• Energy Research

With the fast expansion and increasing complexity of power system to meet the ever growing load demand, more information needs to be transmitted for real-time monitoring, control and management purpose. The timely and accurate transmission of a huge quantity of data poses great challenge to the communication network. This paper proposes a novel real-time compressive sensing based strategy for the load frequency control of a multi-area interconnected power system. According to the proposed strategy, the measured data in each control area is compressed before being transmitted through the communication network, and then recovered accurately by the discrete central controller. The proposed strategy can significantly reduce the size of transmitted data and improve the reliability of the communication network by introducing model predictive control method. Simulation results demonstrate the effectiveness and applicability of the proposed compressive sensing based control strategy.

Increasing number of drivers are switching to electric vehicles (EV) for higher efficiency, lower environmental impact and less maintenance cost. To promote the EV revolution, charging issues of EVs needs to be well addressed. Since the traffic flow of the transportation network and the operating conditions of the power systems are time-varying, it is important to implement real-time charging navigation for EV drivers. In this paper, a novel navigation approach is proposed to search the fast charging station with the lowest overall objective, which consists both of the time consumption and the financial cost. The traffic condition and distribution system loading level are reflected by the time consumption on each road section and locational marginal price (LMP) at each fast charging station, respectively. The LMP can act as a signal to divert the EV load and thus relieve the traffic and power line congestions. The proposed navigation approach is based on the multi-agent system framework utilizing the distributed min/max- consensus and the biased min consensus algorithms. Simulations demonstrate the effectiveness of the proposed navigation approach in different scales of systems.

Accurate power network estimation is critical for the efficient operation and management of distribution networks, especially in the context of integrating renewable energy sources and maintaining grid stability. This paper presents a novel two-stage approach for topology identification and line parameter estimation in distribution networks. The proposed method integrates variational mode decomposition into feature-enhancing techniques to extract meaningful features from noisy, volatile nodal data, effectively addressing the challenges posed by measurement disturbance. Additionally, a graph convolutional network based model is introduced to accurately capture both local and global dependencies within the network topology, enhancing the scalability and robustness of the estimation process. Experimental results demonstrate a significant improvement in accuracy, achieving a 62.3% reduction in identification errors compared to mainstream methods. The proposed framework effectively handles networks under different scales, offering a robust and scalable solution for distribution network analysis and real-time operational applications.

This paper proposes an interval-partitioned uncertainty (IPU) constrained robust dispatch model for an islanded AC/DC hybrid microgrid considering the uncertainties of the generation-load power and the operating states of the bidirectional converter. The IPU sets accurately describe the spatiotemporal distributions of the generation-load power and reduce the conservativeness of traditional single-interval uncertainty set-based robust optimization. To coordinate the scheduling of the uncontrollable renewable generators under uncertain conditions, the operating states of these generators were set as the first- or third-level optimal variables of the min-max-min robust model, which was linearized by the big-M method. The tri-level robust optimization with a mixed-integer recourse problem was quickly solved by a nested column-and-constraint generation algorithm. The validity and rationality of the proposed robust models, uncertainty sets, and solving method were confirmed in case studies.

The optimal dispatch of electric vehicles (EVs) aims to minimize the system operation cost while satisfying the requirements for peak shaving, congestion management and voltage regulation. However, the stochastic mobility of EVs makes dispatch difficult and requires modeling the spatial and temporal distribution of EV availability. In this paper, a trip-chain-based EV resource aggregation model considering EV flexibility similarity is developed. Then, a bilevel optimization model is formulated to enable participation of the EV aggregators (EAGGs) in the day-ahead dispatch while ensuring various system operation constraints. Finally, the proposed bilevel model is transformed into a single-level convex optimization problem that can be conveniently solved by off-the-shelf software. Simulation tests substantiate that the proposed approach is superior to the existing method in terms of higher computational efficiency and lower system operation cost.

To cope with the impact of predicted source-load deviations on the optimal dispatch of ac/dc hybrid microgrids at different time scales, this paper develops a multiple-time-scale (MTS) rolling optimal dispatching framework. A novel day-ahead distributionally robust optimization (DRO) model, based on the predicted means, deviations, and confidence probabilities of the source-load power, reduces the conservativeness of the adaptive robust optimization and provides robust day-ahead scheduling plans. The source-load deviations are effectively compensated by an MTS rolling optimization for the intraday dispatch, which adjusts the operating power of the controllable units. Relaxed penalty cost functions and rigid constraints on the state of charge are added to ensure cyclic regulation of the energy storage. The superiority and validity of the day-ahead DRO model and the intraday MTS rolling optimization model are verified in case studies.