Integrating genetic algorithms, tabu search, and simulatedannealing for the unit commitment problem

This paper presents a new algorithm based on integrating genetic algorithms, tabu search and simulated annealing methods to solve the unit commitment problem. The core of the proposed algorithm is based on genetic algorithms. Tabu search is used to generate new population members in the reproduction phase of the genetic algorithm. A simulated annealing method is used to accelerate the convergence of the genetic algorithm by applying the simulated annealing test for all the population members. A new implementation of the genetic algorithm is introduced. The genetic algorithm solution is coded as a mix between binary and decimal representation. The fitness function is constructed from the total operating cost of the generating units without penalty terms. In the tabu search part of the proposed algorithm, a simple short-term memory procedure is used to counter the danger of entrapment at a local optimum, and the premature convergence of the genetic algorithm. A simple cooling schedule has been implemented to apply the simulated annealing test in the algorithm. Numerical results showed the superiority of the solutions obtained compared to genetic algorithms, tabu search and simulated annealing methods, and to two exact algorithms     

Optimal Sizing and Location of Solar Capacity in an Electrical Network Using Lightning Search Algorithm

Abstract

The transition from centralized power grids to microgrids (MGs) has facilitated the increased integration of renewable energy sources (RES) to the power systems. However, the operational cost in MGs is highly dependent on planning decisions. Therefore, the optimal sizing and placement of RES will offer significant savings in operational costs and reduce power losses in the system. This study proposes a new technique for optimal sizing and placement of solar capacity in the electrical network to minimize the operational cost of conventional generators and real power losses. Due to the complexity and non-linearity associated with the problem, a new natural inspired optimization technique called lightning search algorithm (LSA) is developed to find the optimal solution. To assess LSA, differential evolution algorithm is also used to solve the same problem and a comparison is conducted to prove the superiority of LSA in reducing computation time. The optimization problem is applied to IEEE 14- and 30-bus systems with 24-hr load and solar profile. The obtained results verify the effectiveness of the proposed LSA approach to obtain the optimal sizing and placement of RES and reduce operational cost and power losses.