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Optimal spatial and temporal demand side management in a power system comprising renewable energy sources

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  • Kotur, Dimitrije
  • Đurišić, Željko

Abstract

The increase in installed capacity of renewable energy sources (RES) has a positive effect on the development of smart grids and demand side management (DSM). The reason for this is the intermittent nature of renewable energy, which is directly related to the problem of balancing the production and consumption of power within the power system. By using the DSM, the power consumption in the system comprising RES can be easier adjusted to the power production. The paper proposes an improved concept of DSM through the spatial and temporal DSM. The optimal spatial and temporal DSM aims at determining the power diagram of each individual load bus in order to achieve the optimal state in the whole system. The optimal state of the system can be quantified through the minimum daily energy losses or minimum daily operating costs. A mathematical definition of the optimal spatial and temporal DSM problem is presented as well as the algorithm for its solution. The proposed methodology has been tested by three test networks. The results confirm the overall system performance improvements that include: reduction of energy losses in the system, reduction of the operating costs and the increase of the voltage quality within the system.

Suggested Citation

  • Kotur, Dimitrije & Đurišić, Željko, 2017. "Optimal spatial and temporal demand side management in a power system comprising renewable energy sources," Renewable Energy, Elsevier, vol. 108(C), pages 533-547.
    • Handle: RePEc:eee:renene:v:108:y:2017:i:c:p:533-547
    DOI: 10.1016/j.renene.2017.02.070
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    1. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    2. Schulze, Tim & McKinnon, Ken, 2016. "The value of stochastic programming in day-ahead and intra-day generation unit commitment," Energy, Elsevier, vol. 101(C), pages 592-605.
    3. Javed, Fahad & Arshad, Naveed & Wallin, Fredrik & Vassileva, Iana & Dahlquist, Erik, 2012. "Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting," Applied Energy, Elsevier, vol. 96(C), pages 150-160.
    4. Yaïci, Wahiba & Entchev, Evgueniy, 2016. "Adaptive Neuro-Fuzzy Inference System modelling for performance prediction of solar thermal energy system," Renewable Energy, Elsevier, vol. 86(C), pages 302-315.
    5. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    6. Finn, P. & O’Connell, M. & Fitzpatrick, C., 2013. "Demand side management of a domestic dishwasher: Wind energy gains, financial savings and peak-time load reduction," Applied Energy, Elsevier, vol. 101(C), pages 678-685.
    7. Atikol, Uğur, 2013. "A simple peak shifting DSM (demand-side management) strategy for residential water heaters," Energy, Elsevier, vol. 62(C), pages 435-440.
    8. Zhang, Chi & Wei, Haikun & Zhao, Junsheng & Liu, Tianhong & Zhu, Tingting & Zhang, Kanjian, 2016. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, Elsevier, vol. 96(PA), pages 727-737.
    9. Yang, Yulong & Wu, Kai & Long, Hongyu & Gao, Jianchao & Yan, Xu & Kato, Takeyoshi & Suzuoki, Yasuo, 2014. "Integrated electricity and heating demand-side management for wind power integration in China," Energy, Elsevier, vol. 78(C), pages 235-246.
    10. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    11. Sharafi, Masoud & ElMekkawy, Tarek Y. & Bibeau, Eric L., 2015. "Optimal design of hybrid renewable energy systems in buildings with low to high renewable energy ratio," Renewable Energy, Elsevier, vol. 83(C), pages 1026-1042.
    12. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Demand response in smart electricity grids equipped with renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 64-72.
    13. Richardson, David B. & Harvey, L.D. Danny, 2015. "Optimizing renewable energy, demand response and energy storage to replace conventional fuels in Ontario, Canada," Energy, Elsevier, vol. 93(P2), pages 1447-1455.
    14. Wang, Xiaonan & El-Farra, Nael H. & Palazoglu, Ahmet, 2017. "Optimal scheduling of demand responsive industrial production with hybrid renewable energy systems," Renewable Energy, Elsevier, vol. 100(C), pages 53-64.
    15. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    16. Wang, Xu & Jiang, Chuanwen & Li, Bosong, 2016. "Active robust optimization for wind integrated power system economic dispatch considering hourly demand response," Renewable Energy, Elsevier, vol. 97(C), pages 798-808.
    17. Wu, Yujie & Wang, Jianzhou, 2016. "A novel hybrid model based on artificial neural networks for solar radiation prediction," Renewable Energy, Elsevier, vol. 89(C), pages 268-284.
    18. Liu, Jinqiang & Wang, Xiaoru & Lu, Yun, 2017. "A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system," Renewable Energy, Elsevier, vol. 103(C), pages 620-629.
    19. Dogan, Eyup & Seker, Fahri, 2016. "Determinants of CO2 emissions in the European Union: The role of renewable and non-renewable energy," Renewable Energy, Elsevier, vol. 94(C), pages 429-439.
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    4. Mayank Singh & Rakesh Chandra Jha, 2019. "Object-Oriented Usability Indices for Multi-Objective Demand Side Management Using Teaching-Learning Based Optimization," Energies, MDPI, vol. 12(3), pages 1-25, January.
    5. Yang, Lu & Xie, Pengli & Bi, Chongke & Zhang, Ronghui & Cai, Bowen & Shao, Xiaowei & Wang, Rongben, 2020. "Household power consumption pattern modeling through a single power sensor," Renewable Energy, Elsevier, vol. 155(C), pages 121-133.
    6. Đorđe Lazović & Željko Đurišić, 2023. "Advanced Flexibility Support through DSO-Coordinated Participation of DER Aggregators in the Balancing Market," Energies, MDPI, vol. 16(8), pages 1-26, April.
    7. Shubo Hu & Zhengnan Gao & Jing Wu & Yangyang Ge & Jiajue Li & Lianyong Zhang & Jinsong Liu & Hui Sun, 2022. "Time-Interval-Varying Optimal Power Dispatch Strategy Based on Net Load Time-Series Characteristics," Energies, MDPI, vol. 15(4), pages 1-23, February.
    8. Katsaprakakis, Dimitris Al & Thomsen, Bjarti & Dakanali, Irini & Tzirakis, Kostas, 2019. "Faroe Islands: Towards 100% R.E.S. penetration," Renewable Energy, Elsevier, vol. 135(C), pages 473-484.

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