To address the issue of significant unpredictability and intermittent nature of renewable energy sources, particularly wind and solar power, this paper introduces a novel optimization model based on online reinforcement learning. Initially, an energy management optimization model is designed to achieve plan adherence and minimize energy storage (ES) operation costs, taking into account the inherent challenges of wind power-photovoltaic energy storage systems (WPESS). An online reinforcement learning framework is employed, which defines various state variables, action variables, and reward functions for the energy management optimization model. The state-action-reward-state-action (SARSA) algorithm is applied to learn the joint scheduling strategy for the microgrid system, utilizing its iterative exploration mechanisms and interaction with the environment. This strategy aims to accomplish the goals of effective power tracking and reduction of storage charging and discharging. The proposed method's effectiveness is validated using a residential community with electric vehicle (EV) charging loads as a test case. Numerical analyses demonstrate that the approach is not reliant on traditional mathematical models and adapts well to the uncertainties and complex constraints of the WPESS, maintaining a low-cost requirement while achieving computational efficiency significantly higher than that of the model predictive control (MPC) and deep Q-network (DQN) algorithm.

In this paper, a planning-oriented resilience assessment framework is proposed that can help to design a more resilient power transmission system against extreme weather-induced events, such as typhoon disasters. It consists of three major models, i) a wind field-based probabilistic typhoon model to determine the level of uncertainty in the intensity and path of the typhoon; ii) a spatiotemporal fragility model of the power transmission corridor to quantify the spatiotemporal impacts of extreme wind speed on the failure probability of transmission corridor, and iii) a resilience assessment model of the planning system that allows us to develop quantitative resilience indices from both the system and component perspectives. The combinatorial enumeration method is introduced into the proposed framework to generate various potential typhoon disasters. Meanwhile, the impact-increment-based state enumeration (IISE) method is embedded within the proposed framework to improve its computational efficiency. The proposed method can provide the key information to identify the weak points of the system as well as inform the design of enhanced resilient planning schemes of transmission system against typhoon disasters. Extensive simulations carried out on the IEEE RTS-79 system considering realistic typhoon scenarios demonstrate the effectiveness of the proposed method.