Smart Grids & Energy Markets

We, the Smart Grids & Energy Markets (SGEM) research group at the KIT Institute for Information Systems, develop and evaluate economic mechanisms to coordinate supply and demand in digitalized energy systems. The overall objective is the development and the support of a sustainable energy system considering individual preferences and developments in our society. Alongside our core research, we also work on simplifying adherence to the FAIR data principles, underscoring our commitment to open science.

Team Member:

Leo Semmelmann
+49 (721) 608-48377

leo.semmelmann∂kit.edu

Kim K. Miskiw

+49 (721) 608-48377

kim.miskiw∂kit.edu

Christina Speck

+49 (721) 608-48377

christina.speck∂kit.edu

Oliver Resch

+49 (721) 608-48377

oliver.resch∂kit.edu

Laura Selgrad

+49 (721) 608-48377

laura.selgrad∂kit.edu

Philipp Fritz

+49 (721) 608-48377

philipp.fritz∂kit.edu

 

Research Areas:

 

  • Energy system modelling (e.g., agent-based modelling, etc.)
  • Equity in the energy transition (e.g., landlord-tenant-dilemma, balcony PV, etc.)
  • Energy informatics (e.g., load forecasting, synthetic data generation)
  • Energy policy (e.g., dynamic tariffs, grid charge design, self-consumption policies)
  • District heating
  • Open energy research (e.g., visual analytics, stakeholder engagement, platform monitoring)

 

Research Methods:

 

  • Simulative studies
  • Experimental research
  • Behavioral research
  • Design science research

 

Projects:

 


An Algorithm for Modelling Rolling Intrinsic Battery Trading on the Continuous Intraday Market
Semmelmann, L.; Dresselhaus, J.; Miskiw, K. K.; Ludwig, J.; Weinhardt, C.
2024. Energy Informatics Review, 4 (4)
Multi Power-Market Bidding: Stochastic Programming and Reinforcement Learning
Miskiw, K. K.; Harder, N.; Staudt, P.
2024. Proceedings of the 57th Annual Hawaii International Conference on System Sciences : Hilton Hawaiian Village Waikiki Beach Resort, January 3-6, 2024. Ed.: T. Bui, 3062–3071, IEEE Computer Society
Empirical field evaluation of self-consumption promoting regulation of household battery energy storage systems
Semmelmann, L.; Konermann, M.; Dietze, D.; Staudt, P.
2024. Energy Policy, 194, 114343. doi:10.1016/j.enpol.2024.114343
Explainable Deep Reinforcement Learning for Multi-Agent Electricity Market Simulations
Miskiw, K. K.; Staudt, P.
2024. 20th International Conference on the European Energy Market (EEM), 9 S., Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/EEM60825.2024.10608907
Guiding Policy Communication: Tailoring Energy Education to Diverse Public Sentiments
Speck, C.; Fritz, P.; Weinhardt, C.; Fegert, J.
2024. First International Conference on Sustainable Energy Education (SEED 2024). Book of Proceedings, 378–386, Universitat Politècnica de València
The impact of heat pumps on day-ahead energy community load forecasting
Semmelmann, L.; Hertel, M.; Kircher, K. J.; Mikut, R.; Hagenmeyer, V.; Weinhardt, C.
2024. Applied Energy, 368, 123364. doi:10.1016/j.apenergy.2024.123364
Market Abstraction of Energy Markets and Policies - Application in an Agent-Based Modeling Toolbox
Maurer, F.; Miskiw, K. K.; Acosta, R. R.; Harder, N.; Sander, V.; Lehnhoff, S.
2024. Energy Informatics. Ed.: B. Jørgensen. Pt. 2, 139–157, Springer Nature Switzerland. doi:10.1007/978-3-031-48652-4_10
Privacy‐preserving peak time forecasting with Learning to Rank XGBoost and extensive feature engineering
Semmelmann, L.; Resch, O.; Henni, S.; Weinhardt, C.
2024. IET Smart Grid, 7 (2), 172–185. doi:10.1049/stg2.12137
Generating synthetic load profiles of residential heat pumps: a k-means clustering approach
Semmelmann, L.; Jaquart, P.; Weinhardt, C.
2023. Energy Informatics, 6, Art.-Nr.: 37. doi:10.1186/s42162-023-00284-1
The Impact of Local Energy Market Participants’ Decisions on Efficient Energy Usage: Design of a Multi-Agent System
Speck, C.; Bluhm, S.; Weinhardt, C.; Zwickel, P.; Hagenmeyer, V.
2023. 2023 19th International Conference on the European Energy Market (EEM), Lappeenranta, Finland, 06-08 June 2023, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/EEM58374.2023.10161771
Effects of price-making and portfolio size in stochastic optimization of trading in sequential electricity markets
Kraft, E.; Miskiw, K. K.; Fleten, S.-E.
2023. 13. Internationale Energiewirtschaftstagung (IEWT 2023), Wien, Österreich, February 15–17, 2023
Entsolidarisierung der Stromversorgung durch Haushaltseigenverbrauch?
Miskiw, K. K.; Staudt, P.
2021. Energiewirtschaftliche Tagesfragen, 71 (11), 56–60
Load forecasting for energy communities: a novel LSTM-XGBoost hybrid model based on smart meter data
Semmelmann, L.; Henni, S.; Weinhardt, C.
2022. Energy Informatics, 5 (S1), Art.Nr. 24. doi:10.1186/s42162-022-00212-9
On the Role of Risk Aversion and Market Design in Capacity Expansion Planning
Fraunholz, C.; Miskiw, K. K.; Kraft, E.; Fichtner, W.; Weber, C.
2023. The Energy Journal, 44 (3). doi:10.5547/01956574.44.2.cfra
On the Role of Risk Aversion and Market Design in Capacity Expansion Planning
Fraunholz, C.; Miskiw, K. K.; Kraft, E.; Fichtner, W.; Weber, C.
2021. Karlsruher Institut für Technologie (KIT). doi:10.5445/IR/1000140411
Learning from IT Security Catastrophes: A Post Catastrophe Analysing Checklist
Traup, F.; Speck, C.; Deckers, F.; Lorenz, P.
2021. cii Student Papers - 2021. Ed.: A. Sunyaev, 73–106, Karlsruher Institut für Technologie (KIT)
Investitionsplanung unter Unsicherheit – Ein agentenbasierter Ansatz für liberalisierte Strommärkte
Miskiw, K. K.; Fraunholz, C.; Kraft, E.
2021. 12. Internationale Energiewirtschaftstagung, IEWT: Das Energiesystem nach Corona: Irreversible Strukturänderungen - Wie?. 8. - 10. September 2021, Karlsplatz & Online, TU Wien
Considering Life Cycle Greenhouse Gas Emissions in Power System Expansion Planning for Europe and North Africa Using Multi-Objective Optimization
Junne, T.; Cao, K.-K.; Miskiw, K. K.; Hottenroth, H.; Naegler, T.
2021. Energies, 14 (5), Art. Nr.: 1301. doi:10.3390/en14051301