The potential role of forecasting for integrating solar generation into the Australian National Electricity Market

Transcription

1 The potential role of forecasting for integrating solar generation into the Australian National Electricity Market Ben Elliston 1, Iain MacGill 1,2 1 School of Electrical Engineering and Telecommunications 2 Centre for Energy and Environmental Markets University of New South Wales Sydney, NSW 2052 b.elliston@student.unsw.edu.au ABSTRACT The future construction of utility-scale solar power plants in Australia will present challenges to integrate these plants into the existing electricity industry. This paper considers the likely solar forecasting requirements to facilitate the integration of photovoltaic and concentrating solar thermal plants into the Australian National Electricity Market (NEM). Many market participants may benefit from reliable solar forecasts, but they will have different requirements for accuracy and forecasting horizon. The current state of solar forecasting techniques worldwide is briefly reviewed. Electricity market designs in Europe (in particular, the Spanish day-ahead market) have promoted research in solar forecasting. In Australia, the Bureau of Meteorology is producing solar irradiance forecasts from its current weather forecasting system, and there are growing efforts to develop useful solar forecasts. Nevertheless, there is much further work to be done to integrate solar generators into the NEM, including evaluating the accuracy of solar forecasts, better characterising the solar resource and understanding how forecasting accuracy can improve plant economics and power system operation. Keywords: utility-scale, grid integration, solar forecasting, solar thermal Introduction Australia has recently experienced significant growth in the installation of solar photovoltaic (PV) systems. Feed-in tariffs have only supported small-scale residential

2 and commercial PV systems to date. The Commonwealth Government Solar Flagships program is, however, likely to lead to the construction of several utility-scale (above 150MW) solar power plants, of both PV and solar thermal types, in the near future. Renewable energy policies in Spain and Germany are already giving rise to grid integration issues (Pulvermueller et al. 2009; Bofinger and Heilscher 2006; Wittmann et al. 2008). Widespread deployment of solar electricity generation will, in the longer term, depend crucially on successful integration of these generators into the Australian National Electricity Market (NEM). The variable nature of the solar resource poses significant challenges for successful and economically efficient integration into the electricity industry. Reliable forecasts of solar irradiance can therefore play a vital role. This paper considers the forecasting requirements to integrate solar generators into the NEM. Stakeholders of reliable solar forecasts include solar generators, the Australian Energy Market Operator (AEMO) as the Independent System Operator and Market Operator, and other generators in the NEM. We then review the state of solar irradiance forecasting in the literature. The present Australian solar forecasting capability is described and we conclude with future research directions for this problem. Solar forecasting needs of the NEM Many of the issues facing solar power plant integration in Australia have already been encountered in the wind power sector. The integration issues for solar and wind are similar and much of the effort to integrate wind power has paved the way for integration of solar plants, particularly with respect to forecasting and scheduling. Each of the stakeholders in the NEM has different requirements for solar forecasts. These will be discussed later in this section. AEMO divides generators into three classes: non-scheduled generators with a nameplate rating under 30MW; semi-scheduled intermittent generators over 30MW; and scheduled all other generators over 30MW. All of the short-listed Solar Flagships projects carry a nameplate rating well in excess of 30MW (Department of Resources, Energy and Tourism 2010). The projects based on PV technology range in size between 150MW p and 195MW p and the solar thermal projects between 150MW el and 250MW el. It seems probable that these plants will be registered with AEMO as semi-scheduled generators. 1 3 December 2010, Canberra, ACT, Australia 2

3 Wind farms were initially treated as non-scheduled generators, allowing wind farms to generate maximum power in each dispatch interval except under particular network conditions (Cutler 2009). South Australian licensing conditions introduced after wind power expanded significantly in the State then saw a number of wind farms required to register as scheduled plants. In 2008, the semi-scheduled generators classification was introduced across the NEM. A semi-scheduled generator bids into the spot market as a scheduled generator does, and may be subject to directives from the power system operator to reduce power output under certain system circumstances. AEMO forecasting requirements AEMO already uses a centralised wind forecasting system for power system operation known as the Australian Wind Energy Forecasting System (AWEFS). AWEFS was funded by the Commonwealth Department of Resources, Energy and Tourism and developed by a third party vendor, who continues to operate the system on behalf of AEMO (AEMO 2009). The forecasting system is predominantly used for maintaining power system security rather than for optimising wind farm scheduling. AWEFS provides forecasts on a range of horizons from five minutes to two years. AEMO is likely to have similar requirements for solar generation. Indeed, AEMO have already anticipated extending the wind forecasting system to include solar resource information (AEMO 2009). For AEMO, the main purpose of solar forecasts will be to maintain power system security. AEMO will require forecasts on a range of time horizons. Very short-term forecasts (minutes) are relevant to the management of ancillary services. Medium-term forecasts of hours to a day ahead can help inform unit commitment decision making to maintain adequate supply reserves. Longer term forecasts (days to weeks and beyond) will support decision making for security of supply issues including generator maintenance and possible forecast reserve shortfalls. Solar generator forecasting requirements Solar generators will use solar irradiance forecasts to optimise plant operation and profitability. Two solar generation technologies are of interest: photovoltaics, both flat plate and concentrating types, and concentrating solar thermal power (CSP). Each of these will be considered in turn. The power output of photovoltaic cells is proportional to irradiance. A small PV array can ramp down very quickly if a cloud moves overhead in otherwise sunny conditions. A much larger array, or an array distributed over a wider geographic area, may temper the ramp rate by increasing the time it takes for a cloud to completely shade the array. For a 100MW array, the time taken to traverse the 1 3 December 2010, Canberra, ACT, Australia 3

4 array is in the order of minutes (Boerema 2010). Once the cloud clears, power may ramp up again as quickly as it declined. CSP plants rely on direct beam irradiance and usually have a non-linear response to irradiance (Hammer et al. 2009). CSP plants with storage can ride through cloud events. However, even CSP plants without specific storage facilities will possess some degree of thermal mass in the working fluid, allowing the plant to ride through brief periods of cloud cover. The thermal mass will also allow the system to ramp down more slowly and predictably than PV if cloud cover is persistent. For PV and CSP plants, the operator wishes to know what the power output of the plant will be at a future time in order to make profitable market decisions. When thermal storage is involved in a CSP system, forecasts will form another input into the decision making process, that is managing the thermal store. A semi-scheduled or scheduled generator in the NEM is required to submit 48 halfhourly dispatch offers (4am to 4am) by noon the previous day. This gives a total forecasting horizon of 40 hours. The offers consist of the energy quantity for the halfhour and a price. As the dispatch time approaches, the NEM permits re-bidding on five minute dispatch intervals. An offer for a given dispatch interval can be revised until moments before the start of the interval. The energy quantity can be revised, but the price cannot. The only commercially significant price under these arrangements is the final five minute wholesale spot prices (averaged to provide a half hour price). Other countries can have quite different arrangements. The Spanish electricity industry operates a day-ahead market that also requires generators to anticipate 24 hours of generation, 16 hours ahead of dispatch (Pulvermueller et al. 2009). In contrast to the NEM, there are only six opportunities through the day to adjust bids on a shorter forecast horizon. Generators are fined for deviating from the energy quantity it has bid (above or below). Hence, reliable forecasts are critical to improving profitability. In the NEM, a semi-scheduled or scheduled generator pays ( causer pays ) for ancillary services that AEMO must call upon to meet demand and control frequency. If a generator fails to supply the energy it has bid, it is subject to causer pays costs to pay for ancillary services that are purchased to ensure that demand is met. These costs are effectively a penalty for deviation. To minimise causer pays costs, a solar generator could greatly benefit from accurate dispatch offers. The re-bidding mechanism in the NEM means that solar generators can tolerate reasonably poor day-ahead forecasts, as bids can be continuously revised up until dispatch (Thorncraft et al. 2008). However, solar generators in the NEM will benefit from very short-term forecasts covering the five minute duration of the next dispatch interval. What is less clear is the potential benefits for these generators of longer-term forecasts. Many of the conventional generation plants in the NEM participate in derivative markets that enable them to secure a fixed price for their generation in future 1 3 December 2010, Canberra, ACT, Australia 4

5 time periods. Successful risk management with these contracts requires that generators are able to reliably dispatch at least their contract volume at these future periods. This is clearly a significant challenge for highly variable and somewhat unpredictable wind and solar generation. Market participants with a portfolio of plants that might include a number of wind farms or solar plants as well as flexible conventional plant such as gas turbines could benefit significantly from useful forecasts out to a day or more in managing their fossil fuel units. Wittmann et al. (2008) have published one of very few papers on market operation of a CSP plant using solar forecasts. The work compares the daily market revenue of a CSP plant bidding into the Spanish day-ahead market using meteorological and chemical (aerosol) forecasts of direct normal irradiance (DNI). The relevant finding of this work is that, due to the penalty mechanism in the Spanish market, the forecast accuracy required by a solar generator is time dependent. The time at which there is a supply deviation and the direction of the deviation (surplus or deficit) has a significant impact on revenue. This is also relevant in the Australian context. Short-term forecasts must be more accurate around mid-day because this is when absolute forecasting errors can be greatest. Thermal storage can play a role in minimising the impact of these costs. Non-solar generator forecasting requirements Other generators (wind, other renewable, or fossil fuel) will require accurate solar irradiance forecasts on a longer time horizon (hours to days) to improve their bidding into the spot market. If these generators (and AEMO) can predict when solar irradiance is likely to be low or highly variable, dispatch planning can be performed to ensure generation is available from other sources. The ramp rates and start-up times of conventional plants will dictate the time horizon required for the forecast. For example, multiple days of cloudy weather, forecast with one day s notice, could be met by an inflexible fossil-fuelled plant if sufficient time is given to bring on and ramp up additional generation. Similarly, more responsive plants (e.g. gas turbines) could meet brief shortfalls with less notice. Such dispatch considerations should be reflected in the derivative markets associated with future regional prices in the NEM. Forecasting solar irradiance The literature describes two classes of techniques for irradiance forecasting: statistical processing of satellite images and numerical weather prediction (NWP). The former is generally considered to be suited to short-term forecasts up to six hours; the latter to forecasts up to two days ahead or beyond (Heinemann et al. 2006). 1 3 December 2010, Canberra, ACT, Australia 5

6 Numerical weather prediction Global scale NWP models such as that used by the European Centre for Medium- Range Weather Forecasts (ECMWF) are the basis for longer term solar irradiance forecasts, but have a coarse spatial and temporal resolution. To improve this resolution, it is necessary to apply the NWP output to a regional model or use statistical post-processing of the NWP output. Glahn and Lowry (1972) performed the earliest work on the application of statistical post-processing. In Model Output Statistics (MOS), a statistical relationship is established between the predicted variable and the forecast values of a number of meteorological variables, as produced by a numerical weather model. A technique called screening regression is used to select the independent variables to include in regression equations. Glahn and Lowry (1972) applied MOS to forecast various meteorological conditions twice daily: probability of precipitation, surface wind speed, maximum temperature, cloud cover and probability of frozen precipitation. For cloud cover, which is of particular interest, a simple coding of the predicted variable classifies the degree of cloud cover around each weather station. The values range from zero to eight, where zero represents a clear sky and eight is completely obscured. Satellite image forecasting Short-term global irradiance forecasts can be produced by processing successive satellite images taken by geostationary satellites. These satellites, using sensors sensitive to the visible and near-infrared band, observe the amount of radiation reflected back to the satellite sensor from the surface, clouds and the atmosphere. Figure 1 shows a sample image taken over Australia by the MTSAT-2 weather satellite operated by the Japanese Meteorological Agency. For over a decade, efforts have been on-going to improve the determination of surface global irradiance using satellite images. Weymouth and Marshall (1999) established a solar radiation data service at the Bureau of Meteorology (the Bureau) using images from weather satellites in the region. European research groups have been carrying out similar work using satellite data from the Meteosat satellite over Europe. Several algorithms for forecasting surface irradiance are described in the literature, but all are based on the idea of predicting the detail of a future satellite image and using some combination of the established irradiance estimation models to determine surface global irradiance. Hammer et al. (1999) describe a statistical technique to predict cloud motion using motion vector fields. This forecast is most accurate on a 30 minute to two hour horizon. This technique assumes that the motion vectors for cloud features between 1 3 December 2010, Canberra, ACT, Australia 6

7 Figure 1: Infrared image of Australia at 0500 UTC, July 15, 2010 (Japan Meteorological Agency) the current and previous satellite images will be the same as the motion vectors between the current and next image. It also assumes that the intensity of pixels in a given feature do not change along the motion path. Hammer and colleagues found that images with high variability (a high average difference in intensity between adjacent pixels) are harder to predict. In addition to forecasting global irradiance, concentrating PV and CSP plants require forecasts or historical DNI data for a site. This has led to the development of numerical models for estimating DNI from global irradiance data. Favourable renewable energy policies in Spain, where direct beam insolation is high, have caused rapid development of medium-scale solar thermal plants (around 50MW). Solar thermal plant developers wish to determine the energy yield of their plants at a given site, but may not accept delays collecting long-term DNI data from ground instruments. Where historical global irradiance data is available, either as observations from pyranometers or derived from satellite data, numerical models permit the direct and diffuse components of global irradiance to be estimated (Gastn et al. 2009; Hammer et al. 2009; Pagola et al. 2009; Ridley et al. 2010). Ridley et al. (2010) developed a multiple predictor model capable of predicting diffuse horizontal irradiance from global horizontal irradiance. This model was developed due to the poor results obtained when applying European and North American DNI models in Australia. A modified form of it is used by the Bureau as part of the process of estimating DNI. Australia has few solar observation stations, so it is a stated objective of the model to use as few measured predictor variables as possible to reduce the need for data from ground measuring equipment. 1 3 December 2010, Canberra, ACT, Australia 7

8 Local sensors For the very short-term forecasting needs of solar generators described earlier, the use of local sensors may produce superior results to space-based forecasts in a cost effective way. Modica et al. (2010) emphasises the scheduling needs of mini-grids and the need for very short term solar forecasting. It is in this work that the use of a ground-based camera is suggested for the first time, providing localised and short term forecasts. As the cost of such equipment will be small compared to utility-scale plants, the benefit of additional local sensors should be investigated. Australian solar forecasting capability The Bureau routinely produces solar irradiance forecasts from a regional model. Dennis (2004) used daily solar irradiation forecasts to improve the control of auxiliary heating in solar hot water systems. However, there has been very little use of solar irradiance forecasts in Australia to date. The National Meteorological & Oceanographic Centre (2010) at the Bureau has recently converted its NWP model suite to a new suite called the Australian Community Climate and Earth-System Simulator (ACCESS). ACCESS offers higher spatial resolution and enhanced model physics. In particular, the suite improves the physical modelling of cloud. Consequently, the accuracy of solar irradiance forecasts is expected to improve. The new NWP model produces direct and diffuse components of irradiance and with a forecast horizon of 48 hours. It is expected that solar irradiance forecasts will have improved since they were evaluated by Dennis (2004). However, solar irradiance forecasts from the new suite require further evaluation. The technique described to forecast irradiance using motion vector fields derived from satellite images is cost effective and has good spatial coverage. This method can be used to improve day-ahead bid offers for solar generators in the NEM, but even shorter term forecasts may be required. The limitation of this approach is the imaging frequency of weather satellites. The satellite images produced by the Bureau are obtained from the Japanese MTSAT-2 satellite which has a temporal resolution of 60 minutes (although its replacement will offer 10 minute resolution from 2015). The Meteosat Second Generation satellite used in the EU refreshes images every 15 minutes and has a higher spatial resolution than MTSAT-2 (Hammer et al. 2009). Further work Solar forecasting research is very active in Europe due to the commercial realities of operational utility-scale plants and the expectation of more renewable generation as a result of government policies. The same concerns are now emerging in Australia 1 3 December 2010, Canberra, ACT, Australia 8

9 and it can be expected that solar forecasting efforts are now a topic of growing interest. It has been shown that the current structure of the NEM should not cause major difficulties for the early deployment of solar generators. The ability to re-bid until just before the dispatch interval removes risks associated with uncertainty of longterm forecasts. However, this paper has raised a number of areas that merit closer examination: the accuracy of solar forecasts produced by the current Bureau numerical weather prediction model; the performance of the European motion vector field techniques on satellite images produced by the Bureau and what consequences the reduced temporal resolution has on short-term forecasts covering the dispatch interval; the accuracy of solar forecasts on different time horizons and whether these are sufficient to meet the constraints of ramp rates of current conventional generators in the NEM; the cost effectiveness of improving the accuracy of solar forecasts in order to reduce causer pays costs; and the implications and potential value of forecasts for market participants managing their generation portfolios. Acknowledgements We thank Merlinde Kay (UNSW) and Lawrie Brown (UNSW@ADFA) for their feedback. Biography of presenter Ben Elliston is a PhD candidate in the School of Electrical Engineering and Telecommunications at UNSW. After 10 years of developing optimising compilers and other programming tools, he commenced a PhD in 2010 to pursue his interest in renewable energy. His research investigates integrating solar electricity into the grid at high penetration. 1 3 December 2010, Canberra, ACT, Australia 9

10 References AEMO (Australian Energy Market Operator) 2009, AEMO AWEFS, World Wide Web electronic publication. Last accessed August 4, URL: Boerema, N. 2010, Renewable Energy Integration into the National Electricity Market: Characterising the Energy Value of Wind and Solar Generation. Honours thesis, University of New South Wales. Bofinger, S. and Heilscher, G. 2006, Solar electricity forecast approaches and first results, in 21st European Photovoltaic Solar Energy Conference, pp Cutler, N. 2009, Characterising the Uncertainty in Potential Large Rapid Changes in Wind Power Generation, PhD thesis, School of Electrical Engineering and Telecommunications, University of New South Wales. Dennis, M. 2004, Active Control of Split System Domestic Solar Water Heaters, PhD thesis, Department of Engineering, Australian National University. Department of Resources, Energy and Tourism 2010, Solar flagships project descriptions round 1, World Wide Web electronic publication. Last accessed August 5, URL: Gastn, M., Pagola, I., Fernndez, C., Ramrez, L. and Mallor, F. 2009, A New Adaptive Methodology of Global-to-Direct Irradiance Based on Clustering and Kernel Machines Techniques, in Proceedings of 15th SolarPACES Conference, Berlin, Germany. Glahn, H. R. and Lowry, D. A. 1972, The Use of Model Output Statistics (MOS) in Objective Weather Forecasting, Journal of Applied Meteorology 11(8), Hammer, A., Heinemann, D., Lorenz, E. and Lckehe, B. 1999, Short-term forecasting of solar radiation: a statistical approach using satellite data, Solar Energy 67(1-3), Hammer, A., Lorenz, E., Kemper, A., Heinemann, D., Beyer, H., Schumann, K. and Schwandt, M. 2009, Direct normal irradiance for CSP based on satellite images of Meteosat second generation, in Proceedings of 15th SolarPACES Conference, Berlin, Germany. Heinemann, D., Lorenz, E. and Girodo, M. 2006, Forecasting of Solar Radiation, in E. Dunlop, L. Wald and M. Suri, eds, Solar Energy Resource Management for Electricity Generation from Local Level to Global Scale, Nova Science Publishers, New York, chapter 7, pp URL: December 2010, Canberra, ACT, Australia 10

11 Modica, G. D., d Entremont, R., Mlawer, E. and Gustafson, G. 2010, Short-Term Solar Radiation Forecasts in Support of Smart Grid Technology, in First Conference on Weather, Climate, and the New Energy Economy, 90th American Meteorological Society Annual Meeting. National Meteorological & Oceanographic Centre 2010, ACCESS NWP Data, World Wide Web electronic publication. Last accessed August 5, URL: Pagola, I., Gastn, M., Fernadez, C., Torres, J. L., Silva, M. A. and Ramrez, L. 2009, Comparison and Fitting of Several Global-to-Beam Irradiance Models in Spain, in Proceedings of 15th SolarPACES Conference, Berlin, Germany. Pulvermueller, B., Schrdter-Homscheidt, M., Pape, B., Casado, J. and Riffelmann, K.-J. 2009, Analysis of the Requirements for a CSP Energy Production Forecast System, in Proceedings of 15th SolarPACES Conference, Berlin, Germany. Ridley, B., Boland, J. and Lauret, P. 2010, Modelling of diffuse solar fraction with multiple predictors, Renewable Energy 35(2), Thorncraft, S., Outhred, H., Clements, D. and Barker, F. 2008, Market-Based Ancillary Services in the Australian National Electricity Market for Increased Levels of Wind Integration, Wind Engineering 32(1), Weymouth, G. and Marshall, J. L. 1999, An operational system to estimate global solar exposure over the Australian region from satellite observations, Australian Meteorological Magazine 48(3), Wittmann, M., Breitkreuz, H., Schroedter-Homscheidt, M. and Eck, M. 2008, Case Studies on the Use of Solar Irradiance Forecast for Optimized Operation Strategies of Solar Thermal Power Plants, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 1(1), December 2010, Canberra, ACT, Australia 11