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The penetration of distributed renewable energy (DRE) greatly raises the riskof distribution network operation such as peak shaving and voltage stability.Battery energy storage (BES) has been widely accepted as the most potentialapplication to cope with the challenge of high penetration of DRE. To cope withthe uncertainties and variability of DRE, a stochastic day-ahead dynamicoptimal power flow (DOPF) and its algorithm are proposed. The overall economyis achieved by fully considering the DRE, BES, electricity purchasing andactive power losses. The rainflow algorithm-based cycle counting method of BESis incorporated in the DOPF model to capture the cell degradation, greatlyextending the expected BES lifetime and achieving a better economy. DREscenarios are generated to consider the uncertainties and correlations based onthe Copula theory. To solve the DOPF model, we propose a Lagrangerelaxation-based algorithm, which has a significantly reduced complexity withrespect to the existing techniques. For this reason, the proposed algorithmenables much more scenarios incorporated in the DOPF model and better capturesthe DRE uncertainties and correlations. Finally, numerical studies for theday-ahead DOPF in the IEEE 123-node test feeder are presented to demonstratethe merits of the proposed method. Results show that the actual BES lifeexpectancy of the proposed model has increased to 4.89 times compared with thetraditional ones. The problems caused by DRE are greatly alleviated by fullycapturing the uncertainties and correlations with the proposed method.

Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage