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The purpose of this paper is to solve commitment problem of generating units in thermal power plants and to find the optimal dispatches of the committed units.

The unit commitment (UC) problem has been solved in two stages. In the first stage, the optimal units are identified using contribution factor. Initially, the generating units to be committed for each interval in the time horizon are obtained without considering the unit operational constraints such as minimum up time, minimum down time and initial state. Then the unit operational constraints are enforced and the optimal UC schedule is obtained. In the second stage, sequential approach with a matrix framework has been proposed to obtain the optimal dispatches of the committed units.

The simple methodologies have been developed for unit selection and to find the optimal dispatches of the committed units. The results of proposed methodology illustrate an improvement in the savings of total cost. The proposed approach is computationally efficient for solving large‐scale systems and successive UC problems.

UC has a major role in electric thermal power plant operation. The problem with one day and one week scheduling horizon has a large potential of use, especially for small‐ and medium‐scale power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation. The techniques developed for UC problem will provide a support to electric power companies for their economic operation and the concepts presented are useful in both graduate teaching and research to understand the UC problem.

The contribution of the paper is the simple methodologies which have been developed for unit selection and economic dispatch.

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 purpose of this paper is to solve the problem of committing electric power generators (unit commitment, UC), considering network constraints.

The UC is first solved with a local search based meta‐heuristic, following the assumption that all generators and loads are connected to a single network node. For evaluation purposes, the economical production levels of the units committed are computed by running a pre‐dispatch algorithm where network constraints are not included. If a good quality solution is reached, an economic dispatch (ED) with network constraints is performed, where the geographic location of generators and loads are considered. Therefore, the production level of each committed generator is performed that leads to the global lowest solution cost, regarding both the generators' costs and constraints and the power system network constraints.

The algorithm proposed is computationally efficient, given the time available for decision making. In addition, the solution for this algorithm, in terms of minimization of total costs, is generally better than the solution of the two phases approach. Some contractual and legal aspects related with the injection in network connections can also be included in the model.

UC with network constraints has a large potential of use, especially for small and medium size power systems. It reflects reality in a closer way and provides a more complete and realistic knowledge about the system in operation.

The paper presents an approach where the ED with network constraints is integrated with the UC procedure. The model described is currently implemented in an EMS package offered in the market – making it a case of successful transfer from science to industry.

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.

The purpose of this paper is to present a new system frequency response model with participation of wind-hydro-thermal units to overcome frequency deviations.

The extracted minimum frequency equation is considered as a constraint in security-constrained unit commitment calculations. Because of high-order polynomials in the frequency transfer function and high degree of nonlinearity of minimum frequency constraint, Routh stability criterion method and piecewise linearization technique are used to reduce system order and linearize the system frequency response model, respectively.

The results of this paper indicate that by using this model, the hourly minimum frequency is improved and is kept within defined range.

This combined model can be used to evaluate the frequency of the power system following unexpected load increase or generation disturbances. It also can be used to investigate the system frequency performance and ensure power system security which are caused by peak load or loss of generation in presence of renewable energies.

Generation scheduling (GS) is the most prominent and hard-hitting problem in the electrical power industry especially in an integrated power system. Countless techniques have been used so far to solve this GS problem for proper functioning of the units in the power system to dispatch the load economically to consumers at once. Therefore, this work aims to study for the best possible function of integrated power plants to obtain the most favourable solution to the GS problem.

An appropriate method works in a proper way and assures to give the best solution to the GS problem. The finest function of incorporated power plants should be mathematically devised as a problem and via that the aim of the GS problem to minimize the total fuel cost subject to different constraints will be achieved. In this research work, the latest meta-heuristic and swarm intelligence-based technique called grey wolf optimization (GWO) technique is used as an optimization tool that will work along with the formulated problem for correct scheduling of generating units and thus achieve the objective function.

The recommended GWO technique provides the best feasible solution which is optimal in its performance for different test cases in the GS problem of integrated power plant. It is further found that the obtained solutions using GWO method are better than the former reports of other traditional methods in terms of solution excellence. The GWO method is found to be unique in its performance and having superior computational efficiency.

Decision making is significant for effective operation of integrated power plants in an electrical power system. The recommended tactic implements a modern meta-heuristic procedure that is applied to diverse test systems. The method that is proposed is efficient in providing the best solutions of solving GS problems. The suggested method surpasses the early techniques by offering the most excellent feasible solutions. Thus, it is obvious that the proposed method may be the appropriate substitute to attain the optimal operation of GS problem.

Renewable energy sources are discontinuous and infrequent in nature, and it is tough to predict them in general. Further, integrating renewable energy source-based plants with the conventional plant is extremely difficult to operate and maintain. Operation of integrated power system is full of challenges and complications. To handle those complications and challenges, the GWO algorithm is suggested for solving the GS problem and thus obtain the optimal solution in integrated power systems by considering the reserve requirement, load balance, equality and inequality constraints.

The proposed system should be further tested on diverse test systems to evaluate its performance in solving a GS problem and the results should be compared. Computation results reveal that the proposed GWO method is efficient in attaining best solution in GS problem. Further, its performance is effectively established by comparing the result obtained by GWO with other traditional methods.

The purpose of this study is to solve the optimal operation strategy problem of plug-in electric vehicles (PEV) parking as a demand response (DR) program and hydro storage as an energy storage system in a smart grid environment using a heuristic algorithm.

Studying the smart grid with DR, renewable energy resources and energy storage systems is necessary. To do this, the heuristic optimization algorithm is developed to solve the scheduling problem. This deterministic algorithm benefits from the definition of appropriate fitness functions.

For validation, it is shown that reduction of 1.1%–12.5% in pollution and 8.8%–34.8% in total cost are achieved, as compared with literature. Also, the suggested operation strategy of PEVs parking and hydro storage results in reducing the total cost by 6.21%.

DR programs such as PEV parking play a major role in smart grid developments. Also, energy storage systems such as hydro storage lead to better performance of distributed generations and lower costs and pollution by thermal units. However, based on the literature, the effects of PEV parking and hydro storage on smart grid operation strategy are not considered. Therefore, contributions of this study are: effects of hydro storage on the smart grid are considered, effects of PEV parking on the smart grid are considered, a heuristic algorithm is developed to solve operation strategy problem for PEV parking and hydro storage in a smart grid environment.

The purpose of this paper is to illustrate how emission constraints imposed by the emission trading scheme (ETS) in the European Union, as well as transmissions capacity, can affect the outcome of the generation scheduling. The aim is to demonstrate the application of the generation scheduling tool which includes both the ETS and transmission constraints, and helps evaluate their effect on emission reduction, costs, and generators' behavior and availability. It can also be used to help generators make strategic decisions regarding utilization and purchases of carbon allowances.

The paper extends the generation scheduling formulation to allow for additional constraints modeling. The formulation is based on the mixed integer programming approach with linearization of generation cost and emission functions, and the possibility to split the system into zones in order to investigate transmission congestion.

The paper presents six case studies that include unconstrained and constrained operation, both from the emission and transmission points of view. It also illustrates the effect of free allocations versus auctioning. The case studies look into the system with wind generation that can be constrained due to transmission limits, and their impact on emission reductions. This is often the case in systems where most of the wind generation is located in the area which does not have sufficiently strong links to the rest of the system where the majority of loads are.

The extension of the work will be inclusion of stochastic nature of emission prices and wind availability. It will also be used for further studies on systems with high wind penetration and insufficient transmission capacity.

The generation scheduling tool and the results from the paper could be useful for generators when making decisions on how to use or purchase their emission allocations, as well as for evaluation of the adverse affect of transmission congestion on carbon emission reductions.

This piece focuses on the microprocesses of decisions. It distills the complex cognitive processes inherent in decision making into pragmatic utility by articulating several “games”. These games, as they are described in the article, routinely undermine the best‐intentioned proposals, initiatives, strategies and good ideas as they are played in ways that elude most participants. The cognitive decision processes (dubbed “games” here) are described as: Framing; Criteria setting; Misuse of analogy; Misuse of rationality; and Commitment building. The key purpose of the article is to distill complex processes into manageable dialect to improve awareness. Argues that awareness and understanding of these processes is essential to achieve influence in decision making, to avoid the pitfalls of fuzzy choice, and to promote mastery over the decision‐making process.

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.