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The impact of electricity tariffs on residential demand side flexibility: results of bottom-up load profile modeling

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Abstract

Energy systems based on renewable energy sources require increasing demand side flexibility. Also, changes in the underlying cost structure, i.e., decreasing variable costs and increasing infrastructure investments as well as varying consumer needs should be reflected in the setup of future markets, including retail markets and tariffs. While various studies focus solely on tariffs with variable energy prices to leverage residential demand side flexibility, we incorporate tariffs with capacity-based price components in our analysis. The latter enable electricity providers to offer more differentiated tariffs, considering individual consumer needs and a balanced cost allocation. To compare the impact of different tariffs on residential demand side flexibility, we develop a bottom-up load model. This model not only simulates but also optimizes residential load profiles in the presence of different tariffs. The model is calibrated based on data from a large-scale field trial. Our results show that tariffs with variable energy prices induce larger demand side flexibility, but the impact of tariffs with variable capacity prices is more predictable and reliable from a suppliers perspective. Potential regulatory adjustments are identified enabling sustainable business models, rewarding demand side flexibility and facilitating the technical implementation.

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Notes

  1. RELO was used on a computer with the operating system Win Server 2008, two AMD Opteron 6134 processors with 2.3 GHz and 64 GB RAM.

  2. The number of variables and constraints varies significantly depending on the appliance type and its considered shifting ranges, which also affects the computing time. For instance, the optimization of fridges and freezers typically requires about 16 variables and 17 constraints while domestic hot water or space heating requires about 192 variables and 288 constraints. The default configuration of IBM ILOG CPLEX was used.

  3. In many applications, \(\phi ^{PV}\) may be assumed to be zero. However, there may be cases where \(\phi ^{PV}>0\), e.g., if those who live in the house do not own the PV modules but pay a fee for each kWh of PV power as defined in an agreement with the owner of the PV modules.

  4. The number of variables, binary decision variables and constraints differs significantly between shortage situations depending on the appliance types and their shifting ranges. For instance, a model run with 1000 households, a maximum power level restricted over one year and 100% smart households requires approximately from 100 to 17,000 constraints, 30 to 11,000 variables and 30 to 8500 binary decision variables.

  5. The hourly EEX prices for 2011 are available at http://www.eex.com.

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Acknowledgements

Valentin Bertsch acknowledges funding from the Energy Policy Research Centre of the Economic and Social Research Institute. The usual disclaimer applies.

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Authors and Affiliations

  1. Chair of Energy Economics, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Marian Hayn & Wolf Fichtner

  2. Institute of Operations Research, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Anne Zander & Stefan Nickel

  3. Economic and Social Research Institute, Dublin, Ireland

    Valentin Bertsch

  4. Department of Economics, Trinity College Dublin, Dublin, Ireland

    Valentin Bertsch

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  1. Marian Hayn
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  5. Valentin Bertsch
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Correspondence to Valentin Bertsch.

Appendix

Appendix

See Table 9.

Table 9 Hourly probabilities for the Bernoulli distributions of manual load shifting
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Hayn, M., Zander, A., Fichtner, W. et al. The impact of electricity tariffs on residential demand side flexibility: results of bottom-up load profile modeling. Energy Syst 9, 759–792 (2018). https://doi.org/10.1007/s12667-018-0278-8

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