Pith. sign in

REVIEW 3 major objections 3 minor 53 references

Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland

T0 review · 3 major / 3 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper introduces a public, geo-referenced dataset that places solar panels, batteries, heat pumps, and electric vehicles at more than two million nodes of Switzerland's medium- and low-voltage distribution grids, with hourly time…

desk verdict A genuinely useful, well-documented open dataset for Swiss distribution grid research, but the uniform-random DER siting assumption is a real limitation that the validation cannot detect. read the letter →

arxiv 2506.08724 v3 pith:GVGHG4MN submitted 2025-06-10 eess.SY cs.SY

Lorenzo Zapparoli , Alfredo Oneto , María Parajeles Herrera , Blazhe Gjorgiev , Gabriela Hug , Giovanni Sansavini This is my paper
classification eess.SYcs.SY
keywords distributedenergyresourcesdistributiongridsSwitzerlandphotovoltaicsystemsbatterystorageheatpumpselectricvehiclesgridflexibility
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper's contribution is a public, geo-referenced dataset that places distributed energy resources — rooftop PV, batteries, heat pumps, and electric vehicles — onto Switzerland's synthetic medium- and low-voltage distribution grids at more than two million connection points. For each device class the dataset gives installed capacities, hourly time series for a full year, and parameters describing operational flexibility: PV curtailment, battery state-of-energy control, heat-pump use of building thermal inertia with an indoor temperature comfort band, and EV smart charging (V1G) with shifting limits. Scenarios are provided for 2030, 2040, and 2050, aligned with national energy forecasts. Planners and researchers can therefore use it as a common, reproducible foundation for studying grid congestion, flexibility provision, and policy in Swiss distribution networks, and as a template for building similar national datasets elsewhere.

What carries the argument

The load-bearing object is the dataset itself, organized as CSV files whose unique node identifiers tie every device and time series to a specific geo-referenced medium- or low-voltage grid node. The construction pipeline is what makes it coherent: building-level rooftop PV potential is scaled to national targets, BESS co-location rates are applied, building-registry data is matched to thermal archetypes for heat pumps, and municipality-level EV profiles are spread across nodes by load share. Flexibility is encoded through explicit parameter sets — PV curtailment as generation versus installed capacity, BESS state of energy with efficiencies, HP thermal capacitance and conductance with COPs and hourly outdoor temperatures, and EV charging bounds with a daily flexible-energy budget — so downstream users can run dispatch or grid studies without re-deriving the models.

What would settle it

Compare the building-level PV and heat-pump locations in the 2030 dataset with actual connection registers from one or more Swiss distribution system operators. If real installations are systematically concentrated on south-facing roofs, in higher-income municipalities, or in areas with local subsidy programs, the uniform-random allocation will diverge measurably, and the divergence can be quantified by a chi-square or Kolmogorov-Smirnov test on the spatial distributions.

Watch

Extended reading notes

Core claim

The paper claims to have built the first country-wide, open dataset that couples DER deployment and operation to actual geo-referenced distribution grid topologies. Rooftop PV potential per building is combined with national penetration targets; buildings are then selected to meet those targets, and PV/BESS/HP devices are assigned to nearby grid nodes with statistical cutoffs, distance thresholds, and nodal power caps. Municipal EV charging profiles are allocated by load share, and non-controllable loads come from archetype demand profiles scaled by nodal peak loads. The dataset also encodes flexibility: HP parameters (thermal conductance and capacitance, COP, weather station temperatures) allow users to compute heating consumption under thermostat control; EV profiles include upper/lower shifting bounds and daily flexible energy; BESS has capacity, power, and efficiency values. Validation checks logical consistency, compares national energy totals with an official scenario report, and reports Spearman and Pearson correlations of municipal allocations with population.

Load-bearing premise

The dataset's spatial realism rests on the assumption that buildings chosen uniformly at random to meet national deployment targets are a good stand-in for real adoption patterns, which are likely shaped by income, roof orientation, and local incentives.

Editorial extensions

If this is right

  • Distribution grid studies in Switzerland no longer depend on proprietary DSO data; any research group can load the same national grid and DER scenario.
  • Flexibility assessments can compare 2030, 2040, and 2050 directly, because the dataset holds scenario parameters and device parameters across all three years.
  • The nodal allocations and load profiles allow testing of voltage control, hosting capacity, and feeder loading at both single-grid and national scale.
  • The method can be replicated for any country with rooftop-PV potential maps, building registers, and EV mobility data.
  • The explicit flexibility parameters enable reproducible studies of V1G, battery arbitrage, and heat-pump load shifting.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because buildings are chosen uniformly at random, the spatial allocation likely underestimates clustering of DERs in affluent or incentive-friendly areas; studies of overloaded feeders should treat the dataset as a smoothed deployment rather than a worst case.
  • The assumption that non-controllable demand stays unchanged to 2050 may understate winter peaks if electrification of other sectors progresses faster than expected.
  • The dataset could serve as a synthetic control group for privacy-preserving grid research, allowing algorithms for optimal power flow or load forecasting to be benchmarked on public data before use on real utility data.
  • One testable extension is to rerun the allocation with spatially correlated adoption, such as seeding by municipality income or roof suitability, and compare feeder-load indicators to quantify how much the uniform-random choice matters.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 3 minor

Summary. The paper describes a publicly available, geo-referenced dataset of distributed energy resources (PV, BESS, heat pumps, EVs) and non-controllable loads allocated to Switzerland's synthetic medium- and low-voltage distribution grids, covering more than two million connection points. The dataset provides hourly time series and flexibility parameters for 2030, 2040, and 2050, with scenario parameters derived from Swiss Federal Office of Energy (SFOE) projections. The methods for each DER type, grid integration, and flexibility parametrization are documented in detail, and the data are accompanied by integrity-check scripts and a data loader. The paper claims that the dataset is validated through logical consistency checks, coherence comparisons with SFOE Energy Perspectives 2050+, and geographical correlation with population.

Significance. If the dataset is credible and reusable, it is a valuable public resource for distribution grid planning, flexibility studies, and energy policy analysis in Switzerland, filling a gap for high-resolution, country-wide DER data. The paper's strengths include fully public code and data, a modular structure, detailed documentation of all input sources and processing steps, and automated integrity checks that are reproducible. However, the validation strategy has significant weaknesses—notably the circularity of the coherence check and the weak spatial validation of deployment assumptions—that need to be addressed before the dataset's claims of alignment and spatial realism can be accepted.

major comments (3)
  1. [Technical Validation, coherence of energy consumption and generation] Table 5 compares the dataset's national energy values with the SFOE Energy Perspectives 2050+ report (ref 30), but the scenario parameters in Table 2 (PV penetration, BESS co-allocation, HP penetration, COP, envelope factors) are taken from the same report or interpolated from it. This makes the agreement in Table 5 largely circular: the dataset is constructed to match those targets, so the comparison is not an independent validation. The discrepancies that do appear (e.g., PV 18.2 vs 24.6 TWh and HP 11.6 vs 8.7 TWh in 2050) are explained by exclusions and category differences, but the reference itself is not an external benchmark. The paper should either validate against independent data (e.g., current deployment statistics, actual load measurements, or an alternative scenario study) or explicitly reframe Table 5 as an internal consistency check rather than a validation against national forecasts.
  2. [Photovoltaic data, Battery energy storage systems data, Heat pumps data, and Technical Validation, geographical…] The spatial allocation of PV, BESS, and HP relies on uniformly random selection of buildings to match national penetration targets (described in the Photovoltaic data, Battery energy storage systems data, and Heat pumps data sections). This assumption directly determines where DERs appear on feeders and thus controls the outcomes of the dataset's stated applications, such as reliability assessment with spatially heterogeneous DER deployment. The only spatial validation, Table 6, shows correlation of municipal DER totals with population. This is an expected consequence of building density and cannot distinguish uniform-random siting from adoption driven by roof orientation, income, or local incentives. The authors disclose the assumption but do not test its spatial consequences. I recommend adding a sensitivity analysis that compares the uniform-random allocation with, for example, an allocation weighted by the rooftop PV potential already present in the input data (ref 27), or with regional adoption patterns, and reporting how feeder-level indicators (e.g., PV penetration per LV grid, overvoltage risk) change.
  3. [Non-controllable load data and Table 5] The non-controllable load profiles are assumed to remain unchanged for 2030, 2040, and 2050 (Non-controllable load data section). This assumption is inconsistent with the SFOE reference in Table 5, where final non-controllable electricity consumption declines from 49.9 TWh in 2030 to 41.4 TWh in 2050 due to energy efficiency measures. As a result, the dataset's load is fixed at 48.8 TWh for all three years, making the 2040 and 2050 scenarios materially different from the national forecast. In addition, the HP demand in the dataset exceeds the reference by a large margin (8.8 vs 5.6 TWh in 2030; 11.6 vs 8.7 TWh in 2050). The authors attribute this to large HPs connected at MV that the SFOE report places in a separate category, but this explanation is not quantified. A breakdown of HP energy by voltage level and a reconciliation with the SFOE total would be needed to establish that the dataset is actually aligned with national projections.
minor comments (3)
  1. [Abstract] The first paragraph of the abstract contains the sentence 'this work introduces a comprehensive database...' with a lowercase 't' at the beginning of a sentence; this should be capitalized.
  2. [Manuscript header] The running header on page 2 reads 'Data DEScR iPTOR'; this appears to be a formatting artifact and should be corrected.
  3. [Table 2] The rows 'Temperature Unchanged' and 'Demand Unchanged' are clear but could be clarified by stating that these refer to the assumption of unchanged temperature profiles and unchanged non-controllable load profiles, respectively, consistent with the main text.

Circularity Check

1 steps flagged · score 4.0 of 10

Dataset construction is transparent and largely self-contained, but the PV 'coherence validation' compares against a reference built from the same input rooftop-potential dataset and the same SFOE penetration targets, making that check partly tautological.

  1. self definitional [Technical Validation, 'Coherence of energy consumption and generation' (Table 5), with Methods, 'Photovoltaic data' and Table 2.]
    "The coherence of energy consumption and generation in the dataset is assessed by comparing the results with the Energy Perspectives 2050 + report from the SFOE30. ... Table 5 reports the dataset's national values, consumption projections from Energy Perspectives30, and PV rooftop generation27 scaled by the expected deployment reported in Table 2."

    For PV, the 'Reference' column in Table 5 is not an independent benchmark: it is the same rooftop-PV potential dataset (ref 27, used as an input) scaled by the same SFOE expected deployment fractions (ref 30, Table 2) that were used to select the random building subsets for the dataset. Thus the PV comparison verifies only that the selection, trimming, and capping pipeline approximately preserved the input energy; it cannot validate the deployment scenario itself. The HP, EV, and load comparisons are less circular because they involve separate consumption projections, but the shared SFOE origin of the penetration targets means the overall coherence check is a consistency check rather than an external validation.

full rationale

The paper does not exhibit fatal circularity: the central deliverable is a public, geo-referenced dataset assembled from many independent inputs, including synthetic grid models, the Swiss building register, weather data, rooftop-PV potential, and an EV mobility dataset. Uniform-random siting is a stated modeling assumption, not a hidden reuse of the output, and the spatial population-correlation check, while weak, is an external sanity check rather than a circular reduction. The main circular step is localized to the PV validation: the reference PV generation is constructed from the same input PV-potential dataset and the same SFOE penetration values used to generate the allocation, so the agreement in Table 5 is partly by construction. This warrants a moderate score of 4 because the central dataset content still has independent substance and the circularity is confined to a single validation claim, not to the generation of the nodal allocations themselves.

Assumptions & free parameters 9 free parameters · 7 assumptions · 0 invented entities

The assembled dataset depends on a chain of input data sources and modeling choices. The most consequential are the representativeness of the synthetic grids, the accuracy of the PV and building-registry inputs, the validity of SFOE projections, and the paper's own assumptions (uniform random adoption, unchanged load profiles, distance and trimming cutoffs). The ledger lists the explicit numerical choices and the premises that, if false, would invalidate the dataset's usefulness.

free parameters (9)
  • PV roof penetration rates = 27%, 45%, 100% for 2030, 2040, 2050
    Chosen to align with SFOE/national projections (Table 2). The 2050 value is a full-deployment assumption.
  • BESS/PV co-allocation rates = 31%, 51%, 70% for 2030, 2040, 2050
    2050 follows SFOE projection; 2030 and 2040 are linearly interpolated from Europe's 2021 BESS/PV co-allocation (ref 31).
  • BESS storage duration and round-trip efficiency = 2.5 h and 85%
    Fixed estimates from NREL (ref 32) and Cole & Karmakar (ref 33).
  • HP penetration rates = commercial 14/21/28%; residential 36/52/65%
    Directly from SFOE Energy Perspectives 2050+ (Table 2).
  • Fixed HP coefficients of performance = 3.49, 3.79, 4.12
    Projected from SFOE (ref 30); variable-COP coefficients for 2040/2050 are scaled from 2030 literature values (ref 38).
  • Envelope factors for building insulation = 90%, 78%, 70% for 2030, 2040, 2050
    Hand-chosen improvements applied to building conductance values for future years.
  • HP specific nominal power per construction type = 60, 45, 35 W/m2
    Midpoints of Swiss sizing guideline ranges for heavy, medium, and light constructions (ref 36).
  • Nodal PV and HP peak capacity caps = 100 kW (LV), 1 MW (MV)
    Imposed to avoid extreme feeder loading; values adopted from refs 25 and 26.
  • Distance thresholds and trimming fractions = 50 m (LV), 3 km (MV), 5% trimming
    Used to match buildings between the OpenStreetMap-based grids and the Swiss building registry; based on statistical trimming (ref 46).
assumptions (7)
  • domain assumption The synthetic Swiss distribution grid models (refs 12, 49) are representative of actual medium- and low-voltage grids.
    All DER and load allocations are integrated into these grids; if the grids are unrepresentative, every nodal allocation inherits the bias. Invoked in Methods, Grid integration.
  • domain assumption The rooftop PV potential dataset (ref 27) correctly estimates Swiss rooftop generation and capacity.
    Nominal PV capacities are back-calculated from this dataset using Eq. (1); errors propagate into PV and BESS values.
  • domain assumption The Swiss building registry (ref 34) and building archetype thermal parameters (ref 35) are complete and representative.
    HP allocations and thermal conductance/capacitance values are derived from these sources.
  • domain assumption SFOE Energy Perspectives 2050+ (ref 30) projections are a valid basis for future DER scenarios.
    Penetration targets, COP values, and envelope factors all come from this report.
  • domain assumption The EV mobility dataset (refs 39-42) represents Swiss EV charging behavior and flexibility potential.
    Municipal profiles are aggregated from this model; scaling by penetration assumes constant per-vehicle behavior.
  • ad hoc to paper Non-controllable load profiles remain unchanged through 2050.
    Explicitly stated in Methods: 'Non-controllable load profiles are assumed to remain unchanged for 2030, 2040, and 2050.'
  • ad hoc to paper Uniformly random selection of buildings for DER adoption preserves spatial deployment characteristics.
    Used for PV, BESS, and HP allocation; validated only by population correlation, not by comparison with real adoption drivers.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland." pith.science (2026). https://pith.science/paper/GVGHG4MN

@misc{pith2026250608724,
  author       = {Pith},
  title        = {Pith review of: Future Deployment and Flexibility of Distributed Energy Resources in the Distribution Grids of Switzerland},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GVGHG4MN}},
  note         = {Machine review of arXiv:2506.08724}
}
read the original abstract

The decarbonization goals worldwide drive the energy transition of power distribution grids, which operate under increasingly volatile conditions and closer to their technical limits. In this context, localized operational data with high temporal and spatial resolution is essential for their effective planning and regulation. Nevertheless, information on grid-connected distributed energy resources, such as electric vehicles, photovoltaic systems, and heat pumps, is often fragmented, inconsistent, and unavailable. This work introduces a comprehensive database of distributed energy resources and non-controllable loads allocated in Switzerland's medium- and low-voltage distribution grid models, covering over 2 million points of connection. Remarkably, this data specifies the flexibility capabilities of the controllable devices, with a set of projections aligned with national forecasts for 2030, 2040, and 2050. The database supports studies on flexibility provision of distributed energy resources, distribution grid resilience, and national energy policy, among other topics. Importantly, its modular structure allows users to extract national- and local-scale information across medium- and low-voltage systems, enabling broad applicability across locations.

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

53 extracted references · 51 canonical work pages

  1. [1]

    Papailiou, K. O. Springer Handbook of Power Systems (Springer Nature, 2021)

    work page 2021

  2. [2]

    & Y ang, Q

    Ehsan, A. & Y ang, Q. State-of-the-art techniques for modelling of uncertainties in active distribution grid planning: A review. Applied Energy 239, 1509–1523 (2019)

    work page 2019

  3. [3]

    & Osma-Pinto, G

    Caballero-Peña, J., Cadena-Zarate, C., Parrado-Duque, A. & Osma-Pinto, G. Distributed energy resources on distribution grids: A systematic review of modelling, simulation, metrics, and impacts. International Journal of Electrical Power & Energy Systems 138, 107900 (2022)

    work page 2022

  4. [4]

    & Shahidehpour, M

    Xu, X., Li, Y ., Y an, Z., Ma, H. & Shahidehpour, M. Hierarchical central-local inverter-based voltage control in distribution grids considering stochastic pv power admissible range. IEEE Transactions on Smart Grid 14, 1868–1879 (2023)

    work page 2023

  5. [5]

    & Jain, A

    Y ao, Y ., Ding, F ., Horowitz, K. & Jain, A. Coordinated inverter control to increase dynamic pv hosting capacity: A real-time optimal power flow approach. IEEE Systems Journal 16, 1933–1944 (2022)

    work page 2022

  6. [6]

    Gupta, R. et al. Spatial analysis of distribution grid capacity and costs to enable massive deployment of pv, electric mobility and electric heating. Applied Energy 287, 116504 (2021)

    work page 2021

  7. [7]

    & Hakvoort, R

    Eid, C., Codani, P ., Perez, Y ., Reneses, J. & Hakvoort, R. Managing electric flexibility from distributed energy resources: A review of incentives for market design. Renewable and Sustainable Energy Reviews 64, 237–247 (2016)

    work page 2016

  8. [8]

    Henriquez-Auba, R. et al. Sharing economy and optimal investment decisions for distributed solar generation. Applied Energy 294, 117029 (2021)

    work page 2021

Show all 53 references
  1. [9]

    Jaramillo, A. F . M. et al. Distributed energy resources electric profile identification in low voltage grids using supervised machine learning techniques. IEEE Access 11, 19469–19486 (2023)

    2023

  2. [10]

    Anvari, M. et al. Data-driven load profiles and the dynamics of residential electricity consumption. Nature Communications 13, 4593 (2022)

    2022

  3. [11]

    & Y ang, W

    Haider, R., Xu, Y . & Y ang, W . Towards closing the data gap: A project-driven distributed energy resource dataset for the u.s. grid. In e-Energy ’23 Companion: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, 52-58 (Association for Com...

    2023

  4. [12]

    & Sansavini, G

    Oneto, A., Gjorgiev, B., Tettamanti, F . & Sansavini, G. Large-scale generation of geo-referenced power distribution grids from open data with load clustering. Sustainable Energy, Grids and grids 101678 (2025)

    2025

  5. [13]

    & Sansavini, G

    Kopka, T., Akbari, B., Oneto, A. & Sansavini, G. Assessing maximum grid loadability under pv deployment for distribution planning support: a swiss case study. In CIRED 2024 Vienna Workshop, vol. 2024, 863–866 (IET, 2024)

    2024

  6. [14]

    & Sansavini, G

    Zapparoli, L., Gjorgiev, B. & Sansavini, G. Optimal duration of reserve capacity ancillary services for distributed energy resources. arXiv (2025)

    2025

  7. [15]

    & Tse, C

    Li, S., Wang, S., Zhao, Q., Liu, D. & Tse, C. A risk assessment framework for large-scale synthetic power distribution networks considering historical hurricane disasters. IEEE Transactions on Power Systems (2025)

    2025

  8. [16]

    Contreras, D. A. & Rudion, K. Computing the feasible operating region of active distribution networks: Comparison and validation of random sampling and optimal power flow based methods. IET Generation, Transmission & Distribution 15, 1600–1612 (2021)

    2021

  9. [17]

    Jiang, X., Zhou, Y ., Ming, W . & Wu, J. Feasible operation region of an electricity distribution network. Applied Energy 331, 120419 (2023). Correlations with population distribution Distributed energy resource Spearman correlation Pearson correlation PV 0.76 0.65 BESS 0.90 0...

    2023

  10. [18]

    & Wogrin, S

    Konrad, A., Gaugl, R., Maier, C. & Wogrin, S. Implementing dynamic power feed-in limitations of photovoltaic systems in distribution grids for generation expansion planning. Energy Strategy Reviews 59, 101760 (2025)

    2025

  11. [19]

    & Goebel, C

    Okoyomon, E. & Goebel, C. A framework for assessing the generalizability of gnn-based ac power flow models. In Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems, E-Energy ’25, 476-488 (Association for Computing Machinery, New Y ork, ...

    2025

  12. [20]

    & Sansavini, G

    Oneto, A., Gjorgiev, B. & Sansavini, G. Wasserstein-based kernels for clustering: Application to power distribution graphs. Available at SSRN 5197297 (2025)

    2025

  13. [21]

    & Kazempour, J

    Dvorkin, V ., Fioretto, F ., Van Hentenryck, P ., Pinson, P . & Kazempour, J. Differentially private optimal power flow for distribution grids. IEEE Transactions on Power Systems 36, 2186–2196 (2021)

    2021

  14. [22]

    Gebbran, D., Mhanna, S., Chapman, A. C. & Verbič, G. Multiperiod der coordination using admm-based three-block distributed ac optimal power flow considering inverter volt-var control. IEEE Transactions on Smart Grid 14, 2874–2889 (2023)

    2023

  15. [23]

    U., Mateo, C., Gómez San Román, T

    Ziegler, D. U., Mateo, C., Gómez San Román, T. & Prettico, G. Multistage distribution expansion planning leveraging load flexibility. Electric Power Systems Research 228, 110094 (2024)

    2024

  16. [24]

    & Rajagopal, R

    Navidi, T., El Gamal, A. & Rajagopal, R. Coordinating distributed energy resources for reliability can significantly reduce future distribution grid upgrades and peak load. Joule 7, 1769–1792 (2023)

    2023

  17. [25]

    Vinicius, G. T. F ., Corigliano, S., Dimovski, A., Bolognesi, M. & Merlo, M. Rural electrification planning based on graph theory and geospatial data: A realistic topology oriented approach. Sustainable Energy, Grids and Networks 28, 100525 (2021)

    2021

  18. [26]

    & Merlo, M

    Corigliano, S., Carnovali, T., Edeme, D. & Merlo, M. Holistic geospatial data-based procedure for electric network design and least- cost energy strategy. Energy for Sustainable Development 58, 1–15 (2020)

    2020

  19. [27]

    & Scartezzini, J.-L

    Walch, A., Castello, R., Mohajeri, N. & Scartezzini, J.-L. Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty. Applied Energy 262, 114404 (2020)

    2020

  20. [28]

    & Vulic, N

    Brodnicke, L., Ganter, A., Tröber, S., Sansavini, G. & Vulic, N. Dynamic grid emission factors and export limits reduce emission abatement and cost benefits of building pv systems. Energy and Buildings 323, 114772 (2024)

    2024

  21. [29]

    Eip-eth collaboration: Rethinking future swiss electricity supply

    Energy Infrastructure Partners & ETH Zurich. Eip-eth collaboration: Rethinking future swiss electricity supply. https://nexus-e.org/ eip-eth-collaboration-rethink-future-swiss-electricity-supply/ Accessed: 2025-4-7 (2024)

    2024

  22. [30]

    Energy perspectives 2050+

    Swiss Federal Office of Energy (SFOE). Energy perspectives 2050+. Tech. Rep., SFOE Accessed: 23-10-2024 https://www.bfe.admin. ch/bfe/en/home/policy/energy-perspectives-2050-plus.html (2021)

    2021

  23. [31]

    European market outlook for residential battery storage 2022-2026

    Solar Power Europe. European market outlook for residential battery storage 2022-2026. Tech. Rep., SolarPower Europe Accessed: 2025-4-7 https://www.solarpowereurope.org/insights/outlooks/european-market-outlook-for-residential-battery- storage-2022-2026 (2022)

    2022

  24. [32]

    Annual technology baseline: Residential battery storage

    National Renewable Energy Laboratory. Annual technology baseline: Residential battery storage. https://atb.nrel.gov/ electricity/2023/residential_battery_storage#MIKY3RBI Accessed: 2025-4-7 (2023)

    2023

  25. [33]

    & Karmakar, A

    Cole, W . & Karmakar, A. Cost projections for utility-scale battery storage: 2023 update. Tech. Rep., National Renewable Energy Laboratory Contract No. DE-AC36-08GO28308 https://www.nrel.gov/docs/fy23osti/85332.pdf (2023)

    2023

  26. [34]

    Gwr publicly available data Accessed: 2025-4-7 https://www.housing-stat.ch/de/madd/public.html

    Swiss Federal Statistical Office. Gwr publicly available data Accessed: 2025-4-7 https://www.housing-stat.ch/de/madd/public.html. (2022)

    2022

  27. [35]

    D., Bacher, J.-P

    Pongelli, A., Priore, Y . D., Bacher, J.-P . & Jusselme, T. Definition of building archetypes based on the swiss energy performance certificates database. Buildings13, 40 Multidisciplinary Digital Publishing Institute, (2023)

    2023

  28. [36]

    Dimensionierung von Wärmepumpen

    Swiss Federal Office of Energy (SFOE). Dimensionierung von Wärmepumpen. Tech. Rep., SFOE Accessed: 2025-2-6 (2001)

    2001

  29. [37]

    Grundlagen für energetische Berechnungen von Gebäuden

    Schweizerischer Ingenieur- und Architektenverein (SIA). Grundlagen für energetische Berechnungen von Gebäuden. Tech. Rep. SN 504380:2022, Schweizerischer Ingenieur- und Architektenverein (SIA), Postfach, CH-8027 Zürich (2022)

    2022

  30. [38]

    & Girard, R

    Rogeau, A., Vieubled, J., Ruche, L. & Girard, R. A generic methodology for mapping the performance of various heat pumps configurations considering part-load behavior. Energy and Buildings 318, 114490 (2024)

    2024

  31. [39]

    & Hug, G

    Parajeles Herrera, M., Schwarz, M. & Hug, G. Spatio-temporal modeling of large-scale bev fleets’ charging energy needs and flexibility. In 2024 International Conference on Smart Energy Systems and Technologies (SEST), 1–6 (2024)

    2024

  32. [40]

    & Zerrahn, A

    Gaete-Morales, C., Kramer, H., Schill, W .-P . & Zerrahn, A. An open tool for creating battery-electric vehicle time series from empirical data, emobpy. Scientific data 8, 152 (2021)

    2021

  33. [41]

    Parajeles Herrera, M. P . H. & Hug, G. Modeling charging demand and quantifying flexibility bounds for large-scale bev fleets. arXiv (2025)

    2025

  34. [42]

    & Hug, G

    Parajeles Herrera, M. & Hug, G. Charging demand and flexibility bounds for large- scale bev fleets - the case study of switzerland https://doi.org/10.5281/zenodo.15194721 (2025)

    2025 doi

  35. [43]

    & Kristina, O

    Portia, M., Julien, M., Mathias, N., Georgios, M. & Kristina, O. Optimal transformation strategies for buildings, neighbourhoods and districts to reach co2 emission reduction targets. Energy and Buildings 207, 109569 (2020)

    2020

  36. [44]

    Residential & commercial building energy demand profiles under different retrofit scenarios (cesar model)

    JASM. Residential & commercial building energy demand profiles under different retrofit scenarios (cesar model). https://data. sccer-jasm.ch/demand-hourly-profile-retrofits-cesar/ Accessed: 01-04-2025

    2025

  37. [45]

    Quarter-hourly values of electricity consumption in grid levels 5 and 7 in the city of zurich, since 2015 https://data

    Stadt Zürich. Quarter-hourly values of electricity consumption in grid levels 5 and 7 in the city of zurich, since 2015 https://data. stadt-zuerich.ch/dataset/ewz_stromabgabe_netzebenen_stadt_zuerich Accessed: 5-4-2025. (2022)

    2022

  38. [46]

    & Y oung, D

    Cheng, K. & Y oung, D. S. An approach for specifying trimming and winsorization cutoffs. Journal of Agricultural, Biological and Environmental Statistics 28, 299–323 (2023)

    2023

  39. [47]

    Planet dump retrieved from

    OpenStreetMap contributors. Planet dump retrieved from. https://www.openstreetmap.org (2017)

    2017

  40. [48]

    Federal Register of Buildings and Dwellings https://www.bfs.admin.ch/bfs/en/home/register/gebaeude- wohnungsregister (2015)

    Federal Statistics Office. Federal Register of Buildings and Dwellings https://www.bfs.admin.ch/bfs/en/home/register/gebaeude- wohnungsregister (2015)

    2015

  41. [49]

    E., Tettamanti, F ., Gjorgiev, B

    Oneto, A. E., Tettamanti, F ., Gjorgiev, B. & Sansavini, G. Synthetic low- and medium-voltage grids for switzerland https://doi. org/10.5281/zenodo.15167589 (2025)

    2025 doi

  42. [50]

    Zapparoli, L. et al. Future deployment and flexibility of distributed energy resources in the distribution grids of switzerland (2025). https://doi.org/10.5281/zenodo.15056134

    2025 doi

  43. [51]

    swissboundaries3d File: swissboundaries3d_2022-01_2056_5728.shp, Accessed: 2025-04-01 https://www.swisstopo.admin.ch/de/landschaftsmodell-swissboundaries3d (2022)

    Federal Office of Topography swisstopo. swissboundaries3d File: swissboundaries3d_2022-01_2056_5728.shp, Accessed: 2025-04-01 https://www.swisstopo.admin.ch/de/landschaftsmodell-swissboundaries3d (2022)

    2022

  44. [52]

    Rooftop photovoltaic (pv) potential data for the swiss building stock https://doi.org/10.5281/zenodo.3609833 (2020)

    Walch, A. Rooftop photovoltaic (pv) potential data for the swiss building stock https://doi.org/10.5281/zenodo.3609833 (2020)

    2020 doi

  45. [53]

    Temperature profiles for weather stations

    Meteo Swiss. Temperature profiles for weather stations. https://www.meteoswiss.admin.ch/services-and-publications/service/open- data.html. Accessed: 9-4-2025 acknowledgements The research published in this publication was carried out with the support of the Swiss Federal Offic...

    2025

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.