Optimization of dual-stage controllers in renewable energy sources-based interconnected power systems through refinement of the African Vultures Optimization Algorithm

Abstract

With an emphasis on a three-region system, this paper explores automatic generation control within a conventional framework. Area 2 uses thermal-thermal sources, Area 3 uses gas-thermal sources, and Area 1 uses thermal-biodiesel sources. IPD(1 + I) is a unique cascade controller that combines integral-proportional-derivative (IPD) components with one-plus-integral (1 + I) components. To find the best gains and settings for the IPD(1 + I) controller, a meta-heuristic method called the African Vulture Optimization Algorithm (AVOA) is applied. Reducing the integral squared error (ISE), as determined by the performance index (Pi), is the main goal. The performance of the IPD(1 + I) controller is evaluated by contrasting it with a range of secondary controllers. Hybrid Peak Area-ISE (hPA-ISE), a novel performance metric, is also assessed in addition to the conventional ISE. The incorporation of renewable energy sources, such solar thermal energy and solid oxide fuel cells, significantly improves the system’s performance. Interestingly, under normal conditions, the IPD(1 + I) controller’s parameter settings continue to work for both sinusoidal and random load situations, obviating the need for additional tuning.

Keywords

• AGC: Automatic Generation Control
• AVOA: African Vulture Optimization Algorithm
• Bio-diesel sources
• Gas-thermal sources
• hPA-ISE: Hybrid Peak Area-ISE
• ISE: Integral squared error