Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy

Ali, Hossam Hassan; Kassem, Ahmed M.; Al‐Dhaifallah, Mujahed; Fathy, Ahmed

doi:10.1109/access.2020.3004299

Cited by 60 publications

(24 citation statements)

References 56 publications

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“…The sooty terns optimizer algorithm (STOA) has been introduced in [39] for optimizing the MPC in LFC systems. Further, multi-verse optimization (MVO) based MPC controller has been proposed in [40]. The ABC optimizer-based terminal siding-more controller (TSMC) has been presented for LFC in [41].…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

et al. 2021

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Several issues have been risen due to the recent vast installations of renewable energy sources (RESs) instead of fossil fuel sources in addition to the replacement of electric vehicles (EVs) for fuel-powered vehicles. Mitigating frequency deviations and tie-line power fluctuations has become driving challenge for the control design of interconnected power systems. RESs represent continuously varying power generators due to their nature and dependency on the environmental conditions. In this context, this article presents a new modified hybrid fractional order controller for load frequency and EVs control in interconnected power systems. The new controller combines the benefits of two widely employed fractional order controllers, including the FOPID and TID controllers. In addition, a new practical application of recent artificial ecosystem optimization (AEO) method has been proposed in this article for determining simultaneously the optimum controller parameters. The proposed controller and optimization method are validated on two areas interconnected power system with different types of RESs and with considering the natural characteristics of sources, EVs and load variations. Obtained simulation results verify the superior performance of the proposed controller and optimization method for achieving high mitigation of frequency fluctuations and tie-line power deviations, increased robustness, enhanced system stability over a wide range of parameters uncertainty and fast response during transients.INDEX TERMS Artificial ecosystem optimization, electric vehicles (EVs), fractional order controller, load frequency control, renewable energy sources.

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Section: A Literature Reviewmentioning

confidence: 99%

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

et al. 2021

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“…The optimized modern controllers have been applied in LFC of interconnected power systems. Model predictive control (MPC) has been optimized using sooty terns optimizer (STO) in [33], [34] and multi-verse optimization (MVO) in [35]. As well, sliding mode control (SMC) has been designed and optimized using ABC in [36], [37].…”

Section: B Literature Reviewmentioning

confidence: 99%

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

et al. 2021

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Energy transition from traditional generation sources into new renewable energy generation has become essential for facing climate changes. However, increased penetration levels of renewable energy sources (RESs) make power systems subjected to low inertia problems. Moreover, the continuously growing numbers of electric vehicles (EVs) have made the substantial need for their contribution in power systems. Therefore, this paper proposes a combined fractional-order controller using the parallel combination of tiltintegral-derivative with filter (TIDF) and hybrid fractional-order (HybFO) controllers for robust frequency regulation in interconnected power systems. The proposed controller is advantageous in combining the merits of two fractional-order controllers that result in more robust and effective load frequency control (LFC) at wide range and different types of disturbances. Furthermore, a new application of marine predator optimization algorithm (MPA) is proposed for simultaneously determining the optimum controller parameters in the different power system areas. The existing EVs contribute in performing additional functionality in power systems. EVs help in damping out the frequency and tie-line power oscillations in the proposed work. The two-area interconnected power system is selected as a case study with the installed photovoltaic (PV), and wind generations in addition to distributed EVs among areas. The obtained results show the superiority and suitability of the proposed controller over the traditional controllers in the literature. Additionally, the effectiveness of the MPA is validated and compared with recent meta-heuristic optimization algorithms.INDEX TERMS Electric vehicles (EVs), interconnected power systems, load frequency control (LFC), marine predator optimization algorithm (MPA), renewable energy sources (RESs).

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“…Centralized MPC is introduced for voltage control in a distributed power system in [38]. However, proper tuning is necessary for MPC parameters including; the prediction horizon, control horizon, sampling time, and the weighting factors to enhance the system performance [39,40]. Artificial intelligence (AI) algorithms can deal with the optimization issue of the controller gains to enhance the system performance [41,42].…”

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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Multi-Verse Optimizer for Model Predictive Load Frequency Control of Hybrid Multi-Interconnected Plants Comprising Renewable Energy

Cited by 60 publications

References 56 publications

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

Optimum Modified Fractional Order Controller for Future Electric Vehicles and Renewable Energy-Based Interconnected Power Systems

Frequency Regulation of Electric Vehicle-Penetrated Power System Using MPA-Tuned New Combined Fractional Order Controllers

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

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