Muon detectors

The scenarios used to evaluate critically the evolution of the generation mix and the effect on environmental CO2 emissions are based on the policy scenarios of Section 4.3.2 and Section 5.3.

The chosen scenarios, together with the baseline (business as usual) scenario, are derived from the literature (Isle-pact, 2012; European Commission, 2013; Botelho, 2015; Nunes, 2015). A total of four energy-related policy scenarios are considered together with a business-as-usual case. These

172

four other scenarios are energy efficiency measures, tourism impacts, EV expansion (as detailed in Section 5.3) and renewables policies (from Section 4.3.2). These scenarios are posited to be potentially interesting drivers for the future of the evolving electricity system and can give insights into the environmental and energy security concerns of the isolated island system.

At this point it is appropriate to comment on an aspect of terminology used within the thesis. As noted in Section 3.6, the research programme started with a set of scenario planning assessments concerning electricity futures in the Açores. The scenario planning was qualitative and followed the well-established, two-axis four quadrant approach as described in Lindgren and Bandhold (2009).

In this chapter however, as with Sections 4.2.3, 4.3.3 and 5.3, the term scenario is used in a rather different sense. Scenario is considered within these sections as an SD long-term analysis (policy or influence based) into the future in which one aspect is given dominance or particularly emphasised in some way. This allows for the scenarios presented in this chapter.

In addition, the synthesis model presented within this chapter brings together a range of considerations previously located in the smaller and more closely bounded models focussed on, for example, the renewable integration futures or endogenous electricity demand futures. The synthesis model therefore has the capability to be constrained or given emphasis in various ways.

At the risk of somewhat oversimplifying, for giving dominance to one policy or influence, which can be focussed on or not within the model. By exploring the synthesis model through such scenarios it becomes possible to reveal key aspects of the potential for electricity futures on the island of São Miguel. Such insights can then be held in mind when considering the behaviour of the synthesis model with the combined scenarios, found in Section 6.4.2, of all policies and influences. The different individual scenarios used within this chapter and for the model are now described.

Scenario 1: Business as usual

This scenario extrapolates the current trend of key factors and policies within the island system as detailed here. The scenario uses system data from 2005 to 2015, together with the past and present

173 policies and the prevailing economic and social conditions. Two important factors are the island population, which is determined by the current birth and death rates, and the GDP per capita; both being extrapolated up to 2050 from the 2005-2015 historical data. The existing policy for energy efficiency target of 6% decrease in consumption across all consumer-type sectors over the following 15 years starting from 2012 is implemented. The renewable policy, which was enacted in mid-2008 to achieve approximately 75% renewable generation by 2020, was adjusted to 45% renewable installation. This adjustment of the policy is made to reflect a business-as-usual with realistic rates of actual installations in 2015 (EDA, 2016). Also, a medium term goal of 30% reduced CO2 emissions by 2020 is implemented within this scenario. There is also a 12 MW goal for a small reservoir energy storage project to begin in 2018, considered as a policy objective for the system (Botelho, 2015). In addition, and as in Section 5.3, there are no EV policies or pronounced market influences apart from a normal increase in EVs (based on the purchasing rate of about 4 new EVs per year in 2015) which currently results in a total of approximately 50 vehicles in 2015. Further to this, the growth rate in the number of overnight tourist stays is determined from the 2005-2016 historical data to be 0.14%

(SREA, 2016), starting with an initial value of 96000 in January 2005 and extrapolated into the future. Using these system aspects as key inputs, the long-term trends and impacts within the system are simulated.

Scenario 2: Tourism impact

This scenario, which is similar to the one examined in Section 5.3, explores the effects of changing tourism on the generation mix and environmental impacts of the electricity system. It uses the Scenario 1 characteristics apart from the growth in the number of overnight tourist stays. For tourism growth rates, given from the historical data (SREA, 2016), two different cases are studied, namely, a one and a half times increase of the growth rate from 0.14% per month to 0.21% per month from 2016 until 2050 and a doubling of the growth rate from 0.14% per month to 0.28% per month from 2016 until 2050. These cases are of interest to EDA and the Regional Directorate for

174

Energy of the Açores (Botelho, 2015; Nunes, 2015). It is assumed here that no new hotel construction is warranted because of the low average occupancy rate on the island of 32% (Isle-pact, 2012; SREA, 2016), and the growth rate has only been doubled for this scenario (leading to a tripling of the average occupancy rate to 96% which does not warrant new hotel builds).

Scenario 3: Energy efficiency measures

This scenario corresponds to Scenario 1, apart from variations in the energy efficiency policy as described in Section 5.3. Two case studies are considered: namely, the doubling (to 12%) and tripling (to 18%) of the original policy targets across the policy timeline of 15 years. The energy efficiency policy measures are proposed to be discontinued after 15 years (Botelho, 2015; Nunes, 2015). Longer timelines are considered for the sensitivity analysis shown in Appendix B2. It is also assumed that the energy efficiency measures are fully adopted by the consumers (no adoption dynamics is considered for this model). However, the extreme case for non-adoption of the doubling and tripling measures will be reflected in the “business as usual” (Scenario 1) case.

Scenario 4: EV expansion

This scenario examines the possible influence of EV expansion. As with the previous scenarios, it corresponds to Scenario 1 apart from the EV expansion. It is assumed that light-duty vehicles are the target for EV expansion. The baseline case is compared to three other policy cases as in Section 5.3: a market-based adoption policy for diffusion of technologies (Bass, 1969), a target of approximately 2500 EVs by 2020 (Botelho, 2015), and a combination of the 2020 EV and the market-based adoption policies.

Scenario 5: Renewables policies

This scenario features two case studies of renewable policies based on aspects of Scenario 1. The CO2 emissions policy emphasis and installed renewable capacity targets within the system as described in Section 4.3.2 are the basis for the scenarios. For this scenario, a target of 30% reduction in CO2 emissions and 45% installed renewable capacity within the system by 2050 is examined. In

175 addition, a more aggressive 50% reduction in CO2 emissions with 75% installed renewable capacity within the system by 2030 is also considered.