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Optimal economic programming of an energy hub in the power system while taking into account the uncertainty of renewable resources, risk-taking and electric vehicles using a developed routing method

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  • Zhang, Tairan
  • Sobhani, Behrouz

Abstract

Energy distribution network operation will be a major problem in the systems shortly because most changes will emerge in this domain. The presence of energy hubs, natural gas grids and dispersed generation resources at the distribution network's moderate- and low-voltage levels is the first challenge facing the energy distribution network operator. When PHEV vehicles are included in the energy hub, batteries can act as a general storage system and increase the possible integration of RES within power system networks. For the optimal operation of an energy hub that involves RES, PHEV, electrolyzer, fuel cell vehicles, boiler, hydrogen tank, rectifier, a heat storage system and inverter, a new model has been proposed in this paper. A new model has been developed for estimating consumption-related uncertainty of PHEVs during trips using information gap decision-making theory IGDT under risk aversion and risk-taking strategies. Additionally, a model for the uncertainty of RES is put out based on scenario development and reduction. A unique solution based on routing is presented since tackling the problem mentioned above might be error-prone mathematically. In this model, the possibility of entrapment in local optimum is reduced by increasing local and global searches. Finally, the proposed method is evaluated on a sample system and in different scenarios. Consequently, the proposed method maximizes target operation under risk-free and risk-averse strategies while minimizing target performance under risk-taking strategies.

Suggested Citation

  • Zhang, Tairan & Sobhani, Behrouz, 2023. "Optimal economic programming of an energy hub in the power system while taking into account the uncertainty of renewable resources, risk-taking and electric vehicles using a developed routing method," Energy, Elsevier, vol. 271(C).
    • Handle: RePEc:eee:energy:v:271:y:2023:i:c:s0360544223003328
    DOI: 10.1016/j.energy.2023.126938
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    References listed on IDEAS

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