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In the current study, the exergoeconomic analysis of a cogeneration pulp and paper plant under bi-operating modes at different environment temperatures of 290, 295, 305, 310, and 320 K was investigated. Two operating modes of the cogeneration system, i.e., a hybrid operating mode using a power boiler and a recovery boiler and a singular operating mode using a power boiler only, were considered. Natural gas was utilized as the main fuel in the power boiler, while black liquor with heavy fuel oil was employed in the recovery boiler. The total capital investment, total operating, exergy, unit exergy, and exergy destruction costs for each operating mode were evaluated. The results indicated that the total capital investment cost was 3420.27 $/h, and the total capital investment and the operation and maintenance costs in the hybrid and singular modes were 5853.27 and 6226.67 $/h, respectively. As well, the unit exergy cost and the exergy cost of steam introduced by the power boiler were 39.31 $/MWh and 2503.69 $/h, respectively. Where these values for the recovery boiler were 34.69 $/MWh and 554.30 $/h, respectively. Furthermore, the exergy destruction costs at an environment temperature of 320 K for the power and recovery boilers were 1436.6 and 213.63 $/h, respectively. In the hybrid mode, the soda at a 10% concentration was recovered, with a rate of 32 t per hour desired for the cooking process in the pulp mill. In addition, the use of black liquor as a bio-fuel in the recovery boiler saved 14% of the consumption of natural gas. • Exergoeconomic analysis was performed for the pulp and paper industry. • Hybrid and singular operating modes for different ambient temperatures were studied. • Unit exergy cost, exergy cost, and exergy destruction cost were evaluated. • Using black liquor as a bio-fuel in RB saved about 14% of natural gas consumption. [ABSTRACT FROM AUTHOR]

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 present study reports the characterization of reverse osmosis (RO) technology at water treatment plant Cogen-2 in paper and Board mills, Pakistan. RO is a commonly used process to obtain de-mineralized water for high-pressure boiler operation in thermal power plants. Scaling and fouling in three-stage RO plants is a major challenge in chemical industry due to the use of raw brackish water in the power plant of paper and board mills. In our study, the feed water quality of RO was changed from soft water to raw water to make it economical. The cleaning frequency was increased three times than normal, which was unsafe for operation and it was required to control scaling and fouling to achieve the desired result. Differential pressures behavior of all stages for 2-month data was observed without acid treatment, and the results of Langelier Saturation Index (LSI) control parameters (temperature, pH, total dissolved solids, calcium hardness, and alkalinity) clearly showed the abnormality. To optimize scaling and fouling of RO, the LSI factor was controlled in total reject water for the next 2 months by acid treatment in feed water. Duration of chemical cleaning and membranes' life has been extended by fouling and scaling control. Understanding the effect of operational parameters in RO membranes is essential in water process engineering due to its broad applications in drinking water, sanitation, seawater, desalination process, wastewater treatment, and boiler feed water operation. The product flow increased from 18.3 to 19.9 m3/h, and this was due to a decrease in the rejection flow from 8.2 to 6.7 m3/h. The total reject stream pressure also increased from 8.1 to 9 bar. A lower value of LSI of 1.6 is obtained in the reject water stream after the acid treatment. [ABSTRACT FROM AUTHOR]

In this work, a techno-economic assessment of carbon dioxide capture from limekiln flue gas of a pulp and paper mill (Mill A) and a linerboard mill (Mill B) using a Monoethanolamine (MEA) absorption desorption process was carried out. We coupled the ASPEN Plus simulator with a derivative-free optimization (DFO) tool to identify the optimal configuration for minimizing the total capture cost. The capture costs were calculated using CAPCOST, a modular program for equipment cost estimation, and appropriate coefficients. Eight degrees of freedom, the direct contact cooler stages, the absorber stages, the stripper stages, the solvent lean loading, the solvent weight concentration, the stripper inlet temperature, the flue gas inlet temperature, and the amount of CO2 captured, were selected for process and flowsheet optimization. Additionally, we evaluated the effect of steam integration and Sect. 45Q of the existing federal tax credit for carbon capture and sequestration on CO2 capture costs. The total capture costs per tonne of CO2 were $64.9 for Mill A and $69.7 for Mill B. When steam integration and Sect. 45Q are considered, the costs dropped to − $2.5 and $2.6 for Mill A and Mill B, respectively. The sensitivity of CO2 capture cost to changes in the inlet flue gas flowrate, flue gas CO2 mol%, and the electricity and MEA prices were investigated. The sensitivity analysis results revealed that the capture costs vary from − $5.9 to $5.9 per tonne of CO2 captured. [ABSTRACT FROM AUTHOR]

• This is a case study of a small modular reactor supplying steam and electricity to a pulp and paper mill. • Resulting steam conditions in the mill are analyzed using steady-state and dynamic models. • Models of the paper mill were built in Aspen HYSYS and Dymola, a Modelica Ecosystem. • Integrated SMRs with pulp and paper mills is feasible but may present cost limitations. Small modular reactors (SMRs) are reactor designs producing less than 300 MWe and are generally planned for deployment as multimodule nuclear power plants. The possibility of factory-manufactured, flexibly sized plants expands the opportunities for nuclear power to different communities and industries, including manufacturing plants that currently utilize fossil fuels to produce both steam and electricity. This paper examines the feasibility of coupling a NuScale SMR with a midsize pulp and paper mill in the Southeastern United States. A steady-state mill model was developed in Aspen HYSYS, based on real data from the operation of the mill, and modified it to include the SMR while maintaining steam quality requirements and making as few changes as possible to existing equipment. Dynamic plant models were also developed Dymola to demonstrate possible plant conditions, using three configurations. Preliminary results suggest that, while SMR coupling is physically feasible, its economic feasibility is limited by the differences in steam and electricity demands. Because of limitations in the amount of steam the mill can take from the SMR, sizing the SMR for the plant's steam demand may result in an electricity deficit, or vice versa. Dynamic analyses show that the addition of a thermal storage system could reduce such deficits, but this entails its own challenges. Each plant must determine the best configuration and control scheme for itself, based on its electricity and heat needs, including the peak duration and intensity for both. Ultimately, an implementation of SMRs with manufacturing processes would benefit from partnering with a local utility to purchase excess electricity generated by the SMR. This will help manufacturing facilities meet their environmental and cost-savings goals, in addition to meeting the need for cost-effective baseload power across the United States. [ABSTRACT FROM AUTHOR]

Climate warming has gradually become a major problem threatening human survival, and countries have begun to pay attention to carbon emissions. Energy conservation and emission reduction has become a central task in China's economic development since the 14th Five-Year Plan. As the main force of carbon emissions in China, thermal power industry is bound to become the focus of attention in China's low-carbon development strategy and energy conservation and emission reduction. Moreover, with the marketization of the power industry, the state has joined the market competition at the power generation sectors and the power sale sectors, and implemented the "opening the middle of the two pipes." Therefore, the coverage of influence of carbon emissions and carbon investment behavior of power generation companies is not limited to itself, but will also be extended to the supply chain level. Based on the above background, this paper evaluates the scientific rationality of low-carbon investment projects of thermal power enterprises from the perspective of low-carbon supply chain, which not only can help enterprises achieve a win-win situation of economic and environmental benefits, but also contribute to the carbon emission reduction of the entire supply chain, thereby promoting China's entire social and economic energy conservation and emission reduction work., (© 2023. The Author(s).)

Forest based power plants are alternatives in the diversification of domestic energy supply in Brazil, given the growing demand for electricity in recent decades. Evidences of space-time clusters contribute to the understanding of regional development associated with correlated activity. Therefore, this paper analyzed the space-time conglomerates for Brazilian forest-based power plants, from 2000 to 2019. The data used were from the Generation Information System of the National Electric Energy Agency (ANEEL)-SIGA. It detected the existence of clusters by means of scan statistics via space-time permutation, considering the high level of conglomeration. The results show the Center-South region of Brazil with the highest concentration of conglomerates, with black liquor and forest residues being the most used energy resources. The clusters with the highest installed power were in the early 2010s, with the black liquor plants. The regions with the formation of fast-growing forest plantations promoted the existence of conglomerates associated with the pulp and paper and steel industry complexes. It is concluded that there was a conglomeration of forest power plants in the central-south region of Brazil, in which they help in decision-making and guidance of public policies for forestry projects for energy. [ABSTRACT FROM AUTHOR]

Qualification data presented included satisfactory ultrasonic inspection results including attenuation data, tensile, hot tensile, and Charpy toughness/FATT data, normalised creep rupture/creep strain data out to beyond 10,000 hours, low cycle fatigue data, and thermomechanical fatigue data. B SESSION 1: HIGH TEMPERATURE MATERIALS ISSUES b B Paper 1-1: Role of inclusions on degradation in creep life and rupture ductility of ferritic power plant steels b F Abe (National Institute for Materials Science (NIMS), Tsukuba, Japan) Dr Abe presented the results of metallographic investigation and creep test data to clarify how boron nitride, aluminium nitride, and manganese sulphide inclusions affected the creep performance of martensitic and ferritic steels. B SESSION 6: PLENARY SESSION b B Paper 6-1: Life assessment of high temperature plant - The role of materials design data b DJ Allen European Technology Development, Leatherhead, Surrey, UK Dr Allen discussed assessment of the remaining life of high temperature plant components and the role of materials design data both for the traditional low alloy steels and the relatively new high Cr martensitic steels. [Extracted from the article]

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]

Due to the advantages of micro‐grids including power losses and voltage drops reduction, reliability improvement and reduction in transmission network cost, numerous clean micro‐grids including renewable resources such as wind, tidal and solar are developed in different countries of world. The investment cost of the renewable energy‐based generation units especially tidal barrages is high, and to generate the cost‐effective electricity from the renewable resources, the optimization process should be performed. In this paper, optimal planning of a micro‐grid containing energy storage device and new mixture of renewable resources including tidal barrage and photovoltaic system is performed. For this purpose, optimal characteristics of barrage type tidal plant including number of turbines, sluices and hydro‐pumps, turbine tip and hub diameters and sluice width are determined to maximum energy of tidal plant is generated. Then, number of photovoltaic systems and batteries are obtained to supply the required load in minimum cost. To optimize the objective functions, different heuristic approaches including particle swarm, genetic and imperial competitive algorithms (ICAs) are applied, in the paper. To study the effectiveness of the proposed approach, optimal planning of a micro‐grid is performed. It is deduced from the numerical results that the particle swarm method has performed best in determining the optimal solution. [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]

DC collection and transmission is one of the major development directions of large‐scale photovoltaic (PV) power system. In order to achieve low‐cost, high‐efficiency and long‐distance transmission of PV power, this paper adopted a DC grid‐connected topology by using multi‐modular cascaded DC‐DC converters, forming an input‐independent and output‐series (IIOS) system. In this system, an LLC type DC‐DC converter based on IGCT and magnetically integrated transformer was proposed in this paper, which can significantly increase the capacity, efficiency and power density of DC‐DC converter. And the equalization unit based on half‐bridge topology was adopted for the case of uneven irradiation of the PV inputs. In addition, this paper proposed an equalization control method based on the Nearest Level Modulation (NLM), which greatly simplifies the design of the acquisition and control system. Furthermore, the parametric design of a 50MW PV system was presented, and the simulation was carried out to verify the feasibility and advantages of the system in this paper. The proposed IIOS system is more suitable for high‐capacity, long‐distance PV power transmission, which can serve as a reference for the development of large‐scale photovoltaic systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the investigations was to characterise and describe the modern creep-resistant Thor 115 steel for supercritical power units. Design/methodology/approach: The investigations were performed based on the knowledge and experience of the author, the preliminary results of his own research and the available literature on the subject-matter of the considerations presented in the paper. Findings: Modern steel Thor 115 was subjected to overall analysis in terms of its use as a potential heat-resistant structural material for power plant components. Based on the preliminary results of own research and the available literature data, it has been shown that the analysed steel may be a structural material of full value if the assumed creep resistance in the service temperature range of 600-650°C is confirmed. Research limitations/implications: The comprehensive analysis of degradation of microstructure of the steel after ageing (and/or creep) requires TEM examinations. Finding the correlation between the creep and ageing conditions and changes in the microstructure of the steel. Practical implications: The investigations carried out as part of the paper and the considerations on the subject-matter of the analysed steel may be the basis for the development of a database of material characteristics for steels, alloys and welded joints. Originality/value: The analysis of chemical composition, heat treatment and mechanical properties and the investigations of microstructure of Thor 115 steel are presented. [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]