4.7 Article

Reducing Generation Uncertainty by Integrating CSP With Wind Power: An Adaptive Robust Optimization-Based Analysis

Journal

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY
Volume 6, Issue 2, Pages 583-594

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSTE.2015.2396971

Keywords

Adaptive robust optimization (RO); concentrating solar power (CSP); thermal energy storage; uncertainty; wind power

Funding

  1. National Key Basic Research Program of China (973 Program) [2013CB228203]
  2. Foundation for Innovative Research Groups of the National Natural Science Foundation of China [51321005]
  3. NSFC-RCUK_EPSRC [51361135703]
  4. EPSRC [EP/L014351/1] Funding Source: UKRI
  5. Engineering and Physical Sciences Research Council [EP/L014351/1] Funding Source: researchfish

Ask authors/readers for more resources

The uncertainty of wind power generation brings problems in power system operation, such as requiring more reserves and possible frequency issues. In this paper, we propose an idea of combining concentrating solar power (CSP) plants with wind farms to reduce the overall uncertainty in the joint power output. Taking advantage of the dispatchability of CSP, the uncertainty of joint power generation is expected to decrease. Based on the operational model of CSP plants with thermal storage system, we search for the narrowest but robust bounds of the joint power output with a given uncertainty of the wind power output and solar power availability, and within operational constraints of CSP plants. The problem is formulated as an adaptive robust optimization (RO) problem, containing mixed-integer variables at the second stage. We introduce an algorithm that combines a nested column-and-constraint generation (C-CG) method and an outer approximation (OA) method to solve the problem. The case studies show that robust intervals for the joint power output can be obtained, and the obtained intervals can be significantly narrower than the original intervals of wind power.

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