Fossil Fuel Power Plants Research Articles

Large offshore wind farms have minimal direct impacts on air quality

Abstract Wind power has rapidly grown over the past decade because it is clean, renewable, and abundant. However, wind farms can affect local weather and possibly alter the transport, diffusion, and concentration of air pollutants. Given the unprecedented expansion of offshore wind farms planned along the U.S. East Coast by the Bureau of Ocean Energy Management (BOEM), this study aims to investigate if and how those future offshore wind farms might directly affect air pollution along the densely populated East Coast, in particular, the concentrations of ozone (O3), fine particulate matter (PM2.5), sulfur dioxide (SO2), and nitrogen dioxide (NO2). These pollutants are regulated at the federal and state levels and are harmful to human health. We exclusively study the direct effects of the wind turbines on air pollution (via meteorological changes), rather than investigating the indirect impacts of replacing fossil-fuel power plants with wind farms. We first run a numerical meteorological model, the Weather Research and Forecast (WRF) model, to simulate the meteorology along the U.S. East Coast during the summer of 2018 in two scenarios, with and without the wind farms. Then we use the output of these two sets of simulations from the WRF model as input to the Comprehensive Air Quality Model with extensions (CAMx) to simulate the changes in air quality in the study domain due to the wind farms. On average, we find a minor increase in O3 levels within the wake of the New Jersey WEA. The minor changes to O3 can be attributed to the slight temperature increase below the turbine hub height, within the rotor area, as well as a significant decrease in wind velocity in the wake of the turbines and a slight increase in VOCs. In addition, we report that the other three pollutants remain unchanged in the presence of wind farms. In summary, the direct impacts on air pollution by the BOEM-planned offshore wind farms are expected to be negligible.

Технико-экономические показатели энергокомплекса при использовании ветроэнергетических установок для покрытия собственных нужд ТЭЦ

In accordance with the Russia Energy Strategy until 2035, within the framework of the implementation of energy-saving technologies in the power-generating sector, it is planned to use hybrid energy, which is one of the ways of transition from conventional generation to generation with a high share of renewable energy sources. However, to implement such complexes, it is necessary to conduct research that considers the natural and climatic characteristics of the region, its resource capabilities, as well as the mutual influence of conventional and renewable energy sources in the structure of generating equipment capacities. The wind potential at the construction site has been analyzed to assess the feasibility of incorporating wind turbines. Based on the technical characteristics of wind turbines and meteorological conditions, the model of wind turbines to be installed has been selected. Technical and economic indicators of the energy complex operation, providing for the use of wind power plants to partially cover the house load of CHPs have been obtained. The indicators are determined on an annual, monthly basis using the physical method of attributing the total fuel costs for heat and electricity output in case of their combined production. Operation of wind power plants as a part of the energy complex with CHP in winter has a positive effect on the process of thermal efficiency of combined heat and power generation, and in summer, on the contrary, leads to deterioration of the efficiency indicators. Relatively small fuel savings per year when using wind power plants as a part of the energy complex with CHP does not allow to consider the project cost-effective. The obtained results should be considered when making system-wide decisions on the development of renewable energy sources in parallel operation with fossil fuel power plants.

Determining the reliability function of the thermal power system in power plant 'Nikola Tesla, Block B1' using the Weibull distribution

The assessment of thermal power system reliability represents a key step towards maintaining the stability and efficiency of the system. Through the application of mathematical models, risk analysis, and maintenance strategies, it is possible to optimise system performance and reduce the risk of failures. This paper presents an assessment of the reliability of the thermal energy system in fossil fuel power plant ‘Nikola Tesla, Block B1’, based on a twelve-year failure database. By implementing the theory of reliability, based on statistics and theory of possibility, and using the simple and complex two-parameter Weibull distribution, the theoretical functions of the failure rate are determined. A significant advantage of such a study is the opportunity of an early and in-depth understanding of the logic and mechanisms of system risk behaviour, and a more precise assessment of its functioning throughout future exploitation.

Weather as Fuel — The Wicked Problem of Renewable Energy

Abstract A key component of mitigating climate change is reducing society’s dependence on fossil fuels, which will require replacing them with clean energy sources. The key strategy being pursued in the United States (and most other countries) is to rapidly scale up our use of renewable energy sources. Electricity generation provided by wind turbines and solar photovoltaic panels, in particular, has been growing rapidly, and most energy experts expect them to be the dominant forms of electricity generation in the future. Given the dependence these energy sources have on weather conditions (windiness and cloudiness, respectively), it is useful to view weather as the “fuel” for these renewables in the same way that coal or natural gas serves as the fuel in generating electricity in a fossil-fuel power plant. This paper uses a novel thought experiment to drive home this concept, while also exploring the complexity of providing reliable power on a grid dominated by renewable generation. The paper then shows how the transition to renewable energy falls into the class of problems known as “wicked problems,” and discusses approaches that will be needed to make progress. The current status of addressing these complex issues is reviewed through the lens of the wicked problem paradigm.

A data-driven regression model for predicting thermal plant performance under load fluctuations

The global energy demand is still primarily reliant on fossil-fueled thermal power plants. With the growing share of renewables, these plants must frequently adjust their loads. Maintaining, or ideally increasing operational efficiency under these conditions is crucial. Increasing the efficiency of such systems directly reduces associated greenhouse gas emissions, but it requires sophisticated models and monitoring systems. Data-driven models have proven their value here, as they can be used for monitoring, operational state estimation, and prediction. However, they are also sensitive to (1) the training approach, (2) the selected feature set, (3) and the algorithm used. Using operational data, we comprehensively investigate these model parameters for performance prediction in a thermal plant for process steam generation. Specifically, four regression algorithms are evaluated for the prediction of the highly fluctuating live steam flow with two training approaches and three feature subsets of the raw dataset. Furthermore, manual and automatic clustering methods are used to identify different states of operation regarding the fuel amounts used in the combustion chamber. Our results show that the live steam flow is predicted with excellent accuracy for a testing period of one month (R2=0.994 and NMAE=0.55%) when using a dynamic training approach and a comprehensive feature set comprised of 48 features representing the combustion process. It is also seen that the statically trained model predicts various load changes with strong accuracy and that the accuracy of the dynamically trained model can be approached by incorporating the cluster information into the static model. These models reflect the plant’s physical intricacies under varying loads, where deviations from the predicted live steam flow indicate unwanted long-term drifts. They can be directly implemented to help operators detect inefficiencies and optimize plant performance.

Maintaining reliability in a 100% decarbonized power sector: The interrelated role of flexible resources

Reliability will remain a key requirement for decarbonized power sectors, but also a concern with the high penetration of variable renewable energy (VRE). The traditional approach for reliability is by producing enough energy and adapting rapidly to changes in load with the dispatch of fossil fuel power plants, especially natural gas power plants. However, this will have to evolve as decarbonization progresses. Can various flexibility resources such as transmission, storage and demand response provide the same reliability benefits? This paper uses a detailed, capacity expansion and hourly operation model to investigate the possibility and the cost of reaching different levels of planning reserve margins (PRM), in the multi-regional context of the North American Northeast. We find that while it is possible to entirely avoid natural gas power plants or nuclear ones to obtain high PRM, it would come at a very high cost and would require high levels of transmission, storage and demand response. We also detail why these three flexibility resources are needed in combination. The implications for energy policies are significant: in order for power system's cost to remain reasonable, reliability must count on some carbon-neutral natural gas or nuclear generation, along with interties with neighboring systems, storage and high levels of demand response.

Blowin’ in the wind: Long-term downwind exposure to air pollution from power plants and adult mortality

We estimate the causal effects of long-term exposure to air pollution emitted from fossil fuel power plants on adult mortality. We leverage quasi-experimental variation in daily wind patterns, which is further instrumented by the county orientation from the nearest power plant. We find that the county’s fraction of days spent downwind of plants within 20 miles in the last 10 years is associated with increased mortality from COVID-19 through the third peak in mortality in January 2021. This effect is more pronounced in fenceline communities with high poverty rates, low health insurance coverage, and low educational attainment.

Addressing the limitation of Operational Flexibility in Power Generation with Pumped Hydro Energy Storage System – Indian Perspective

The global climate change scenario has impacted and taken corrective measures in shifting the sourcing of primary energy and efficient end-use pattern with more focus to utilise renewable energy resources to replace the depleting fossil fuels. Electric power generation capacity in the Indian subcontinent has gradually been improved to substantially contribute to intermittent and variable renewable power generation mix up to 40% of grid demand. However, the injection of grid-integrated renewable power as per the availability of generation potential will necessitate limiting the power generation from generating plants running on fossil fuel. But the operational flexibility of fossil fuel power plants is mostly designed to run efficiently with a 55% minimum technical load on the machines. In the above situation, stable grid availability and operation with a desirable spinning reserve may be taken care of suitably with an on-grid energy storage system, and in the Indian context, a pumped hydro energy storage system may play a critical role to take care of grid operational variability with adequate measures on frequency and voltage correction through periodic power generation and power draw. In this paper, the feasibility of a pumped hydro storage system from an Indian power sector perspective has been reviewed about the present national power scenario.

Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants

Fossil fuel power plants are a significant contributor to global carbon dioxide (CO2) and nitrogen oxide (NOx) emissions. Accurate monitoring and effective reduction of these emissions are crucial for mitigating climate change. This systematic review examines the current state of research on the application of machine learning techniques in evaluating the emissions from fossil fuel power plants. This review first briefly introduces the continuous emission monitoring (CEM) systems and predictive emission monitoring (PEM) systems that are commonly used in power plants and highlights that machine learning models can significantly improve PEM systems through their capability to process and interpret large datasets intelligently to transform traditional emission monitoring systems by enhancing their precision, effectiveness, and cost-efficiency. Compared to previously published review articles, the key contribution and innovation in this present review is the discussion of machine learning models in CO2/NOx emissions according to the different algorithms used, including their advantages and disadvantages in a systematic way, which aims to help future researchers to develop more effective machine learning models. The most popular machine learning model includes reinforcement learning, a forward neural network, a long short-term memory neural network, and support vector regression. While each model method has its own advantages and disadvantages, we noted that training data quality, as well as the proper selection of model parameters, plays an important role. The challenges and research gaps, such as model transferability, a deep understanding of the physics of CO2/NOx emissions, and the availability of high-quality data for training machine learning models, are identified, and recommendations as well as potential future research directions to address these challenges are proposed and discussed.

PSLBII-6 Measuring greenhouse gas emissions from biogas facility as manure treatment and comparing with 2006 IPCC guideline calculation

Abstract To achieve global net-zero emission goals by 2050, Korea government announced 2050 carbon neutral scenarios. Substitution of fossil fuel power plants to renewable energy plants is the main strategic long-term vision. It included increasing the number of a biogas facility to retrieve methane (CH₄) for energy sources from manure. As increasing the biogas facility with using manure, it is important to quantify and monitor the greenhouse gas (GHG) such as CH₄ and nitrous oxide (N₂O). The objective of this research was to measure the GHG emissions from the biogas facility as the baseline to construct the country-specific emission factors, and to compare the emissions between site measured data and 2006 IPCC guideline (2006 IPCC GL). This facility was constituted with feedstock, anaerobic digestion, biogas storage, and liquid fertilizer storage. The facility treats 70 tons of swine manure from 20,000 pigs and 30 ton of food waste per day and generates 3,000 m3 /d of biogas. All gases were vented by negative pressure from the odor treatment tower, except the generator and its blowers. Quantification of the total GHG emissions from the biogas facility was conducted for 24 h every 2 wk, from July 2022 to October 2023, using CH₄/N2O Analyzer (Los Gatos Research, San Jose, CA). The total emissions of CH₄ and N2O were 12,545 ± 2,297 kgCH4/yr and 442 ± 1,123 kg N2O/yr. To compare total GHG emissions from the facility, it was changed by carbon dioxide (CO2) equivalent. The results showed that when the system of biogas facility operated normally, it can reduce the total GHG emissions by up to 60% compared with 2006 IPCC GL calculation. However, if the system does not circulate properly, it can result in 18 times greater emissions due to N2O, leading to significant increase in GHG emissions. To reduce GHG from biogas facilities, it is crucial to ensure continuous operation of organic matter in and out, and increase the amount of sufficient aeration in liquid fertilizer storage.

Integration of deep eutectic solvent with adsorption and membrane-based processes for CO2 capture: An innovative approach

CO2 capture strategies have been implemented to address climate change by reducing CO2 emissions from large-scale sources such as fossil fuel power plants and natural gas upgrading. Deep eutectic solvents (DESs) are a new class of green solvents with good CO2 affinity, simple synthesis steps, inexpensive raw materials, non-toxic and non-volatile properties. For these reasons, they are a promising platform for CO2 capture applications and have tremendous untapped potential to enhance the performance of the adsorption and membrane-based separation processes. Unlike existing reviews which have focused on the functionalities of DESs, as well as the physicochemical and CO2 solubility properties of DESs, this mini-review discusses the integration of DESs with adsorption and membrane processes to evaluate their CO2 separation performance. Moreover, simulation and computational studies on DES-based systems, including confinement in porous solids and membranes, are discussed. Finally, perspectives on developing DES-based adsorbents and DES-based membranes for the CO2 separation field are presented. This review provides insight into the potential of integrating DESs with CO2 capture technologies to enhance the performance of hybrid systems in relation to standalone approaches.

Development of a coupled agent-based generation expansion planning tool with a power dispatch model

Power companies need to adapt their generation expansion planning in response to changing market, climate and regulatory conditions as global warming, electrification, and technology breakthroughs continue. To fortify energy system resilience, it is critical to understand the collective effects of their autonomous decisions on power systems operations and reliability. To this end, we developed an integrated framework, an agent-based model (ABM) coupled with a power dispatch model (PDM) (referred to as ABM-PDM), tested on the Texas 123-bus transmission system in the Electric Reliability Council of Texas (ERCOT) region. Agents (power generation companies) can invest in natural gas, solar, and wind technologies to maximize profits from 2021 to 2050, using market information from the PDM based on their capital budget and perceived costs, financial incentives for renewable energy, and climate risks. We applied ABM-PDM to assess how power companies respond to future technological advancements and climate change. After demonstrating model credibility, we explored 25 combinations of cost and capacity factors reflecting a variety of technological evolution trajectories. Results indicated that to replace wind over solar for replacing existing fossil-fuel power plants due to lower costs and higher capacity factors. Additionally, as more agents invest, the energy market becomes more competitive, and system-wide electricity prices drop. We also analyzed the impacts of temperature increases on investments using seven projections, from 0 to 6 °C, during the modeling period. The results showed that as temperatures rise, agents invest more to accommodate the increasing loads. ABM-PDM incorporates risk attitude and learning into companies’ decision-making, providing additional information on generation expansion for the non-optimal future of power systems.

Comparison of entransy loss analysis and the first law of thermodynamics in the optimization of organic Rankine cycle coupled with parabolic trough collector

Due to the numerous environmental issues associated with fossil fuel power plants, using solar energy to generate electricity is a viable alternative. The organic Rankine cycle (ORC) is a thermodynamic process used to convert low- and medium-temperature heat sources into electricity, often utilizing organic fluids as the working medium. Entransy is a relatively new concept that many readers may not be familiar with. Moreover, entransy loss (Ġloss) is derived from the entransy concept, which quantifies the inefficiency in transferring thermal energy through a system. In this study, (Ġloss) is used for the first time when designing an ORC cycle coupled with parabolic trough collectors. The entransy loss relations were driven with assumption that the heat capacity is a function of temperature. A genetic algorithm is a search heuristic inspired by natural selection. It is used to find optimal or near-optimal solutions to complex problems by evolving a population of candidate solutions. Two scenarios utilized the genetic algorithm in MATLAB to optimize the system (scenario 1: maximizing the output power and scenario 2: maximizing the Ġloss). In addition, the optimization parameters included turbine inlet temperature (Ttur), boiler pressure (Pboil), condenser pressure (Pcond), and the temperature of the collector fluid at the boiler outlet (Thf,out). This optimization was performed for the temperature of the collector fluid at the boiler inlet (Thf,in) in the range 310–400 °C at 10 °C intervals with four working fluids (i.e., toluene, cyclohexane, MM, and water). The land area and the beam solar radiation were considered to be 100 hectares and 800 W/m2, respectively. The results indicated that according to scenario one, at temperatures of 310–320 °C, the maximum power was obtained for the case of toluene fluid with values 59.8 and 63.5 MW. For the collector fluid temperature from 330 to 400 °C, water had the most optimal power with values ranging from 66.2 to 88.2 MW. Furthermore, toluene exhibited superiority to two other organic fluids in the 330–400 °C temperature range after water, with net power values ranged between 65.7 and 76.3 MW. The results indicated that the maximum entransy loss does not correspond to the maximum output power because the application preconditions of the entransy loss concept are not all satisfied. Across all working fluids and Thf,in, scenario 2 resulted in lower optimal output power, cycle efficiency, and system efficiency compared to scenario 1.

Optimal Design and Operation of Electrolytic Hydrogen Production and Storage System for Solar Photovoltaic Power Smoothing

Even though the increasing penetration of solar photovoltaic (PV) energy into the electric grid can reduce the carbon emission of power generation, the intermittency and variability of renewable solar energy lead to frequent and steep ramping operations of conventional fossil fuel power plants. Hydrogen production via water electrolysis using solar power can serve as a controllable load and provide a short/long duration energy storage to mitigate the grid fluctuations (i.e., PV smoothing) and improve the resiliency of power grid. The generated green hydrogen will be further stored under high pressure for subsequent utilizations (e.g., hydrogen fuel cell electric vehicle refueling).Polymer electrolyte membrane (PEM) electrolyzer with high efficiency and quick dynamic response is used to generate hydrogen [1,2]. The capacity factors of PV field and PEM electrolyzer can affect the performance of PV smoothing on the cloudy day. Even though PEM electrolyzers directly producing high pressure hydrogen are more energy efficient than ambient pressure electrolyzers followed by mechanical compression, higher pressure leads to more undesired hydrogen back diffusion to the oxygen side, which causes lower hydrogen production efficiency, cell degradation, and safety risk of hydrogen explosion limit [3,4]. Optimal design and operation of electrolyzer under fluctuating solar power are formulated based on an integrated hydrogen-based energy storage system (renewable solar coupled with green hydrogen production and storage). Different PV smoothing scenarios as well as optimal size and operation conditions of the PEM electrolyzer are investigated.The high-fidelity dynamic model of a PEM electrolyzer cell/stack is established with consideration of two-dimensional (“through-plane” and “in-plane”) mass/heat transfer coupled with electrochemical kinetics. The electrochemical reactions are considered using Butler-Volmer kinetics with varying surface molar concentrations of components. Maxwell-Stefan diffusion equation is adopted to calculate the gas-phase species molar concentration in the backing and catalyst layers. Hydrogen permeation back through membrane is included with consideration of both diffusion and convective transports as a function of operational pressure.To evaluate the transient response of the PEM electrolyzer stack, real-time PV data based on an Orlando Utilities Commission (OUC) solar farm is smoothed using the developed power signal control algorithm. The optimal PV smoothing control algorithm and the electrochemical dynamic stack model show the effectiveness of hydrogen-based energy storage system in smoothing the PV signal to improve the grid stability and flexibility.[1] Kumar, S.S. and Himabindu, V., 2019. Hydrogen production by PEM water electrolysis–A review. Materials Science for Energy Technologies, 2(3), pp.442-454.[2] Götz, M., Lefebvre, J., Mörs, F., Koch, A.M., Graf, F., Bajohr, S., Reimert, R. and Kolb, T., 2016. Renewable Power-to-Gas: A technological and economic review. Renewable energy, 85, pp.1371-1390.[3] Trinke, P., Bensmann, B., Reichstein, S., Hanke-Rauschenbach, R. and Sundmacher, K., 2016. Hydrogen permeation in PEM electrolyzer cells operated at asymmetric pressure conditions. Journal of The Electrochemical Society, 163(11), p.F3164.[4] Scheepers, F., Stähler, M., Stähler, A., Rauls, E., Müller, M., Carmo, M. and Lehnert, W., 2020. Improving the efficiency of PEM electrolyzers through membrane-specific pressure optimization. Energies, 13(3), p.612.

Strategy for developing the utilization of organic waste as an alternative source of electricity in Indonesia

Background: Indonesia has pledged to reach its Net Zero Emission target by 2060, necessitating the shift to renewable energy sources. To achieve this, Indonesia must transition from its current reliance on fossil fuel power plants to renewable energy generators, ensuring the same or greater electricity capacity. One viable renewable energy source is organic waste. This study aims to explore strategies for developing organic waste as an alternative energy source to bolster Indonesia's energy resilience and environmental sustainability. Method: The research employs a qualitative approach, including literature reviews and qualitative descriptive analysis. Result: Waste to Energy (WtE) processes convert waste into electricity and/or heat, aiding in greenhouse gas reduction, improving waste management efficiency, and supporting sustainable development. WtE technologies can utilize both thermochemical and biochemical methods to convert waste into energy. The Indonesian government is known to have 12 projects to accelerate the installation of Waste Processing into Electrical Energy, both the Waste Power Plants that have been in operation and under construction and using gasification as the method. The development of WtE faces various challenges ranging from completeness and consistency of regulations, high tipping fees, complex cooperation mechanisms and business models, problems with the characteristics and nature of Indonesian waste that need special handling, and rejection from residents. Conclusion: Strategies that can be implemented in developing WtE in Indonesia include drafting policies and regulations, increasing public awareness, collaboration with the private sector, choosing the right technology, developing infrastructure, increasing the efficiency of waste collection, and international partnerships. Novelty/Originality of this study: This research offers concrete strategies to develop Waste to Energy (WtE) technology in Indonesia, including policy formulation, increasing public awareness, and collaboration with the private sector to utilize organic waste as a renewable energy source to support the 2060 Net Zero Emission target.

Carbon capture and co-pollutants in a networked power system

Abstract We evaluate how the availability of Carbon Capture (CC) in a networked electricity system affects the emissions of both carbon and of co-pollutants, under a range of plausible technical, economic, and policy scenarios about CC technology, the pace of renewable deployment, the structure of the power grid, and climate policy. We employ a Power Flow model of a three-node, mixed-source network in which fossil fuel power plants may invest in CC via retrofit. Our stylized model retains some of the complexities of a real power system while allowing for a detailed analysis of the impact of power plant operations and transmission constraints. We find that, in a networked system, the availability of CC may lead some generation to move from Natural Gas to Coal, thus leading to a significant increase in co-pollutants. This is of particular concern during the mid-transition, a period when both carbon and non-carbon electrical generation is active. The introduction of CC can lead to an increase in co-pollution even as the energy system transitions toward renewable energy and, surprisingly, co-pollution outcomes can be worse under a stronger decarbonization policy. This insight is important and timely in light of recent rules incentivizing the use of CC. Systems in the early stages of the energy transition may experience an increase in co-pollution if the co-pollutant dynamics are not considered in the first steps of CC policy design.

Carbon capture and storage at Malaysia power plants: Evaluation of its feasibility and requisite enablers

Rapidly growing GHG emissions are significantly altering the world climate, necessitating a global decarbonization efforts to avert irreversible catastrophic failure of the world ecosystem. Malaysia is pledging to achieve carbon neutrality by 2050 and has subsequently identified Carbon Capture and Storage (CCS) as one of the solutions for carbon removal from its energy sector by 2050. However, the implementation of CCS presents formidable challenges, encompassing both technical and commercial complexities, especially for a nation like Malaysia, which has limited experience in large-scale CCS deployment. This study evaluates the feasibility of CCS implementation in Malaysia's fossil fuel power plants by conducting a comprehensive techno-economic assessment, taking into considerations the prevailing energy landscape of the country. This assessment is very crucial to demonstrate the feasibility of this plan within the timeframe, considering the limited research have been done to assess a national-scale implementation, as well as to provide the important insights for future policy implication to promote CCS adoption. The CCS scenarios are assessed through an analytical approach, and comparative analyses are conducted between alternative paths. The result shows that a large-scale implementation of CCS at Malaysia's fossil fuel power plant to achieve net-zero emission by 2050 are feasible given a strong enforcement on emission policy and establishment of CCS legal framework. CCS can potentially reduce 60% of Malaysia's energy sector total emissions, and a solar-supported CCS helps to reduce it further. Levelized Cost of Electricity analysis reveals that the implementation of CCS leads to only a minimal increase between pre- and post-CCS LCOE when carbon price is considered. A SWOT-PESTLE analysis was also conducted to identify gaps in regulatory framework, financial and social aspects. It can be concluded that CCS is a techno-economically viable solution, however Malaysia's readiness for CCS implementation is still at a very novice stage, and aggressive actions are needed to enable a successful full-scale implementation.

Feasibility study of hybridizing a solar power plant with a small modular reactor for seawater desalination

The article discusses the technical and economic analysis of a seawater desalination plant, where the power source is a hybrid of solar and nuclear energy, as they are considered the cleanest energy sources compared to fossil fuel power plants. The source of nuclear energy in this study is a small modular reactor (SMR). This plant also uses a hybrid of seawater desalination systems: either a reverse osmosis plant with a multi-effect distillation (RO + MED) unit, or a reverse osmosis plant with a multi-stage flash distillation (RO + MSF) unit. Small modular reactors can be used for other applications besides generating electricity, as they produce high-temperature steam which can be used in many industrial processes such as hydrogen production and seawater desalination. Small modular reactors are also considered to be more cost effective, safer, more flexible and have a greater number of applications compared to high power reactors. The analysis is based on calculating the cost of producing of one cubic meter of fresh water using this hybrid desalination plant and comparing the results with those of desalination plant integrated with a power plant that uses exclusively nuclear energy as a source of thermal and electrical power, which uses the VVER-1200 reactor. Also, this study studies the impact of the degree of hybridization, that is, the ratio of power used from solar energy to power used from nuclear energy, on the cost of desalination of one cubic meter of water, as well as on the quality of the desalinated water.

Lagrangian relaxation for continuous‐time optimal control of coupled hydrothermal power systems including storage capacity and a cascade of hydropower systems with time delays

AbstractThis work considers a short‐term, continuous time setting characterized by a coupled power supply system controlled exclusively by a single provider and comprising a cascade of hydropower systems (dams), fossil fuel power stations, and a storage capacity modeled by a single large battery. Cascaded hydropower generators introduce time‐delay effects in the state dynamics, which are modeled with differential equations, making it impossible to use classical dynamic programming. We address this issue by introducing a novel Lagrangian relaxation technique over continuous‐time constraints, constructing a nearly optimal policy efficiently. This approach yields a convex, nonsmooth optimization dual problem to recover the optimal Lagrangian multipliers, which is numerically solved using a limited memory bundle method. At each step of the dual optimization, we need to solve an optimization subproblem. Given the current values of the Lagrangian multipliers, the time delays are no longer active, and we can solve a corresponding nonlinear Hamilton–Jacobi–Bellman (HJB) Partial Differential Equation (PDE) for the optimization subproblem. The HJB PDE solver provides both the current value of the dual function and its subgradient, and is trivially parallelizable over the state space for each time step. To handle the infinite‐dimensional nature of the Lagrange multipliers, we design an adaptive refinement strategy to control the duality gap. Furthermore, we use a penalization technique for the constructed admissible primal solution to smooth the controls while achieving a sufficiently small duality gap. Numerical results based on the Uruguayan power system demonstrate the efficiency of the proposed mathematical models and numerical approach.

Modeling and uncertainty quantification of CO2 absorption process using phase separation solvent

Absorption using an amine solvent is one of the primary means for capturing CO2 from many combustion sources such as fossil fuel power plants. CO2 capture systems by absorption need a large amount of thermal energy to strip CO2 from the amine solvent. In recent years, absorption using a phase separation solvent, which forms two phases when CO2 is absorbed, has been demonstrated as a promising technique to reduce the energy consumption. In this study, we examine several process models for the CO2 absorption process using a phase separation solvent, and quantify the uncertainty of the model parameters. We estimated model parameters, including equilibrium and mass transfer parameters, from two sources of experimental data: solubility tests, as well as lab-scale absorber tests. Model parameters were estimated by Bayesian inference using the Markov chain Monte Carlo method. The proposed approach successfully quantified the uncertainties of the model parameters.