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A new combined control algorithm for PV-CHP hybrid systems

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  • Kneiske, T.M.
  • Braun, M.
  • Hidalgo-Rodriguez, D.I.

Abstract

Due to the 2012 change in the renewable energy act the feed-in tariffs, and therefore the number of newly installed photovoltaic systems decreased dramatically in Germany. Particularly in the residential sector as the biggest market new business ideas for photovoltaic systems were developed. Hence a photovoltaic and combined heat-and-power system, which provides not only electricity but also heat. This complex system requires flexible control strategies. A new combined control algorithm is proposed that in contrast to the standard strategies can operate even under incorrect weather and load forecasts without creating discomfort. Furthermore is it applicable for cloud-based solutions. In this paper a high-level model predictive control based on a mixed integer linear programming problem is combined with an additional low-level, rule-based controller. The low-level control compares the set-points of the optimization with the actual values and corrects the set-points according to each system component until the next optimization takes place. The results show that a hybrid system can be successfully controlled by a combined control approach. In case of a cloud-based application the need for an optimization can be reduced by a factor of four without diminishing comfort or facing much higher operational costs. It has also been shown that the combined control algorithm can be used as an energy management of microgrids.

Suggested Citation

  • Kneiske, T.M. & Braun, M. & Hidalgo-Rodriguez, D.I., 2018. "A new combined control algorithm for PV-CHP hybrid systems," Applied Energy, Elsevier, vol. 210(C), pages 964-973.
    • Handle: RePEc:eee:appene:v:210:y:2018:i:c:p:964-973
    DOI: 10.1016/j.apenergy.2017.06.047
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    1. Romero Rodríguez, Laura & Salmerón Lissén, José Manuel & Sánchez Ramos, José & Rodríguez Jara, Enrique Ángel & Álvarez Domínguez, Servando, 2016. "Analysis of the economic feasibility and reduction of a building’s energy consumption and emissions when integrating hybrid solar thermal/PV/micro-CHP systems," Applied Energy, Elsevier, vol. 165(C), pages 828-838.
    2. Merei, Ghada & Moshövel, Janina & Magnor, Dirk & Sauer, Dirk Uwe, 2016. "Optimization of self-consumption and techno-economic analysis of PV-battery systems in commercial applications," Applied Energy, Elsevier, vol. 168(C), pages 171-178.
    3. Zhang, Di & Evangelisti, Sara & Lettieri, Paola & Papageorgiou, Lazaros G., 2015. "Optimal design of CHP-based microgrids: Multiobjective optimisation and life cycle assessment," Energy, Elsevier, vol. 85(C), pages 181-193.
    4. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    5. Petersen, Steffen & Bundgaard, Katrine Wieck, 2014. "The effect of weather forecast uncertainty on a predictive control concept for building systems operation," Applied Energy, Elsevier, vol. 116(C), pages 311-321.
    6. Li, Bei & Roche, Robin & Miraoui, Abdellatif, 2017. "Microgrid sizing with combined evolutionary algorithm and MILP unit commitment," Applied Energy, Elsevier, vol. 188(C), pages 547-562.
    7. Fu, Xueqian & Sun, Hongbin & Guo, Qinglai & Pan, Zhaoguang & Zhang, Xiurong & Zeng, Shunqi, 2017. "Probabilistic power flow analysis considering the dependence between power and heat," Applied Energy, Elsevier, vol. 191(C), pages 582-592.
    8. Kriett, Phillip Oliver & Salani, Matteo, 2012. "Optimal control of a residential microgrid," Energy, Elsevier, vol. 42(1), pages 321-330.
    9. Sukumar, Shivashankar & Mokhlis, Hazlie & Mekhilef, Saad & Naidu, Kanendra & Karimi, Mazaher, 2017. "Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid," Energy, Elsevier, vol. 118(C), pages 1322-1333.
    10. Wang, Xiaonan & Palazoglu, Ahmet & El-Farra, Nael H., 2015. "Operational optimization and demand response of hybrid renewable energy systems," Applied Energy, Elsevier, vol. 143(C), pages 324-335.
    11. Sultana, W. Razia & Sahoo, Sarat Kumar & Sukchai, Sukruedee & Yamuna, S. & Venkatesh, D., 2017. "A review on state of art development of model predictive control for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 391-406.
    12. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    13. Fathima, A. Hina & Palanisamy, K., 2015. "Optimization in microgrids with hybrid energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 431-446.
    14. Nyholm, Emil & Goop, Joel & Odenberger, Mikael & Johnsson, Filip, 2016. "Solar photovoltaic-battery systems in Swedish households – Self-consumption and self-sufficiency," Applied Energy, Elsevier, vol. 183(C), pages 148-159.
    15. Balcombe, Paul & Rigby, Dan & Azapagic, Adisa, 2015. "Environmental impacts of microgeneration: Integrating solar PV, Stirling engine CHP and battery storage," Applied Energy, Elsevier, vol. 139(C), pages 245-259.
    16. Lorenzi, Guido & Silva, Carlos Augusto Santos, 2016. "Comparing demand response and battery storage to optimize self-consumption in PV systems," Applied Energy, Elsevier, vol. 180(C), pages 524-535.
    17. 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.
    18. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
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    Cited by:

    1. José Manuel Salmerón Lissén & Laura Romero Rodríguez & Francisco Durán Parejo & Francisco José Sánchez de la Flor, 2018. "An Economic, Energy, and Environmental Analysis of PV/Micro-CHP Hybrid Systems: A Case Study of a Tertiary Building," Sustainability, MDPI, vol. 10(11), pages 1-15, November.
    2. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    3. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    4. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    5. Kneiske, T.M. & Niedermeyer, F. & Boelling, C., 2019. "Testing a model predictive control algorithm for a PV-CHP hybrid system on a laboratory test-bench," Applied Energy, Elsevier, vol. 242(C), pages 121-137.
    6. Li, Dacheng & Guo, Songshan & He, Wei & King, Marcus & Wang, Jihong, 2021. "Combined capacity and operation optimisation of lithium-ion battery energy storage working with a combined heat and power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    7. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    8. Tanja M. Kneiske, 2023. "Reducing CO 2 Emissions for PV-CHP Hybrid Systems by Using a Hierarchical Control Algorithm," Energies, MDPI, vol. 16(17), pages 1-24, August.
    9. Kwan, Trevor Hocksun & Shen, Yongting & Yao, Qinghe, 2019. "An energy management strategy for supplying combined heat and power by the fuel cell thermoelectric hybrid system," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    11. Dong, Zhe & Liu, Miao & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2019. "Automatic generation control for the flexible operation of multimodular high temperature gas-cooled reactor plants," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 11-31.
    12. Jolando M. Kisse & Martin Braun & Simon Letzgus & Tanja M. Kneiske, 2020. "A GIS-Based Planning Approach for Urban Power and Natural Gas Distribution Grids with Different Heat Pump Scenarios," Energies, MDPI, vol. 13(16), pages 1-31, August.
    13. Chen, Scarlett & Kumar, Anikesh & Wong, Wee Chin & Chiu, Min-Sen & Wang, Xiaonan, 2019. "Hydrogen value chain and fuel cells within hybrid renewable energy systems: Advanced operation and control strategies," Applied Energy, Elsevier, vol. 233, pages 321-337.

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