Abstract
Economic load dispatch (ELD) became recently a mandatory tool for reliable and economic power system operation. ELD improves the security of the power supply while minimizing the generation costs. Daily load curve imposes conflicting requirements on ELD scheme, such as fulfilling demand during peak periods while limiting the cost. Demand response (DR) is an intelligent approach that ensures load demand fulfillment without introducing additional costs. DR allows shifting the electricity consumption to the non-peak periods. Therefore, DR could be considered as a virtual power plant. ELD is usually solved as nonlinear constraint optimization problem. Three simple, fertile, and robust meta-heuristic optimizations are competing in this article for solving ELD incorporating DR as virtual power plant. These are ant lion optimizer (ALO), particle swarm optimization (PSO), and artificial immune system (AIS). The objectives are minimizing the overall cost, system losses, and emission levels. Variety of distinctive study systems are considered to test the functionality and feasibility of the proposed algorithms; they are 6-bus, 30-bus, and 118-bus IEEE systems. MATLAB environment is used for coding ALO, PSO, AIS, and the systems under concern. The comprehensive results show that incorporating DR in ELD reduces the costs/losses/emissions without affecting the power system security and reliability. The comparative results verify the robustness and reliability of ALO in minimizing the overall operating cost and emission levels of the power systems under concern while confining with the constraints. Moreover, they show that ALO has the highest accuracy and convergence efficiency while enjoying the reduced computation storage and execution time.