Day-Ahead Energy and Reserve Dispatch Problem under Non-Probabilistic Uncertainty

The current energy transition and the underlying growth in variable uncertain renewable-based generation challenge proper operation of power systems. Classical probabilistic uncertainty models, e.g., stochastic programming or robust optimisation, have been used widely to solve problems such as day-ahead reserve dispatch problem enhance decisions with a insight renewable real-time. By doing so, scheduling system becomes, production consumption electric power, more reliable (i.e., because potential deviations) while minimising social costs given balancing actions. Nevertheless, these classical models are not valid when is imprecise, meaning that operator may rely on unique distribution function describe uncertainty. Given Distributionally Robust Optimisation method, our approach can be implemented for any non-probabilistic, interval rather than only sets functions (ambiguity set probability distributions). In this paper, aim apply two advanced non-probabilistic models: Interval ϵ-contamination, where imprecision in-determinism (uncertain parameters) considered. We propose kinds theoretical solutions under decision criteria—Maximinity Maximality. For an illustration solutions, we proposed case study inspired by 24-node IEEE reliability test system.