With the increase in global geopolitical risks and the frequent occurrence of extreme climate in recent years, the electricity prices in Europe have shown large fluctuations. Electricity price has an important impact on the cost of production and living, while electricity demand will also affect other energy markets. A double-layer system based on the spillover effects from a systematic perspective is constructed in this paper to explore the connectedness between different electricity markets and other related energy markets in Europe, considering the impact of climate risks. The results show that there are certain spillover effects among electricity markets in different countries, with a temporary upward trend in the beginning of the Russia–Ukraine conflict, and the electricity markets in the UK and Germany have a more important role in Europe. There are two-way spillover effects between the electricity market and fossil fuel markets, carbon market and carbon emission. Since 2022, the electricity market is affected by gas prices, while it has a certain impact on carbon emissions. The heating degree day (HDD) has significant spillover effects on the electricity market and other energy markets, while the spillover effects of the cooling degree day (CDD) are relatively small.

The expansion of the scale of the Power Internet of Things stimulated by the development of the Energy Internet makes the growth in demand for the effective authentication and access control technologies in the cross-domain data exchange. Based on the cross-chain technology of the blockchain and the cuckoo filter, this paper proposes a cross-domain authentication scheme for Power Internet of Things. Firstly, a cross-chain authentication architecture is established. Combined with the existing authentication technologies used in intra-domain authentication, a cross-domain authentication process based on the cross-chain technology is proposed to realize the automatic transmission of the authentication credentials from application domain to authentication domain. The cuckoo filter is deployed on the blockchain as smart contracts, and the user certificate fingerprint is inserted into the filter to realize user registration, query, and revocation, which reduces the cost of the user certificate management. Experimental results show the effectiveness and feasibility of our scheme. Based on the proposed authentication scheme, a cross-domain access control scheme based on roles and object classes is presented, by treating the object classes as controlled objects and then applying the role-based access control to the object classes, on the condition that the heterogeneous domains in the Energy Internet have the same kinds of resources.

Quantitative assessment of data risks is a focal point in the field of data security. In this paper, we establish a multi-level power data security risk indicator system based on Data Security Capability Maturity Mode (DSMM) to delineate risk indicators covering the entire data lifecycle. We propose a method for representing expert evaluation information based on probabilistic hesitant fuzzy sets to mitigate issues arising from individual expert biases and the number of experts. By transforming the quantification of power data risks into a multi-attribute decision-making problem, we present a method for power data risk assessment based on probabilistic hesitant fuzzy multi-attribute group decision-making. Experimental results demonstrate that the proposed approach can effectively calculate power data risk levels, providing theoretical support for power data security protection.

With the fast development of electric power enterprise information, special structured storage and management system is becoming more and more important. As a uniform interface for multitude of data sources, the efficiency to extract unstructured and semi-structured data existing in webpage is a key issue for Web data integration. In this paper, we grope for the problem of building domain adaptation wrappers for web data extraction. We design the domain adaptation extraction framework based on bootstrapping method. Meanwhile, we discuss the main technologies. We can get the extraction model for recruitment site and then random sampling pages at other power system sites for training the latest wrapper, a uniform data accessing infrastructure in power system domain can be built. In addition, the wrapper has high multipurpose, realizing the domain adaptation extraction. The result of experiment indicates our method can improve the accuracy of extraction in various fields.

Operating modern power grids requires coordinated solutions for optimal dispatch, state estimation, price prediction, and contingency analysis, yet these tasks are usually modeled separately. We present PhysBridge, a response-oriented pretraining framework in which historical windows provide context while solver-generated perturbation responses, active constraints, and local sensitivities define the pretraining targets. PhysBridge combines a temporal-graph encoder with a Primal-Dual Response Bridge (PDR-Bridge) that performs safeguarded local refinement and outputs transferable primal, projection-dual, sensitivity, and health tokens. Across IEEE 14-, 30-, 57-, 118-, and 300-bus systems, the benchmark contains 26.3 million solver-generated perturbation-response pairs; in the 300-bus transfer setting, pretraining uses only the 21.04 million nominal pairs from the 14-, 30-, 57-, and 118-bus source topologies. Across three runs on unseen IEEE 300-bus transfer with 10% labels, PhysBridge obtains a 2.21 ± 0.05 % OPF cost gap, 96.3 ± 0.2 % feasibility, 1.84 ± 0.05 $/MWh LMP MAE, and 91.6 ± 0.3 % contingency/fault event macro F1. Relative to a matched rollout-only model trained on the same source corpus without response targets or bridge refinement, these values correspond to a 50.8% lower OPF gap and a 7.4 percentage-point higher event F1. The bridge requires 12.4 ms per 300-bus forward pass and activates fallback on 4.6% of stressed windows. Comparisons with supervised-only OPF specialists that do not receive matched solver-generated pretraining are interpreted as task-relevant performance under the proposed pretraining regime, not as architecture-only superiority.