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

Ricardo Bessa, Corinna Möhrlen, Vanessa Fundel, Malte Siefert, Jethro Browell, Sebastian Haglund El Gaidi, Bri-Mathias Hodge, Umit Cali, George Kariniotakis
2017 Energies  
2017) Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10 (9). Abstract: Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The reliability of system operations and smooth integration of wind energy into electricity markets has been strongly supported by years of improvement in weather and
more » ... t in weather and wind power forecasting systems. Deterministic forecasts are still predominant in utility practice although truly optimal decisions and risk hedging are only possible with the adoption of uncertainty forecasts. One of the main barriers for the industrial adoption of uncertainty forecasts is the lack of understanding of its information content (e.g., its physical and statistical modeling) and standardization of uncertainty forecast products, which frequently leads to mistrust towards uncertainty forecasts and their applicability in practice. This paper aims at improving this understanding by establishing a common terminology and reviewing the methods to determine, estimate, and communicate the uncertainty in weather and wind power forecasts. This conceptual analysis of the state of the art highlights that: (i) end-users should start to look at the forecast's properties in order to map different uncertainty representations to specific wind energy-related user requirements; (ii) a multidisciplinary team is required to foster the integration of stochastic methods in the industry sector. A set of recommendations for standardization and improved training of operators are provided along with examples of best practices. and uncertain, because it depends on local meteorological conditions. Wind power forecasting has thus been utilized to reduce and forecast the uncertainty associated with wind power output. Traditionally, this has been in form of a single point forecast; however, advanced forecasting techniques can provide more information, often in form of uncertainty forecasts. Presently, these uncertainty forecasts have not gained widespread usage in power system operations or electricity market bidding, but there are significant opportunities to improve economic applications [1] as well as reliability applications [2] in the power industry by utilizing such additional forecast information in novel ways. Uncertainty information is routinely conveyed in public weather forecasts to provide some indication of the probability of an event's occurrence. Everybody has experienced the morning weather forecast claiming that "there is an 80% chance of rain this afternoon" and thereafter grabbed an umbrella on the way out of the door. Due to this inherent possibility for an end-user of forecasts to take action, when forecasts lack precision, it is important to understand how wind power forecasts are currently being utilized by the electric power industry and analyze the gaps that prevent more prevalent usage of such uncertainty information in wind power applications [3, 4] . The most common usage of wind power forecasts in power system operations is in Unit Commitment (UC), Economic Dispatch (ED) [5] processes and in allocating required reserve [6] . These are the scheduling processes by which power system generators are assigned an on/off status and output level for future time periods. For UC, this is typically performed in the day-ahead, and thus deterministic wind power forecasts used in this process typically range from 12-48 h ahead of the operating hour. Deterministic forecasts of load and renewable generation are critical in this process as it is currently designed due to the search of a single "optimal" solution. For reserve requirements, deterministic rules (e.g., covering a certain percentage of the historical load or wind point forecast errors) are generally used by system operators. There is a very extended literature on stochastic approaches for the various power system functions like congestion management, energy trading, UC, ED, reserves estimation, power flow and optimal power flow, storage placement and sizing, etc. [3] . The consensus today is that there is a mismatch between the proposed methods in the literature and their adoption by the industry, and we do not speak about the uncertainty forecasts but the decision making tools that use these forecasts. Nevertheless, there are exceptions. For example, the probabilistic-dynamic rule of the Electric Reliability Council of Texas (ERCOT) for non-spinning reserve (updated on a daily basis based on historical data, the solar forecast, and the power system state forecast) [7], the probabilistic method for allocating the maximum import net transfer capacity between Portugal and Spain [8] or the probabilistic method from Red Eléctrica de España (REE) for allocating replacement reserve [6] . Another application for uncertainty forecasts in the electric power industry is to provide graphical tools to enhance the "situational awareness" of critical events in the grid operation that enhances the confidence and efficiency with which grid operators may change dispatch schedules in real-time to maintain system reliability in critical situations according to updated forecast information [7, 9, 10] . This is one area in which uncertainty information has made more of an impact, especially for system operators that receive some sort of probability information. This is often in the form of a value that tells about the probability of exceedance of a given parameter, e.g., the 20th percentile, which need to have a 80% probability of exceedance in a well-calibrated forecast. While useful, this simplified representation does not provide all of the information available from the forecasts and can thus lead to sub-optimal decisions due to a lack of information. Despite uncertainty forecasts being widely available, in general, for electricity market optimal bidding (i.e., minimum imbalance costs), mostly point forecasts are being used to derive wind power bids for a given wind farm or pool of wind farms to the day-ahead, intra-day, or real-time markets [11, 12] . According to [13] , common practice consists of scaling point forecasts and schedule backup capacity during periods of high uncertainty, which helps to avoid over-promising generation and increases reliability, but leads to decreased economic potential.
doi:10.3390/en10091402 fatcat:o6ebcce4wfgj5gnkujy3ggkk6a