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Stochastic mid-term generation scheduling incorporated with wind power

Authors
Journal
International Journal of Electrical Power & Energy Systems
0142-0615
Publisher
Elsevier
Identifiers
DOI: 10.1016/j.ijepes.2014.07.076
Keywords
  • Mid-Term Generation Scheduling
  • Point Estimate Methods
  • Wind Power Generation
  • Modified Gravitational Search Algorithm
  • Self-Adaptive Strategy
Disciplines
  • Computer Science

Abstract

Abstract A challenge now facing system operator is how to schedule optimally the generation units in a wind integrated power system over a one year time horizon considering the effects of wind forecasting and variability; also, regarding the effects of load uncertainty. By the same token, this paper first develops a new formulation for Stochastic Mid-term Generation Scheduling (SMGS). In the formulation, 2m+1 point estimate method is developed to accurately estimate the output variables of Mid-term Generation Scheduling (MGS) problem. Then, the formulation is combined with adaptive modified gravitational search algorithm and a novel self-adaptive wavelet mutation strategy for the establishment of new robust algorithm for the present problem. It is noteworthy to say that the classical methods considered certain wind information in the deterministic solution of the MGS problem which is not the realistic approach. However, this study improves modeling of wind–thermal system in the MGS problem by considering possible uncertainties when scheduling the generators of power system. The proposed model is capable of taking uncertainty of load and wind into account. The proposed method is applied on two test cases and the numerical results confirmed the efficiency and stability of the proposed algorithm.

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