Distributed Generation Planning in Multi-Energy Microgrids

^* New Prince Shri Bhavani college of Engineering and Technology, Anna University, nksundar@newprinceshribhavani.com
^† College of technical engineering, The Islamic university, Najaf, Iraq .
^‡ Department of Management Uttaranchal Institute of Management, Uttaranchal University, Dehradun-248007, India
^§ Department of Electrical & Electronics Engineering, IES College of Technology, India 462044 IES University, MP 462044 India, Bhopal, Madhya Pradesh .
^** Department of ECE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai -127
⁶ Professor, Department of Information Technology, Vishwakarma Institute of Information Technology, India Email: dharmesh.dhabliya@viit.ac.in, Pune, Maharashtra

^† Corresponding Author: ahmedabbas85@iunajaf.edu.iq
^‡ Deepabisht.bisht28@gmail.com
^§ research@iesbpl.ac.in
^** n.t.velusudhaece@psvpec.in

Abstract

This review focuses on Distributed Generation Planning within Multi-Energy Microgrids (MES), a transformative approach where various energy forms like electricity, heat, and cooling interact optimally. MES offers significant technical, economic, and environmental benefits, making it crucial to explore intricate modelling and assessment techniques for such systems, including microgrids, virtual power plants (VPPs), and energy hubs. Amid global energy challenges and environmental crises, renewable energy adoption is vital. Distributed Generation (DG) plays a pivotal role, but integrating it at scale poses challenges. Microgrids are a viable solution, and this review introduces their core concepts, emerging trends, and challenges, such as integrating more renewable energy sources, multienergy forms, complex architectures, demand-side management, and advanced storage. Furthermore, the paper delves into Multi-Energy Microgrid planning complexities, particularly in off-grid areas. It presents a stochastic investment model that minimizes costs and carbon emissions while optimizing the distributed energy resource mix, locations, and sizes. This model considers intricate energy flows and uncertainties associated with renewable generation and energy demands. Case studies confirm significant cost and emission reductions, establishing it as a promising multi-energy microgrid planning solution for the future.