Background
With the increasing complexity and scale of modern energy systems, optimization algorithms play a crucial role in ensuring operational efficiency, reliability, and sustainability. These algorithms are pivotal in addressing various challenges, including load balancing, renewable energy integration, demand response management, and grid stability. Given the diverse nature of these challenges, a wide range of optimization algorithms have been developed, each with its unique strengths, weaknesses, and applicability to specific problem contexts within energy systems.

Objective of the Thesis
The aim of this thesis is to compile a comprehensive collection of optimization algorithms used in energy systems and classify them according to their characteristics and suitability for different use cases. This classification will serve as a valuable resource for identifying the most appropriate optimization techniques for specific problems in energy system management and planning.

• Conduct a thorough literature review to identify optimization algorithms used in the context of energy systems, covering areas such as generation, distribution, consumption, and storage.
• Analyze the identified optimization algorithms to understand their theoretical foundations, computational complexity, scalability, and the types of problems they are best suited to solve.
• Categorize the collected optimization algorithms, highlighting their strengths and limitations in the context of specific use cases within energy systems.
• Depending on the type of thesis and interests, the exact scope and focus will change.