Cost-effective primary frequency response at high asynchronous generation levels

Transcription

1 Cost-effective primary frequency response at high asynchronous generation levels Juha Kiviluoma Wind Integration VTT Technical Research Centre of Finland Espoo, Finland Frans Van Hulle XP Wind Herent, Belgium Andrej Gubina Electricity Research Centre University College Dublin Dublin, Ireland Nicolaos Cutululis DTU Wind Energy Technical University of Denmark Roskilde, Denmark Abstract Restrained generation from wind and solar PV can provide a frequency response that is fast in comparison to conventional generation. However, the reduced inertia in a dispatch with little conventional generation can cause very fast initial drop in the frequency and it may be difficult to mitigate that with wind and solar PV alone. In the paper, we outline a method to compare frequency control strategies in such a system, present typical response characteristics of different options, and simulate some example cases for frequency containment in the Iberian power system. The results are demonstrative and additional analysis is required to draw more robust conclusions and to conclude on the costs and benefits of using wind and PV for frequency response. Keywords- primary frequency control; asynchronous generation; wind power; solar PV; unit commitment; economic dispatch; cost comparison I. INTRODUCTION In a power system, an outage results in a change of system frequency. While synchronous generators provide inertia, which slows down the frequency decline immediately after an outage asynchronous wind power and PV generation don t inherently do the same. If no actions are taken, at high instantaneous penetration levels of asynchronous generation (AG), such as wind power and PV generation, the power system s frequency response to outages or other so-called N-1 events will be inadequate. There are several options to mitigate the problem. In the current approach AG is restrained preventively to keep enough synchronous generation online to provide an adequate primary response. In the second option, AG participates in the primary response by operating wind and solar PV at less than the currently available AG capacity (restrained) and the AG s control systems enable a primary response based on frequency at the point of connection. The increased response from AG can act against the change of system frequency, decreasing the minimum required online synchronous generation. As the third option, some wind generators can provide the so-called synthetic inertia for a short period of time by extracting energy from the rotational momentum of the wind turbine. The fourth option is to use dedicated network-based fast-responding hardware like capacitor banks, batteries, flywheels or pumped hydro units in combination with the fast power electronics devices or FACTS. All of these options and their combinations entail different costs as well as a different frequency response levels and characteristics. Also wind and PV generators have distinct curtailment-related costs and response characteristics. In order to compare the options, it is necessary to establish what constitutes an adequate response from each option before the cost comparison. This paper explores a methodology for the cost comparison, presents typical parameters for the response options, and shows initial results. The analysis is limited to the first seconds after the fault and only primary reserve under droop control is simulated. In the proposed ENTSO-E terminology this is frequency containment reserve (FCR) for conventional generation and a combination of FCR and fast frequency response (FFR) for wind and PV generation. The latter has been proposed by the Irish TSOs and adopted in the REserviceS EU-project setup [1] and used in this paper. II. THE METHOD A. Unit commitment and economic dispatch model In order to evaluate the economics of various frequency control options, one has to consider the cost of running thermal or hydro units at part-load, which is necessary to provide primary frequency response. Similarly, the possible cost of restraining wind and solar generation for the

2 provision of upward reserve has to be optimized from the system point of view. An approximation of this can be achieved with a unit commitment and dispatch model that includes part-load efficiencies, power plant cycling costs and power plant start-up costs. In our case the WILMAR model is used to provide an hourly dispatch for a full year in a high wind penetration scenario. WILMAR is run in a MIP mode where power plant start-ups and shutdowns are modeled with a binary decision. The part-load efficiencies [1], [3] and the cost of wear and tear due to cycling [4] have been approximated based on the sources. The WILMAR model reserves a pre-defined amount of primary reserve. The model does not include frequency or voltage related constraints and can therefore decide on a dispatch schedule that would not be secure. B. Primary response model Occasions where frequency deviations could occur are analysed with a separate dynamic frequency response model. The model takes the online units, their generation level, and demand from WILMAR as an input. For WILMAR, one hour is a flat block, but in reality within the hour the frequency reserves are used to balance the shorter term variations in the system. Therefore, situations where the intra-hour reserves have been heavily deployed should be identified in order to ensure adequate response also in these situations. The dynamic frequency response model is then used to simulate the frequency response to an outage during such situations. The model consists of blocks that represent the frequency related characteristics and controls of power plant turbines, frequency-dependent loads, rotating mass loads, wind and PV generation as well as tie-lines. The model is built in Matlab/Simulink environment. In this paper only primary frequency control is investigated. The lack of secondary frequency control and automatic regulation is parallel to a situation where they have been exhausted within the intra-hour operations. C. Iteration of dispatch and frequency response models For each of the analyzed options, a sufficiently secure frequency response is iterated. The system security after the response is approximated by the frequency nadir after the fault. If this goes below a selected level, the amount of primary response has to be increased in WILMAR and the dispatch is re-optimized. Since the primary response module is fast to solve, it is possible to estimate the increase in the frequency nadir when using different generator types for additional primary response. WILMAR can then use this information in addition to the cost per MW of primary response in the dispatch re-optimization. This paper does not yet iterate between WILMAR and the primary response module. D. Cost comparison Since each case has been iterated to provide adequate frequency response in possibly difficult operational situations, the costs of the dispatch can be compared based on the system costs provided by WILMAR. The operational cost differences are then augmented with annualized capital costs from the devices that enable the frequency response. It is assumed that conventional generators are capable of primary response in any case and no additional cost is considered for them. Cost comparison is not performed in this paper. III. FREQUENCY RESPONSES The work for the paper is carried out within the REserviceS IEE EU-project ( which has already established cost models for the response from wind and solar PV as well as for the other plants in the system [10] [11] [12]. Relevant findings are summarized here. Characteristics of load and network devices are also presented. A. Response from conventional generation The conventional generators in the study have typical values for droop control, inertia and ramp rates. The droop for all units was set so that the response is fully activated after a frequency drop of 0.5 Hz from the nominal 50 Hz. The parameters of the procured units were sent to the primary response model. The full activation time for primary response was around 5 10 seconds for thermal units and 15 seconds for hydro units. The primary response range was about 0.05 p.u. for thermal units and 0.1 p.u. for hydro units. It was assumed that nuclear units do not provide primary frequency response. B. Demand response Load behavior in the network can be similar to tacit energy storage in electrical loads with constant impedance characteristics (predominantly resistive loads). Such loads possess an inherent self-regulation capacity where by reduction of the voltage in case of an outage, their active power is reduced for a few minutes, [5]. In motors, due to their constant apparent power S characteristics the voltage drop translates into the increase in the current, exacerbating the voltage instability problem. An automated frequency response from load could be formed in at least two ways: - Interruptible contracts with large consumers: for example in Ireland, this is called Short Term Active Response (STAR), [6]. STAR is an interruptible load type service by which electricity consumers agree to have either their entire load or a portion of their load disconnected, without notice, during an under-frequency incident, typically between times per annum. With each interruption typically of the order of 5 minutes duration, STAR assists in the recovery of the power system frequency to normal operating conditions following the loss of a major generation in-feed to the power system. The TSO makes payment for the service based on the level of load that an electricity consumer makes available for interruption. - Virtual power plant: when many smaller loads on the MV and LV networks are aggregated, possibly combined with distributed generation sources and jointly dispatched, a so-called virtual power plant can provide network-based demand response of adequate size and speed to provide frequency response on the HV network. Thermostatically controlled loads may lead to temporarily significantly increased active power consumption due to the cold-load pick up phenomenon within (5-50) minutes after these loads try to simultaneously reconnect after an extended outage [5]. However, although these effects of the MV and LV-network connected loads can be felt also on the transmission network sufficiently to impact the system frequency response after the fault, the time constants are far too long for fast frequency response, impacting mainly the restoration efforts.

3 In this paper we have assumed a composition of resistive and motor load in which a 1 % drop in frequency causes a 1 % drop in load. We did not consider large rotating motor loads, whose behavior is dependent on the derivative of the frequency. In the Iberian case study presented below, the total demand for primary reserve in the WILMAR model was 1026 MW. C. Response from wind power plants Wind power technology today in general is able to cope with the required characteristics for active power control. However, frequency support services during frequency drops require faster response. The necessary improvements in capabilities for enabling adequate frequency support services in the future seem to be directed more towards equipment and algorithms for communication and control rather than wind turbine components. The ability of delivering active power control depends on the technology used. There is a limited number of early designed fixed speed and variable speed wind turbines in which active power control is not possible or it is very constrained (up to 1-2 % active power variation). These turbines are estimated to represent less than 20 % the current installed capacity in Europe. It is therefore safe to assume the majority of the wind turbines can participate in fast frequency response. Individual variable speed wind turbines (Type 3 and 4) are able to go from the lowest power level to full rated power in a maximum time response of less than 10 seconds. According to the survey results presented in [10], the response time varies between 1-2 seconds and up to 4-6 seconds for up- and downward regulation. At wind farm level, the total response time increases with the delay of the communication. Furthermore, the response time will be influenced by the generation level or the level of curtailment of wind generation. According to one wind power plant operator, going from 10 to 100 % active power production may take up to 30 sec. In general, for variable speed wind power plants, active power control capability is generally available and very fast ramp rate control can be achieved. Wind power plants are now required to be able to automatically modify the active power output depending on the system frequency. This is also called droop control. At the wind turbine level, the maximum initial delay to provide droop control is less than 1 second according to responses from the wind industry [10]. For wind turbines it is possible to define a dead band for frequency control mode operation. At wind power plant level, the same capability is also available but there is an extra delay of the control response between 500 ms and 2 s on top of the delay at wind turbine level. This is due to the communication and/or processing time at the wind farm controller. While the capabilities for frequency control mode are in general available, how this is done is not standardized. In some cases, at wind power plant level, switching between frequency sensitive modes (FSM) remotely is a procedure that can be done only by the manufacturer even though the capability is available in the wind turbines. There might be cases that operators do not have a direct access to the wind farm to enable the functionality on the wind farm controller themselves. Regarding the capability of receiving and processing active power set points, the requirements are usually very well defined in grid codes. For example, [Energinet.dk, 2010] requires that wind power plants, in case the active power set point is to be changed, such change must be commenced within two seconds and completed no later than 10 seconds after receipt of an order. The accuracy of the control performed and of the set point must not deviate by more than ±2 % of the set point value or by ±0.5 % of the rated power, depending on which yields the highest tolerance. All wind turbine manufacturers provide with their products the methods for processing active power set points. The accuracy of response is between ±2 % and ±5 % based on 1 minute average, according to the responses provided with the questionnaire. At the wind farm level, there is an additional settling time for set points generally below 2 seconds, but it could be up to 5-10 seconds. D. Response from solar PV In order to mitigate overvoltage at distribution grids or to enable positive frequency response, solar PV installations need to be able to reduce their active power. An example of this is set by German grid codes, where the Distribution System Operator (DSO) may demand a temporal active power reduction in case of compromised system operation, overloading, frequency problems, static or dynamic system stability problems. The generators must be capable of reducing their active power at steps of maximally 10 % of the agreed active connection power P ac. [11] Reduction alone is not sufficient for frequency response; also droop control has to be enabled. Based on the interviews conducted in REserviceS project and described in Deliverable 4.1 [11], the maximum initial delay to provide droop control at the inverter level is < 500 ms according to responses from the questionnaires. However an inverter manufacturer mentioned that the maximum response delay could be increased to 2-4 s when the control mode is based on a hysteresis. [11] At PV system level, the capability is also available but there is an extra delay of the control response between 500 ms and 2 s on top of the delay at the inverter level. This is due to the communication and/or processing time at the plant controller. [11] E. Response from network devices Frequency control mainly involves rapid shifting of energy to the system to increase the frequency or from the system to reduce it. As most of the energy storage equipment store energy in DC, flexible power electronics devices are typically used to connect storage to the network. The network-based response can be broadly classified into following two categories: - Energy storage: in HV network, serially connected storage devices such as batteries, superconducting magnetic energy storage (SMES), flywheels and super condensers are employed usually in combination with the power electronics to control active power in the network and therefore provide for frequency control, [7][8][9]. The role of power electronics (either IGBT or some other advanced FACTS devices, e.g. SSSC or STATCOM) lies mainly in fast and optimal adjustment of the injected/withdrawn power from the system to the energy source or vice versa. In fact, the best arrangement is to use storage systems for short-duration spinning reserves of about 15 minutes duration, and then use fast-starting turbines as standing reserves, [7]. Although the storage and FACTS devices are tied to significant

4 investment costs, it is safe to assume that in high AG penetration scenarios, there will already be a significant number of these network devices installed for voltage support. They can be readily used for frequency control without much additional investment involved. - Inter-area frequency control: universal power flow controller (UPFC) can be used to control the frequency in one area (system) through shifting of the active power flow between two areas, influencing the frequency in both of them as desired. For the purpose of this study, we will model a typical energy storage system with a fast-acting grid interface setup as batteries with STATCOM, as described in detail in [9]. IV. PARAMETERS FOR THE SCENARIOS The study case is the Iberian system (Spain and Portugal) with time series from 2012 modified by increased penetration of wind and PV generation with respective annual energy shares of 28.5 % and 13.7 %. It was assumed that there is no connection to the rest of the Europe, which has a large impact on the frequency response. This assumption was taken in order to simplify the model. The results are therefore not accurate for a large interconnection like the former UCTE. The scenarios used in the results sections had different conventional generation units online as can be seen from the aggregated parameters visible in Table I. In all scenarios a hypothetical 1 GW unit was tripped one second to the simulation. TABLE I. ONLINE CONVENTIONAL FLEET PARAMETERS IN THE SCENARIOS No FCR/FFR from wind / PV Scenarios FCR/FRR from Wind FCR/FRR from solar PV Inertia constant (s) Capacity of online conventional units (GW) For wind and PV generation there was an initial response delay of 1 s and 500 ms respectively. After that PV increased generation in direct relation to frequency, but wind had a ramp limitation of p.u./s. V. EXAMPLE RESULTS FROM THE SCENARIOS This section presents a couple of examples of FCR/FRR in the Iberian system. In the first comparison, a response to a hypothetical 1 GW unit trip is investigated for three cases of WILMAR simulations. A dispatch hour with high share of renewables was chosen (at noon 11 th of June). In the first scenario, noprimwindpv, Wind and solar PV were not allowed to participate in the primary frequency response. Therefore more conventional power had to be online than in scenarios where wind or PV were allowed to participate in the FCR/FRR ( PrimWind and PrimPV respectively). In these scenarios, wind and PV could have in principle covered the whole load, but the unit commitment decided against that mainly due to the start-up costs it was more cost-efficient to keep some coal and gas generation online. Still, the amount of online conventional generation was lower in these scenarios, which led to lower inertia and faster initial drop in frequency after a fault (Fig. 1). After the initial frequency drop, the response from wind and especially from PV is faster than that from conventional units. Wind has a longer delay than PV and it is slower to ramp up. For PV the ramp-up was assumed to be instantaneous after the initial communication delay. Frequency (Hz) Simulation time (s) Figure 1. noprimwindpv PrimWind PrimPV FCR/FRR in three scenarios The longer communication delay of wind power leads to some oscillations in the frequency. These get worse if the amount of wind in primary response is increased. Fig. 2 shows what happens if half or all of the wind generation intentionally running below available capacity would be under FCR/FRR droop control. The base case has approximately 1 GW of primary response, which consists of 930 MW of restrained wind generation and 100 MW of thermal units. In the 50% case additional 1 GW of restrained wind generation is included in the primary response. In the 100% case rest of the available restrained wind generation, a further 2 GW, is added to the primary response. Frequency (Hz) PrimWind 50% wind 100% wind Simulation time (s) Figure 2. Frequency response from wind power as more wind participates in the primary frequency response.

5 VI. CONCLUSIONS A method to compare the cost-effectiveness of different types of frequency response was outlined. The paper presents typical response characteristics for different categories of response: conventional units, wind power, solar PV, network devices with storage and demand response. Example results from the Iberian system are presented. Based on the example results, wind generation and solar PV generation intentionally running below full capacity can provide a frequency response that is fast in comparison to conventional generation. However, the reduced inertia in a dispatch with little conventional generation can cause a very fast initial drop in the frequency and it may be difficult to mitigate that with wind and solar PV alone. The results of the paper are for demonstration purpose only and additional analysis is required to draw more robust conclusions and to analyze also the costs and benefits of using wind and PV for frequency response. From these preliminary results it is already clear that there is a need to dampen the response from wind power or to decrease the communication delay in order to eliminate the oscillations visible in Fig. 2. ACKNOWLEDGMENT The writing of the paper has been funded by the IEE EUproject REserviceS. The WILMAR model for Iberia was set-up by Miguel Azevedo at VTT and part of data came from Gustavo Quiñonez Varela from Acciona. Data for frequency response from wind and PV are derived from the REserviceS deliverable D3.1 and D4.1. Their authors are gratefully acknowledged. REFERENCES [1] H. Holttinen, J. Kiviluoma, N.A. Cutululis, A. Gubina, A. Keane, F. van Hulle, Ancillary services: technical specifications, system needs and costs. REserviceS Deliverable D2.2., December [2] B. Kirby, Ancillary services: technical and commercial insights, July, [3] D. Lew, G. Brinkman, N. Kumar, P. Besuner, D. Agan, S. Lefton, Impacts of Wind and Solar on Fossil-Fueled Generators: Preprint, 10 pp.; NREL Report No. CP , August [4] N. Kumar, P. Besuner, S. Lefton, D. Agan and D. Hilleman, Power Plant Cycling Costs. NREL/SR , Apr [5] F. Gubina, Power System Operation. Facutly of Electrical Engineering, Unviersity of Ljubljana Press, 2006, ISBN [6] Eirgrid, Short Term Active Response (STAR), An Interruptible Load Service, Application Information Pack, April 2009, pdf [7] E. M. John et al., FACTS devices with battery-based energy storage - extending the reach of traditional grid stability systems, IEEE PES T&D 2012, Orlando, May [8] M. L. Lazarewitz, A. Rojas, Grid frequency regulation by recycling electrical energy in flywheels, IEEE PES GM 2004 [9] M. Holmberg et al., SVC Light with Energy Storage for Frequency Regulation, 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply, paper ID 667 [10] M. Faiella, T. Hennig, N.A. Cutululis, F. van Hulle, Capabilities and costs for ancillary services provision by wind power plants, REserviceS Deliverable D 3.1., April, [11] P. Kreutzkamp, K. De Brabandere, O. Gammoh, J. De Decker, M. Rekinger, S. Varga, D. Craciun, Ancillary Services by Solar PV Capabilities and Costs, REserviceS Deliverable D 4.1, May [12] J. Kiviluoma, A. Gubina, Ancillary services costs for different services and different conventional generators, REserviceS D2.3., January 2013.