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Power systems throughout the world are experiencing increasing levels of power electronics interfaced generation in their generation portfolio. As these devices have a significantly different dynamic behavior than conventional synchronous generators, it is expected that this trend will pose power system stability related challenges. This paper presents the results of a questionnaire conducted within the MIGRATE project. The aim of this questionnaire, to which more than 20 European transmission system operators (TSOs) responded, was to identify and prioritize these challenges. The TSOs identified challenges related to rotor angle stability (two), frequency stability (three), voltage stability (five), and power electronics interactions and resonances (two). In a follow-up survey, the TSOs were asked to rank the challenges based on their severity, probability of occurrence, and time of manifestation. The decrease of inertia was ranked the highest among the 11 issues. Additionally, the TSOs gave insight into current practices with regards to system monitoring and analysis. Based on the ranking, mitigation measures are currently being designed in order to facilitate an even higher amount of power electronics interfaced renewable energy sources in the power system.

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A mixed integer linear programming (MILP) approach for the joint simulation of electric control reserve and electricity wholesale markets is presented. This generation dispatch model extends an existing integrated grid and electricity market (IGEM) model covering the Continental European electric power system. By explicitly incorporating the markets for primary and secondary control reserves (PCR and SCR), the model can reproduce the decisions of generating unit operators on which markets get involved. Besides, the introduction of the integrality conditions allows considering start-up costs and the calculus of generating units to pass through the economically unattractive periods with low or even negative prices in order to avoid another start-up. Since this model is too large to be solved with common MILP solvers for the intended simulation time of one year, temporal and geographical interdependencies are used to solve it heuristically. The heuristic therefore splits the model into various sub-problems so that on the one hand, the number of variables, especially of integer variables, per sub-problem is reduced significantly and on the other hand, the relevant interdependencies remain considered. The heuristic is evaluated in terms of accuracy and computation time by means of two case studies. Both case studies show satisfactory accuracy and significant advantages in computation time.