Modified sub-gradient based combined objective technique and evolutionary programming approach for economic dispatch involving valve-point loading, enhanced prohibited zones and ramp rate constraints

A security constrained non-convex power dispatch problem with prohibited operation zones and ramp rates is formulated and solved using an iterative solution method based on the feasible modified sub-gradient algorithm (FMSG). Since the cost function, all equality and inequality constraints in the nonlinear optimization model are written in terms of the bus voltage magnitudes, phase angles, off-nominal tap settings, and the Susceptance values of static VAR (SVAR) systems, they can be taken as independent variables. The actual power system loss is included in the current approach and the load flow equations are inserted into the model as the equality constraints. The proposed modified sub gradient based combined objective technique and evolutionary programming approach (MSGBCAEP) with as decision variable and cost function as fitness function is tested on the IEEE 30-bus 6 generator test case system. The absence of crossover operation and adoption of fast judicious modifications in initialization of parent population, offspring generation and normal distribution curve selection in EP enables the proposed MSGBCAEP approach to ascertain global optimal solution for cost of generation and emission level shown in Table 6 and displayed in Figure 2 and Figure 3 respectively

Particle Swarm Optimization Technique with Time Varying Acceleration Coefficients for Load Dispatch Problem

Economic load dispatch is a non linear optimization problem which is of great importance in power systems . While analytical methods suffer from slow conversion and curse of dimensionality particle swarm optimization can be an efficient alternative to solve large scale non linear optimization problem.A lot of advancements have been done to modify this algorithm. This paper presents an overview of Classical PSO and then PSO with TVAC. Results are compared first with GA then CPSO and PSO with TVAC. DOI: 10.17762/ijritcc2321-8169.15068

Chaos Firefly Algorithm With Self-Adaptation Mutation Mechanism for Solving Large-Scale Economic Dispatch With Valve-Point Effects and Multiple Fuel Options

This paper presents a new metaheuristic optimization algorithm, the firefly algorithm (FA), and an enhanced version of it, called chaos mutation FA (CMFA), for solving power economic dispatch problems while considering various power constraints, such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options. The algorithm is enhanced by adding a new mutation strategy using self-adaptation parameter selection while replacing the parameters with fixed values. The proposed algorithm is also enhanced by a self-adaptation mechanism that avoids challenges associated with tuning the algorithm parameters directed against characteristics of the optimization problem to be solved. The effectiveness of the CMFA method to solve economic dispatch problems with high nonlinearities is demonstrated using five classic test power systems. The solutions obtained are compared with the results of the original algorithm and several methods of optimization proposed in the previous literature. The high performance of the CMFA algorithm is demonstrated by its ability to achieve search solution quality and reliability, which reflected in minimum total cost, convergence speed, and consistency

Comparison of Constraints Handling Methods for Economic Load Dispatch Problem using Particle Swarm Optimization Algorithm

The main goal of economic load dispatch (ELD) problem is to find an optimal operating condition for the committed generating units in order to minimize total operational cost while satisfying the constraints. The ELD problem becomes more complicated and non-convex when valve point effects of the generator are considered. The penalty function approach (PFA) is widely used to handle the constraints in ELD problem due to simple implementation. However, it requires a proper penalty factor tuning and provides inconsistent result. This paper investigates the performances of modification of infeasible particle (MIP) method based on particle swarm optimization (PSO) for solving ELD problem. The performances of MIP and PFA methods have been compared in terms of optimal result, convergence characteristic and robustness. The proposed MIP and PFA have been tested on three standard test systems (consists of 3, 6 and 40 generating units) to validate their effectiveness. The simulation result confirmed that MIP has better convergence characteristic and more robust compared to PFA. Therefore, the MIP approach can be applied in any optimization algorithm for solving constraint ELD problem effectively