To ensure the continuous operation of the VSC-MTDC, there has been much concern regarding how to isolate the single-pole earth fault by means of protective relaying. However, when the boundary components are not on the DC transmission lines, conventional main protections for the HVDC systems perform less satisfactorily. To solve this problem, a local measure-ment-based ultra-high-speed main protection is proposed in this manuscript. The proposed method is achieved by the co-operation of the single-end directional start-up criterion and the surge arrival time difference between the 0-mode and 1-mode traveling waves. According to the simulation results, the proposed method has satisfactory performance against internal faults and is quite reliable in external fault and operating condi-tion changes. Meanwhile, the reach is also wide enough to cover most of the protected line. Comparison studies are also con-ducted among the proposed protection and three types of exist-ing protection schemes, highlighting the advantages of the pro-posed protection in terms of selectivity, operating speed and resistive tolerance.

Recently, various strategies for energy management have been proposed to improve energy efficiency in smart grids. One key aspect of this is the use of microgrids. To effectively manage energy in a residential microgrid, advanced computational tools are required to maintain the balance between supply and demand. The concept of load disaggregation through non-intrusive load monitoring (NILM) is emerging as a cost-effective solution to optimize energy utilization in these systems without the need for extensive sensor infrastructure. This paper presents an energy management system based on NILM and the Internet of Things (IoT) for a residential microgrid, including a photovoltaic (PV) plant and battery storage device. The goal is to develop an efficient load management system to increase the microgrid’s independence from the traditional electrical grid. The microgrid model is developed in the electromagnetic transient program PSCAD/EMTDC to analyze and optimize energy performance. Load disaggregation is obtained by combining artificial neural networks (ANNs) and particle swarm optimization (PSO) to identify appliances for demand-side management. An ANN is applied in NILM as a load identification task, and PSO is used to optimize the ANN algorithm. This combination enhances the NILM technique’s accuracy, which is verified using the mean absolute error method to assess the difference between the predicted and measured power consumption of appliances. The NILM output is then transferred to consumers through the ThingSpeak IoT platform, enabling them to monitor and control their appliances to save energy and costs.

With the development of electric vehicles, millions of electric vehicle service equipment (EVSE) are integrated into the power grid. These EVSEs are weakly protected from cyber-attacks. By exploiting vulnerabilities, attackers could establish a botnet integrating hundred-MW-level EVSE loads from the Internet. Dynamic load-altering attack (DLAA) is powerful to amplify the load disturbance to destabilize the power grid. However, different from large-scale loads controlled by existing DLAA, the EVSE botnet is formulated by tens of thousands of bots widely distributed on various power grid nodes, rendering it challenging to be coordinated. In this article, we analyze the approach to launching a DLAA from the distributed EVSE botnet. Firstly, we adopt a time-division model to evaluate the impact of communication limitations on the attack vectors of the botnet. Then we develop an analytical model to calculate the coordinated effect of attack vectors from different nodes and with varying phase angles; thus, we provide a foundation for the optimal coordination of distributed bots. Subsequently, considering the power grid has adopted defense measures to eliminate or suppress the attack, we formulate an optimization algorithm to make the attack robust against these potential defense measures. Numerical simulations validate the effectiveness of the proposed approach.

Extreme natural hazards may damage the pelagic island energy system (PIES) integrating distribution systems, cold storages and desalination stations, resulting in the electric service interruption, stored food deterioration, and freshwater shortage. To alleviate the energy demand of residents as much as possible before the arrival of mainland emergency resources, a novel self-sustaining strategy of post-disaster PIESs with mobile multi-energy storages is proposed in this paper. First, mobile multi-energy storages are used to delivery electricity, ice, and water among stations to increase the resource supply resilience. Their differences in resource delivery time and transportation energy consumption are modelled. On this basis, mobile multi-energy storages are coordinated with diesel generators, cold storages, desalination devices, network reconfiguration, and islanded grid merging for self-sustaining of post-disaster PIESs. The utility loss evaluation model of the common electric load shedding, food deterioration, and freshwater shortage is established, in order to better reflect and respond to the needs of residents. Robust optimization methods are also used to address the uncertainties of the renewable generation, multi-type load demand, and especially energy transmission (i.e., traffic congestion time and line repair time), making the restoration strategy reliable. Finally, case studies verified the superiority of the proposed strategy.

This paper develops a novel decentralized periodic energy trading framework for a pelagic islanded microgrid. Compared with situations of the microgrid connecting directly to the main grid and of the short-periodic energy trading system, in this paper we tackle the following challenges: the limitation of energy resources, the two types of end users (i.e., strategic users and normal users), the high uncertainty of renewable energy generations (REGs), and the non-convexity of optimization problem. To overcome these four obstacles unavoidably existing in practice, we propose the models of user satisfaction with the priority power supply for strategic users. Robust optimization approach is adopted to address the uncertainty associated with the REGs. A non-convex optimization problem derived from the game-theoretic approach and backward induction is solved via designing a decentralized bi-level iterative algorithm, then a Stackelberg equilibrium strategy is obtained, where the aggregator maximizes its revenue and each user minimizes its energy cost. Simulation results are provided to show the effectiveness and benefits of the proposed periodic energy trading framework for pelagic islanded microgrids.

This paper presents an advanced fault control for transformerless modular multilevel converters (MMCs) in AC/DC hybrid distribution networks (HDNs) under the single-phase grounding (SPG) fault. The characteristics of voltages and transmission paths of zero sequence currents in AC networks with different voltage levels and neutral grounding types under SPG faults are analyzed. According to the analysis, the SPG fault results in two issues: 1) the voltage imbalances in nonfaulted networks, which have negative influence on the operation of nonfaulted networks; 2) the overcurrent of MMC-based terminals results in the shutdown of MMCs instead of fault ride-through. To solve these two issues, two types of zero sequence fault control are proposed, which are suitable for different fault ride-through abilities of transformerless MMCs in AC/DC HDNs. The proposed fault control is implemented by an advanced zero sequence current controller (ZSCC). Furthermore, to reduce the effect of fault control on the sensitivity of current differential protection based on the zero sequence fault current, an improved set of current reference value is addressed. Finally, the theoretical analysis and proposed fault control are verified through simulations performed in MATLAB/Simulink.