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An efficient two-stage algorithm for decentralized scheduling of micro-CHP units

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  • Jochem, Patrick
  • Schönfelder, Martin
  • Fichtner, Wolf

Abstract

In this paper we present an efficient two-stage hierarchical decomposition algorithm aiming at determining economically improved operation schedules for residential proton exchange membrane fuel cell micro-combined heat and power (PEMFC micro-CHP) units and optimizing local charging of electric vehicles (EV) in the same household. Based on an individual short-term load forecasting (STLF) approach (imperfect forecast) for households implemented as an adaptive network-based fuzzy inference system (ANFIS), a mixed-integer linear program (MILP) and a two-stage greedy algorithm are used for determining optimized schedules based on a rolling-window approach. The results of the case study performed for eight variants in exemplary German households reveal that with both the MILP and the algorithmic approach, significant economic savings can be achieved compared to the standard heat-led strategy. Compared to the MILP, however, the two-stage algorithm has the additional advantage of a reduced computing time of only about 115. Deviations from the MILP solutions are mostly smaller than 3 percent regarding the annual supply costs. Moreover, the comparison between the use of perfect and imperfect demand forecasts quantifies additional average losses due to forecasting errors of 2 percent and 3.3 percent at the maximum. Altogether, the algorithmic approach seems to be convincing for real applications in households due to its good results, high reliability, easy implementation, and short computing times. The combination of a micro-CHP unit and an EV is highly synergetic.

Suggested Citation

  • Jochem, Patrick & Schönfelder, Martin & Fichtner, Wolf, 2015. "An efficient two-stage algorithm for decentralized scheduling of micro-CHP units," European Journal of Operational Research, Elsevier, vol. 245(3), pages 862-874.
    • Handle: RePEc:eee:ejores:v:245:y:2015:i:3:p:862-874
    DOI: 10.1016/j.ejor.2015.04.016
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    2. Schwarz, Hannes & Bertsch, Valentin & Fichtner, Wolf, 2015. "Two-stage stochastic, large-scale optimization of a decentralized energy system - a residential quarter as case study," Working Paper Series in Production and Energy 10, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    3. Jochem, Patrick & Babrowski, Sonja & Fichtner, Wolf, 2015. "Assessing CO2 emissions of electric vehicles in Germany in 2030," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 68-83.
    4. Arsalis, Alexandros, 2019. "A comprehensive review of fuel cell-based micro-combined-heat-and-power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 391-414.
    5. Maximilian Roth & Georg Franke & Stephan Rinderknecht, 2022. "A Comprehensive Approach for an Approximative Integration of Nonlinear-Bivariate Functions in Mixed-Integer Linear Programming Models," Mathematics, MDPI, vol. 10(13), pages 1-17, June.
    6. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    7. Mojica, Jose L. & Petersen, Damon & Hansen, Brigham & Powell, Kody M. & Hedengren, John D., 2017. "Optimal combined long-term facility design and short-term operational strategy for CHP capacity investments," Energy, Elsevier, vol. 118(C), pages 97-115.
    8. Boškoski, Pavle & Debenjak, Andrej & Mileva Boshkoska, Biljana, 2018. "Rayleigh copula for describing impedance data—with application to condition monitoring of proton exchange membrane fuel cells," European Journal of Operational Research, Elsevier, vol. 266(1), pages 269-277.
    9. Hannes Schwarz & Valentin Bertsch & Wolf Fichtner, 2018. "Two-stage stochastic, large-scale optimization of a decentralized energy system: a case study focusing on solar PV, heat pumps and storage in a residential quarter," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(1), pages 265-310, January.
    10. Langenmayr, Uwe & Wang, Weimin & Jochem, Patrick, 2020. "Unit commitment of photovoltaic-battery systems: An advanced approach considering uncertainties from load, electric vehicles, and photovoltaic," Applied Energy, Elsevier, vol. 280(C).
    11. Yang, Hongming & Xiong, Tonglin & Qiu, Jing & Qiu, Duo & Dong, Zhao Yang, 2016. "Optimal operation of DES/CCHP based regional multi-energy prosumer with demand response," Applied Energy, Elsevier, vol. 167(C), pages 353-365.
    12. Lauven, Lars-Peter & Geldermann, Jutta & Desideri, Umberto, 2019. "Estimating the revenue potential of flexible biogas plants in the power sector," Energy Policy, Elsevier, vol. 128(C), pages 402-410.
    13. Apitzsch, Tilman & Klöffer, Christian & Jochem, Patrick & Doppelbauer, Martin & Fichtner, Wolf, 2016. "Metaheuristics for online drive train efficiency optimization in electric vehicles," Working Paper Series in Production and Energy 17, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    14. Ji, Ling & Zhang, Bei-Bei & Huang, Guo-He & Xie, Yu-Lei & Niu, Dong-Xiao, 2018. "Explicit cost-risk tradeoff for optimal energy management in CCHP microgrid system under fuzzy-risk preferences," Energy Economics, Elsevier, vol. 70(C), pages 525-535.

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