Comparison Investigation into Power System Optimization and Constraint-Based Generator Load Scheduling Using Metaheuristic Algorithms

In this paper, a novel flower pollination algorithm (FPA) is implemented to solve the problem of combined economic emission dispatch (CEED) in the power system. The FPA is a new metaheuristic optimization technique, which takes a biological approach to flower pollination. The FPA mimics the characteristics of flower pollination according to the survival of the fittest concept. CEED represents a combination of the emission and economic dispatch functions, formulated into a single function using the penalty factor. In this paper, the effect of valve point loading in the power system network is considered to obtain minimum fuel cost, minimum emissions, and optimum power generation. The performance of the proposed algorithm is evaluated using two test systems, namely 10 and 14 generating units by contemplating the valve point loading effect as well as transmission loss. The results of the 10 and 14 system units are compared with a learning-based optimization technique to demonstrate the effectiveness of the FPA. The findings reveal that the proposed FPA gives better performance than other algorithms with minimum fuel cost and emissions.

A New Method of Artificial to Solve the Optimization Problems without the Violated Constraints

There are some problems in optimization that cannot be derived mathematically. Various methods have been developed to solve the optimization problem with various functional forms, whether differentiated or not, to overcome the problem, which are known as artificial methods such as artificial neural networks, particle swarm optimization, and genetic algorithms. In the literature, it is said that there is an artificial method that frequently falls to the minimum local solution. The local minimum results are proof that the artificial method is not accurate. This paper proposes the Large to Small Area Technique for power system optimization, which works based on reducing feasible areas. This method can work accurately, which that never violates all constraints in reaching the optimal point. However, to conclude that this method is superior to others, logical arguments and tests with mathematical simulations are needed. This proposed method has been tested with 24 target points using ten functions consisting of a quadratic function and a first-order function. The results showed that this method has an average accuracy of 99.97% and an average computation time of 62 seconds. The proposed technique can be an alternative in solving the economic dispatch problem in the power system.