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Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation

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  • Zhu, Xingxu
  • Hou, Xiangchen
  • Li, Junhui
  • Yan, Gangui
  • Li, Cuiping
  • Wang, Dongbo

Abstract

In this paper, a distributed online prediction optimization algorithm is proposed to optimize the operation of distributed energy resources (DERs) considering multi-period constraints. First, we build a time-varying operation optimization model of DERs in multi-area distribution networks considering the operation constraints among different periods (e.g., the relationship of DERs energy within a certain amount of time). Second, we design an online prediction algorithm to solve the time-varying model in a distributed way. Specifically, it decouples the sensitivities of power flow states among different distribution areas. Based on this, a model is set up to characterize the mapping relationship among power flow states and the increments of DER output powers among different periods, and to predict the power flow states in the future only depending on the local and some aggregated information of each area. Thus, the solution of the time-varying optimization problem is decomposed into solving several subproblems in a distributed way. Finally, the proposed algorithm is verified in a 502-node distribution system. It achieves the optimal operation of DERs with satisfying the constraints of the energy plan formulated to reserve enough energy for the regulation in future periods. Compared with the centralized method, the proposed algorithm achieves similar optimization results with significant reduction of calculation time.

Suggested Citation

  • Zhu, Xingxu & Hou, Xiangchen & Li, Junhui & Yan, Gangui & Li, Cuiping & Wang, Dongbo, 2023. "Distributed online prediction optimization algorithm for distributed energy resources considering the multi-periods optimal operation," Applied Energy, Elsevier, vol. 348(C).
    • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009765
    DOI: 10.1016/j.apenergy.2023.121612
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    References listed on IDEAS

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