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Optimal Planning of Standalone Hybrid Energy Systems in the Presence of Multiple Uncertainties

Ghazvini, Mahram | 2013

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  1. Type of Document: Ph.D. Dissertation
  2. Language: Farsi
  3. Document No: 45401 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Abbaspour-Tehrani-Fard, Ali; Fotuhi-Firuzabad, Mahmud
  7. Abstract:
  8. As a cost-effective and reliable alternative to supply remote areas, standalone Hybrid Energy Systems (HES) are recently under investigation to address various concerns associated with technical, financial and environmental issues. HESs are composed of renewable resources, fossil fuel generators, storage devices and power electronic converters, which are able to supply electrical and thermal loads. HESs have advantages of both renewable and fossil fuel technologies. HESs not only decrease the fuel consumption and maintenance cost of the diesel generator, but also reduce the effects of unpredictable and costly nature of renewable energy resources. Standalone hybrid energy systems should compete with other alternatives such as grid extension and diesel-only systems. Therefore, development of procedures to achieve optimum design and planning of the HES is crucial in order to attain a cost-effective, reliable and competitive system. On the other hand, Net Present Cost (NPC) of the HES depends on both of the components’ size and the operation strategy. Consequently, in the design process of the HES, its components’ size and operation strategy should be simultaneously optimized as they are both correlated with each other.
    In this thesis, optimization of the HES is performed from two aspects of the components’ size and operation strategy. To effectively optimize the operation strategy of the HES, the proposed optimization algorithm determines optimal set points of the control system. In this regard, new control set points are defined, and a new operation strategy is proposed based on the defined set points. The Multi-objective particle swarm optimization (MOPSO) method is used to simultaneously minimize objective functions associated with the cost, energy not served (ENS) and emissions. In addition, uncertainties related to renewable resources’ availability, load forecasting, and components’ outages are considered in the optimization process, and a new approach is proposed to optimize the HES in an uncertain environment. In this regard, the aforementioned uncertainties are probabilistically modeled and incorporated in the MOPSO approach using Monte Carlo Simulation (MCS) method. The applicability and effectiveness of the proposed methods are investigated through some numerical analyses. In addition, the obtained results from the proposed approaches were compared with those of the existing methods.
  9. Keywords:
  10. Optimum Programming ; Monte Carlo Simulation ; Power Management ; Hybrid Energy System ; Multiobjective Partial Swarm Optimization (MOPSO) ; Uncertainty Modeling ; Probabilistic Optimization

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