Journal
IEEE TRANSACTIONS ON POWER SYSTEMS
Volume 37, Issue 4, Pages 3259-3273Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3023474
Keywords
Heat pumps; Computational modeling; Cogeneration; Solar heating; Planning; Biological system modeling; Resistance heating; Capacity expansion planning; sector coupling; low-carbon energy systems; heat pumps; modelling; net neutrality
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Funding
- DeV-KopSys project, funding initiative Erneuerbar Mobil by the Federal Ministry for Environment, Nature Conservation and Nuclear Safety [FKZ 16EM4005-1]
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This study investigates different modeling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system and highlights the implications of using model aggregation techniques through a comparative case study.
Pursuing long-term climate stabilisation scenarios subjects power, heat, industry, and transport sectors to undergo fundamental transitions towards fully renewable energy systems. Cost-efficient strategies involve the integration of energy vectors, introducing additional cross-sectoral electricity demand and flexibility for traditional power sectors. When performing cross-sectoral capacity expansion planning (CEP) analyses of low-carbon energy systems for decision making support, the underlying mathematical optimisation problems easily become intractable. To overcome the computational challenges of optimising real-world instances of future energy systems with adequate representations of temporal, spatial, and technological detail, this work investigates different modelling approaches for flexible heat pump systems at the power and heat sector interface of the long-term energy system model SCOPE SD. A comparative case study carried out for a net-neutral decarbonisation scenario in Europe and Germany highlights the implications of using model aggregation techniques. It is shown that the proposed modelling approaches can present computational benefits at only limited cost of result accuracy. However, poorly implemented grouping strategies can lead to high result inaccuracies and even a lack of performance improvements.
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