A microgrid (MG) is a cyber-physical system with coupled power and communication networks. The centralized secondary control of MGs with periodical communications restricts system efficiency and resilience. This article proposes a distributed event-triggered secondary control scheme in islanded MGs with its cyber-physical implementation. The proposed control scheme operates with the reduced frequency of communications depending on the MG states change “events” (e.g., load variations and communication failures). Besides, the secondary control objectives, including frequency/voltage regulation and accurate real/reactive power sharing, are decoupled into two timescales. Instead of designing event-triggering conditions (ETCs) for each secondary control functions, only ETCs for power sharing control in slower timescale are designed. Thus, the communication burden is significantly reduced since communications among neighbor controllers are only needed at the event-triggered time. The proposed controller has been tested on a hardware-in-the-loop (HIL) platform, where the physical system is modeled in the OPAL-RT and the cyber system is realized in Raspberry Pis. The control effectiveness is validated by the HIL results.

This paper proposes a low-carbon operation model for an energy hub (EH) that combines the distributionally robust optimization (DRO) method with the Stackelberg game. Firstly, a bilevel single-leader-multi-follower Stackelberg game model is presented where the EH is the leader while users and electric vehicles (EVs) are regarded as two followers. Then, the Kullback-Leibler (KL) divergence-based DRO model is developed to deal with the uncertainty of renewable generation (RG) in the EH. Besides, Karush–Kuhn–Tucker (KKT) conditions, strong duality theory, and big- M approach are combined to transform the bilevel model into a single-level model. The reformulated single-level operation model is incorporated into the KL-based DRO approach. Furthermore, since the crafted column and constraint generation (C&CG) algorithm can prevent possible numerical problems caused by the exponential function and accelerate the solution speed, the crafted C&CG algorithm with linearization for the upper-level slave problem is proposed to iteratively solve the KL-based DRO integrated with Stackelberg game. Finally, numerical case studies are conducted with all simulation results confirming the effectiveness of the proposed model and method.

Extreme events like floods, tsunamis, hurricanes, etc., can threaten the normal operation of district power and thermal networks in multi-energy distribution systems (MDSs). Exploiting the reconfigurable properties of the combined network can provide a cost-effective solution to enhance the MDS resilience level and operation flexibility by redistributing multi-energy loads. In this paper, a resilient restoration approach is proposed for a MDS to coordinate multi-stage dispatch decisions in a distributed manner. First, the MDS operation is modelled with the power and thermal network constraints. As the district thermal network model is nonlinear, massive auxiliary flow variables are introduced to make the reconfiguration problem tractable, which significantly reduces the computational burdens. Second, to handle diverse uncertainties from the outcomes of disasters, loads, and renewable generation, both the pre-restoration and real-time stage measures are coordinated via a two-stage stochastic programming method. To further save the solution time, a distributed algorithm named progress hedging is used to decompose the original large problem into several small sub-ones. At last, case studies are conducted to demonstrate the effectiveness of the proposed method compared to other traditional restoration benchmarks.

Given the rapid development of the ship industry and increasing ship operating cost and gas emissions, this article studies the optimal operation of a multienergy ship (MES) microgrid to simultaneously reduce the ship operating cost and gas emissions. Apart from the conventional diesel generators, electric energy storage, and photovoltaic cells, the combined cooling heat and power (CCHP) unit is introduced for higher energy utilization efficiency. Besides, to flexiblize the CCHP operation and enhance the multienergy interaction, the thermal storage and power-to-thermal conversion unit are also included in the MES microgrid. Then, a multiobjective coordinated energy dispatch and voyage scheduling method are presented. After the constraints linearization, the coordination method is formulated as a mixed-integer linear programming problem, which is solved by the augmented ϵ-constraint method through Cplex solver. Finally, the proposed method is verified on a testing MES microgrid with necessary sensitivity analysis, and by comparing it with traditional benchmarks, its advantages are demonstrated. The simulation results indicate that the proposed method is effective for reducing the operating cost and emission, and it could provide references for the optimal operation of the emerging multienergy cruising ships.