Load Frequency Control of a Multi-Microgrid System Incorporating Electric Vehicles

• Published in: Electric power components and systems Vol. 49; no. 9-10; pp. 867 - 883
• Main Authors: Khokhar, Bhuvnesh, Dahiya, Surender, Parmar, Krishna Pal Singh
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 15.06.2021; Taylor & Francis Ltd
• Subjects: Algorithms; atom search optimization; Control systems; Controllers; Derivatives; Distributed generation; distributed generation sources; electric vehicle; Electric vehicles; Electrical loads; Energy storage; energy storage systems; Frequency control; Frequency deviation; Integrals; load frequency control; multi-microgrid system; Optimization; renewable energy sources; state-of-charge; Storage systems; TID controller; Vehicle-to-grid
• ISSN: 1532-5008; 1532-5016
• DOI: 10.1080/15325008.2022.2049648

Owing to high cost of conventional energy storage systems, battery of electric vehicles (EVs) is now being considered as their partial replacement to facilitate the demand side response. EVs can act as controllable bidirectional sources to restrain the frequency deviations in power system via vehicle-to-grid control. Consequently, this article proposes a load frequency control (LFC) scheme of a multi-microgrid (MMG) system incorporating EVs. A tilt integral derivative (TID) controller is enforced as the LFC controller in the proposed MMG system. To optimize the gains of the TID controller, a recently developed atom search optimization algorithm is implemented as a novel initiative. Diverse loading patterns that include random, sinusoidal, and pulse load disturbance patterns are considered in the MMG to establish the competence of the proposed control scheme. Simulation results reveal that the proposed LFC scheme enhances the dynamic responses of the MMG system in terms of attenuated oscillations, improved transient specifications and minimized objective function in the frequency and tie-line power deviations, and thus, satisfies the LFC requirements. The results are compared with some standard algorithms. Lastly, sensitivity of the proposed controller is validated subject to ±30% variation in various MMG parameters.