Approach limitations

For the sake of comprehensiveness, some simplifications have been made during this research. This section summarizes the main simplifications of this work.

In regards to climate parameters, some limitations may be pointed. Daily and three-hourly data of temperature, irradiance and wind speed were interpolated to hourly data using simple methods. Those interpolations could be significantly improved by implementing more complex approaches that may more accurately describe the behavior of the climate parameters. Even though the water supply was addressed using a multiyear calibration, it is only driven by monthly precipitation. Hydrological models could be applied to better simulate the available water supply.

The spatial resolution of the climate parameters used to ascertain the energy supply was considerably large. Photovoltaics’ generation was determined using temperature and irradiance averaged per NUTS III, while for wind power the driving factor was wind speed averaged for the Centre region. Water supply was averaged at a national level, driving the hydropower and run-of-the-river generation. The renewable sources of energy generation were then averaged to deliver a single time-series to introduce in the energy modelling tool. Ideally, the spatial resolution to determine those generations should be finer in order to include differences in the resource availability and behavior.

Power system modelling also shows significant shortcomings. One of the biggest limitations is the use of a single point in space to simulate the power system. It neglects the power constraints in the national transmission lines by ignoring the spatial distribution of supply and demand. Due to the same constraint, each supply source is modelled as one large power plant, discarding the individual characteristics of each power plant. Thermal power plants are great examples of this limitation: only two may be modelled, implying that the model does not allow to simulate condensing power plants using different fuels with different kinds of operation and characteristics. Hydro dam storage capacity is also a good example, the model considers a single reservoir that does not include geographical distribution of both the water supply and of the dams.

The modelling of hydro pump also raises two issues: 1) there are no limitations of the water availability in the downstream, i.e., while excess electricity is being generated and

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while the reservoir is not at its full capacity, the downstream water is pumped to the reservoir, independently on the level of river flow in that downside of the dam; and 2) the water supply is completely used for electricity generation, ignoring other uses of water (e.g. irrigation). Another limitation in the power system modelling is the hourly resolution: it is not enough to explore grid stabilization issues; for that, a thinner resolution is required.

Focusing on electricity demand, some simplifications were implemented. Even though a wide range of possible demand evolutions was provided to cover divergent paths, they still might be debatable. The assumptions taken for the services, industry, and agriculture sectors were simply gathered according to the literature. The electricity demand for the mobility sector was built in a detailed bottom-up manner for the light vehicles, e.g. the number of vehicles and respective driving patterns, and the same approach could be extended to heavy-duty freight vehicles. Also, some of the mobility players were not taken into account, such as it is the case for heavy-duty passenger vehicles, railways, navigation, and aviation.

The main limitation in the modelling of the electricity demand was that solely the residential electricity demand was assumed to depend on climate. Below, the limitations from the approach taken to model the residential demand are listed.

Residential electricity demand depends on socio-economic context, signal prices, user’s behavior, climate, etc. The projection of its development is strongly dependent on the assumptions and on the chosen approach to model it. Thus, as for any complex framework, the method proposed to model residential electricity demand in the future has some limitations, which are discussed below.

To determine the residential electricity demand, several sets of dwellings are created and characterized by their building characteristics, the existence of space heating and cooling systems, etc. Each parameter of a dwelling is selected according to its probability distribution function, without considering possible correlations with other characteristics besides age (e.g. a dwelling with double glazing windows may be more likely to be well insulated). One of the limitations of this method is not considering possible correlations among variables. For example, more efficient equipment is expected in houses with improved thermal performance.

A future energy efficiency improvement of heat pumps was disregarded. The residential space heating and cooling needs were determined considering several sources for internal gains (e.g. occupants, electric appliances, lighting, etc.), excluding non-electric appliances. One significant limitation is the disregard of economic factors such as price-driven mechanisms that may change consumer’s behavior, and economic growth that may affect ownership of electric appliances.

Energy storage in batteries was considered under two alternatives. First, due to a model limitation, electric vehicles and second-life batteries assume a charging/discharging rate of 0.1C (see subsections 4.4.2.1 and subsection 4.4.2.4); also, they are modelled as a single large battery, emphasizing the limited spatial resolution of the model. Second, the capacity of the dedicated energy storage was determined by applying a simple algorithm to calculate the imports energy need above the defined cross-border interconnection capacity defined. It does not take into account charging/discharging efficiency rates as it aims at illustrating the potential requirement for additional storage capacity.

In what regards overall emissions, the results aims at illustrating the differences in CO2 emissions between demand-flexibility scenarios. It considers a basic approach that neglects emissions from other activities not focused on this work (e.g. aviation).

Finally, hydrogen or electricity for its production was not included in this work, for any sector. In mobility, biofueled-vehicles were considered as opposed to hydrogen-fueled vehicles. For energy storage, second-life electrochemical batteries were assumed as opposed to hydrogen (or other storage technologies such as compressed air energy storage). Also, other uses could consider hydrogen, such as in industrial processes. Its implementation would require an adjustment of electricity demand due to different efficiencies of the technologies, for example, compared to electrochemical batteries. The introduction of hydrogen would add more complexity to the built system, but it is one of the most interesting follow-ups for this work.