M.Sc. Sina Hajikazemi
Adversarial attack on energy system models
+49 (0) 6151 16-21719
fax +49 (0) 6151 16-21715
S3|10 305
Landgraf-Georg-Str. 4
64283 Darmstadt
Research Interest
- Robust optimization
- Bilevel programming
- Parametric programming
- Sensitivity analysis
Research Project:
How much do the decision outputs of the energy system models depend on each set of input parameters?
Can big companies imperceptibly change the input parameters in order to change the decision outputs in a desired direction?
To answer the above questions, I am currently working on sensitivity analysis of the mathematical optimization based energy system models. The focus of my research is mainly on the bilevel linear programming models with millions of variables which is NP-hard in the general form.
Open theses
Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Bachelor Thesis
Energy planning models are essential for analyzing energy and climate policies at national and global scales. However, these models face various uncertainties, categorized into uncertainties in input parameters, such as future fuel prices, and uncertainties in the structure of the model, such as the complexities and constraints inherent in different technologies. While methods such as global sensitivity analysis, stochastic programming, and Monte Carlo simulation address parameter uncertainties, they often overlook uncertainties in the model structure. In addition, policymakers are faced with considerations outside the scope of conventional modeling, such as political feasibility, regulatory challenges, and the timing of actions. As a result, policymakers may choose feasible but suboptimal solutions due to the challenges of quantifying intangibles in energy optimization models. Modeling to Generate Alternatives (MGA), a technique borrowed from the operations research literature, is a valuable approach to address structural uncertainties inherent in energy planning models as well as uncertainties in input parameters. MGA efficiently explores the feasible region around the optimal solution and generates alternative solutions with maximum diversity. By providing a spectrum of viable options beyond the conventional optimal solution, MGA provides invaluable insights for policy makers. These alternative solutions shed light on trade-offs and considerations often overlooked in conventional energy planning models, enabling policymakers to make more nuanced and informed decisions amid uncertainty and real-world constraints. This thesis focuses on implementing this approach in a German energy transition model (see github.com/EINS-TUDa/CESM) and exploring the results and insights it can provide to decision makers. Project Tasks:
- Understand the Modeling to Generate Alternatives (MGA) methodology.
- Apply the MGA methodology to the German energy transition model using the Compact Energy System Modelling Tool (CESM).
- Investigate the outcomes of the MGA implementation and identify the insights it offers for policymakers.
- Evaluate the strengths, weaknesses, and obstacles associated with the MGA methodology. Prerequisites: Proficiency in Python programming.
- Gain basic knowledge of mathematical programming techniques necessary for basic optimization tasks relevant to energy planning models.
- Develop a fundamental understanding of energy planning models, including their components, basic methodologies, and applications in energy policy analysis.
- Learn basic skills in reporting and justifying the outcomes of energy planning models, including simple interpretation of findings and basic assessment of model validity.
Supervisor: Sina Hajikazemi
Earliest start: immediately
Type: Bachelor Thesis
Electric transmission grids are critical energy infrastructures in every country. Intelligent attackers may attempt to damage specific components of the grid to cause maximum load shedding, and grid operators respond by solving the power flow problem to minimize load shedding using the remaining intact components. This raises the question: How vulnerable is the grid to adversarial attacks?
This project focuses on implementing and understanding an iterative optimization algorithm proposed by Javier Salmeron et al. [1] for the Electricity Network Interdiction problem. The algorithm formulates the problem as a bilevel programming problem, where the attacker aims to maximize load shedding, and the grid operator aims to minimize load shedding through optimal power flow in the attacked network.
Project Tasks:
1. Implementation: Implement the optimization algorithm in Python, ensuring clean and well-structured code.
2. Documentation: Provide clear and concise documentation for the implemented code, explaining key functions and algorithms.
3. Testing: Develop and execute test cases to validate the functionality of the implemented code.
4. Version Control: Utilize Git for effective code management and version control.
5. Presentation: Prepare a concise presentation that explains the project's objectives, methodology, and findings.
Prerequisites:
• Proficiency in Python programming.
• Basic understanding of mathematical optimization concepts.
Reference:
[1] Salmeron, Javier, Kevin Wood, and Ross Baldick. "Worst-case interdiction analysis of large-scale electric power grids." IEEE Transactions on Power Systems 24.1 (2009): 96-104.
Short Bio
2010-2014: BSc Civil Engineering, Sharif university of technology, Iran
2014-2016: MSc Applied Mathematics(Operational Research), Ferdowsi university of Mashhad, Iran
since 2022: PhD student at TU Darmstadt