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Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing

Author

Listed:
  • Wang, Fei
  • Ge, Xinxin
  • Yang, Peng
  • Li, Kangping
  • Mi, Zengqiang
  • Siano, Pierluigi
  • Duić, Neven

Abstract

This paper addresses the optimal decision problem of a distributed energy resources (DER) aggregator who manages wind turbines, solar PV systems and battery energy storage (BES) units while implementing real-time pricing (RTP) demand response program. The DER aggregator can procure electricity by bidding in the electricity market and scheduling its DER to meet the load demand of its customers. In the bidding and scheduling processes, the intrinsic uncertainties of distributed renewable generations and customer’s responsiveness to RTP program have brought economical risks to the DER aggregator, which will lower the DER aggregator’s profit. However, most of the current researches only consider the uncertainty of renewable generations while neglecting the uncertainty of customer’s responsiveness. To this end, a robust optimization-based day-ahead optimal bidding and scheduling model is proposed for DER aggregator by jointly considering these two uncertainties. The objective of the proposed model is to maximize the aggregator’s profit via optimally determining the hourly bidding quantities in the day-ahead market and the scheduled output power of distributed renewable generations and BES units. Case studies demonstrate that the proposed robust optimization model can help DER aggregator reduce the bidding and scheduling costs to obtain a higher expected profit.

Suggested Citation

  • Wang, Fei & Ge, Xinxin & Yang, Peng & Li, Kangping & Mi, Zengqiang & Siano, Pierluigi & Duić, Neven, 2020. "Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricing," Energy, Elsevier, vol. 213(C).
  • Handle: RePEc:eee:energy:v:213:y:2020:i:c:s0360544220318727
    DOI: 10.1016/j.energy.2020.118765
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    References listed on IDEAS

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    1. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    3. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    4. Alipour, Manijeh & Zare, Kazem & Seyedi, Heresh & Jalali, Mehdi, 2019. "Real-time price-based demand response model for combined heat and power systems," Energy, Elsevier, vol. 168(C), pages 1119-1127.
    5. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    6. Singh, Nitin & Mohanty, Soumya Ranjan & Dev Shukla, Rishabh, 2017. "Short term electricity price forecast based on environmentally adapted generalized neuron," Energy, Elsevier, vol. 125(C), pages 127-139.
    7. Fazlalipour, Pary & Ehsan, Mehdi & Mohammadi-Ivatloo, Behnam, 2019. "Risk-aware stochastic bidding strategy of renewable micro-grids in day-ahead and real-time markets," Energy, Elsevier, vol. 171(C), pages 689-700.
    8. Mohammadi Rad, Amin & Barforoushi, Taghi, 2020. "Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets," Energy, Elsevier, vol. 192(C).
    9. Mazidi, Mohammadreza & Monsef, Hassan & Siano, Pierluigi, 2016. "Design of a risk-averse decision making tool for smart distribution network operators under severe uncertainties: An IGDT-inspired augment ε-constraint based multi-objective approach," Energy, Elsevier, vol. 116(P1), pages 214-235.
    10. Nizami, M.S.H. & Hossain, M.J. & Amin, B.M. Ruhul & Fernandez, Edstan, 2020. "A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading," Applied Energy, Elsevier, vol. 261(C).
    11. Ghahramani, Mehrdad & Nazari-Heris, Morteza & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2019. "Energy and reserve management of a smart distribution system by incorporating responsive-loads /battery/wind turbines considering uncertain parameters," Energy, Elsevier, vol. 183(C), pages 205-219.
    12. Rajamand, Sahbasadat, 2020. "Effect of demand response program of loads in cost optimization of microgrid considering uncertain parameters in PV/WT, market price and load demand," Energy, Elsevier, vol. 194(C).
    13. Sun, Mei & Ji, Jian & Ampimah, Benjamin Chris, 2018. "How to implement real-time pricing in China? A solution based on power credit mechanism," Applied Energy, Elsevier, vol. 231(C), pages 1007-1018.
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