Search

The paper aims to investigate the fuel and system options for a floating power plant (FPP) considering economic performance and the decarbonization goals of the International Maritime Organization. Various case studies have been assessed using a reference FPP, encompassing the instant and future retrofitting scenarios. The ready-to-use scenarios involve alternative fuel and organic Rankine cycle-based waste heat recovery system usage. Nuclear energy systems have been evaluated within the reference FPP since they are suitable candidates for achieving zero-carbon objectives and providing low-cost electricity. A simulation framework created in Python has calculated the fuel consumption regarding the power requirement and organized the approaches used in the study. An environmental model comparing the systems has been built to calculate upstream and operational emissions. The cost projection model for 2030 and 2050 has assessed the economic performance. Technique for Order of Preference by Similarity (TOPSIS) one of the multi-criteria decision-making approaches has ranked the systems considering the outcomes of economic and environmental models over the years. Findings demonstrate that the current fuel usage scenario of the FPP is not suitable both environmentally and economically. The other emissions can be near zero and greenhouse gases can be decreased by up to 15.95% using alternative fuels. Nuclear energy is a strong candidate to meet the 2050 targets, but its viability is largely based on economic performance., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The combined production of electricity and thermal energy (combined heat and power or co-generation) is the most efficient and convenient approach to reduce costs for energy at industrial power plants and district heating plants that use natural gas as fuel to produce thermal energy for various needs. This paper analyses the operational characteristics of the combined heat and power plant, which has been operating since January 1, 2021, at the Voždovac Heating Plant as part of the Belgrade district heating system. The combined heat and power plant consists of three gas engine units with a total nominal electric power of 10 MW and thermal power of 10.1 MW, which use natural gas as fuel. The combined heat and power plant is used for district heating and preparing domestic hot water while electricity is sold to the local electric grid. The analysis in this paper focuses on the plant's operational characteristics: the number of working hours, the total energy consumption and energy production, the efficiency as well as the operational and maintenance costs. Also, the impact of the drastic changes in the prices of natural gas, electricity, and maintenance costs in the last year on the financial profitability of the combined heat and power plant was analysed in particular. [ABSTRACT FROM AUTHOR]

At present, the operation and maintenance of photovoltaic power generation systems mainly comprise regular maintenance, breakdown maintenance, and condition-based maintenance, which is very likely to lead to over- or under-repair of equipment. Therefore, a preventive maintenance and replacement strategy for PV power generation systems based on reliability as a constraint is proposed. First, a hybrid failure function with a decreasing service age factor and an increasing failure rate factor is introduced to describe the deterioration of PV power generation equipment, and the equipment is replaced when its reliability drops to the replacement threshold in the last cycle. Then, based on the reliability as a constraint, the average maintenance cost and availability of the equipment are considered, and the non-periodic incomplete maintenance model of the PV power generation system is established to obtain the optimal number of repairs, each maintenance cycle and the replacement cycle of the PV power generation system components. Next, the inverter of a PV power plant is used as a research object. The model in this paper is compared and analyzed with the equal cycle maintenance model without considering reliability and the maintenance model without considering the equipment replacement threshold, Through model comparison, when the optimal maintenance strategy is (0.80, 4), the average maintenance cost of this paper's model are decreased by 20.3% and 5.54% and the availability is increased by 0.2395% and 0.0337%, respectively, compared with the equal-cycle maintenance model without considering the reliability constraint and the maintenance model without considering the equipment replacement threshold. Therefore, this maintenance model can ensure the high reliability of PV plant operation while increasing the equipment availability to improve the system economy. [ABSTRACT FROM AUTHOR]

The accumulation of wind power prediction deviations will make it difficult to maintain the long-term stable operation of energy storage. To solve this problem, this paper proposes a hybrid energy storage system configuration method containing second-use batteries. This paper establishes a three-battery hybrid energy storage operation strategy that considers the accumulation of prediction deviation and prevents the accumulation of prediction deviation by changing the energy storage used at the end of the dispatch cycle. It also establishes an optimal allocation model for energy storage capacity, which takes into account the performance parameters and life loss of the second-use batteries and the new power battery. Finally, Gurobi is used to simulate the field data of a wind farm. The simulation results show that this method is effective in preventing the accumulation of prediction deviation while reducing wind power grid deviation and improving the level of energy storage utilization. It can play a certain reference role in the configuration of energy storage for wind farms. [ABSTRACT FROM AUTHOR]

Aiming at the "net-zero carbon" target, a higher proportion of variable renewable energies (VREs) has been integrated into power grids, and pumped storage plants (PSPs) are crucial for guaranteeing the safe and stable operation of hybrid energy systems. As secondary frequency regulation (SFR) is related to the economic operation and the quality of auxiliary services provided by PSPs, it is critical to clarify its performance and compensation. Therefore, the corresponding quantitative evaluations of the SFR of pumped storage units (PSUs) are carried out in this paper. First, the performance of SFR is quantified based on the fuzzy analytic hierarchy process (FAHP). Considering the current situation in China's electricity market, the average compensation of the PSP with each MW is determined to be 9.336 CNY per day, and a method for calculating the compensation of SFR is constructed. Then, taking a real Chinese PSP as an example, based on different wind power deviation signals, the quantitative evaluation of the compensation of SFR is conducted in accordance with the fixed-speed pumped storage units (FSPSUs) and the variable-speed pumped storage units (VSPSUs) in power priority and speed priority modes. The results show that the compensation of FSPSUs and VSPSUs (power priority mode and speed priority mode) is 10,900 CNY per day; 54,400 CNY per day; and 17,300 CNY per day. This paper could contribute to clarifying the compensation of SFR and providing technical support for the development of the auxiliary service market. [ABSTRACT FROM AUTHOR]

Piping is an essential component of processing plants that transfers process fluids from one piece of equipment to another. To detect the unpredictable deterioration of piping systems, periodic or continuous pipe inspection for wall loss is conducted, which determines when preventative or corrective maintenance activities, usually replacements, should be done. Therefore, it is necessary to develop an optimal maintenance strategy to replace piping systems to reduce the overall maintenance expenditure and avert the effects of pipe failure. Most of the literature in this field concentrates on maintenance activities executed during decision-making. This paper investigates creating a system and tool to assist decision-makers in evaluating and selecting the optimal moment to replace a deteriorating pipe based on the current state of the piping system. Considering that preventative replacement of the pipeline is done during a scheduled shutdown, also referred to as a turnaround, this paper investigates making a preventative replacement ruling two turnaround cycles ahead. We account for the varying thickness at different monitoring locations of two piping systems in a refinery by applying a nonstationary gamma process with random effects. We employ the Markov decision process (MDP) and the value iteration algorithm to determine the most effective maintenance replacement policy. The outcome of our analysis is an optimal maintenance policy that minimizes the cost of maintenance. Practical Applications: This paper's practical use is to aid in maintenance planning in refineries, midstream or pipelines, chemical plants, and gas facilities by planning piping replacement in two shut-down cycles ahead of time. The pipe wall is classified into states ranging from the present thickness reading to the retirement thickness. The likelihood of transiting from one state to another is calculated based on the corrosion rate. Given that the pipe wall thickness is classified into states, the current wall thickness, transitional probabilities, and preventative and corrective maintenance costs, including lost profit opportunity, can be inputted into the model to help decision-makers decide if it is optimal to replace the piping in the subsequent two shut-down cycles. This policy will assist in reducing the risk of a pipe failure, which has financial, safety, and environmental consequences. It is also critical not to replace a still-in-good-shape pipe. This model may be customized to pipe systems in the various petrochemical sectors with known retirement thicknesses to estimate the risk of operating near the retirement thickness. [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2 , necessitating a conversion to NOx. Previous studies have applied a constant NO2 -to- NOx conversion factor. In this paper, we develop a more realistic model for NO2 -to- NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH large-eddy simulation model with chemistry for the Bełchatów (PL), Jänschwalde (DE), Matimba (ZA) and Medupi (ZA) power plants, as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO , NO2 and NOx line densities from simulated NO and NO2 columns and derived NO2 -to- NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions and that the conversion factors can be modelled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2 -to- NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between - 50 % and - 42 % to between only - 9.5 % and - 0.5 % in comparison with reported emissions. Single-overpass estimates can be quantified with an uncertainty of 20 %–27 %, while annual NOx emission estimates have uncertainties in the range of 4 %–21 % but are highly dependent on the number of successful retrievals. Although more simulations covering a wider range of meteorological and trace gas background conditions will be needed to generalise the approach, this study marks an important step towards a consistent, uniform, high-resolution and near-real-time estimation of NOx emissions – especially with regard to upcoming NO2 -monitoring satellites such as Sentinel-4, Sentinel-5 and CO2M. [ABSTRACT FROM AUTHOR]

With the rapid development of hydrogen production by water electrolysis, the coupling between the electricity-hydrogen system has become closer, providing an effective way to consume surplus new energy generation. As a form of centralized management of distributed energy resources, virtual power plants can aggregate the integrated energy production and consumption segments in a certain region and participate in electricity market transactions as a single entity to enhance overall revenue. Based on this, this paper proposes an optimal scheduling model of an electricity-hydrogen coupling virtual power plant (EHC-VPP) considering hydrogen load response, relying on hydrogen to ammonia as a flexibly adjustable load-side resource in the EHC-VPP to enable the VPP to participate in the day-ahead energy market to maximize benefits. In addition, this paper also considers the impact of the carbon emission penalty to practice the green development concept of energy saving and emission reduction. To validate the economy of the proposed optimization scheduling method in this paper, the optimization scheduling results under three different operation scenarios are compared and analyzed. The results show that considering the hydrogen load response and fully exploiting the flexibility resources of the EHC-VPP can further reduce the system operating cost and improve the overall operating efficiency. [ABSTRACT FROM AUTHOR]

To optimize the energy supply potential and complementary advantages of distributed energy, this paper focuses on the dispatching optimization of cogeneration virtual power plant considering uncertainty. First of all, wind power, photovoltaic, combined heat and power (CHP) units, electric boilers, and controllable loads are integrated into a CHP virtual power plant. Then, carbon capture and electric-to-gas devices are introduced to realize carbon recycling. Furthermore, quantify the risk of real-time dispatching of virtual power plants through uncertainty scenario generation and conditional value at risk (CVaR) theory, and the multi-objective stochastic dispatching optimization model of virtual power plants is built with the aim at minimizing the operation cost, carbon emissions, and operation risk as the objectives, and the CRITIC weighting method is adopted to solve it. Finally, the calculation results show that: 1) the electric boiler can use wind and photovoltaic power to supply heat for the system, reduce the dependence ofthe virtual power plant (VPP) on the CHP unit, and make the electric output of the unit more flexible. 2) The risk quantification method proposed can fully measure the risk situation in real-time dispatching, arrange the wind and photovoltaic power generation plan and backup plan more reasonably, and enable the VPP to get more benefits while avoiding the risks in real-time dispatching. [ABSTRACT FROM AUTHOR]

With the increase in the integration of renewable sources, the home energy management system (HEMS) has become a promising approach to improve grid energy efficiency and relieve network stress. In this context, this paper proposes an optimization dispatching strategy for HEMS to reduce total cost with full consideration of uncertainties, while ensuring the users' comfort. Firstly, a HEMS dispatching model is constructed to reasonably schedule the start/stop time of the dispatchable appliances and energy storage system to minimize the total cost for home users. Besides, this dispatching strategy also controls the switching time of temperature-controlled load such as air conditioning to reduce the energy consumption while maintaining the indoor temperature in a comfortable level. Then, the optimal dispatching problem of HEMS is modeled as a Markov decision process (MDP) and solved by a deep reinforcement learning algorithm called deep deterministic policy gradient. The example results verify the effectiveness and superiority of the proposed method. The energy cost can be effectively reduced by 21.9% at least compared with other benchmarks and the indoor temperature can be well maintained. [ABSTRACT FROM AUTHOR]

Atmospheric emissions from anthropogenic hotspots, i.e., cities, power plants and industrial facilities, can be determined from remote sensing images obtained from airborne and space-based imaging spectrometers. In this paper, we present a Python library for data-driven emission quantification (ddeq) that implements various computationally light methods such as the Gaussian plume inversion, cross-sectional flux method, integrated mass enhancement method and divergence method. The library provides a shared interface for data input and output and tools for pre- and post-processing of data. The shared interface makes it possible to easily compare and benchmark the different methods. The paper describes the theoretical basis of the different emission quantification methods and their implementation in the ddeq library. The application of the methods is demonstrated using Jupyter notebooks included in the library, for example, for NO 2 images from the Sentinel-5P/TROPOMI satellite and for synthetic CO 2 and NO 2 images from the Copernicus CO 2 Monitoring (CO2M) satellite constellation. The library can be easily extended for new datasets and methods, providing a powerful community tool for users and developers interested in emission monitoring using remote sensing images. [ABSTRACT FROM AUTHOR]

Deploying renewable generation to replace conventional fossil-fuel-based energy supplies provides an important pathway for the decarbonization of steelmaking plants. Meanwhile, it is also crucial to improve the flexibility of blast-furnace-gas-fired combined-cycle power plants (BFGCCs) to ease the accommodation of uncertain renewable generation. To this end, this paper proposes the deployment of a molten salt heat storage (MSHS) system in BFGCCs to store the heat of gas turbine flue gas so that the power–heat coupling of these BFGCCs can be unlocked to enhance the flexibility of the energy supply. A stochastic capacity optimization of an integrated BFGCC–MSHS–wind–solar (BMWS) energy system is presented to determine the optimal installed capacities of a BFG holder, MSHS, wind turbine, and PV panel, aiming to achieve an economic and safe energy supply for the entire system. Multiple scenarios considering uncertain fluctuations in load demands and renewable generation are generated with the Monte Carlo method based on a typical scenario. These scenarios are then reduced to representative scenarios using the synchronous substitution and reduction method for stochastic capacity optimization to enhance the reliability of the results. The case study results demonstrate that configuring MSHS reduces the total annualized cost of the BMWS system by 2.28%. Furthermore, considering the uncertainties of the power/heating load and wind/PV generation can reduce the expected annualized total cost of the BMWS system and the corresponding standard deviation by 5.66% and 81.45%, respectively. The BMWS system can achieve 730.68 tons of equivalent CO2 reduction in 24 h due to the successful utilization of renewable energy. This paper provides an effective approach for the decarbonization of energy generation systems in steelmaking plants. [ABSTRACT FROM AUTHOR]

To fully explore the potential low-carbon and economic advantages of a virtual power plant (VPP) that aggregates multiple distributed resources, the paper proposes a VPP scheduling model that considers the carbon emission flow (CEF) and demand response (DR), which is characterized by electro-carbon coupling and source-load interaction. First, the electric-carbon characteristics of each distributed resource under VPP are modeled, and the source-load electric-carbon coupling characteristic model is modeled through the CEF theory. On this basis, a load-side multi-type DR model is established to achieve the purpose of source-load synergy to reduce carbon emissions from VPP. To this end, a two-stage scheduling model of VPP considering the source- load electro-carbon coupling relationship is established, and the implementation of the model can reduce power generation costs, carbon emissions and promote clean energy, and the simulation results of the improved IEEE-14 node system verify the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

The paper presents a specific method of environmental impact assessment applied in Strategic Environmental Assessment (SEA) for the National Emission Reduction Plan (NERP) in the Republic of Serbia, based on air quality. The specificity of the approach is in the application of a semiquantitative method of multicriteria evaluation based on air dispersion modeling and the integration of SEA goals, indicators and criteria for assessing the impact of the NERP on the quality of air and other environmental elements in this method. When predicting changes in air quality for the planning horizon to 2028, the physical, geographical and climatic characteristics of the area were taken into account, as well as technical measures to reduce SO2 emissions, since this was the dominant pollutant from the Serbian coal-fired power plants studied by the NERP. Air pollution modeling was carried out using the AERMOD software package based on the data collected, and the quantitative results obtained were used in a multicriteria evaluation as part of the SEA. The results of the research indicated the importance of applying this approach in order to significantly increase objectivity in the SEA process, since it is an important element of decision making at the strategic level. In addition, a comparative presentation of the modeling results before and after application of the NERP was an important part of the SEA process, and it provided a clear insight into expected changes in the air quality. This is a key argument for making appropriate policy decisions on spatial, energy, environmental and socio-economic development in the Republic of Serbia, which, like other developing countries, is sluggishly following global trends in energy transition. [ABSTRACT FROM AUTHOR]

The integration of renewable generation presents a promising venue for displacing fossil fuels, yet integration remains a challenge. This paper investigates Demand Response (DR) as a means of economically integrating Renewable Energy Resources (RERs). We propose Incentive-Based DR (IBDR) programs, particularly suitable for small customers. The uncertainties in the electricity market price pose a challenge to IBDR programs, which is addressed in this paper through a novel and robust IBDR approach that considers both the electricity market price uncertainties and customer responses to incentives. In this paper, scenarios are simulated premised on the Western Electricity Coordinating Council (WECC) 240-bus system in which coal-fired power plants become inactivated, while the RER contribution increases in the span of one year. The simulation results indicate that the proposed IBDR program mitigates the issues associated with renewable expansion, such as utility benefit loss and market price volatility. In addition, the proposed IBDR effectively manages up to 30% of errors in day-ahead wind forecasts that significantly reduce financial risks linked to IBDR programs. [ABSTRACT FROM AUTHOR]

In light of the growing urgency surrounding energy and environmental concerns, this paper presents a two-layer iterative energy dispatch strategy tailored for a multi-energy-flow virtual power plant (VPP) operating within the distribution power grid. The proposed strategy unfolds in two key phases. First, it establishes an energy dispatch framework designed specifically for the multi-energy-flow VPP within the distribution power grid. Subsequently, it introduces an improved ant colony algorithm aimed at optimizing the output power of each VPP. In addition, the paper presents an optimization method for substation energy dispatch. This method uses a delay-aware consensus algorithm with the substation dispatch cost increment rate as the consensus variable, taking into account the communication delay between VPPs. Integrating a proportional-derivative (PD) control mechanism enhances the convergence speed of the delay-aware consensus algorithm and enables real-time energy dispatch of the multi-energy-flow VPP. The paper presents its conclusions by validating the efficacy of the proposed approach through simulation, thereby addressing the challenges and adapting to the shifting energy and environmental landscape. [ABSTRACT FROM AUTHOR]

Several recently signed environmental agreements and protocols emphasize the global need to reduce GHG emissions, with a focus on limiting coal consumption due to high NOx and CO2 emissions. However, many countries, including those in the Western Balkans, rely heavily on coal for electricity generation. The outdated thermal power plant infrastructure in these regions poses a major challenge when it comes to meeting modern environmental standards while maintaining efficiency. This study is part of the more comprehensive research which aims to develop an expert system that utilizes existing measurements to estimate key parameters crucial for both energy production and pollution reduction. The focus is on Serbian thermal power plants, particularly plant Nikola Tesla unit B1. One of the critical parameters for optimizing thermal power plant control loops is the heating value of coal, which is challenging to measure in real time due to the coal's varying chemical compositions and caloric values. This paper examines 74 different parameters measured in 59 instances to estimate the hating value of coal at unit B1. Through detailed analysis and feature selection methods, including linear regression, this research aims to identify the most informative parameters for estimating the heating value of coal, which will improve the control system that enables more efficient and environmentally friendly power generation in coal fired thermal power plants. [ABSTRACT FROM AUTHOR]

Low carbon operation of power systems is a key way to achieve the goal of energy power carbon peaking and carbon neutrality. In order to promote the low carbon transition of energy and power and the coordinated and optimized operation of distributed energy sources in virtual power plants (VPP), this paper proposes a framework for collaborative utilization of pumped storage–carbon capture–power-to-gas (P2G) technologies. It also constructs a multi-time scale low carbon economic dispatch model for VPP to minimize the internal resource operation cost of VPP in each time period. During the intraday scheduling stage, the day-ahead scheduling results as the planned output and the energy flow is then dynamically corrected at a short-term resolution in the framework. This allows for the exploration of the low-carbon potential of each aggregation unit within the virtual power plant. The results of the simulation indicate that the strategy and model proposed in this paper can effectively encourage the consumption of renewable energy sources, promote the low-carbon operation of power system power, and serve as a valuable reference for the low-carbon economic operation of the power system. [ABSTRACT FROM AUTHOR]

To enhance the utilization of clean energy sources, such as wind power and photovoltaic within virtual power plants, and mitigate carbon emissions, this paper proposes a virtual power plant participation in the electricity carbon joint market optimization operation model based on ensemble empirical mode decomposition–improved bat algorithm (IBA)–Markov chain new energy output prediction. First, complementary set empirical mode decomposition is performed on historical data to construct a Markov chain based wind power and photovoltaic prediction model optimized by IBA. Second, this prediction model is used to predict the daily generation power of wind power and photovoltaic power. Finally, with the optimization goals of maximizing the benefits and minimizing the carbon costs of virtual power plants, a virtual power plant system participating in the electricity carbon joint market model based on wind power and photovoltaic output prediction results is constructed. At the same time, demand response factors are introduced and solved using the NSGA-II algorithm. Taking a certain park as an example for simulation analysis, the research results show that the combined effect of carbon market and demand response can achieve 99.82% of new energy consumption in virtual power plants without significantly reducing profits, basically achieving complete new energy consumption, demonstrating the effectiveness of the proposed model in this paper. [ABSTRACT FROM AUTHOR]

A run‐of‐the‐river power plant is a renewable energy source used for electricity production. Its power output depends on the varying water flow rate over time, which can impact the reliability of the electric network. Previous research has not studied the effects of water flow rate and temperature variations on the hazard rate of the plant's components. This paper addresses this gap by investigating the impact of these variations on the reliability of electric networks with run‐of‐the‐river power plants. The analysis considers the hazard rates of the plant's components, incorporating the relationship between hazard rate and temperature based on the Arrhenius law. Parameters such as power output, current, power loss, operating temperature, and hazard rate are calculated for different water flow rates and ambient temperatures. Numerical simulations on a test system are conducted to examine the influence of water flow rate and temperature on the reliability indices of the electric network. The results demonstrate that water flow rate and temperature significantly affect the hazard rates of run‐of‐the‐river power plants. This highlights the need to consider these factors in the reliability analysis of electric networks incorporating these renewable resources. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method in this paper can be utilized to assess the enhanced accommodation capability of renewable energy sources following the coupling integration with retrofitted thermal power units. Thermal power units (TPUs) play a crucial role in accommodating the high penetration of renewable energy sources (RESs) like wind turbines (WTs) and photovoltaics (PVs). This paper proposes an evaluation framework to quantitatively analyze the flexibility potential of retrofitted TPUs in enhancing the accommodate capability of RESs through coupling integration and optimal scheduling. Firstly, the coordination framework for coupling TPUs with RESs is outlined, including a comprehensive analysis of benefits and implementation strategies. Secondly, an annual optimal scheduling model for TPUs and RESs is developed, incorporating deep peak regulation services, ladder-type constraints for retrofitted TPUs, and their operational characteristics before and after the coupling integration. Thirdly, indices to evaluate RES accommodation levels and TPU regulation capacities are proposed to quantify the performance of power sources. Finally, a real-world case study is conducted to demonstrate that integrating retrofitted TPUs with RESs through coupling significantly enhances RES utilization by 3.6% and boosts TPUs' downward regulation capabilities by 32%. [ABSTRACT FROM AUTHOR]

In the context of a low‐carbon economy, considering the output uncertainties of the renewable energy sources and the fluctuation of electrical price, the cost–benefit analysis is proposed for multi‐investor virtual power plant (VPP) under different risk preferences, and multi‐investor VPP capacity sizing problem is researched. First, the structural organization and flexible regulation characteristics of multi‐investor VPPs are analyzed. Second, integrated model and cost–benefit analysis indexes of VPP are built. Third, an optimal capacity sizing model of multi‐investor VPP is established, aiming at income maximization. Game theory is used to configure the distributed generators in the VPP under various cooperative modes and noncooperative modes of multiple investors. Fourth, according to the installed proportion of each distributed generator and the risk preference coefficient of each investor, the income allocation correction model based on the improved Shapley value method is proposed to stabilize the optimal VPP alliance. Finally, the demonstration project in the animation industry park of Sino‐Singapore Tianjin Eco‐City is employed as representative scenarios, which is used to validate the effectiveness of the proposed model. The results prove that the method proposed in this paper can guide knowledge for the investors with different risk preferences when planning the optimal capacity of multiple distributed generators in the VPP. [ABSTRACT FROM AUTHOR]

Coal is a mixture of several chemicals, many of which have mutagenic and carcinogenic effects and are a key contributor to the global burden of mortality and disease. Previous studies suggest that coal is related to telomeric shortening in individuals occupationally exposed, however little is known about the effects of mining and burning coal on the telomeres of individuals living nearby. Therefore, the primary objective of this investigation was to assess the impact of proximity to coal power plants and coal mines on the genomic instability of individuals environmentally exposed, while also exploring potential associations with individual characteristics, oxidative stress, inflammatory responses, and the presence of inorganic elements. This study involved 80 men participants from three cities around a thermoelectric power plant and one city unexposed to coal and byproducts. DNA was extracted from peripheral blood samples obtained from each participant, and the telomeres length (TL) was assessed using quantitative real-time polymerase chain reaction (qPCR) methodology. No significant difference was observed between exposed individuals (6227 ± 2884 bp) when compared to the unexposed group (5638 ± 2452 bp). Nevertheless, TL decrease was associated with age and risk for cardiovascular disease; and longer TL was found to be linked with increased concentrations of silicon and phosphorus in blood samples. No correlations were observed between TL with comet assay (visual score), micronucleus test, oxidative stress, and inflammatory results. Additional research is required to ascertain the potential correlation between these changes and the onset of diseases and premature mortality., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

This study introduces a novel integrated quantitative modeling framework to assess the multiple environmental, health, and economic benefits from implementing carbon capture technology in the power sector of Basra province, Iraq. This province is struggling with significant environmental challenges and air pollution caused by extensive oil extraction operations. First, the developed modeling framework quantifies the captured CO 2 emissions and the equivalent avoided PM 2.5 emissions resulting from the use of carbon capture units in existing power plants. This is achieved through a detailed simulation of the monoethanolamine (MEA) capture process using Aspen. Second, the impact of avoided PM 2.5 exposure on public health is evaluated by developing and applying a dynamic dispersion model across the districts where the power plants are located. Third, it quantifies the expected health benefits, using the health impact assessment method. This method is based on a comprehensive meta-analysis of concentration-response functions, and it utilizes a Recurrent Neural Network prediction framework based on the Long-Short Term Memory (LSTM) method to predict the relative risk value of six health outcomes. Finally, the economic value of avoided health burdens is estimated by employing the Value of Statistical Life (VSL) and the Cost of Illness (COI) approaches. According to the findings, implementing new carbon capture units in the selected power plants in the area will lead to a reduction of 7.697 million tons of carbon dioxide per year in the total emission of pollutants from the current power generation units in the region. The integrated assessment results demonstrate a significant reduction in PM 2.5 emissions, amounting to 2299 tons per year, leading to the avoidance of 1328 premature deaths and 217 hospital admissions, resulting in annual savings of $1846 million from the avoided mortalities and morbidities cases and creation of 29,607 green jobs in Basra Province., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In city-integrated energy systems containing electric–thermal multi-energy sources, the uncertainty of renewable energy sources and the fluctuation of loads challenge the safe, efficient, economic and stable operation of the integrated energy systems. This paper introduces a novel approach for the operation of a carbon capture plant/CHP with PV accommodation within a city-integrated energy system. The proposed strategy aims to maximize the utilization of photovoltaic (PV) power generation and carbon capture equipment, addressing issues related to small-scale CHP climbing constraints and short-term output regulation. Additionally, this paper presents a multi-timescale optimal scheduling strategy, which effectively addresses deviations caused by PV fluctuations and load changes. This was achieved through a detailed analysis of the CHP climbing constraints, carbon capture equipment operation and real-time characteristics of PV power generation. This paper introduces a fully distributed neural dynamics-based optimization algorithm designed to address multi-timescale optimization challenges. Utilizing rolling cycles, this algorithm computes both day-ahead and real-time scheduling outcomes for urban integrated energy systems. Theoretical analyses and numerical simulations were conducted to validate the precision and efficacy of the proposed model and algorithm. These analyses quantitatively evaluate the scheduling performance of PV power generation and carbon capture CHP systems across various timescales. [ABSTRACT FROM AUTHOR]

Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions. [ABSTRACT FROM AUTHOR]

In the realm of Natural Gas Combined Cycle (NGCC) power plants, it is crucial to prioritize the mitigation of CO2 emissions to ensure environmental sustainability. The integration of post-combustion carbon capture technologies plays a pivotal role in mitigating greenhouse gas emissions enhancing the NGCC's environmental profile by minimizing its carbon footprint. This research paper presents a comprehensive investigation into the integration of solar thermal energy into the Besmaya Natural Gas Combined Cycle (NGCC) power plant, located in Baghdad, Iraq. Leveraging advanced process simulation and modeling techniques employing Aspen Plus software, the study aims to evaluate the performance and feasibility of augmenting the existing NGCC facility with solar assistance for post-carbon capture. The primary objective of this research is to conduct a thorough simulation of the Besmaya NGCC power plant under its current operational conditions, thereby establishing a baseline for subsequent analyses. Subsequently, a solar-assisted post-combustion capture (PCC) plant is simulated and seamlessly integrated into the existing power infrastructure. To accurately estimate solar thermal power potential at the Baghdad coordinates, the System Advisor Model (SAM) is employed. The integration of solar thermal energy into the NGCC power plant is meticulously examined, and the resulting hybrid system's technical viability and performance metrics are rigorously evaluated. The paper contributes to the field by providing valuable insights into the technical feasibility and potential benefits of incorporating solar thermal energy into conventional natural gas power generation infrastructure, particularly in the context of the Besmaya NGCC plant in Baghdad. The power generation capacity of the plant was set at 750 MW. With this capacity, the annual CO2 generation was estimated at 2,119,318 tonnes/year which was reduced to 18,064 tonnes/year (a 99% reduction). The findings aim to inform future decisions in the pursuit of sustainable and efficient energy solutions, addressing both environmental concerns and energy security in the region. [ABSTRACT FROM AUTHOR]

The development of public transportation is considered a vital issue in reducing traffic as well as urban pollution. City buses play an important role in the city transportation system. In Iran, due to the high average age of city buses, it is necessary to replace the old buses with new ones, To replace the old buses, diesel and CNG, hybrid, and electric buses are proposed as the main alternatives. Global warming and the energy crisis are now considered as two potential serious threats for the world. Therefore, energy consumption and CO2 emissions are examined as two outstanding criteria for comparing candidate buses in this paper. To make an accurate comparison, the amount of energy consumption and CO2 emissions have been calculated based on the well-to-wheel approach. The electric bus well-to-wheel analysis has been done for both electricity generation mix and renewable generation. To perform more accurate calculations and simulations, as a case study, a real driving cycle has been constructed for Tehran. For this approach, a modified micro trip method as a novel solution is presented to synthesize the driving cycle. The results show that due to the high share of fossil power plants (about 92%) in Iran, the use of electric buses in the bus fleet may not have much effect on reducing energy and CO2 eq emissions. By using renewable power plants, the amount of well-to-wheel energy consumption and CO2 emissions decrease significantly (about 56% and 93%, respectively) compared to that for the generation mix. [ABSTRACT FROM AUTHOR]

Power systems are generally designed to be reliable when faced with low-impact, high-probability, and expected power outages. By contrast, the probability of extreme event (extreme weather or natural disasters) occurrence is low, but may seriously affect the power system, from long outage times to damage to major equipment such as substations, transmission lines, and power plants. As, in the short term, it is extremely difficult to completely avoid the damage caused by extreme events, it is important to enhance the resilience of power systems. This study has provided a comprehensive review of power system resilience by discussing its concepts, assessment, and enhancement measures. This article summarized possible impacts and quantitative indicators of various types of disasters on power grids, presented the concept of power system resilience, and analyzed the main characteristics that a resilient system should possess. Moreover, this article further distinguished the differences between the resilience, flexibility, and survivability of a power system. More importantly, this paper has proposed a novel framework and the corresponding metric for assessing resilience, which makes the evaluation of system resilience more accurate. Finally, this paper discussed various measures to enhance power system resilience and outlined potential challenges for future research. [ABSTRACT FROM AUTHOR]

In this paper, an economic life cycle assessment of hydrogen-fueled gas turbine power plant is developed, where hydrogen is produced via electrolysis with electricity generated from renewable resources (green hydrogen). By this way, the continuous green electricity without fluctuation can be generated. With a great potential in solar irradiation, Iran can be a major country for producing green hydrogen. For this purpose, first, we simulate a 100 MW solar PV plant in Fars province and calculate annual electricity generation. Considering capital cost (Capex) and operations and maintenance costs (Opex) of plants including PV, electrolyzer, hydrogen storage and distribution facilities, and gas turbine, we estimate levelized cost of electricity from green H2-fueled gas turbine power plant. Because hydrogen technology has not matured yet, the analysis for both 2021 and 2040 will be conducted. Due to lower marginal production costs, the increase in renewable energy sources decreases electricity wholesale prices. In this paper, we also estimate long-term merit order effect (MOE) of electricity generated by green hydrogen on electricity wholesale price. The results show that in renewable/hydrogen-based forecast for electricity mix of 2040, electricity wholesale price is estimated around 6.38 c$/kWh, nearly 38% less than present wholesale price. [ABSTRACT FROM AUTHOR]

The harmonics of photovoltaic power plants are affected by various factors including temperature, weather, and light amplitude. Traditional power harmonic prediction methods have weak non-linear mapping and poor generalization capability to unknown time series data. In this paper, a Kernel Extreme Learning Machine (KELM) model power harmonic prediction method based on Gray Relational Analysis (GRA) with Variational Mode Decomposition (VMD) coupled with Harris Hawk Optimization (HHO) is proposed. First, the GRA method is used to construct the similar day set in one screening, followed by further using K-means clustering to construct the final similar day set. Then, the VMD method is adopted to decompose the harmonic data of the similar day set, and each decomposition subsequence is input to the HHO-optimized KELM neural network for prediction, respectively. Finally, the prediction results of each subseries are superimposed and numerical evaluation indexes are introduced, and the proposed method is validated by applying the above method in simulation. The results show that the error of the prediction model is reduced by at least 39% compared with the conventional prediction method, so it can satisfy the function of harmonic content prediction of a photovoltaic power plant. [ABSTRACT FROM AUTHOR]

With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants' benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. [ABSTRACT FROM AUTHOR]

In the case of new investment in RES technology, there are two issues related to efficiency assessment. The first one is how to join technical, financial and economic efficiency assessments in one. An investment feasibility study is usually conducted through a financial analysis to obtain the internal rate of return and the net present value. However, a new RES investment is typically financially unprofitable unless the environmental and social aspects are included. The second one is a lack of required financial data. The new RES investment is often innovative and neither the owner nor other entities have financial data on the operating costs and expenditure for the last periods. Therefore, in this paper, we proposed two methods of efficiency assessment. The first one is based on the avoided costs theory. Furthermore, the second one belongs to heuristic methods and is based on the experts' assessment of different kinds of parameters. The purpose of this study is to assess the efficiency of the pilot project of VPPs using two recommended methods. This paper emphasizes the advantages and disadvantages of each method. The actual technical and financial data for the period of six months from the pilot study were calculated. [ABSTRACT FROM AUTHOR]

As a flexible electricity pricing mechanism, critical peak pricing (CPP) is one of the important means of demand response under the electricity market. The existing CPP research does not take into account the carbon emission problem of units and weakens the difference between the use of terminal loads on critical peak days and non-critical peak days in the establishment of electricity price model, so this paper studies the feedback mechanism of CPP on coal consumption of power generation side units and proposes a dynamic CPP mechanism that takes into account terminal consumption satisfaction and coal consumption of power generation side units. Firstly, the influence mechanism of CPP on the power generation side is studied. Secondly, the consumer psychology theory is used to construct a user demand response model under critical peak days and non-critical peak days. Then, based on the difference in load usage of end users on critical peak days and non-critical peak days, a multi-objective CPP optimization model that considers the benefits of coal consumption and end user electricity expenditure on the power generation side is constructed. Finally, three scenarios were established to analyze the sensitivity of the user demand response model parameters, terminal satisfaction constraints, prices, and rate restrictions on CPP pricing, load improvement, and unit coal consumption reduction and verify the model's effectiveness. The results show that the proposed CPP optimization model has a significant effect on load improvement and carbon emission reduction; the user response gradient, terminal satisfaction, prices, and rate restrictions have a greater influence on the model optimization results, while the threshold and saturation values have little influence on the model optimization results., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Combined heat and power (CHP) plants fueled by biomass can collect and utilize waste straw resources in a productive way. This paper considers the impact of regional factors on biomass energy potential and the energy needs of the population, so as to study the differences in construction of biomass CHP plants and the collection scope of raw materials, and proposes evaluating suitability for biomass energy development based on scope of resource collection. Taking five counties in China as its study areas, this paper assesses biomass energy potential. A topology system of biomass CHP plants has been reasonably established in different counties through ArcGIS, the required installed capacity has been calculated according to the number of persons served by such plants. Finally, the collection length and corresponding value range of raw materials of CHP plants along roads has been obtained based on biomass energy potential and energy demand. The result shows that the differences in area, straw yield and biomass fuelization rate depending on regions have a great impact on biomass energy potential, while the residue-to-product ratio of straw and biomass calorific value have less of an impact. When the biomass energy per capita of a region reaches 9.75GJ/person, it is suitable for biomass energy development. The installed capacity in the biomass CHP plant system of each study area is mostly within the scope of 3-59 MW, and the collection length of corresponding biomass resources of such plants along roads is mostly within the scope of 5.09-25.23 km., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)