Electric Generation Units Research Articles

Evaluation of electricity generation subsystem of power-to-gas-to-power unit using gas expander and heat recovery steam generator

Power-to-hydrogen technology allows the effective use of electricity produced during the energy demand valleys, mainly in stochastic renewable energy sources. The dynamics of implementing such systems in the economy is correlated with the development of technologies enabling safe use of hydrogen. Currently, planning investments in power-to-hydrogen requires considering the risks associated with the possibility of reduced hydrogen demand. A way of protection against permanent or periodic reduction in the demand for hydrogen may be investments in electricity generation systems, which, thanks to the use of by-product of the electrolysis process - oxygen, can allow using of not only hydrogen but also natural gas. This paper discusses the thermodynamic and economic study of a hydrogen-to-power system using methanation and oxy-combustion combined cycle. The thermodynamic calculations focus on analysis of heat recovery steam generator (HRSG) structures and determining the energy efficiency of the system. The obtained energy storage efficiency are in the range of 16.29–24.95%. Within economic considerations, an electricity generation unit consisting of methanation and single-pressure HRSG systems cooperating with 100 MW hydrogen generators unit is considered. Sensitivity analyzes are performed and the value of investment outlays in hydrogen production unit and the price of electricity are decision variables.

Life-cycle ecological footprint assessment of grid-connected rooftop solar PV system

ABSTRACT Solar photovoltaic (PV) power generation system is generally considered to be land-intensive in view of the diffuse nature of solar energy. However, a comprehensive assessment in this regard would involve bio-productive land for all types of resource consumption during the lifespan of the system. This study presents such an assessment for a grid-connected rooftop solar photovoltaic (RSPV) system located in a tropical climate. The life cycle ecological footprint (EFT) methodology has been used to derive the results based on material & energy consumption, water & manpower requirements along with waste disposal. This methodology is illustrated through an existing 1 MWp RSPV system installed at an educational institution in India. The total EFT is evaluated as 22.69 global hectare (gha) per year, out of which the environmental impact of solar PV panels is 76% of the total. The EFT per kWP capacity is 0.454 gha/kWP and the EFT per unit solar panel collector area is 0.074 gha/m2. The EFT of the RSPV system per unit electricity generation is 1.59 × 10−5 gha/kWh. However, considering only emissions from the grid-electricity in India, an approximate reduction potential of ecological footprint through RSPV-based electricity is estimated as 95%.

Interstate transport of ozone in eastern United States: An analysis of the impact of southeastern states’ emissions in 2017

Abstract To meet the requirements of the Clean Air Act's “good neighbor” provision, it is necessary to quantify the interstate transport of air pollution and determine if upwind states significantly contribute to noncompliance with the National Ambient Air Quality Standards (NAAQS) at downwind states. This study aims to estimate the contributions of ten southeastern states (Alabama, Florida, Georgia, Kentucky, Mississippi, North Carolina, South Carolina, Tennessee, Virginia and West Virginia) to ozone (O3), and identify significant associations with nonattainment or maintenance at monitors across the eastern United States (US) in 2017. Two widely used air quality models, the Comprehensive Air quality Model with extensions (CAMx) and the Community Multiscale Air Quality model (CMAQ), using different versions of the Carbon Bond (CB) chemical mechanism, were applied to predict 2017 O3 design values (DVFs) from 2011 O3 design values (DVCs) at the US Environmental Protection Agency's (EPA) Air Quality System (AQS) sites throughout the eastern US. Since 2017 design values are already published, the accuracy of the EPA-recommended modeling methodology for projecting current design values into the future only by changing the emissions was evaluated. The assumption that good model performance in the base year would result in accurate future year ozone projections was tested. In general, CAMx/CB6r2 simulated higher DVFs than CMAQ/CB05, with an average difference of 0.5 ppb. As a result, this study identified 24 sites in the eastern US as nonattainment (DVF ≥ 76 ppb) with CAMx/CB6r2 compared to only 16 sites with CMAQ/CB05. Using monitor cell relative response factors (RRFs) instead of 3×3 cell matrix maximum RRFs (the approach used in EPA's proposed Cross-State Air Pollution Rule (CSAPR) modeling) (EPA, 2015a), led to higher DVFs at some monitors and lower DVFs at others. Source apportionment analysis with the CAMx Anthropogenic Precursor Culpability Assessment (APCA) probing tool revealed that NOx emissions dominated over VOC emissions in contributing to O3 in downwind states. Contributions of total anthropogenic NOx emissions and contributions of Electric Generating Unit (EGU) NOx emissions from the southeastern states to O3 at nonattainment and maintenance sites were quantified separately using CAMx/APCA as well as CMAQ/zero-out simulations. Contributions obtained from CAMx/APCA were typically larger than the contributions obtained from CMAQ/zero-out. Therefore, a “significant contribution” determined by CAMx/APCA may be insignificant according to CMAQ/zero-out. “Significant contribution” was defined as an impact larger than 1% of the 2008 O3 NAAQS (i.e., ≥ 0.76 ppb). Alabama, Kentucky, Mississippi, Tennessee, Virginia and West Virginia were linked to 2, 3, 1, 1, 6 and 5 nonattainment sites with CAMx/APCA, while only Kentucky, Virginia and West Virginia were linked to 2, 3 and 2 nonattainment sites with CMAQ/zero-out. Additionally, for significant linkages, EGU NOx contributions were 10–55% of the total anthropogenic NOx contributions. The information produced in this study can be used by southeastern states to help develop “good neighbor” State Implementation Plans (SIPs).

A Building Energy Management System Based on an Equivalent Electric Circuit Model

In recent decades, many EU and national regulations have been issued in order to increase the energy efficiency in different sectors and, consequently, to reduce environmental pollution. In the building sector, energy efficiency interventions are usually based on the use of innovative insulated materials and on the installation of cogeneration and tri-generation units, as well as solar technologies. New and retrofitted buildings are more and more commonly being called “smart buildings”, since they are characterized by the installation of electric and thermal power generation units, energy storage systems, and flexible loads; the presence of such technologies determines the necessity of installing Building Energy Management Systems (BEMSs), which are used to optimally manage their operation. The present paper proposes a BEMS for a smart building, equipped with plants based on renewables (photovoltaics, solar thermal panels, and geothermal heat pump), where the heating and cooling demand are satisfied by a Heating, Ventilation and Air Conditioning System (HVAC) fed by a geothermal heat pump. The developed BEMS is composed of two different modules: an optimization tool used to optimally manage the HVAC plant, in order to guarantee a desired level of comfort inside rooms, and a simulation tool, based on an equivalent electric circuit model and used to evaluate the thermal dynamic behavior of the building. The paper describes the two modules and shows the main results of the validation phase that has been conducted on a real test-case represented by the Smart Energy Building (SEB) located at the Savona Campus of the University of Genoa, Italy.

Disparities in Distribution of Particulate Matter Emissions from US Coal-Fired Power Plants by Race and Poverty Status After Accounting for Reductions in Operations Between 2015 and 2017.

Objectives. To investigate potential changes in burdens from coal-fired electricity-generating units (EGUcfs) that emit fine particulate matter (PM2.5, defined as matter with a nominal mean aerodynamic diameter of ≤ 2.5 µm) among racial/ethnic and economic groups after reduction of operations in 92 US EGUcfs.Methods. PM2.5 burdens calculated for EGUs listed in the 2008, 2011, and 2014 National Emissions Inventory were recalculated for 2017 after omitting emissions from 92 EGUcfs. The combined influence of race/ethnicity and poverty on burden estimates was characterized.Results. Omission of 92 EGUcfs decreased PM2.5 burdens attributable to EGUs by 8.6% for the entire population and to varying degrees for every population subgroup. Although the burden decreased across all subgroups, the decline was not equitable. After omission of the 92 EGUcfs, burdens were highest for the below-poverty and non-White subgroups. Proportional disparities between White and non-White subgroups increased. In our combined analysis, the burden was highest for the non-White-high-poverty subgroup.Conclusions. Our results indicate that subgroups living in poverty experience the greatest absolute burdens from EGUcfs. Changes as a result of EGUcf closures suggest a shift in burden from White to non-White subgroups. Policymakers could use burden analyses to jointly promote equity and reduce emissions.

Seasonal Risk Assessment of Water-Electricity Nexus Systems under Water Consumption Policy Constraint.

Previous studies have estimated power plant cooling water consumption based on the long-term average cooling water consumption intensity (WI: water consumption per unit of electricity generation) at an annual scale. However, the impacts of the seasonality of WI and streamflow on electricity generation are less well understood. In this study, a risk assessment method is developed to explore the seasonal risk of water-electricity nexus based on the Integrated Environmental Control Model, which can simulate variable WIs in response to daily weather conditions and avoid underestimation in WIs as well as nexus risk during dry seasons. Three indicators, reliability, maximum time to recovery, and total power generation loss, are proposed to quantify the seasonal nexus risk under water consumption policy constraint represented by the allowed maximum percentage of water consumption to streamflow. The applications of the method in two representative watersheds demonstrate that the nexus risk is highly seasonal and is greatly impacted by the seasonal variability of streamflow rather than annual average water resources conditions on which most previous studies are based. The nexus is found more risky in the watershed with almost double mean annual streamflow and greater streamflow variability, compared with the watershed with less streamflow variability.

Cleaning up the air for the 2008 Beijing Olympic Games: Empirical study on China's thermal power sector

This study examines the impact of the 2008 Beijing Olympic Games (BOG08) on pollution control strategies in the thermal power sector. We focus on two pollution control strategies: the installation of pollution control equipment and improvement in energy efficiency. By using a difference-in-differences methodology, we investigate if there is a significant difference in the installation of pollution control equipment between provinces under the regional control policy for the BOG08 and other provinces. Furthermore, by using matching methods, we explore the differences in the energy efficiency between power plants in provinces targeted by the policy and those in other provinces. The results suggest that there are no statistically significant effects of the BOG08 on pollution control equipment. On the other hand, energy efficiency measured by the coal consumption per unit of electricity generation by thermal power plants improved in 2007 and 2008 in provinces designated as areas requiring coordinated air pollution control for the Olympic Games. These results are consistent with the hypothesis that faster and cheaper responses played a larger role in pollution reduction in BOG08.

Differential flatness theory‐based approach to the control of gas‐turbine electric power generation units

A differential flatness theory-based control and state estimation method has been developed for electric power units that consist of synchronous generators connected to gas turbines. The dynamic model of the power unit satisfies the properties of differential flatness and this allows for its transformation into an input–output linearized form. Moreover, it is shown that the state-space description of the power system can be written in the canonical (Brunvsky) form. Using this, a solution for the power unit's control and state estimation problem is given. First, a stabilizing feedback controller is designed. Moreover, with the use of differential flatness theory-based implementation of the Kalman Filter it becomes possible to solve the state and disturbances estimation problem of the gas-turbine power unit. The considered filtering method, under the name of Derivative-free nonlinear Kalman Filter, consists of application of the Kalman Filter's recursion on the linearized equivalent model of the power system, and of an inverse transformation that allows for computing estimates for the state variables of the initial nonlinear system. By redesigning the aforementioned Kalman Filter as a disturbance observer one can also identify and annihilate in real time exogenous perturbations.

Fracture Analysis on Turbine Intermediate Regulating Valve of an Electric Generator Unit

A turbine intermediate regulating valve fractured in a power plant, and the reasons for the fracture were analysed by using macro morphology observation, mechanical properties test, chemical composition analysis and microstructure observation, such as scanning electron microscopy (SEM) analysis or metallographic analysis. The results indicated that the chemical composition and strength properties were in accordance with the standard requirements, but the elongation after fracture and the impact toughness value were all lower than the required values. Precipitation of σ phase further weakened the matrix and reduced the strength of the structure. Fretting friction and collision appeared between the valve stem and the anti-rotating pin, and additional stress was generated by the reduction of radial clearance between the pin and valve. Then cracks and brittle expansion occurred in the stress concentration areas close to the anti-rotating pin holes and finally fractured.

Machine Learning Based Cost Effective Electricity Load Forecasting Model Using Correlated Meteorological Parameters

Electricity, a fundamental commodity, must be generated as per required utilization which cannot be stored at large scales. The production cost heavily depends upon the source such as hydroelectric power plants, petroleum products, nuclear and wind energy. Besides overproduction and underproduction, electricity demand is driven by metrological parameters, economic and industrial activities. Therefore, the region specific accurate electric load forecasting can help to effectively manage, plan, and schedule appropriate low cost electricity generation units to decrease per unit cost and provision of on time energy for maximum financial benefits. Machine learning (ML) offers different supervised learning algorithms including multiple linear regression, support vector regressors with different kernels, k-nearest neighbors, Random Forest and AdaBoost to forecast the time series data, but the performance of these algorithms is data dependent. It is vitally important to consider correlated metrological parameters of the specific region for accurate prediction of electricity load demand using ML based forecasting models to minimize the price per unit. In this study, an algorithm is proposed to select least cost electric load forecasting model (lcELFM) using correlated meteorological parameters. We developed least cost forecasting models by minimizing root mean squared error, mean absolute error, and mean absolute percentage error. For simulations, the recorded electricity demand data is taken from a substation of water and power development authority Muzaffarabad city from $1^{\mathrm {st}}$ January 2014 to $31^{\mathrm {st}}$ December 2015. The meteorological time series data are obtained from the substation of Pakistan meteorological department for the same period and same region. Empirical results demonstrate the robustness of the proposed algorithm to select lcELFM. Moreover, SVR (Radial) based electric load forecasting model proves to be the robust model when built using correlated features (temperature and dew point) for the said region and in turn can save up to PKR 0.313 million daily.

Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment

This paper presents a two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the competitive interactions between the electrical generation units. The first stage is focused on the day-ahead scheduling of the thermal power plants while the balancing market dispatch is considered in the second stage using the stochastic producers and quick dispatch units. To capture the uncertainties associated with electricity demand and wind speed, Latin hyperbolic sampling and fast forward selection methods are applied for scenario generation and reduction processes, respectively. The flexibility of the studied system is analyzed considering the variations of the key parameters of thermal units and the transmission line’s capacity. For this work, the modified IEEE 14-bus standard test system integrated with the components of the studied system is selected as the case study. After solving the problem, the maximum potential of clean energy production units is used in comparison with fossil fuel-based units through the optimal scheduling of the wind-thermal-hydropower-pumped storage system. Given the numerical results, reducing the flexibility of the system by reducing the ramp up/down parameters, increasing the minimum up/down parameters, and reducing the transmission line capacity has been led to increase of 6.47%, 7.3%, and 9.77% in the total energy cost, respectively.

Energy-water nexus in electricity trade network: A case study of interprovincial electricity trade in China

The large amount of electricity generation is exerting tremendous pressure on water resources. Moreover, the spatial mismatch between water and electricity demand is aggravating national water scarcity through virtual water transfers in electricity trade. To investigate the impact of electricity trade on water resource and further provide insights into the optimization of national electricity trade pattern from virtual water strategy perspective, the impact evaluation model for interprovincial electricity trade network by evaluating and quantifying the “positivity” or “negativity” of electricity trade is constructed. The interprovincial virtual water transfer network is constructed, and the hybrid virtual water content is proposed to represent provincial water consumption factor per unit electricity generation. An impact assessment system is proposed and impact evaluation indicator is defined to identify and evaluate the “positivity” or “negativity” of each electricity trade. The results show that total volume of virtual water transfer embodied in Chinese electricity trade in 2015 is 5638.4 million m3, in which the negative virtual water transfer is 1410.5 million m3, which comprises 25.0% of the total volume of virtual water transferred. The most positive trade pair is “Sichuan-to-Shanghai”, while the most negative trade pair is “Sichuan-to-Chongqing”. By assessing the “positivity” or “negativity” of electricity trade, this study identifies critical trade pairs that mitigate or aggravate water scarcity risk, and provide insights into improvement and optimization of national electricity trade pattern.

Develop of Control Water Supply Pump Based on Supervisory Control and Data Acquisition on Instrumentation and Power Generator Control in Grade X Students Majoring In Electric Power in the PGRI 3 Vocation School Malang

The quality of education can affect the quality of the learners were produced. The importance to improve the quality of education so learners produced increased anyway. According to Asyhar, one way to improve the quality of education is the use of media of instruction. Instrumentation and generator control the Generator operation of SCADA on power plant aims to train students designing and mengoperasiakan the existing SCADA system at the power plant. One operation of SCADA in electricity generation at the WTP (Water Treatment Plant), namely on Water Supply Pump to control water pump. SCADA learning media packed in prototype trainer Water Supply Pump -based Supervisory Control And Data Acquisition SCADA learning to support. Aiming to understand the way design of SCADA, create, communicate, SCADA program and operate it. The model used is the development model of development Sugiyono. Stages of development model Sugiyono are: (a) the potential and problems, (b) data collection, (c) product design, (d)design validation, (e) the revised design, (f) a trial product, (g)product revision, (h) a trial use, (i) the revision of the product, and (j) production mass. The results of the research and development shows that the eligibility level trainer control water supply pump has a percentage of 91.9% of the 3 (three) validator experts which means decent products used with little revision, 83.72% of test small groups by 10 (ten) grade X PBB meaning decent products used without revision, 90.53% of tests by a large group of thirty (30) students of class X PBA product which means it is very worthy of being used without any revisions. The conclusions of this research and development are decent products used in the learning process. The product can help learning activities can help so that the students of class X students majoring in electric power can be better understand the operation of scada in electricity generation units.

Off-design performance analysis and optimization of the power production by an organic Rankine cycle coupled with a gas turbine in an offshore oil platform

The organic Rankine cycle has been widely explored for energy recovery from several heat sources. In this paper, the maximization of the energy recovery of the exhaust gases from the General Electric LM 2500 gas turbines in a floating production offloading unit is studied in order to meet the demands of heat (pressurized hot water) and electricity over oil production lifetime. An off-design model of the system is developed to check the viability of an organic Rankine cycle under dynamic operating conditions. Energy and exergy analysis of the proposed system are conducted to determine main irreversibilities and performance characteristics of the system and, finally, the economic benefits of the energy recovery system application are discussed. The organic Rankine cycle is very flexible in the heat recovery in dynamic demand conditions, contributing up to 20.3% in electricity generation, which causes an increase in overall system efficiency of up to 11.3% and up to 18.3% in the utilization factor, as well as an average reduction of 22.0% in carbon dioxide emissions in the floating production offloading unit electricity generation process. The exergy efficiency of the system reaches 55.8% and the heat recovery devices are the most irreversible equipment in organic Rankine cycle, contributing up to 68% of the total exergy destroyed. The economic analysis demonstrated the attractiveness of the organic Rankine cycle implementation, allowing a net present value to Brazil's current economic scenario of up to USD 16.8 million.

Sustainability Analysis of Electricity Generation Technologies Based on Life‐Cycle Assessment and Life‐Cycle Cost—A Case Study in Liaoning Province

Electricity generation in China is currently dominated by thermal power generation (TPG). To achieve sustainable development during electricity generation, this study compares the sustainability of the six main approaches for electricity generation in Liaoning Province: thermal power, nuclear power, wind power, hydropower, photovoltaic power, and biomass power generation. First, the environmental impact and unit electricity generation cost (UEGC) of each electricity generation approach is calculated and analyzed from the perspectives of both life‐cycle assessment (LCA) and life‐cycle cost (LCC). Then, the energy potential of electricity generation approaches in Liaoning Province is calculated. Finally, the entropy‐weighted method is used to combine the environmental impacts, UEGC, and energy potentials of each energy source, producing a mathematical evaluation model for determining the sustainability of electricity generation. As a result, the six approaches are ranked in terms of electricity sustainability in Liaoning Province as follows: wind power, nuclear power, biomass power, hydropower, solar power, and thermal power. In future development of the electricity industry, Liaoning Province should increase the proportion of wind power, nuclear power, and biomass power generation in total installed capacity and reduce the proportion of thermal power in total installed capacity.

Fault diagnosis of gas-turbine power units with the derivative-free nonlinear Kalman Filter

A method is developed for diagnosing faults and cyberattacks in electric power generation units that consist of a gas-turbine and of a synchronous generator. By proving that such a power generation unit is differentially flat its transformation into an input–output linearized form becomes possible. Moreover, by applying the Derivative-free nonlinear Kalman Filter, state estimation for the power unit is performed. The latter filtering method, consists of the Kalman Filter's recursion on the linearized equivalent model of the power unit, as well as of an inverse transformation providing estimates of the initial nonlinear system. By subtracting the estimated outputs of the Kalman Filter from the measured outputs of the power unit the residuals’ sequence is generated. The residuals undergo statistical processing. It is shown that the sum of the squares of the residuals’ vectors, weighted by the associated covariance matrix, forms a stochastic variable that follows the χ2 distribution. By exploiting the statistical properties of this distribution, confidence intervals are defined, which allow for detecting the power unit's malfunctioning. As long the aforementioned stochastic variable remains within the previous confidence intervals the normal functioning of the power unit is inferred. Otherwise, a fault or cyber-attack is detected. It is also shown that by applying the statistical method into subspaces of the system's state-space model, fault or cyber-attack isolation can be also performed.

Distributed, Neurodynamic-Based Approach for Economic Dispatch in an Integrated Energy System

In an integrated energy system, the growing number of distributed heat and electric power generation units will bring new technical challenges to the existing centralized economic dispatch strategies. This paper proposes a distributed optimization approach for the economic system operation in a multienergy system by considering various equality and inequality constraints to accommodate the integration of intermittent renewable generations. The proposed distributed neurodynamic-based approach only requires the information exchange among neighboring units and offers flexibility, adaptivity, scalability, faster convergence, and lower communication burden compared with some traditional centralized methods. The simulation results of two integrated energy systems validate the effectiveness of the proposed distributed approach. Comparisons with other centralized and distributed optimization methods quantify the advantages of the proposed distributed approach in terms of convergence speed and computation complexity.

High resolution, extreme isotopic variability of precipitation nitrate

Deposition of atmospheric nitrate (NO3−) in precipitation can be an important source of reactive nitrogen (N) to ecosystems, particularly in regions with high nitrogen oxide (NOx = nitric oxide (NO)+nitrogen dioxide (NO2)) emissions. However, high resolution deposition data are lacking for most systems. We conducted hourly precipitation sampling across six growing season storms in a forested area historically subjected to some of the highest levels of chronic N deposition in the United States. To characterize the influence of electricity generating unit (EGU), vehicle, and biogenic NOx emissions on NO3− deposition, we calculated the total NOx emitted from these sources within a 100 km radius of air mass back trajectories determined for Fernow Experimental Forest (West Virginia, USA). We combined these emissions estimates with established 15N isotope values for NOx sources in a three end-member mixing model to predict source-based δ15N values of deposition reaching the study site on an hourly basis. To evaluate the effect of NOx oxidation pathways on measured δ15N-NO3- values, we compared observed hourly isotope values to a coupled δ15N and Δ17O array representing N isotope exchange between atmospheric oxidized N molecules. Within individual events, δ15N, δ18O, and Δ17O values ranged by as much as 19.5‰, 28.9‰, and 13.8‰, respectively. This extreme short-term isotopic variability suggests a dynamic mix of NOx sources, oxidation pathways, and fractionation processes contributing to HNO3 formation. During every storm, precipitation δ15N-NO3- values were lower than those expected to result from predominant HNO3 formation pathways or oxidation of estimated NOx emissions along back trajectories, suggesting a systematic underestimation of NOx contributions to atmospheric HNO3 formation from isotopically depleted soil emissions. Together, these analyses represent the most comprehensive assessment to date relating high temporal resolution δ15N-NO3- observations to NOx emission sources, oxidation chemistry, and isotopic fractionation effects. We present the first observations of extreme intra-storm δ15N, δ18O, and Δ17O variability, emphasizing the need for improved constraints on soil NOx emissions, forest canopy effects, and their role in atmospheric NO3− deposition and isotope dynamics in forests.

Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China

With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously.

Robust stochastic optimal short-term generation scheduling of hydrothermal systems in deregulated environment

Hydrothermal systems play a significant role in electric energy systems as important power generation units, which have been studied in previous researches considering remarkable efforts. The optimal short-term hydrothermal scheduling (STHS) aims to attain optimal production scheduling of thermal and hydro plants for determining minimum operation cost of providing demand in the determined time interval. Different constraints should be studied in the solution of such issue containing limitations associated with water discharge, water storage, power production of plants, power balance of the system and water balance of hydro plants. In addition, valve impacts of thermal plants and complex hydraulic coupling of hydro plants are the other operational and technical constraints. In this study, the robust stochastic STHS is studied considering market price and demand uncertainties. Accordingly, robust optimization method is employed in this study to model price uncertainties. In addition, the uncertainties of load demand are handled using scenario-based modeling procedure. The scheduling problem of hydrothermal system is studied in a deregulated environment, where the hydrothermal system belongs to the private company and is capable to sell the surplus generated power to the market. Accordingly, the company aims to obtain maximum profit by selling power to market in addition to supplying power demand of the company. The introduced scheme for stochastic robust STHS is simulated and the provided solutions are investigated to verify the effectiveness of the scheme.