Search Results (145)

With the advancement of the “dual carbon” goal, the power system is accelerating its transition towards a clean and low-carbon structure, with a continuous increase in the penetration rate of renewable energy generation (REG). However, the volatility and uncertainty of REG output pose severe challenges to power grid operation. Traditional distribution networks face immense pressure in terms of scheduling flexibility and power supply reliability. Active distribution networks (ADNs), by integrating energy storage systems (ESSs), soft open points (SOPs), and demand response (DR), have become key to enhancing the system’s adaptability to high-penetration renewable energy. This work proposes a DR-aware scheduling strategy for ESS-integrated flexible distribution networks, constructing a bi-level optimization model: the upper-level introduces a price-based DR mechanism, comprehensively considering net load fluctuation, user satisfaction with electricity purchase cost, and power consumption comfort; the lower-level coordinates SOP and ESS scheduling to achieve the dual goals of grid stability and economic efficiency. The non-dominated sorting genetic algorithm III (NSGA-III) is adopted to solve the model, and case verification is conducted on the standard 33-node system. The results show that the proposed method not only improves the economic efficiency of grid operation but also effectively reduces net load fluctuation (peak–valley difference decreases from 2.020 MW to 1.377 MW, a reduction of 31.8%) and enhances voltage stability (voltage deviation drops from 0.254 p.u. to 0.082 p.u., a reduction of 67.7%). This demonstrates the effectiveness of the scheduling strategy in scenarios with renewable energy integration, providing a theoretical basis for the optimal operation of ADNs. Full article

Under China’s “dual-carbon” strategic goals and the advancement of smart city development, the rapid adoption of electric vehicles (EVs) has deepened the spatiotemporal coupling between transportation networks and distribution grids, posing new challenges for integrated energy systems. To address this, we propose a collaborative optimization framework for power–transportation coupled networks that integrates multi-modal data with physical priors. The framework constructs a joint feature space from traffic flow, pedestrian density, charging behavior, and grid operating states, and employs hypergraph modeling—guided by power flow balance and traffic flow conservation principles—to capture high-order cross-domain coupling. For prediction, spatiotemporal graph convolution combined with physics-informed attention significantly improves the accuracy of EV charging load forecasting. For optimization, a hierarchical multi-agent strategy integrating federated learning and the Alternating Direction Method of Multipliers (ADMM) enables privacy-preserving, distributed charging load scheduling. Case studies conducted on a 69-node distribution network using real traffic and charging data demonstrate that the proposed method reduces the grid’s peak–valley difference by 20.16%, reduces system operating costs by approximately 25%, and outperforms mainstream baseline models in prediction accuracy, algorithm convergence speed, and long-term operational stability. This work provides a practical and scalable technical pathway for the deep integration of energy and transportation systems in future smart cities. Full article

Metal materials are widely used in power grid infrastructure, but they are prone to metal corrosion due to long-term exposure to various environmental conditions, resulting in significant losses. The existing superhydrophobic coatings have good anti-corrosion performance, but poor wear resistance. Therefore, it is extremely important to improve the wear resistance of superhydrophobic coatings. In this study, a kind of fluorine-modified SiO₂ particle was prepared with pentafluorooctyltrimethoxysilane (FAS-13) as the low surface energy modifier, following the fabrication of a superhydrophobic coating on metal substrate via a PDMS-doped spray deposition method to reinforcement wear resistance property. XPS, FT-IR and Raman spectra confirmed the successful introduction of FAS-13 on SiO₂ particles, as evidenced by the characteristic fluorine-related peaks. TGA revealed that the fluorine modified SiO₂ (F-SiO₂) particles exhibited excellent thermal stability, with an initial decomposition temperature of 354 °C. From the perspective of surface morphology, the relevant data indicated a peak-to-valley height difference of only 88.7 nm, with Rq of 11.9 nm and Ra of 8.86 nm. And it also exhibited outstanding superhydrophobic property with contact angle (CA) of 164.44°/159.48°, demonstrating remarkable self-cleaning performance. And it still maintained CA of over 150° even after cyclic abrasion of 3000 cm with 800 grit sandpaper under a 100 g load, showing exceptional wear resistance. In addition, it was revealed that the coated electrode retained a high impedance value of 8.53 × 10⁸ Ω·cm² at 0.1 Hz after 480 h of immersion in 5 wt% NaCl solution, with the CPE exponent remaining close to unity (from 1.00 to 0.97), highlighting its superior anti-corrosion performance and broad application prospects for metal corrosion prevention. Full article

In response to the decline in the inertia level of the power system caused by the large-scale integration of new energy, this paper proposes a grid-side pumped storage configuration strategy considering inertia constraints. The general pumped storage configuration ignores the duration of pumped storage and selects only single-duration units for capacity and power configuration. A single unit cannot balance rapid frequency response and long-term energy transfer, forcing thermal power to operate at high costs continuously to provide inertia support, while also causing a sharp increase in wind and solar power curtailment. This paper breaks through the limitations of the traditional single-duration pumped storage configuration and proposes a configuration-operation collaborative optimization strategy that combines inertia constraints and pumped storage duration selection. Firstly, starting from the system’s inertia requirements, the minimum inertia required by the system is obtained, respectively, based on the constraints of the system’s frequency change rate and the lowest point of the frequency. Furthermore, the minimum inertia demand constraint of the power system is constructed, and a capacity configuration strategy for grid-side pumped storage is proposed with the goal of minimizing the total operating cost of the power system throughout its entire cycle, taking into account the penalty term of the peak-valley difference index of the load curve and the penalty of the inertia guarantee value of medium and long-term units, while considering the inertia constraint. Finally, the effectiveness and superiority of the proposed method were verified through simulation analysis. Full article

To address the challenge of the synergistic optimization of carbon reduction and economic operation in the integrated energy systems (IES) of industrial parks, this paper proposes an optimization scheduling model that incorporates carbon trading and supply–demand response (SDR) coordination mechanisms. This model is based on an IES coupling power-to-gas (P2G) and carbon capture and storage (CCS) technologies. First, the K-means clustering algorithm identifies three typical daily scenarios—transitional season, summer, and winter—from annual operation data. Then, we construct a synergistic optimization model that integrates a carbon trading mechanism, tiered carbon quota allocation, and SDR coordination. The model is solved via mixed-integer linear programming (MILP) to minimize total system operating costs. Systematic comparative analysis across six scenarios quantifies the incremental benefits: P2G–CCS coupling achieves a 15.2% cost reduction and 49.3% emission reduction during transitional seasons; supply–demand response contributes 3.5% cost and 5.6% emission reductions; technology synergies yield an additional 21.6 percentage points of emission reduction beyond individual contributions. The integrated system achieves 100% renewable energy utilization and optimizes peak-to-valley differences across electricity, heating, and cooling loads. Carbon price sensitivity analysis reveals three response stages—low sensitivity, rapid reduction, and saturation—with the saturation point at 200 CNY/t (28.6 USD/t), providing quantitative guidance for tiered carbon pricing design. This research provides theoretical support and practical guidance for achieving low-carbon economic operations in industrial parks. Full article

Given the urgent demand for flexible peak-shaving in power systems and underutilized distributed photovoltaic (PV) regulation potential, this paper proposes a distributed PV peak-shaving control strategy based on the temporal coupling self-organizing map (TC-SOM) neural network and a bi-level model. First, the SOM algorithm is improved for efficient feature extraction and accurate clustering of distributed PV data, realizing rational PV cluster division. On this basis, a bi-level peak-shaving model for distributed PV is constructed, forming a hierarchical peak-shaving mechanism from node demand to PV clusters to individual PVs to ensure inter- and intra-cluster coordination. This hierarchical structure embodies symmetric response logic, enabling balanced interaction between upper-layer node demand guidance and lower-layer PV execution, as well as inter-cluster coordination. Simulations on the IEEE-33 node system confirm its effectiveness: it significantly smooths the load curve, reduces peak–valley differences, and optimizes the flexible utilization of distributed PV through coordinated control, aggregation management, and curtailment regulation, providing strong support for precise PV cluster regulation and stable operation of high-proportion PV-integrated power grids. Full article

Under the backdrop of the “dual carbon” strategy, the rapid increase in renewable energy penetration has exacerbated challenges such as widening peak–valley load gaps and insufficient grid regulation capacity, highlighting the urgent need to establish a market-oriented collaborative dispatching mechanism. This paper proposes a peak-shaving and valley-filling dispatching approach based on a multi-agent system (MAS) to enhance both the regulatory capability and economic efficiency of power grids. A multi-agent collaborative architecture is established on the generation side, where behavioral modeling and interaction simulations of generation, load, and energy storage agents are conducted using the NetLogo platform to emulate dynamic responses under market conditions. On the grid side, dynamic electricity pricing and energy storage control strategies are implemented. An integrated time-of-use electricity pricing mechanism is designed that incorporates environmental pollution factors, supply–demand state factors, and price-smoothing factors to dynamically adjust tariffs. A price-responsive load demand model and a dynamic threshold-based energy storage control strategy are developed to facilitate flexible regulation. On the load side, an optimized dispatch model is formulated with dual objectives of minimizing system operating costs and reducing the standard deviation of the net load profile. The Beetle Antennae Search (BAS) algorithm is employed to solve the model, striking a balance between economic efficiency and stability. Case study results demonstrate that, compared with traditional dispatch methods, the coordinated optimization of the BAS algorithm and the dynamic pricing mechanism proposed in this paper achieves a dual improvement in solution efficiency and economy. This ultimately reduces the system’s peak-to-valley difference by 10.92% and operating costs by 66.2%, proving its effectiveness and superiority in power grids with high renewable energy penetration. Full article

Pumped storage power stations provide essential benefits to power grids by cutting peak loads, filling valleys, and boosting renewable energy integration rates. They serve as the foundation for developing a new power system based on renewable energy. Pump turbines are currently evolving to provide higher heads, larger capacities, and higher rotating speeds. The performance and dependability of its basic components have a direct impact on the safety and stability of unit operation. Based on this, this research looks into the modal characteristics and structural aspects of essential stationary components, such as the pump-turbine head cover. By comparing the mechanical performance of two different structural designs (Design A and Design B), Design B features an overall thickness 1.5 times that of Design A and incorporates an upper flange structure. Its design aims to enhance the component’s resistance to bending and deformation, optimize stress distribution while reducing peak stress values, and improve modal characteristics. This approach elevates the overall structural performance of the fixed components to accommodate the complex operating conditions of ultra-high-head pumped storage units. It was discovered that Design B had greater bending and deformation resistance than Design A, as well as better stress distribution and lower maximum stress values. This study further indicates that variations in structural design lead to significant differences in modal characteristics and overall structural performance. In particular, the thicknesses of the head cover’s main body and stiffening ribs are critical parameters that govern the modal behavior and structural properties of stationary components. These insights provide critical technical guidance for optimizing the design of stationary parts, such as the head cover, in pumped storage power plant units. Full article

To address the impact of source-load uncertainty on voltage security and power flow distribution, this study proposes an active distribution network (ADN) active–reactive power coordinated optimal dispatch strategy that incorporates the grid balance degree (GBD). First, we analyzed GBD by defining it across three key dimensions: power flow, voltage, and network structure. We combined GBD with economic indicators to establish a pre-assessment indicators system to determine grid operational status. Second, to address source-load uncertainty, a dispatch model is established, incorporating a load factor penalty term into the optimal objective. Nonlinear terms in the model are linearized for efficient solution using the Gurobi solver. Finally, GBD is adjusted through measures such as load factor penalty threshold, voltage constraint upper/lower limits, and network restructuring. The optimal dispatch strategy is selected by comparing pre-assessment indicators of operational states across different dispatch schemes. Case studies demonstrate that compared to the baseline dispatch scheme, the proposed strategy achieves a 17.4% reduction in heavy load rates, a 5.7% decrease in power flow entropy, a 5.2% reduction in voltage peak-to-valley difference rates, and a 3% decrease in network losses. Although operating costs slightly increase by 0.56%, the operation reliability of the distribution network and power quality are further optimized. This fully demonstrates the gain value of GBD optimization and provides a reference for the optimization of ADN dispatch to achieve high operational reliability. Full article

Under China’s dual carbon goals, the surging summer demand for air conditioning (AC) has widen the power grid’s peak-to-valley difference, posing challenges to conventional supply-side solutions. To address this issue, effective demand-side coordination is essential. However, existing demand response schemes typically optimize single resources in isolation and overlook the dynamic evolution of user participation. This study proposes a price-guided coordination mechanism integrating vehicle-to-grid (V2G) and AC loads to establish a closed-loop value chain among the grid, aggregators, and users. A bi-level optimization framework is developed to balance the interests of these stakeholders. The model incorporates a V2G discharging willingness component that considers user psychology and battery degradation, as well as an AC response model reflecting thermal comfort. Simulation results demonstrate that the proposed mechanism effectively mitigates peak loads and narrows the peak-to-valley difference while enhancing off-peak electricity consumption. It accommodates the spatiotemporal and user-type heterogeneity of response behaviors, yielding notable economic gains for all participants. This research validates a comprehensive strategy for improving grid flexibility, protecting stakeholder interests, and optimizing user engagement, offering both theoretical insight and practical guidance for diversified resource integration. Full article

With the large-scale integration of electric vehicles (EVs) into the power grids, the disorderly charging of EVs may cause local overload and exacerbate peak-valley load difference. The current pricing strategies primarily focus on the supply side while neglecting user urgent charging demands and the impact of carbon trades; hence, an electricity–carbon joint pricing strategy is proposed in this paper. The strategy includes the selection of optimal charging modes based on the charging demand emergency, charging service satisfaction indicators, as well as the establishment of an electricity–carbon joint trading framework. A Stackelberg game model is further developed between the charging stations (CS) and EV users, which is solved under Karush–Kuhn–Tucker (KKT) conditions and duality theory. Simulation experiments have been performed, and the results demonstrate that this strategy can smooth the grid supply and reduce the CS operational costs via the increased carbon revenue while simultaneously satisfying EV users’ emergency power demands. Full article

Free-form grid structures offer both aesthetic appeal and structural efficiency in long-span roof design and application, yet the potential of morphological design to optimize thermal performance has been long overlooked. This study proposes a multi-objective synergistic optimization framework which can improve the thermal environment and mechanical performance simultaneously for the roof. Focusing on public buildings in hot–humid climates, the research investigates the impact of roof geometry on indoor temperature under extreme thermal loading conditions and long-term thermal loading conditions. Furthermore, the evolution of thermal performance during mechanical performance-driven surface optimization is systematically analyzed. Subsequently, a dynamic proportional adjustment factor is introduced to explore the performance of the optimized results under different performance weights, with thermal and mechanical performance serving as the optimization objectives. Results demonstrate that thermal performance-driven optimization generates saddle-shaped free-form surfaces with alternating peak–valley configurations to achieve self-shadowing effects, reducing indoor temperature by approximately 2 °C but significantly compromising structural stiffness. Conversely, strain energy minimization yields moderate indoor temperature reductions, revealing a positive correlation between strain energy decrease and thermal performance improvement. In the multi-objective optimization considering thermal and mechanical properties, when the strain energy ratio is 0.5–0.7 (optimization balance zone), the indoor temperature decreases, while the structural stiffness and stability bearing capacity increase. This study provides a morphological–structural–environmental synergistic design reference for low-carbon long-span building roofs. Full article

The rapidly growing demand of electric vehicle (EV) charging is one of the main challenges to modern electrical distribution systems. Accurate modelling of the EV charging load is crucial for charging load prediction and optimization. However, previous methods based on the charging behaviors of private EVs are hard to collect user’s private data. In this study, charging load data from 962 charging and battery swapping stations (CBSSs), classified into dedicated charging stations, public charging stations, and battery swapping stations, collected during 2021–2022, are analyzed to investigate seasonal variations in the charging coincidence factor. A data-driven probabilistic model of charging load, based on the Gaussian Mixture Model, is developed to address various scenarios, including new station construction, capacity expansions, and optimized charging strategies. This model is applicable to different types of CBSSs. A real-world 10 kV feeder system is employed as a case study to validate the model, and a delayed charging strategy is proposed. The results demonstrate that the proposed model accurately estimates charging load peaks after new construction and expansion in 2023, with an error rate under 3%. Furthermore, the delayed charging strategy achieved a 24.79% reduction in maximum load and a 31.96% decrease in the peak–valley difference. Its implementation in the real-world feeder significantly alleviated nighttime overloading in 2024. Full article

Addressing the problems of wind power’s anti-peak regulation characteristics, increasing system peak regulation difficulty, and wind power uncertainty causing frequency deviation leading to power imbalance, this paper considers the peak shaving and valley filling function and frequency regulation characteristics of energy storage, establishing a day-ahead and intraday coordinated two-stage optimization scheduling model for research. Stage 1 establishes a deterministic wind power prediction model based on time series Autoregressive Integrated Moving Average (ARIMA), adopts dynamic peak-valley identification method to divide energy storage operation periods, designs energy storage peak regulation working interval and reserves frequency regulation capacity, and establishes a day-ahead 24 h optimization model with minimum cost as the objective to determine the basic output of each power source and the charging and discharging plan of energy storage participating in peak regulation. Stage 2 still takes the minimum cost as the objective, based on the output of each power source determined in Stage 1, adopts Monte Carlo scenario generation and improved scenario reduction technology to model wind power uncertainty. On one hand, it considers how energy storage improves wind power system inertia support to ensure the initial rate of change of frequency meets requirements. On the other hand, considering energy storage reserve capacity responding to frequency deviation, it introduces dynamic power flow theory, where wind, thermal, load, and storage resources share unbalanced power proportionally based on their frequency characteristic coefficients, establishing an intraday real-time scheduling scheme that satisfies the initial rate of change of frequency and steady-state frequency deviation constraints. The study employs improved chaotic mapping and an adaptive weight Particle Swarm Optimization (PSO) algorithm to solve the two-stage optimization model and finally takes the improved IEEE 14-node system as an example to verify the proposed scheme through simulation. Results demonstrate that the proposed method improves the system net load peak-valley difference by 35.9%, controls frequency deviation within ±0.2 Hz range, and reduces generation cost by 7.2%. The proposed optimization scheduling model has high engineering application value. Full article

The large-scale integration of new power loads, such as electric vehicles and energy storage devices, has led to challenges including insufficient regulation capacity and low resource coordination efficiency in low-voltage distribution transformer areas. To address these issues, this paper proposes an optimal configuration model for flexible interconnected distribution transformer areas based on load aggregation. First, a flexible interconnection architecture is constructed using multi-port power electronic conversion devices, enabling mutual power support and voltage stabilization between adjacent areas. Second, a load aggregator scheduling model is established to quantitatively assess the dispatchable potential of electric vehicle charging loads. On this basis, a multi-objective optimization configuration model is formulated with the objectives of minimizing the comprehensive cost of the system and minimizing the average peak-valley difference of substation transformer loads. Case study results demonstrate that the proposed model significantly improves both economic efficiency and operational reliability. Compared to the traditional independent operation mode, the coordinated optimization scheme reduces the comprehensive system cost by 29.6% and narrows the average load peak-valley difference by 50.8%. These findings verify the synergistic effectiveness of flexible interconnection and load aggregation technologies in enhancing equipment utilization, reducing distribution losses, and improving power supply resilience. Full article