A multi-stage risk-averse planning approach for multi-type ESSs considering Markov transition of persistent low output scenario

The rapid expansion of renewable energy, particularly wind and photovoltaic (PV) power generation, increases the vulnerability of power systems to persistent low output scenarios (PLOS), which pose significant security risks. To address uncertainties in the timing, duration, and frequency of PLOS while considering operational non-anticipativity, this paper proposes a multi-stage energy storage planning model incorporating a Markov process. A nested conditional value at risk (CVaR) framework is employed to manage uncertainty. To efficiently solve the large-scale multi-stage mixed-integer stochastic problem, a modified stochastic dual dynamic integer programming (SDDiP) algorithm is proposed. In order to accelerate the convergence speed of the algorithm, techniques such as regularization, dynamic sampling, dynamic cut selection, and parallel computation are designed. Case studies on the IEEE 118-bus system validate the effectiveness of the proposed approach.