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A Heuristic Algorithm for Combined Heat and Power System Operation Management

Author

Listed:
  • Muhammad Faisal Shehzad

    (Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy)

  • Mainak Dan

    (Interdisciplinary Graduate Programme, Nanyang Technological University Computational Intelligence Laboratory, Blk N4, B1a-02, Singapore 639798, Singapore)

  • Valerio Mariani

    (Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy)

  • Seshadhri Srinivasan

    (Berkeley Education Alliance for Research in Singapore, Singapore 138602, Singapore)

  • Davide Liuzza

    (Fusion and Technology for Nuclear Safety and Security Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00044 Rome, Italy)

  • Carmine Mongiello

    (Energy Technologies and Renewable Sources Department, Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 80055 Portici, Italy)

  • Roberto Saraceno

    (AtenaTech srl, 00044 Rome, Italy)

  • Luigi Glielmo

    (Group for Research on Automatic Control Engineering, Department of Engineering, University of Sannio, Piazza Roma 21, 82100 Benevento, Italy)

Abstract

This paper presents a computationally efficient novel heuristic approach for solving the combined heat and power economic dispatch (CHP-ED) problem in residential buildings considering component interconnections. The proposed solution is meant as a substitute for the cutting-edge approaches, such as model predictive control, where the problem is a mixed-integer nonlinear program (MINLP), known to be computationally-intensive, and therefore requiring specialized hardware and sophisticated solvers, not suited for residential use. The proposed heuristic algorithm targets simple embedded hardware with limited computation and memory and, taking as inputs the hourly thermal and electrical demand estimated from daily load profiles, computes a dispatch of the energy vectors including the CHP. The main idea of the heuristic is to have a procedure that initially decomposes the three energy vectors’ requests: electrical, thermal, and hot water. Then, the latter are later combined and dispatched considering interconnection and operational constraints. The proposed algorithm is illustrated using series of simulations on a residential pilot with a nano-cogenerator unit and shows around 25–30% energy savings when compared with a meta-heuristic genetic algorithm approach.

Suggested Citation

  • Muhammad Faisal Shehzad & Mainak Dan & Valerio Mariani & Seshadhri Srinivasan & Davide Liuzza & Carmine Mongiello & Roberto Saraceno & Luigi Glielmo, 2021. "A Heuristic Algorithm for Combined Heat and Power System Operation Management," Energies, MDPI, vol. 14(6), pages 1-22, March.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1588-:d:516142
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    References listed on IDEAS

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    1. Vögelin, Philipp & Koch, Ben & Georges, Gil & Boulouchos, Konstatinos, 2017. "Heuristic approach for the economic optimisation of combined heat and power (CHP) plants: Operating strategy, heat storage and power," Energy, Elsevier, vol. 121(C), pages 66-77.
    2. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    3. Kim, Jong Suk & Edgar, Thomas F., 2014. "Optimal scheduling of combined heat and power plants using mixed-integer nonlinear programming," Energy, Elsevier, vol. 77(C), pages 675-690.
    4. Akbar Maleki & Marc A. Rosen & Fathollah Pourfayaz, 2017. "Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications," Sustainability, MDPI, vol. 9(8), pages 1-20, July.
    5. Olatomiwa, Lanre & Mekhilef, Saad & Ismail, M.S. & Moghavvemi, M., 2016. "Energy management strategies in hybrid renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 821-835.
    6. Batrancea Larissa & Rathnaswamy Malar Maran & Batrancea Ioan & Nichita Anca & Rus Mircea-Iosif & Tulai Horia & Fatacean Gheorghe & Masca Ema Speranta & Morar Ioan Dan, 2020. "Adjusted Net Savings of CEE and Baltic Nations in the Context of Sustainable Economic Growth: A Panel Data Analysis," JRFM, MDPI, vol. 13(10), pages 1-17, October.
    7. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    8. Yokoyama, Ryohei & Shinano, Yuji & Wakayama, Yuki & Wakui, Tetsuya, 2019. "Model reduction by time aggregation for optimal design of energy supply systems by an MILP hierarchical branch and bound method," Energy, Elsevier, vol. 181(C), pages 782-792.
    9. Barbieri, Enrico Saverio & Melino, Francesco & Morini, Mirko, 2012. "Influence of the thermal energy storage on the profitability of micro-CHP systems for residential building applications," Applied Energy, Elsevier, vol. 97(C), pages 714-722.
    10. Batrancea Ioan & Rathnaswamy Malar Kumaran & Batrancea Larissa & Nichita Anca & Gaban Lucian & Fatacean Gheorghe & Tulai Horia & Bircea Ioan & Rus Mircea-Iosif, 2020. "A Panel Data Analysis on Sustainable Economic Growth in India, Brazil, and Romania," JRFM, MDPI, vol. 13(8), pages 1-19, August.
    11. Steven K. Rose & Richard Richels & Geoffrey Blanford & Thomas Rutherford, 2017. "The Paris Agreement and next steps in limiting global warming," Climatic Change, Springer, vol. 142(1), pages 255-270, May.
    12. Muhammad Kashif Rafique & Zunaib Maqsood Haider & Khawaja Khalid Mehmood & Muhammad Saeed Uz Zaman & Muhammad Irfan & Saad Ullah Khan & Chul-Hwan Kim, 2018. "Optimal Scheduling of Hybrid Energy Resources for a Smart Home," Energies, MDPI, vol. 11(11), pages 1-19, November.
    13. Wouters, Carmen & Fraga, Eric S. & James, Adrian M., 2015. "An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study," Energy, Elsevier, vol. 85(C), pages 30-44.
    14. Nazari-Heris, M. & Mohammadi-Ivatloo, B., 2015. "Application of heuristic algorithms to optimal PMU placement in electric power systems: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 214-228.
    15. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
    16. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
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