B40 and B60 combinations with similar GNPs concentrations. The prepared blend properties were measured, and good thermophysical properties were found. The trial includes testing the engine emissions and performance at altering loads ranging from 0 to 12 kg for all blends. The artificial neural network (ANN) tool is used to forecast the accuracy of experimental results. Compared to pure diesel, the B60GNP100 blend at 12 kg load condition showed the lowest brake specific fuel consumption at 12.58 % and the highest brake thermal efficiency at 27.13%. Emissions were estimated using a gas analyzer, and the outcomes indicated that the biodiesel blends have controlled levels of CO, CO2, NOx, and HC compared to pure diesel. The ANN model with 99.99% accuracy was developed using experimental data, confirming the accuracy of the experimental results with lower simulation time and cost. Additionally, the B60GNP100 blend yielded better results compared to previous studies.

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Weather Condition Clustering for Improvement of Photovoltaic Power Plant Generation Forecasting Accuracy

Simultaneous costs minimizing in electricity and gas micro-grids with the presence of distributed generation

Distributed generation can actively participate in the day-ahead markets, real-time power balance, and wholesale gas markets to achieve various goals, such as supplying gas to various electric power generation plants. A multi-objective network with two types of loads is considered in this paper. The reason for the simultaneous optimization of these two networks is that these two energy carriers are dependent on each other and gas is needed to produce electricity, so this issue can be addressed with a multi-objective function. The simulation carried out in this article is coded in GAMS software as a mixed integer linear programming (MILP). The efficiency of gas turbines and fuel cells in this article is dependent on their working point, and considering the exact model of these resources and the relationships related to the calculation of their fuel consumption is non-linear. On the other hand, a binary variable has been used to show the charging and discharging state of the storage and the on-and-off state of the gas turbines. Therefore, the problem considered in this article is a MILP problem. The results of this article are the proper planning of charging and discharging of the energy storage system with the proper planning of the power generation of different energy sources considering the network loads in two optimized and non-optimized scenarios.

Characterization of Humic Acid Salts and Their Use for CO2 Reduction

The European Union aims to be climate neutral by 2050. To achieve this ambitious goal, net greenhouse gas emissions must be reduced by at least 55% by 2030. Post-combustion CO2 capture methods are essential to reduce CO2 emissions from the chemical industry, power generation, and cement plants. To reduce CO2, it must be captured and then stored underground or converted into other valuable products. Apromising alternative for CO2 reduction is the use of humic acid salts (HASs). This work describes a process for the preparation of potassium (HmK) and ammonium (HmA) humic acid salts from oxidized lignite (leonardite). A detailed characterization of the obtained HASs was conducted, including elemental, granulometric, and thermogravimetric analyses, as well as 1H-NMR and IR spectroscopy. Moreover, the CO2 absorption capacity and absorption rate of HASs were experimentally investigated. The results showed that the absorption capacity of the HASs was up to 10.9 g CO2 per kg. The CO2 absorption rate of 30% HmA solution was found to be similar to that of 30% MEA. Additionally, HmA solution demonstrated better efficiency in CO2 absorption than HmK. One of the issues observed during the CO2 absorption was foaming of the solutions, which was more noticeable with HmK.

Effect of multi-unit and multi-type DG installation using integrated optimization technique in distribution power system planning

With the rise in load demand, improving the voltage profile and reducing line loss is vital to ensure reliable power delivery to the customer. However, increasing power plant generation capacity is limited by environmental and economic factors, requiring a careful assessment of locally optimal options. Distributed Generation (DG) installation into the electricity grid is one of the reliable remedial actions to ensure a smooth power delivery. Installation of DG requires an optimization process to identify the appropriate placement and sizing. Inaccurate sizing and placement of the DG installation may result to over-compensation or under-compensation phenomena. This paper proposes a novel approach termed the Integrated Immune Moth Flame Evolutionary Programming technique (IIMFEP) for optimizing the installation of DG sources in distribution systems. It handles various scenarios, including multi-DG single-type and multi-DG multi-type installations, with the main objective of minimizing power loss in the system. This technique employs a hybrid approach that combines elements of immune algorithms, moth flame optimization, and evolutionary programming to achieve more accurate and efficient results. Two cases were considered in this study termed Case 1 and Case 2. Results in Case 1 discovered that the optimal sizing and placement of four Type III DGs exhibit the lowest power loss worth 2.74 kW (98.78 % reduction) for the 69-Bus RDS, while for the 118-Bus RDS the power loss is 319.89 kW (75.36 % reduction). In Case Study 2, the combination of DG Types I and III provided the highest power loss reduction. With one DG Type I and two DG Type III units installed, power loss was reduced by 97.15 % to 6.41 kW for the 69-Bus RDS and by 62.01 % to 493.21 kW for the 118-Bus RDS. The proposed IIMFEP managed to alleviate the setback experienced in the traditional EP, AIS and MFO which found to be stuck at local optimum. The IIMFEP method is compared to Moth Flame Optimization, Artificial Immune System and Evolutionary Programming and validated using the IEEE 69-Bus and 118-Bus Radial Distribution Systems, resulting in outstanding performance.

Thermoelectric generators versus photovoltaic solar panels: Power and cost analysis

In the current study, the concept of building a power plant using thermoelectric generator (TEG) modules is investigated, both technically and economically. The hypothesized thermoelectric generation power plant is a modular system, consisting of a large array of electrically connected thermoelectric generator units for generating clean electricity with-out greenhouse gas (GHG) emissions, noise, or hazardous solid wastes. The envisioned thermoelectric generation power plant (TEGPP) considered here is assumed to utilize solar radiation as a heat source, and water as a heat sink. The viability of such a concept is examined in the current study based on available specifications of a high-output thermo-electric generator module released in the market (TEG1-24111-6.0). Benchmarking is car-ried out considering a high-efficiency photovoltaic (PV) panel in the market (SunPower SPR-MAX3-400), assuming that it operates under standard solar radiation of 1,000 W, and with a standard panel temperature of 25 C, causing it to give an output electric power of 400 W (DC or direct current). It is found that in order to have an electric power of a thermoelectric generator unit similar to that of a photovoltaic panel of equal surface area, the temperature at the hot side of the thermoelectric generator unit should be about 70 C if the cold-side temperature is 30 C. However; under this output power equiva-lence, the price of the thermoelectric generator unit is about 90 times that of a photovol-taic panel of equal size (based on prices of October 2023). At an elevated hot-side tem-perature of 300 C for the thermoelectric generator unit (with the cold-side temperature being still 30 C), the thermoelectric generator unit can generate electric power that is about 25 times the power generated by a photovoltaic panel of an equal geometric area. This big boost in the output power still does not counteract the large cost difference be-tween the thermoelectric generator technology and the photovoltaic technology, where the per-watt(electric) cost in the case of thermoelectric generators is 3.5 times its value in the case of photovoltaic panels. Thus, the TEG technology in its intensified generation mode is still relatively more expensive compared to the PV technology. The practical im-plications of the current study are excluding the thermoelectric generation (based on the Seebeck effect) concept from large-scale commercial power plants, and viewing thermoe-lectric generators as sources of small electric power for waste heat management or con-venient power sources for small mobile devices.

Financial Viability of Thermal Power Generation Plants in the Transition to Renewable Energy

The energy transition will occur due to the natural decline in profitability of thermal power generation assets, and their place in the energy matrix will be taken over by renewable energies. Ensuring electric supply security, a cornerstone of the energy trilemma, is a priority for both developed and developing countries. Nations heavily reliant on hydrological resources employ thermal energy as a backup, making the exploration of financial feasibility for such projects pertinent. This study introduces an economic valuation model for a dispatchable thermal power generation plant in the Colombian market. Associated uncertainty is assessed based on revenue from market sales, bilateral contracts, and firm power remuneration – elements constituting the cash flow and converging into profitability and risk analysis. These are calculated through the Monte Carlo simulation methodology. Additionally, a sensitivity analysis is conducted, accounting for variables such as the "Reliability Payment" settlement price, capacity factor, and costs linked to coal supply. The results emphasize the need for firm power remuneration as a crucial incentive for the success of such projects. Furthermore, the criticality of dispatch level in relation to profitability is verified. Finally, the impact of coal taxes and supply negotiations on financial feasibility is assessed.

Research on Operation Characteristics of Passive Containment Heat Removal System in Typical Accident Conditions

In order to verify the feasibility of the design of the third generation of nuclear power plant independently developed in China, this study investigated the operation characteristics of the passive containment heat removal system under five working conditions using the passive containment heat removal system general performance verification facility: the design conditions, low water level conditions, low pressure conditions in the containment, extreme accidents conditions and resistance increasing conditions are analyzed, and the thermal characteristics, power requirements and thermal parameters of the containment under different operating conditions are analyzed. The results show that the heat dissipation capacity of the passive containment heat removal system exceeds the design requirements under the design conditions. Under off-design low pressure conditions, the pressure in the system is significantly affected. The decrease in the cooling water tank’s water level not only did not adversely affect the operation of the system but instead led to a significant increase in the system’s heat dissipation power. In the case of extreme accidents, the system may initially stall, but a stable natural cycle can then be established. As the system resistance coefficient increases, the heat dissipation power of the passive containment heat removal system exhibits an approximate linear decreasing trend.

Determining Fault Location in Transmission Lines Using Differential Equation Algorithms

A transmission line, in the context of electrical engineering and power systems, is a high-voltage or high-tension line that is used to transmit electrical power over long distances from power generation sources (such as power plants) to substations, where the electricity is then distributed to homes, businesses, and industrial facilities. Transmission lines are the backbone of the electrical grid, serving as the highways for electricity delivery. Their efficient and reliable operation is essential for ensuring that electricity is generated, transmitted, and distributed effectively, meeting the needs of consumers while maintaining grid stability and resilience. Transmission line protection refers to a set of techniques and devices used in electrical power systems to detect and respond to faults or abnormal conditions that can occur in high-voltage transmission lines. These lines are a critical part of the electrical grid and are responsible for transporting electricity over long distances from power generation plants to distribution substations. This paper proposes a different differential equation algorithm to locate faulty section on transmission lines. As seen from performance curves, the proposed protection algorithm is able to distinguish normal and faulty conditions.

Experimental analysis of a pilot plant in Organic Rankine Cycle configuration with regenerator and thermal energy storage (TES-RORC)

Applying thermal energy storage in a Regenerative Organic Rankine Cycle system is a possible combination of energy storage for renewable and waste energy sources. This research presents an experimental analysis of a regenerative organic Rankine cycle pilot plant, incorporating a thermal energy storage chamber based on a phase change materials to obtain thermal inertia in the system. The primary heat source is the residual heat of combustion gases at a temperature of approximately 320 °C coming from a generating set that operates within a thermal power generation plant. The prototype uses a twin-screw expander modified to work as a turbine. Novec 649 was used as the working fluid which is an organic compound. The experimental evaluation of the regenerative organic Rankine cycle with thermal energy storage has been carried out through energy and exergetic efficiency analysis to verify the operation of the pilot plant. Values of 13.2 % were obtained for the exergetic efficiency and 2.67 % for the global efficiency of the cycle, with a maximum expander work of 11.8 kW. It was demonstrated that the thermal energy storage system provides energy in response to the plant's operating inertia for 17.5 min, sufficient time for the gradual shutdown of the plant equipment, maintaining the working power of the regenerative organic Rankine cycle.

Integration and capacity optimization of molten-salt heat storage in coal-fired power plant with carbon capture system

Developing a new generation of economical, low-carbon, and flexible coal-fired power plants (CFPP) is crucial for achieving the low-carbon transformation of the energy industry. Deploying molten salt heat storage (MSHS) in the CFPP can effectively enhance its peak shaving capability and reduce the limitations of carbon capture on wide range operation of the plant. To this end, this paper proposes four integration schemes for MSHS into the power plant-carbon capture system. The thermal efficiencies, exergy efficiencies, and peak shaving capabilities of the entire plant under different integration schemes are evaluated and compared. The results indicate that the optimal integration scheme involves utilizing hot reheat steam to heat the cold molten salt and further provide heat for the carbon capture system during the heat charging process and utilizing hot molten salt to heat the outlet water of the feed water pump during the heat discharging process, which achieves the highest thermal efficiency of 38.00 % and exergy efficiency of 37.09 % across the entire cycle. A novel integrated design, scheduling, and control optimization (IDSCO) method is proposed to find the optimal capacity configuration of MSHS, in which the investment, maintenance, fuel, carbon emission, and long-timescale and short-timescale load tracking deviation penalty costs are fully considered. A design parameter-aware control system is developed based on a multi-parametric programming model predictive control approach to ensure a fair evaluation of closed-loop dynamic performance under different configuration capacities. The simulation results demonstrate that the deployment of MSHS can significantly enhance the flexibility of the power plant-carbon capture system and the proposed IDSCO approach can reduce 17.05 % of load tracking deviation penalty cost and 1.38 % of totalized cost compared to the conventional configuration approach. This paper effectively leverages the potential of MSHS in enhancing the operational flexibility of CFPP-PCC system, thereby providing useful guidance for future deployment of MSHS within CFPP-PCC system.