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WIREs Energy Environ.
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Handling renewable energy variability and uncertainty in power systems operation

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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. This article is categorized under: Wind Power > Economics and Policy Energy Infrastructure > Systems and Infrastructure Energy Systems Analysis > Systems and Infrastructure
Generic FRT voltage versus time characteristic curve.
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Architecture of a wind power dispatch center.
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Wind power reduction in Portugal due to extreme wind speed conditions originated by the cyclone Klaus in January 2009 (the wind power generation values are only from telemetered wind farms, available in real time, and not the total wind power generation in Portugal). (Reproduced with permission from Ref 131. Copyright 2009, REN.)
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Example of the expected energy not supplied as a function of the reserve level for a look‐ahead time and taking into account different sources of uncertainty: conventional and load uncertainty; conventional, load and wind power uncertainty.
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Generation variability of solar power due to clouds: (a) hourly average; (b) 15 min average.
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(a) Forecast intervals, centered in the median, and limited by its lower and upper bounds, which are forecasted quantiles. (b) Twenty statistical‐based scenarios with temporal dependency of errors and that respect the marginal distribution of the probabilistic forecasts [i.e., plot in (a)].
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Wind Power > Economics and Policy
Energy Systems Analysis > Systems and Infrastructure
Energy Infrastructure

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