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Short-Term Impacts of DR Programs on Reliability of Wind Integrated Power Systems Considering Demand-Side Uncertainties

Moshari, A ; Sharif University of Technology | 2016

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  1. Type of Document: Article
  2. DOI: 10.1109/TPWRS.2015.2449778
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2016
  4. Abstract:
  5. Meeting the reliability requirements in wind integrated power systems is a challenging problem due to the volatility of wind power generation. Wide implementation of demand response (DR) programs is an effective solution for this problem. This paper studies the impacts of DR programs on short-term reliability assessment of wind integrated power systems. A new algorithm including the effects of time and components initial states is presented for short-term reliability evaluation. This algorithm involves a multi-segment optimal power flow (OPF) approach to model the lead-time of DR and reserve resources. Taking into account the uncertainties associated with DR programs, a new model for these programs is also proposed. In addition, a reliability modeling is developed for wind energy conversion systems to be employed in short-term studies. The results show that DR programs can eliminate the negative impacts of wind energy volatility on power system reliability. It is also shown that the actual operating conditions, such as the uncertainties in demand-side participation and elasticity estimation, and the lead-time of remedial resources, may significantly affect the efficiency of DR programs. Therefore, it seems that these issues should also be included in the reliability-based planning of DR resources. © 2015 IEEE
  6. Keywords:
  7. Demand response (DR) programs ; Multi-segment optimal power flow (OPF) ; Reliability assessment ; Smart grids ; Uncertainty ; Acoustic generators ; Electric load flow ; Electric power generation ; Electric power transmission networks ; Energy conversion ; Reliability ; Reliability analysis ; Wind power ; Demand response programs ; Optimal power flows ; Reliability assessments ; Smart grid ; Wind generation ; Smart power grids
  8. Source: IEEE Transactions on Power Systems ; Volume 31, Issue 3 , 2016 , Pages 2481-2490 ; 08858950 (ISSN)
  9. URL: http://ieeexplore.ieee.org/document/7160790