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Service Engineer RAO expert

Belgium, Brussels Hoofdstedelijk Gewest, BrusselsServices

Job description

Your new career in Coreso

Founded in 2008, Coreso is a Regional Coordination Centre (RCC) encompassing nine European electricity Transmission System Operators (TSOs) shareholders. Our mission is to coordinate the different national power grids and proactively support European TSOs to ensure security of supply on a European basis. Thus, we provide services to secure energy transmission across the entire European continent 24 hours a day, 7 days a week, to anticipate the operations both in the short term and the long term, from a year-ahead until intraday (few hours before real time).

Within the European context of progressing market mechanisms, continuous growth of renewable energy, ambitious grid development and further harmonization, new coordination challenges are numerous. Thus, the secure operation of the European electricity system will continue to represent a truly international challenge.

With our team, Coreso aims to be at the first row in building up, the adequate operational processes to cope with those game-changing trends and with evolving regulatory environment. That is why, to achieve our ambitious goals, the corresponding operational human expertise is essential.

Let us give you a preview of your future mission!

Being a Service Engineer RAO expert, your main tasks will be:

  • Supporting Coreso for the Remedial Action Optimiser* (RAO) – Summary below
  • Providing Coreso guidance on how the Optimisation can be implemented and potentially support the coding of a simple Prototype (Proof of Concept)
  • Supporting Coreso in the discussion with the developer of the RAO
  • Challenging the technical orientation which will be offered by the RAO developer
  • Training a junior Coreso employee on the Optimisation concepts

Job requirements

What are we looking for?

  • Good understanding of mathematical optimisation and AI-driven decision making support systems.
    • Experience in Mixed-Integer Linear Programming and Metaheuristic-based optimisation algorithms.
    • Experience in dealing with external optimisation expert/internal during software acquisition processes.
  • Previous experience in the energy sector, in particular power system operations.
    • Bonus: Power system software development for TSOs or RCCs
    • Basic knowledge on SOGL and CACM EU Regulations
    • Basic understanding of a CSA and CCC Process
  • Be able to see beyond the mathematical model, understand the real needs and provide coherent solutions.
  • Mastery of English
  • Expertise in technical deliverables management
  • Knowledge of RCCs tasks in Europe
  • Knowledge of the TSO business and its challenges
  • Very good analytical, synthesis and writing skills
  • Very good organizational skills and autonomy
  • Very good teamwork skills
  • Very good communication skills, good interpersonal skills
  • Used to Multitasking
  • Sense of curiosity and enthusiasm

We offer a market conform salary package with extra-legal benefits on top! Curious to know more, apply now!

What is RAO?

The main objective of the Remedial Action Optimization is to quickly identify (within 20 minutes) the most economically efficient set of preventive and curative remedial actions (PRAs and CRAs) that make the system under study secure for the N state and post-contingency states for all time stamps while observing a range of inter-temporal operational and technical constraints. This optimization is applied for the entire regions (CORE, Italy North, …) for all 24 hours of the day and has access to both costly (e.g., generation re-dispatch) and non-costly actions (e.g., topology changes and phase shifting transformer tap positions).

This solution must be robust against the impact of uncertainty in a broad range of power system variables, e.g., renewable generation, demand, unit availability, topology. Robustness would entail ensuring a solution that remains secure under a certain degree of change in system conditions, at a reasonable increase in cost and that wherever possible the exhaustion of any given remedial action is avoided. 

For clarity, the solution of the RAO must simultaneously consider the flow on all elements to be secured, under both the intact N-0 condition and all post-contingency conditions. However, the problem formulation itself need not be closed or necessarily consider all these factors simultaneously.

The inputs of this module are extensive and the most essential are:  

  • one common grid model (CGM) per Time Stamp to be optimized,  
  • lists of the available Preventive RAs and Curative RAs,  
  • lists of the contingencies to be studied,  
  • lists of the cross-border network elements (XNEs) and monitored network elements,  
  • TSO constraints on the number of remedial actions that may be taken,  
  • parametrization of the objective function, 
  • the degree of uncertainty in the input data and the required degree of robustness, 
  • the results of the security analysis of the grid models prior to optimization, and  
  • where available the results of the most recent previous optimization applied to the time horizon under study. 

The main outputs of this module are: 

  • The optimized set of proposed PRAs and CRAs, 
  • The overall cost of the solution, 
  • The results of an AC contingency analysis for the optimized grid models, 
  • A report on the remaining available range of action on those PRA/CRA providers activated, 
  • Comparison of the optimization results with the results of the most recent previous optimization applied to the same time horizon, and 
  • The influence factors for every RA for a range of system topology changes. 

The main expected challenges when developing the RAO are: 

  1. Guaranteeing a solution in the time provided. 
  2. Ensuring the solution generated by the optimization routine, which is likely to be based on an approximation of the power system, remains valid for an AC load flow assessment with minimal modification. 

Developing a proper parameterization of the objective function that captures the diverse needs of the user, e.g., the balancing of penalty costs for small overloads against the preference for minimizing the number of remedial actions to be taken. 

Belgium, Brussels