Development of MADB of P‐I controller using LMI technique in a renewable energy based AGC system and study its application in a deregulated environment including energy storage device

Abstract

In recent decades, inclusion of renewable resources is considered as one of the most promising options for the long run uninterrupted power supply without depending on conventional resources. Thus, the renewable energy generation will get more attention and massive growing, so that the goal of 40% share of electricity in the worldwide energy portfolio in 2050 would be realized. But during replacement of renewable energy by conventional energy, engineers are facing a lot of problem due to solar generation, wind generation change their characteristic rapidly with weather condition, which may cause large synchronizing imbalance between different units and generate large system delay or communication delay in large interconnected grid. In this article, the authors propose linear matrix inequalities techniques to developed margin of allowable delay for delay dependent stable hybrid system. Initially, to judge the efficacy of proposed chaotic atomic search optimization (CASO) algorithm over other evolutionary algorithms with PID controller, a thermal‐hydro gas system is considered. The second part of this article is motivated by the fact that proposed CASO algorithm with P‐I controller is superior in contrast to bacterial foraging algorithm technique. In addition to this, some energy storage devices such as fuel cell, aqua electrolyzer, and ultra‐capacitor are used to achieve a better dynamic response within specified delay margin. Moreover, to study the impact of communication delays (delay margin) due to the loss of synchronism between solar‐wind (for their unpredicted environmental features) and thermal unit (nonlinearities like generation rate constraints, boiler dynamics, and governor with the dead band) are considered in thermal unit and the simulation results verify with the effectiveness of the proposed approach on providing a balance between the delay margin and the damping performances is evaluated under deregulated environment. The simulation results help to make an inter‐relation between the delay margin and the controller gain (P‐I) which help the system operator in designing controllers gain for stable operation of the proposed hybrid system.

An Interactive Operating Demand Response Approach for Hybrid Power Systems Integrating Renewable Energy Sources

“…Also electrical grids are progressively moving O towards deregulation, creating a competitive energy market. In a deregulated environment, distribution companies (Disco) may contact directly with generation companies (Gencos) at distinct locations [12].…”

“…16, which highlights the improvement in system dynamics achieved by implementing DR as compared to the response without DR. QOHHO Optimized designed controller coefficients for Scenario-I are reported in Table III and the improvement in the dynamic response of the test power system considering DR strategy is presented in Table Ⅳ. At steady state, the scheduled tie line power can be computed as [12]: )…”

“…GRC of thermal  0.0005 s, Capacity of the first cr1 () P and second areas cr2 () P  2000 MW , 12 1, a  system's anticipated loading  0.5,…”

Hybrid whale optimization algorithm‐scaled cascade fractional order hybrid controller applied to renewable based Electric Vehicle generating systems

“…In most cases, linear controllers have been used in combination with turbine speed tracking system. Classical controllers like PI, 40 PID, 41 LQ & LQG 42 and linear robust controllers have been extensively discussed in various works in different field of engineering operation. Nuclear, hydroelectric and solar power plants, however typically have nonlinear systems components, whereas linear controllers have lower reliability and poor system performance 43 .…”

Optimal control of electrical vehicle incorporated hybrid power system with second order fractional‐active disturbance rejection controller

“…Literature in References 10,32,48,49 have performed GA, PSO, flower pollination‐algorithm and BBO techniques for controller gain optimization respectively. Numerous other optimization techniques namely Davidon–Fletcher–Powell algorithm, elephant‐herding‐optimization, chaotic‐atomic‐search‐optimization, whale‐optimization‐algorithm, Harris‐Hawk‐optimization have been employed for controller tuning in References 50‐54. In Reference 55, the authors present Magnetotactic Bacteria Optimization (MBO), an efficient and precise optimization approach.…”