Open AccessEnhancing load frequency control of multi-area multi-sources power system including conventional and renewable energy units with nonlinearities
Author(s) -
Mohamed Abdul Raouf Shafei,
Ahmed Nabil Abd Alzaher,
Doaa Khalil Ibrahim
Publication year - 2020
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v19.i1.pp108-118
Subject(s) - control theory (sociology) , automatic generation control , automatic frequency control , pid controller , renewable energy , electric power system , diesel generator , controller (irrigation) , wind power , control engineering , engineering , computer science , power (physics) , automotive engineering , diesel fuel , control (management) , temperature control , telecommunications , agronomy , physics , electrical engineering , quantum mechanics , artificial intelligence , biology
The foremost aims the Load Frequency Control (LFC) is to maintain the frequency at nominal value and minimize the unscheduled tie line power flow between different control areas. The penetration of renewable energy sources into the grid is a recent challenge to the power system operators due to their different modelling rather than conventional units. In this paper, enhancing load frequency control of multi-area multi-sources power system including renewable units system with nonlinearities is proposed using a new application of proportional–integral–derivative controller with proportional controller in the inner feedback loop, which is called as PID-P controller. To investigate the performance of the proposed controller, a thermal with reheater, hydro, wind and diesel power generation units with physical constraints such as governor dead band, generation rate constraint, time delay and boiler dynamics are considered. The proposed controller parameters are optimized using different heuristic optimization techniques such: Linearized Biogeography-Based Optimization technique, Biogeography-Based Optimization technique and Genetic Algorithm. The ability of the system to handle the large variation in load conditions, time delay, participation factors, and system parameters has been verified comprehensively.