A Systematic Review of Uncertainty Handling Approaches for Electric Grids Considering Electrical Vehicles

Author:

Auza Anna12ORCID,Asadi Ehsan1ORCID,Chenari Behrang1ORCID,Gameiro da Silva Manuel1

Affiliation:

1. Associação para o Desenvolvimento da Aerodinâmica Industrial—ADAI, Department of Mechanical Engineering, University of Coimbra, Rua Luís Reis Santos, Pólo II, 3030-788 Coimbra, Portugal

2. Faculty of Economics, University of Coimbra, Av. Dr. Dias da Silva 165, 3004-512 Coimbra, Portugal

Abstract

This paper systematically reviews the techniques and dynamics to study uncertainty modelling in the electric grids considering electric vehicles with vehicle-to-grid integration. Uncertainty types and the most frequent uncertainty modelling approaches for electric vehicles are outlined. The modelling approaches discussed in this paper are Monte Carlo, probabilistic scenarios, stochastic, point estimate method and robust optimisation. Then, Scopus is used to search for articles, and according to these categories, data from articles are extracted. The findings suggest that the probabilistic techniques are the most widely applied, with Monte Carlo and scenario analysis leading. In particular, 19% of the cases benefit from Monte Carlo, 15% from scenario analysis, and 10% each from robust optimisation and the stochastic approach, respectively. Early articles consider robust optimisation relatively more frequent, possibly due to the lack of historical data, while more recent articles adopt the Monte Carlo simulation approach. The uncertainty handling techniques depend on the uncertainty type and human resource availability in aggregate but are unrelated to the generation type. Finally, future directions are given.

Funder

European Regional Development Fund

CCDRC

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Reference124 articles.

1. International Renewable Energy Agency (IRENA) (2023, April 14). World Energy Transitions Outlook 2022. Available online: https://irena.org/publications/2021/March/World-Energy-Transitions-Outlook.

2. Nebel, A., Krüger, C., and Merten, F. (2011, January 6–8). Vehicle to Grid and Demand Side Management—An Assessment of Different Strategies for the Integration of Electric Vehicles. Proceedings of the IET Conference on Renewable Power Generation (RPG 2011), Edinburgh, UK.

3. IEA (2020). Global EV Outlook 2020: Entering the Decade of Electric Drive?.

4. Choi, Y. (1993). Paradigms and Conventions, University of Michigan Press.

5. A comprehensive review on uncertainty modeling techniques in power system studies;Aien;Renew. Sustain. Energy Rev.,2016

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