While Electric Vehicles (EVs) have the potential to contribute to low-carbon transportation, the burgeoning adoption of EVs and uncoordinated charging can place a significant burden on the power grid. This paper addresses the challenges of the integration of massive EVs into smart grids by introducing a comprehensive framework with heterogeneous flexibility aggregation. First, a Spatio-Temporal Heterogeneous Graph Neural Network (STH-GNN) is proposed to accurately predict EV charging demands by analyzing complex spatio-temporal relationships. Furthermore, a bi-level coordinated charging scheduling framework optimizes grid operations and individual EV charging schedules, relieving grid burden and enhancing efficient energy use based on the STH-GNN prediction. It solves the gaps between prediction errors and real-time actual charging demand. Additionally, the EV flexibility set is modelled to effectively aggregate heterogeneous EV resources, allowing for optimized deployment of large-scale EV assets by constructing the inner approximation of the Minkowski sum of the individual flexibility sets. The proposed method is verified in simulation, utilizing data from a realistic urban setting with diverse EV charging behaviours and grid conditions. Simulations demonstrate the effectiveness of the STH-GNN in accurately forecasting EV charging demands across different times and locations. The bi-level charging scheduling framework successfully manages grid burden while showcasing significant improvements in operational efficiency and cost reduction. Results validate the proposed model’s robustness and scalability, proving its potential applicability in real-world smart grid environments.

Due to dynamic characteristic of demand response and stochastic nature of power generation, it brings great challenge to smart energy management. In this paper, a demand response model is created with two-level dynamic pricing transaction among grid operator, service provider and customers, which also involves customers’ active participation with load shifting issue. To effectively control system load on the demand side, an improved deep reinforcement learning approach is proposed with a recursive least square (RLS) technique to deal with the dynamic pricing demand response problem, which accelerates the on-line training and optimization efficiency. On the power generation side, a probabilistic penalty-based boundary intersection (PBI) based multi-objective optimization algorithm is improved to optimize the economic cost, emission rate and statistic voltage stability index (SVSI) simultaneously with generated stochastic scenarios, which can ensure energy conservation and environmental protection, as well as system security. The case results reveal that the proposed two-level optimization strategy successfully deals with energy management with dynamic pricing demand response.Note to Practitioners—This paper is motivated by solving stochastic energy management issue of isolated power system with dynamic pricing demand response. Those existing methods merely focus on the load demand or power generation side, and the methods for demand response issue lacks efficient on-line learning ability, while this work proposes a recursive least square based deep reinforcement learning approach to tackle with the two-level dynamic pricing demand response issue, scenario based PBI multi-objective optimization is proposed to solve the power dispatch issue on power generation side, and the numerical analysis results suggest that the proposed optimization strategy can deal with the whole energy management issue well. The future work will focus on the dynamic power-load coordination in the energy management issue.

The development of mobile charging facilities for electric vehicles (EVs) has provided significant help in alleviating the pressure on active distribution networks (ADN) and traffic flow. This article proposes using the interaction between mobile charging facilities for EVs and the ADN to improve the power quality while ensuring the utility of mobile charging facility operators. First, the utility function of mobile charging facility operators is established with normal operation and emergency operation modes. The normal operation is to dispatch the mobile charging facilities for EVs requesting to be charged, while maximizing the charging benefits. To ensure the power quality for the ADN, the emergency operation is proposed to realize the power interaction between the mobile charging facilities and power grid. Furthermore, we propose an electricity price incentive mechanism to encourage optimal charging and discharging for mobile charging facilities. During the emergency operation, coordination between the mobile charging facilities and ADN is formulated as a Stackelberg game. We propose a sensitivity-based electricity price regulation algorithm and theoretically prove its equilibrium. Simulation results confirm the effectiveness and superiority of this approach, showing that the mobile charging facility and ADN can achieve a mutually beneficial outcome.

Meter pointers exhibit stable and anti-interference capabilities, rendering them extensively utilized in industrial environments. However, automated reading poses a significant challenge due to the fact that current segmentation methods struggle to isolate the fine-grained pointers and scales for accurate reading calculations. This challenge can be alleviated by enhancing the feature extraction capability of the segmentation network. As is well-known that Attention plays an essential role in human vision by selectively focusing on convex parts, and attention-based methods have been applied to various computer vision tasks. Therefore, we propose a new image segmentation network called multipath attention network (MPANet) for pointer meter recognition in the complex industrial environments. The designed network employs an attention gate mechanism to proficiently capture local features stemming from various pathways during skip-connection and upsample processes. In addition, our network incorporates deep supervision by merging the outputs of the final three layers to extract abundant low-dimensional information. To further improve the performance of encoders and decoders, a residual U-block is employed, thereby forming an enhanced U-shaped network structure. In the experiments, we employ HD95, Dice, and Recall as evaluation metrics. MPANet demonstrates superior performance compared to state-of-the-art networks on three our self-collected datasets, showing improvements of over 1% across all metrics. In addition, we validate the efficacy of MPA as a plug-and-play module and the benefits of applying deep supervision to multidecoder network.

In this article, a multiple time-scale voltage regulation is proposed based on the three-level coordinated control approach. This approach aims at solving the voltage issues in different scopes of space-time for active distribution networks. First, to address the global voltage issue of the entire network on a relatively slow-time scale, a multimode switching control is designed based on the Petri-net, which can effectively switch the on-load tap changer to extend its service life. Second, a multiobjective optimization considering the voltage differences with the security boundary and the transmission loss of the entire network is proposed. As such the global voltage issue can be handled by cooperating with the first-level switching control. Third, to solve the local voltage issue on a fast-time scale, a fully distributed optimal control integrating the active/reactive power sharing of all the distributed units is proposed. This control facilitates the control efficiency and active power consumption for renewable energy sources. Finally, the efficiency and effectiveness of the proposed method are validated under different scenarios in case studies.

The rapid development of electric vehicles (EVs) brings great challenges to the charging infrastructure construction and the smooth operation of power systems, which facilitates emerging of a new charging scheme based on mobile charging robots (MCRs) cluster. In this article, a novel framework with three-level optimization and control on multiple time scales is proposed, and the interactions among the MCR operator, power systems and EVs are realized by means of the cloud-edge-terminal coordination-based architecture. In the first level, the operation dispatch scheduling of the MCRs is formulated as a multiobjective optimization problem considering the voltage security of power grids, which improves the charging service efficiency of MCRs on a slow time scale. In the second level, a new solution algorithm based on the proposed resource competition and occupation model is used to handle the charging decision for the MCR operator, which greatly reduces the calculation time and ensures the solution accuracy simultaneously. In the third level, a local coordinated control is proposed to enable cooperation among the MCRs on a fast time scale, which provides charging services for EVs within the optimization interval of the second level and achieves the fairness of the residual state of charge of the MCRs. Finally, simulation results validate the effectiveness and superiority of the proposed three-level coordination with comparisons of existing methods.

In this article, a source–grid–load-collabora tion-based control framework is proposed to improve the power quality of active distribution networks (ADNs) with high penetration of low-carbon energy. First, hybrid dynamics of ADNs are characterized by addressing the voltage regulation and operation economics in each operation mode, and the mode switching control is designed in line with the operation principle of the on-load tap changer, where voltage security events are used to build the event-triggered functions. Second, multiobjective optimization is formulated with consideration of the system-wide operation cost and distribution circuit loss of the ADN in a relatively slow time scale, while in the fast time scale, all the inverter-based distributed generators, energy storages, and static var compensator devices are coordinated at the source–load side, through which multiple voltage issues, including voltage profile issue and voltage increment issue, can be addressed in a fully distributed manner. Finally, simulation results validate the effectiveness and robustness of the proposed method based on the modified IEEE 33-bus system.

In this article, a cloud-edge collaboration based control framework is proposed for the voltage regulation and economic operation in incremental distribution networks (IDN). The voltage regulation and economic operation, usually considered in separated aspects, can be integrated in a hierarchical control method by coordinating the active power and reactive power of distributed generators (DGs) and distributed storages (DSs) in an “active” mode. Promising the voltage security of the IDN, the upper level multiobjective optimization is formulated to maximize the consumption of the DGs, moreover, the lower level model predictive control (MPC) aims to regulate the dynamics of the DGs and DSs based on the established state space model. Time delay in the downstream channel is considered due to the open environment of the proposed control framework, which can be eliminated by using the PCM derived from the MPC considering model uncertainty. Finally, simulation results demonstrate the validity and robustness of the proposed method.

High penetration and fluctuation of renewable energy resources are threatening the voltage security of the integrated hybrid energy system (IHES). This article focuses on providing a fully distributed method to ensure the voltage security and information privacy of the IHES. First, a multiagent system based control scheme is presented, in which the upper level agent is responsible for the voltage security of the whole system, and the local dynamic performance of each distributed energy resource (DER) unit is formulated in the lower level unit agent. Second, a distributed model predictive control (DMPC) based coordinated voltage control method is proposed, by which only partial information exchange is needed in the interaction. Furthermore, the diverse requirements on voltage quality of each microgrid in the IHES are considered in this article and it provides a novel paradigm in this field compared with the traditional methods. To study the stability of the resultant DMPC, the Nash equilibrium is achieved and theoretically proved to ensure the convergence of the proposed method. Finally, the validity of the proposed method is verified by virtue of the simulation and experimental test results.

Hybrid and intermittent characteristics of the distributed energy resources (DERs) bring great challenges to the security control and economic dispatch (ED) of the microgrids. To bypass these hurdles, this article proposes a multiagent system-based integrated design of security control and ED to guarantee the effective and economical operation of the interconnected microgrids. First, a hierarchical control scheme is constructed by two-level unit agents, in which the switching control and dynamic regulation are fully implemented with the corresponding hybrid behaviors based on the differential hybrid Petri-net (DHPN) model. Based on the DHPN model, a novel dynamic ED integrated with security control is proposed to overcome the issues that cannot be solved in conventional models. Furthermore, to reduce the computational complexity and unified the mathematical model of the DERs, the inverter-based power control strategy is converted to a predictive control model which can be decomposed into several subsystems. In the optimization process, all the subsystems are implemented in a fully distributed, communication free, and rolling optimization manner based on the distributed model predictive control (DMPC). The validity of the proposed design is demonstrated according to the simulation results in case studies.