Electricity demand

The WEC studies provide data for generation, installed capacities and overall electricity demand for each region. However, they do not provide electricity demand profiles (e.g., at hourly resolution), which are required for computing simplified power flows across the interconnections. Thus, additional input data was required.

An internal survey within the working group was conducted in order to acquire the missing piece of information, i.e. the hourly-sampled electricity demand profiles for one complete year, at country/regional level. Subsequently, the load curve of every region was obtained by aggregating the profiles (when available) of all countries belonging to that region. All required demand curves were not available, but around 90% of the total worldwide electricity demand of 2016 was covered. For example, Figure 4-9 shows the typical daily load curve of Region 9 - Europe 1.

Figure 4-9: The daily load pattern in Europe, winter & summer 2013. Source [83].

Additional assumptions are required to estimate hourly demand profiles for the target year of the study (i.e., 2050). Concretely, the 2050 load curves are derived from the available 2016 data via homothetic transformations, with a scaling factor for each region given by the ratio between the electricity demand projected in 2050 [69] and the annual load in 2016. This transformation method was preferred over the translation method after discussions within the WG. As for the shape of the future hourly profiles, it is assumed that behavioral changes in the use of electricity (e.g., integration of electric vehicles or heat pumps) will cancel each other out, thus leading to similar patterns as the ones we currently observe, yet with a scaling effect due to the increased overall demand. Plots in Figure 4.10 reveal the results of the homothetic transformation proposed above.

Figure 4-10. The annual load profiles for 2016 and 2050, with homothetic and translation assumptions (left). Detail emphasizing one week of data (right).

In order to properly account for the shifted consumption patterns across regions in the model, as induced by the dispersed geographical positioning of the consumers at different longitudes, the time reference for all demand data is set to Universal Time Coordinated (UTC) time. The collected and processed hourly load profiles (Figure 4-11) provide useful information about currently existing patterns in the use of electricity around the world, with seasonal variations easily observable in some regions. Further investigation of seasonality in load time series is conducted via the equation below, where max⁡(2016) (resp. min⁡(2016)) represent the 2016 peak load (resp. minimum load) for each region. On this basis, seasonal patterns are considered as being strong when the seasonal effect ratio reaches values above 25%.

Demand 2016 - total Homothety - 2050 Translation - 2050

4500000

Homothety - 2050 Translation - 2050

Figure 4-11: Selection of electricity demand profiles for 2016.

𝑠𝑒𝑎𝑠𝑜𝑛𝑎𝑙_{𝑒𝑓𝑓𝑒𝑐𝑡}= ⁡max(2016) − min⁡(2016) min⁡(2016)

Figure 4-12 shows that significant seasonal effects can be identified for all but one region in the Northern Hemisphere (with the exception of Region 6, North-West Asia). Region 8 (Middle East) tops this chart, with 62% score, mostly driven by steep electricity demand increase due to cooling requirements over summer time. Several other regions share the same seasonal behavior biased towards summer peaks for similar reasons (e.g., Region 1 – North America, Region 4 – East Asia, Region 11 - North Africa).

On the other side, one can observe relatively high seasonal scores for Regions 9 and 10 (i.e., Europe and UPS), which feature their peak demand during winter time, when domestic demand (e.g., for heating or lighting) increases.

Figure 4-12. Quantification of demand time series seasonality.

The plots and the associated table in Figure 4-13 provide basic information with respect to the yearly amplitude of electricity demand in Regions 4 and 9 (i.e., East Asia and Europe), as well as regarding the potential smoothing effect that may result from the aggregation of all regions via unlimited transmission capacity. For instance, Region 4 is characterized by a peak load which is 36% higher than the average year-round demand (850 GW), while the minimum demand amounts to a 45% decrease from the same average value. In Region 9, a similar situation is observed. Peak load is 45% higher than the average demand over the year (375 GW), while the consumption nadir represents a 36% drop compared to the mean demand. Finally, the aggregation of all demand time series indeed results in a flatter profile throughout the year. In this case, a peak load of 23% above a 2500 GW average demand is recorded, whilst a 17% drop corresponds to the minimum demand year-round.

Region 4

Region 9

All Regions Mean

(GW) 850 375 2500

Min

(%) - 45 - 36 - 17

Max

(%) 36 45 23

Figure 4-13. Statistical properties (e.g., minimum, maximum, mean) of 2016 demand time series for Region 4 – East Asia, Region 9 – Europe and for the aggregation of all regions.

An insightful way to assess the electricity demand variations over different time horizons (e.g., daily or weekly) is to compute heat maps. Figure 4-14 below displays the 2016 electricity demand resulting from the aggregation of all considered regions. In this plot, yellow areas represent the time where electricity load is around the average of the yearly value (i.e., 2500 GW), green areas represent instances when demand is relatively low, while red areas signify time instances when demand is higher than average. It can be observed in this plot that the aggregation of all regions still yields a yearly profile reaching its peak during summer time (due to the influence of high-demand areas, such as Region 1 – North America or Region 4 – East Asia), while preserving the weekly cyclicity currently seen in most of the regions.

Figure 4-14. Hourly-resolution heat map of electricity demand resulting from the aggregation of all regions during 2016.

This type of visual representation proves useful when trying to identify (on a more qualitative basis) the interconnection potential between the regions included in this study. In this regard, it might be of interest (from a pure signal complementarity standpoint, without considering any other exogenous factors, e.g.

the cost of interconnection) to link regions with opposite colors (e.g., green and red) displayed at similar time references. The following figure 4-15 centralizes the heat maps of all regions of interest (with the exception of Region 13 – North Atlantic, given by the assumption of no demand in this area) and provides a first glance on which groups of regions would complement each other from a demand aggregation perspective. For example, the signal in Region 1 (North America) is complementary to the ones in Region 9 (Europe) or Region 10 (UPS), but it is rather correlated with the demand profiles in Region 4 (East Asia) and Region 11 (North Africa). Furthermore, one might say that connecting Region 4 (East Asia) with Regions 5 (South-East Asia), 9 (Europe) or 10 (UPS) is justifiable, but not with Regions 7 (South Asia), 8 (Middle East), or even 3 (Oceania).

Figure 4-15. Hourly-resolution heat maps of electricity demand for all regions with an associated demand profile (2016).

Friday

Saturday

Sunday

Monday

Tuesday

Wednesday

Thursday 01/01/2016

31/12/2016

Daysof a week

Weeks of 2016

11

5 6

7 8

3 10

1 9

2 12

12 1

2

9

10

4

3 13

The data presented in this section, together with the economic assumptions introduced in Chapter 5, will serve as input to the optimization problem defined in Chapter 7. Further information concerning regional generation and demand data is provided in Appendix F.