Preliminary Impacts of Wind Power Integration in the Hydro-Quebec System
Page content transcription
If your browser does not render page correctly, please read the page content below
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 35 Preliminary Impacts of Wind Power Integration in the Hydro-Quebec System André Robitaille1, Innocent Kamwa2, Annissa Heniche Oussedik2, Martin de Montigny2, Nickie Menemenlis2, Maurice Huneault2, Alain Forcione2, Richard Mailhot3, Jacques Bourret4 and Luc Bernier4 1Hydro-Québec Production, 75 boul René Lévesque Ouest 9e étage, Montréal, Québec, Canada, H2Z 1A4 E-mail: firstname.lastname@example.org 2Institut de recherche d’Hydro-Québec (IREQ), 1800 boul. Lionel-Boulet, Varennes, Québec, Canada, J3X 1S1 3Hydro-Québec TransÉnergie, C.P. 10000, Succ. Pl. Desjardins, tour Est, étage B1, Centre Hydro, Complexe Desjadins, Montréal, Québec, Canada, H5B 1H7 4Hydro-Québec Distribution, 75 boul René Lévesque Ouest, 22e étage, Montréal, Québec, Canada, H2Z 1A4 ABSTRACT Recent studies undertaken by Hydro-Québec evaluate three aspects of the integration of wind generation on their system reliability/security. In an operations setting, the impacts on intra-hourly operating reserves and on extra-hourly balancing reserves are examined. On an operations planning horizon, the wind power capacity credit is evaluated for winter peak loading conditions, when very cold temperatures risk disabling part of the wind generation. Depending on the study, various mathematical tools were used to generate the statistical characteristics of the load and anticipated wind generation: time-series analysis, wind simulation at new/future wind plant sites, power system simulation and a posteriori determination of forecast errors. However, in each case the measure used to quantify the impact of wind generation has been related to the change in the variance of the total system uncertainty as a result of the addition of wind power generation. Keywords: Wind-hydro, impacts, reliability, reserves, load-following, regulation, balancing, capacity credit 1. INTRODUCTION Hydro-Québec (HQ) has taken advantage of the vast hydropower potential on its territory to develop a green and renewable energy base. Its installed hydro capacity stands at 97% of its total generation capacity of about 40 GW. It is now turning increasingly to wind power as a complementary source of renewable energy. Accordingly, it has been decided that the installed wind capacity will reach 10% of that of hydro by 2016, or about 4000 MW, and that this ratio will most likely be maintained thereafter. Presently, the impacts of this rapid integration of wind power on the power system are being evaluated. System reliability/security issues have been prioritized, while technical and economical effects on water management and market-related issues are targeted for a second phase of the study. Accordingly, the reliability/security-related aspects considered and presented here are the impacts of wind power on
36 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM 1. Operating reserves, to mitigate the effects of sudden changes in the power system (contingencies) and of slower frequency deviations due to load and wind generation variability in the intra-hourly time frame. These reserves essentially address power system security. 2. Balancing reserves, to mitigate the consequences of inherent load and wind generation forecast errors over the time horizon of 1 to 48 hours. These reserves essentially address economic aspects of short-term supply adequacy. 3. System capacity adequacy, to address reliability aspects of long term supply adequacy taking into account coincident load and wind power series with real weather conditions, and further the forced outages of the wind turbines induced by very cold temperatures (under −30°C). Important variables influencing the amount of required reserves are the capacities and geographical dispersion of the wind power plants, the magnitude and profile of the load, the coincidence between load and wind power, and the (winter) meteorological conditions at peak load. These impacts and underlying variables are not specific to a massively hydro- electric system; nevertheless, a thorough analysis of each of these aspects sheds light on the advantages that a large hydro capacity can offer in efficiently integrating wind power in the electrical system. This paper summarizes three studies undertaken at HQ [1−5], each assessing one of the above impacts. The studies are based on the addition into the power system of 3000 MW of wind power capacity over 23 wind power plants, either presently built or under contract to be built by 2016. Figure 1 illustrates their locations in the province. Figure 1: Locations of the 23 wind power plants considered in the study.
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 37 2.PRELIMINARIES 2.1. Reserve types and their magnitudes at HQ HQ maintains six types of reserves, which are grouped in two broad categories as shown in Table 1. These are listed below from the most to the least fast-acting. The first five types taken together constitute the operating reserves. Of these, the first three react to contingencies. Stability or spinning reserves, typically 1000 MW, serve to stabilize power/voltage immediately following a system contingency. They are sized to cover 60% of the largest single loss of generation. The 10-minute and 30-minute reserves further aid in the recovery of system frequency in the minutes following the contingency. The 10-minute reserves, which consist of non-firm sales, interruptible load and a large portion of stability reserves, typically are also set around 1000 MW. The 30-minute reserves, typically about 500 MW, cover 50% of the second most severe single loss of generation. The next two types of reserves act to counter the variability of system load and wind generation. Frequency regulation reserves use the automatic generation control (AGC) to counter slow frequency variations in a time frame of minutes; they operate over a 500 MW (minimum) modulation range. Load-following reserves operate over a longer time frame within the hour. They do not have a strictly defined standard, since this function can be performed practically without any constraint on the amount of power that can be obtained. This is because most often power is readily available in capacity and ramping rate from HQ’s large hydro-generation base. The energy-balancing reserves, or simply balancing reserves, act to counter uncertainties in load and wind generation forecasts over a time horizon of 1 to 48 hours ahead. They can vary from 1500/1200 MW (winter/summer) in the day-ahead time frame to 500 MW in real-time two-hours ahead. These reserves consist mostly of calls to non-firm export sales, interruptible load, gas turbines, etc. Since HQ participates actively in neighboring electricity markets through asynchronous links, balancing reserves aim at assuring both the short-term supply adequacy for its customers and the honoring of commercial commitments in those markets. 2.2. Wind power modeling To support the realization of its integration studies, HQ simulated the important variables at each wind power plant site under consideration. Hourly time series of wind speed, air temperature and wind generation covering a period of 36 years (1971−2006) were reconstituted based on historical measurements from Environment Canada weather stations and meteorological mats, wind power plant layouts, local topography information, and diagnostic extrapolation models. These series were validated in two ways. First, as 3 wind plants were already in operation in 2009, HQ extended the time series for 3 years and compared them with actual measurements at those sites  . Secondly, due to the Table 1: Reserve types Operating reserves Balancing reserves For contingencies For load and wind power For load and wind power forecast variability uncertainties (horizon 1–48 hours) Stability reserve AGC (regulating, minutes) Balancing Reserves 10 minutes Load following 30 minutes (intra-hour)
38 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM importance of the quality of the time series when the wind turbines could be shutdown under −30°C, Hydro-Quebec proceeded to cross-validate and update the reconstituted data for 14 historical peak load events using a high resolution numerical weather forecast model . 3. FREQUENCY REGULATION AND LOAD-FOLLOWING RESERVES 3.1. Introduction to the HQ approach Within the one-hour time frame, a preliminary analysis rapidly identified that the contingency-related reserve categories are not sensitive to wind energy integration. That is essentially because the wind plants are limited in size (less than 200 MW) and are geographically dispersed over 1000 km. The loss of a wind power plant, or more likely of only part of a plant, would have little impact on the stability of the system. Hence in this time frame, only the AGC and load-following reserve capacities deserve further investigation. Two methodologies for computing additional regulation capacity requirements due to the presence of wind power were applied to the Quebec network: that developed by the Oak Ridge National Laboratory (ORNL)  and that proposed by the Bonneville Power Administration (BPA) for its Rate Case 2010 . These require regulation signals as input. For the generation of regulation signals, REGAGC and REGLF, two distinct approaches were considered, one based on statistical time-series analysis and the other on simulation. These elements are illustrated in Figure 2 below, with the signal-generating functions in the top boxes, the capacity adjustment function in the central box, and a look at results highlighting the impacts of wind generation in the lower box. Each of these elements is presented in the following sections. After reviewing the pros and cons of each, HQ’s ISO, TransÉnergie (TÉ), opted for the simulation approach for the following reasons: Load data Load time-series Computation of Computation of regulation signals using regulation signals Wind data analytical methods using the simulator Wind time-series REGAGC REGAGC Network data REGLF REGLF Computation of additional regulation reserves BPA and ORNL Impacts Figure 2: Computational scheme for the impact of wind power generation on the AGC and LF reserve capacity requirements.
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 39 1. Unlike in most other North-American jurisdictions, TÉ’s Area Control Error (ACE) is dependent only on the frequency deviation and not on tie-line power imbalances. The analytical approach does not use and thus cannot handle frequency deviation whereas the simulation approach can. 2. Simulation provides a means to monitor many other impacts of wind power such as the frequency of stop-starts, the efficiency degradation of AGC units and, in particular, the AGC’s regulating range. The simulation approach is currently under development at IREQ in a simulator called SIRE , further described in Section 3.2.2. It reproduces the power system behavior in the presence of wind generation over a long time horizon with a 1-minute resolution. With the two methodologies fed successively with input signals from the two regulation signal generating approaches, four series of results are generated. These results are presented and compared in a final section. 3.2. Preparation of simulation data In preparation of the computation of reserve requirements, regulation signals were generated based on the following two approaches. 3.2.1. Signals based on load and wind time series Contrary to a simulation approach, this approach is not based on using a detailed modeling of the underlying power systems. In fact, it consists in applying analytical methods to chronological series of the demand and wind generation over a number of years [19, 20]. For our study, hourly demand and wind generation forecasts were derived for the year 2016 as follows. The Load Serving Entity first derived its 2016 demand forecasts based on the actual 2006 demand profile and on realistic load growth assumptions. Then, 11 years of hourly demand data were simulated from this baseline forecast to best fit the climatologic conditions observed from 1995 to 2006. Assuming similar meteorological conditions in 2016, the simulated 2016 hourly demand should mimic quite accurately the time-series generated in the long term statistical analysis. Similarly, the hourly wind generation data comes from 11-year historical reconstitutions of the anticipated 3000-MW wind plants generation capacity [6, 7] as described in Section 2.2. Following studies by BPA and the California Independent System Operator, real-time hour- ahead wind forecasts are based on a simple 2 hour persistence model. However, for the simulator-based studies, a 1 hour persistence model was deemed more realistic in the Hydro- Québec Energy Management System (EMS) context. The minute by minute demand and wind generation data were then interpolated according to . Table 2 summarizes some of the typical features of the minute by minute data of the long term demand and wind generation time series. Generally speaking, wind generation proportionally has more variability and less real-time predictability than the load. Figure 3 illustrates the overall characteristics of wind generation integrated into the HQ network. The daily maximum (blue) and minimum (red) values of the hourly penetration rates are shown in Figure 3 (a) (i.e. 365 values per year for each curve). Figure 3 (b) presents the daily maximum (blue) and minimum (red) hourly ramping of the wind generation. The overall peak load of 41,126 MW occurred on January 2004, while the absolute minimum load of 13 998 MW was reached in June 1996. The highest penetration rate is about 18% at the hours when the high wind generation is coincident with the low load.
40 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM Table 2: Summary statistics of minute by minute data from November 1, 1995 to October 31, 2006 In % with respect In % with respect Demand1 to the Mean to the max (MW) Demand Demand Standard deviation of variability 1 min 38.5 0.2 0.1 Standard deviation of variability 1 h 837 4 2 Absolute average real-time 522 2.5 1.2 forecasting error (1 h look-ahead time) Standard deviation of real-time 686 3.2 1.6 forecasting errors (1 h look-ahead time) Wind In % with respect In % with respect generation1 to mean wind to rated wind (MW) generation generation Standard deviation - variability 1 min 7.5 0.7 0.3 Standard deviation - variability 1 h 171 15.6 5.7 Absolute average real-time 202 18.4 6.7 forecasting error (2 h-persistence model) Standard deviation of real-time 227 21 7.6 forecasting errors (2 h-persistence model) Mean Demand = 21 204 MW, 1 Max Demand = 41 774 MW; Mean Wind = 1 099 MW, Max Wind = 2 801 MW, Rated Wind = 3000 MW. 3.2.2. Signals generated by the Hydro-Québec power grid simulator Other power grid simulators have been developed for specific needs seemingly similar to ours. A simulator developed by KEMA  is used mainly for market and storage systems modeling. The e-terraSimulator model proposed by AREVA  can simulate real-time power grid control, but it is designed for operator training. These simulation tools are ill-suited for fast simulation of long time periods multi-scenarios. The new simulator developed by Hydro-Québec, called SIRE, is devoted to the quantification of the impacts of wind integration on quantities related to power grid control in the context of the transmission service provider. It is based on a multi-agents framework, built using a specification published by Sandia National Laboratories . It was designed for fast simulation of the planning and real-time phases, while taking into account security and regulation rules of the transmission system provider. This study has been made possible by the availability of comprehensive and accurate data from three sources: – One year duration (year 2006) of actual and forecasted global demand (i.e. domestic load plus power exchanges with neighboring grids) obtained from the EMS/SCADA historian. – For each wind power plant under consideration, one year duration (year 2006) of simulated time series of wind power generation [6, 7]. – One year of hourly topologies of the network, in the form of load flow cases, taken from the state estimator historian. These input feed the simulator to produce a complete simulation of the operation of the power system. Conventional generation is allocated based on the historical commitment ranks of the units in response to load and wind generation. Given generation and load patterns
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 41 (a) Hourly penetration rate 18 16 14 12 10 % 8 6 4 2 0 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 (b) Hourly wind generation ramping (wind base = 3000 MW) 30 20 10 Hour % 0 −10 −20 −30 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Figure 3: (a) Daily maximum and minimum of hourly penetration; (b) Ramping rates in % of rated wind generation. and a description of the network, all the electrical variables in the network are computed using an AC power flow. This signal-generating approach has the advantage of modeling all the network variables and the constraints imposed by regulation rules and operational limitations such as the regulating range and up-down margins of the AGC system. In the analytical time series signal- generating approach, these impositions would have gone unconsidered. 3.3. Reserve capacity evaluation Independently of the approach used for generating regulation signals time series data, to date measures to quantify the impact of wind power generation on reserve capacity requirements have been based on statistical methods (most notably in ). Most often, the adopted measure has been the difference between the variance of the variability of the net load, defined as the load minus wind generation, versus that of the load alone. Their application and
42 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM detailed assessment on the HQ network, in the context of wind integration impact studies for the intra-hourly time horizon, was reported in . The two methodologies considered here, introduced earlier, are described below. We note important differences between them: from a general viewpoint they pursue different philosophies; in particular one assumes statistical independence of the input signals whereas the other considers the covariance between the input signals. 3.3.1. The ORNL method based on standard deviation This methodology, developed by ORNL , was applied to Nordic countries in  to assess the impacts of wind integration on reserves. The main idea is to use the increase in the standard deviation of the regulation signals as a measure of the impact of wind integration. More precisely, if one assumes that the variability of load and wind are normally distributed and uncorrelated, then the corresponding regulation signals will also be normally distributed and uncorrelated. Then σ 2 (REG NL ) = σ 2 (REG L ) + σ 2 (REGW ) (1) where σ(REGNL) is the standard deviation of signal REG. REG can represent the regulation signal associated with Automatic Generation Control (AGC) or load-following (LF), and X represents load or wind. The NL, L and W indices refer to net-load, load and wind respectively. The additional regulation requirement to cover the accrued net load variability brought by the integration of wind generation, ∆REG ORNL (Wind), is then determined as ∆REG ORNL (Wind ) = n × ( (σ 2 (REG L ) + σ 2 (REGW ) − σ (REG L ) ) (2) Here n is a suitable number selected to cover the risk associated with almost all occurrences of wind variability. Since the faster acting reserves cannot rely on backup actions, their values of n must be higher than those of slower reserves. Hence, typically n is chosen between 4 and 6 for AGC and between 2 and 2.5 for load-following. More specifically, in order to cover most of the variability, Hydro-Quebec adopted n = 4 for AGC and n = 2 for load-following. 3.3.2. The BPA method based on an allocation formulation This approach  was first proposed by BPA for wind generation projects in their control area . The principle is to establish the total reserve capacity requirement, and then to attribute to the proportions that the wind and load components each contributes to the total using the 99.5% covariance allocation concept  . The total regulation capacity requirement REG NL , whether for AGC or for load-following, was computed to cover the net load variability at a 99.5% exceedance level, based on the regulation signal statistical characteristics over the corresponding time frames, i.e. Prob REG NL 99.5% ≥ REG NL = 0.995 (3) where REG NL, is the net load regulation defined as a random variable. The value 0.995 of the exceedance level would correspond to an n of 2.58 should the variability be Gaussian. The relative contribution of each component is given by:
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 43 cov(−REGW , REG NL ) σ 2 (REGW ) + cov(−REGW , REG L ) WindShare = = σ 2 (REG NL ) σ 2 (REG NL ) (4) cov(REG L , REG NL ) σ 2 (REG L ) + cov(REG L , −REGW ) LoadShare = = σ 2 (REG NL ) σ 2 (REG NL ) where COV(X,Y) represents the covariance between the variables X and Y, COV (–X,Y) = COV(Y, –X), confirms the equality of the covariances with commuted arguments, REGNL = REGL _ REGW expresses the regulation on the net load as the difference between those on the load and the wind components. The covariance coefficients in equation (4) account for the correlation between wind and load regulation signals. Contrary to the ORNL method, equation (4) was applied in this study to data collected at each hour (h) of the day, resulting in diurnal cycles of wind and load shares of AGC and load-following. Given the hourly relative wind and load shares, the absolute contribution of each entity (in MW) was obtained as follows: WindShareMW (h) = WindShare (h ) × REG NL 99.5% (h) (5) LoadShareMW (h ) = LoadShare (h ) × REG NL 99.5% (h) For year 2006, Figure 4 shows diurnal cycles of wind and load shares of the forecasted load-following obtained from the SIRE simulator. Note that at this stage the wind and load shares are expressed in per unit, such that their sum is exactly equal to one. Figure 5 illustrates the diurnal contribution of each entity in MW. In this case the forecasted load- following is obtained from the SIRE simulator with data from year 2006. The maximum contribution from wind, equal to 726 MW, is obtained at 4:00AM whereas the maximum contribution from load, equal to 2628 MW, is obtained at 16:00PM. Thereafter, single optimum reserve values were allocated to wind and load contributions by weighting the maximum reserve values throughout the diurnal cycle values as follows: max (WindShareMW (h)) REGWBPA (opt ) = 99⋅5% × max(REG NL % (h)) max (WindShareMW (h)) + max ( LoadShareMW (h)) (6) max ( LoadShareMW (h)) REG LBPA (opt ) = × max(REG N99L⋅5% (h)) max (WindShareMW (h)) + max ( LoadShareMW (h)) Wind share and load share (Forecasted load following) 1.00 0.80 0.60 Pu 0.40 0.20 0.00 0h 1h 2h 3h 4h 5h 6h 7h 8h 9h 10h 11h 12h 13h 14h 15h 16h 17h 18h 19h 20h 21h 22h 23h Load share 0.87 0.78 0.77 0.74 0.73 0.81 0.86 0.85 0.83 0.82 0.85 0.87 0.87 0.87 0.86 0.86 0.89 0.87 0.86 0.81 0.86 0.87 0.87 0.88 Wind share 0.13 0.22 0.23 0.26 0.27 0.19 0.14 0.15 0.17 0.18 0.15 0.13 0.13 0.13 0.14 0.14 0.11 0.13 0.14 0.19 0.14 0.13 0.13 0.12 Figure 4: Relative contribution of wind and load to the load-following forecasted by SIRE (year 2006).
44 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM Wind share and load share (Forecasted load following) 3500 3000 2500 2000 MW 1500 1000 500 0 0h 1h 2h 3h 4h 5h 6h 7h 8h 9h 10h 11h 12h 13h 14h 15h 16h 17h 18h 19h 20h 21h 22h 23h Load shareMW 2082 1940 1866 1938 1946 2054 2465 2456 1963 1691 1467 1579 1378 1495 1695 2125 2628 2403 1828 1483 1647 1301 1272 1412 Wind shareMW 305 544 561 670 726 475 404 425 394 376 258 240 209 232 287 358 321 366 302 350 267 201 190 201 REGNL(99.5%) 2386 2484 2428 2608 2672 2529 2869 2881 2357 2067 1725 1819 1586 1728 1982 2483 2949 2768 2130 1832 1915 1502 1462 1613 Figure 5: Contribution of wind and load (in MW) to the load-following forecasted by SIRE (year 2006). where REGW (opt ) represents the added regulation requirement due to the presence of BPA wind generation using the BPA methodology. 3.4. Comparison of results Recall that – The two statistical methodologies described in Section 3.3 were applied to regulation signals obtained using both the analytical  and simulation  time- series-based approaches. – The results associated with the analytical regulation time series were obtained using eleven years duration of simulated time series data, whereas those associated with the SIRE simulator were obtained using one year duration of simulated time series. – For the BPA method, the sum of wind and load reserves is exactly equal to reserves requirements for the net load. With this in mind, Table 3 shows the additional requirements in AGC and load-following capacity resulting from the integration of 3000 MW of wind generation on the Hydro-Quebec network, computed using the two methodologies each fed by the two approaches described previously. In the case where we select the BPA variance allocation methodology with signals generated by the simulator, the supplementary AGC and load-following reserves required to Table 3: Additional reserve requirements due to the addition of wind generation Additional needs in terms of reserves Approach for (% of wind power capacity) Methodologies for regulation signals AGC LF Total impacts evaluation computation MW % MW % MW % ORNL (n × σ ) Simulator (SIRE)2 28 1 142 5 170 6 Analytical time series1 13 0.4 203 7 216 7 BPA (variance based Simulator (SIRE)2 88 3 638 21 726 24 allocation) Analytical time series1 54 1.8 618 20.6 672 22 1 The analytical time-series for AGC and load-following were obtained using the formulas in  . 2 In the simulator, the AGC is derived using realistic real-time operation rules to manage minute to minute load imbalances. The load-following is derived from realistic real-time operation and forecasted 10 minute operating reserves calculated by the simulator.
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 45 accommodate 3000 MW of installed wind capacity will amount to 3 and 21% respectively on a yearly average basis. However, selecting the n × σ criterion and the simulator as regulation signals generator resulted in much lower incremental reserves requirements with only 1% and 5% increase of the AGC (n = 4) and load-following (n = 2) respectively. Despite the similar additional reserve requirements produced by the simulator and analytical approaches, the former offers one major advantage in that it is better suited for comprehensive impact studies on transmission systems. Indeed, using realistic system operation rules, a load flow calculation and an optimal dispatch allows obtaining more accurate results associated with a large number of physical variables. Going through one full year of operations simulation with detailed transmission network and generation dispatch, the simulator calculated that 3000-MW of wind generation could increase the number of alternators start-ups and shut-downs by approximately 1340, with a maximum daily increase of 34 starts-stops and an average increase of 4 starts-stops per day. In summary, the simulator appears to be the proper tool for obtaining the most accurate analysis of the impacts of wind generation on the efficient use of equipment connected to the transmission system, including the generators ensuring the AGC and load-following services. By providing the regulating range and up-down margins of the AGC system as additional output results, the simulator also allows to relate in a more direct manner the additional reserves requirements imposed by wind generation with the current terms of agreement covering the frequency regulation service in Quebec network. 4. BALANCING RESERVES 4.1. Introduction In HQ, Balancing Reserves (BRs) assure short-term reliability to its power system over a time horizon of one hour to 48 hours ahead. Recently, several studies in the literature have re- evaluated the actual reserve levels required when incorporating wind integration into their systems and have proposed increasing the level of these reserves. Furthermore, they identified the need for computing these reserves dynamically [16–19]. 4.2. Methodology One methodology to compute balancing reserves, integrating several sources of uncertainties, is power system reliability theory . It is based on the criterion of loss of load probability (LOLP), referred to here as risk in order to distinguish it from the long term reliability. It is equivalent to the probability that the available generation, including reserves, is not sufficient to satisfy completely the demand. Reserves are computed such as to meet at each instant a given risk target. The methodology adopted here borrows from the traditional reliability theory and adapts it to the time-horizon of 1 to 48 hours ahead. In its final formulation, the balancing reserve requirement is a function of a distribution of a net forecast error composed of load, wind generation and generation unavailability forecast errors rather than of the forecasts themselves [3, 4]. R0 (t ) = Prεd (t ) + εu (t ) − εw (t ) ≥ BR (7) = 1 − Prεd (t ) + εu (t ) − εw (t ) ≤ BR where
46 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM εd (t ) + εu (t ) − εw (t ) represents the net forecast error and R0 (t) is the risk corresponding to a given level of balancing reserves. Subscripts d, u and w represent demand, conventional generator unavailability and wind generation respectively. The inputs to this method are the distributions of all the forecast errors over the lead times from 1 to 48 hours. A major difference with the other two studies reported here is that these distributions were developed a posteriori from actual past forecasts and their corresponding measurements by subtracting one from the other. Having at hand the distributions of forecast errors facilitates both the aggregation of the individual forecast errors into a net forecast error and the graphical representation of results. The anticipated risk was then computed at each forecast lead time. It is the value of a function of the net forecast error distribution corresponding to a predetermined level of balancing reserves. Alternatively, given a target level of risk, the associated balancing reserve requirements can be quantified. Repeating this computation for each lead time over a given time horizon, it reveals the temporal evolution of risk or of the balancing reserve requirements. It is clear that the wind forecast errors are functions of the wind generation level. The methodology captures this fact by providing wind generation forecast error statistical characteristics as a function of wind generation levels . Consequently, the risk and the balancing reserves depend on the wind generation level, justifying the need of a dynamic computation of the reserves. This methodology was used to evaluate additional balancing reserves required to integrate 3000MW of wind power capacity into the Hydro-Québec system. This was done by comparing the balancing reserves required to maintain the same level of risk before and after the integration of wind generation over numerous system conditions. 4.3. Results Figure 6 illustrates a graphical representation of the methodology using actual HQ data. Given some system characteristics at a given instant, a risk versus balancing reserves curve is computed and is represented by the curve R d+u . The risk, R 0 , of 17%, corresponds to some Risk and additional BRs in the presence of two wind generations 30 Risk wo wind 25 Risk w wind Risk w wind ∆R ∆R 20 ∆BR R0 Risk (%) 15 ∆ BR Rd + u − W 10 Rd + u − W 5 Rd + u BR0 0 300 400 500 600 700 800 900 1000 BRs (MW) Figure 6: Qualitative illustration of the risk and additional balancing reserves for two different wind generation penetration levels whose forecast errors are represented by zero mean Gaussian distributions.
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 47 nominal balancing reserves level, BRnom = 500 MW (obtained by reading on curve Rd+u ). The additional risk incurred, ∆R, and the additional reserves ∆BRs required following the integration of two different wind generation capacities are also shown on this figure. Similar risk versus balancing reserve curves for these two cases are marked Rd+u+w (full-lined curve) for the smaller and Rd+u+w (dotted curve) for the larger of the two wind generation additions, respectively. Adding a certain amount of wind generation into the system and keeping the same amount of balancing reserves increases the system risk by an amount of ∆R. In order to maintain the same risk before and after the additions of wind generation, it is necessary to provide the system with additional balancing reserves in the amount of ∆BRs. We point out that at each instant the original risk without wind generation, R0, presented to the system depends on the statistical characteristics of the load forecast uncertainties, on those of unavailable generation and on the nominal balancing reserves level, BRnom. Further, looking at the time evolution of the variables, since the forecast uncertainties may vary over time, the hour of the day and the season, it follows that the risk R0 incurred with a constant level of nominal balancing reserves varies over time. Alternatively, the balancing reserves BRs required to maintain a given risk level also varies over time. The additional risk, ∆R sustained by the system when integrating wind generation, and therefore the additional balancing reserves, ∆BRs, depend on the original risk, R0, corresponding to the given level of reserves, BRnom , and on the statistical characteristics of the added wind generation forecast error. The two quantities ∆R and ∆BRs also vary over time. The overall methodology of the computation on balancing reserves as implemented practically over the entire time 1–48 hour horizon for the HQ system is illustrated in Figure 7 below. Figure 8 below presents a wind generation forecast and some typical corresponding output. Figure 8(a) shows a given wind generation realization over a period of 48 hours. It is shown to span 4 generation levels, to each of which is associated a set of forecast error characteristics. Figure 8(b) shows the risk encountered without and with this wind generation (top) accompanied by the required ∆BRs (bottom) beyond the predetermined balancing reserves to maintain risk at the same level as before the integration of the wind power. The bump in the risk curves around 16:00h (lead time of 28 hours) reflects the particular signature of load forecast errors. Using Hydro-Québec data, with the nominal balancing reserves, the risk levels encountered without wind generation reach up to 5% over the day-ahead horizon. This may seem unusually high, but contrary to the regulating reserves acting in the intra-hour time horizon, utilities have the leisure to accept larger risk levels here because looking forward they can still call on uncommitted yet available resources to remedy undesirable occurrences. Since the remedies Imminent forecasts Figure 7: Illustration of computational scheme for balancing reserves, including imminent forecasts and input forecast uncertainties (left), the risk-BRs-lead time relation, and the output evolution of risk or BR over time.
48 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM (a) (b) 10 1400 8 Without wind With wind Risk (%) 1200 6 Generation (MW) 1000 4 800 2 0 600 10 20 30 40 150 ∆BR (MW) 400 100 200 50 0 0 0 10 20 30 40 10 20 30 40 Lead time (h) Lead time (h) Figure 8: (a) : Wind generation separated into 5 levels, illustrated by different colors; (b): risk with and without wind generation (top), and added balancing reserves ∆BRS to maintain the same risk as before the incorporation of wind generation (bottom). are implemented at extra cost, the choice of risk level is essentially an economic consideration associated with the deployment of resources committed at the last minute. The additional reserves required to maintain the same risk to the system as before the incorporation of wind generation are dependent on the wind generation and load level scenario presented to the system. These reserves are particularly sensitive on the wind generation forecast level and associated forecast errors. As a function of wind generation level, in some rare cases it was observed that additional reserves may reach as high as 13%. This corresponds to the case of high load forecast uncertainties and high wind generation levels. This methodology allows us to quantify the balancing reserve requirements, with and without wind generation, based on a risk criterion. With the same procedure we have also determined the added reserve requirements to maintain a specified level of risk before and after the integration of 3000 MW of wind power capacity. The methodology revealed the importance of an accurate representation of the distribution of wind forecast errors and justifies the need of a dynamic computation of the reserves. The methodology developed here is general. It does not rely on the gaussianity or independence of the parameters involved. It can be applied to any time horizon where the input data contain inherent uncertainties. In summary, with current HQ balancing reserves being relatively high and risk levels relatively low, for the day ahead horizon, little additional balancing reserves are required to integrate 3000 MW of wind power capacity most of the time. The 5% maximum risk level revealed in our simulations was not predetermined, but rather was revealed by the present study. It seems to be acceptable, since current practice in operations planning seems satisfactory. 5. WIND POWER CAPACITY CREDIT 5.1. Introduction 5.1.1. Climate and load As is the case for a few northern countries and Canadian provinces, the Quebec annual peak electricity demand occurs during the winter, and is well correlated to actual air temperature and wind at major load centers. The peak usually occurs during cold spells when the minimum temperature reaches around –30°C or lower during two or more consecutive days.
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 49 5.1.2. Climate and wind generation The winter season is also generally favorable to a good wind power generation, on average. However, in order to protect the turbines against structural damage, the wind generation is halted when the actual temperatures at the turbine site reach a limit set by design. The limit is chosen by manufacturers mainly by comparing the value of expected lost energy over the life of the turbine with the cost of lowering this limit. Based on the climates in which wind capacity is actually deployed, today’s turbines are usually available either with a standard operational limit of −20°C, or with a “cold package” limit of −30°C. With the Quebec climate, turbines in the control area are of the latter type, but might still face periods of low temperature induced forced stoppages. However, due to the geographic dispersion of wind power plants and their varying distances from load centers, these stoppages are not necessarily or systematically coincident with system peak load events. 5.1.3. Scenario In such a context, an appropriate evaluation of capacity contribution is crucial to ensure system security and reliability at minimum capacity supply cost . Accordingly, the capacity contribution of wind power in the Quebec control area as been studied in detail for the “3000 MW in 2016” wind scenario. In depth description of the study is available . A brief summary is provided here. 5.2. Methodology A custom-made Monte-Carlo simulation model was used. The model relied on wind and load data series that were matched on an hourly time-step, over a 36 year period using real weather data combined with seven different weekday load patterns. The model takes into account forecasting errors and conventional generation outages. The capacity contribution results from the comparison of two simulations leading to the same reliability target with the loss of load expectation (expectation of not having enough resources to meet the demand) equal to one day per ten years: – A first simulation includes the 3000 MW wind power scenario. – In the second simulation, the wind power is replaced by conventional generation resources having a 0% outage rate. The amount of conventional generation added in the second scenario that would provide the same reliability as in the first scenario is then used as a benchmark for the capacity contribution of wind power. 5.3. Results Obviously, such simulations rely on the availability and realism of data over the full 36 year period. Accordingly, hourly load data was provided by highly reliable demand models and based on historical hourly weather time series. However, in absence of real historical wind generation and with the complex spatial and temporal correlations between weather, wind generation and load plus meteorologically triggered stoppages, care had to be given to the evaluation of the underlying long term wind power time series. These were obtained using historical meteorological data available from weather stations that were extrapolated at the power plant sites using a physics-based diagnostic model [6, 7]. Thus, additional evaluations were performed in order to evaluate the sensitivity of wind power capacity contribution estimations to wind power data. Such evaluations indicated that the results were sensitive to wind power hourly data during a limited number of very cold
50 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM events occurring along the 36 year period. That was not surprising, due to the correlation between extreme cold events and high risk periods of not having enough resources to meet the demand. Following the results of the sensitivity analysis on the preliminary wind generation series, these were then supplemented by in depth analysis of fourteen critical extreme cold weather events, using high resolution numerical weather “hindcasting” models and weather reanalysis data . After the inclusion of this new dataset, the capacity contribution of 3000 MW of wind power was found to be equivalent to 900 MW of firm conventional generation. Results were found to be very sensitive to wind data during a limited number of extreme cold events over the 36 years period. That finding also suggests that such evaluations are improved by long time series and by better on site weather data covering critical historical events. 6. CONCLUSIONS 6.1. AGC and load-following It is generally accepted that the evaluation of the impacts using a statistical model to generate system data is not as accurate as an approach based on simulation, which is founded upon far more realistic systems operation hypotheses. By providing the regulating range and up-down margins of the AGC system as additional output results, the simulator also allows to relate, in a more direct manner, the additional reserve requirements imposed by wind generation with the current terms of agreement covering the frequency regulation service in Quebec interconnection. 6.2. Balancing reserves The methodology developed here for the computation of balancing reserves based on risk revealed that the additional reserves are highly dependent on the wind generation together with load forecast error characteristics, thus justifying the use of dynamic reserves. These reserves may reach as high as 13% of wind generation in some instances. The frequency of the occurrence of such an event depends on the meteorological data. Further, since the reserves come at a cost, the risk we would want to maintain with these additional reserves is an economic decision. 6.3. Capacity credit The capacity credit was established at 30% of total wind nameplate capacity which amounts, for the studied 3000 MW scenario, to an equivalent of about 900 MW of firm conventional generation capacity. 6.4. Next phase Up to now, priority had been given to evaluate the impact of a “3000 MW of wind in 2016” integration scenario on the reliability related aspects of the Hydro-Quebec system. Supplemental studies will now address the impacts of wind power that are specific to the water management and market related processes. REFERENCES HQ studies summarized in this paper  Kamwa I., Héniche A. and de Montigny M. (2009) Assessment of AGC and Load- Following Definitions for Wind Integration Studies in Québec, Proceedings of the 8th
W IND E NGINEERING VOLUME 36, N O . 1, 2012 PP 35-52 51 International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, T. Ackermann (ed), Energynautics GmbH, Paper no. 129, Bremen, Germany.  M. de Montigny, A. Héniche, I. Kamwa, R. Sauriol, R. Maihot, D. Lefebvre, “A new simulation approach for the assessment of wind integration impacts on system operations,” 9th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms – Quebec, 18–19 Oct, 2010.  N. Menemenlis, M. Huneault, J. Bourret, A. Robitaille, “Calculation of Balancing Reserves Incorporating Wind Power into the Hydro-Québec System over the Time Horizon of 1 to 48 Hours”, 8th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks of Offshore Wind Farms, 14–15 October, 2009, Bremen, Germany.  N. Menemenlis, M. Huneault, A. Robitaille, “Computation of Dynamic Operating Balancing Reserve for Wind Power Integration over the Time Horizon of 1–48 Hours”, 9th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks of Offshore Wind Farms, 18-19 October, 2010, Québec, Québec, Canada.  Bernier L. and Sennoun A. (2010) Evaluating the Capacity Credit of Wind Generation in Québec, in Proceedings of the 9 th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, T. Ackermann (ed), pp 198–205, Energynautics GmbH, Quebec, Canada. Data for Wind Generation Simulation  Hélimax Énergie Inc. (2008) Reconstitution de séries historiques de production éolienne – Parcs éoliens de la Gaspésie (990 MW), Prepared for Hydro-Québec Distribution; 61 pages (http://www.regie-energie.qc.ca/audiences/3648-07/ RepDDRHQD3648/B-14-HQD-03-01_annexe4_3648_22fev08.pdf).  Hélimax Énergie Inc. (2009) Reconstitution de séries historiques de production éolienne – Appel d’offres pour 2000 MW, Prepared for Hydro-Québec Distribution; 74 pages (contact email@example.com).  Choisnard J., Roch M., Desgagné M., Charron M., Antic S. and Bourret J. (2010) High- resolution historical wind power time series simulation using state-of-the-art NWP model and on-site calibration, in Proceedings of the 9th International Workshop on Large-Scale Integration of Wind Power into Power Systems as well as on Transmission Networks for Offshore Wind Farms, T. Ackermann (ed), pp 108–115, Energynautics GmbH, Quebec, Canada. Additional References on AGC and Load-Following  E. Hirst, B. Kirby, “Separating and Measuring the Regulation and Load-Following Ancillary Services,” Utilities Policy, vol. 8, pp. 75–81, June 1999.  BPA Wind Integration Team: “Regulation, Load Following and Generation/Load Imbalance”, Study paper for the 2010 BPA rate Case, September 2008: http://www.bpa.gov/corporate/ratecase/2008/2010_BPA_Rate_Case/docs/TR-10_ WIT%20Study%20Paper_091008.pdf.  http://www.kema.com/services/consulting/markets/market-modeling.aspx
52 P RELIMINARY I MPACTS OF W IND P OWER I NTEGRATION IN THE H YDRO -Q UEBEC S YSTEM  WindLogics, Xcel Energy Northern States Power (NSP), “Renewable Energy Research and Development Project (RD-57),” 2008. http://www.xcelenergy.com/Site CollectionDocuments/Docs/WEFS-Final-Report.pdf  D. Smathers, L. Kidd, S. Goldsmith, L. Phillips, D. Bakken, A. Bose, D. McKinnon, Software Requirements Specification for Management for Grid Control, Sand Report: SAND2003- 1215, Sandia National Laboratories, 2003, 82p.  2010 BPA Rate Case, Presentation of Power Services & Transmission Services to the Wind Integration Rate Customer Workshop, 23 January 2009 http://www. bpa.gov/corporate/ratecase/2008/2010_BPA_Rate_Case/docs/TR-10%20 Workshop_Wind%20Integration%20Rates_012309.pdf.  P. Albrecht, “Risk Based Capital Allocation”, in Encyclopedia of Actuarial Science, Wiley & Sons 2004, [on line], http://www.sfb504.uni-mannheim.de/publications/dp03- 02.pdf Additional References on Balancing Reserves  H. Holttinen, M. Milligan, B. Kirby, T. Acker, V. Neimane, T. Molinski, “Using Standard Deviation as a Measure of Increased Operational Reserve Requirement for Wind Power,” Wind Engineering, 32(4), 2008, pp. 445–451.  Eastern Wind Integration and Transmission Study, prepared for NREL by EnerNex Corporation, NREL/SR-550-47078, January 2010.  Western Wind and Solar Integration Study, prepared for NREL by GE Energy, NREL/SR-550-47434, May 2010.  2006 Minnesota Wind Integration Study, Volume I and II, Prepared by EnerNex Corporation, Nov. 2006.  T. Ackerman, Wind Power in Power Systems, Ed. Ackermann, Wiley, 2005, pp 143–167.  R. Billington, R. N. Allan, Reliability Evaluation of Power Systems, Second Ed., Plenum Press, New York, 1996. Additional Reference on Capacity Credit  North American Electric Reliability Council (2009) Accommodating High Levels of Variable Generation; Chapter 3.2 – Resource Adequacy Planning; pp 36–42, Princeton, NJ, USA.
You can also read
NEXT SLIDES ... Cancel