In this paper, a hierarchical bipartite graph (HBG) matching method is proposed to promote transactive vehicle-tovehicle (V2V) power exchanges in a distribution power system. A distribution system which includes EVs is divided into a series of subsystems according to EV locations, where V2V power exchanges are optimized for scheduling charging/discharging periods among transacting EVs in each subsystem. Each subsystem’s power surplus or deficit will then be regulated by power exchanges among neighboring subsystems and the utility grid. The one-to-multiple and multiple-to-one modes are adopted in the proposed HBG algorithm for the promotion of transactive V2V power exchanges. Also, individual EV’s electricity trading price can track that of the subsystem by adjusting respective power exchange objectives adaptively. Several case studies are presented to validate the effectiveness of the proposed method, where transactive V2V power exchanges are improved and the energy efficiency in a distribution power system is enhanced.

This article proposes a novel N–1 interval optimal power flow (NIOPF) scheme that considers N–1 contingencies and interval uncertainties in renewable generation. Based on the scenario-equivalent decoupling method (SEDM), the NIOPF addresses the problems associated with existing methods, including constraint violations and low solution efficiency. First, the NIOPF model is formulated by integrating security constraints involving interval variables under both N–0 normal and N–1 contingency scenarios, along with scenario transition constraints. Second, a credible domain reduction method (CDRM) based on the Minkowski difference is proposed to handle interval constraints, converting them into tractable deterministic constraints that guarantee the system operates within safe limits across all uncertain scenarios. Then, the SEDM represents numerous N–1 scenarios equivalently as two positive and negative deviation scenarios, effectively reducing the dimensionality of N–1 scenarios. Next, a two-stage optimization model that integrates the N–0 normal operating scenario with the two equivalent deviation scenarios is constructed and solved iteratively to improve computational efficiency. Finally, case studies on the modified IEEE 30-bus system verify that the proposed SEDM ensures constraint satisfaction under uncertainties while improving computational efficiency. Further tests on the modified IEEE 118-bus and PEGASE 1354-bus systems confirm its engineering applicability.

To achieve sustainable environmental development, numerous countries and governments have been vigorously promoting the proliferation of electric vehicles (EVs) through a series of policy measures and economic subsidies. With the increasing number of EVs, multiple EV charging modes are being researched to satisfy owners’ requirements. In this paper, an EV charging method considering multiple power exchange modes’ coordination is proposed for meeting owners’ requirements with cost-effectiveness. In the proposed method, the battery swapping (BS) station, building-to-vehicle (B2V) station, and grid-to-vehicle (G2V) station are considered. In G2V stations, EVs charge from the power grid. In B2V stations, distributed renewable energy generation is considered as the energy provider. This study contemplates the use of photovoltaic power systems in B2V stations for the charging of EVs. In BS stations, the power exchange among batteries and the power grid is considered. The battery energy storage is utilized for reducing the battery degradation cost (BDC) and power cost. EVs are dispatched to the corresponding charging stations according to the electric price, BDC, and so on. In the dispatching process, the particle swarm optimization (PSO) algorithm and Hungarian algorithm are applied. Several case studies are presented to validate the effectiveness of the proposed method and the power matching between EVs and charging modes is discussed.

Accurate online state-of-health (SOH) estimation can improve the operational efficiency of lithium-ion batteries (LIBs) and ensure the safety of energy storage systems. However, the complex electrochemical properties of LIBs make accurate SOH estimation challenging. To overcome this challenge, we propose a secondary structural ensemble learning (SSEL) cluster. The proposed SSEL cluster includes multiple SSEL frameworks established separately within different SOH data intervals, allowing the identification of stable feature-SOH relationships. The adaptability and basic accuracy of each SSEL framework are guaranteed by various base learners and the corresponding stacking model and bagging model fusion. Each framework remains unique and specialized owing to the adoption of back propagation neural networks, which adjust learner weights based on the feature-SOH relationship at each interval. The effectiveness of the SSEL cluster was verified using the Oxford Battery Degradation Dataset 1. Comparisons showed that the proposed estimation method performs better than traditional machine learning methods.