Mitigating the Load Frequency Fluctuations of Interconnected Power Systems Using Model Predictive Controller

Abstract

The penetration of renewable energy sources into the conventional power systems are evolving day by day. Therefore, in this paper, a photovoltaic (PV) connected thermal system is discussed and analyzed by keeping PV to operate at maximum power point (MPP). The main problem in the interconnection of these systems is load frequency fluctuations due to different load changing conditions. The model predictive controller (MPC) has the ability to predict the target value at real-time with fast convergence. Therefore, MPC is proposed to negate this problem by giving minimum oscillation. The comparison analysis is carried out with other conventional controllers, including genetic algorithm-based PI, firefly algorithm-based PI and PI controller. Simulation results clearly exhibit the outclass performance of MPC over all other controllers.

Variable structure model predictive controller based gain scheduling for frequency regulation in renewable based power system

“…Furthermore, the designing of the converter to operate PV at MPP and designing of the inverter which converts DC to AC power to make system compatible for interconnection with the thermal system is also modeled. While designing, the PV system consist of 30 kW linked arrays with a constant voltage source of at PV array side and where the MPP is raised up to 4.5MW 30 …”

“…Gain between AC and DC voltage can be calculated using Equation (1) 30 Where, is known as the gain for AC and DC voltage in the system and the value of Z is generally less than .…”

Impact of Renewable Energy Sources into Multi Area Multi-Source Load Frequency Control of Interrelated Power System

“…In this model, altered parametric standards are used for generator regulation and for turbine-governor to shelter the diversity of production categories in the control area. The expressed components and blocks are defined as follows: ΔPm is mechanical power, Δf is frequency deviation, ΔPL is load disturbance, ΔPC is supplementary frequency control action, DSys is equivalent damping coefficient, HSys is equivalent inertia constant, βis frequency bias, αi is participation factor, Riis drooping characteristic, ΔPP is primary frequency control action, ΔPRESis renewable energy source power fluctuation, Mi(s) is a governor-turbine model, ACEis area control error [24], and finally, ΔP′L and ΔP′tie are amplified local load alteration and tie-line power vacillation signals, respectively. In the revised LFC structure, the efficient ACE signal must signify the influences of renewable energy on planned stream over tie-line and local power variation through area frequency.…”

Adaptive fuzzy based optimized proportional‐integral controller to mitigate the frequency oscillation of multi‐area photovoltaic thermal system

“…Recently advanced controllers are reported to have better control performance 29,30 . Keeping the influence of renewable source in a context in disrupting the frequency different control strategies have been applied for multi‐area PV‐thermal system.…”

“…Keeping the influence of renewable source in a context in disrupting the frequency different control strategies have been applied for multi‐area PV‐thermal system. MPC was introduced to control the LFC problem 29 . Furthermore, the genetic and firefly based tuned (GA‐PI), (FA‐PI) controllers are used to control LFC problem under PV‐thermal system these controllers are optimizing PI controller in order to minimize the frequency oscillations 30 .…”