Ant lion optimizer versus particle swarm and artificial immune system for economical and eco‐friendly power system operation

Abstract

Summary 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.

Research on electrical load distribution using an improved bacterial foraging algorithm

“…In Case 1, taking 10 units in a power plant (Basu 2016) as an example, the coal consumption characteristic parameters of the unit and the upper and lower limits of the unit load are shown in the following Table 1: In the case of a 10-unit medium-sized power plant, this paper uses the improved crisscross hybrid bacterial foraging algorithm (ICSBFO), the crisscross algorithm (CSO) (Meng et al, 2015), the bacterial foraging algorithm (BFO), and the particle swarm algorithm (PSO) (Hatata and Hafez 2019) to solve the 10-unit load optimization distribution model of a medium-sized power plant. Since different unit parameters and valve point effect parameters greatly influence the results of unit load economic dispatch, this paper uses the methods proposed by them in their respective articles to simulate the unit parameters of the same case.…”

“…The other group of intelligent algorithms is also gradually being developed is applied to the ELD problems. (Hatata and Hafez 2019) is optimized by the ant lion algorithm (ALO) compared with the PSO algorithm and artificial immune system (AIS). The results found that the ALO in dealing with ELD has higher efficiency and convergence precision.…”

Ant Lion Optimization: Variants, Hybrids, and Applications

“…Also, in [157], [158] authors proposed an approach to solve optimal reactive power dispatch (ORPD). Also, Alazemi and Hatata [159] Considered Demand Response as a Visual Power Plant to solve the Optimum Economic Dispatch problem.. Also, Hatata and Hafez [160] employed ALO, PSO, and AIS in solving ELDP problem by trying to minimize cost, emission levels, and system losses. In [159], another attamp to solve economic dispatch with respect to demand response as a visual power.…”