La Voluntariedad de los Sistemas de Seguridad y Salud

Published investigations of µCHP, as discussed in this section and otherwise, have applied an assortment of modelling approaches to determine µCHP performance, in terms of carbon reduction potential and/or economic viability. There has been a significant variation in the predicted relative CO2 savings potential reported for µCHP systems. Hamada et al [113] have estimated the carbon abatement potential of a PEM µCHP system as 20%. Hawkes & Leach [58] compared SE-, ICE- and SOFC-based systems, under thermal and electrical load following regimes, and RCS between 10% and 19%. Peacock & Newborough [57] reported carbon savings between 40% and -3% for 3kWe FC-based and 1kWe SE-based µCHP systems, respectively.

The effect of changing grid intensity was investigated by De Paepe et al [77], for a range of SE-, ICE- and FC-based µCHP, where they assumed that exported electricity was assigned a CI equal to that of grid imports. With a Belgium average grid intensity of 0.272kgCO2/kWh, they reported RCS of between -6% and 12%, rising to 17%-48% when compared with a grid intensity of 0.617kgCO2/kWh. Due to the capacities of modelled systems (1-9.5kWe), they note that 85-90% of generated electricity is exported, which underlines the sensitivity of CO2 savings to the CI assigned to export electricity. Peacock & Newborough agree, stating that due to the high proportion of electrical export (44–74%) from systems with relatively high Pe, the CO2 savings attributable to such systems largely depend on the assumption of equal carbon intensities of electricity import and export carbon.

Prior research [3] agrees that the variation in reported CO2 savings can be attributed to variation in prime mover technology, system design (whose aspects include the integration of storage technologies, operating regimes and control algorithms), externalities (carbon intensities and prices of fuel, import and export electricity), and the magnitude and shape of the dwelling’s demand profile. It is pointed out that, between the investigations reported in this chapter, an array of electrical and thermal efficiencies have been used to represent each prime mover technology. In addition, various modelling approaches have been applied which implement or disregard, to varying degrees, transient performance, part-load performance and other operating

restrictions of µCHP systems. The limitations or economic emphasis of the modelling approaches, as discussed in this section, that were adopted by published investigations cast some doubt on the validity of predicted CO2 savings attributed to µCHP systems. As the range of CO2 savings reported can be very low, or even negative, especially for low-ƞe prime mover designs, it is conceivable that many µCHP systems may not provide significant carbon savings. This is supported by field trial results from the Carbon Trust’s Micro-CHP Accelerator programme [19], which reported relative carbon savings (versus a condensing boiler) of -5% to 5% for domestic µCHP (<5kWe), and 6% to 11% for commercial µCHP (5-10kWe).

Therefore, the Building Integrated Micro-Generation (BIM-G) model was conceived to facilitate the investigation of μCHP systems. The originality of the BIM-G modelling and analysis methodology is the transient, bottom-up approach to demand definition, coupled with micro-generation and storage performance modelling. A major point of novelty of the BIM-G model is that a dynamic link exists, integrating supply calculations and demand estimation. This permits the supply:demand matching algorithms to account for the effect of previous energy generation, at whatever output level, on the energy demand during the preceding iteration. This is a departure from other modelling approaches discussed previously, which were constrained by static relationships between demand and supply profiles, based on historic measurements of demand. The approach taken by BIM-G allows transient performance, part-load performance and other operating restrictions to be modelled with high temporal precision. This is a departure from other modelling approaches, such as Peacock & Newborough [3], or Hawkes & Leach [58], which were constrained by static relationships between demand and supply profiles, based on historic measurements of demand.

As discussed by Ferguson & Ugursal [78], there exists a need for a modelling tool that can not only estimate performance of µCHP systems, but evaluate different system designs and control strategies, and determine the optimum sizing of systems based on particular technologies under different demand conditions. The application of the BIM- G model in this project will investigate the relationship between performance and

thermal demand, in order to inform the design and specification processes for µCHP systems.

Regardless of the magnitude of RCS identified in this project, it should be acknowledged that µCHP systems have yet to prove themselves on a commercial basis. Only once µCHP, of whichever prime mover technologies, proves itself to be reliable, cost effective and environmentally friendly in operation, will mass deployment be a possibility. As discussed in Section 1.1, governments are offering financial support to encourage consumer uptake, however to achieve widespread adoption, µCHP systems must offer an affordable solutions for homeowners, landlords and builders.

The focus of this research is carbon abatement potential of µCHP, as calculated relative to a base-case energy system using a condensing boiler, hence it is considered the most important performance metric. Whilst this project does not provide an economic analysis of µCHP performance, it is prudent to acknowledge that the operational lifetime of the µCHP system will have a major impact on the financial feasibility of such systems. Therefore, the investigation presented in Chapter 4 onwards includes analysis of cumulative annual operating hours and thermal cycling (i.e. start-stop cycles) of the prime mover, both of which are understood to limit life expectancy [56] [69]. This issue of thermal cycling is significant enough to spur developers to include minimum run time conditions within the control logic of their µCHP systems [70][71].