SSA‐optimized cascade optimal‐PIDN controller for multi‐area power system with RFB under deregulated environment

Abstract

In today's rapidly evolving interconnected electric power system network, ancillary service such as automatic generation control (AGC) plays a critical and important role in safeguarding the system's power supply quality by establishing an equilibrium between generation and load demanded plus losses. A power system with a robust and smart AGC strategy is obligatory for better power quality and system security. Cascade control schemes cope up better with nonlinearity by tuning the gain of controller of power system amid continuous load changing. Therefore, in this article, a supplementary control approach that is, cascade optimal controller‐proportional integral with derivative filter (COC‐PIDN) is suggested for AGC performance advancement of the power systems under deregulated environment. The OC designed consuming full state feedback control approach acts as master controller and PIDN plays the role of slave controller. The nature inspired salp swarm algorithm is employed to adjust the PIDN parameters. Initially, the efficacy of the COC‐PIDN control‐method is explored on a two‐area restructured reheat thermal system and then to validate its excellence and extensibility, the study is protracted to restructured two‐area thermal‐hydro system. Extensive analysis of the findings establishes that the dynamic performance of the COC‐PIDN controller is more valuable than the OC and COC‐PID approaches. The results of COC‐PIDN get further improved in presence of redox flow battery. Finally, the sensitivity analysis as well the acquired results with the COC‐PIDN controller with system nonlinearities demonstrate its robustness.

Enhancing frequency regulation in multi‐area interconnected MPS with virtual inertia using MPC + PIDN controller

“…The ESS has been producing virtual inertia power in both sectors in this study. When the frequency varies, the suggested VI control will direct the necessary power to the i‐th location 10 : $$$$ \Delta {P}_{inertia,i}=\frac{K_{VI,i}}{1+{sT}_{VI,i}}\left[\frac{d\left(\Delta {f}_i\right)}{dt}\right], $$$$ where is the time constant‐based VI for modeling the dynamic control of ESS in the area‐i and is the VI control gain in the area‐i. The value of for a per‐unit system is equal to that of $$$ \Delta f $$$ and $$$ \Delta {f}_i $$$ for the area‐i frequency deviation.…”

“…By incorporating virtual inertia, AGC can effectively regulate grid frequency and balance power between generation and consumption in real-time, even in the presence of fluctuating renewable energy outputs. 10 AGC includes grid frequency control under both regular and irregular operating conditions. Additionally, it involves managing power flow within the tie-lines that connect various regions within the power plant.…”

“…This virtual inertia feature enables RESs, such as solar and wind, to actively participate in load frequency management and grid stability. By incorporating virtual inertia, AGC can effectively regulate grid frequency and balance power between generation and consumption in real‐time, even in the presence of fluctuating renewable energy outputs 10 . AGC includes grid frequency control under both regular and irregular operating conditions.…”