• Energy Research

In response to extreme events, substantial research efforts have focused on developing load restoration strategies for networked microgrids (MGs). However, existing distributed control approaches only consider independent time periods, which cannot capture the time-coupled flexibility of storage units. Additionally, mobile energy storage systems (MESSs) have been deployed for resilience enhancement due to their advantages in mobility and flexibility. However, existing research on networked MGs utilizes simplistic energy management approaches for MG modeling without detailed network structures, which cannot capture the mobility of MESSs. To address these issues, this paper proposes a three-stage stochastic distributed control approach based on rolling optimization to enhance the resilience of networked MGs with MESSs. Specifically, a stochastic linearized OPF is formulated in the first stage to capture the flexibility of MESSs and uncertainties, while a consensus algorithm is utilized in the second stage to calculate the power exchange among MGs. Finally, a detailed non-linear AC OPF algorithm is employed in the third stage to capture technical constraints relating to stability properties towards accurate optimization results. Case studies considering load distinction, multiple line outages, and limited generation resources are developed to demonstrate the effectiveness of the proposed distributed control approach in accurate and timely decision-making.

Abstract High-impact and low-probability (HILP) events can cause severe damage to power systems. Networked microgrids (MGs) with distributed generation resources provide a viable solution for the resilience enhancement of power systems. However, most literature utilizes centralized control methods based on energy management systems to model networked MGs and employs static storage units to enhance resilience, which are both unrealistic. In this paper, a hierarchical control approach based on detailed AC OPF algorithm is developed to capture technical constraints relating to voltage, angle and power loss as well as obtaining accurate solutions of power exchange between MGs, while the routing of electric vehicles (EVs) is incorporated into the model to reduce load shedding during extreme events. Uncertainties relating to load profiles and renewable energy sources are captured via stochastic programming. The impacts of limited generation resources and different levels of contingencies (including multiple line faults) are captured in the model to mimic a realistic scenario and verify the effectiveness of the proposed resilience enhancement strategy. Appropriate sensitivity analyses are suggested to investigate the influence of uncertain event occurrence time, tie-line capacity and EV scheduling horizon.

Low-carbon transitions require joint efforts from electricity grid and transport network, where electric vehicles (EVs) play a key role. Particularly, EVs can reduce the carbon emissions of transport networks through eco-routing while providing the carbon intensity service for power networks via vehicle-to-grid technique. Distinguishing from previous research that focused on EV routing and scheduling problems separately, this paper studies their coordinated effect with the objective of carbon emission reduction on both sides. To solve this problem, we propose a multi-agent reinforcement learning method that does not rely on prior knowledge of the system and can adapt to various uncertainties and dynamics. The proposed method learns a hierarchical structure for the mutually exclusive discrete routing and continuous scheduling decisions via a hybrid policy. Extensive case studies based on a virtual 7-node 10-edge transport and 15-bus power network as well as a coupled real-world central London transport and 33-bus power network are developed to demonstrate the effectiveness of the proposed MARL method on reducing carbon emissions in transport network and providing carbon intensity service in power network.