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A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response

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  • Giaouris, Damian
  • Papadopoulos, Athanasios I.
  • Patsios, Charalampos
  • Walker, Sara
  • Ziogou, Chrysovalantou
  • Taylor, Phil
  • Voutetakis, Spyros
  • Papadopoulou, Simira
  • Seferlis, Panos

Abstract

Multi-vector microgrids that utilise several forms of energy storage are becoming popular in smart grid topologies due to their ability to cope with problems induced in the power network from the usage of distributed generation. While these microgrids appear to be pivotal in future energy systems, they impose several problems in the design and operation of the network mainly due to their complexity and the different properties that each energy subsystem has. In this work, we propose a novel, generic and systematic way of modelling the assets in a microgrid including the energy management method that is used to control the operation of these assets under multiple stochastic loads. This gives a unique tool that allows the testing/derivation of multiple energy management methods including demand side response and the usage of forecasting tools to optimise the performance of the system. A thorough study of the proposed method, using data from a real hybrid energy system (built in Greece), proves that the performance and efficiency of the system can be significantly improved while at the same time the requirement for external power supply or the consumption of fossil fuels can be reduced. The main concept is based on a state space modelling approach that transforms the power network into a hybrid dynamical system and the implemented energy management method into the evolution operator. The model integrates structural, temporal and logical features of smart grid systems in order to identify and construct multiple different energy management strategies EMS which can then be compared with respect to their ability to best serve the considered demands. Other than coping with several energy carriers, the model inherently accounts for forecasting, handles multiple random loads with time dependant importance and supports the use of demand side response strategies. Conclusions drawn from numerical case studies are used to demonstrate the advantages of the proposed method. In this work we clearly show that by using 20 different energy management methods and analysing their performance through a multi-criteria assessment approach we obtain non-trivial energy management approaches to support the operation of a multi-vector smart-grid considering variable external demands.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:226:y:2018:i:c:p:546-559
    DOI: 10.1016/j.apenergy.2018.05.113
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    3. Aatabe, Mohamed & El Guezar, Fatima & Vargas, Alessandro N. & Bouzahir, Hassane, 2021. "A novel stochastic maximum power point tracking control for off-grid standalone photovoltaic systems with unpredictable load demand," Energy, Elsevier, vol. 235(C).
    4. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    5. Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Chapaloglou, Spyridon & Varagnolo, Damiano & Marra, Francesco & Tedeschi, Elisabetta, 2022. "Data-driven energy management of isolated power systems under rapidly varying operating conditions," Applied Energy, Elsevier, vol. 314(C).
    7. Lin Wang & Anke Xue, 2021. "Equivalent Modeling of Microgrids Based on Optimized Broad Learning System," Energies, MDPI, vol. 14(23), pages 1-11, November.
    8. Xu, Fangyuan & Wu, Wanli & Zhao, Fei & Zhou, Ya & Wang, Yongjian & Wu, Runji & Zhang, Tao & Wen, Yongchen & Fan, Yiliang & Jiang, Shengli, 2019. "A micro-market module design for university demand-side management using self-crossover genetic algorithms," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    9. Khawaja, Yara & Allahham, Adib & Giaouris, Damian & Patsios, Charalampos & Walker, Sara & Qiqieh, Issa, 2019. "An integrated framework for sizing and energy management of hybrid energy systems using finite automata," Applied Energy, Elsevier, vol. 250(C), pages 257-272.
    10. Rosato, Antonello & Panella, Massimo & Andreotti, Amedeo & Mohammed, Osama A. & Araneo, Rodolfo, 2021. "Two-stage dynamic management in energy communities using a decision system based on elastic net regularization," Applied Energy, Elsevier, vol. 291(C).
    11. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.

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