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Enterprise AI Analysis: Chaos quasi-opposition arithmetic algorithm-based Robust improved frequency regulation for restructured hybrid power system integrating renewable energy sources

Energy Management

Chaos quasi-opposition arithmetic algorithm-based Robust improved frequency regulation for restructured hybrid power system integrating renewable energy sources

This paper introduces a novel 2DOF-TFID-FOPD controller optimized by a Chaotic Quasi-Opposition-Arithmetic Optimization Algorithm (CQOAOA) for enhanced frequency regulation in multi-area power systems with high renewable energy penetration. The proposed method significantly reduces settling time, overshoot, and ITSE values compared to existing controllers, ensuring robust stability under various disturbances and system uncertainties. Experimental validation on an IEEE-118 test system confirms its real-world applicability and effectiveness.

Quantified Impact for Your Enterprise

Implementing the CQOAOA-optimized 2DOF-TFID-FOPD controller can yield significant operational improvements for utility companies managing complex power grids.

0 Settling Time Reduction
0 Overshoot Reduction
0 ITSE Value Improvement

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

The deep integration of renewable energy sources (RESs) and deregulated power market operations has significantly increased frequency instability and tie-line power oscillations in multi-area interconnected power systems. The fundamental AGC controllers (PID, TIDN, and FOTID) exhibit limited adaptability, slower settling response, and higher overshoot under stochastic RESs fluctuations. To address these challenges, a novel kind of two-degree of freedom tilt controller, fractional integral derivative with a fractional order proportional derivative (2DOF-TFID-FOPD) controller, has been created to enhance settling time, undershoot, and overshoot in the instance of high penetration of RESs. 2DOF-TFID-FOPD achieves superior results compared to a basic controller when compared to several contemporary controllers. A novel Chaotic Quasi-Opposition-Arithmetic Optimization Algorithm (CQOAOA) technique has been introduced to optimize the diverse gain parameters of a novel controller. We exhibit the advantages of CQOAOA by comparing it with certain popular meta-heuristic techniques. The proposed controller enhances transient shaping flexibility by integrating tilt and fractional dynamics within a 2DOF framework, while CQOAOA improves global search capability and convergence speed during parameter tuning. Comparative simulation results demonstrate that the proposed strategy reduces settling time by 67.81%, overshoot by 64.98%, and ITSE value by 79.82% compared to optimized 2DOF-TFIDN control. This proposed system has been further analysed by considering the different scenario structures with renewable sources like solar and wind. The research has been escorted undergoing a variety of operating situations, including step, multi, and random disturbances. The efficacy and robustness of the proposed control strategy are further tested and implemented on a large power system, i.e., the IEEE-118 test system, successfully. The comprehensive result analysis of this work delivers convincing verification of the efficacy and efficiency of the proposed control domain in real power systems scenarios, ultimately improving the performance as anticipated. Additionally, the whole setup has been verified using OPAL-RT 4510 experimentally, followed by real-time analysis, making the proposed restructured model of enhanced frequency regulation strategy and its validation attainable.

The 2DOF-TFID-FOPD controller provides combined advantages of two degrees of freedom, tilted derivative action, fractional-order integral action, flexibility, and enhanced dynamic performance for non-integer-order schemes. This allows for simultaneous control of setpoint tracking and disturbance rejection, a significant improvement over conventional PID and TID controllers. The controller's design integrates tilt and fractional dynamics within a 2DOF framework, leading to superior transient shaping and robustness against system uncertainties and renewable energy fluctuations. Its performance is validated through various load patterns, including step, multi-step, and random disturbances.

A novel modified Chaotic Quasi-Opposition Arithmetic Optimization Algorithm (CQOAOA) is proposed to optimize the diverse gain parameters of the 2DOF-TFID-FOPD controller. This algorithm integrates chaotic mapping and quasi-opposition learning to improve exploration and exploitation capabilities, leading to faster convergence and better global search compared to existing meta-heuristic techniques like VPL, HHO, PSO, AOA, QOAOA, and GWO. The CQOAOA's effectiveness is demonstrated by its ability to achieve significantly reduced ITSE values, contributing to the overall stability and performance of the power system.

0 Reduction in Settling Time achieved by proposed controller.

Enterprise Process Flow

Set AOA parameters & initialize population
Initialize QOAOA & CQOAOA
Generate Quasiopposite Solution
Update MOA & MOP with chaotic map
Determine best N solutions
Calculate fitness value of 2N solutions
If r2 > 0.5: Implement Division Operator (D)
Else: Implement Multiplication Operator (M)
If r3 > 0.5: Implement Subtraction Operator (S)
Else: Implement Addition Operator (A)
Increment Iteration Counter
Check Convergence Criteria
Feature Conventional PID/TID 2DOF-TFID-FOPD (Proposed)
Controller Adaptability
  • Limited adaptability to stochastic RES fluctuations
  • Slower settling response
  • Higher overshoot
  • Enhanced adaptability for high RES penetration
  • Superior settling time
  • Reduced overshoot
Transient Shaping Flexibility
  • Basic control actions (P, I, D)
  • Less flexible for complex system dynamics
  • Integrates tilt and fractional dynamics
  • 2DOF framework for setpoint tracking & disturbance rejection
Optimization Capability
  • Relies on traditional/simpler optimization (e.g., FGS)
  • Prone to local minima, slow convergence
  • Optimized by CQOAOA (chaotic, quasi-opposition, arithmetic)
  • Improved global search, faster convergence

IEEE-118 Test System Validation

The proposed CQOAOA-optimized 2DOF-TFID-FOPD controller was rigorously tested on a large-scale power system, the IEEE-118 bus system, which is a standard benchmark for evaluating controller performance in complex network conditions with renewable energy integration. The system incorporated thermal, hydro, and gas units, along with realistic GRC and GDB nonlinearities.

Outcome: The results demonstrated the controller's efficacy and robustness in maintaining frequency stability and regulating tie-line power under various disturbances (step, multi-step, random) and system uncertainties. Experimental validation using OPAL-RT 4510 further confirmed its real-time applicability, successfully improving performance as anticipated in a restructured power system model.

Calculate Your Potential ROI

Estimate the financial and operational benefits of implementing this advanced control strategy in your enterprise.

Estimated Annual Savings $0
Annual Hours Reclaimed 0

Your Implementation Roadmap

A typical phased approach to integrate advanced frequency regulation into your existing infrastructure.

Phase 1: Assessment & Strategy (2-4 Weeks)

Comprehensive analysis of existing grid infrastructure, current control systems, and specific frequency stability challenges. Develop a tailored implementation strategy and define key performance indicators (KPIs).

Phase 2: Controller Integration & Simulation (4-8 Weeks)

Design and integrate the CQOAOA-optimized 2DOF-TFID-FOPD controller into simulation environments (e.g., MATLAB/Simulink, OPAL-RT). Rigorous testing under various load conditions, renewable energy fluctuations, and fault scenarios.

Phase 3: Pilot Deployment & Validation (6-12 Weeks)

Deploy the controller in a controlled pilot environment or a specific sub-area of the grid. Real-time validation against predefined KPIs, fine-tuning parameters, and training operational staff.

Phase 4: Full-Scale Rollout & Optimization (Ongoing)

Gradual expansion of the controller to the entire power system. Continuous monitoring, adaptive learning, and iterative optimization to maximize efficiency, stability, and integration with future grid technologies like FACTS and energy storage.

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