The increasing penetration of electric vehicles (EVs) and hydrogen fueling stations (HFSs) brings up additional power demand with high flexibility, which shows great potential to enhance the performance of the distribution network. This paper proposes a reliability-oriented optimal operation model considering the coordinated flexibility of EVs and HFSs to reduce interruption loss during outages for the distribution network. In the proposed model, to incorporate the flexibility of HFSs, the multi-state Markov model considering multi-mode operation and inherent reliability is formulated for HFSs. To estimate the comprehensive demand of different types of vehicles, a novel behavior model is established to consider the respective characteristics of EVs and hydrogen-based fuel cell electric vehicles, and support vector regression (SVR) is deployed for traffic volume forecasting. The Monte Carlo simulation (MCS) technique is utilized for system state analysis during contingencies. Based on the simulation results, the system reliability, operation economy, and renewable energy accommodation capability are comprehensively evaluated. The proposed method is tested on the modified IEEE 34-bus system considering multi-time periods. The results indicate that, compared with existing methods that do not account for coordination with HFSs, the proposed coordinated method reduces the expected energy not supplied (EENS) and loss of load probability (LOLP) by 33.8% and 18.9%, respectively, and lowers the interruption costs by 25.9%.
The unit commitment (UC) problem is a typical mixed integer linear programming (MILP) problem in power systems. It often encounters significant computational challenges in power industries. With the development of power systems, the structure of the UC model and the scenarios that need to be considered become more complex and unpredictable, which increases the hardness of the UC solution. However, a systematic investigation of the key factors determining UC solution difficulty remains a hanging problem, limiting the development of targeted efficient algorithms. This paper presents a comprehensive analysis of three critical factors determining UC computational complexity: constraint stress, root relaxation quality, and convergence criteria. Through the indicator definition, this paper quantitatively evaluates proposed key factors and reveals the influence mechanisms of the proposed key factors on the hardness of UC solution through extensive case statistical analysis. The analysis of this paper provides effective references for efficient and customized UC solution algorithms.
Rapid renewable energy deployment driven by global decarbonization goals is accelerating worldwide. However, seasonal variability and evolving climate patterns pose great challenges to the long-term adequacy of power supply. This study focuses on the long term climate risks associated with inadequate renewable supply. First, the climate risk is defined and quantified as the seasonal inadequacy of renewable supply. Then, the risk formulation is incorporated into the optimal planning model as a three-stage frame work. Finally, a 2030 China-provincial case study is performed. The case results show that the climate-risk-aware planning scheme raises total system costs by approximately 0.5% but cuts expected load curtailment by nearly 40%. Additionally, residual climate risk is concentrated in a few coastal and southwestern provincial-level regions, primarily in winter, and in southern coastal regions in summer. The introduction of climate risk reshapes capacity portfolios, raises costs, and exposes seasonal reliability gaps in coastal areas.
Dielectric elastomers are a class of smart materials capable of converting electrical energy into mechanical motion, offering great potential for lightweight and efficient actuation systems. In this work, a dielectric elastomer-transmission-wing (DETW) structure was developed to transform the in-plane vibration of a dielectric elastomer actuator into flapping motion. The static performance of the dielectric elastomer actuator was first analyzed, followed by the fabrication of a flapping-wing prototype. An energy-based model was established for DETW using the Lagrangian approach, which incorporates air load and key dissipative factors such as material viscosity. The model enables a clear understanding of how mechanical loads influence the flapping behavior and dynamic response of the structure. Experiments further confirmed the feasibility of the DETW concept and the prediction accuracy of the model.
Despite growing interest in decentralized multilevel energy markets, challenges persist in security, privacy, and coordination among distributed energy resource operators (DEROs), distribution system operators (DSOs), and transmission system operators (TSOs). Current approaches often rely on centralized structures and lack a unified optimization and communication framework. Hence, this paper proposes a decentralized transactive energy architecture for interconnected transmission-distribution networks, structured as a multilevel electricity market (MLEM). DEROs engage in peer-to-peer (P2P) transactions at the distribution level, while DSOs and TSOs coordinate grid-level operations through a hierarchical optimization structure. A nested ADMM-based algorithm is introduced, where an inner loop computes P2P exchanges in closed form and an outer loop enables decentralized coordination across the hierarchy. A blockchain security layer with a PoS-inspired validator-selection mechanism ensures data integrity, and the communication process is evaluated using an NS-3 wireless network model. The framework is tested on a combined 9-bus transmission and two 15-bus distribution networks. The method reaches consensus in 37 outer and 238 inner iterations; the 1-hour horizon solves in 66.98 s, with only an 8.35% increase for 24 h. TSO-DSO schedules converge within 1% accuracy, and the blockchain maintains a stable 1-second block. The results confirm reliable convergence and consistent communication performance.
