Handling renewable energy variability and uncertainty in power systems operation

Abstract

The concerns about global warming (greenhouse-gas emissions), scarcity of fossil fuels reserves, and primary energy independence of regions or countries have led to a dramatic increase of renewable energy sources (RES) penetration in electric power systems, mainly wind and solar power. This created new challenges associated with the variability and uncertainty of these sources. Handling these two characteristics is a key issue that includes technological, regulatory, and computational aspects. Advanced tools for handling RES maximize the resultant benefits and keep the reliability indices at the required level. Recent advances in forecasting and management algorithms provided means to manage RES. Forecasts of renewable generation for the next hours/days play a crucial role in the management tools and protocols of the system operator. These forecasts are used as input for setting reserve requirements and performing the unit commitment (UC) and economic dispatch (ED) processes. Probabilistic forecasts are being included in the management tools, enabling a move from deterministic to stochastic methods, which conduct to robust solutions. On the technological side, advances to increase mid-merit and base-load generation flexibility should be a priority. The use of storage devices to mitigate uncertainty and variability is particularly valuable for isolated power system, whereas in interconnected systems, economic criteria might be a barrier to invest in new storage facilities. The possibility of sending active and reactive control set points to RES power plants offers more flexibility. Furthermore, the emergence of the smart grid concept and the increasing share of controllable loads contribute with flexibility to increase the RES penetration levels.

Wind Power Ramps Driven by Windstorms and Cyclones

“…In Table 2, TP represents the events that have been foreseen with the windstorm/cyclone detection methodologies and observed within the considered area which originated wind power ramps; FP represents the events that were foreseen within the considered area although not observed; FN represents the wind power ramp events observed that were not foreseen with the proposed methodologies; and TN represents no events foreseen/observed [36]. Different performance scores can be achieved based on the contingency table, namely the Bias Score (Bias) (5), the Extreme Dependency Score (EDS) (6), and the Hanssen & Kuipers Skill Score (KSS) (7). The EDS and KSS range between 0 and 1.…”

“…Currently, for the efficient and safe operation of a power system [4,5], a reliable wind power production forecast is required, that when coupled with a load forecast system, enables a reduction in the need to balance the energy on the reserve markets, at high costs [6]. Despite the fact that most TSOs currently use wind power forecast systems, these still face substantial errors (in phase and magnitude), especially during periods with severe variations in the wind power production [7].…”

Wind power probabilistic forecast in the Reproducing Kernel Hilbert Space

“…A first set of results has to do with some aspects of the training process of the models, specifically the computational time 1 and the robustness of the employed algorithms. Table II shows the averaged computational time in hours, t c , employed to train a single configuration of the model (i.e., 19 quantile submodels for specific values of the model parameters).…”

“…Presently, the increasing share of wind power in the generation portfolio of several control areas is demanding for a revision of the operational practices and management tools [1]. For instance, the Transmission System Operators (TSO) of Portugal and Spain studied alternative methods for setting the operating reserve requirements based on wind power uncertainty forecasts [2] [3].…”

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

“…Equation (3) means that the observed wind power is completely contained inside the simultaneous forecast intervals during all hours of the time horizon T.…”