Electricity generation technology cost projections: 2017-2050

[摘要] The continuing importance of electricity futures to the nation and the fast rate of change in costs of some technologies, means that regularly updated electricity generation technology cost projections remain an important public resource. CSIRO’s approach to cost projections is based on using the most objective and data driven method available which simultaneously projects both global and local deployment of electricity generation technology and the cost reductions achieved due to the well-known learning by doing effect.In the past, CSIRO has provided standalone projections or contributed to studies led by other organisations at irregular intervals since 2011, with the last being the Australian Power GenerationTechnology Report in 2015. Those projections are significantly out of date and therefore it is appropriate that CSIRO provide this 2017 update to cost projections.Model for Electricity (GALLM-E) and have constructed a similar model for transport (GALLM-T) to assist in projecting battery storage costs which, while potentially very important for the electricitysector, are being deployed at a faster rate in the transport sector (making this sector the strongest source of learning and thus cost reductions). We have also updated our assumptions on globalrenewable resources availability for each region and extended the learning rate for selected technologies which have demonstrated an extended period of higher learning.The updated projections indicate the trend we have seen in the last few years whereby solar photovoltaics, wind and battery storage technologies reduce their costs at a faster rate than most other technologies continues. Compared to previous projections, these substantial cost reductions in renewables have also begun to impact the cost and adoption of other technologies. For example, in our 4-degrees climate ambition scenario we project very low adoption of carbon capture and storage (CCS) and subsequently very little cost reductions in that technology. In effect, the ongoing cost reductions in renewables has crowded out investment in other technologies.In the 2-degrees climate ambition scenario the renewable energy resource constraints, together with a stronger carbon price signal, do allow for investment in a wider diversity of electricity generation technologies. Significant adoption and cost reductions in CCS are projected. However, they are less than those that we projected for a similar scenario in the 2015 projections. This is because in either climate ambition scenario, the world deploys a greater share of low cost renewables in response to emission constraints compared to other options.The capital cost projections are primarily designed to be included in Australian electricity modelling studies as scenario inputs. However, we also provide levelised cost of electricity (LCOE) estimates which are useful for providing a quick indication of what the capital cost projections mean for the future cost of electricity. LCOE estimates have been justifiably criticised for an absence of standardised plant performance and financial assumptions and failing to take intoaccount the additional cost of supporting variable generation technologies to be reliable. These two flaws mean that LCOE comparisons within (i.e. between technologies) and across alternativeLCOE projection studies can be meaningless.To address these issues we provide information about our LCOE calculation assumptions and present a preliminary estimate of the cost of supporting variable renewable generation to be reliable as their share of electricity generation increases. This “extended” LCOE estimate, which includes the additional costs of very high renewable shares, indicates that the LCOE of renewables begins to approach that of CCS and nuclear power (i.e. increasing towards $120/MWh at 90% variable renewable share compared to $30-50/MWh under the conventional LCOE calculation). However, given variable renewable electricity gener...

[发布日期] 2017-12-14 [发布机构] CSIRO

[效力级别] Environment and Resource Economics [学科分类] 地球科学(综合)