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Optimal real time cost-benefit based demand response with intermittent resources

Author

Listed:
  • Zareen, N.
  • Mustafa, M.W.
  • Sultana, U.
  • Nadia, R.
  • Khattak, M.A.

Abstract

Ever-increasing price of conventional energy resources and related environmental concern enforced to explore alternative energy sources. Inherent uncertainty of power generation and demand being strongly influenced by the electricity market has posed severe challenges for DRPs (Demand Response Programs). Definitely, the success of such uncertain energy systems under new market structures is critically decided by the advancement of innovative technical and financial tools. Recent exponential growth of DG (distributed generations) demanded both the grid reliability and financial cost–benefits analysis for deregulated electricity market stakeholders. Based on the SGT (signaling game theory), the paper presents a novel user-aware demand-management approach where the price are colligated with grid condition uncertainties to manage the peak residential loads. The degree of information disturbances are considered as a key factor for evaluating electricity bidding mechanisms in the presence of independent multi-generation resources and price-elastic demand. A correlation between the cost–benefit price and variable reliability of grid is established under uncertain generation and demand conditions. Impacts of the strategies on load shape, benefit of customers and the reduction of energy consumption are inspected and compared with Time-of-Used based DRPs. Simulation results show that the proposed DRP can significantly reduce or even eliminate peak-hour energy consumption, leading to a substantial raise of revenues with 18% increase in the load reduction and a considerable improvement in system reliability is evidenced.

Suggested Citation

  • Zareen, N. & Mustafa, M.W. & Sultana, U. & Nadia, R. & Khattak, M.A., 2015. "Optimal real time cost-benefit based demand response with intermittent resources," Energy, Elsevier, vol. 90(P2), pages 1695-1706.
  • Handle: RePEc:eee:energy:v:90:y:2015:i:p2:p:1695-1706
    DOI: 10.1016/j.energy.2015.06.126
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    References listed on IDEAS

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    Cited by:

    1. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    2. repec:eee:rensus:v:80:y:2017:i:c:p:367-379 is not listed on IDEAS
    3. Li, Lanlan & Gong, Chengzhu & Tian, Shizhong & Jiao, Jianling, 2016. "The peak-shaving efficiency analysis of natural gas time-of-use pricing for residential consumers: Evidence from multi-agent simulation," Energy, Elsevier, vol. 96(C), pages 48-58.
    4. Loganthurai, P. & Rajasekaran, V. & Gnanambal, K., 2016. "Evolutionary algorithm based optimum scheduling of processing units in rice industry to reduce peak demand," Energy, Elsevier, vol. 107(C), pages 419-430.
    5. Majzoobi, Alireza & Khodaei, Amin, 2017. "Application of microgrids in providing ancillary services to the utility grid," Energy, Elsevier, vol. 123(C), pages 555-563.
    6. repec:eee:energy:v:141:y:2017:i:c:p:1681-1694 is not listed on IDEAS
    7. Fera, M. & Macchiaroli, R. & Iannone, R. & Miranda, S. & Riemma, S., 2016. "Economic evaluation model for the energy Demand Response," Energy, Elsevier, vol. 112(C), pages 457-468.

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