The increasing penetration of distributed renewable energy resources (REs) poses significant challenges to the voltage regulation and hosting capacity of modern distribution networks. Energy storage systems (ESSs), particularly when deployed across both user-side and grid-side levels, provide valuable flexibility for mitigating renewable fluctuations and supporting network operation. However, conventional state-of-charge (SoC) management strategies typically impose rigid terminal constraints, forcing ESSs to return to predefined states at the end of each scheduling horizon. This practice restricts intertemporal flexibility and limits the effective utilization of storage resources. To address these limitations, this study proposes a boundary-free SoC optimization framework for multi-source ESSs. By removing terminal SoC constraints, the proposed framework enables continuous multi-period operation and releases additional flexibility for renewable energy accommodation. To effectively utilize this flexibility, a multi-agent active/reactive power control model is developed to coordinate user-side ESSs, grid-side ESSs, and distributed renewable energy resources. Furthermore, a hybrid-attention multi-agent proximal policy optimization (HAMAPPO) algorithm is designed to support efficient decision-making. The hybrid attention mechanism enables heterogeneous agents to focus on role-specific information, thereby improving coordination performance and learning efficiency. Simulation results demonstrate that the proposed framework effectively reduces distribution network operational costs, user-side operating costs, and voltage deviations. These findings confirm the effectiveness of boundary-free SoC optimization in unlocking storage flexibility and improving distribution network hosting capacity under high renewable penetration conditions.

The increasing penetration of renewable energy resources and electric vehicles (EVs) introduces considerable uncertainty into distribution networks, where renewable intermittency and uncoordinated charging can cause voltage violations, congestion, and reduced hosting capability. Conventional static assessment methods, based on fixed operating assumptions, fail to capture the spatiotemporal interactions induced by renewable variability and aggregated charging demand under network constraints. To address these challenges, this study proposes a dynamic hosting domain (DHD) framework based on reinforcement learning. The DHD defines the time-varying feasible region of system states, providing a quantitative representation of admissible operating boundaries with explicit physical safety guarantees. It adapts to spatiotemporal charging variations and incorporates a margin-based index for hosting capacity evaluation. A station-level delay perception model is embedded into the reward design to account for aggregated service-quality constraints as macroscopic economic feedback, discouraging excessive charging postponement while preserving operational feasibility at the system level. Furthermore, a federated reinforcement learning architecture based on proximal policy optimization (PPO) enables collaborative policy learning among multiple charging stations without raw data sharing, thereby enhancing scalability, privacy preservation, and spatiotemporal coordination under non-IID conditions. Case studies on renewable-integrated distribution networks demonstrate faster convergence, enhanced voltage stability, and lower operational costs compared with benchmarks. The proposed DHD framework offers a unified pathway for secure, efficient, and behavior-aware EV charging management in renewable-energy-integrated systems.

To address the challenges of power fluctuations caused by the integration of distributed generation (DG) and the difficulty in simultaneously managing peak-valley load regulation due to diverse user energy demands in a microgrid system, this paper presents a coordinated optimal configuration method for serving a hybrid energy storage system (HESS), which explicitly considers the differentiated requirements from both the supply-side and the demand-side. In the presented method, an improved empirical mode decomposition (EMD) method is first presented to decompose the DG power into high-frequency, medium-frequency, and low-frequency bands. Based on the complementary technical and economic characteristics of different energy storage types, a coordinated regulation strategy for HESS in the multiple time-frequency domains is developed. Second, a coordinated optimal configuration model for HESS is further established. This model integrates key performance indicators, including maximum fluctuation and renewable energy utilization rate on the supply-side and the peak-valley difference reduction rate on the demand-side. Finally, a distributed optimization algorithm based on an improved alternating direction method of multipliers (ADMM) is developed to solve the coordinated configuration model. The experimental results demonstrate that the presented method can effectively smooth the DG power fluctuations and reduce the load peak-valley difference. The renewable energy utilization rate reaches 100%, and the peak-valley difference reduction rate reaches approximately 80%. The presented method successfully achieves the coordinated optimal configuration of HESS on both the supply and demand sides, providing a theoretical underlying infrastructure for the configuration of energy storage in the microgrid system with high penetration of renewable energy.

With the proposal of global low-carbon goals, research on new energy systems has become a major focus. As an emerging technology, electric vehicle (EV) can effectively reduce fossil energy consumption and enhance the integration of renewable energy sources. However, since EV usage patterns are highly dependent on human behavior, their uncertainty and complexity pose new challenges to the safe operation of distribution networks. Existing studies mainly focus on optimizing EV scheduling and orderly charging while overlooking the comprehensive carrying capacity of the distribution network for EV. Therefore, this paper proposes a method to enhance EV carrying capacity based on multi-agent cooperative control in the distribution network. First, a comprehensive EV carrying capacity index system is constructed, covering safety, stability, and quality aspects, and the Delphi method is used to integrate multiple indicators into a single optimization objective. Then, an improved particle swarm optimization (PSO) algorithm is applied to solve the multi-agent cooperative control model, aiming to enhance the EV hosting capacity of the distribution network. Finally, case studies based on the IEEE 33-bus system validate the effectiveness of the proposed method. The results demonstrate that the proposed approach significantly improves the maximum EV acceptance capacity and grid penetration rate while ensuring the safe, stable, and high-quality operation of the distribution network with minimal economic cost variation, providing effective support for the further development and deployment of EV.

ObjectiveThe Energy Router (ER) is an intelligent power electronic device featuring bidirectional regulation and multi-port interconnection, enabling efficient coordination of diverse energy types and voltage levels. However, ER planning involves multiple coupled factors—such as deployment location, connection topology, and capacity allocation—which result in a complex, high-dimensional optimization problem. To tackle this challenge, a two-stage planning method is proposed aiming to integrates network partitioning, source-load centroid identification, and bi-level optimization modeling to determine optimal ER deployment and configuration.MethodsThe proposed planning method is structured in two stages to address the complexity of ER deployment and configuration under high DG penetration. In the first stage, the distribution network is abstracted as a weighted graph, where the line weights are constructed based on electrical betweenness. A Louvain-based community detection algorithm is applied to partition the network into subregions. Within each subregion, the power source and load centroid are identified by relaxing the source-load distribution and calculating a synthetic centroid. The centroid nodes are then mapped to actual physical nodes, which serve as candidate locations for ER deployment. The number of ERs is determined by the number of subregions. In the second stage, a bilevel optimization model is established. The upper level determines the port number and interconnection scheme of each ER, with the objective of minimizing the total system cost, including ER investment and operational expenses. Based on the upper-level decisions, the lower level optimizes the port capacity configuration and dispatch strategy under uncertain conditions of wind, solar, and load. A two-stage adjustable robust optimization model is employed to ensure operational feasibility across a range of worst-case disturbance scenarios. The column-and-constraint generation (C&CG) algorithm and duality theory are adopted to iteratively solve the robust model, ensuring both computational tractability and robustness of the planning solution.Results and Discussions Case studies on a modified IEEE-33 bus system demonstrate the effectiveness of the proposed method. Compared with a baseline case without ER deployment, the proposed method reduces the annual system cost by 27.14% and improves ER return on investment (ROI) by 81.9% over existing single-ER strategies. The proposed ER planning also ensures 100% DG utilization and significantly reduces voltage deviation, with the maximum voltage offset decreasing by 48.73% compared to the baseline. The ERs operate at near-full capacity during peak hours, achieving average operational efficiency of 96.1% and extended periods of full-load operation. Sensitivity analyses indicate that deploying two ERs strikes an optimal balance between cost and ROI, outperforming both under- and over-deployment scenarios. Reducing the unit port capacity (e.g., to 10 kVA) improves voltage regulation and enhances port utilization due to increased configuration flexibility. Under different DG penetration levels (from 1.0 to 1.6), the ER layout remains largely stable. Higher penetration levels yield greater cost reduction and ROI, with maximum ROI observed under the 1.6 scenario. Although higher port capacities introduce some redundancy during low-output periods, overall efficiency remains above 92%, demonstrating robust adaptability to varying DG outputs.ConclusionsThe proposed two-stage ER planning method effectively addresses the challenges posed by high DG penetration in radial distribution networks. By combining network partitioning, gravity center-based deployment, and robust bi-level optimization, the method enhances economic efficiency, renewable energy accommodation, and voltage stability. Simulation results verify its adaptability, cost-effectiveness, and scalability under diverse operating conditions, making it a practical solution for next-generation distribution network planning.

The increasing deployment of distributed energy resources has driven significant interest in peer-to-peer (P2P) energy trading frameworks, particularly for optimizing distributed energy storage service provision (DESSP). Traditional bidding concession mechanisms primarily employ linearized models that oversimplify real-world market interactions and fail to capture the nonlinear relationship between participants’ concession strategies and market-clearing dynamics. To address this gap, this study proposes a novel P2P market design integrating a two-stage framework and a bidding concession model. Initially, we construct an iterative bidding model to quantify the nonlinear relationship between concession behavior and clearing prices, minimizing market mismatches and reducing dependence on the distribution network. We then introduce a two-stage P2P trading framework, incorporating day-ahead and intraday markets to mitigate deviations in renewable generation and load through DESSP. Finally, we construct a cross-framework credit mechanism, integrating credit into the trading rank to enhance transaction completion and market integrity and regulate pricing practices. Experimental results demonstrate that the proposed framework decreases reliance on the distribution network by 26.29%, improves local energy matching, and reduces total operational costs by 12.12%. The credit mechanism further stabilizes market dynamics, reducing operational costs by an additional 3.36%. These findings demonstrate the effectiveness of our proposed approach in enhancing the efficiency, stability, and fairness of P2P energy markets, providing valuable insights for future distributed energy trading systems.

Currently, renewable energy sources (RES) have been widely deployed in the smart grid, especially in the active distribution network (ADN). However, the inherent uncertainty of the renewable energy output significantly impacts the economy of the ADN operation. It is suggested that the utilization of flexible resources (FR) can effectively even out the uncertainty of RES. Nevertheless, the non-marketization of FR may prevent the emerging park-level integrated energy systems (PIES), important entities in ADN, from sufficiently offering their potential flexibilities. Therefore, this paper presents a two-stage local flexibility trading mechanism for motivating multi-PIESs to provide their flexible resources (PFR) to improve the operational flexibility of ADN. The first stage determines the dispatching plan of ADN according to the day-ahead forecasted values of RES. The second stage enables the multi-PIESs to trade their PFR with ADN to adjust the day-ahead dispatching plan in real-time to correct the forecasted errors of RES. In terms of implementing the two stages, firstly, the capacity of PFR with involving the adjustable tie line power of PIES is quantified using an optimization-based as sessment model. Secondly, based on the change of the operation cost before and after the sale of PFR, a pricing model of PFR is established. And then, to determine the trading amount of PFR, a real-time ADN economic dispatching model with the network constraints is further constructed, which aims at minimizing the comprehensive operation cost. Afterwards, a marginal-based method is employed to obtain the clearing price of PFR. Finally, to ensure the feasibility of the trading results and to provide the accurate dispatching strategies for the PFR trading in next time interval, a rolling dispatch for the multi-PIESs is carried out. Case studies demonstrate that the presented flexibility trading mechanism significantly reduces the power curtailment of ADN, the operation costs of ADN and the multi-PIESs.

With the large-scale integration of wind and solar power into the power grid, their volatility and uncertainty pose significant challenges to the flexible and safe operation of the power system. In order to achieve flexible operation of the power system and fully tap into the flexible regulation ability of the power system. A market trading mechanism model considering demand side flexibility resources has been proposed. Firstly, explore the potential and market behavior of demand side flexibility resources; Establish a game model that considers the participation of flexible ramping products(FRP) and new energy generation companies in the day ahead market by comprehensively analyzing and determining the providers of flexible ramping products; Finally, the effectiveness of the model was verified through case studies.

AbstractThe volatility of renewable energy generation impacts the safe and stable operation of power systems. Moreover, load uncertainty complicates renewable energy consumption. Therefore, accurately extracting load patterns using artificial intelligence (AI) technology is crucial. Load classification is an effective way to master load behaviour. However, issues in the collected load data, such as data class imbalance, significantly affect the accuracy of traditional load classification. To address this problem, this study proposes a novel classification method based on data augmentation and few‐shot learning, significantly enhancing the training efficiency of algorithm recognition. This addresses the challenge of real‐data recognition in power systems. First, time‐series load data are converted into images based on the Gramian angular field method to extract time‐series data features using a convolutional neural network. Subsequently, the data are augmented based on variational autoencoder generative adversarial network to generate samples with distributions similar to those of the original data. Finally, the augmented few‐shot data are classified using the embedding and relation modules of the relation network. A comparison of the experimental results reveals that the proposed method effectively improves power load classification accuracy, even with insufficient data.

With the high penetration of renewable energy sources (RES), the traditional distribution network has evolved into an active distribution network (ADN) which can effectively monitor and optimize the scheduling of RES. However, the operational flexibility of ADN is significantly influenced by the uncertainty of numerous power loads and distributed RES. As an important flexible resource, energy storage can participate in the economic dispatch of active distribution grids. Therefore, this paper presents an optimal economic dispatch method for enhancing the operational flexibility of ADN with distributed energy storage. Firstly, the energy storage and distributed power sources are modeled. Secondly, a robust optimal economic dispatch model is presented by characterizing the uncertainty of renewable energy output through the uncertainty set in order to fully utilize renewable energy and lower system operation costs. Thirdly, based on the models of energy storage and distributed power sources, a robust optimal scheduling model for the ADN with distributed energy storage is set up. Afterwards, the presented models are solved using an solver. Finally, by comparing the experimental results, the operating cost of the active distribution network using the robust optimal scheduling method of this paper and the traditional deterministic method are 313000 yuan and 28650000 yuan, respectively. The results of case study demonstrate the method's validity.

Energy routers, as an emerging means of current control, can effectively improve the network transmission loss of distribution networks as well as the problem of distributed power consumption. However, the planning of energy routers involves more variables, which are less concerned in current research. In this regard, based on the autonomous partitioning of distribution networks containing Distributed Generation (DG), a two-stage planning model for energy routers considering the matching of source/load barycenter in subregions of distribution networks is proposed to configure the access locations, connection lines, and port capacities of energy routers in distribution networks. Based on the improved IEEE 33-node system, the effectiveness of the proposed energy router planning method is verified, which can effectively improve the distributed power consumption rate of the distribution network and optimize the system operation economy through the optimal configuration of energy routers.

The application of attention mechanisms, especially channel attention, has achieved huge success in the field of computer vision. However, existing methods mainly focus on more sophisticated attention modules for better performance, but ignore global and local contexts in the frequency domain. This work focuses on the channel relationship and proposes a novel architectural unit called Frequency Global and Local (FGL) context block. It adaptively recalibrates global-local channel-wise feature responses by explicitly modeling interdependencies between channels in the frequency domain. The proposed lightweight FGL module is efficient well generalizable across different datasets. Meanwhile, the FGL context block significantly improves the performance of existing convolutional neural networks (CNNs) at a slight computational cost. Our FGL module is extensively evaluated with applications of image classification, object detection, and semantic segmentation with the backbones of ResNets, MobileNetV2, and MobileNeXt. The experimental results indicate that our module is more efficient than its counterparts. Our model is open-sourced at https://github.com/YunDuanFei/FGL .

双碳目标背景下,新能源并网需求的大量增加对区域综合能源系统灵活运行能力提出了新的要求.为了有效挖掘系统灵活性资源开发潜力,该文基于热网及热负荷精细化模型,提出一种计及多能灵活性的区域综合能源系统日前多目标优化运行策略.首先,建立供热管网传输模型以及建筑储能特性模型,精细化刻画热能传输动态过程;其次,建立多能灵活性资源模型,定量分析制定调度计划时的热电机组可调节容量;随后,建立考虑多能潮流的区域综合能源系统模型,并提出一种以系统灵活性不足率及综合运行成本为目标函数的日前调度策略.算例结果表明,该文所提的多能灵活性资源模型以及多目标优化调度策略可有效降低系统综合运行成本、提升系统可再生能源消纳率,缓解了系统用能高峰期供能压力.

This paper proposes a cloud energy storage service mechanism for the distributed energy storage scenario in industrial parks, and studies the pricing of cloud energy storage resources in this mechanism, which is oriented to the new power system. By optimally solving the distributed energy devices and energy demand of the campus users, the users' energy storage idle and energy storage demand are derived, and the cloud energy storage service platform benefits by matching the idle and demand quantities. For the pricing of cloud energy storage resources, this paper uses classical social welfare function and Nash social welfare function as the objective function to build a two-layer optimization model, and uses particle swarm algorithm to solve the cloud energy storage pricing model. Simulation analysis shows that the mechanism can effectively improve the utilization of energy storage resources and reduce the cost of energy use while ensuring the overall benefits of the platform and users.

The sharing economy is a new economic mode that can effectively promote the optimal allocation and utilization of the existing resources. Its integration with the distributed energy domain has a great significance in enhancing the utilization rate of equipment and reducing the cost of energy consumption of users. Therefore, this paper presents a distributed photovoltaic (PV) sharing service mechanism for the distributed PV existing resources of users in the park. Firstly, the architecture of the distributed PV sharing service mechanism is established, and the business process of the service mechanism is designed. Then, a model of distributed PV sharing user group is established, which can effectively analyze the sharing power, the net energy cost, and the purchased and sold power of the sharing user group. After that, a distributed PV sharing benefit model, a benefit allocation model based on a heuristic method, and a cost settlement model are established. Afterwards, the coupling relationship between the sharing benefit and the tariff profit interval and the amount of sharing electricity is discussed, and the heuristic benefit allocation method is compared and interpreted from the perspective of the tariff. Finally, it is verified through case study that the presented mechanism can effectively promote the localized sharing of distributed PV energy and reduce the cost of energy consumption of users.

Due to the advantages, such as being environment-friendly, the penetration of renewable energy has increased rapidly. However, the uncertainty and randomness of renewable energy source output severely impact economic dispatch, especially for those park-level integrated energy systems. To improve the economy of system operation and the accuracy of scheduling results, this paper presents a two-stage rolling dispatch strategy, in which a refined model of power flow, natural gas pipelines, and heat flow are established. The multi-energy flow model is linearized by the piecewise linearization method and second-order cone relaxation method. Moreover, a virtual energy storage model considering the thermal inertia in district heat systems and building enclosures is constructed. Further, to quantify the capacity of adjustable resources in park-level integrated energy systems, a multi-energy flexible source model is set up and integrated. Ultimately, to even out the prediction errors, a two-stage rolling dispatch is carried out, which corrects the results of day-ahead schedule plans. Based on the aforementioned works, case results demonstrate the effectiveness of the presented approach, both in renewable energy accommodation rate (with a promotion of 13.3%) and system operation economy (with a reduction of 14.55% in operation costs). It is recognized that the constructed models play important roles in improving the system flexibility and the presented operation strategy can effectively enhance the accuracy of the dispatch plans in real-time operation.

With the growing share of new energy installations, traditional power systems are under increasing pressure. Distributed energy storage systems not only mitigate the volatility caused by renewable resources but also enable load balancing and adjustments in user energy consumption patterns. To provide users with a better understanding of their energy storage habits, a park user identification method is proposed based on an enhanced KMEANS algorithm and multidimensional indicators for distributed energy storage. The enhanced K-MEANS, integrated with the Borderline SMOTE algorithm, addresses data class imbalances. Selection of the optimal number of clusters K is achieved using evaluation metrics such as SSE, silhouette coefficient, and CH. Preliminary assessment of user energy potential is based on daily electricity load patterns. To address low energy storage utilization among distributed energy storage users, a comprehensive multidimensional potential index system is introduced. Weighting of these indicators is determined using the Delphi method, and users are ranked based on their energy usage potential using the TOPSIS method. The effectiveness of this proposed method is demonstrated through experimental validation.

Due to the rapid development of Internet of Things (IoT) technology, there is a closer and more complex coupling relationship between energy network and information network in the integrated energy park than ever that included electricity, heat and other energy forms, constituting the IoT-Enable integrated energy park (IIEP). In the IIEP, massive data information can be comprehensively collected and managed, which provides the possibility of energy sharing among the internal prosumers. In order to improve the utilization rate of equipment and reduce the energy costs of prosumers in the IIEP, this paper presents a distributed energy sharing service mechanism. Firstly, the architecture and business process of the sharing service mechanism are designed. And then, the coordinated operation model and strategy aiming at minimizing the net energy cost of all prosumers in the IIEP are established. Furthermore, to provide clarity on the benefits and costs for each participating entity, the sharing benefit allocation and the cost settlement models are set up. Finally, the results of case study demonstrate that the presented sharing service mechanism effectively improves the overall utilization of distributed energy supply and storage systems, promotes local sharing and consumption of distributed energy, and reduces the energy costs of prosumers.

With the development of wind and solar power, the randomness and uncertainty of the power system have brought challenges to the optimal dispatching of active distribution networks. A dispatching model based on the comprehensive carrying capacity index system is proposed to improve the safety and stability of the active distribution network under uncertain factors and to excavate the potential of controllable resources. Firstly, the comprehensive carrying capacity indicator system is proposed from the three sides respectively, including network and load. Then economic optimal dispatching model is established on the basis of the indicator system. Finally, the model and its effectiveness are verified by the simulation analysis of the improved IEEE 33 nodes system by Particle Swarm Optimization.

随着我国能源市场的逐步开放,综合能源服务得到广泛关注,而园区多能供给服务是其重要的发展趋势之一.在此背景下,以多能供给服务商管理园区综合能源系统为应用场景、服务商和用户为博弈参与者、服务商净收益和用户综合效用为目标函数,?建立考虑用户效用评价的服务商能量管理及定价策略的主从博弈模型.通过组合赋权法得到用户综合效用函数,构建含电、热、冷、气4类负荷的用户模型,并结合服务商收益优化模型,提出服务商和用户主从博弈定价机制,通过双方的互动博弈,?动态改变双方策略,得到博弈均衡结果.最后通过算例分析证明了所提能量管理及定价策略具有合理性和有效性.