Abstract
This paper presents an algorithm to solve the unit commitment problem in a power system. The proposed algorithm employs the Salp swarm algorithm technique to search the optimum unit schedule for a particular daily demand pattern and specific time horizon. Different constraints are taken into consideration, transition cost (start-up and shut down )cost, mean-up time, mean-down time, spinning reserve, and power balance. The proposed algorithm is applied to 10-units and 26-unit. The obtained results are compared with other methods. It reveals the robustness of the proposed algorithm in terms of minimizing overall running costs.
Highlights
The change in human lifestyle and the tremendous development in large industrial projects in recent years are among the factors that have led to an increase in electrical energy consumption
Artificial intelligence techniques have been applied for solving UCP, evolutionary Programming [11], simulated annealing [12] particle swarm optimization [13], Genetic Algorithm (GA) [14], hybrid PSO, Grey Wolf Optimizer (PSO-GWO)[20], Particle Swarm Optimization (PSO) [21], priority list based evolutionary algorithm (PLEA) [25] and Ant Colony Search Algorithm (ACSA) [19 This paper presents an optimum solution for the unit commitment problem based on Salp Swarm Algorithm [SSA], a proposed optimization technique, was used to solve the unit commitment problem for a 10-unit power system and a 26-unit power system
The results obtained by applying the proposed algorithm Salp Swarm Algorithm is (721182 $/day) at the number of population 20 and maximum iteration 100.computation time was (74.89sec.) Figure 4 shows the cost convergence characteristics of 26 units Table 7 is clarified compares the result obtained from the salp swarm algorithm [SSA] with the other methods this shows there are differences in results and there is improvement and minimizing in the operation cost
Summary
The change in human lifestyle and the tremendous development in large industrial projects in recent years are among the factors that have led to an increase in electrical energy consumption. Under these circumstances, energy companies and investors are struggling to meet the evolution of energy consumption by making a trade-off between maintaining an adequate security margin and supplying the power with a low operational cost. Energy companies and investors are struggling to meet the evolution of energy consumption by making a trade-off between maintaining an adequate security margin and supplying the power with a low operational cost To this end, an optimum unit commitment is aimed to support such activities. The second kinds of constraints are related directly with generating units such as slope limit, slope limit, and limit Lowest to highest and downtime [2]
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