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Decision support tools for advanced energy management

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  • Mařík, Karel
  • Schindler, Zdenek
  • Stluka, Petr

Abstract

Rising fuel costs boost energy prices, which is a driving force for improving efficiency of operation of any energy generation facility. This paper focuses on enhancing the operation of distributed integrated energy systems (IES), system that bring together all forms of cooling, heating and power (CCHP) technologies. Described methodology can be applied in power generation and district heating companies, as well as in small-scale systems that supply multiple types of utilities to consumers in industrial, commercial, residential and governmental spheres. Dispatching of such system in an optimal way needs to assess large number of production and purchasing schemes in conditions of continually changing market and variable utility demands influenced by many external factors, very often by weather conditions. The paper describes a combination of forecasting and optimization methods that supports effective decisions in IES system management. The forecaster generates the future most probable utility demand several hours or days ahead, derived from the past energy consumer behaviour. The optimizer generates economically most efficient operating schedule for the IES system that matches these forecasted energy demands and respects expected purchased energy prices.

Suggested Citation

  • Mařík, Karel & Schindler, Zdenek & Stluka, Petr, 2008. "Decision support tools for advanced energy management," Energy, Elsevier, vol. 33(6), pages 858-873.
    • Handle: RePEc:eee:energy:v:33:y:2008:i:6:p:858-873
    DOI: 10.1016/j.energy.2007.12.004
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    References listed on IDEAS

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    1. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
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    2. Suganthi, L. & Samuel, Anand A., 2012. "Energy models for demand forecasting—A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1223-1240.
    3. Justyna Smagowicz & Cezary Szwed & Dawid Dąbal & Pavel Scholz, 2022. "A Simulation Model of Power Demand Management by Manufacturing Enterprises under the Conditions of Energy Sector Transformation," Energies, MDPI, vol. 15(9), pages 1-27, April.
    4. Macek, Karel & Mařík, Karel, 2012. "A methodology for quantitative comparison of control solutions and its application to HVAC (heating, ventilation and air conditioning) systems," Energy, Elsevier, vol. 44(1), pages 117-125.
    5. Sadegheih, A., 2009. "Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques," Energy, Elsevier, vol. 34(10), pages 1539-1551.
    6. Pouya Ghadimi & Seyed Mousavi & Wen Li & Sami Kara & Bernard Kornfeld, 2016. "A Combined Approach for Production Parameter Selection and On-site Energy Supply Management in Manufacturing Industry," Modern Applied Science, Canadian Center of Science and Education, vol. 10(8), pages 230-230, August.
    7. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    8. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    9. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    10. Difs, Kristina & Bennstam, Marcus & Trygg, Louise & Nordenstam, Lena, 2010. "Energy conservation measures in buildings heated by district heating – A local energy system perspective," Energy, Elsevier, vol. 35(8), pages 3194-3203.
    11. Chung, Mo & Park, Hwa-Choon, 2010. "Development of a software package for community energy system assessment – Part I: Building a load estimator," Energy, Elsevier, vol. 35(7), pages 2767-2776.
    12. Lin, Q.G. & Huang, G.H., 2010. "An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level," Energy, Elsevier, vol. 35(5), pages 2270-2280.
    13. Facci, Andrea Luigi & Andreassi, Luca & Ubertini, Stefano, 2014. "Optimization of CHCP (combined heat power and cooling) systems operation strategy using dynamic programming," Energy, Elsevier, vol. 66(C), pages 387-400.
    14. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    15. Cappa, Francesco & Facci, Andrea Luigi & Ubertini, Stefano, 2015. "Proton exchange membrane fuel cell for cooperating households: A convenient combined heat and power solution for residential applications," Energy, Elsevier, vol. 90(P2), pages 1229-1238.
    16. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.

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