# Mdp1 _sciences_
###### Abstract  
As the penetration of renewable energy generation in electric grids becomes more substantial, its contribution to the variability of the net load becomes more noticeable. Particularly in small or weak grids, the rate at which the output power of a wind farm decreases may become a concern to grid operators. In the present work, a novel approach, called forecast-based curtailment (FBC), is shown to be able to self-mitigate downward ramps on short time scales at a very small energy penalty, compared to conventional mitigation schemes, such as flat curtailment or up-ramp limitations. FBC allows to achieve compliance with ramp limits imposed by system operators at a very small energy cost and modest additional upfront investments. wind power variability; short-term forecast; curtailment 2021 6 May 20  
regulation, and sometimes Automatic Generation Control (AGC). These requirements are often justified on the grounds of the variability of the VRE plant, confusing services _provided by_ the plant _to_ the grid with actions designed to mitigate the variability of the VRE plant output itself. This distinction is important, since though the VRE plant contributes to the variability of the net load (generally by increasing it), the marginal increase of one additional plant is often small. In either case, given the discretionary power often invested in grid operators, some restriction as to the tolerable level of variability of the power output is likely to be imposed on individual power plants seeking an interconnection permit. Such a requirement may often be phrased as a maximal negative slope in power output tolerated by the grid. The main ideas for managing and mitigating wind power variability in general and (negative) ramps in particular can be summarized as follows. Not unexpectedly, most authors consider some kind of storage device (generally battery-based) as the main tool for smoothing wind power output, often proposing specific algorithms for the optimized use of such devices [8, 9, 10, 11]. Another line of thought is the overall reduction of wind power variability by an appropriate siting of wind farms, thereby tapping into the complementarity of wind generation at different locations [12, 13]. This geographic diversification goes a long way towards smoothing the overall wind power generation in the system; its impact is, however, generally limited to the planning stage, at least if all wind farms are expected to always produce their maximum possible power output. An obvious option for the management of an already built-out fleet of wind farms is to selectively restrict the output of certain farms at given times, in order to \"tailor\" the output according to pre-established criteria for ramps or variability. This can be done by flat-curtailing a given wind farm [14] (i.e., restricting the output to a maximal value or fraction of the rated output), or restricting the rate of increase (or positive ramp rate) to a certain value. The rationale behind this latter idea is that a positive ramp will be generally followed by a negative one, so restricting the former will on the average reduce the latter. This approach was studied in an empirical manner by [PERSON] [15] for the case of the Spanish grid, demonstrating that the general ideas works, albeit at a considerable energy penalty. A different approach consists in accepting the wind power output as it is, but managing its impact on the grid. This can be done by considering wind power ramps as an explicit variable in the dispatch operations, allowing for sufficient operating reserves to be allocated to account for the possibility of large wind power ramps. [PERSON] et al. [16] took such an approach by considering ramps as a risk variable in their stochastic unit commitment problem; ramps were not predicted but used as an external variable in their optimization problem. Being able to predict the general ramping characteristics is of course useful for any optimization approach, so the development of suitable wind farm models is important too. [PERSON] et al.