The use of bi-directional converters (BDCs) is crucial for enhancing power exchange in hybrid AC/DC networked microgrids (NMGs). However, the dynamic nature of their conversion efficiency and the non-convex conversion direction expression of BDC models result in a highly non-convex programming problem, which leads to significant computational challenges. This paper proposes a least squares approximation approach to simplify the complex trigonometric function that characterizes the dynamic changes in BDC conversion efficiency with power. The simplified expression transforms the original non-convex relationship into a computationally efficient convex form. Subsequently, we systematically explore and validate the sufficient conditions to achieve non-simultaneous rectification and inversion behaviors of BDCs in a convex form. These explored conditions are further customized and extended to various practical application scenarios. Case studies are conducted on a hybrid AC/DC NMG with 66 nodes. The results demonstrate that our proposed least squares approximation method transforms the computationally intractable model into one that is solvable. Additionally, the explored conditions contribute to a substantial reduction in solution time by more than two orders of magnitude. These results verify the superiority of our proposed method, and showcase its applicability in practical scenarios.

The benefit of risk-based (RB) security-constrained optimal power flow (SCOPF) model lies in its ability to improve the economic performance of a power system while enhancing the system's overall security level. However, the RB-SCOPF model is difficult to solve due to the following two characteristics: 1) the overload severity of a circuit changes with the loading condition on it, thus is hard to express with a deterministic function, and 2) the risk index is a function of the state variables in both normal and contingency states, which greatly increases the scale of optimization. To handle the first issue, a new expression of severity function is proposed so that it is possible to decompose the model into a SCOPF subproblem and a risk subproblem. To deal with the second issue, a nested Benders decomposition with multi-layer linear programming method is proposed. Illustrations use the ISO New England bulk system is provided to demonstrate the feasibility of the proposed method. Analysis is presented to demonstrate the merits of the RB-SCOPF over the traditional SCOPF model.

Electric Vehicles (EV) significantly contribute to reducing carbon emissions and promoting sustainable transportation. Among EV technologies, hybrid energy storage systems (HESS), which combine fuel cells, power batteries, and supercapacitors, have been widely adopted to enhance energy density, power density, and system efficiency. Bidirectional DC-DC converters are pivotal in HESS, enabling efficient energy management, voltage matching, and bidirectional energy flow between storage devices and vehicle systems. This paper provides a comprehensive review of bidirectional DC-DC converter topologies for EV applications, which focuses on both non-isolated and isolated designs. Non-isolated topologies, such as Buck-Boost, Ćuk, and interleaved converters, are featured for their simplicity, efficiency, and compactness. Isolated topologies, such as dual active bridge (DAB) and push-pull converters, are featured for their high voltage gain and electrical isolation. An evaluation framework is proposed, incorporating key performance metrics such as voltage stress, current stress, power density, and switching frequency. The results highlight the strengths and limitations of various converter topologies, offering insights into their optimization for EV applications. Future research directions include integrating wide-bandgap devices, advanced control strategies, and novel topologies to address challenges such as wide voltage gain, high efficiency, and compact design. This work underscores the critical role of bidirectional DC-DC converters in advancing energy-efficient and sustainable EV technologies.

Short-term load forecasting (STLF) plays a crucial role in the efficient and economical management of power systems. While artificial neural networks have achieved significant success in STLF, they suffer from the limitation of providing a black box representation, making it challenging to obtain an analytical expression between features and loads. This limitation hampers subsequent quantitative analysis, which is crucial for artificial intelligence based decision-making processes. To address this issue, this paper proposes a novel STLF approach through the utilization of Kolmogorov-Arnold Networks (KANs). By leveraging KANs, the interpretability of model parameters can be enhanced. As a result, detailed analytical expressions of the model can be derived. To validate the proposed approach, we conducted experiments by comparing the forecasting performances among KANs, multi-layer perceptrons and XGBoost on a publicly available dataset from Switzerland. Numerical results demonstrate the effectiveness of the proposed KAN-based STLF method in accurately forecasting short-term loads. Additionally, the KAN-based approach provides the advantage of yielding the analytical expression for STLF, enabling further insights and analysis.

The demand for computing power has increased at a rate never seen before due to the quick development of artificial intelligence (AI) technologies and applications. Consequently, AI data centers, referring to computing facilities specifically designed for large-scale artificial intelligence workloads, have become one of the fastest-growing electricity consumers globally. Therefore, it is essential to understand the load characteristics of AI data centers and their impact on the grid. This paper provides a comprehensive review of the evolving energy landscape of AI data centers. Specifically, this paper (i) presents the energy consumption structure in AI data centers and analyzes the key workload features and patterns in four stages, emphasizing how high power density, temporal variability, and cooling requirements shape total energy use, (ii) examines the impacts of AI data centers for power systems, including impacts on grid stability, reliability and power quality, electricity markets and pricing, economic dispatch and reserve scheduling, and infrastructure planning and coordination, (iii) presents key technological, operational and sustainability challenges for AI data centers, including renewable energy integration, waste heat utilization, carbon-neutral operation, and water–energy nexus constraints, (iv) evaluates emerging solutions and opportunities, spanning grid-side measures, data-center-side strategies, and user-side demand-flexibility mechanisms, (v) identifies future research priorities and policy directions to enable the sustainable co-evolution of AI infrastructure and electric power systems. The review aims to support utilities, system operators, and researchers in maintaining reliable, resilient, and sustainable grid operation in the context of the rapid development of AI data centers.

In multi-agent active distribution networks, the information exchanges in the ADMM algorithm for the decentralized distribution-level optimal power flow (D-OPF) may expose sensitive load flows of tie-lines across adjacent agents. This may be overheard by adversarial agents for business competition. To preserve this privacy, this paper proposes a differentially private consensus ADMM (DP-C-ADMM) algorithm, which can offer a mixture solution of both realistically optimal generator outputs and obfuscated-but-feasible load flows of tie-lines. And $\epsilon -$ differential privacy holds for load flows of tie-lines across agents over iterations. Case study justifies the theoretical properties of this algorithm up to specified privacy parameters.