Coordinated decentralized reactive power optimization of multi-area power systems with discrete variables based on the alternating direction method of multipliers and Ward equivalence

Shen, Zhijun; Liu, Mingbo; Lu, Wentian; Lin, Shian-Ru

doi:10.1016/j.ijepes.2018.10.004

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…However, both these proposed algorithms ignore the discrete controlling devices and are therefore only able to dispatch the optimal DG inverter reactive powers. Recently, researchers have attempted to include these discrete controlling devices with the ADMM based voltage optimisation strategies [19], [20]. The algorithm proposed in [19] uses the branch and bound method to estimate the tap positions of the LTCs in its ADMM based voltage optimization method.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Solution of Stochastic Volt/VAr Control in Radial Networks

Nazir

Pal

Jabr

2020

IEEE Trans. Smart Grid

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This paper presents a fully distributed algorithm for the stochastic Volt/VAr control (VVC) problem in active distribution networks. Exact convexification of the VVC problem is achieved through the use of second-order cones on the continuous relaxation of the original optimization structure. The global optimum solution of the relaxed problem is obtained through the application of the alternating direction method of multipliers (ADMM). In order to minimize the effect of rounding off on the final solution, an adaptive threshold discretization technique is used. A two-stage control strategy is adopted where the discrete controllers, like load tap changers and switched capacitors, are dispatched at the beginning of the optimization interval and the continuous controllers, like distributed generation (DG) inverters, are adjusted in real time according to an optimized decision rule. The superiority of the proposed algorithm is demonstrated through numerical simulations on the UKGDS-95 and the IEEE-123 bus systems.

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Section: Introductionmentioning

confidence: 99%

“…Also the algorithm could not dispatch the switched capacitor banks. On the other hand, the reactive power optimization problem solved in [20] is directly cast in an MINLP framework and hence lacks any mathematical guarantee for finding the global optimum of the system.…”

Section: Introductionmentioning

confidence: 99%