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Abstract This paper proposes a two‐stage stochastic scheduling model for urban distribution network resilience enhancement against ice storms, which coordinates mobile deicing equipment routing and distributed energy resources dispatching. An improved line ice thickness prediction model and a photovoltaic power generation prediction method in accordance with conditional generative adversarial networks are proposed to provide data boundaries for scheduling strategy. Facing the uncertainty of line failure, a two‐stage scenario‐based distribution network optimization model is established. At first stage, the mobile deicing equipment routing strategy is decided to mitigate the impact caused by ice storms. The Monte‐Carlo simulation method is introduced to describe the uncertainty of line failure due to ice acceleration. For the second stage, based on the results of photovoltaic forecasting and possible distribution line failure scenario generated by Monte‐Carlo simulation method, the optimal distributed energy resources dispatching strategy can be obtained through the mixed integer programming. The proposed model is simplified to a mixed integer linear programming model that can be solved by a commercial solver. The test results on the modified IEEE 33‐node system and modified 69‐node system demonstrate that the proposed method can effectively improve the resilience performance of urban distribution network under ice storms.

Due to an ever-increasing market penetration of plug-in hybrid electric vehicles (PHEVs), the charging demand is expected to become a main determinant of the load in future distribution systems. In this paper, we investigate the problem of controlling in-home charging of PHEVs to accomplish peak load shifting while maximizing the revenue of the distribution service provider (DSP) and PHEV owners. A leader-follower game model is proposed to characterize the preference and revenue expectation of PHEV owners and DSP, respectively. The follower (PHEV owner) decides when to start charging based on the pricing schedule provided by the leader (DSP). The DSP can incentivize the charging of PHEV owners to avoid system peak load. The costs associated with power distribution, line loss, and voltage regulation are incorporated in the game model via power flow analysis. Based on a linear approximation of the power flow equations, the solution of sub-game perfect Nash equilibrium (SPNE) is obtained. A case study is performed based on the IEEE 13-bus test feeder and realistic PHEV charging statistics, and the results demonstrate that our proposed PHEV charging control scheme can significantly improve the power quality in distribution systems by reducing the peak load and voltage fluctuations.

In this article, we attempt to solve the issue of optimal scheduling for vessels monitoring video data uploading in maritime cyber physical systems, during the period of sailing from the origin port to destination port. We consider the terrestrial infrastructure-based networking, delivering video packets assisted by the shoreside infostations to the authorities on land. Each video packet has respective elements (i.e. release time, deadline, processing time, and weight) to describe, in which deadline is utilized to demonstrate the time domain limitation before that to upload it successfully. In order to cope with the computation complexity of traditional scheduling algorithms in intermittent infostations scenario, time-capacity mapping method is exploited to transfer it to a continue scenario when classic scheduling algorithms could be utilized with lower time complexity. An ingenious mathematic job-machine scheduling formulation is indicated with the goal of minimizing the total penalties of tardiness of uploaded video packets, taking into account the tardiness and the weights of jobs simultaneously. A genetic based algorithm, as well as an improved genetic algorithm–based optimization scheme, is proposed to target this optimization formulation. Specially, the genetic based algorithm as well as the improved genetic based algorithm are described in detail, including a novel chromosome representation, a heuristic initialization procedure, as well as a modified crossover and mutation process. The effectiveness of the proposed schemes is verified by the simulation results.

An innovative paradigm named Cooperative Cognitive Maritime Cyber Physical Systems (CCMCPSs) is developed to achieve high-speed and low-cost communication services. The analysis of the available white space at sea, as well as the framework, is presented. Specifically, a bilevel game with two stages of PUs-to-SUs (primary users to secondary users) and SUs-to-SUs is proposed, to address the resource allocation issue of Decode-and-Forward (DF) relay mode with maximal-ratio combining (MRC) receiving mode in destination. Stackelberg game with priority is employed between PU and SUs, while a symmetrical system model is considered among SUs-to-SUs. The game theoretic procedure that converges to Nash equilibrium based on the utility and payoff function is illustrated. Simulation results demonstrate that our proposed strategy could effectively increase the throughput as well as the payoffs of the system.