To achieve low-carbon operation of a distribution network, new energy resources like photovoltaics (PVs) have been extensively integrated into it. However, this integration poses significant challenges to the supply-demand balance. Specifically, the generation of PVs is stochastic, causing power fluctuations. Additionally, the increase in power data and the open nature of the network will cause network congestion and communication disturbances. To address these issues, an active power support (APS) strategy is developed with the following innovations. First, an adaptive mutation-based generation prediction algorithm incorporating a multi-extreme learning mechanism (ELM) is proposed to optimize the prediction model and provide reliable predicted generation data for regulation. Second, a demand-driven path optimization method is proposed to prioritize critical data transmission, ensuring that regulatory service demands are met while mitigating congestion. Third, a hierarchical control strategy utilizing multifactor matching and a sliding mode controller (SMC)-based virtual leader-following consensus algorithm is designed to generate optimal control commands for PVs and suppress disturbances. Finally, adequate simulations demonstrate that the proposed method reduces the prediction error by at least 10.1% compared to existing methods, adjusts transmission paths based on data importance and service needs to mitigate congestion, and suppresses communication disturbances within 1s, thereby enabling effective APS.

Uncertainty aggregation characterization of multi spatial–temporal distributed energy resources (DERs) is crucial for effective decision-making and control in power systems. In this article, we propose a cloud-edge-end collaboration approach to quantify the aggregated uncertainty of power generation from multi spatial–temporal DERs. First, considering the temporal dynamic and electrical topology correlation of DERs, a local uncertainty aggregation model based on a spatial-temporal graph neural network (STGNN) is developed. This model can effectively extract the spatial-temporal characteristics of data. Second, addressing the data silo problem caused by the unwillingness of various stakeholders managing the DERs to share data due to privacy concerns, an uncertainty aggregation model training mechanism based on an adaptive secure federated learning is proposed. This mechanism enables collaborative modeling of uncertainty aggregation models across stakeholders while preserving user privacy. In addition, it improves the quality of local model training by adaptively extracting parameter information from the global model for local model initialization. Moreover, since the probability distribution of the aggregated uncertainty is unknown, this article combines STGNN with the weighted quantile regression model to characterize the aggregated uncertainty without prior assumptions about the distribution, and by assigning differentiated weights to aggregation results under different confidence levels based on their importance, the proposed method can better meet the diverse needs of power grid. Finally, simulations conducted on the IEEE 33-bus system and IEEE 69-bus system validate the effectiveness of the proposed method.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A "source-storage-load" coordinated master-slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply-demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source-storage-load coordinated control method based on the "ramping speed" ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source-storage-load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

In order to improve the voltage quality of buses in active distribution networks with high photovoltaic penetration, power compensation of controllable resources is widely used. How to develop a method to reduce communication burden while facilitating their optimal coordination is rarely discussed. This article designs an optimal voltage regulation method via hybrid power compensation. First of all, an event-triggered mechanism based on multiconstraint of changes in power flow is designed, thereby dividing all instants into trigger and nontrigger types. Both voltage offset at trigger instants and voltage fluctuation at nontrigger instants are mitigated by using a hybrid coordinated power compensation. By establishing a quantitative index, the optimal tradeoff performance for hybrid power compensation is analyzed under the event-triggered way. Finally, the case study verifies the effectiveness of the proposed method.

The proportion of distributed resources connected to the distribution network is gradually increasing. But in most scenarios, resources operate in passive response mode and cannot give full play to their active regulation potential. To awaken the regulation capability of distributed resources for active support of system frequency stability, a transmission and decision-making co-designed architecture is studied in this paper when a local supply-demand imbalance in the distribution network causes frequency instability. The architecture contains “data module”, “transmission module”, and “decision-making module”. Firstly, in the “data module”, based on the theory of multi-extreme learning machines and power flow calculation, the generation prediction of energy sources is performed to provide the data basis for generating regulation commands. Secondly, in the “transmission module”, through the nodal current equation-based sliding mode control and fast path reconstruction, the response strategy of the communication disturbance problem is proposed to provide the transmission support for generating regulation commands. Finally, based on the theory of “multiple factors matching” and “source-load interaction”, the source and load-side regulation commands are generated in the “decision-making module” by combining the transmitted data. Related case studies are carried out to verify the effectiveness of the proposed strategies.

In the multi-microgrid system, once a microgrid is severely disturbed into the alert or emergency state, the effective multi-mode management method is necessary to make the system restore the balance of supply and demand rapidly. Therefore, a hierarchical multi-mode management strategy is proposed in this study, which includes three steps:1. The first step is to predict and fit the support capacity of the neighbor microgrids and to determine whether these microgrids need to participate in the support, which constitutes the upper layer. To this end, an event-triggered mode management strategy is proposed considering the multi-source fitting and line loss factor, etc. 2. When the support of neighbor microgrids is not required, the second step is to manage local source-storage-load to restore the balance of supply and demand rapidly, which constitutes the lower layer. To this end, another event-triggered management strategy is designed to generate management commands without pre-processing data; 3. To establish the mathematical models of a microgrid in hybrid mode consisting of continuous operation status and discrete management commands, the related small signal model is designed as the third step, which can analyze the system stability conveniently. Finally, the effectiveness of methods is verified by case studies.

Nowadays, with the development of communication technology and its application in islanded microgrid, the pure power grid has gradually become a kind of cyber-physical system. In this system, the communication data and consensus algorithm are widely used in the secondary control of energy resources. However, in the communication process, there are also risks of network attacks, such as denial of service attack. To deal with this kind of attack existing in the data uploading channel during secondary control, the evolutionary game-based defense mechanism is designed, and the main works are as follows: firstly, the caused influence by attack on the control effect is analyzed through constructing a small-signal model. Secondly, two defense strategies including “Adjacent prediction” and “Path reconstruction” are designed. Facing different attack situations, the most suitable strategy can be selected via the evolutionary game. Thirdly, a game-based active defense strategy is designed, which can simulate the attack probability in the near future so that the defense can be prepared in advance. Finally, based on Laplace transformation and$\text{H}_{\infty } $robust theories, the parameter design in defense strategies is completed. The effectiveness of the above strategies is shown in multiple case studies.

To improve the voltage and power sharing of distributed generations, the hierarchical controls are widely used in microgrids while the dependent communication network makes it difficult to ensure the performance under limited bandwidth. In this paper, a predictive voltage hierarchical controller is designed. With the delayed secondary PI compensation signals, an inner-loop robust control is designed in the primary controller, ensuring stable voltage tracking. Then, a predictive controller is designed to provide neighbor and local predictions, such that the data integrity is improved by an accurate predictive compensation under bidirectional data-loss. Final case study results verify the effectiveness of the proposed method.

In this article, a cyber-physical cooperative control strategy is proposed for islanded microgrid (MG), which divides the MG into cyber and physical layers. And the main designs in these two layers are two event-triggered mechanisms, where one mechanism is used to improve the voltage and frequency stability of MG considering the packet loss problem, the other is used to reduce the communication burden in the control process. More specifically, the control process of the first mechanism can be understood as we use these event-triggered mechanisms to complete the secondary control in the physical layer based on the information in the cyber layer. In this mechanism, the packet loss situation in one communication channel is divided into three categories: 1) to handle the case where the loss rate is small, an adaptive virtual leader-following consensus controller (AVLFCC) is proposed in the cyber layer; 2) to handle the case where the loss rate is large and the forecasted data can be used, a hybrid forecast supplement method (HFSM) is proposed in the physical layer; and 3) to handle the case where the loss rate is large and the forecasted data cannot be used, a path reconstruction method combined with a novel sliding-mode control (SMC) is proposed in the cyber layer. In the second mechanism, an event-triggered protocol is designed for the consensus controller to reduce the communication burden based on the designs in 1)-3). Finally, based on these designs in the two mechanisms, a novel secondary controller is designed. And the experimental results have confirmed the validity of the contributed strategy.

Voltage regulation is imperative for the successful operation of electricity distribution networks, especially with a high penetration 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 article 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 online adjustment of weights and samples. Simulations on a sample distribution network demonstrate the effectiveness of our presented approach.

A voltage regulation scheme considering communication security is proposed for photovoltaic (PV) power system. The scheme is a two-level regulation to, respectively, reduce overall voltage deviation (VDE) and voltages difference (VDI). First, the evaluation indexes of VDE and VDI are built. Then, primary regulation through a powers compensation scheme is used. Considering communication topology change and delay under upper bound, secondary regulation through consensus protocol is developed. In addition, communication packet-loss and large delay are solved by predictive compensation. Finally, effectiveness of the proposed method is verified by simulation in MATLAB.