[Paper Review] A New Approach to the Solution of Economic Dispatch Using Particle Swarm Optimization with Simulated Annealing

V. Karthikeyan, S. Senthilkumar+1 more|arXiv (Cornell University)|Jul 11, 2013

Electric Power System Optimization4 citations

TL;DR

This paper proposes a hybrid metaheuristic approach combining Particle Swarm Optimization (PSO) and Simulated Annealing (SA) to solve the Economic Dispatch (ED) problem in power systems. By integrating PSO's global search capability with SA's local search refinement, the method achieves lower fuel costs and faster convergence than conventional techniques, as validated through a three-unit test case in MATLAB.

ABSTRACT

A new approach to the solution of Economic Dispatch using Particle Swarm Optimization is presented. It is the progression of allocating production amongst the dedicated units such that the restriction forced are fulfilled and the power needs are reduced. More just, the soft computing method has received supplementary concentration and was used in a quantity of successful and sensible applications. Here, an attempt has been made to find out the minimum cost by using Particle Swarm Optimization Algorithm using the data of three generating units. In this work, data has been taken such as the loss coefficients with the max-min power limit and cost function. PSO and Simulated Annealing are functional to put out the least amount for dissimilar energy requirements. When the outputs are compared with the conventional method, PSO seems to give an improved result with enhanced convergence feature. All the methods are executed in MATLAB environment. The effectiveness and feasibility of the proposed method were demonstrated by three generating units case study. Output gives hopeful results, signifying that the projected method of calculation is competent of economically formative advanced eminence solutions addressing economic dispatch problems.

Motivation & Objective

• To address the economic dispatch problem in power systems by minimizing fuel cost while satisfying operational constraints.
• To overcome the limitations of conventional methods and standard PSO in converging to global optima for non-convex ED problems.
• To enhance the performance of PSO by integrating Simulated Annealing for improved local search and avoidance of local minima.
• To validate the effectiveness of the hybrid PSO-SA approach using a standard three-generator unit test system.
• To demonstrate the feasibility and efficiency of the proposed method in achieving economically optimal power generation dispatch.

Proposed method

• The hybrid algorithm combines Particle Swarm Optimization (PSO) with Simulated Annealing (SA) to improve convergence and global optimization capability.
• PSO is used for global exploration of the solution space, leveraging particle velocity and position updates based on individual and global best solutions.
• Simulated Annealing is integrated to refine solutions by allowing occasional uphill moves, reducing the risk of premature convergence to local optima.
• The algorithm iteratively updates particle positions and velocities using the standard PSO equations, while SA controls the acceptance of new solutions based on a temperature-dependent probability.
• Constraints such as generator output limits and loss coefficients are incorporated into the fitness function to ensure feasible solutions.
• The entire algorithm is implemented and tested in the MATLAB environment using a three-generator case study.

Experimental results

Research questions

• RQ1 Can the integration of Simulated Annealing with Particle Swarm Optimization improve the convergence and solution quality for the economic dispatch problem?
• RQ2 How does the hybrid PSO-SA method compare to conventional methods in terms of fuel cost reduction and convergence speed?
• RQ3 To what extent does the hybrid approach avoid local minima in non-convex economic dispatch problems?
• RQ4 Is the proposed method robust and effective for small-scale power systems with practical operational constraints?
• RQ5 What is the impact of incorporating loss coefficients and generator limit constraints on the performance of the hybrid algorithm?

Key findings

• The proposed PSO-SA hybrid method achieved a lower total fuel cost compared to conventional methods in the three-unit test system.
• The algorithm demonstrated enhanced convergence behavior, reaching optimal or near-optimal solutions more reliably than standard PSO.
• The integration of Simulated Annealing significantly reduced the likelihood of getting trapped in local minima, improving solution robustness.
• The method successfully satisfied all operational constraints, including generator output limits and loss coefficients.
• The results from the MATLAB simulation confirmed the feasibility and effectiveness of the hybrid approach for solving economic dispatch problems.
• The case study showed that the proposed method is capable of producing economically superior and high-quality dispatch solutions.