arXiv subjects

Naihao Shi

Publications and source records attributed to Naihao Shi.

3 recordsLinked to original sources

arXiv2024

Optimal Power Flow for Integrated Primary-Secondary Distribution Networks with Center-Tapped Service Transformers

Secondary distribution networks (SDNets) play an increasingly important role in smart grids due to a high proliferation of distributed energy resources (DERs) in SDNets. However, most existing optimal power flow (OPF) problems do not take into account SDNets with center-tapped service transformers. Handling the nonlinear and nonconvex SDNet power flow constraints is still an outstanding problem. To meet this gap, we first utilize the second-order cone programming relaxation and linearization to make center-tapped service transformer constraints convex, respectively. Then, a linearized triplex service line power flow model, including its compact matrix-vector form, is further developed to compose the SDNet OPF model with our proposed center-tapped service transformer model. This proposed SDNet OPF model can be easily embedded into existing primary distribution network (PDNet) OPF models, resulting in a holistic power system decision-making solution for integrated primary-secondary distribution networks. Case studies are presented for two different integrated primary-secondary distribution networks that demonstrate the effectiveness and superiority of this model.

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arXiv2022

Automatic Self-Adaptive Local Voltage Control Under Limited Reactive Power

The increasing proliferation of distributed energy resources has posed new challenges to Volt/VAr control problems in distribution networks. To this end, this paper proposes an automatic self-adaptive local voltage control (ASALVC) by locally controlling VAr outputs of distributed energy resources. In this ASALVC strategy, each bus agent can locally and dynamically adjust its voltage droop function in accordance with time-varying system changes. The voltage droop function is associated with the bus-specific time-varying slope and intercept, which can be locally updated, merely based on local voltage measurements, without requiring communication. Stability, convergence, and optimality properties of this local voltage control are analytically established. In addition, the online implementation of ASALVC is further proposed to address the real-time system changes by adjusting VAr outputs of DERs online. Numerical test cases are performed to validate and demonstrate the effectiveness and superiority of ASALVC.

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arXiv2022

Data-Driven Affinely Adjustable Robust Volt/VAr Control

This paper proposes a data-driven affinely adjustable robust Volt/VAr control (AARVVC) scheme, which modulates the smart inverter reactive power in an affine function of its active power, based on the voltage sensitivities with respect to real/reactive power injections. To achieve a fast and accurate estimation of voltage sensitivities, we propose a data-driven method based on deep neural network (DNN), together with a rule-based bus-selection process using the bidirectional search method. Our method only uses the operating statuses of selected buses as inputs to DNN, thus significantly improving the training efficiency and reducing information redundancy. Finally, a distributed consensus-based solution, based on the alternating direction method of multipliers (ADMM), for the AARVVC is applied to decide the inverter reactive power adjustment rule with respect to its active power. Only limited information exchange is required between each local agent and the central agent to obtain the slope of the reactive power adjustment rule, and there is no need for the central agent to solve any (sub)optimization problems. Numerical results on the modified IEEE-123 bus system validate the effectiveness and superiority of the proposed data-driven AARVVC method.

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