Global energy and emissions scenarios for effective climate change mitigation—Deterministic and stochastic scenarios with the TIAM model

Syri, Sanna; Lehtilä, Antti; Ekholm, Tommi; Savolainen, Ilkka; Holttinen, Hannele; Peltola, Esa

doi:10.1016/j.ijggc.2008.01.001

Cited by 24 publications

(32 citation statements)

References 13 publications

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“…This has the advantage of accounting for the cost of uncertainty, relaxing the assumption of perfect foresight and analysing hedging strategies. Stochastic programming has been applied to a large variety of input assumptions in energy systems modelling, like energy prices (Krey et al, 2007), resource availability (Usher & Strachan, 2012), technology parameters (Labriet et al, 2012), climate sensitivity (Syri et al, 2008), the stringency of mitigation targets (Kanudia & Loulou, 1998) and the stochasticity of intermittent renewable resources (Seljom & Tomasgarda, 2015).…”

Section: Annexmentioning

confidence: 99%

Impact of technology uncertainty on future low-carbon pathways in the UK

Fais

Keppo

Zeyringer

et al. 2016

Energy Strategy Reviews

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Energy and climate policy-making requires strong quantitative scientific evidence to devise robust and consistent long-term decarbonisation strategies. Energy system modelling can provide crucial insights into the inherent uncertainty in such strategies, which needs to be understood when designing appropriate policy measures.This study contributes to the growing research area of uncertainty analysis in energy system models. We combine consistent and realistic narratives on several technology dimensions with a global sensitivity analysis in a national, bottom-up, optimizing energy system model. This produces structured insights into the impact of low-carbon technology and resource availability on the long-term development of the UK energy system under ambitious decarbonisation pathways. We explore a variety of result metrics to present policy-relevant results in a useful and concise manner. The results provide valuable information on the variability of fuel and technology use across the uncertainty space (e.g. a strong variation in natural gas demand). We demonstrate the complementarities and substitutability of technologies (e.g. the dependency of hydrogen technologies on the availability of CCS). We highlight critical low-carbon options and hedging strategies (e.g. the early decarbonisation of the electricity sector or the stronger use of renewable sources as a hedging against failure in other technologies) and demonstrate timing and path dependencies (e.g. the importance of early decarbonisation action in the presence of multiple technology uncertainty). The results also show how the availability of a given technology can have wider impacts elsewhere in the energy system, thus complicating the management of a long-term energy transition.

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Section: Annexmentioning

confidence: 99%

Impact of technology uncertainty on future low-carbon pathways in the UK

Fais

Keppo

Zeyringer

et al. 2016

Energy Strategy Reviews

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“…Regarding the costs, we reviewed different studies on the present costs of wind installation [7], offshore sites [65], and the future costs [59,66]. It is expected that the investment cost of wind generation in Finland would decrease by 5% by 2030 with expanding the installed capacity [67]. The cost of power imbalance entailed by wind into the power system is also considered in the cost calculations, amounting 3-4 €/MWh of wind production [68,69].…”

Section: Higher Wind Integrations Under Today's Conditionsmentioning

confidence: 99%

Wind Integration into Energy Systems with a High Share of Nuclear Power—What Are the Compromises?

2015

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Towards low-carbon energy systems, there are countries with ongoing plans for expanding their nuclear power capacity, and simultaneously advancing the role of variable renewable energy sources (RES), namely wind and solar energy. This crossroads of capital-intensive, baseload power production and uncontrollable, intermittent RES may entail new challenges in the optimal and economic operation of power systems. This study examines this case by hourly analysis of a national-level energy system with the EnergyPLAN modeling tool, coupled with wind integration simulations (including uncertainty) implemented using MATLAB. We evaluate the maximum feasible wind integration under different scenarios for nuclear power plants, energy demand, and the flexibility of energy infrastructure for a real case study (Finland). We propose wind-nuclear compromise charts to envision the impact of any mix of these two technologies on four parameters: total costs, power exchange, carbon emissions, and renewable energy integration. The results suggest that nuclear power constrains the room for maximum uptake of wind energy by a descending parabolic relationship. If nuclear power production exceeds 50% of the total power demand, wind will be unlikely to penetrate in shares over 15% of the respective demand. Moreover, we investigate the role of four flexibility options: demand side management, electrical energy storage, smart electric heating, and large-scale heat pumps (backed with thermal energy storage). Heat pumps (which are in connection with combined heat and power (CHP) and district heating systems) offer the highest efficiency in balancing excess power from variable RES. However, power-to-heat options offer a limited OPEN ACCESSEnergies 2015, 8 2494 capability for absorbing high-level excess power, as oversupply arises mainly in the periods with relatively low demand for heat. This calls for longer-term energy storage and/or other flexibility options to achieve the planned targets in wind-nuclear scenarios.

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“…Earlier scenario literature has considered this type of dynamic climate policy only in few papers (Syri et al, 2007;Johansson et al, 2008;Webster et al, 2008;Loulou et al, 2009), and in a very simplified setting, where the uncertainty is resolved in its entirety suddenly at a single point of time, usually in 2040. In this report we present a more sophisticated treatment of the dynamic climate policy problem, using a simplified, stochastic model using marginal abatement cost curves.…”

Section: A Cost-optimal Pathway For the 2c Targetmentioning

confidence: 99%