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Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system

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  • Sharma, Akanksha
  • Jain, Sanjay K.

Abstract

This paper deals with the investigation of a day-ahead reactive power ancillary service procurement problem to minimize cost and voltage deviation under wind power generation uncertainties in a pool-based deregulated system. This reactive power procurement problem is formulated as a dynamic bi-objective optimization problem and is solved using a developed Pareto-based multi-objective artificial electric field algorithm (MO-AEFA). The developed MO-AEFA utilizes nondominated sorting principle and an external archive to store Pareto optimal solutions. Sign and reorder mutation operators are used to avoid trapping in local optima and enhance population diversity. The numerical results obtained from MO-AEFA are compared with other algorithms to validate the efficacy of proposed approach. The optimization algorithm utilizes a fast Newton power flow employing sparse matrix techniques to diminish computational burden. The capacitor switching is decided with consideration of marginal prices. The proposed methodology has been tested on modified IEEE 30-bus and IEEE 118-bus test systems. The performance of both test systems is analyzed for two studies namely, VAr dispatch without wind integration, and VAr dispatch under wind integration. The analysis with wind integration is further investigated for different wind penetration levels. The convergence characteristic of Pareto solutions is measured through statistical distance and diversity metrics.

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  • Sharma, Akanksha & Jain, Sanjay K., 2021. "Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system," Energy, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:energy:v:223:y:2021:i:c:s0360544221002772
    DOI: 10.1016/j.energy.2021.120028
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    References listed on IDEAS

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    1. Yin, WanJun & Ming, ZhengFeng & Wen, Tao, 2021. "Scheduling strategy of electric vehicle charging considering different requirements of grid and users," Energy, Elsevier, vol. 232(C).

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