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Impact of forecasted heat demand on day-ahead optimal scheduling and real time control of multi-energy systems

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  • Stienecker, Malte

Abstract

For optimizing and scheduling multi-energy system’s operation in real applications, forecasts are needed. Therefore, forecasts impact the scheduling results and the degree to with an optimized schedule can be implemented. In the present investigation, real data from a German hospital with a combined heat and power unit, gas boiler and thermal energy storage (TES) is used as case study. For this case study, different heat load forecasts are generated by artificial neural networks and a persistence forecast, optimal operation is scheduled via MILP based on these forecasts, and the determined schedules are implemented stepwise in a simulative environment. Forecasts are evaluated by mean absolute percentage error (between 6 and 13%) and normalized mean error (between −2 and 12%). Two performance indicators are selected for evaluation, comparing the planned, realized, and idealized operation (the latter based on perfect foresight) with each other: balancing energy and degree of self-generation. Using the best-performing forecasts, self-generation is half a percentage point lower than in the idealized case and 2% negative balancing energy are needed. In general, underestimating heat load is beneficial to avoid balancing energy (high reliability), whereas overestimating is beneficial to achieve a high degree of self-generation (low operating costs). The gained experience helps to implement optimal scheduling and control effectively.

Suggested Citation

  • Stienecker, Malte, 2024. "Impact of forecasted heat demand on day-ahead optimal scheduling and real time control of multi-energy systems," Energy, Elsevier, vol. 297(C).
    • Handle: RePEc:eee:energy:v:297:y:2024:i:c:s0360544224009290
    DOI: 10.1016/j.energy.2024.131156
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    References listed on IDEAS

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    1. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    2. Malte Stienecker & Anne Hagemeier, 2023. "Developing Feedforward Neural Networks as Benchmark for Load Forecasting: Methodology Presentation and Application to Hospital Heat Load Forecasting," Energies, MDPI, vol. 16(4), pages 1-22, February.
    3. Tao Hong & Pierre Pinson & Yi Wang & Rafal Weron & Dazhi Yang & Hamidreza Zareipour, 2020. "Energy forecasting: A review and outlook," WORking papers in Management Science (WORMS) WORMS/20/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    4. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    5. Fang, Tingting & Lahdelma, Risto, 2016. "Optimization of combined heat and power production with heat storage based on sliding time window method," Applied Energy, Elsevier, vol. 162(C), pages 723-732.
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