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The purpose of this paper is to solve economic emission dispatch problem in connection of wind with hydro-thermal units.

The proposed hybrid methodology is the joined execution of both the modified salp swarm optimization algorithm (MSSA) with artificial intelligence technique aided with particle swarm optimization (PSO) technique.

The proposed approach is introduced to figure out the optimal power generated power from the thermal, wind farms and hydro units by minimizing the emission level and cost of generation simultaneously. The best compromise solution of the generation power outputs and related gas emission are subject to the equality and inequality constraints of the system. Here, MSSA is used to generate the optimal combination of thermal generator with the objective of minimum fuel and emission objective function. The proposed method also considers wind speed probability factor via PSO-artificial neural network (ANN) technique and hydro power generation at peak load demand condition to ensure economic utilization.

To validate the advantage of the proposed approach, six- and ten-units thermal systems are studied with fuel and emission cost. For minimizing the fuel and emission cost of the thermal system with the predicted wind speed factor, the proposed approach is used. The proposed approach is actualized in MATLAB/Simulink, and the results are examined with considering generation units and compared with various solution techniques. The comparison reveals the closeness of the proposed approach and proclaims its capability for handling multi-objective optimization problems of power systems.

With the inclusion of significant wind power into the power system, the unit commitment (UC) has become challenging due to frequent variations in wind power, load and requirement of reserves with sufficient ramp rate. The pumped storage units with lesser startup time and cost can take care of these sudden variations and reduce their impact on power system operation. The aim of this paper is to provide a solution model for UC problem in a hybrid power system.

The model developed has been implemented through GAMS optimization tool with CONOPT solver. The model has been called into MATLAB platform by using GAMS‐MATLAB interfacing to obtain solutions.

The model provides an efficient operating schedule for conventional units and pumped storage units to minimize operating cost and emission. The effects of wind power and load profiles on emission, operating cost and reserve with enough ramping capabilities have been minimized with the use of pumped storage unit. The commitment schedule of thermal and pumped storage units have been obtained with significant wind power integrated into the system for best cost commitment (BCC) and for a combined objective of cost and emission minimization.

This paper finds that the operating cost and emission in a commitment problem can be reduced significantly during variable wind and load conditions in a hybrid system. The model proposed provides operational schedules of conventional and pumped storage units with variable wind power and load conditions throughout operating horizon. The coordinated optimization approach has been implemented on a hybrid system with IEEE‐30 bus system.

This study aims to analyze the technical, environmental, economic and thermal comfort impacts of implementing passive measures and heating systems in Ciudad Verde, a large-scale social housing project located at the periphery of Bogota, Colombia.

A system dynamics (SD) model is proposed to evaluate scenarios through counterfactual experiments, including technical, environmental and economic components. Model inputs are obtained from building energy simulation models and data collected from official reports, public policy documents and construction records.

Results suggest that the use of heating systems is the best choice to achieve thermal comfort conditions throughout the day. However, both the capital expenditures and CO₂ emissions associated with such system make their adoption very difficult. In line with that, the use of heating systems in combination with passive measures stands out as a viable solution since their costs are affordable and their use contributes to reducing CO₂ emissions.

The proposed model recreates the dynamics underlying social housing construction processes, the adoption of heating systems and passive measures in low-income dwellings and their corresponding impact on CO₂ emissions and indoor thermal comfort conditions. The model can be employed as a support tool in the formulation of social housing policies associated with thermal comfort specifications. In this way, the model represents a first step toward incorporating thermal-related variables into the decision-making processes related to social housing planning and development.

The purpose of the paper is to solve hydrothermal scheduling (HTS) problem for energy-efficient management by allocating the optimal real power outputs for thermal and hydroelectric generators.

HTS can be formulated as a complex and non-linear optimization problem which minimizes the total fuel cost and emissions of thermal generators subject to various physical and operational constraints. As the artificial bee colony algorithm has proven its ability to solve various engineering optimization problems, it has been used as a main optimization tool to solve the fixed-head HTS problem.

A meta-heuristic search technique-based algorithm has been implemented for hydrothermal energy management, and the simulation results show that this approach can provide trade-off between conflict objectives and keep a rapid convergence speed.

The proposed methodology is implemented on the standard test system, and the numerical results comparison indicates a considerable saving in total fuel cost and reduction in emission.

The purpose of this paper is to focus on the cost-effective and environmentally sustainable operation of thermal power systems to allocate optimum active power generation resultant for a feasible solution in diverse load patterns using the grey wolf optimization (GWO) algorithm.

The economic dispatch problem is formulated as a bi-objective optimization subjected to several operational and practical constraints. A normalized price penalty factor approach is used to convert these objectives into a single one. The GWO algorithm is adopted as an optimization tool in which the exploration and exploitation process in search space is carried through encircling, hunting and attacking.

A linear interpolated price penalty model is developed based on simple analytical geometry equations that perfectly blend two non-commensurable objectives. The desired GWO algorithm reports a new optimum thermal generation schedule for a feasible solution for different operational strategies. These are better than the earlier reports regarding solution quality.

The proposed method seems to be a promising optimization tool for the utilities, thereby modifying their operating strategies to generate electricity at minimum energy cost and pollution levels. Thus, a strategic balance is derived among economic development, energy cost and environmental sustainability.

A single optimization tool is used in both quadratic and non-convex cost characteristics thermal modal. The GWO algorithm has discovered the best, cost-effective and environmentally sustainable generation dispatch.

Aims to introduce a self‐adjusting robotic painting process for automotive fuel containers, capable of predicting the required correction action to avoid further defect production.

Presents the development, testing and on‐site implementation of a robotic thermal machine vision system designed for evaluating coat thickness and coverage attributes. Computer simulation is used to study the effect of the painting robot's program on the film build‐up.

Effective technique for the real‐time detection of anti‐corrosive coat's pinholes and pop‐ups. A systematic study for this paint deposition scheme.

The presented detection system and the simulation program methodology could be further studied and modified for other painting applications.

Provides insights validated with on‐site results and systematic study for the automated or the manual adjustments of the robotic painting parameters.

Introduces a novel application of thermal imaging for evaluating coated surfaces. In addition, a first reported case study of automotive fuel container's painting process. Presents potential application to reduce the defects generation thus, improving quality, and reducing production cost.

China has proposed two-stage goals of carbon peaking by 2030 and carbon neutralization by 2060. The carbon emission reduction effect of the power industry, especially the thermal power industry, will directly affect the progress of the goal. This paper aims to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency of the thermal power industry and proposes policy suggestions for realizing China’s carbon peak and carbon neutralization goals.

This paper evaluates and compares the carbon emission efficiency of the thermal power industry in 29 provinces and regions in China from 2014 to 2019 based on the three-stage slacks-based measure (SBM) of efficiency in data envelopment analysis (DEA) model of undesired output, excluding the influence of environmental factors and random errors.

Empirical results show that during the sample period, the carbon emission efficiency of China’s thermal power industry shows a fluctuating upward trend, and the carbon emission efficiency varies greatly among the provincial regions. The carbon emission efficiency of the interregional thermal power industry presents a pattern of “eastern > central > western,” which is consistent with the level of regional economic development. Environmental factors such as economic level and environmental regulation level are conducive to the improvement of carbon emission efficiency of the thermal power industry, but the proportion of thermal power generation and industrial structure is the opposite.

This paper adopts the three-stage SBM–DEA model of undesired output and takes CO₂ as the undesired output to reveal the spatial-temporal characteristics and influencing factors of carbon emission efficiency in China’s thermal power industry. The results provide a more comprehensive perspective for regional comparative evaluation and influencing factors of carbon emission efficiency in China’s thermal power industry.

Recent developments in the electricity generation sector of Nigeria necessitated the re-assessment of its contribution to air emission level in the country as information provided by previous inventory is nearly out-of-date. The purpose of this paper is to quantify the carbon dioxide (CO₂) emissions generated from existing thermal power plants in the country.

Thermal power plants in Nigeria and their installed capacities were identified, and estimation of CO₂ emission from each of the plants was carried out using the emission factor method. In addition to the direct emissions generated through the combustion operation of the power plants, indirect emissions resulting from upstream activities such as extraction, production, and transportation of fuels consumed by the thermal power plant was determined using the same method.

In total, 40 thermal power plants are currently operational in Nigeria. Additional 18 thermal plants are at different stages of completion. The operational thermal plants have average generation output of 40 percent of their installed capacity and produce 87.3 million metric tonne (mmt)/annum CO₂ emissions. In total, 66.9 percent of the estimated emissions are direct emissions, i.e. fuel combustion emissions; the rest are indirect emissions. Additional 67.9 mmt was estimated as expected overall emissions from the thermal power plants under construction. Considering the global warming potential of CO₂, proactive measures must be taken to regulate its emissions from the country’s thermal power plants.

This paper bridged the information gap existing in the emission inventory from the Nigeria electricity sector by providing up-to-date data on the contribution of the sector to greenhouse gas emission level in the country.

Rapidly increasing the proportion of installed wind power capacity with zero carbon emission characteristics will help adjust the energy structure and support the realization of carbon neutrality targets. The intermittency of wind resources and fluctuations in electricity demand has exacerbated the contradiction between power supply and demand. The time-of-use pricing and supply-side allocation of energy storage power stations will help “peak shaving and valley filling” and reduce the gap between power supply and demand. To this end, this paper constructs a decision-making model for the capacity investment of energy storage power stations under time-of-use pricing, which is intended to provide a reference for scientific decision-making on electricity prices and energy storage power station capacity.

Based on the research framework of time-of-use pricing, this paper constructs a profit-maximizing electricity price and capacity investment decision model of energy storage power station for flat pricing and time-of-use pricing respectively. In the process, this study considers the dual uncertain scenarios of intermittency of wind resources and random fluctuations in power demand.

(1) Investment in energy storage power stations is the optimal decision. Time-of-use pricing will reduce the optimal capacity of the energy storage power station. (2) The optimal capacity of the energy storage power station and optimal electricity price are related to factors such as the intermittency of wind resources, the unit investment cost, the price sensitivities of the demand, the proportion of time-of-use pricing and the thermal power price. (3) The carbon emission level is affected by the intermittency of wind resources, price sensitivities of the demand and the proportion of time-of-use pricing. Incentive policies can always reduce carbon emission levels.

This paper creatively introduced the research framework of time-of-use pricing into the capacity decision-making of energy storage power stations, and considering the influence of wind power intermittentness and power demand fluctuations, constructed the capacity investment decision model of energy storage power stations under different pricing methods, and compared the impact of pricing methods on optimal energy storage power station capacity and carbon emissions.

1. Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

2. Investment strategy of energy storage power stations on the supply side of wind power generators.

3. Impact of pricing method on the investment decisions of energy storage power stations.

4. Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

5. A two-stage wind power supply chain including energy storage power stations.

Electricity pricing and capacity of energy storage power stations in an uncertain electricity market.

Investment strategy of energy storage power stations on the supply side of wind power generators.

Impact of pricing method on the investment decisions of energy storage power stations.

Impact of pricing method, energy storage investment and incentive policies on carbon emissions.

A two-stage wind power supply chain including energy storage power stations.

Dynamic economic and emission dispatch (DEED) aims to optimally set the active power generation with constraints in a power system, which should target minimum operation cost and at the same time minimize the pollution in terms of emission when the load dynamically changes hour to hour. The purpose of this study is to achieve optimal economic and emission dispatch of an electrical system with a renewable generation mix, consisting of 3-unit thermal, 2-unit wind and 2-unit solar generators for dynamic load variation in a day. An improved version of a simple, easy to understand and popular optimization algorithm particle swarm optimization (PSO) referred to as a constriction factor-based particle swarm optimization (CFBPSO) algorithm is deployed to get optimal solution as compared to PSO, modified PSO and red deer algorithm (RDA).

Different model with and without wind and solar power generating systems; with valve point effect is analyzed. The thermal generating system (TGs) are the major green house gaseous emission producers on earth. To take up this ecological issue in addition to economic operation cost, the wind and solar energy sources are integrated with the thermal system in a phased manner for electrical power generation and optimized for dynamic load variation. This DEED being a multi-objective optimization (MO) has contradictory objectives of fuel cost and emission. To get the finest combination of the two objectives and to get a non-dominated solution the fuzzy decision-making (FDM) method is used herein, the MO problem is solved by a single objective function, including min-max price penalty factor on emission in the total cost to treat as cost. Further, the weight factor accumulation (WFA) technique normalizes the pair of objectives into a single objective by giving each objective a weightage. The weightage is decided by the FDM approach in a systematic manner from a set of non-dominated solutions. Here, the CFBPSO algorithm is applied to lessen the total generation cost and emission of the thermal power meeting the load dynamically.

The efficacy of the contribution of stochastic wind and solar power generation with the TGs in the dropping of net fuel cost and emission in a day for dynamic load vis-à-vis the case with TGs is established.

Cost and emission are conflicting objectives and can be handled carefully by weight factors and penalty factors to find out the best solution.

The proposed methodology and its strategy are very useful for thermal power plants incorporating diverse sources of generations. As the execution time is very less, practical implementation can be possible.

As the cheaper generation schedule is obtained with respect to time, cost and emission are minimized, a huge revenue can be saved over the passage of time, and therefore it has a societal impact.

In this work, the WFA with the FDM method is used to facilitate CFBPSO to decipher this DEED multi-objective problem. The results reveal the competence of the projected proposal to satisfy the dynamic load demand and to diminish the combined cost in contrast to the PSO algorithm, modified PSO algorithm and a newly developed meta-heuristic algorithm RDA in a similar system.