Demand Forecasting Reporting Period: 4 th Dec th Mar 2018

Transcription

1 N A T I O N A L G R I D P A G E 1 O F 25 C O M M E R C I A L, E L E C T R I C I T Y C O M M E R C I A L O P E R A T I O N S Demand Forecasting Reporting Period: 4 th Dec th Mar 2018 EXECUTIVE SUMMARY This report provides an overview of National Grid s national demand, solar and wind forecasting performance for the reporting period 4 th of December 2017 to the 11 th of March 2018, and the work currently underway to reduce these errors. The magnitude of forecasting errors is discussed at the different lead times of within-day, day ahead, 2-14days ahead and 2-52 weeks ahead. Demand forecast s late evening and overnight cardinal point forecast errors were generally comparable to or reduced compared to the previous period. This was in spite of more volatile and difficult to forecast weather. However, the errors for the remainder of our cardinal point forecasts increased, including that of the early evening DP cardinal point due to Triad Avoidance. On the 1 st of March, we experienced the peak national demand of 49.8GW for the whole reporting year (1 st April th March 2018), which for the first time occurred outside of the Triad Period. This happened due to unseasonal, extreme cold and windy weather, which also brought snow to the majority of the country. This contrasted with an overnight minimum of 18.3GW on Christmas Day. Wind generation forecasting improved compared to the previous Quarter, albeit still reporting errors generally above the target threshold of 4.75%. We attribute the improvement to a significant campaign of wind power forecasting model upgrades undertaken in late November, which helped to underpin our performance through to March. Our wind forecasts were also generally unbiased during this period. Both our wind generation and demand forecasting model suites will continue to attract focussed updates and improvements to underpin our forecasting performance. In spite of a backdrop of changing system conditions we have continued to provide high quality demand and wind generation forecasts. With system changes such as increasing distributionconnected generation and new technologies such as batteries and smart meters, maintaining transmission-system demand forecast accuracies will become increasingly challenging. To meet these new challenges we plan to build actively upon our existing work and projects with the final objective to deliver real value to market participants and final consumers. 1

2 INTRODUCTION Under section 4H of the Transmission Licence, National Grid is obliged to publish a quarterly report describing our demand, wind and solar generation forecasts from within-day to 52 weeks ahead. We are also required to describe the actions that we have taken that have affected forecast errors and an analysis of the causes and future actions to reduce these errors. This report contains an overview of the differences between national demand, wind and solar forecasts and their outturns with different lead-in times during the Qtr. 4 reporting period 4 th of December 2017 to 11 th of March Some key demand outturns of the Qtr.4 reporting period are given below. Key Numbers Date Half hour Power (GW) Peak Demand 1 st Mar Overnight minimum 25 th Dec Daytime minimum 24 th Feb Maximum PV output 25 th Feb For the first time, we saw the peak demand for the year occurring outside the Triad period - specifically on the 1 st of March More information is found on this within the Case Study later in this report. At the end of the reporting period installed capacities for wind and solar reached 31.8GW. This is 1.3GW higher than reported at the end of the previous Quarter. The greatest increase was reported for metered wind capacity at 1GW. Embedded wind and solar capacities, which directly affect our demand forecasts, continue to increase, as forecast for the foreseeable future. Generation Type Capacity (GW) Metered Wind 12.8 Embedded Wind 5.9 Embedded Solar 13.1 Total Wind & Solar 31.8 This report describes in detail the national electricity demand (national demand/indo equivalent) forecasts and its major components: Embedded PV generation Embedded wind generation Weather forecast input data We also report on the metered wind generation forecasting performance, as well as report on the work we have undertaken to improve both demand and wind generation forecasts. Special attention is paid to the incentivised forecasts, made one, two and seven days ahead. 2

3 DEMAND FORECAST ERRORS We produce forecasts at multiple time horizons, from within-day up to five years ahead. Overall we typically produce over 100 forecasts for the peak demand for a particular day over the preceding five years. Forecasts at or greater than 14 days ahead are based on seasonal average weather, whilst those from within day to 13 days ahead use the latest available weather forecasts. It should be noted that most weather forecast models run from 0 to 6 days ahead, and that the accuracy of weather forecasts decreases significantly from the 7 th to the 13 th day ahead. The table below shows the mean absolute error for all cardinal points, and for all lead times up to 13 days into the future. This table encompasses more than just our incentivised forecasts, which are restricted to one, two and seven days ahead. With forecast lead-time increasing (i.e. a forecast projecting further into the future), the errors grow, as can be expected. In comparison with Qtr. 3, and consistent with the time of year, the mean absolute errors for most cardinal points and lead-times have increased. Some exceptions include the overnight cardinal points for (in particular 1F, 1A and 03) from within-day to around 2days ahead. errors < errors< errors< errors 1000 errors > 1000 The major components of the demand forecast errors are explained in detail in Appendix 1. During this Quarter, 13DA forecasts for 1F, 1A and 4C were not produced, hence are omitted from the table and the figures below. The graphs below show how the mean absolute error for each cardinal point forecast changed with forecast horizon, from 0 to 13 days ahead. The data is broken into forecasts for the overnight, daytime and evening cardinal points. 3

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5 I N C EN T I VI SED D EM AN D F O R EC AST S This section of the report shows a comparison between the previous quarters (Qtr.1, Qtr.2 & Qtr.3) and the current quarter (Qtr.4) for the incentivised demand forecasts - day ahead (DA), 2 days ahead (2DA) and 7 days ahead (7DA). We continually try to improve the performance of our forecasting models against a background of increasing uncertainty. Challenges include the further growth of both weather dependent and independent embedded generation, as well as new technologies such as smart meters and energy storage. The first set of 3 graphs below shows the quarterly MAE for each of the incentivised timescales. The DP cardinal point errors for most months in Qtr. 4 increased due to the effect of Triad avoidance actions (Triad period November February inclusive), as the forecasts are required to be made without taking these actions into account whilst the out turn does take these actions to account. 5

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7 The following 3 graphs present the performance of the forecasts relative to the forecasting incentive targets for each timescale. A negative value indicates that the average forecast error for that cardinal point exceeded the incentive target error. 7

8 The fact that the majority of the performance measures are negative shows that the targets continued to be very difficult to meet. The graph also shows that the incentive targets for this Quarter were generally more challenging compared to previous Quarter s targets. The above 3 graphs also highlight that it was particularly challenging to meet the required targets for DP due to Triad avoidance behaviour mentioned before. 8

9 M O N T H L Y C O U N T O F L AR G E ER R O R S ( G R EAT ER T H AN M W ) The graph below depicts the count of errors above 1000 MW since 1 st April It looks at the incentivised forecast horizons only, i.e. day ahead, 2 days ahead and 7 days ahead Monthly errors >1 GW per forecasting horizon 0 Apr-17 May-17 Jun-17 Jul-17 Aug-17 Sep-17 Oct-17 Nov-17 Dec-17 Jan-18 Feb-18 Mar-18 DayAhead 2DayAhead 7DayAhead The graph illustrates that the number of large errors (greater than 1000 MW) increases as lead time increases The total number of large demand forecast errors, greater than 1GW, increased slightly compared to the previous Quarter; consistent with expectations for this time of year. Note that the number of errors in March is low due to the fact that the date period of this report covers only until 11 th March. CASE STUDY WINTER PEAK DEMAND 1 st MARCH 2018 The peak national demand during this Quarter was 49.8 GW, which occurred on Thursday the 1 st of March at half-hour ending 18:30. This also corresponded to the peak demand for the winter and the whole of the incentives reporting period from 1 st April th March 18. Whilst historic winter peaks have been higher, this was the first time in our records that the peak national demand occurred outside of the Triad Avoidance period, i.e. from November to February. This peak value of national demand occurred during a week of severe cold weather, caused by easterly winds bringing a cold front from Russia into Europe. The observed temperature at the time of the winter peak (-1.6 C at 18:00) was almost 10 C lower than the normal temperature for this time of day and year. The day also saw snow affecting a large part of the country as well as high winds. 9

10 Due to these extreme weather conditions, and the observed and expected system and market stress, additional effort and time was invested into producing our forecasts during this challenging period. As the peak occurred towards the back end of the cold weather week, this did allow for some learning of how behaviour had adjusted to the extreme conditions. Our final day-ahead forecast was 817MW under-forecast, which whilst somewhat outside of the incentive target error range, was within range given the difficult conditions. Peak time forecasts of all our forecast lead times were generally under-forecast and did not converge significantly. Part of the reason can be attributed to the uniqueness of the event and the unpredictable behaviour observed, whilst the difficulties of forecasting the underlying weather sufficiently accurately was also a driver WEEKS AHEAD PEAK DEMAND FORECASTS The graph below shows our 2-52 weeks ahead peak national demand forecast, as published in January 2017 on the BMRS website for the reporting period 4 th December th March 18. This forecast is based on seasonal average weather. The graph also compares our forecast with both actual demand and weather corrected actual demands. For the reporting period, the mean absolute error between the 2-52week peak national demand forecast and actual demand was 1.8GW - the same value as during the previous Quarter. The maximum error was between the forecast and actual demand was 6.1GW on the 23 rd January 2017, compared to 6.2GW during the previous quarter. Errors during this period are affected by the fact that long term forecasts do not take into account only real weather forecasts, but also triad avoidance actions, which normally occur during the November to February window. Forecasts are required to be made without taking these actions into account, whilst the outturn does of course reflect such actions if they occur. 10

11 Mar-12 Jun-12 Sep-12 Dec-12 Mar-13 Jun-13 Sep-13 Dec-13 Mar-14 Jun-14 Sep-14 Dec-14 Mar-15 Jun-15 Sep-15 Dec-15 Mar-16 Jun-16 Sep-16 Dec-16 Mar-17 Jun-17 Sep-17 Nov-17 Solar capacity/output (MW) EMBEDDED PV During the previous Quarter, solar generation capacity increased from 12.9GW in Dec 17 to just over 13GW in March 18. The graph below depicts the daily maximum national solar generation output (based on the Sheffield Solar PV live data) and the installed solar generation capacity up until mid-february Smoothed Solar Capacity Daily Max Solar Output D ESC R IPT IO N O F SO L AR G ENER AT IO N AND F O R EC AST ER R O R S Our solar generation forecasts, as published on BMRS and the Data Explorer section of the National Grid website, arise from an internal PV generation forecasting model, based on a number of parameters:- Estimated capacities, Weather forecasts from our weather provider and Empirically derived models connecting radiation and national generation, using data from our NIA collaboration with Sheffield Solar. Our weather forecasts extend from the current day out to 13 days ahead, and so our solar generation forecasts have the same time horizon. Beyond this, we use seasonal normal weather. The figure below gives an illustration of our mean absolute solar generation forecast error for daylight hours, against lead time of the forecast in days ahead (DA). The error is measured against the national generation as estimated from our Sheffield Solar NIA project. Some further details on the trends are given in the analysis section. 11

12 It can be observed that our mean absolute PV error increased slightly over almost all forecast time horizons from Q3 to Q4 (with an average of 679MW in Q4 compared to 623MW in Q3), although the values are not atypical for the year as a whole. AC T IO NS AF F EC T ING SO L AR G ENER AT IO N F O R EC AST S AND ER R O R S During the period 4 th Dec 11 rd March, a number of actions were taken to optimise the solar generation forecast. As part of our regular PV model reviews, we refreshed the PV generation models twice leading to improvements in the mean absolute error. These improvements generally reduce with time until the next refresh. ANAL YSIS O F T HE C AUS ES O F SO L AR G ENER AT IO N F O R EC AST ER R O R S & F UT UR E AC T IO NS As a number of parameters feed into the forecast of solar generation, errors in each of these contribute to the discrepancy with the actual real generation. However, as error in capacity or outturn can only be estimated theoretically (as there are no standard benchmarks nor solar metering available to National Grid), we take the estimates of capacity and outturn as exact, and break our forecast error down into just two components Model Error and Weather Error:- Error = P (weather forecast) Observed Outturn = (P (weather actual) Observation) + (P (weather forecast) P (weather actual)) = Model Error + Weather Error where P is the function being used to convert weather data into solar generation data. 12

13 The radiation forecast is the main weather variable used from our weather forecasts. The figure below illustrates the values of mean absolute radiation forecast error seen during this Quarter. The figure is plotted for daylight hours only It can be seen that solar radiation forecast errors remained low in Qtr. 4, and comparable to those in Qtr. 3, which is also consistent with being at the start and end of winter. This is also discussed further in the Weather Analysis section. We know from sensitivity analysis of our internal models, that an error of 50kJ/m2 in solar radiation is equivalent to around a115mw error in our national PV forecast. Using this scaling to compare the values of solar radiation error to those of PV power error, we can see that the weather forecast error again drives a significant portion of our overall solar power forecast error. In this Quarter however, the solar power forecasting model error was at least as significant. This seems to have arisen from a long period until the first model refresh, and a shorter impact than previously experienced. We have made reviews of the newly proposed NIA solar power models and anticipate implementing these in the very near future. 13

14 WIND FORECASTS The key input to our forecasts is the wind speed forecast. The graph below shows the mean absolute error in wind speed forecast in m/s at different time horizons. As an indication of the impact of the errors, in the mid part of a wind power curve, between around 5 and 15 m/s, a 1 m/s error in wind speed forecast equates to around a 10% error in wind power forecast for the generator. Mean abs wind speed error (m/s) 13DA m/s 12DA m/s 11DA m/s 10DA m/s 9DA m/s 8DA m/s 7DA m/s 6DA m/s 5DA m/s 4DA m/s 3DA m/s 2DA m/s DA m/s In Day m/s The wind incentive measures our performance in forecasting the wind power that is available, and does not require us to forecast the actual wind output after the control room have taken bid -offeracceptances (BOAs) on wind farms to manage constraints or other system issues in real time. In order to report this, the forecasts for each individual wind farm for each half hour are recorded. For the half hour where a BOA is taken on that farm, the unit is discounted for the calculation of corrected forecast, corrected outturn and corrected installed capacity. The percentage error for the forecast for that half hour is then calculated from the corrected values, and a daily average is calculated which is used in the incentive formula to calculate the outcome for that day. In terms of performance for Qtr.4, the graphs below show the comparison of corrected forecast and actual wind generation for the period 4 th of December 2017 until 11 rd of March 2018, as well as the target daily average percentage error of 4.75% used to calculate the outcome of the incentive. 14

15 01-Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Mar Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Dec Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Jan Forecast and Actual Wind Generation (BOA Corrected) 04 Dec Jan Forecast Actual 14 Daily Percentage Forecast Accuracy Compared with Target 04 Dec Jan Daily APE Target Forecast and Actual Wind Generation (BOA Corrected) 1 Feb- 11 March Forecast Actual 15

16 01-Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Feb Mar Mar Mar Mar Mar Mar Daily Percentage Forecast Accuracy Compared with Target 1 Feb - 11 March 18 Daily APE Target Just prior to the existing Quarter, we embarked on a significant update to our wind power forecasting models, the effect of which was sustained through until March. During the current Quarter, our average forecasting errors after BOA s were 4.84%, 5.22%, 5.3%, and 4.52% for December, January, February, and March (up to 11 March 2018). Whilst for the most part still above the target of 4.75%, the accuracies were a significant improvement on Quarter 3. The second component of the wind incentive scheme is the bias incentive. In this component, the objective is to have as many over-forecasts as under-forecasts, with a target of 50:50, a breakeven of 60:40 and a maximum penalty of 70:30. There is also a separate term in the bias incentive that states that if any two individual forecast half-hours are 70% biased or greater, the maximum penalty is incurred. For the wind incentive, this means that if any two out of 48 half hourly forecasts are 70% biased or greater than the maximum penalty is incurred. This equates to 9 out of 31 forecasts in a month being one side of zero and 22 being the other side. In December we experienced no biased half-hours, whilst in January, and February we experienced just one biased half-hour each month outside of the 30:70 range. W IND G ENER AT IO N MO D E L L ING On the 28 th of November we completed a significant update of our wind power models for the transmission connected wind farms. The updated power models were used for the whole of this Quarter and this led to significantly lower wind power forecast errors than we otherwise would have experienced. In order to sustain the improvements, revisiting of the models will continue as before and we plan to explore new potential technologies over the coming months from our NIA projects. 16

17 ( C) WEATHER ANALYSIS The demand forecast models use a number of inputs. One of the major inputs is the weather forecasts for selected locations around the country. These come with their own set of errors. Depending on the situation, weather forecast errors can occasionally be compensated for manually provided there is any certainty on the expected error. The graphs below show the mean absolute errors in the forecasts that we receive for our key weather parameters over the 0 to 13 day- ahead forecast horizons. Temperature is measured in C, with an error of 1 C approximating to a demand forecast error of ~500 MW. Solar radiation is measured in kj/m 2, with an error of 50 kj/m 2 approximating to an error of around 115 MW in our PV forecasts. Note that the graph below shows the MAE calculated across all hours of the day (in contrast to the data in the PV section where only daylight hours MAE is shown) Wind speed is measured in m/s. A forecast error of 1 m/s within the range of 5 15 m/s approximates to an error of 10% of capacity. Each graph shows the mean absolute weather forecast error at different lead times, as well as a comparison to the same error for the previous Quarters In Day Q1:Q4 MAE of Forecasted Temperature from in-day to D13 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 Forecasting horizon (days ahead) Q4 Q3 Q2 Q1 The quality of the temperature forecast is very similar to other Quarters up to a week ahead. From D+7 onwards a striking change in forecast quality can be seen. 17

18 (m/s) During this Quarter we experienced both larger and more frequent deviations from seasonal normal temperatures, whereas by their nature, the D+7 forecasts tend towards seasonal averages. A particularly strong example of extreme cold weather occurred from late February, when cold weather settled over the UK, brought by easterly winds from Russia. The comparison to seasonal normal/climatic weather will be presented and discussed more at the end of this section. (kj/m 2) In Day Q1:Q4 MAE of Forecasted Radiation (all hours) from in-day to D13 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 Forecasting horizon (days ahead) Q4 Q3 Q2 Q1 The accuracy of the solar radiation forecast during Quarter4 was the best over all the quarters. For solar radiation, the insolation envelope is much smaller during the winter season. This leads to a smaller range of possible outcomes. On average this leads to a smaller mean absolute error. Q1:Q4 MAE of Forecasted wind speed from in-day to D In Day D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 Forecasting horizon (days ahead) Q4 Q3 Q2 Q1 18

19 04/12/ /12/ /12/ /12/ /01/ /01/ /01/ /01/ /01/ /02/ /02/ /02/ /02/ /03/ /03/2018 The error in the wind speed is fairly comparable to the third Quarter, up to a week ahead, being as they are both within the winter periods. The winter months tend to be among the windiest and most volatile months of the year, leading to higher mean absolute errors. As per temperature forecasts, from D+7 onwards, the error increased more than during the previous Quarter. Similar to forecasts of temperature, extreme conditions are harder to forecast (for example storm Emma which occurred in late February). COMPARISON TO SEASONAL NORMAL WEATHER In the graphs below, we take a closer look at how the midday temperature and wind speed outturns compared to the seasonal normal values Observed National Average Temperature against 30-year average at noon Observations Climate 19

20 04/12/ /12/ /12/ /12/ /01/ /01/ /01/ /01/ /01/ /02/ /02/ /02/ /02/ /03/ /03/ Observed National Average wind speed against 30-year average at noon Observations Climate During this recent Quarter, both of the above parameters show significant variation compared to the seasonal normal values. The wind speeds show greater variability than temperatures, although the average wind speed was balanced around the seasonal average. Temperatures showed a number of very extreme cold conditions for the time of year. The two extreme cold events at the beginning and end of the Quarter also caused the Quarter to be colder on average than the seasonal norm. The cold spell at the end of February/beginning of March was particularly extreme. The 1 st of March saw the lowest noon temperature observed. This day was also affected by snow and features in the top 5 highest errors for the two day ahead and seven day ahead forecasts (see Appendix 2). Extreme and unusual weather conditions can lead to significantly different demand and wind outturns compared to forecast, which can markedly affect performance figures over short to medium length periods of time. 20

21 WORK TO IMPROVE FORECASTS Some of our work to improve our forecasting accuracy has been mentioned in earlier sections, but for completion we highlight and collate these activities again here. The internal PV generation forecast model has been updated twice, once in February and again more recently in March, both of which led to immediate accuracy improvements. We made fuller reviews of the two solar power models from our recent NIA projects, and anticipate updating our solar power models shortly. We continue to update our demand models each season as well as review our wind power forecasting models. Work is continuing to build upon the work completed with our external partners and NIA projects, as well as our strategic review of the Energy Forecasting system. 21

22 CONCLUSION A detailed analysis was presented describing the errors and sources of errors in National Grid s demand, solar and wind generation forecasts for the period 4 th December 2017 to 11 th March In addition, the work that has been done to improve these forecasts was described. Our late evening and overnight cardinal point demand forecast errors were generally comparable to or reduced compared to the previous Quarter. This was in spite of more volatile and difficult to forecast weather. The errors for the remainder of our cardinal point forecasts increased however, including in particular for the evening DP cardinal point, due to Triad Avoidance. For this Quarter there was a slight widening of the gap between our incentivised targets and our forecast errors. The total number of large demand forecast errors, greater than 1GW, increased slightly compared to the previous Quarter; consistent with expectations for this time of year. During this period we observed a peak national demand of 49.8GW on the 1 st of March 2018, and an overnight minimum of 18.3GW on Christmas Day. The peak national demand of 49.8GW was also the peak for the whole reporting period (1 st April th March 2018), and was particularly notable for occurring for the first time outside of the Triad Period (November to February). The above event occurred during a week of extremely cold weather, which provided a unique set of challenges. As the peak occurred towards the end of the week, we were able to apply learning from earlier days to optimise our performance, with the final error coming in at 817MW underforecast. This period illustrated well the challenges involved in forecasting during unusual sets of conditions, and the effect that short period events can have on overall performance. Our embedded PV generation forecast errors were slightly elevated, but not atypical for the year as a whole. We anticipate making a significant update to our solar power forecasting models during the first quarter of financial year 2018/19. Wind generation forecasting improved compared to the previous Quarter, whilst still reporting errors generally above the target threshold of 4.75%. We attribute this improvement to the campaign of wind power forecasting model upgrades undertaken in late November, which helpe d to underpin our performance through to March. Our wind forecasts were also generally unbiased during this period. Both our wind generation and demand forecasting model suites will continue to attract focussed updates and improvements to underpin our forecasting performance. In spite of a backdrop of changing system conditions we have continued to provide high quality demand and wind generation forecasts. With system changes such as increasing distribution-connected generation and new technologies, maintaining transmission-system demand forecast accuracies will be increasingly challenging. To meet these new challenges we plan to build actively upon our existing work and projects with the final objective to deliver real value to market participants and final consumers. 22

23 APPENDIX 1 SOURCES OF DEMAND FORECAST ERROR Forecast error is made up of many independent, but interacting components. On some days small errors due to several different components can result in a large overall error, while on other days two very large errors can cancel out leaving a net very accurate forecast. The components of error are: Model error - errors in demand model coefficients. Coefficients are estimated from data.. Residual in demand forecast models, which represents the variation in underlying demand unexplained by model. Short term demand fluctuations (1 day to 1 week) unexplained by the model. Estimated by the forecasters choice of basic demand. Error in outturn demand demand data received by NG differs from real outturn demand because of metering errors, data transfers errors. In addition outturn demand is calculated on an assumption that the frequency is a constant 50 Hz. As frequency deviates from this figure the actual demand differs from the calculated demand. We can estimate that this introduces an error of up to ± 100 MW in the outturn demand. Error in weather forecast data difference between forecast and actual observed weather at weather stations can be measured, and our models can be used to retrospectively estimate the impact of these errors on our demand forecasts Error in wind and PV capacity our embedded wind and solar models assume a certain capacity, and its geographical location. National Grid has limited data on such installations, and data has to be gathered from publically available data sources. This data is neither exhaustive nor up to date. Error in solar radiation weather forecasts the solar radiation forecasts are a key input to our forecasts for PV generation, and can be a significant source of errors. Error in PV models we use models to convert forecast solar radiation into forecast PV load factors. The models use radiation forecast at a single point to represent solar radiation incident on a very large area on which PV installations are installed. We currently use 53 solar radiation measuring stations across GB, so on average each measuring point represents an area of 8840 km 2. The error can be estimated by comparing model output with model estimates of outturn PV generation, as discussed next. Error in outturn PV NG in collaboration with the Sheffield Solar team of Sheffield University produces an estimate of outturn PV generation. It is based on a limited sampling from a number of installations combined with complex algorithms. There is an approximately 10% error in this estimate of PV generation. Error in wind speed weather forecasts we currently receive wind speed forecasts for around 100 locations in the country. Some of these are weather station locations where observations are available, allowing us to quantify the error at these locations, but other forecasts are for sites near large wind farms where outturn data may not be available. We can however estimate the error from the points that we can measure. Error in embedded wind generation models we use models to convert the nearest available wind speed forecast into a generation forecast for embedded wind farms. As per embedded PV, there is no available output data available to National Grid. Error cannot be quantified. We assume these errors are broadly comparable to the errors in metered wind farm forecast. 23

24 APPENDIX 2 BREAKDOWN OF LARGE INDIVIDUAL ERRORS DAY AHEAD FORECASTING ERRORS Date CP Forecast Actual Error Forecaster Note This turned out to be a very sunny day B Radiation was under-forecast by 485 kj m DP B DP B DAY AHEAD FORECASTING ERRORS This day had an estimated 1800MW demand reduction for TRIAD avoidance This was during the lead-up to the cold spell that affected all of Great-Britain. On this day, there were certain area s affected by snow, but not the entire country. The behavioural response was smaller than anticipated. This day had an estimated 2000MW demand reduction for TRIAD avoidance This day had a high degree of uncertainty associated with an inactive frontal system that was moving west-east over the country. This lead to some drizzle, but generally bright conditions. Radiation was underforecast by 202 kj m -2 Date CP Forecast Actual Error Forecaster Note This was a very dull day. Radiation was over-forecast by 194 kj m -2. Temperature B was under-forecast by 1.9 C. A rain front slowly moved west-east over the country, lingering in the east in the afternoon DP This was an unseasonably warm evening, with observed temperatures at 17:00 approximately 6 C higher than the seasonal average. Wind speeds were under-forecast by 4 m s DP B DP On this day there was an estimated 1800 MW demand reduction for TRIAD avoidance. Temperatures were underforecast by 0.8 C This was a very cold day with observed temperatures at 12:00 approximately 9 C below seasonal average. Radiation was over-forecast by 247 kj m -2. There was snow on the ground and on the solar panels. A high demand day. This day had an estimated 1600MW demand reduction for TRIAD avoidance. 24

25 7 DAY AHEAD FORECASTING ERRORS Date CP Forecast Actual Error Forecaster Note This was a very cold day with observed temperatures at 12:00 approximately 9 C below seasonal average. Temperatures B were over-forecast by 2.9 C. Radiation was over-forecast by 602 kj m -2. Wind speeds were over-forecast by 5 m s -1 There was snow on the ground and on the solar panels DP The temperature was 2.5 C warmer than it was forecasted. Wind was under-forecast by 8 m/s. It was also duller during the day compared to the forecasted brightness (some of the load that on a brighter day would come on around DP was already on) B This day was a very sunnier day and sunnier than forecast. Radiation was under-forecast by 906 kj m -2 and temperatures were 0.6 C higher than forecast B It was sunnier, warmer and significantly windier than forecast. Radiation was underforecast by 168 kj m -2, temperatures were under-forecast by 1.5 C. Wind was underforecast by 8 m/s B This day was much sunnier and warmer than forecast. Radiation was underforecast by 463 kj m -2, temperature were under-forecast by 2.3 C, wind was underforecast by 5 m s -1 25