Large-scale economic dispatch by genetic algorithm

1995 ◽  
Vol 10 (4) ◽  
pp. 1919-1926 ◽  
Author(s):  
Po-Hung Chen ◽  
Hong-Chan Chang
Keyword(s):  
Genetic Algorithm ◽  
Large Scale ◽  
Economic Dispatch

Modern Methods of Optimization in Models of Hydrothermal Coordination and Emission of Contaminating Particles in Power-Generating Plants

2020 ◽  
pp. 353-376
Author(s):  
Gilberto Pérez Lechuga ◽  
Ugo Fiore ◽  
Francisco Martínez Sánchez
Keyword(s):  
Genetic Algorithm ◽  
Customer Service ◽  
Large Scale ◽  
Random Search ◽  
Economic Dispatch ◽  
Mathematical Optimization ◽  
Hydrothermal Coordination ◽  
Modern Methods ◽  
Hydroelectric Plants

The problem of hydrothermal coordination is a classic example of nonlinear and large-scale dynamic mathematical optimization that involves the problem of the economic dispatch plus other constraints associated with the coordination of a pair of systems integrated into a network (i.e., the production of energy from thermoelectric and hydroelectric plants). A large number of methods and models that represent this problem, from classical methods of optimization to modern methods based on metaheuristics, exist. In this chapter, the classic structure of the problem is analyzed and new constraints are proposed, including those based on the level of customer service. Some recent modern formulations of the model are shown, and the existing literature is reviewed showing the modern methods of optimization based on heuristics. The authors illustrate it by optimizing an instance consisting of three thermo plants and three hydro plants through random search and genetic algorithm.

scholarly journals A Novel Heap based Optimizer for Scheduling of Large-scale Combined Heat and Power Economic Dispatch

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed R. Ginidi ◽  
Abdallah M. Elsayed ◽  
Abdullah M. Shaheen ◽  
Ehab E. Elattar ◽  
Ragab A. El-Sehiemy
Keyword(s):  
Large Scale ◽  
Economic Dispatch ◽  
Combined Heat And Power ◽  
Power Economic

scholarly journals Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

2021 ◽  
Vol 13 (3) ◽  
pp. 1274
Author(s):  
Loau Al-Bahrani ◽  
Mehdi Seyedmahmoudian ◽  
Ben Horan ◽  
Alex Stojcevski
Keyword(s):  
Large Scale ◽  
Thermal Power ◽  
Optimization Technique ◽  
Economic Dispatch ◽  
Pso Algorithm ◽  
Search Space ◽  
Economic Benefits ◽  
Optimization Techniques ◽  
Ramp Rate ◽  
Dynamic Economic Dispatch

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

Health monitoring sensor placement optimization based on initial sensor layout using improved partheno-genetic algorithm

2020 ◽  
pp. 136943322094719
Author(s):  
Xianrong Qin ◽  
Pengming Zhan ◽  
Chuanqiang Yu ◽  
Qing Zhang ◽  
Yuantao Sun
Keyword(s):  
Genetic Algorithm ◽  
Health Monitoring ◽  
Large Scale ◽  
Complex Structure ◽  
Sensor Placement ◽  
Placement Problem ◽  
Optimal Sensor Placement ◽  
Placement Optimization ◽  
Sensor Placement Optimization ◽  
Sensor Locations

Optimal sensor placement is an important component of a reliability structural health monitoring system for a large-scale complex structure. However, the current research mainly focuses on optimizing sensor placement problem for structures without any initial sensor layout. In some cases, the experienced engineers will first determine the key position of whole structure must place sensors, that is, initial sensor layout. Moreover, current genetic algorithm or partheno-genetic algorithm will change the position of the initial sensor locations in the iterative process, so it is unadaptable for optimal sensor placement problem based on initial sensor layout. In this article, an optimal sensor placement method based on initial sensor layout using improved partheno-genetic algorithm is proposed. First, some improved genetic operations of partheno-genetic algorithm for sensor placement optimization with initial sensor layout are presented, such as segmented swap, reverse and insert operator to avoid the change of initial sensor locations. Then, the objective function for optimal sensor placement problem is presented based on modal assurance criterion, modal energy criterion, and sensor placement cost. At last, the effectiveness and reliability of the proposed method are validated by a numerical example of a quayside container crane. Furthermore, the sensor placement result with the proposed method is better than that with effective independence method without initial sensor layout and the traditional partheno-genetic algorithm.

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