Grid, pipelines and other infrastructure - REMIND-MAgPIE
| Corresponding documentation | |
|---|---|
| Previous versions | |
| Model information | |
| Model link | |
| Institution | Potsdam Institut für Klimafolgenforschung (PIK), Germany, https://www.pik-potsdam.de. |
| Solution concept | General equilibrium (closed economy)MAgPIE: partial equilibrium model of the agricultural sector; |
| Solution method | OptimizationMAgPIE: cost minimization; |
| Anticipation | |
General distribution costs
REMIND-MAgPIE represents electricity/gas/hydrogen grids as well as distribution costs for solids and liquids in terms of a linear cost-markups on final energy use.
Variable renewable energy sources
Variable renewable electricity (VRE) sources such as wind and solar PV require storage to guarantee a stable supply of electricity [1]. Since the techno-economic parameters applied to CSP include the cost of thermal storage to continue electricity production at nighttime, REMIND-MAgPIE assumes that CSP requires only limited additional storage for balancing fluctuations.
The approach used in REMIND-MAgPIE follows the idea that storage demands for each VRE type rise with increasing market share. This is because balancing fluctuations becomes ever more challenging with higher penetration[2].
<figure id="fig:REMIND-MAgPIE_3.2.1 6">
</figure>
For modeling reasons, there is a 'generalized storage unit', tailor-made for each VRE. This construct consists of a VRE-specific mix of short- and medium-term storage as well as curtailment. Examples are redox-flow batteries for short-term storage, electrolysis and hydrogen storage for medium-term storage, as well as curtailment to balance seasonal fluctuations. A specific combination of these three real-world storage options is determined in order to match the VRE-specific fluctuation pattern. From this combination of actual storage technologies, we calculate aggregated capital costs and efficiency parameters for the 'generalized storage unit' of a specific VRE.
To calculate the total storage costs and losses at each point in time, the calculated 'generalized storage unit' of a VRE is scaled with this VRE's scale-factor VRE. The capital costs of the generalized storage units decrease through learning-by-doing with a 10% learning rate.
Costs for long-term HVDC transmission are included following a similar logic as storage costs. REMIND-MAgPIE assumes that grid requirements increase with market share. Furthermore, since resource potentials for PV (suitable for decentralized installation) are not as localized as those for wind and CSP, REMIND-MAgPIE assumes that grid costs for PV are comparatively smaller.
Both storage and grid requirements are partly regionalized: in regions where high demand coincides with high wind (EUR) or solar (USA, ROW, AFR, IND, MEA) incidence, storage requirements are slightly reduced. If a region is small or has homogeneously distributed VRE potentials (EUR, USA, IND, JPN), grid requirements are lower.
For a market share of 20%, marginal integration costs (including storage, curtailment and grid costs) are in a range of 19-25 USD/MWh for wind, 20-35 USD/MWh for PV, and 8-15 USD/MWh for CSP. For more details on the modeling of VRE integration in REMIND-MAgPIE, see Pietzcker [3].
Carbon capture and Storage
REMIND-MAgPIE represents several carbon capture and storage (CCS) applications. First, CCS can curb emissions from fossil fuel combustion. In REMIND-MAgPIE, CCS technologies exist for generating electricity as well as for the production of liquid fuels, gases, and hydrogen from coal and gas. Secondly, it is possible to combine biomass with CCS to generate net negative emissions. Such bioenergy CCS (BECCS) technologies are available for electricity generation (e.g., biomass integrated gasification combined cycle power plant), biofuels (e.g., biomass liquefaction), hydrogen, and syngas production. Thirdly, CCS can be used to reduce atmospheric CO2 emissions from the industry sector.
The sequestration of captured CO2 is explicitly represented in the model by accounting for transportation and storage costs [4]. There are regional constraints on CO2 storage potentials which are largely based on IEA [5]. In total, the global storage potential amounts to around 1000 GtC . It is smaller for EUR with 50 GtC, Japan with 20 GtC, and India with 50 GtC. The yearly injection rate of CO2 is assumed not to exceed 0.5% of total storage capacity due to technical and geological constraints. This creates an upper limit of 5 GtC per year for global CO2 injection.
- ↑ Pietzcker RC, Stetter D, Manger S, Luderer G (2014b) Using the sun to decarbonize the power sector: The economic potential of photovoltaics and concentrating solar power. Applied Energy 135:704–720. doi: 10.1016/j.apenergy.2014.08.011
- ↑ Current electricity systems already require substantial flexibility due to varying demand. This flexibility allows for the use of low shares of individual VRE (below ~10%) without any adaptations or storage requirements, as seen in many of today’s electricity networks. Furthermore, many regions have some limited potential for (cheap) pumped hydro storage, leading to low storage costs at low market shares of VRE.
- ↑ Pietzcker RC, Stetter D, Manger S, Luderer G (2014b) Using the sun to decarbonize the power sector: The economic potential of photovoltaics and concentrating solar power. Applied Energy 135:704–720. doi: 10.1016/j.apenergy.2014.08.011
- ↑ Bauer N (2005) Carbon capture and sequestration: An option to buy time? Ph.D. Thesis, University of Potsdam
- ↑ IEA (2008b) CO2 Capture and Storage – A key carbon abatement option. International Energy Agency