of studies on system effects
Annex 3.A1. Case studies: Power system and economic assumptions
Model of the power system
Power system analysis requires gathering a sizeable amount of detailed information on electricity systems. Evolution of the electricity demand, load factors of VRE and hydro run-of-the river power plants, water inflows in run-of-the reservoirs, size and seasonal availability of interconnections among the different regions simulated are some of the required information.
A considerable effort has therefore been devoted to gather consistent and reliable data on the real systems to be modelled, in order to provide a realistic representation of a large, well-interconnected power system as the basis for this modelling exercise. The present study has strongly benefitted from the large amount of information on EU power systems published by the European transmission system operators (TSOs) and utilities.
Much of the input data for the main region (region 1) have been derived from real data of the French power system in 2015, which are published by the French transmission system operator RTE. These data include the load as well as the realised load factors from solar PV, wind farms and run-of-the-river “fatal” hydroelectric resources, which are available with a 30 minute interval. These data have been aggregated to match the hourly interval used in the modelling. The capacities of hydroelectric resources were also derived from the currently available resources in France: this applies to run-of-the-river “fatal” capacities, reservoir-based hydro reserves, as well as the pumped hydro capacities. Overall, region 1 has:
• 10 GW of run-of-the-river hydroelectric capacity;
• 10 GW of reservoir hydroelectric;
• 4.5 GW of pumped hydroelectric capacity.
The “water value”, i.e. the amount of water that periodically fills the reservoir, has been aggregated on a weekly interval.
Region 2 is artificially constructed in order to set up an interesting modelling exercise for being able to study the behaviour of the main region. Region 2 reflects the characteristics of the six power systems of the countries interconnected with France: Belgium, Germany, Italy, Spain, Switzerland and the United Kingdom. However, this region has been redesigned (i.e. scaled downwards) to have the same yearly electricity demand as that of the main region.
The two regions have therefore the same “size” in electricity terms. The load and the VRE load factors renewables have been obtained as a weighted average of the respective country data available. Similarly, the hydroelectric capacities are based on the real data for those countries.
Overall, region 2 has:
• 7.5 GW of run-of-the-river hydroelectric capacity;
• 7.5 GW of reservoir hydroelectric;
• 8 GW of pumped hydroelectric capacity.
As not all information was available in neighbouring countries, the load factors for hydroelectric resources and “water values” for region 1 have also been applied in region 2.
The demand curve for 2015 has been scaled using a factor of 1.14 to reflect the expected increase in the electricity demand by 2050 in OECD countries (see World Energy Outlook [IEA, 2015a]). The load factors of VRE and hydro resources have also been scaled to reflect the average load factor taken for the cost estimation of these resources, which are representative
of values in OECD countries (respectively 30% for onshore wind, 40% for offshore wind, 15% for solar PV and 50% for hydro). However, in this process the maximal load factor for each hour was limited to a maximal value of 90% (95% for offshore wind).
Figure 49 shows the load duration curve (left) as well as the load factor distributions (right) in the two regions. As clearly visible in these figures, both the demand and the VRE load factors are much more smoothed in region 2 than that of the main region, due to the their aggregation over a much larger area. This goes some way towards explaining why the uptake of VRE is far more vigorous in region 2 than in region 1 in the least-cost efficiency low-cost renewable scenario (see above).
Figure 49. Load duration curves and RES load factor distributions in the two regions
Finally, it is interesting to calculate the correlation coefficient between the generation from renewable resources and the electricity demand. Indeed, the correlation between RES generation and electricity demand provides an indication of the value of the electricity generated. The more the RES generation is correlated with the electricity load, the more valuable for the system is the RES generation and the higher is the market value of the electricity produced by RES. Table 9 provides the correlation coefficient between RES generation and electricity demand in the two regions considered1. In 2015 the correlation coefficient between solar PV and load is close to zero (but positive) in France, indicating that the value of the first MW2 of solar PV installed on the region should be closer to the average price of electricity. This reflects the characteristics of the French electricity demand, which peaks in the late afternoon winter time, when solar PV generation is minimal. The correlation coefficient is much higher in region 2, thus reflecting a better correlation with demand in neighbouring regions. Opposite trends are observed for wind, where generation is better correlated with demand in the main region. Hence, the market value of wind is expected to be higher than that of solar PV in the main region, while the opposite holds for region 2 (calculated market values for the main region are provided in Figure 48). It should be noted that the correlation coefficient between demand and VRE load factor, especially for wind power, may change from year to year depending on specific meteorological conditions. The above considerations are thus valid only for 2015.
1. The “correlation coefficient” is defined such that -1 ≤R≤1, whereby R=0.99 is almost perfect correlation, R=-0.99 is perfect anti-correlation and R near 0 means that there is no correlation.
2. The market value of electricity produced by solar PV and wind decreases with their penetration level.
The results indicated in Table 9 are therefore indicative of the value of the first MW of VRE installed.
0
Table 9. Correlation between renewable generation and demand in the two regions considered
Solar PV Wind onshore Hydro run-of-the-river
Main region 0.015 0.223 0.355
Region 2 0.372 0.097 0.276
Assumptions on other components of system costs
This section provides the data and assumptions used to estimate the components of system costs that could not be captured by the modelling work performed within this study:
connection costs, balancing costs and grid costs, i.e. T&D costs.
Connection costs, i.e. the costs for connecting the power plant to the nearest point of the high-voltage or medium-voltage transmission grid, are not accounted for in the IEA/NEA estimates of plant-level costs used in this exercise and therefore are not part of the optimisation process of the generation mix. The estimates of connection costs used in this study are based upon the data from Nuclear Energy and Renewables: System Costs in Decarbonising Electricity Systems (NEA, 2012) and are expressed as a fraction of the investment costs of each generation plant. Note that OCGT and hydroelectric pumped storage plants have the same annualised connection costs as those of CCGT plants and hydroelectric reservoir, respectively.
These data and the resulting annualised investment costs for each generating technology are summarised in Table 10 below. The component “connection costs” is calculated for each scenario in the following way: for each scenario the cost of connecting all power plants in the generation mix is calculated by multiplying the installed capacity of each power plant type by its specific connection cost, drawn for each technology from the literature. The “connection cost” component of the system costs is then obtained by calculating the difference between the total connection costs of each individual scenario and those of the base case, divided by the net amount of electricity produced by VRE.
Table 10. Plant-level data used for calculating the connection costs Fraction of
investment costs (%) Annualised cost (USD/MW/year)
OCGT - 4 530
CCGT 5% 4 530
Nuclear 5% 20 694
Wind 8% 13 730
Solar PV 5% 7 350
Hydro run-of-the-river 5% 16 185
Hydro reservoir 5% 12 233
Hydro pump storage - 12 233
Source: NEA, 2012.
Data for balancing and grid costs are derived from the information available in the public literature and are summarised in Table 11. It should be noted that the model used in the optimisation of the generation mix accounts for the uncertainty of VRE generation by adapting the required level of reserves as a function of the VRE penetration level. Balancing costs are therefore already accounted for, although only partially, in the calculation of profile costs. For this reason, 50% of the balancing costs found in the literature and reported in Table 11 are effectively used in the assessment of total system costs.
Most of the reported data concern low or mid-penetration levels (generally from a few percent up to 30%-40% of wind or solar PV penetration), and a few quantitative estimates are available for VRE penetration levels beyond 50%. The data used for a penetration levels of 10%, 30% and 50% represent the (lower-end) spectrum of results found in the literature, while the values at 75% are obtained as a linear extrapolation.
Table 11. Estimates for grid and balancing costs Penetration
level (%) Grid costs (USD/MWhVRE)
Balancing costs (USD/MWhVRE) Literature Used
Wind
10% 3 1.0 0.5
30% 5 2.0 1.0
50% 8 4.0 2.0
75% 11 6.0 3.0
Solar PV
10% 1 0.5 0.25
30% 2 1.0 0.5
50% 4 1.0 0.5
75% 7 1.5 0.75
Source: NEA estimated based on literature (see Chapter 2).
Note on balancing costs: The left column shows the reference values reported in the literature review, the right column the value used in the calculation (50% of the reference value).
The additional costs for T&D networks to accommodate significant amounts of decentralised and variable renewable energies such as wind and solar PV requires further systematic study. The figures underlying the analysis in this report remain largely based on Nuclear Energy and Renewables: System Costs in Decarbonising Electricity Systems (NEA, 2012), which was based on a meta-analysis of the available literature. Newer estimates indicate that network costs in systems with high shares of VRE might indeed be very large. A recent study (Berthélemy et al., 2018) summarising work by a modelling group around Pantelis Capros of TU Athens thus indicates that in France reducing the share of electricity by nuclear power plants to 50% as early as 2030 would imply investment costs for grid expansion of around EUR 90 billion during the decade 2030-2040, which would exceed the costs of new generating capacity over the same decade.