Effect of energy storage systems on automatic generation control of interconnected traditional and restructured energy systems

Abstract

Summary During major disturbances in electric power system (PS) penetrated with renewable energy sources, primary and supplementary automatic generation control (AGC) strategies usually show inefficiency in mitigating the frequency and power oscillations because of sluggish control action. The frequency and power deviations should be controlled to retain the generation‐demand balance, which reinforce the quality and stability of overall PS. The fast‐acting energy storage systems (ESSs) having very small time constants like capacitive energy storage (CES) and redox flow battery (RFB) are utilised in this study to improve these dynamic responses. To conduct the analysis, initially, a two‐area nonreheat thermal PS with extra generations from wind turbine system (WTS) and dish‐stirling solar thermal system (DSTS) is explored extensively, and then to validate the efficacy of the method, the approach is tested on two‐area nonreheat thermal system having governor deadband (GDB) nonlinearity, reheat thermal, and restructured multisource thermal gas systems. An imperialist competition algorithm (ICA) optimised fuzzy PID‐filter‐(1 + PI) controller named as FPIDF‐(1 + PI) is utilised as supplementary controller, and its performance with CES/CES‐RFB is compared with ICA‐optimised FPIDF with/without CES and existing optimal PI/PID/PIDF/FPID controller without CES. Investigation of dynamic responses for sudden variation in power demand unveils the superiority of the control approach compared with others regarding settling time, peak undershoot, and performance index. Analysing the impact of ESSs on the responses divulges that the amalgamation of CES‐RFB in PS imparts better system dynamics. The robustness analysis suggests that ICA‐optimised controller with ESSs performs excellently and robustly for ±25% variation in PS parameters, random load disturbances, and nonlinearities.

Impact of ultra‐capacitor on automatic generation control of electric energy systems using an optimal FFOID controller

“…Artificial neural network (ANN) 13 and fuzzy logic controller (FLC) 4,[14][15][16][17][18] are helpful to offer enhanced system performance at different operating conditions. Artificial neural network (ANN) 13 and fuzzy logic controller (FLC) 4,[14][15][16][17][18] are helpful to offer enhanced system performance at different operating conditions.…”

“…Artificial neural network (ANN) 13 and fuzzy logic controller (FLC) 4,[14][15][16][17][18] are helpful to offer enhanced system performance at different operating conditions. Lately, investigators have proposed several smart optimization methods like ICA 16,17 to tune fuzzy PI/PIDF-(1 + PI) and BFOA to tune fuzzy PI/PID 18 controllers in PSs. Furthermore, FLC is easy to apply on real systems.…”

“…9,16,17,21,22 It is enthused by the human social development idea. 9,16,17,21,22 It is enthused by the human social development idea.…”

“…9,16,17,21,22 It is enthused by the human social development idea. 9,16,17,21,22 The versatility and superior performance of ICA motivated to explore its implementation in this study to tune the suggested fuzzy fractional order integral derivative (FFOID) controller for various electric systems. 9,16,17,21,22 The versatility and superior performance of ICA motivated to explore its implementation in this study to tune the suggested fuzzy fractional order integral derivative (FFOID) controller for various electric systems.…”

“…,16 the presented study utilized a new FFOID controller for various PSs with/without UC. The preeminent objectives and novelties of the current work are as follows:…”