The error induced by using representative periods in capacity expansion models: system cost, total capacity mix and regional capacity mix

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (similar to 5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (similar to 20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (> 50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects

The cost of a future low-carbon electricity system without nuclear power – the case of Sweden

To achieve the goal of deep decarbonization of the electricity system, more and more variable renewable energy (VRE) is being adopted. However, there is no consensus among researchers on whether the goal can be accomplished without large cost escalation if nuclear power is excluded in the future electricity system. In Sweden, where nuclear power generated 41% of the annual electricity supply in 2014, the official goal is 100% renewable electricity production by 2040. Therefore, we investigate the cost of a future low-carbon electricity system without nuclear power for Sweden. We model the European electricity system with a focus on Sweden and run a techno-economic cost optimization model for capacity investment and dispatch of generation, transmission, storage and demand-response, under a CO2 emission constraint of 10 g/kWh. Our results show that there are no, or only minor, cost benefits to reinvest in nuclear power plants in Sweden once the old ones are decommissioned. This holds for a large range of assumptions on technology costs and possibilities for investment in additional transmission capacity. We contrast our results with the recent study that claims severe cost penalties for not allowing nuclear power in Sweden and discuss the implications of methodology choice

Revenue and risk of variable renewable electricity investment: The cannibalization effect under high market penetration

Wind and solar power depress market prices at times when they produce the most. This has been termed the ‘cannibalization effect’, and its magnitude has been established within the economic literature on current and future markets. Although it has a substantial impact on the revenue of VRE technologies, the cannibalization effect is neglected in the capital budgeting literature, including portfolio- and real options theory. In this paper, we present an analytical framework that explicitly models the correlation between VRE production and electricity price, based on the production costs of surrounding generation capacity. We derive closed-form expressions for the expected short-term and long-term revenue, the variance of the revenue and the timing of investments. The effect of including these system characteristics is illustrated with numerical examples, where we find the cannibalization effect to decrease projected profit relative to investment cost from 33% to between 13% and −40%, depending on the assumption for the future VRE capacity expansion rate. Using a real options framework, the investment threshold increases by between 13% and 67%, due to the inclusion of cannibalization

Into a cooler future with electricity generated from solar photovoltaic

The fast-growing global cooling demand due to income growth in tropical countries necessitates substantial investments in new generation capacity. Despite the synergy between the temporal behavior of cooling demand and solar PV production, it is not clear whether the increased cooling demand will make solar PV more cost-effective or less so. We use a capacity expansion model to investigate the cost-effectiveness of investing in solar PV to meet the electricity demand linked to cooling for seven different regions under various CO2 emission targets. Solar PV plays a dominant role in meeting the additional electricity demand for cooling, and the share of solar PV in the additional generation capacity ranges from 64% to 135%. Additionally, powering electric cooling with mainly solar PV is cheaper than powering the rest of the demand. These results suggest that solar PV may comprise the backbone of electricity supply for cooling in the future electricity system

Historical wind deployment and implications for energy system models

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation

An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand

An Autopilot for Energy Models – Automatic Generation of Renewable Supply Curves, Hourly Capacity Factors and Hourly Synthetic Electricity Demand for Arbitrary World Regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand

Are biofuel mandates cost-effective? - An analysis of transport fuels and biomass usage to achieve emissions targets in the European energy system

Abatement options for the hard-to-electrify parts of the transport sector are needed to achieve ambitious emissions targets. Biofuels based on biomass, electrofuels based on renewable hydrogen and a carbon source, as well as fossil fuels compensated by carbon dioxide removal (CDR) are the main options. Currently, biofuels are the only renewable fuels available at scale and are stimulated by blending mandates. Here, we estimate the system cost of enforcing such mandates in addition to an overall emissions cap for all energy sectors. We model overnight scenarios for 2040 and 2060 with the sector-coupled European energy system model PyPSA-Eur-Sec, with a high temporal resolution. The following cost drivers are identified: (i) high biomass costs due to scarcity, (ii) opportunity costs for competing usages of biomass for industry heat and combined heat and power (CHP) with carbon capture, and (iii) lower scalability and generally higher cost for biofuels compared to electrofuels and fossil fuels combined with CDR. With a -80% emissions reduction target in 2040, variable renewables, partial electrification of heat, industry and transport, and biomass use for CHP and industrial heat are important for achieving the target at minimal cost, while an abatement of remaining liquid fossil fuel use increases system cost. In this case, a 50% biofuel mandate increases total energy system costs by 123–191 billion €, corresponding to 35%–62% of the liquid fuel cost without a mandate. With a negative -105% emissions target in 2060, fuel abatement options are necessary, and electrofuels or the use of CDR to offset fossil fuel emissions are both more competitive than biofuels. In this case, a 50% biofuel mandate increases total costs by 21–33 billion €, or 11%–15% of the liquid fuel cost without a mandate. Biomass is preferred in CHP and industry heat, combined with carbon capture to serve negative emissions or electrofuel production, thereby utilising biogenic carbon several times. Sensitivity analyses reveal significant uncertainties but consistently support that higher biofuel mandates lead to higher costs