Did you ever consider the use of systems and control methods ?

There is a lot of research in this area in which large scale networks are included as well. As a paradigm in your area where you have to adjust quite regularly your decisions on the basis of quite a number of data, the language of systems and control seems to be important. Just give it a try . I am no longer doing active research in this area, but I still follow the literature. And, really , it is worthwhile to have a look at relevant papers in the internet. By the way, the JCW systems and control group at the university of Groningen is outstanding. Maybe this group is willing and able to assist you in maybe a new proposal for PhD research . Kind regards. Johannes Willem Nieuwenhuis.

Dear Dr. Nieuwenhuis,

I am sincerely grateful for your insightful suggestion regarding the application of systems and control methods in my research. Your recommendation to explore this paradigm, especially in the context of large-scale networks and dynamic decision-making, is invaluable.

I am particularly interested in the work of the Jan C. Willems Center for Systems and Control at the University of Groningen, which you highlighted. Their focus on complex systems and networks aligns with my recent research interests.

Given your extensive experience, I would greatly appreciate any further guidance or resources you could provide. Additionally, if you could recommend a contact within the Jan C. Willems Center or any other relevant institution who might assist in developing a PhD proposal in this area, it would be immensely helpful.

Thank you once again for your support and valuable advice.

Kind regards,

Elias Saadeh

When integrating renewable energy sources (such as solar and wind) into electrical grids, optimization techniques such as stochastic programming and linear programming are used for planning and management. Each has its advantages and challenges, but there are some drawbacks and limitations that must be considered when choosing the appropriate approach.

1. Linear Programming (LP) in Renewable Energy Integration

Linear programming is used to model problems that can be represented by linear relationships between variables and constraints. When used in renewable energy integration, it assumes that inputs (such as electricity demand and solar power production) are deterministic or accurately predictable.

Drawbacks and Limitations:

Inflexibility with uncertainty: Linear programming does not take into account the stochastic nature of renewable energy generation, which can lead to unrealistic solutions when unexpected fluctuations in weather occur.

Inability to handle dynamic fluctuations: It does not easily manage multiple scenarios, making it less ideal for systems that rely heavily on variable renewable sources.

Unrealistic Assumptions: Often assumes a linear relationship between variables, while reality is more complex and requires non-linear models in some cases.

2. Stochastic Programming (SP) in Renewable Energy Integration

Stochastic programming takes into account uncertainty in inputs by modeling different scenarios of renewable generation probabilities. Solutions are designed based on a probability distribution of energy sources, which helps improve decisions under uncertainty.

Disadvantages and Limitations:

High computational complexity: Requires significant computational resources due to the need to simulate multiple possible power generation scenarios.

Difficulty in obtaining accurate probability distributions: The success of the model depends on the availability of reliable data on weather and production changes, which may be unavailable or expensive to obtain.

Increased complexity in interpretation and implementation: Requires advanced analytical skills and may be difficult to adopt in industrial settings compared to simple linear models.

Comparison of the two approaches

Which approach is better?

The choice of approach depends on the nature of the system:

If the system has a low share of renewable energy and relies mainly on conventional sources → Linear programming may be sufficient.

If renewable energy represents a large share of the electrical grid, and uncertainty is a major factor → **Programming