Title: Stochastic Dynamic Optimal Power Flow in Distribution Network with Distributed Renewable Energy and Battery Energy Storage,
Abstract: The penetration of distributed renewable energy (DRE) greatly raises the risk
of distribution network operation such as peak shaving and voltage stability.
Battery energy storage (BES) has been widely accepted as the most potential
application to cope with the challenge of high penetration of DRE. To cope with
the uncertainties and variability of DRE, a stochastic day-ahead dynamic
optimal power flow (DOPF) and its algorithm are proposed. The overall economy
is achieved by fully considering the DRE, BES, electricity purchasing and
active power losses. The rainflow algorithm-based cycle counting method of BES
is incorporated in the DOPF model to capture the cell degradation, greatly
extending the expected BES lifetime and achieving a better economy. DRE
scenarios are generated to consider the uncertainties and correlations based on
the Copula theory. To solve the DOPF model, we propose a Lagrange
relaxation-based algorithm, which has a significantly reduced complexity with
respect to the existing techniques. For this reason, the proposed algorithm
enables much more scenarios incorporated in the DOPF model and better captures
the DRE uncertainties and correlations. Finally, numerical studies for the
day-ahead DOPF in the IEEE 123-node test feeder are presented to demonstrate
the merits of the proposed method. Results show that the actual BES life
expectancy of the proposed model has increased to 4.89 times compared with the
traditional ones. The problems caused by DRE are greatly alleviated by fully
capturing the uncertainties and correlations with the proposed method.