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Why Space Weather Is Relevant to Electrical Power Systems

2015; American Geophysical Union; Volume: 14; Issue: 1 Linguagem: Inglês

10.1002/2015sw001306

1542-7390

C.T. Gaunt,

Geomagnetism and Paleomagnetism Studies

Space WeatherVolume 14, Issue 1 p. 2-9 CommentaryFree Access Why Space Weather Is Relevant to Electrical Power Systems C. T. Gaunt, Corresponding Author C. T. GauntSearch for more papers by this author C. T. Gaunt, Corresponding Author C. T. GauntSearch for more papers by this author First published: 19 October 2015 https://doi.org/10.1002/2015SW001306Citations: 25AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Introduction In March 1989, engineers in the electricity utility Hydro-Quebec attributed the collapse of their power system to unusual activity on the Sun. The public was critical of this excuse for another power failure. Even now, when I describe part of my research interest as being geomagnetically induced currents (GICs), some engineers think I am teasing them with science fiction. Two things stand out from the Quebec power failure of 1989. The first is that it should not have been such a surprise, because space weather forecasting had commenced as early as 1965, and in Canada since 1974 [Lam, 2011], and several publications had already linked space weather and GICs in power systems. Second, the event initiated significant directed-objective research to improve the understanding of the hazard and subsequently attracted many researchers to the complex problems. The oldest of the papers [Boteler, 1991] included in this American Geophysical Union (AGU) Special Collection of GIC-related papers describes the modeling and relevance of space weather studies to GICs and power systems vulnerability. Twenty-five years later, the research has established a fund of knowledge to inform policy and guide further research and action. To understand the opportunities, it is useful to review what has been achieved. The various papers are grouped by their main contribution to the causal chain of "Solar wind models-Geomagnetic disturbances-GICs-Grid vulnerability" relationships. Solar Wind Modeling Coronal mass ejections (CMEs) from the Sun cause most of the severe geomagnetic disturbances (GMDs). Many models of the propagation of plasma (solar wind) from the Sun to the Lagrange point L1, about 1.5 million kilometers toward the Sun from Earth, are omitted in this review that focuses on papers that specifically mention GICs. Until recently, solar wind models were restricted by the Earth's limited perspective of the Sun, but Savani et al. [2013] describe how imaging from the STEREO instruments enabled examination of the momentum flux of a CME from the Sun to L1 and the magnetosphere. Even before satellites were placed in solar orbit, Zanetti et al. [1994] and Kappenman et al. [1997] identified the correlation between ionospheric currents, geomagnetic measurements, and GICs. Instruments on the SOHO and ACE satellites, launched in 1995 and 1997 and orbiting at the L1 point, provide the inputs for various models from L1 to the magnetosphere, ionosphere, and ground. Weigel et al. [2002] showed that the geomagnetic field fluctuations coupled to the solar wind were affected by latitude and local time. Huttunen et al. [2008] identified the importance of the turbulence in the sheath regions of the CMEs for the magnitude of the substorms driving large GICs. Taktakishvili et al. [2011] used SOHO/Large Angle and Spectrometric Coronagraph measurements of geoeffective CMEs with the Wang Sheeley Arge-ENLIL model to predict their arrival times at Earth. A 3-D magnetohydrodynamic model of coupling between the solar wind, magnetosphere, and ionosphere, developed by Den et al. [2006] and running on National Institute of Information and Communication Technology supercomputers, models the timing and extent of GMDs. Weimer [2013] determined empirically that the interplanetary magnetic field and solar wind velocity at the L1 point, with the ionospheric conductivity, predict the low-frequency geomagnetic variation. Blanch et al. [2013] examined the effects of the solar wind shock wave on the ionosphere and geomagnetic field. The CalcDeltaB postprocessing model computes the ground-level geomagnetic variation from currents calculated from first principle magnetosphere-ionosphere models [Rastätter et al., 2014]. Kim et al. [2014] determined that both CME data and solar wind data from the L1 point are needed to correctly forecast all GMDs. Yu and Ridley [2008] described how predictions must be tested against measurements with ground-based magnetometers. Oler [2004a, 2004b] and Pulkkinen et al. [2011, 2013] have compared the validity of some prediction models. A few models carry through to the GICs, as described by Trichtchenko et al. [2007], Pulkkinen et al. [2009], and Zhang et al. [2012]. Geomagnetic Disturbance Modeling Ground-Based B Field and E Field Monitoring All over the world, magnetic observatories measure GMDs [Love and Finn, 2011; Love and Chulliat, 2013]. These measurements are an indispensable part of the validation of all the models—solar wind models between Sun and Earth, and downstream to GICs. Most measurements are of the geomagnetic B field and its time derivative dB/dt, using magnetometers. Magnetotellurometer measurements of both electric field or E field and B field are made at some observatories [Kotzé et al., 2015], as well as at other locations. Various relationships between GMDs and the GICs induced in power systems and pipelines have been reported by Viljanen [1997], Boteler and Jansen van Beek [1999], and Clilverd et al. [2010]. Disagreement over the indices characterizing the severity of GMDs arises from the dual effects of intensity (magnitude) and duration of the disturbances and resulting GICs, and the difficulty of identifying threshold values. The disagreement is illustrated by two papers: Oler [2004a, 2004b] argues the K index is adequate, while Kappenman [2005] argues that other indices are needed, such as to express the accumulated variation dB/dt. Field Models The E field is induced by and mathematically derived from the B field. Various approaches have been proposed and applied to networks of power lines and pipelines in different parts of the world. Some of the models are understood most easily by the applications, for which a few are selected here. The plane wave method was applied by Liu et al. [2009] to networks in China. Assuming a uniform plane wave in the ionosphere and uniform ground conductivity, the E field is calculated from the sampled B field by integrating over a defined time period. The GICs in a grounded network node, such as where transformer neutrals are grounded, are calculated from the E field using two network coefficients a and b that depend on the topology and resistances of the network. In a similar application of the plane wave model in South Africa, Ngwira et al. [2008] demonstrated an improvement by replacing the uniform ground with a layered ground. They also showed the generally applicable a and b coefficients for a node can be derived by backfitting measured GICs to the model. However, the modeling of GICs becomes less accurate with increasing distance from the magnetic observation station [Ngwira et al., 2009]. The backfitting of the a and b coefficients in Sweden was used to determine the model of the ground conductivity [Wik et al., 2008]. Instead of assuming a uniform geomagnetic field over the whole network, the interpolated field was derived from the Spherical Elementary Current System. Interestingly, the researchers reported that the calculated GIC was systematically lower than the measured GIC for large currents. Love and Swidinsky [2014], by analyzing the B field and E field data from Kakioka Observatory, were able to derive the ground conductivity and a galvanic tensor that relate the fields at that location, obtaining a good fit for the low-frequency variation. Viljanen et al. [1999] apply the complex image method (CIM) to the calculation of the geoelectric field with a spatial structure consistent with the driving ionospheric currents, on a grid with spacing of the order of the shortest distances between the grounded nodes of the network. Pirjola and Boteler [2002] find the results of the CIM to be very similar to the line current model and series expansion. Dong et al. [2015] and Matandirotya et al. [2015] have introduced finite element analysis to improve the ground conductivity modeling, and the former has applied it to discontinuities such as coastlines. Contrasting with the "conventional" methods, Simpson [2011, 2012] proposes finite-difference time-domain techniques to solve Maxwell's equations for the coupling between ionospheric currents and power systems, finding the techniques gives different results for the significance of the ground structures. The approach requires supercomputer facilities. Significant Parameters The models identify several parameters affecting the estimation of GICs. Gilbert [2005] examined the ocean-land interface. Pirjola [2008] studied the effect of the resistance of the transformer neutral grounding. Pulkkinen et al. [2006] investigated the cadence of the sampling of the magnetic field. Evidence from two extreme storms indicates that localized peak E field enhancement differs significantly from regionally averaged fields [Ngwira et al., 2015]. High-latitude enhancement of a GMD, occurring under some conditions of sudden commencement events, was identified by Fiori et al. [2014]. Superficially similar enhancement of sudden impulse events occurs in low-latitude regions when GMDs are amplified by equatorial electrojets [Carter et al., 2015]. The spectral content of sudden storm commencement events was shown by Kappenman [2003] to contribute significantly to GIC magnitude in midlatitude and low-latitude regions. On the other hand, Pulkkinen and Kataoka [2006] showed by S transform analysis that the spectral properties of GMDs causing GICs can be related to specific GMD characteristics, such as substorms or pulsations. GICs Regional Studies An important body of literature describes the calculation of GICs in various parts of the world and compares the results with actual measurements. The variability of the local time observations of particular GMD events and different latitudes, ground conductivity regions, and characteristic electricity networks expose the limits of the various models identified in sections 2 and 3. In addition to the studies used as examples of different modeling approaches in section 3.2, in China, South Africa, and Sweden, the following papers illustrate the growing international participation in GIC research during the past 10 years. Pirjola et al. [1999] compared measured and calculated GICs for pipelines in Finland. Viljanen et al. [2006] extended the analysis to 7 years of measurements, including the Halloween storm of October 2003, and the application to the Finnish natural gas pipeline nowcasting service. Thomson et al. [2005] reported calculations and measurements in Scotland from the 2003 Halloween storm, and the development of a space weather forecasting service. Torta et al. [2012] described their calculations and measurements in Spain. Viljanen et al. [2013] assessed the variation of the magnitudes of GICs across Europe, comparing them with the conditions in Finland, affected by generally stronger GMDs and lower ground conductivities. Juusola et al. [2015] studied the ionospheric currents and GICs in Europe during strong storms. Calculations and measurements in China illustrate the effects on geographically large, very high voltage networks [Liu et al., 2008; Zhang et al., 2015]. Calculations and measurements in Japan indicated a strong correlation of GICs with geomagnetic fields, rather than with their time derivatives [Watari et al., 2009a, 2009b]. Southern Hemisphere studies, in addition to those in South Africa, have been reported in Brazil [Trivedi et al., 2007], New Zealand, including a transformer failure ascribed to GICs [Marshall et al., 2012], and Australia [Marshall et al., 2013]. Wei et al. [2013] compared the GMD intensities and calculated GICs across North America for the Quebec and Halloween events and taking into consideration the ground conductivity according to different physiographic regions. Extreme Values An underlying theme in all the regional studies and several of the modeling papers is a concern about extreme events: how serious could an extreme GMD or GIC be? In a broad discussion of the hazards of magnetic storms, Love et al. [2014] lead to this issue. The question of assigning an extreme disturbance value for a level of risk or frequency of occurrence only arose relatively recently. Pulkkinen et al. [2008] identified several of the factors affecting the extreme values, including the number of severe and extreme events reported, magnetospheric responses to the solar wind, the ground conductivity, and the averaging effect of the length of line in which a GIC is induced. They fitted statistical distributions to the data they collected and compared the extrapolation with values derived from records of the 1859 Carrington event. Using data from only European observatories, Thomson et al. [2011] adopted the approach of estimating the rate of change of the horizontal geomagnetic field dH/dt and some related parameters for 100–and 200 year return periods. They illustrated the variation of intensity with geomagnetic latitude, with the maximum values occurring between 55° and 60°, and identified an anomalous location. Kataoka [2013] related extreme geomagnetic disturbances to the maximum sunspot number for each solar cycle and predicted that a GMD comparable with the Carrington event has a probability of 4–6% in the next decade. Love [2012] approaches the estimation of extreme events through Poisson probability instead. Pulkkinen et al. [2012] used a model of the ground structure to convert geomagnetic variation into geoelectric fields with 10 s cadence. They derived values of 20 V/km for the E field at high geomagnetic latitudes with poorly conducting grounds and showed how this parameter, responsible for driving the GICs in the power system, varied with geomagnetic latitude and ground conductivity. Ngwira et al. [2013] extended the study to show that the boundary of the high geoelectric fields experienced at high geomagnetic latitudes corresponds with the auroral oval. They also reported enhancement of the geoelectric fields by the equatorial electrojet in low-latitude regions. The extreme E field is transformed into the expected GICs flowing in the network in Great Britain by modeling the whole electrical network and improving the ground conductivity models [Beggan et al., 2013]. Using various models of the GMD, the network nodes most consistently at risk of high GICs were identified. Tsurutani and Lakhina [2014] take a very different approach to estimating the extreme values, modeling a "perfect storm" resulting from an extreme CME. They conclude that an event twice as extreme as the Carrington event is possible but do not estimate its probability of occurrence. Grid Vulnerability Much of the interest in GICs arises from their potential to damage power systems and disrupt societies that depend on reliable electricity supply, as illustrated by Jansen and Pirjola [2004]. Several approaches have been adopted toward assessing the impact of GICs and identifying mitigation approaches. Economic Impact Forbes and St. Cyr [2004] published an initial assessment of the correlation between GMD events, taken as a proxy for GICs, with transmission congestion or increased prices in the real-time energy market of the PJM transmission system market operator. Their analysis indicated a statistically significant correlation existed, and they determined the economic impact. However, Kappenman [2006] refuted the results of the correlation analysis, basing his argument on data in PJM and Nuclear Regulatory Commission reports, and claimed the cost impact had been significantly overestimated. In an extension of their initial study to 12 power grids in different parts of the world, Forbes and St. Cyr [2008] found that real-time market conditions and network losses were statistically related to magnetic field variations, again taken as a proxy for GICs. Forbes and St. Cyr [2010] subsequently published a study of the difference between expected day ahead and real-time prices in the PJM energy market, declared constraints for 500 kV transformers and the occurrence of GMDs. Again, they found statistically significant correlation, and they attributed the effects not only to transformer constraints but also to increased reactive power requirements and dispatch of generation to maintain stability margins. A study of insurance claims associated in time with GMD events [Schrijver et al., 2014] reported that the economic effects of GICs and harmonics extend even to low voltage distribution networks, initiating damage to electrical and electronic equipment. System Risk The risk to power systems derives from the effects that GICs have on the components of the system, particularly on transformers. The very low frequency GIC, often referred to a quasi-DC, causes a transformer core to be partially saturated in one half of the power frequency cycle, initiating harmonic distortion, voltage unbalance, and increased heating and nonactive (reactive) power loss. Some of the complexity of the transformer response to GIC is described by Zhang et al. [2011]. In turn, the transformer response affects other components, including capacitor banks, static VAR compensators, protection relays (as described by Pulkkinen et al. [2005]), and the capacity of the system to deliver power. Kappenman [2004] and Kappenman and Radasky [2005] described the vulnerability of large, high-voltage grids to GICs. Examples of disruption of power systems by GICs in Canada, UK, and Sweden are described by Pirjola et al. [2005]. Interestingly, the then-prevailing idea that GICs are "a high-latitude problem" associated with the auroral zone has been modified by several subsequent studies reviewed in section 4.1. In research paralleling that of their economic impact studies, Forbes and St. Cyr [2012] found that space weather forecasting can affect a system operator's dispatch of generation so as to reduce the vulnerability of a system to disruption by GICs. Mitigation The appropriate response to system risk and the economic impact of GICs would appear to be taking steps to avoid or mitigate the potential disruption. Indeed, Schrijver [2015] indicates that the economic benefits of mitigation could exceed the cost. However, there are various difficulties: Posner et al. [2014] identified the inadequacy of existing solar monitoring instruments and the models for operational forecasting; the geophysical models of space weather and ground conductivity are not yet adequately integrated into GIC forecasting [Lanzerotti, 2012]; the physical implementation of reducing GICs by installing neutral reactors and series capacitors is complex [Arajärvi et al., 2011]; and most customers of space weather information lack the knowledge of how to respond to it [Schrijver and Rabanal, 2013]. Concern about the effects of an extreme event has become evident in strategic and policy levels of the electricity industry and government. A study for the U.S. Department of Homeland Security concluded possible catastrophic damage to transformers and disruption could not be excluded, based on the available information [Zurbuchen, 2012]. A joint study by the U.S. Federal Emergency Management Administration and the National Oceanic and Atmospheric Administration concluded an extreme GMD affecting power supplies in some areas for several days or longer is a serious concern and justifies efforts to improve the understanding of the processes leading to vulnerability, so that appropriate mitigation steps can be implemented [MacAlester and Murtagh, 2014]. And the U.S. Federal Energy Regulatory Commission has issued rules requiring that electricity utilities review the vulnerability to GICs and implement changes where necessary [Kappenman, 2013]. Outlook More than half the papers reviewed were published during the last 5 years—an explosion of research toward a better understanding of GMDs and GICs. Better physics, engineering, and economic models have been developed in response to concerns about extreme events. However, there are still significant gaps. The vulnerability of power systems depends on many factors, including the topology of the grid, the type, design and condition of transformers and protection systems, the loads supplied, and the parameters of the GICs generated by GMDs. Despite the research, reliable causal relationships have not been identified for power system failure, operating constraints or transformer damage initiated by GICs. The calculation and forecasting of GICs depends on a series of models with many layers of uncertainty. The intensity of disturbances depends on whether B fields are measured over seconds, minutes, or hours, and characterized as extreme values at particular locations, or averaged over the length of a line or regionally over a whole grid. The transform to E fields depends on models of local or regional ground conductivity and the frequency of the components of the GMD. Accordingly, forecasts are not yet sufficiently precise and dependable for utilities' regular operational use. It is obvious that more measurements are needed before the many models can be integrated. At ground level, more magnetometers, measurements of ground conductivity and GICs, and records of power system disturbance and transformer degradation initiated by GICs are required. In space, more instruments are needed to measure the parameters of CMEs and solar winds. Obtaining better data for modeling and forecasting is a policy matter. Scientific space missions, geophysical measurements, and electricity utility data collection all incur costs. Government departments, the Federal Energy Regulatory Commission, and other regulators cannot prudently protect society from the potential effects of extreme space weather events without first stimulating an improved understanding of the science and engineering. Indeed, space weather research has a double benefit—it contributes to the general body of scientific knowledge and it can help to avoid the surprise of another Quebec-like blackout. 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