Optimal scheduling of energy storage systems considering the two-layer interaction of distribution networks

Hua, Xiao; Hou, Baoyu; Zheng, Zhanglei; Yu, Yang; Han, Zheng; He, Pinquan; Luo, Lizi

doi:10.1109/ssci51031.2022.10022186

Cited by 1 publication

(1 citation statement)

References 16 publications

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“…Zhu et al (2018) proposed a consensus algorithm based on the charging and discharging characteristics of electric vehicles, which solved the difficulty of microgrid distributed dispatching caused by the integration of intermittent power supply and electric vehicles. Hua et al (2022) proposed an optimal distribution grid distributed dispatching model, considering the two-layer interaction of the energy storage system, and used the consensus algorithm to minimize the dispatching cost. Hu et al (2020) improved the consensus algorithm based on the segmented voltage-power droop control of the key node to mitigate the voltage violation problems using the distributed energy storage unit.…”

Section: Introductionmentioning

confidence: 99%

Hybrid bilevel optimization-based interaction between the distribution grid and PV microgrids with differentiated demand response

Shao,

Liu,

Yao

et al. 2023

Front. Energy Res.

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Demand response plays an important role in improving the balance of power generation and consumption between the distribution grid and photovoltaic (PV) microgrids. However, due to the uncertainty and volatility of PV output, as well as the different operation goals of PV microgrids, a conventional single-tier optimization approach is infeasible to realize the coordinated interaction between the distribution grid and PV microgrids. To address these challenges, we propose a second-order cone and improved consensus algorithm-based hybrid bilevel optimization algorithm for the interaction between the distribution grid and PV microgrids. First, we construct price-based and incentive-based differentiated demand response models to deal with various supply and demand dynamics of the distribution grid and PV microgrids. Building upon this foundation, we construct a hybrid bilevel optimization model. In the lower level, distributed optimization is adopted, and an improved consensus algorithm is used to optimize power output of PV microgrids to maximize the revenue based on output power of upper-level generator sets. In the upper level, centralized optimization is adopted, and second-order cone programming is employed to minimize the grid loss in the distribution grid based on the power output of lower-level PV microgrids. Hybrid bilevel optimization is iterated until the convergence condition is satisfied. Simulation results verify the proposed algorithm for achieving a coordinated interaction between the distribution grid and PV microgrids.

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Section: Introductionmentioning

confidence: 99%