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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.

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.

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.

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.

The efficiency of the emission trading system (ETS) may help to control the total emission amount. The purpose of this paper is to investigate the generating cost issue in environmental/economic power dispatch, under the premise that the ETS has already been established.

The emission benefit and price level factors are introduced for transforming the bi‐objective optimization problem with the fuel cost and emission cost minimization into a single objective. In the developed mathematical model, both the total emission amount from all units and the permitted emission amount from each generating unit are taken into account. The successive linear programming method is employed to solve the optimization problem.

Simulation results of the IEEE 30‐bus test system show that a proper trading mechanism of emission permits is very important for generation companies to control the total emission amount and to reduce the overall generation cost.

Further research is needed to find out the impact on the generating cost caused by trading price fluctuation and the coping strategies.

The results can help to meet the requirements of current generating optimal dispatch.

The purpose of this paper is to solve the optimal power dispatch problem of thermal generating units with cubic fuel cost and emission functions.

The proposed Simplified Direct Search Method (SDSM) is developed from the Direct Search Method (DSM) that is a prevailing method for solving economic dispatch (ED) problems. The SDSM performs a direct search on solution space that starts with the minimum generation limits and provides the most economical schedule in a single execution for all load demands that the system can meet.

A simple methodology is developed to obtain the optimal dispatches of the generators in a thermal power plant. The results of the proposed methodology illustrate improvements in the savings of total cost and marginal reduction in transmission loss. It is also suitable for solving environmental constrained power dispatch problems. The proposed approach is computationally efficient for large‐scale systems.

A simple methodology has been developed to obtain the real power dispatches of thermal generating units with higher order fuel cost and emission functions.

Trade in contraband amounts to billions of dollars each year, and yet the buyers of these products are still a mystery. The purpose of this study was to model the decision to purchase illicit goods, using four predictor measures: product type, buying situation, perceived criminal risk, and price. Part‐worth conjoint analysis was used to obtain individual weights of main effects and selected interaction effects on the willingness to purchase. Individual respondents evaluated the purchase of illicit goods differently. Cluster analysis was used to segment the respondents. Discriminant analysis was used to assess variable importance. The overall model was shown to be significant. Although the results varied by cluster, the main effects of product type, buying situation and price were all significant predictors of willingness to buy. The interactions of risk with product type and price with product type were also significant predictors for some clusters.

This study aims to present a novel optimization technique to solve the combined economic emission dispatch (CEED) problem considering transmission losses, valve-point loading effects, ramp rate limits and prohibited operating zones. This is one of the most complex optimization problems concerning power systems.

The proposed algorithm has been called advanced particle swarm optimization (APSO) and was created by applying several innovative modifications to the classic PSO algorithm. APSO performance was tested on four test systems having 14, 40, 54 and 120 generators.

The suggested modifications have improved the accuracy, convergence rate, robustness and effectiveness of the algorithm, which has produced high-quality solutions for the CEED problem.

The results obtained by APSO were compared with those of several other techniques, and the effectiveness and superiority of the proposed algorithm was demonstrated. Also, because of its superlative characteristics, APSO can be applied to many other engineering optimization problems. Moreover, the suggested modifications can be easily used in other population-based optimization algorithms to improve their performance.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

The purpose of this paper is to construct option pricing models for real estate development by considering and incorporating institutional arrangements, direct interactions and financial constraints in the model. It extends the application of real option theory from the framework borrowed from financial option pricing, and considers the case where a development company has restrictions from outside environment and financial constraint. It explores the effects of these additional practical factors on real asset project value and development timing. This paper makes contributions to bridge the theoretical models and practical applications.

Real estate development is modelled in the binomial option pricing framework with the considerations of time‐to‐build, foregone rent if delaying, institutional environment and capital budgeting. The investment timings are derived from the models and sensitivity analysis is conducted to explore the effects of these factors.

Apart from the factors in traditional option pricing theory, this paper confirms that the contractual covenants, positive synergies between properties and financial status of the firm, which enhance or restrict real flexibility embedded in the development land, influence project value and investment timing. Numerical examples illustrate the effects of these factors. It is argued that the valuation of real options should place emphasis on industry‐specific characteristics and start from the perspective of the firm rather than individual options.

The models constructed in this paper and the results can be directly used in the practical real estate development.

This paper incorporates many practical factors in real estate development which are not investigated in previous studies. It values the option project from the firm perspective rather than project perspective as previous studies. It also shows the effects of institutional arrangement and firm factors on project value and development timing.