ENTSOG GRIP South - Main Report

In addition to the factors affecting the whole South Region, other factors to highlight that have influenced the change structure of the Spanish energy mix and as a consequence, have derived in the significant drop of

natural gas consumption for SpanishCCGT’s are the following: 1. Increase of the renewable contribution to the energymix:

Tomeet the EU2020 target. In the case of Spain, wind generationhas experienced a huge increase in the last years, not only in installed capacities and contribution to the energymix, but also its load factor that, for example, is growing by 3 points this year. 2. National regulation introduced in 2011domestic coal production subsides, giving it preferential access to the powermarket. These factors together, imply a need of reviewing the figures of gas for power generation published in TYNDP 2013–2022, to gather the actual situation. So let´s see more in detail, the behaviour of the Spanish Energy market, as well as the variables affecting the natural gas for power generation, and themethodology implement- ed for the analysis. Long term estimationsmethodology The first groups of combined cycles gas turbines started operating in the spring of 2002 and now, in 2013, we find 67 groups already in commercial operation. In the early years of generation with this technology, high load factors were registered, around 42% reaching a maximum of 48% in 2008. The average growth rate of installed capacity for this technology, in the first five years, was 48%. Based on this historical behaviour long- termestimationsweremade in terms of installedcapacity and futureprojects, taking intoaccount the factorspre- viouslymentioned, as well as the level of maturity in themarket, themethodology to estimate the gas for power generation has evolved. The new methodology takes into account the big amount of variables influencing the generation mix (wind, hydro, solar…), and theneed todeepen on theSpanish electricitymarket behaviour, to outline the role occupied by natural gas in that energymarket, by using the technique of scenario simulation. 1) Thismethodology carries out an analysis based on threedifferent assumptions depending on electricity demand, development of renewable energy and cost of fuels. Thus, for a correct analysis of the generationmix, it would be needed to have a thorough understanding of each of the variables that are part of it, highlighting: \\ Electricity demand \\ Wind generation The thermal generation (thermal gap) represents the last resort in the Spanish generationmix to cover electricity demand. It should be noticed that this thermal gap is impacted by the variability of renewables and level of electricity demand. The thermal gap will be split according to the cost of production associated with each fuel (natural gas and coal). These costs aremarkedmainly by: \\ International coal prices \\ International spot market Price of natural gas \\ CO ² emissionprice Todevelop thenew scenarios for theGRIP, inall of them the level of thenuclear generationhasbeenmaintained, as the installed capacities for this technology do not change in the period under study. Concerning hydro power an average hydro-year has been considered, as installed capacity neither changes. Two different assumptions of electricity demand growth and special regime growth (high and low) have been set up, giving a total of four scenarios built with all the possible combinations between demand growth (high and low) and Special Regime growth (mainly wind and solar, high and low) . For each of the four scenarios, there are 3 alternatives to distribute the thermal gap, depending on the relative prices of coal and gas (price equilibri- um, competitive coal price related to gas price and competitive gas price related to coal). So, four different sce- narios with 3 possibilities each, give a range of 12 scenarios. \\ Nuclear generation \\ International flows \\ Hydro generation \\ Rest (rest of renewables, fuel, auto consumptions, etc.) \\ Thermal generation: natural gas+coal (Thermal gap)

1) It is not just one or several predictivemodels, but explicit knowledge of the sector and its significant variability: deductivemodels, generating scenarios based on the different variables influencing and their respective uncertainties.

24 |

ENTSOG–GRIPSouth 2013–2022