Search

The problem of economic dispatch is one of the significant topics in modern power system control, monitoring, and operation studies due to economic and environmental issues. The multi-area economic dispatch problem (MAED) is the extended version of the economic dispatch problem in modern, and interconnected power systems, especially in competitive environments, which leads to the improvement of power networks economically and technically. The main goal of the MAED problem is to find the optimal amounts of generation and power interchange between adjacent areas by minimizing the generation, and transmission costs, satisfying different operational, and physical constraints governing the problem. This study endeavors to present a comprehensive classification of different techniques, and methods applied to the multi-area economic dispatch problem while reviewing the most prominent studies in this field. Also, it covers comprehensive formulations of the problem and some important issues in the field of probabilistic MAED. Furthermore, some concepts, such as used test systems, and hardware specification are addressed. Finally, suggestions and future directions are highlighted. [Display omitted] • Presenting the formulation of MASED, MADED, RCMAED, RCMAEED, MAEED, and MANCEED. • Classify the methods based on problem-solving, and mechanism handling techniques. • Describing the mathematical-based optimization, and meta-heuristic methods. • Addressing the single-objective, and multi-objective proposed algorithms. • Detailing the probabilistic MAED techniques, and some data of the proposed methods. [ABSTRACT FROM AUTHOR]

The term smart grid refers to a modernization of the electrical network consisting in the integration of various technologies such as dispersed generation, dispatchable loads, communication systems and storage devices which operates in grid-connected and islanded modes. As a result, traditional optimization techniques in new power systems have been seriously influenced during the last decade. One of the most important technical and economical tools in this regard is the Optimal Power Flow (OPF). As a fundamental optimization tool in the operation and planning fields, OPF has an undeniable role in the power system. This paper reviews and compares the OPF approaches mainly related to smart distribution grids. In this work, the main OPF approaches are compared in terms of their objective functions, constraints, and methodologies. Furthermore, computational performances, case study networks and the publication date of these methods are reported. Finally, some basic challenges arising from the new OPF methodologies in smart grids are addressed. [ABSTRACT FROM AUTHOR]

The unit commitment problem is one of the most significant and basic issues in the monitor, control, and operation of modern power systems, which has always been a subject of great concern to researchers and operators as the most extensive human-made system. Before restructuring, one of the main objectives of unit commitment problem was the minimization of the total operation cost of power plants subject to various constraints, including unit and network ones. As the privatization and restructuring process became more serious, the primary purpose of the unit commitment problem has been changed to maximizing the total profit, which led to the emergence of a new concept known as profit-based unit commitment problem. Accordingly, the maximization of the profit for generation companies, all over the studied period, is a top-priority direction. This paper presents a comprehensive overview of the profit-based unit commitment problem in restructured power systems by investigating the most important studies on this topic and providing a complete classification. It also outlines the challenges facing researchers in this field, offers new insights, and suggests future directions. • Describing a comprehensive formulation for profit-based unit commitment problem. • Presenting a complete and comprehensive overview of profit-based unit commitment problem. • Reporting the used case study systems, simulation tools, hardware, and run-time. • Briefly reviewing the fitness functions, parameter setting, convergence criterion, and the obtained profit values. • Addressing the main challenging issues regarding the mentioned problem. [ABSTRACT FROM AUTHOR]