1- The review of literature reveals that some studies have assessed the feasibility of DG from the efficiency and economic perspectives; by using techniques other than optimization (Xuan et al., 2006; Wu and Rosen, 1999; Marantan et al., 2002; Gunes, 2001; Dentice d’Accadia et al, 2003; Dorer et al., 2005; Bhattacharyya & Quoc Thang, 2004; Mone et al., 2001; Hawkes & Leach, 2007). We discussed that due to the diversity of variables in play, namely efficiency parameters of different types of DG, demand magnitude and pattern, and market conditions, there is a considerable scope for employing optimization techniques. Among optimization models developed for DG, mostly consider only cogeneration systems (no other DG types), and the focus is mainly on economic aspects (Arcuri et al., 2007; Cho et al., 2009; Hawkes & Leach, 2005a & 2005b; Mavrotas et al., 2008; Monteiro et al., 2009; Rezvan et al., 2012). Relatively few studies also incorporate environmental aspects (Osman et al., 2008; Ren & Gao, 2010a & 2010b; Rubio-Maya et al., 2011; Rezvan et al., 2012).
2- Among studies that incorporate environmental aspects into the analysis (Osman et al., 2008; Ren & Gao, 2010a & 2010b; Rezvan et al, 2012; Rubio-Maya et
al., 2011), only one study (Osman et al., 2008) explicitly performs an LCA, and the
others are limited to assess the average CO2 or GHG emissions of DG operation. The same conclusions stand for the study that has assessed multi-DG for the building sector, i.e. Rezvan et al. (2012).
3- Most models developed for DG applications in buildings use MILP models. This is partly due to the flexibility offered by using binary variables in formulating problems involving fixed (or setup) costs (Williams, 2013).
4- The findings by (Arcuri et al., 2007; Bhattacharyya & Quoc Thang, 2004; Lozano
et al., 2010; Mone et al., 2001; Monteiro et al., 2009) highlight the importance of
considering national regulatory frameworks in the design and operation of DG, including the effects of parameters such as buy-back rate on the results.
5- Some studies (Hawkes and Leach, 2007; Rubio-Maya et al., 2011) have assessed the performance of cogeneration systems when operating in different load following modes, i.e. electricity following, heat following or both. The least cost operating strategy was shown to vary between technologies and the case-study. The development of an optimization framework for cogeneration system is therefore advocated.
6- Robust optimization techniques have not been extensively applied to DG modelling. The only robust model developed (Rezvan et al., 2011) considers uncertainty in demand and no robust modelling has been developed for other uncertain coefficients, such as cost or environmental impacts.
The review of LCA studies performed for DG led to following conclusions:
1- When dealing with DG renewable sources, an analysis framework is required for a thorough energy and environmental assessment (Chicco & Mancarella, 2009). In particular, the energy and environmental burden of conventional generation is mainly laid in its operation, while the environmental burden of renewables is by large due to plant building and decommissioning. These aspects can be adequately addressed by means of cradle-to-grave LCA techniques (Alsema et al., 2009; Chicco & Mancarella, 2009; Horne et al, 2009).
2- Analyzing upstream processes to produce fuels, and their associated emissions, is important; otherwise, the emissions resulting from electricity generation of the various cogeneration options are underestimated. For NG technology options, upstream GHG emission rates can be up to 25% of the direct emissions from the energy system (Weisser, 2007). The robust design of any policy to mitigate environmental impacts by replacing incumbent fuels with NG therefore requires a proper assessment of upstream emissions of fuels (Howarth et al., 2011 & 2012; Hayhoe et al., 2002; Venkatesh et al., 2011). No study was found to explicitly consider upstream emissions of NG as the main fuel to DG, while the implications of upstream stages of NG and LNG chains to their total GHG footprint have been recently questioned (Cathles et al., 2012; O’Sullivan & Paltsev, 2012; Skone et al., 2011; Stephenson et al., 2011; Venkatesh et al , 2011).
3- A number of studies have assessed the environmental impacts of NG chains for different geographical locations, including for Europe (Arteconi et al., 2010; Cathles et
al., 2011 & 2012; Howarth et al., 2011 & 2012; Okamura et al., 2007; O’Sullivan &
Paltsev, 2012; Skone et al., 2011; Stephenson et al., 2011; Tamura et al., 2001; Venkatesh et al., 2010). Portuguese NG supply is different from the European average mix, with possibly higher upstream emissions due to the high share from LNG chain. No LCA study was found to assess the upstream emissions of gas production from Nigeria and Algeria, which together represent the entire Portuguese NG mix and 18% of NG consumed in Europe. On top of the impacts due to liquefaction and transportation of LNG, relatively high production impacts of Nigerian NG have been acknowledged (Anomohanran, 2012).
4- The results of LCA of DG are dependent on the location: the emissions per output from solar systems depend on total energy produced by the system that depends on the location of installation. Cogeneration systems are also connected to NG transmission network, its upstream emissions depending on the supply mix. We established that Portuguese NG network system is detached from the European system and the average upstream emissions from Europe are not representative for
Portugal. Some authors point out the limits of applying “generic” data to assessing changes in “unique” environments (Horne et al. 2009). A proper assessment of potential of DG in Portugal therefore asks for an LC study framed specifically for the Portuguese case. This also helps policy designers to reduce the impacts of building sector by knowing the type and magnitude of emissions of DG, both renewable and NG fuelled, in Portugal.
5- Performing LCA on a level of case-study or supply objects (e.g. commercial buildings) provides significant methodological advantages (Pehnt, 2008). By explicitly calculating the different flows of energy between different objects of supply and demand, such framework helps to calculate the “actual” impacts from operation of DG, rather than assuming a constant average load of operation for the system. Moreover, by taking into account the demand side, it is possible to ensure that full amount of heat is actually used, or alternatively, calculate for the level of waste.
This doctoral thesis develops an optimization framework for the operation of DG in Portugal, taking into account both economic and environmental aspects. Four types of environmental impacts are assessed: Cumulative Energy Demand (CED), Greenhouse Gases (GHG), Acidification, and Eutrophication. As advocated by literature and justified in this chapter, cradle-to-grave LCA is the suitable technique employed to assess the environmental impacts of DG. The LCA model calculates the total impact arising from meeting the building energy demand, including the emissions due to production and decommissioning of energy systems. The model presents a detailed LCA of upstream emissions of NG, supplied from Algeria and Nigeria, to calculate the accurate emissions of DG in Portugal. A methodological framework is also developed to capture the uncertainty in upstream GHG emissions of NG, to increase the model robustness and provide insight for policy making. The results of LCA are used as the input to a multi-objective mathematical optimization model developed for the design and operation of DG in commercial buildings. Two types of solar systems (ST and PV) and three types of cogeneration technologies (MT, ICE, SOFC) comprise the DG that are coupled with conventional energy systems (boiler
and grid) to meet the building energy demand. ACs are coupled with CHP systems while the extra cooling load can be sourced by CC. The possibility of selling on-site produced electricity to the grid in the context of the legal framework of Portugal as well as dynamic pricing of electricity at peak and off-peak hours are also taken into account. The results of the model are tested on a case-study of a hotel complex to illustrate its application. Finally, a cost and demand robust optimization model is developed to deal with demand and cost uncertainty for optimal energy planning of DG.