A Papavasiliou

AH Murphy

M Alexiadis

Pierre Pinson

T Gneiting

X Zhu

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Test

English

Comments on: Space-time wind speed forecasting for improved power system dispatch

Pinson, Pierre

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Dec 19, 2017Comments on: Space-time wind speed forecasting for improved power systemdispatch.Pinson, PierrePublished in:TestLink to article, DOI:10.1007/s11749-014-0352-zPublication date:2014Link back to DTU OrbitCitation (APA):Pinson, P. (2014). Comments on: Space-time wind speed forecasting for improved power system dispatch. Test,23(1), 26-29. DOI: 10.1007/s11749-014-0352-zTEST manuscript No.(will be inserted by the editor)Comments on: Space-time wind speed forecasting forimproved power system dispatchPierre PinsonReceived: 21.10.2013 / Accepted: ??First, I would like to acknowledge Zhu, Genton, Gu and Xie (ZGGX) for theirsubstantial effort in jointly looking at the problems of improving short-term windand corresponding power forecasts, and of using these forecasts as input to arelevant decision-making problem. It here involves the least-cost scheduling ofconventional generators (economic dispatch) in a power system with substantialwind energy penetration.Forecasts are to be issued as a basis for decision support: it then appears soundto evaluate whether or not forecast improvements yield benefits to those employingthese forecasts as input to decision making. When it comes to renewable energy(more particularly wind power), forecasting and operational problems have beenthe focus of considerable research over the last 3 decades, from very few works inthe 1980s to tens (if not hundreds) of manuscripts published every year nowadays.While the work of ZGGX is to be seen as a good reference presenting approachesto improved forecasting and integration of forecasts in operational problems, Ibelieve some additional aspects may be discussed here in order to give a morecomplete picture. These aspects mainly relate to recent advances in renewableenergy forecasting and to the use of probabilistic forecasts in operational problems.1 Advances in renewable energy forecastingNumerous approaches to wind power forecasting exist today. Most of these ap-proaches have subsequently been adapted and extended for the case of other re-newable energy sources, for instance, solar and wave energy. This can be explainedby the similarities in these prediction problems, involving the future evolution ofrelevant atmospheric and/or oceanic variables, as well as their conversion to elec-tric power through dedicated energy conversion devices, e.g., wind turbines, solarThe author is supported by the Danish Council for Strategic Research through the project “5s- Future Electricity Markets”, no. 12-132636/DSF.P. PinsonTechnical University of Denmark, Dpt. of Electrical Engineering, Kgs. Lyngby, DenmarkE-mail: ppin@dtu.dk2 Pierre Pinsonpanels and wave energy converters. The fact that more emphasis has been placedon wind energy, so far, can certainly be explained by the pre-dominance of thisenergy source with respect to other renewable energy alternatives. A good generaloverview of existing methodologies for wind power forecasting can be found inGiebel et al. (2011), while some of the authors of ZGGX also recently publisheda more targeted review of the state of the art, focused on statistical models andtheir application in power system operations Zhu and Genton (2012).Accounting for spatio-temporal effects in order to improve the quality of windpower forecasting is a possibility that has been explored for the last 15 years, afterthe original proposal of Alexiadis et al. (1999). In the present manuscript, ZGGXbuild on the methodology and dataset originally analysed by Gneiting et al. (2006)for the particular setup of the Columbia River Basin in the Pacific Northwest re-gion of the United States of America. Accounting for such effects certainly is easierfor cases where some locations are obvious sensors, for instance, owing to thermalbreezes and topographic features. In a more general setup where spatio-temporaleffects are dynamic and conditional to prevailing weather conditions, more complexapproaches may be necessary. In that sense, the proposal of ZGGX comprises aninteresting generalization of the Regime-switching Space-Time Diurnal (RSTD)model of Gneiting et al. (2006) by allowing for the defining boundaries of theregimes to be more data-driven and to evolve over time. Another form of gener-alization would be to consider varying-coefficient models, whose parameters are afunction of wind direction, as for the example case of Pinson (2012). A drawbackof the varying-coefficient model alternative, however, may come from the increasedamount of observations necessary for estimation. On the positive side they requireless work and expert knowledge in relation with the definition of regimes. Otherregime-based approaches may be thought of, where the regimes can be definedexplicitly or implicitly. From a more general point of view, it is not clear whichapproach may be best, depending upon test case and quantity of data available.In contrast, we know today that the improvement of short-term wind and powerforecasts will require new types of observations at high spatial and temporal reso-lutions, as for the example of X-band and C-band weather radar images (Trombeet al. , 2013), as well as dedicated statistical models.Owing to the complexity of the wind power generation processes, being nonsta-tionary, nonlinear and double-bounded, forecasts will always contain a variable anddynamic part of error. This has motivated a strand of literature concentrating onprobabilistic approaches to wind power prediction, aiming at fully informing aboutfuture power generation. From an operational point of view, accounting for uncer-tainty is a real plus that can allow formulating a number of operational problemsin a stochastic optimization framework. Probabilistic forecasts may take variousforms depending on the type of operational problem at hand, and of its formula-tion in an optimization framework (Pinson , 2013). For instance, quantile forecastsare sufficient for some of the simplest electricity market participation problems,robust optimization formulations of market-clearing mechanisms require intervalforecasts (Zugno and Conejo , 2013), while the least-cost scheduling of conventionalgenerators cast in a stochastic programming framework calls for scenarios of windpower generation (Papavasiliou and Oren , 2013). The forecasting methodology ofZGGX is thought of in a probabilistic framework based on a truncated Normalassumption for the predictive densities of wind speed. The authors then made thechoice of which single-valued forecasts should be extracted from predictive densi-Comments on: Space-time wind speed forecasting 3ties, more precisely, based on a linear loss function. This has the advantage thatresulting point forecasts can be readily used in existing deterministic approachesto economic dispatch. Additional benefits could be obtained by fully utilizing theinformation contained in the original probabilistic forecasts.2 Operational problems, quality and value of renewable energyforecastsForecasters naturally aim at improving their predictions by enhancing their mod-elling approaches. For instance, here, ZGGX propose to further account for spatio-temporal correlations in wind observations. Better modelling the physical processesinvolved does not mean that the predictions will be better, unconditionally. In thatvein, Murphy (1993) elegantly explained the difference between forecast qualityand value as two sides of how forecasts may be seen as better. They correspondto the views of the forecaster on one side and to those of the forecast user onthe other side. Roughly, it boils down to the forecaster and forecast user havingdifferent loss functions. A forecaster typically aims at minimizing a quadratic lossfunction, as if taking part in a forecast competition where the Root Mean SquareError would be the lead criterion. In contrast, a forecast user has more varied andcomplex loss functions in relation with his specific decision-making problem, mostlikely expressing potential financial gains and losses. Reconciling forecast qualityand value is not an easy task. As mentioned in the above, already deciding aboutwhich single-valued forecasts to extract from predictive densities relies on a givenloss function. For an extensive treatment of these aspects, please see Gneiting(2011) and references therein.For the economic dispatch problem considered by ZGGX, it could well be thatthe optimal point forecasts to extract from the truncated Normal predictive den-sities do not correspond to their median (with nominal level of 0.5). The nominallevel of the optimal quantile could be slightly higher, in case it would be less costly(in expectation) to place the power system in a situation where it more likely thatwind generation is less than anticipated; or conversely a little bit higher if theopposite situation is preferred. In contrast, one of the advantages from consider-ing probabilistic forecasts as input to decision-making problems formulated in astochastic optimization framework is that the decision about best single-valuedforecasts to extract from predictive densities becomes implicit.More generally, if not considering spatio-temporal aspects only, I would stronglyencourage other authors to follow an approach similar to that of ZGGX, in or-der to evaluate the operational benefits of forecast improvements for real-worlddecision-making problems. There exists a wealth of such problems related to re-newable energy generation in power systems management and electricity marketsstill worth looking at. One might then be surprised by the fact that, for someproblems, even slight improvements in forecast accuracy could yield substantialoperational benefits, while, in other cases, even sizeable forecast improvementshave no value for the forecast users.4 Pierre PinsonReferencesAlexiadis M, Dokopoulos P, Sahsamanoglou H (1999). Wind speed and powerforecasting based on spatial correlation models. IEEE T Ener Conv 14: 836–842Giebel, G., Brownsword, R., Kariniotakis, G., Denhard, M. and Draxl, C. (2011).The state-of-the-art in short-term prediction of wind power: A literatureoverview, 2nd edition. Tech Rep, Technical University of Denmark [online: or-bit.dtu.dk]Gneiting T (2011). Making and evaluating point forecasts. J Amer Stat Assoc 106:746–762.Gneiting T, Larson K, Westrick K, Genton MG, Aldrich E (2006). Calibrated prob-abilistic forecasting at the stateline wind energy center: The regime-switchingspace-time method. J Am Stat Assoc 101: 968–979Murphy AH (1993). What is a good forecast? An essay on the nature of goodnessin weather forecasting. Wea Forecasting 8: 281–293.Papavasiliou A, Oren SS (2013). Multi-area stochastic unit commitment for highwind penetration in a transmission constrained network. Oper Res 61: 578–592Pinson P (2012). Very-short-term probabilistic forecasting of wind power withgeneralized logitnormal distributions. J Royal Stat Soc C 61: 555–576Pinson P (2013). Wind energy: Forecasting challenges for its optimal operationalmanagement. Stat Sci, available onlineTrombe PJ, Pinson P, Vincent CL, Bøvith T, Cutululis NA, Draxl C, Giebel G,Hahmann A, Jensen NE, Jensen BP, Le NF, Madsen H, Pedersen LB, SommerA (2013). Weather radars The new eyes for offshore wind farms. Wind Energy,available online [DOI: 10.1002/we.1659]Zhu X, Genton M (2012). Short-term wind speed forecasting for power systemoperations. Int Stat Rev 80: 2–23Zugno M, Conejo A (2013). A robust optimization approach to energy and reservedispatch in electricity markets. Under review [Tech. Rep. version available atorbit.dtu.dk]

Comments on: Space-time wind speed forecasting for improved power system dispatch.

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