Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high level of photovoltaic (PV) systems. Power compensation control (PCC) that uses both reactive power compensation and active power curtailment has shown promising results in alleviating voltage rise problems. It crucially relies on real-time communications among distributed PV systems. However, the transmission of state measurements and control signals in PCC is hampered by inevitable communication delays. Therefore, it is important to not only estimate the maximum tolerable communication delay (MTCD) but also develop an alternative technique for PCC under abnormal communication delay (ACD) conditions. This paper presents a delay-tolerant predictive PCC for voltage regulation in distribution feeders. After estimating the MTCD based on voltage and power mutation, it uses normal PCC for effective operation when communication delay is within MTCD, or switches to predictive PCC under ACD conditions. An accurate prediction is achieved using a double neural network with on-line adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

The curtailment of photovoltaic (PV) power injection is one of the ways to mitigate the overvoltage issue in residential distribution networks with high levels of PVs. The reactive power compensation (RPC) of PV inverter, when applied in conjunction with the active power curtailment (APC) of battery energy storage system (BESS), is more effective in improving the voltage profile. Hence, a coordinated power compensation algorithm (PCA) is proposed in this article to regulate the voltage and reduce the power curtailment while serving requisite load demand. Both RPC and APC are integrated to improve the voltage regulation margin in a low-voltage distribution network. Using bus voltage sensitivities, the integrated RPC and APC are performed on the most sensitive PV inverter and BESS for voltage regulation. The proposed PCA has a large capability in voltage regulation as it performs RPC before APC, reducing the amount of total PV power curtailment. Simulation studies are conducted on distribution feeders to demonstrate the effectiveness of the proposed approach.

In this article, to reduce the deviation of frequency/voltage and power sharing in islanded microgrids, a distributed coordinated control based on multiagent system (MAS) is proposed. A hierarchical control structure is designed in this strategy. For the poor dynamic performance in the primary control, coordinated compensation terms are added to the traditional droop control-loop to reduce the transient fluctuation in frequency/voltage under load change. In the secondary control, an event-triggered consensus protocol is applied to restore frequency/voltage, obtain equal per-unit power sharing, and relieve communication pressure. Also, the communication network is viewed as an unreliable switching graph under communication channel interruption. To this end, a data prediction compensation mechanism is proposed to ensure the secondary control on the right track. Furthermore, to predict the lost data caused by communication channel interruption, an improved exponential smoothing model based on time series is proposed. Finally, several simulation scenarios have been carried out in MATLAB/Simulink to illustrate the validity of the theoretical results.

Virtual power plants (VPPs) possess significant potential to support power systems’ frequency regulation through the aggregation of demand-side distributed energy resources. However, due to their heavy reliance on information technology, VPPs inevitably face diverse communication issues, such as cyber-attacks, privacy disclosure, and packet losses, which poses a serious challenge to ensure efficient power control for frequency regulation. To this end, this paper proposes a multiagent system-based VPPs cooperative power control strategy to achieve frequency regulation in a resilient, optimal, and scalable manner. First, a joint design scheme of VPPs cooperative power control and information interaction mechanism is presented to determine the optimal regulation power of VPPs for frequency regulation, where a consensus-based collaborative strategy is designed for cyber-attacks and a homomorphic encryption technology is introduced for privacy protection. Also, an event-triggered mechanism is adopted for communication burden. Second, a dynamic power control scheme for inverter is presented to enable the internal units of VPPs to track the power reference from the joint design scheme, in which an improved model predictive control method is suggested for packet losses to enhance control performance. Finally, simulation results reveal that, when confronting diverse communication issues, the proposed strategy exhibits significant effects in defending against cyber-attacks, ensuring privacy security, alleviating the communication burden, and achieving fast response, thus effectively facilitating VPPs frequency regulation.

The aggregation and control of massive electric vehicles (EVs) are crucial for grid frequency regulation (FR). However, challenges such as disordered charging, high computational and communication burdens need to be addressed. To this end, a hierarchical hybrid modeling and switching control method for EV aggregation (EVA) is proposed. For modeling, a hybrid state set for EVs comprising three discrete states and one dynamic state is established at the local level. The dynamic state's flexibility allows EVs to charge orderly while considering user demands. At the aggregation level, a Markov-based EVA state space model is designed, integrating the user's willingness-to-pay (WTP) index and hybrid state. It estimates the EVA's FR capacity (FRC) with a lower communication burden and reduces computational burden by simplifying control dimensions. For control, a model predictive control (MPC)-based state switching method is designed at the aggregation level, considering user's FR willingness and power cancellation issue. Furthermore, a predictive compensation mechanism is designed to address model parameter errors resulting from asynchronous control cycles. At the local level, a probabilistic response method is proposed for responding to dispatched control signals, which reduces battery degradation through the state of charge (SOC) based response probability generation. Simulation results validate the method's effectiveness.

The measurement of semantic similarity between concepts is an important research topic in natural language processing. In the past, several approaches for measuring the semantic similarity between concepts have been proposed based onWordNet orWikipedia. However, improvements in the measurement accuracy of most methods have led to a dramatic increase in time complexity, and the existing methods do not effectively integrate WordNet and Wikipedia. In this paper, we focus on designing an efficient semantic similarity method based on WordNet and Wikipedia. To improve the accuracy of WordNet edgebased measures, we propose an edge weight model for combining edge and density information, which assigns a weight to each edge adaptively based on the number of direct hyponyms of the subsumer. Second, to improve the computational efficiencies of the existingWikipedia link vector-based measures, we propose a new Wikipedia link feature-based semantic similarity method that converts Wikipedia links into semantic knowledge and replaces the TF-IDF statistical weight model in the existing measures. In addition, we propose two new word disambiguation strategies to further improve the accuracy of Wikipedia linkbased measures. Finally, to fully exploit the advantages of WordNet and Wikipedia, we propose two new aggregation schemas for combining WordNet “is-a” semantics and Wikipedia link semantics to replace the current aggregation schemas that combineWordNet “is-a” semantics with category semantics inWikipedia. The experimental results show that our aggregation models are outstanding in terms of accuracy, efficiency and word coverage compared to state-of-the-art similarity measures.