A wind power forecast corresponds to an estimate of the expected production of one or more wind turbines (referred to as a wind farm) in the near future, up to a year. Forecast are usually expressed in terms of the available power of the wind farm, occasionally in units of energy, indicating the power production potential over a time interval.
Time scales of forecasts Forecasting of the wind power generation may be considered at different time scales, depending on the intended application: very short-term forecasts (from seconds up to minutes) are used for the real-time turbine control and electrical grid management, as well as for market clearing; short-term forecasts (from 30 minutes up to hours) are used for dispatch planning, intelligent load shedding decisions; medium-term forecasts (from 6 hours up to a day) are used for to make decisions for switching the turbine on or off for safety or conditions on the market; long-term forecasts (from a day up to a week or even a year) are used for long term planning (to schedule the maintenance or unit commitment, optimize the cost of operation). Maintenance of offshore wind farms may be particularly costly, so optimal planning of maintenance operations is of particular importance.
For the last two possibilities, the temporal resolution of wind power predictions ranges between 10 minutes and a few hours (depending on the forecast length). Improvements of wind power forecasting has focused on using more data as input to the models involved, and on providing uncertainty estimates along with the traditionally provided predictions.
Reason for wind power forecasts In the electricity grid at any moment balance must be maintained between electricity consumption and generation – otherwise disturbances in power quality or supply may occur. Wind generation is a direct function of wind speed and, in contrast to conventional generation systems, is not easily dispatchable, so fluctuations of wind generation require power substitution from other sources that might not be available on a short notice (it takes 6 hours to fire up a coal plant and 12 hours for a nuclear one). The problem gets more complex once the wind power starts providing more than a small percentage of the overall electricity supplied to the grid. Better forecasts allow utilities to deploy less spinning reserves, usually natural gas-based generators.
The forecasts are typically requested by utilities on two separate time scales: short-term (minutes to 6 hours) forecast is used to adjust the spinning reserves; long-term (day to week) forecast is used by the utility to plan the energy mix or buy electricity from other providers. Typically this information is needed on a "day ahead" basis (e. g., by 6AM), but markets tend not to operate on weekends and holidays, so occasionally longer forecasts are used.
The challenges utilities face when wind generation is injected into a power system depend on the share of that renewable energy and how well connected the grid is. Wind power in Denmark meets almost half of the country's electricity demand, while the instantaneous penetration (that is, the instantaneous wind power production compared to the consumption to be met at a given time) is sometimes above 100%. Unlike some windy parts of the US, which lack sufficient grid capacity, this is easily exported.
The transmission system operator (TSO) is responsible for managing the electricity balance on the grid: at any time, electricity production has to match consumption. Therefore, the use of production means is scheduled in advance in order to respond to load profiles. The load corresponds to the total electricity consumption over the area of interest. Load profiles are usually given by load forecasts which are of high accuracy. For making up the daily schedule, TSOs may consider their own power production means, if they have any, and/or they can purchase power generation from Independent Power Producers (IPPs) and utilities, via bilateral contracts or electricity pools. In the context of deregulation, more and more players appear on the market, thus breaking the traditional situation of vertically integrated utilities with quasi local monopolies. Two main mechanisms compose electricity markets. The first one is the spot market where participants propose quantities of energy for the following day at a given production cost. An auction system permits to settle the electricity spot price for the various periods depending on the different bids. The second mechanism is the balancing of power generation, which is coordinated by the TSO. Depending on the energy lacks and surplus (e.g. due to power plant failures or to intermittence in the case of wind power installations), the TSO determines the penalties that will be paid by IPPs who missed in their obligations. In some cases, an intra-day market is also present, in order to take corrective actions.
In order to illustrate this electricity market mechanism, consider the Dutch electricity market. Market participants, referred to as Program Responsible Parties (PRPs), submit their price-quantity bids before 11 am for the delivery period covering the following day from midnight to midnight. The Program Time Unit (PTU) on the balancing market is of 15 minutes. Balancing of the 15-minute averaged power is required from all electrical producers and consumers connected to the grid, who for this purpose may be organised in sub-sets. Since these sub-sets are referred to as Programmes, balancing on the 15-minute scale is referred to as Programme Balance. Programme Balance now is maintained by using the production schedules issued the day before delivery and measurement reports (distributed the day after delivery). When the measured power is not equal to the scheduled power, the Programme Imbalance is the difference between the realised sum of production and consumption and the forecast sum of production and consumption. If only production from wind energy is taken into account, Programme Imbalance reduces to realised wind production minus forecast wind production. The programme imbalance is the wind production forecast error.
Programme Imbalance is settled by the System Operator, with different tariffs for negative Programme Imbalance and positive Programme Imbalance. A positive Programme Imbalance indicates more energy actually produced than forecast. by wind energy the realised wind production is bigger than the forecast wind production. And vice versa, in the case of a negative Programme Imbalance by wind energy.
Note that the costs for positive and negative imbalances may be asymmetric, depending on the balancing market mechanism. In general, wind power producers are penalized by such market system since a great part of their production may be subject to penalties.
In parallel to be used for market participation, wind power forecasts may be used for the optimal combined operation of wind and conventional generation, wind and hydro-power generation, or wind in combination with some energy storage devices. They also serve as a basis for quantifying the reserve needs for compensating the eventual lacks of wind production.
General methodology Several techniques of varying degrees of sophistication are used for short-term prediction of wind generation: persistence method is naïve: it assumes that the wind power in the next time interval will stay the same as the current measured instantaneous power. Forecast efficiency naturally quickly deteriorates with time going forward, and typically this method is used as a base-level set of numbers for the predictions of other methods to be compared against; physical methods that use the numerical weather prediction results, recalculate them into the wind speed at the generation site utilizing the physical characteristics of the area around the wind farm and convert the speed to power predictions using the turbine power curve; statistical methods are based on models that assume linear or nonlinear relationship between the numerical weather prediction results and the wind power, with the coefficients trained using the historical data. Two broad subclasses of the statistical models are: time series; ANNs; hybrid methods.
Advanced approaches for short-term wind power forecasting necessitate predictions of meteorological variables as input. Then, they differ in the way predictions of meteorological variables are converted to predictions of wind power production, through the so-called power curve. Such advanced methods are traditionally divided into two groups. The first group, referred to as physical approach, focuses on the description of the wind flow around and inside the wind farm, and use the manufacturer's power curve, for proposing an estimation of the wind power output. In parallel the second group, referred to as statistical approach, concentrates on capturing the relation between meteorological predictions (and possibly historical measurements) and power output through statistical models whose parameters have to be estimated from data, without making any assumption on the physical phenomena.
Prediction of meteorological variables Wind power generation is directly linked to weather conditions and thus the first aspect of wind power forecasting is the prediction of future values of the necessary weather variables at the level of the wind farm. This is done by using numerical weather prediction (NWP) models. Such models are based on equations governing the motions and forces affecting motion of fluids. From the knowledge of the actual state of the atmosphere, the system of equations allows to estimate what the evolution of state variables, e.g. temperature, velocity, humidity and pressure, will be at a series of grid points. The meteorological variables that are needed as input for wind power prediction obviously include wind speed and direction, but also possibly temperature, pressure and humidity. The distance between grid points is called the spatial resolution of the NWPs. The mesh typically has spacing that varies between few kilometers and up to 50 kilometers for mesoscale models. Regarding the time axis, the forecast length of most of the operational models today is between 48 and 172 hours ahead, which is in adequacy with the requirements for the wind power application. The temporal resolution is usually between 1 and 3 hours. NWP models impose their temporal resolution to short-term wind power forecasting methods since they are used as a direct input.