Ramp-rate limits in unit commitment and economic dispatch incorporating rotor fatigue effect

1994 ◽  
Vol 9 (3) ◽  
pp. 1539-1545 ◽  
Author(s):  
C. Wang ◽  
S.M. Shahidehpour
Keyword(s):  
Unit Commitment ◽  
Economic Dispatch ◽  
Ramp Rate ◽  
Fatigue Effect ◽  
Ramp Rate Limits

scholarly journals Economic Dispatch Solution Using Moth-Flame Optimization Algorithm

2018 ◽  
Vol 214 ◽  
pp. 03007 ◽  
Author(s):  
Mohd Herwan Sulaiman ◽  
Zuriani Mustaffa ◽  
Muhammad Ikram Mohd Rashid ◽  
Hamdan Daniyal
Keyword(s):  
Optimization Technique ◽  
Economic Dispatch ◽  
Minimum Cost ◽  
Ramp Rate ◽  
Total Cost ◽  
Prohibited Operating Zones ◽  
Operating Limits ◽  
Moth Flame Optimization Algorithm ◽  
Ramp Rate Limits ◽  
Practical Constraints

This paper proposes an application of a recent nature inspired optimization technique namely Moth-Flame Optimization (MFO) algorithm in solving the Economic Dispatch (ED) problem. In this paper, the practical constraints will be included in determining the minimum cost of power generation such as ramp rate limits, prohibited operating zones and generators operating limits. To show the effectiveness of proposed algorithm, two case systems are used: 6-units and 15-units systems and then the performance of MFO is compared with other techniques from literature. The results show that MFO is able to obtain less total cost than those other algorithms.

Solving Economic Dispatch Problems with Practical Constraints Utilizing Grey Wolf Optimizer

2015 ◽  
Vol 785 ◽  
pp. 511-515 ◽  
Author(s):  
Lo Ing Wong ◽  
Mohd Herwan Sulaiman ◽  
Mohd Rusllim Mohamed
Keyword(s):  
Canis Lupus ◽  
Economic Dispatch ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Ramp Rate ◽  
Nonlinear Characteristics ◽  
Simulation Results ◽  
Alpha Beta ◽  
Ramp Rate Limits ◽  
Practical Constraints

This paper presents the application of a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus) for solving economic dispatch (ED) problems. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting: searching for prey, encircling prey and attacking prey are implemented. In this paper, GWO was demonstrated and tested on two well-known test systems with practical constraints. A comparison of simulation results is carried out with those published in the recent literatures. The results show that the GWO algorithm is able to provide very competitive results for nonlinear characteristics of the generators such as ramp rate limits, prohibited zone and non-smooth cost functions compared to the other well-known meta-heuristics techniques.

scholarly journals Solving Power Economic Dispatch Problem with a Novel Quantum-Behaved Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Li Ping ◽  
Jun Sun ◽  
Qidong Chen
Keyword(s):  
Power Systems ◽  
Local Search ◽  
Optimization Algorithms ◽  
Economic Dispatch ◽  
Ramp Rate ◽  
Solution Quality ◽  
Nonlinear Features ◽  
Gaussian Probability Distribution ◽  
Ramp Rate Limits ◽  
Search Capability

This paper proposes the shrink Gaussian distribution quantum-behaved optimization (SG-QPSO) algorithm to solve economic dispatch (ED) problems from the power systems area. By shrinking the Gaussian probability distribution near the learning inclination point of each particle iteratively, SG-QPSO maintains a strong global search capability at the beginning and strengthen its local search capability gradually. In this way, SG-QPSO improves the weak local search ability of QPSO and meets the needs of solving the ED optimization problem at different stages. The performance of the SG-QPSO algorithm was obtained by evaluating three different power systems containing many nonlinear features such as the ramp rate limits, prohibited operating zones, and nonsmooth cost functions and compared with other existing optimization algorithms in terms of solution quality, convergence, and robustness. Experimental results show that the SG-QPSO algorithm outperforms any other evaluated optimization algorithms in solving ED problems.

Solution of Non-Smooth Economic Dispatch Using Interactive Grouped Adaptive Bat Algorithm

2019 ◽  
Vol 8 (1) ◽  
pp. 88-114 ◽  
Author(s):  
Belkacem Mahdad
Keyword(s):  
Power Loss ◽  
Economic Dispatch ◽  
Bat Algorithm ◽  
Total Power ◽  
Ramp Rate ◽  
Solution Quality ◽  
Search Mechanism ◽  
Metaheuristic Methods ◽  
Valve Point Effect ◽  
Ramp Rate Limits

This article presents the application of new grouped adaptive Bat algorithm (GABA) based metaheuristic method to improve the solution of economic dispatch (ED) problem considering valve point effect, prohibited zones, ramp rate limits and total power loss. The Bat algorithm is a new swarm intelligence algorithm inspired by the echolocation phenomenon in bats. The Bat algorithm is easy to program, and like many metaheuristic methods has an exploration and exploitation phases which require fine adjustment to achieve the near global solution. A grouped search mechanism is introduced to enhance the performances of the original Bat algorithm. The robustness of the proposed algorithm in term of solution quality and convergence characteristic have been demonstrated of three test systems of various complexities 6 units considering simultaneously the prohibited zones, ramp rate limits and total power loss, 13 and 40 units considering valve point effect. Results show clearly the efficiency and superiority of the proposed algorithm compared with various techniques reported in the recent literature.

scholarly journals Application of BARON Solver for Solution of Cost Based Unit Commitment Problem

2020 ◽  
Vol 12 (4) ◽  
pp. 807-827
Author(s):  
Vineet Kumar ◽  
◽  
R Naresh ◽  
Keyword(s):  
Power Plants ◽  
Unit Commitment ◽  
Thermal Power ◽  
Operating Cost ◽  
Optimal Solution ◽  
Ramp Rate ◽  
Unit Commitment Problem ◽  
Commitment Problem ◽  
Ramp Rate Limits ◽  
Algebraic Modelling

This paper presents the solution to cost-based unit commitment (CBUC) problem with and without ramp rate limits of thermal power plants using general algebraic modelling system (GAMS) with BARON solver. The BARON solver in GAMS environment takes care of different units and system constraints to find an optimal solution. To validate the effectiveness of the proposed GAMS solution, simulations have been performed on six different systems consisting of 10-units, 20-units, 40-units, 60-units, 80-units and 100-units, respectively. The analysis also includes the valve-point loading along with the ramp rate limits of thermal units. Results obtained with BARON solver in GAMS have been compared with other approaches available in literature. Comparative analysis shows that the performance of GAMS is better as compared to other existing techniques in terms of operating cost obtained and satisfaction level of constraints.

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