This paper suggests a way to improve teamwork and reduce uncertainties in operations by using a game theory approach involving multiple virtual power plants (VPP). A generalized credibility-based fuzzy chance constraint programming approach is adopted to address uncertainties stemming from renewable generation and load demand within individual VPPs, while robust optimization techniques manage electricity and thermal price volatilities. Building upon this foundation, a hierarchical Nash-Stackelberg game model is established across multiple VPPs. Within each VPP, a Stackelberg game resolves the strategic interaction between the operator and photovoltaic prosumers (PVP). Among VPPs, a cooperative Nash bargaining model coordinates alliance formation. The problem is decomposed into two subproblems: maximizing coalitional benefits, and allocating cooperative surpluses via payment bargaining, solved distributively using the alternating direction method of multipliers (ADMM). Case studies demonstrate that the proposed strategy significantly enhances the economic efficiency and uncertainty resilience of multi-VPP alliances.

Cooperative operations among multiple unmanned underwater vehicles (UUVs) are emerging as a prevailing trend for executing complex marine missions. Accordingly, the multi-UUVs mobile underwater acoustic networks, which serve as the foundation for information exchange between UUVs, have become a key research focus. However, the high mobility of UUVs causes dynamic node positions, leading to unstable channels and time-varying topologies and excessive network maintenance overhead. To address these problems, a Multi-Dimensional Spatio-Temporal Evolution Network graph prediction-based Greedy Perimeter Stateless Routing protocol (MDSTN-GPSR) is proposed to achieve more reliable routing in multi-UUV mobile swarm networks. To address the issues of routing holes and detours caused by the high mobility of UUVs, a multidimensional spatio-temporal evolution network graph prediction model is proposed to construct the time-varying network location maps, topology, and conflict graph for routing calculation. A Low-Overhead Neighbor- Aware Route Maintenance Mechanism is introduced to reduce the scope and frequency of routing, thereby minimizing overhead. The proposed method uses a Cross-Layer Optimization-based Multi-Factor Forwarding Mechanism. This mechanism selects paths based on both physical link quality and MAC scheduling slots, in order to improve the delivery rate and reduce end-to-end delay. Simulation results show that, compared with the GPSR routing protocol, MDSTN- GPSR reduces average end-to-end delay by 10.14% and increases packet delivery rate by 20.26%, adapting better to the time-varying topology of multi-UUV underwater mobile swarm networks.

Under single-timescale model control, the control efficiency of supermarket refrigeration systems is low. Therefore, a real-time control method for supermarket refrigeration systems based on time-series hierarchical transformers is proposed. A time-series hierarchical transformer model is constructed, with hierarchical modeling and cross-layer fusion to predict key refrigeration parameters for the next 5-30 minutes. Based on the prediction results, combined with system operation constraints and control objectives, a three-layer real-time control strategy is designed: “bottom-layer hard real-time closed loop + middle-layer energy efficiency optimization + upper-layer predictive scheduling”. Finally, the optimal control quantity is converted into a standard signal and sent to the actuator. Feedback data is collected in real time, and model parameters and control quantities are dynamically adjusted to form a complete closed-loop control. The experiment demonstrates that the research method reduces the control delay by an average of approximately $\mathrm{1}-\mathrm{1}. 7$ seconds under various operating conditions. When the passenger flow reaches 600 people, the energy consumption is reduced by approximately 35 kWh compared to the control method 1, and by approximately 14 kWh compared to control methods 2 and 3, thereby enhancing the efficiency of the supermarket refrigeration system control.

The flexibility requirements of network topologies in advanced distribution networks (ADNs) have led to increasingly complex distribution network topologies, posing a significant challenge for topology identification. The integration of a vast array of internet of things (IoT) devices at existing distribution network terminals facilitates the real-time collection and transmission of physical measurement data, thus providing new ideas for identifying distribution network topologies. This paper introduces a distribution network topology progressive identification method grounded in measurement data captured by IoT devices. First, the study employs similarity mining of IoT measurement data to identify the topological connection relationships within the low-voltage distribution network. Second, by integrating real-time measurement data with historical data, the medium-voltage-side data are consolidated, and the topology of the medium-voltage distribution network is identified by an inverse power flow model based on a fixed parameter ratio. Furthermore, to address the limitations of low recognition accuracy and non-unique recognition outcomes associated with the aforementioned methods, this study examines the regional similarity among distribution networks and proposes a hyperparameter to increase the recognition accuracy. Finally, different case studies show that the proposed method maintains a topological identification accuracy of more than 80%, and shows a wide range of applicability in different types and sizes of distribution networks.

Ground fracturing technology is often used to treat hard roof in recent years. Current research on the mechanism of ground fracturing controlling hard roof mainly focuses on numerical simulation. However, the present numerical simulation methods reveal distinct limitations. Therefore, we developed a numerical model based on the material point method (MPM) to reveal the mechanism of ground fracturing. The model uses the convected particle domain interpolation (CPDI) technique to improve accuracy and a strain-softening model to describe the mechanical properties of rock mass. At first, the reliability of the model proposed in this study is verified by comparing the similar physical simulation test results of the same working face. Based on verification, hydraulic fractures are embedded in the 1# hard roof layer to simulate the impact of ground fracturing on the rock mass. Then the impact of hydraulic fractures on longwall mining is studied numerically. The results document that the hydraulic fractures are activated and communicated with the mining-induced fractures under the disturbance of excavation. This effect promotes the local slip caving of the hard roof, thereby reducing the advance abutment stress of the working face during the collapse of the hard roof.

With the widespread adoption of distributed photovoltaic (PV) systems, PV microgrids are integral to the smart grid. Blockchain technology applied to interconnected PV microgrid clusters enables secure and efficient energy trading markets, optimizing PV energy use and management of energy transactions. A blockchain-based decentralized energy trading mechanism for interconnected PV microgrid clusters is proposed, accommodating PV output variability and implementing a refined trading cycle. It predicts microgrid net load for the next trading period, calculates initial offers, and employs a tariff adjustment mechanism for enhanced trading success. A consensus mechanism based on proof of reputation (PoR) and Ripple cascade ensures transaction security and integrity. Simulation tests validate the proposed energy trading mechanism's effectiveness.

The rapid serial visual presentation (RSVP) paradigm has been widely adopted to help professionals distinguish target images more efficiently in brain-computer interface (BCI) systems. However, existing RSVP-based BCI systems only provide information on the existence of targets (i.e. the classification of targets and non-targets) and do not include their position and size (i.e. the localization of targets), which limits their application in high-demand tasks. In this study, we propose a novel RSVP-based system designed to simultaneously record electroencephalography (EEG) and eye-movement signals to classify images and localize targets. Our system includes an EEG and eye-movement multi-modal learning (EEMM) model designed to synthesize complementary descriptions. The EEMM consists of three modules: offset reconstituted convolution (ORC), eye-movement convolution (EMC) and multi-modal channel attention dense (MCAD). Fifteen subjects participated in an experiment using the system. The results showed that the proposed system achieved 88.24% and 0.5643 in the balanced-accuracy (BA) and F1-score metrics on classification task, and 78.00% and 0.3972 on the localization task, which exhibited excellent performance.

Achieving net-zero emissions requires transitioning from a fossil fuel-based energy system to one dominated by renewable energy sources. VSC-MTDC systems play a crucial role in this transition by efficiently integrating renewable energy, particularly offshore wind farms. In this paper, a novel hierarchical optimal control strategy without additional PFCs is proposed to achieve cost-effective control of wind farm-integrated MTDC grids after contingencies. Two objectives are considered in the proposed hierarchical control strategy: maintaining effective power line sharing and providing frequency support for AC areas without enough power reserve. A multi-objective optimal control algorithm is implemented in the secondary control layer to optimise the adaptive droop control parameters and determine the voltage reference parameter of VSCs. Adaptive droop control is implemented in the primary layer based on the obtained control solution. A five-terminal MTDC connected with offshore wind farms and AC grids is employed as the test system. The effectiveness of the proposed hierarchical control strategy is verified through system analysis and simulation results carried out using OPAL-RT RT-LAB libraries and MATLAB/Simulink Simscape Blockset.

The material point method (MPM) is an effective approach for simulating longwall mining; however, previous studies have utilized a nonobjective stress rate, specifically the Cauchy stress rate, in their constitutive equations, thereby overlooking the influence of object rotation. To address this issue, this paper introduces an objective stress rate, the Jaumann stress rate, to improve the stress update process, enhancing the accuracy of longwall mining simulations. The effectiveness of the numerical method presented in this paper was validated using a three-point bending test and a longwall mining case study. Building upon this validation, the study systematically explores the effects of hydraulic fracture parameters on hard roof control. Specifically, it was found that when fractures penetrate through the hard roof, the first caving span is significantly reduced and the advanced abutment stress in the coal seam is greatly enhanced. Increasing fracture spacing effectively prevents rotational collapse and reduces the advanced abutment stress ahead of the working face. Moreover, hydraulic fractures should be positioned within the range of mining-induced fractures to effectively facilitate rock strata collapse. In conclusion, this research provides a scientific basis for optimizing the parameters of fracturing technology in practical coal mining operations.

This study explores enhancing the resilience of electric and natural gas networks against extreme events like windstorms and wildfires by integrating parts of the electric power transmissions into the natural gas pipeline network, which is less vulnerable. We propose a novel integrated energy system planning strategy that can enhance the systems’ ability to respond to such events. Our strategy unfolds in two stages. Initially, we devise expansion strategies for the interdependent networks through a detailed tri-level planning model, including transmission, generation, and market dynamics within a deregulated electricity market setting, formulated as a mixed-integer linear programming (MILP) problem. Subsequently, we assess the impact of extreme events through worst-case scenarios, applying previously determined network configurations. Finally, the integrated expansion planning strategies are evaluated using real-world test systems.

This paper establishes a four-port equivalent circuit model of common-mode choke including common mode and differential mode characteristics, which facilitates system simulation. Firstly, impedance characteristic tests are performed, such as common mode, differential mode, and open-circuit. Based on the test results, an optimized equivalent circuit model is developed by enhancing a typical RLC parallel circuit model. The model parameters are solved using a genetic algorithm as a nonlinear optimization problem. Subsequently, a comprehensive four-port equivalent circuit model is established for the common-mode choke, encompassing common mode and differential mode behaviors. Finally, the effectiveness of the proposed choke circuit model is validated by the comparisons of the simulation and the test results.

At present, some provinces and regions in China have established compensation and allocation mechanisms for auxiliary services of peak regulation and frequency modulation, which have played a certain role in promoting clean energy consumption. However, with the further liberalization of the power generation plan and the continuous deepening of the spot market construction pilot, the compensation and allocation mechanism of auxiliary services under the original plan mode has been difficult to meet the needs of the power market construction, and there is a certain lack of connection between peak regulation and the spot market. It is urgent to coordinate and promote the construction of auxiliary service market and spot power energy market. Establish and improve the auxiliary service market system and trading mechanism, and give full play to the decisive role of the market mechanism in guiding the optimal allocation of power resources and reflecting the value of different power resources.

Virtual power plants (VPPs) offer an effective approach for managing distributed energy resources (DERs), including microturbines, distributed generators, demand response aggregators, and energy storage systems. This technology significantly enhances the economic efficiency and flexibility of distribution network systems. This study aims to facilitate a flexible power exchange between the distribution network system and the upper-level grid by aggregating the power flexibility of heterogeneous DERs via a VPP. Additionally, it introduces a methodology for optimal aggregation and disaggregation within a coordinated operation framework between the VPP and distribution network operator (DNO). On one hand, the VPP can determine its day-ahead feasible region and real-time flexible regulation power based on operational constraints of DERs and representative data provided by the DNO, circumventing the need for detailed network information. On the other hand, day-ahead and real-time correction procedures for the DER cost functions are proposed, effectively neutralizing the impact of network operational constraints on these cost functions. Consequently, precise cost functions for both the active power and the flexible regulation power aggregated by the VPP are derived. Employing this aggregated cost function enables the determination of a cost-minimized optimal scheduling solution in real-time by solving a fundamental economic dispatch problem, significantly alleviating computational demands. Finally, case studies demonstrate that the proposed method achieves an error in aggregated power of only 0.77%, compared to the precise computation method that requires comprehensive system information. The proposed optimal VPP disaggregation scheme exhibits power discrepancies of 0.63% and cost discrepancies of 0.91% relative to the precise method. Additionally, when implementing the most cost-effective demand response plan based on the proposed cost function, the average costs for aggregators are reduced by 19.7%.

This paper proposes a joint optimization framework for the demand response-capacity configuration design of the integrated energy system (IES), in which both the economic cost and closed-loop dynamic regulation performance under source-load fluctuations are considered. The joint optimization consists of two layers. The upper layer optimizes the time of use electricity price to adjust the demand response of electric load; while the lower layer optimizes the capacity configuration, scheduling instructions and incentive-based thermal load demand response under typical scenarios. To fairly evaluate the impact of equipment capacity and demand response on the dynamic control performance of the IES, multi-parameter programming-based predictive control approach is applied to develop an offline design and scheduling-perceptive control system. A closed-loop dynamic scheduler is then developed based on the control system, which can optimize the operating instruction of each equipment considering their dynamic operating practice. Simulation studies on a typical off-grid combined heat and power IES show that the proposed approach can effectively match the demand response to the capacity configuration of the IES, helping the system achieve a dynamically flexible and economic energy supply.

There are significant uncertainties and variations in demand response under different operating conditions and incentives. These challenges hinder virtual power plants (VPPs) from fully leveraging the regulatory capabilities of flexible resources. To address this issue, this study introduces an optimal demand response (DR) approach for VPPs, featuring a hierarchical operational framework that accounts for the uncertainty in user participation in DR. First, a refined DR model is developed, incorporating response characteristic parameters based on the DR mechanism. Second, a two-stage distributionally robust optimization model is formulated within a market trading framework for energy scheduling of VPPs. This model aims to minimize operational costs while considering uncertainties in DR. Additionally, the ambiguity set for the uncertainty probability distributions is constructed using a data-driven method. Finally, the study presents an optimal DR disaggregation method that aims to maximize aggregator satisfaction and accounts for the impact of load rebound on the optimization results. Simulation results demonstrate that the proposed method significantly reduces the deviation in demand response by 40% and enhances the profit of the VPP by 20.7%.

Virtual power plants (VPPs) offer system flexibility by aggregating various distributed energy resources (DERs) and simultaneously create profit opportunities for these DERs. A fair and scientific operational model serves as a crucial guarantee for promoting the economically efficient operation of VPPs. Given that DERs are typically managed by various agents, this study introduces a multi‑leader single-follower Nash-Stackelberg game model. This model is designed to facilitate the optimal aggregation of DER agents in a competitive distribution-level market involving multiple players. In the optimization modeling, the DER agents function as leaders, formulating their bidding strategies to maximize profits. Conversely, the VPP acts as a follower, responsible for performing market clearing. The distributed locational marginal price derived from this process serves as the settlement basis for DERs. Furthermore, a level-k reasoning approach is used to simulate the strategic bidding behavior of agents. Case studies demonstrate the effectiveness of the proposed aggregation model in simulating the competitive dynamics among agents. This model offers an expanded set of Nash equilibrium solutions for VPPs, enabling the selection of suitable aggregation schemes tailored to practical requirements. Additionally, the proposed bounded rationality model mitigats the strategic bidding behavior of agents, which consequently leads to a reduction in the system cost.

The virtual power plant (VPP) provides an effective way for the coordinated and optimized operation of distributed energy resources (DERs). To solve the aggregation problem of a VPP containing scattered layouts and heterogeneous performance DERs, this study proposes a dynamic aggregation strategy to improve the flexible regulation ability of the VPP. A VPP aggregation model considering network constraints and temporal coupling constraints of DERs is constructed, while some VPP performance parameters are proposed to characterize and quantify the regulation ability. An uncertainty set considering time-series coupling properties of variables is constructed, and an aggregation method under uncertainty scenarios is proposed based on a two-stage robust optimization model. In addition, a dynamic aggregation strategy is proposed for the application of VPPs in electricity markets. Finally, case studies demonstrates that the proposed method provides a more extensive feasible region compared to other methods, and the deviation power remains within a reasonable range. The dynamic aggregation strategy facilitates the synergistic and correlative operation of DERs, exploits the regulation ability of the VPP in frequency regulation and spinning reserve, and improves the feasibility of practical applications. Simultaneously, the income of the VPP increased by 29.5%.

At present, the classical multi terminal flexible DC power coordination control method mainly solves the problem of power transmission and distribution between multiple AC power grids through DC interconnection. It is necessary to propose a multi terminal flexible DC power coordination control method at the distribution side, so as to realize the unlimited expansion connection of the console port at the AC side, and the plug and play of the DC side port equipment, so as to improve the adaptability of the hybrid AC/DC distribution network to the grid connection of large-scale distributed generators and the wide access of DC loads. Therefore, this paper proposes an adaptive power coordination control strategy suitable for the flexible interconnection of low-voltage distribution stations. Considering the rated capacity and AC load of transformer in the station area, the transmission power of converter in the station area is adjusted to realize the balance of load rate in each station area and maintain the stability of DC side voltage and power balance. Finally, the flexible interconnection simulation model of three stations is built based on Matlab/Simulink. The simulation results verify the feasibility and effectiveness of the proposed control strategy.

Abstract To reasonably evaluate the support capability of grid-forming energy storage in power systems characterized by “double high” characteristics, it is essential to investigate the factors influencing the virtual inertia of grid-forming energy storage. This paper delves into the relationship between two control strategies of grid-forming energy storage and virtual inertia from an energy perspective. Initially, the operational principle and technological advantages of grid-forming energy storage are analyzed. Subsequently, based on the principles of two grid-forming control strategies, mathematical models for both types of grid-forming energy storage are established, elucidating the physical significance of virtual inertia in grid-forming inverters and revealing the adjustability of virtual inertia. Finally, a microgrid simulation system is constructed in PSCAD to validate the theoretical analysis.