Power systems' performance under high renewables' penetration rates: a natural experiment due to the COVID-19 demand shock

The model adopted to study the behavior of electricity load and renewables' share in the generation mix is based on weather and other seasonal effects, such as the time of the day and day of the week. We isolate the effect of the co-occurrence of seasonal variations in the weather by using daily records of maximum temperature, solar irradiance, and wind speed (NOAA 2020a, 2020b). Actual total load is defined as the sum of power generated by plants on TSOs networks, from which the balance (export–import) of exchanges on interconnections between neighboring bidding zones and the power absorbed by energy storage resources is deduced. The total load represents the power demand on the transmission and distribution networks, while any power demand served by distributed networks is not included in the statistics. This aspect influences our measure of the total load, reducing it at times of high generation of renewables in distributed networks. We take such effect into account by performing a set of alternative econometric specifications of the power demand model including daily solar irradiance as a control for distributed energy.

We construct a counterfactual scenario in which the COVID-19 induced net-load shock does not occur to estimate: (a) the total capacity factor of dispatchable technology n at hour h (${\text{C}}{{\text{F}}_{h,n}}$); (b) the share of technology n in the dispatchable generation mix (${S_{h,n}}$):

$$\begin{equation}C{F_{h,n}}\, = \,\left( {{G_{h,n}}} \right)/\,{C_{y,n}}\end{equation} \tag{ 1 }$$

$$\begin{equation}{S_{h,n}} = \,\left( {{G_{h,n}}/\mathop \sum \limits_{}^n {G_h}_{}} \right)\end{equation} \tag{ 2 }$$

where: C is capacity, G is generation of dispatchable technologies, n technology; h is hour, and y is year.

With the 1st variable we evaluate how the demand shock resulting from the COVID-19 lockdown impacts dispatchable technologies' absolute contribution to the power system. The variable measures the share of the total systems' capacity that is generating in each hour of the day over the total available capacity installed in a given year. With the 2nd variable we evaluate the impact of the demand shock on dispatchable technologies' relative contribution to the power system. We evaluate the share of each dispatchable technology in the dispatchable generation mix, as opposed to the total generation mix (including non-dispatchable technologies, amongst which RES), to focus on dispatchable technologies' contribution to system net-load.

The econometric model adopted to study the behavior of hourly dispatchable generation and day-ahead prices includes three explanatory variables: (a) the hourly non-dispatchable renewable generation available on the grid ⁸ ; (b) a linear spline of maximum temperature bins to approximate seasonal demand; (c) a proxy measure of operative costs for fossil fuel generators (${\text{OC}}_d^{{\text{tech}}}$). Further controls include calendar effects (see supplementary methods).

The $\,OC_d^n\,$ are computed as the combination of the daily spot price of fossil fuels ($p_d^n$) and the daily European Emission Allowances' price (${\text{ets}}_d^{}$) multiplied by the country-specific power plants' emission intensity (${\text{eff}}_{}^n$). Our proxy measures the incentives for fossil fuel generators to bid in the day-ahead market, as this is the cost-component of the 'clean spark spread' for gas and 'clean dark spread' for coal, defined as the difference between market prices of electricity ($p_h^{{\text{ely,ahead}}}$) and power plants' unitary operative costs (Abadie 2020):

$$\begin{equation}{\text{OC}}_d^n = p_d^n + {\text{eff}}_{}^n\, \times {\text{ets}}_d^{}\end{equation} \tag{ 3 }$$

$$\begin{equation}{\text{SPARK}}_d^n = {\mkern 1mu} p_h^{{\text{ely,ahead}}} - {\text{OC}}_d^n\end{equation} \tag{ 4 }$$

where d: day; h: hour; n: fossil fuel (gas, coal).

Day-ahead prices result from the day-ahead auction markets, which host most of the electricity sale and purchase transactions (Gestore Mercati Elettrici—GME 2020). In these markets, hourly energy blocks are traded for the next day and offers are accepted based on the economic merit-order criterion and taking into account transmission capacity limits. Actual spot prices on the other hand result from the intra-day market and are based on unit price differentials from the day-ahead price. In both cases the price is determined, for each hour, by the intersection of the demand and supply curves. The clearing prices are influenced by the bids of the thermoelectric plants that are selected as the marginal units. Furthermore, if the net-load is reduced (due to an increase in non-dispatchable renewable energy or to a demand reduction), the thermoelectric plants with higher marginal costs will be cut out from the market and consequently the clearing market prices will fall. Negative prices can arise under conditions of high supply from solar power plants or wind turbines, which have near-zero marginal costs, due to a combination of factors: while low-cost RES are typically not incentivized to reduce production as they receive a market premium in addition to the wholesale market price for each unit of electricity generated ⁹ , the most inflexible fossil-based generators accept negative prices due to the high costs of performing shut-down re-start sequences (Clò et al 2015, Fanone et al 2013).

The contemporaneous occurrence of (a) low fossil fuel prices and carbon allowance prices (IEA 2020, Abadie 2021), (b) variations in RES generation, and (c) lockdown-induced demand reductions requires a methodological framework that can disentangle the price effect (a) from the supply and demand effects (b + c). Methodologies that filter out the effect of the COVID-19 induced shock on fossil fuel prices would provide a more plausible counterfactual scenario to evaluate power systems' reactions to the extreme net-load variations ¹⁰ . We decompose the impact of COVID-19 on wholesale day-ahead prices between (a) demand shock and (b) fossil fuel price drops. While the intra-day spot prices may convey a clearer signal of market interactions, as it better reflects the tightness of the market (leading to higher highs and lower lows in the electricity price) ¹¹ , data availability constrains the analysis to the day-ahead price only (see supplementary methods) ¹² .

Balancing systems are used to maintain and restore the short-term active power balance in integrated electricity systems. They are based on a balancing power market through which TSOs acquire balancing power and an imbalance settlement system that financially clears the imbalances (Gestore Mercati Elettrici—GME 2020). Balancing power is only demanded by the TSOs, hence it is a single-buyer market. The TSO acts in two steps: (a) it contracts reserve capacity from conventional power units in the day-ahead market; (b) it activates in real-time part of the contracted quantities to cover system imbalances. Upward reserves (due to power deficits) are purchased by the TSO at a given 'upward price' which is normally higher than the day-ahead market price, as it reflects not only the increasing marginal cost of production, but also a premium for the flexibility of the generators (Clò and Fumagalli 2019). Downward reserves (due to power surpluses) are activated when the TSOs sell back the excess of energy to flexible generators already sold in the market, at a price that represents the saved operating costs (and are therefore normally lower than the day-ahead market price). Nevertheless, under conditions of large scarcity of downward reserves, downward flexibility providers may bid positive activation prices in order to be paid for the service, leading to negative imbalance prices (Brijs et al 2015). Even in the case of downward reserve prices the market imbalances generate a 'system cost', as end-users are not made whole for the energy not consumed (Clò and Fumagalli 2019). Therefore, we consider both costs due to deficits and surpluses as positive 'system costs' and compute the overall total incurred for balancing purposes over the day.

Other ancillary services excluded from the balancing systems are reactive power compensation and transmission congestion management. These can constitute a large part of the grid costs related to the management of the volatility in RES supply (Hirth and Ziegenhagen 2015).

We gather data from ENTS-E and national TSOs for: (a) balancing markets in Germany, France, United Kingdom and (b) the expenses on the ancillary services' markets in Italy ¹³ . Therefore, for Germany, France and the United Kingdom we consider only a part of the short-term integration costs of variable renewable energy, namely balancing costs, while we are able to include a larger set of grid management costs for Italy. The lack of data availability on the Spanish system's hourly balancing costs in 2020 resulted in the exclusion of the country from the analysis. Our econometric model considers the daily balancing costs (${\text{B}}{{\text{C}}_d}$), computed as the daily sum of all hourly expenses incurred in the real-time balancing market (${\text{BE}}{{\text{X}}_d}$) over the daily total actual load (${\text{A}}{{\text{L}}_d}$) as the dependent variable. The variable is therefore a direct measure of the balancing costs incurred by the consumers, expressed in € MWh⁻¹:

$$\begin{equation}{\text{B}}{{\text{C}}_d} = \frac{{\sum\nolimits_h {{\text{BE}}{{\text{X}}_h}} }}{{{\text{A}}{{\text{L}}_d}}}.\end{equation} \tag{ 5 }$$

The explanatory variables include: (a) the quantities traded over the day in the balancing market (i.e. the total imbalance requirement); (b) the day-ahead wholesale prices; (c) the day-ahead net-load; (d) the capacity factor of each dispatchable generation technology (see supplementary methods). It is important to include day-ahead wholesale prices and the day-ahead net-load as controls as low prices influence market dynamics because thermal generation facilities may turn to the ancillary services instead of the day-ahead market (Graf et al 2020). Further controls include calendar effects. The same model is adopted to study the costs of balancing and ancillary services'. We test a set of alternative model specifications where the influence of the key dependent variables is assumed to be either linear, quadratic or cubic. The inspection of the difference between the model-based counterfactual estimates and the observed balancing costs provides an indication of how much the balancing markets have been affected by unusual market conditions which cannot be captured by the market-based variables we include in the econometric model. Our approach is motivated by recent empirical evidence on the Italian re-dispatch markets during COVID-19 lockdowns, showing that observed ancillary services' costs during the lockdown have been higher than model-based estimations, likely due to new offer strategies or additional operating constraints (Graf et al 2020).

The power system has rapidly responded to COVID-19 lockdowns, when electricity demand was plummeting with the intensification of restrictions' stringency across all countries. Compared to the counterfactual demand that would have occurred in the absence of the COVID-19 lockdowns, the drop in average daily transmission load has increased with the intensification of restrictions across all countries, but especially in Italy and Spain (supplementary figure S1, panel (a)). The 'commerce halt' and 'commerce halt-partial' policies reduced electricity load on average by 10%–15% across the five European countries analyzed. Further stopping all non-essential production activities, resulted in an average reduction of 25% in Italy (surpassing 30% at the onset) and of 22% in Spain (supplementary figure S1, panel (b)). Inspection of the hourly load profile (supplementary figure S1, panel (c)) reveals that all countries experienced a fall in demand from 5 am to 10 pm, with the highest negative spikes around the morning (7–8 am) and evening (6–7 pm) peaks. Higher-than usual domestic activities between 12am and 3pm have compensated the industrial and commercial drops to some extent, leading to smaller reductions. This effect is particularly strong in France, Spain and Italy, suggesting that sectoral responses have differed across countries, possibly depending on different production and consumption behaviors. Going beyond speculation, however, would require the analysis of more granular data, e.g. electricity demand by certain sectors (households, industrial, and commercial).

The fall in the power load has resulted in an upward shift of RES penetration rates in all countries, although the magnitude varies depending on the power system (figure 1). On average, RES have contributed up to 60% of total active generation in Germany (an additional 15% point increase compared to the counterfactual mean shares), around 50% in Italy and Spain (an additional 5%–10% point increase), up to 40% (an 8% point increase) in the United Kingdom and up to 20% in France (3%–5% point increase). A clearer picture of the remarkable increase in RES penetration can be assessed by inspecting the maximum share of RES generation observed during the lockdown phases: RES contributed a maximum of 76% of total active generation in Germany, 60%–70% in Spain, Italy and the United Kingdom and 40% in France.

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Lockdowns have shifted the average contribution of solar energy up by 8%–15% points across all countries except in France (where they were up only by 2%–3% points), during the central hours of the day. As a result, the average hourly share in the mix has ranged from 20% in Spain to 40% in Germany. The impact on wind generation resulted in a smaller absolute change (with the exception of Germany), as wind contributed relatively little or during the hours in which net-load dropped less (night-time). The share of non-dispatchable hydropower has increased by roughly 5% points during evening peaks and night hours in all countries except the UK, where the technology's contribution to the mix is limited. The 1st direct impact of RES contributing up to 60%–70% in power generation due to the COVID-19 demand shocks can be assessed by looking at power generation's market equilibrium, resulting in a reduction of the dispatching of power plants operating at the margin, sharp modulations of the generation mix, and downward pressure on wholesale prices.

We compare the counterfactual capacity factor with the observed hourly mean values during the lockdown phase across countries (we focus our results on the two most stringent lockdowns, the 'commerce halt' lockdown phase in figure 2 and supplementary figure S5 and the 'non-essential activities halt' in supplementary figures S6 and S7). The difference between the observed and counterfactual mean hourly capacity factor of the technologies corresponds to the estimated impact of the net-load shock on the contribution of each technology to the power system in each country (supplementary figure S4). A negative (positive) estimated impact means that the total capacity factor has decreased (increased) with respect to the counterfactual scenario without the COVID-19 induced net-load shock. A decrease (increase) in the total capacity factor means that a smaller (larger) share of the technology's total available capacity contributed to the power system compared to the counterfactual scenario. The change in the curvature of the line through the hour of the day signals that the technology is ramped up/down for flexibility purposes and is used mostly to accommodate peak loads. The variation of the estimated impact on the capacity factor throughout the day is an indication of the extent to which the net-load shock influenced the ramping requirements of each technology. This can signal the extent to which technologies were able to adjust to the circumstances and contribute to the power system alongside a relatively high share of RES to fulfill load requirements. Such a time-varying capacity factor characterizes all technologies with the exception of nuclear-based generation, which exhibits a uniform shape through the hours.

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Among the power plants activated through the day, four alternative effects associated to the COVID-19 measures can be identified: (a) temporary phase-out; (b) partial market exit; (c) increase in flexibility requirements; (d) unaffected by market competition.

(a) Temporary phase-out: if the observed capacity factor has reached values around zero with respect to the counterfactual curvy shape, the technology has been cut out from the market. In this case the technology was likely to operate mostly as a marginal technology during demand peaks in the counterfactual and was hence sharply affected by the fall in demand induced by COVID-19 measures. Coal's capacity factor observed during the lockdown was much lower compared to the counterfactual scenario, meaning that the entire coal fleet in Germany, United Kingdom, France and Spain stood idle during the observed period instead of otherwise being activated at capacity factor ranging between 10% and 40%, depending on the hour of the day. As a result, the observed share of coal on the dispatchable generation mix fell to around 1%–2% in Spain, 5%–7% in Germany, as opposed to counterfactual shares ranging between 15% and 20% (supplementary figure S5).

(b) Partial market exit: if the observed capacity factor is both above zero and lower with respect to the counterfactual's shape, the technology has been only partially displaced from the market. Our estimates suggest that there has been both a fall and a flattening throughout the day of the capacity factor of the gas fleet in Spain and of the coal fleet in Italy, possibly indicating that only a part of the fleet has been pushed out of the market altogether, while the more efficient plants remained in the market to actively contribute to baseload but not flexibility. In Italy a small share of the coal fleet remained active during the COVID-19 induced net-load shock with roughly 20% of available capacity actively contributing to the power system compared to up to 50% of the capacity in the counterfactual scenario. On the other hand, the fall in the capacity margin coupled with a preservation of the ramp-up/ramp-down profile of the lignite fleet in Germany and gas fleet in the UK suggests that part of these fleets continued to provide both baseload and peak-time power ¹⁴ .

(c) Increase in flexibility requirements: more flexible technologies could fulfill market requirements without being cut out from the market, as opposed to cases (a) and (b), due to their lower marginal costs: this is the case of gas-fired generation in Germany (capacity factor increasing uniformly throughout the day by an additional 10% points) and in Italy (capacity factor mostly stable during peak hours and decreasing by 5% points during off-peak hours). Hydropower's flexibility has been considerably exploited due to the net-load shock in Germany, France and Spain, while it has remained stable in Italy. The technology has been used relatively less due to the net-load shock in the United Kingdom, where nonetheless it contributes only to a marginal (<3%) fraction of the dispatchable mix.

(d) Unaffected by market competition: if the observed capacity factor is equal or slightly lower than the counterfactual and with the same overall shape, the technology has likely not been affected by the reduction in power demand leading to higher competition from renewables. A lower than usual capacity factor in this case might be the result of operational and maintenance complexities caused by the lockdowns' disruptions on workers' rather than by competition with other forms of generation (Farrar et al 2020). This is the case of nuclear power, as the technology's observed capacity factor is slightly lower than the counterfactual one in the UK, France and Spain, while it remained stable in Germany. Overall, the decreases in nuclear generation have been less marked than the overall reduction in total dispatchable generation, resulting in an increase in the share of nuclear on the mix.

The import/export balance is a key aspect affecting our assessment of power systems' response to COVID-19. The different responses of gas and coal generation across the countries which are typically reliant on these technologies can be associated to the countries' variation in the import/export balance. The countries which experienced a temporary shut-down of large amounts of coal power capacity (Spain and Germany) or gas (United Kingdom) relied more often on imports to fulfill market flexibility requirements (as underscored by the difference in the distribution of day-ahead imports between the lockdowns and the 2nd quarters of 2017–2019 displayed in the figure 3). Germany typically exports 10 GW–20 GW of power each hour when low or negative wholesale prices occur. This condition has happened often both during the lockdown and in the 2nd quarters of 2017–2019. However, during the lockdown there were also times where similarly low price conditions went hand in hand with imports (figure 3). This may suggest that due to the fall in coal generation, the country relied not only on gas but also on imports for flexibility purposes. France is typically a net exporter. While a number of hours of net importing positions have been registered in the past when day-ahead prices were at least 50–70 € MWh⁻¹, during the lockdowns France has experienced almost no case of positive net imports as prices have been stably below that price range. Italian net imports on the other hand have been strongly reduced during the COVID-19 lockdowns with respect to previous years. Although the country remained a net importer during the lockdowns, volumes dropped and only a few hours registered hourly net imports above 10 GW (half of the maximum hourly net importing position registered during the 2nd quarters of 2017–2019). In Spain and the United Kingdom, net importing positions have not changed drastically, although the distribution points to an increase in exports in Spain and of imports in the UK (figure 3).

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Policy-induced net-load shocks have resulted in a fall in the utilization of fossil fuels, ranking higher in the merit-order curve compared to hydro and nuclear. Accordingly, carbon intensity of the dispatchable generation mix has fallen sharply during the lockdowns, resulting in a large reduction of emissions (see supplementary methods). We compute the COVID-19 induced marginal reduction in the dispatching of each conventional technology and apply country-specific coefficients of the emissions related to their operations (based on Tranberg et al 2019): we find that the net-load shocks have contributed to reduce emissions not only by the average emission factor per kilowatt-hour, but by a higher amount because generation from the more carbon-intense technologies has been cut compared to counterfactual conditions. Across the five countries, total power emissions decreased by about 26 MtCO_2eq (see supplementary table S10). Emission savings have originated both from energy demand reductions (17 MtCO_2eq), and fuel switching (9 MtCO_2eq).

The identification of a statistically significant effect of the operative costs $({\text{OC}}_d^n,{\mkern 1mu} {\mkern 1mu} {\text{OC}}_d^{{\text{gas}}}{\mkern 1mu} {\text{OC}}_d^{{\text{gas}}}{\mkern 1mu} {\text{and}}{\mkern 1mu} {\mkern 1mu} {\text{OC}}_d^{{\text{coal}}}$ ${\text{OC}}_d^{{\text{gas}}}{\mkern 1mu} {\text{OC}}_d^{{\text{coal}}}$ see supplementary methods and supplementary table S2) allows us to construct two counterfactual scenarios of day-ahead wholesale electricity prices: (a) seasonal scenario, in which we simulate the evolution of day-ahead wholesale electricity prices based on the observed variation in renewable energy generation, the observed daily maximum temperature and the calendar effects, holding the value of the operative costs (${\text{OC}}_d^n$) fixed to the mean level in 2019 (green line in figure 3); (b) fuel price scenario, in which we replace the 2019 mean of operative costs (${\text{OC}}_d^n$) with the daily observed operative costs during the lockdowns.

The difference between the seasonal and the fuel price counterfactual scenarios quantifies the impact of the COVID-19 induced shock on fossil fuels' and ETS costs (henceforth 'fuel price effect') on the wholesale day-ahead prices. The difference between the fuel price scenario and the observed day-ahead prices (blue line in figure 4) quantifies the impact of the demand shock on the wholesale day-ahead prices (henceforth 'demand effect').

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The wholesale electricity prices fell on average between 16 and 32 € MWh⁻¹ across countries, corresponding to a percentage fall ranging between 43% and 61%, due to the overall effect of the 'commerce halt' lockdown (see table 1). Our estimated total impact for Italy (47%) is in line with the aggregated shock obtained by Graf et al (2020). The decomposition suggests that both the demand effect and the fuel price effect have caused a significant reduction of day-ahead wholesale prices compared to the day-ahead prices in the counterfactual scenario. We estimate the fuel price shock to cause a reduction in the wholesale electricity prices ranging between 7 € MWh⁻¹ and 14 € MWh⁻¹, while the demand shock reduced wholesale electricity prices by 9 € MWh⁻¹–23 € MWh⁻¹. While the fuel price and demand shock contributions to the total price reduction are roughly equal in most countries, the demand effect is relatively strong in Spain and accounts for 72% of the total fall in prices. The relatively uniform impact of the fossil fuel effect across power systems with heterogeneous dispatchable generation mixes suggests that to provide peak load most countries use relatively expensive gas plants. On the other hand, the relatively small role of the fossil fuel price shock in the Spanish system may be related to the role played by hydropower in the country, which reached a share of up to 35% of the dispatchable generation mix during peak hours during the lockdown (as opposed to 5%–15% in the other countries).

The distribution of the observed hourly day-ahead prices and of the counterfactual hourly day-ahead prices in the counterfactual scenarios provides an indication on the tails of the distribution (panel (b)). Two groups of countries can be distinguished based on the variation in the density functions. In Spain, Italy and the United Kingdom the observed density functions are characterized by a leftward shift in the mean, while the overall shape of the distribution does not change considerably. The shift is stronger in Spain and Italy compared to the UK: in the former two countries the mean price observed during the lockdown (blue) falls outside the left tail of the two counterfactual prices' distributions (95th percentile of green and red distributions).

In Italy, the fall in the costs of peak-load power plants, combined with the demand shock, was sufficiently large to displace the typical import/export balance. As the gap between the (higher) day-ahead prices in the home market with respect to the (lower) day-ahead price of net exporters such as France shrunk due to COVID-19, volumes of imported power have drastically fallen with respect to the 2nd quarters of 2017–2019.

In France and Germany, the mean of the observed distribution does not shift considerably with respect to the counterfactual distributions, while the kurtosis increases considerably. The probability to observe a price of 20 € MWh⁻¹, which is the mean price during the lockdown (blue distribution), is roughly four times higher than the probability associated with the same price in the 'fuel price' counterfactual. Therefore, the demand shock has induced most of the variation in the price distribution of these countries. The similarity in the distribution of observed prices the reduction in price volatility around the mean day-ahead price in Germany and France may derive from greater market integration than the rest of the countries, as the two systems take part to the same regional wholesale market, the Central Western Europe (including France, Belgium, Luxemburg, the Netherlands, Austria and Germany), which is connected through a flow-based market coupling (Felten et al 2019). While across Europe the day-ahead market coupling takes place ex ante the market clearing, in Central Western European a more flexible method has been in place since 2015 that operates simultaneously with the market clearing (Van den Bergh et al 2016) ¹⁵ .

Despite that the occurrence of high negative prices in Germany has been associated with peaks in net exports, overall the country has experienced more frequent net importing positions than during the 2nd quarters of 2017–2019 (see figure 3). The shift towards more imports may be associated with the reduction in dispatchable power flexibility following the temporary phase out of coal generators (as underscored in section 3.2). Similarly, the UK experienced a shift in the distribution of the hourly net import position towards larger import volumes compared to past years' 2nd quarters (see figure 3). This shift should be evaluated in combination with the marked reduction in gas-fired generation during the lockdown, signaling that relatively cheap power from France has been preferred for flexibility purposes to the country's gas-fired fleet.

The fall in net-load demand and the resulting change in the generation structure not only resulted in lower wholesale prices, but also affected the task of balancing the electricity system, possibly resulting in an increase in the costs incurred for grid operations. The stress on the system during COVID-19 lockdowns, characterized by the almost unprecedented condition of very low demand coupled with abundant renewable energy, results from the interaction of forecast errors for RES generation and demand (see supplementary results).

Upward and downward flexibility requirements during the lockdowns may also have been influenced by complementary market conditions. Under typical market conditions, the lack of downward system flexibility, also referred to as 'incompressibility', is exacerbated when baseload units are online due to stringent operational constraints or because they are contracted to provide reserve capacity (Brijs et al 2015). The decrease in baseload technologies' capacity factor during the lockdowns has likely influenced this constraint (see section 3.2). Furthermore, low wholesale prices have reduced the incentives of power generators to offer their electricity in the electricity markets (see section 3.3).

It is important to remark that in our counterfactual out-of-sample predictions we include key variables such as the actual volume of imbalances, the day-ahead prices and the capacity factor observed during the lockdowns. These variables have been remarkably influenced by the COVID-19 induced shocks, as shown in the previous sections. In doing so, we test how well a model based on market conditions calibrated to the pre-COVID-19 period can describe the balancing markets conditions experienced by the power systems during the lockdowns. The comparison between the observed balancing costs and the model-based projections provides contrasting evidence across the four countries analyzed: projected balancing costs during the lockdown are close to the observed balancing costs in France and the UK, while our model systematically underestimates the price spikes that characterized the German and Italian systems (figure 5).

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The inspection of the time series of the balancing costs per unit of demand underscores that Germany experienced very high balancing costs spikes, while Italy experienced an overall increase in the level of ancillary services' costs, which, in both cases, are not captured by the model-based projections. Germany in particular experienced two episodes when daily balancing prices were around 8 € MWh⁻¹ (blue markers in figure 5), a level which is roughly double the maximum registered in the past 3 years (equal to 4.5 € MWh⁻¹). France's balancing market has been characterized by a slight reduction in balancing costs, both as for surplus and for deficit conditions, which resulted in a slight overestimation of balancing costs by the model based on market fundamentals. The UK experienced an increase in balancing costs during the lockdowns compared to previous months, which are well described by our forecasting model based on market fundamentals.

The large abundance of gas capacity in the UK (as shown in section 2) might have played a role in keeping balancing costs much more under control compared to Italy and Germany, where gas-fired capacity was either unaffected (Italy) or even decreased (Germany) due to the day-ahead power market's equilibrium shocks induced by the lockdowns.

The inspection of the type of balancing requirements (surplus vs deficit) which characterized the four countries' markets before and during the lockdowns allows for the identification of a possible driver of the heterogeneity in the model's performance (supplementary figure S10). In France and the UK, the relative importance of downward imbalances (deriving from a generation surplus) and upward imbalances (deriving from a generation deficit) has not changed drastically in the lockdown with respect to the first months of 2020. On the other hand, in Italy and Germany lockdowns were characterized by an unusual increase of downward flexibility costs: in Italy the distribution of balancing costs due to surpluses changed considerably in the 'commerce halt' lockdown and even more sharply in the 'non-essentials activity halt' lockdown, reaching values comparable to day-ahead wholesale prices (higher than 20 € MWh⁻¹). Costs associated with deficits were generally lower than the ones associated with surpluses. The German balancing market experienced an increase of both surplus and deficit costs during the lockdowns, but most of the cost increases derived from the former. The two spikes in balancing costs registered in the German balancing market were associated with a surge in both surplus and deficit prices, resulting from the bids of balancing market participants accepted by the German TSOs: the average price for each unit of activated power reached around −189 € MWh⁻¹ for the surpluses, while the price of the activated balancing power reached as high as 550 € MWh⁻¹ for the deficits (in both cases the prices represent the maximum value registered in 2020, as opposed to an average price of −38 € MWh⁻¹ for surpluses and 74 € MWh⁻¹ for deficits during the 'commerce halt' lockdown and of −12 € MWh⁻¹ for surpluses and 60 € MWh⁻¹ for deficits in the month preceding the lockdowns).

The estimated total balancing costs during the 'commerce halt' lockdown is 60% lower than the observed total costs in Germany (a difference of 23 million €), while it is 13% higher in France (a difference of 3.5 million €) and 4% higher in the United Kingdom (a difference of 2.5 million €) ¹⁶ . As for ancillary services' costs in Italy, the estimated total is 39% lower than the observed costs during the 'commerce halt' (a difference of 80 million €) and 48% lower during the 'non-essential activities halt' (a difference of 195 million €).

By evaluating the difference between our out-of-sample predictions and the observed balancing costs, we shed light on the possible occurrence of new market mechanisms affecting balancing markets. The aspects which could result in a deviation of our estimates from the observed balancing costs include: (a) new offer strategies employed to exercise market power by the participants in the balancing market (for instance, the reduction in the profits of power generators resulting from low day-ahead electricity prices may have triggered an increase in the value of the bids placed by such parties in the ancillary services market); (b) a variation in the operating constraints (such as voltage regulation, reserve requirements or nodal network constraints), which can increase the requirements for re-dispatch actions (Graf et al 2020). Overall, we find evidence for the existence of a combination of such factors from the behavior of balancing markets' costs in Germany and of ancillary services' costs in Italy.