Academic Staff

M.Sc. Abuzar Khalid

Academic Staff

M.Sc. Abuzar Khalid

R11 T07 C07
+49 201 18-33389
+49 201 18-32703
Consultation Hour:
nach Vereinbarung
Lehrstuhl für Energiewirtschaft
Universität Duisburg-Essen, Campus Essen
Fakultät für Wirtschaftswissenschaften
Universitätsstraße 12
45141 Essen

Curriculum Vitae:

02/2020 Universität Duisburg Essen

Wissenschaftlicher Mitarbeiter am Lehrstuhl für Energiewirtschaft (EWL)

10/2015 - 09/2018 Technische Universität München
Masterstudium (M.Sc.) Energietechnik
Schwerpunkt: Erneuerbare Energien, Energiewirtschaft, Energiemärkte

09/2014 - 09/2015 Nationale Universität für Wissenschaft und Technologie
Masterstudium (M. Eng) Maschinenbau

09/2010 - 08/2014 HITEC Universität
Bachelorstudium (B.Sc.) Maschinenbau
Schwerpunkt: Erneuerbare Energien


  • Voswinkel, Simon; Höckner, Jonas; Khalid, Abuzar; Weber, Christoph: Sharing congestion management costs among system operators using the Shapley value. In: Applied Energy, Vol 2021 (2022). doi:10.1016/j.apenergy.2022.119039Full textCitationDetails

    With energy generation becoming increasingly decentralized, the need for congestion management across grid voltage levels is also increasing. To enable fair sharing of congestion costs among grid operators, these costs must be allocated to congested grid elements. We propose using the Shapley value for this purpose. The Shapley value is a cooperative game theory concept that was developed to share a total surplus generated by a coalition of players between the players based on their marginal contributions to the coalition. We apply this concept to share the costs of congestion management between grid elements based on their contributions to overall congestion management costs. To reduce the computational complexity of the Shapley value, we introduce two novel simplification approaches and compare them to existing methods using a numerical example based on CIGRE benchmark grids. The first method exploits the fact that the characteristic function for the congestion costs is obtained from an optimal power flow computation (i.e., a constrained optimization problem). It utilizes knowledge about which constraints are non-binding in the optimization to derive the values of related coalitions without calculating them. The second method takes advantage of the fact that the congestion management cost-allocation game is monotone and derives the values of coalitions based on this property. Both methods are implemented and compared to sampling. Using the first method, we are able to reduce computational complexity to less than 20% of that of the original problem while maintaining exact results. Our second approach is not dependent on detailed knowledge of the underlying optimization problem and can reduce the computational time by almost half with exact results and much further when compromising precision. While the methods are presented through an application example, they can be applied to other games with similar properties.

    • Schinke-Nendza, Aiko; Flatter, Felix; Kramer, Hendrik; Khalid, Abuzar; Uhlemeyer, Björn; Rasti, Sasan Jacob; Trossen, Christian; Mohammadi, Sara; Mayorga Gonzalez, Daniel; Spanel, Udo; Wellssow, Wolfram; Weber, Christoph; Zdrallek, Markus; Schegner, Peter; Kubis, Andreas: 'ZellNetz2050' - A Concept for the Efficient and Effective Operation of Multi-Sector Web-of-Cells Energy Systems. In: CIGRE Session 2022 (2021). Full textCitationDetails

    Tutored Theses:

    • Competitive Analysis of Hydrogen Routes in Selected Industrial Sectors (Master Thesis Business Administration - Energy and Finance, in progress)
    • Data Preparation, Analysis and Forecasts based on Historical Weather Years for a Parsimonious Fundamental Model of a Future Energy System (Master Thesis Economics, in progress)