Low-Earth-Orbit (LEO) satellite constellations are becoming integral to 6G infrastructure, but increasing in-orbit computation accelerates battery degradation and raises sustainability concerns. Meanwhile, renewable-heavy regions worldwide experience persistent energy curtailment due to transmission bottlenecks, leaving substantial clean energy stranded near generation sites. We identify a satellite-grid co-design opportunity: adaptively offloading task-critical data from satellite to data centers co-located with renewable power plants. However, realizing this vision requires jointly considering intermittent and capacity-limited communication windows, as well as time-varying electricity budgets. In this paper, we propose SQSO, a Sustainable and QoS-aware Satellite Offloading framework that models per-interval task offloading as a constrained optimization over dynamic topology and electricity prices. Under this framework, we design $\text{AO}^2$, an adaptive offloading orchestration algorithm to solve the formulated optimization problem. Using Starlink-scale simulations and real-world electricity price traces, $\text{AO}^2$ reduces energy consumption by up to 76.03% and battery life consumption by up to 76.85% compared to state-of-the-art schemes, while also lowering task delay. This work highlights that sustainable scaling of LEO constellations requires co-design of space networking and renewable energy infrastructure, while our solution promotes renewable-aware task offloading and cross-domain collaboration for space-energy integration in the 6G era.

With the increasing scale of distributed energy resource (DER) allocation, local energy system (LES) has emerged as a new form of energy supply. However, the market operation mechanism of LES involving multi-level interaction still need to be revamped. Herein, firstly, an IoT-based hierarchical architecture is proposed, and the functional infrastructures of each level are presented coupling with the multilevel interaction mechanism. Then, the modeling approaches of local consumption, power sharing, and aggregation operation are depicted, especially a discriminative method for the dynamic construction of virtual power plant (VPP) is proposed. On this basis, to address the problem of computational burden and poor interpretability of centralized optimization methods involving personalized demand of massive end-users, a novel trade matching scheme is proposed. Besides that, a typical scenario generation method with Latin hypercube sampling (LHS) and k-means clustering is introduced to measure the risk of the spot market, and quantify the impact of price uncertainty on the aggregation operation decision and end-user strategic behavior. Finally, simulation validation is carried out covering diverse end-users, and the results show that the proposed method can better guide LES for local power sharing or aggregation operation, effectively reduce the operation cost, and resist the decision-making risk of spot market.

The fifth-generation (5G) communication technology development makes it possible for distributed resources to participate in power system frequency regulation. This paper models the whole-chain communication time delay of distributed frequency regulation resources participating in automatic generation control (AGC) via a 5G network. The whole-chain communication time delay consists of time delay in the radio access network, backhaul network, and transmission and core networks. We integrate the time delay model into the power system frequency regulation model to reveal its impact on frequency regulation performance. In addition, we analyze the feasibility of the distributed variable renewable energy (VRE) participating in AGC and calculate the capabilities needed for different VRE penetrations. Finally, we provide a case study on the HRP-38 system. The results show that the communication time delay will substantially impact the frequency regulation performance when the VRE penetration is above 40%. In addition, distributed VRE may be a promising AGC resource. Distributed VRE that accounts for approximately 5% of the total installed VRE capacity is needed to participate in AGC via a 5G network, which could maintain the frequency performance of the power system with above 65% VRE penetrated at a normal level.

Constructing virtual power plants (VPPs) based on cellular base stations (CBSs) can effectively enable the CBSs to participate in power system operations. Then, like VPPs constructed by other distributed resources, it is essential for CBSbased VPPs to conduct self-scheduling at the day-ahead stage. However, the operational flexibility of CBS-based VPPs has yet to be systematically investigated, so the dispatch potential cannot be fully explored. This paper proposes a self-scheduling framework based on the device-level modeling of CBS operational flexibility. Both the DC part and the AC part of CBSs are systematically studied. In the scheduling framework, the backup storage units at the DC part are arranged to utilize the spare capacity to conduct temporal arbitrage while guaranteeing the power supply reliability requirement; the cooling infrastructures at the AC part are dispatched with optimized schemes with temperature prerequisites. The synergy effect improves the scheduling of CBSbased VPPs and reduces operation costs. Case studies validate the proposed framework.

The concept of capacity credit (CC) has been proposed to evaluate the contribution of generators for system adequacy since the 1960s. CC determines the extent to which the added unit affects the generation adequacy of the system. The equivalent reliability builds the bridge between the evaluated unit and the virtual firm capacity. This article firstly summarizes the reliability equivalent criteria for CC of generators and lists various CC definitions. Second, the methodologies for evaluating CC are categorized into three parts in terms of the modeling of the reliability function and the solving strategies. Then, a theoretic expression from several analytical methods is proposed to better interpret the characteristics of the CC function. Some discussions of CC are conducted afterwards. The applications of CC on power system planning and capacity market are presented. Finally, we propose four aspects of future work well worth further investigations.

Since its inception, the smart grid concept has revolutionized power systems worldwide. Concurrently, the energy industry has witnessed significant changes, such as the clean energy transition, digitalization, and the artificial intelligence (AI) revolution. These changes have profoundly impacted power systems technology and energy consumers. As such, power and energy systems have evolved into a multidisciplinary research area encompassing power engineering, information and communication technologies (ICTs), computer and data science, control and optimization theory, and social sciences. In this context, power systems worldwide have moved beyond the smart grid, transforming in terms of technology, physical tructure, and business model. Therefore, this proceeding aims to summarize current developments, recognize new trends, and collect experiences worldwide, focusing on distribution system digitalization and marketization, renewable energy penetration, and electronic device integration.

This article examines the emerging challenges in modeling and analyzing the electric power system due to the widespread growth of variable renewable energy (VRE), particularly in the form of distributed energy resources (DERs), which are displacing traditional large power plants. Many of these resources are connected to the system through power electronic interfaces, also known as inverter-based resources (IBRs), which are reshaping the system dynamics and lowering the grid strength and inertia. Understanding the dynamic behavior of the power system should be critical to addressing the potential stability concerns, refining the grid requirements, and developing effective and reliable measures among many alternatives. However, conventional methodologies for resource integration and network expansion studies, as well as application-specific electromagnetic transient (EMT) studies, need to be improved. This article thus presents recent academic and industrial efforts to advance the existing approaches, especially by incorporating the uncertainty in model parameters of DERs, variability of VRE, and EMT dynamics of IBRs for the grid planning and operations studies such as the impact of DERs on load modeling and system-wide dynamic performance. In addition, this article showcases recent developments to expand the study boundaries by synergizing the strengths of the industry-accepted approaches along with real system studies for Korea’s electric power systems in particular.

Multienergy systems (MESs) are able to unlock the energy system flexibility using the coupling across multiple energy sectors. Such coupling contributes to improving the overall energy efficiency and promoting the accommodation of renewable energy. Among a wide range of literature, this article provides a perspective of network analytics on how to model, optimize, and conduct low-carbon analysis on MESs. The energy sector coupling involves different levels, for example, from a single building to nationwide. In this article, we categorize multienergy networks into two levels, that is, the district level that covers a relatively small area such as a campus or a community, where the energy conversion and utilization is the major focus, and the multiregion level that covers a relatively large area such as a big city, a province, or the whole country, where the energy transmission is the major concern. We first review the state-of-the-art multienergy networks standardized modeling approaches including: 1) energy hub (EH) model for district level energy networks; 2) network models, including power, heat, and gas steady-state and dynamic network models, for multiregion level energy networks; and 3) load models, including electricity, heat, and gas load forecasting models. Second, we explore the planning and operation methods for both district level and multiregion level energy networks. Third, we introduce a special technique named the carbon emission flow (CEF) model that is able to calculate the equivalent CO2 emission associated with the energy flows in multienergy networks. We also demonstrate how the technique can help multienergy networks planning and operation toward a low carbon society. Finally, we envision several further key research topics in the field of multienergy networks.

The two-way flow of information and energy is an important feature of the Energy Internet. Data analytics is a powerful tool in the information flow that aims to solve practical problems using data mining techniques. As the problem of electricity thefts via tampering with smart meters continues to increase, the abnormal behaviors of thefts become more diversified and more difficult to detect. Thus, a data analytics method for detecting various types of electricity thefts is required. However, the existing methods either require a labeled dataset or additional system information, which is difficult to obtain in reality or have poor detection accuracy. In this paper, we combine two novel data mining techniques to solve the problem. One technique is the maximum information coefficient (MIC), which can find the correlations between the nontechnical loss and a certain electricity behavior of the consumer. MIC can be used to precisely detect thefts that appear normal in shapes. The other technique is the clustering technique by fast search and find of density peaks (CFSFDP). CFSFDP finds the abnormal users among thousands of load profiles, making it quite suitable for detecting electricity thefts with arbitrary shapes. Next, a framework for combining the advantages of the two techniques is proposed. Numerical experiments on the Irish smart meter dataset are conducted to show the good performance of the combined method.