This editorial introduces the Special Issue “Methodologies and Applications of Digital Twin for Renewable-Dominant Power Systems”, which highlights key research addressing the challenges associated with integrating renewable energy sources into power systems. Digital twins provide an advanced framework for modeling, simulation, and optimization of these complex systems, driven by the need to address the impact of power electronics and multi-source energies. The contributions featured in this issue focus on the application of DT technologies in renewable-dominant systems, offering solutions for enhancing system stability, optimization, and operational efficiency.

Mutual symbiosis of electroactive bacteria (EAB) and denitrifier may be the key for solving the refractory carbon and residual nitrogen in wastewater treatment plant effluent. However, its application is hampered by unclear co-metabolic model and uncertain electron transfer. Here, we achieved 3–5 times increase in refractory carbon degradation, 40 % improvement in denitrification, and 36.0 % decrease in N₂O emission by co-culturing P. aeruginosa strain GWP-1 and G. sulfurreducens. Such an enhancement is obtained by both refractory carbon co-metabolism and interspecies electron transfer (IET) between GWP-1 and G. sulfurreducens. Importantly, IET was quantified via isotopic approach, which revealed that G. sulfureducens supplies more electrons to GWP-1 when the system was fed with cellulose (0.071 mM) than glucose (0.012 mM). This study demonstrates that the residual refractory carbon and nitrogen in treated wastewater could be further converted by mutual symbiosis of EAB and denitrifiers, which paves a synergic way for pollution and carbon reduction.

Background: Denitrification in wastewater treatment is severely limited under low-temperature and low-carbon (“dual-low”) conditions, hindering sustainable nitrogen removal. Biofilm systems, though energy-efficient, suffer from reduced efficiency in such environments due to impaired interspecies electron transfer (IET). Granular activated carbon (GAC), a conductive mediator, offers potential to enhance IET between electroactive microorganisms (EAMs) and denitrifiers, yet its role in dual-low systems remains underexplored. This study investigates GAC’s capacity to optimize biofilm functionality and mitigate greenhouse gas (GHG) emissions under these constraints. Results: Under dual-low conditions (4–6°C, C/N = 4), GAC increased denitrification efficiency by 19.4–21.9% and reduced N₂O emissions by 10.6–22.9%. Metatranscriptomes revealed upregulation of denitrifying genes (e.g., nosZ) and electron transport pathways (e.g., omcB in Geobacter). FISH/SEM confirmed GAC-driven coacervates of EAMs and denitrifiers, linked by nanowires, enhancing direct electron transfer. Microbial diversity decreased, but functional redundancy improved, with Pseudomonas fluorescens and Geobacter sulfurreducens dominating. TOC removal rose under low temperatures, indicating enhanced carbon utilization. Conclusions: GAC fosters synergistic EAM-denitrifier partnerships, enabling efficient denitrification and GHG mitigation in cold and carbon-limited (“dual-low”) biofilm systems, advancing sustainable wastewater management.

Multi-energy networks facilitate the interactions of multiple energy carriers such as electricity, heat, cooling, and natural gas for manifold benefits in the construction and operation of energy systems. They also provide a unified platform to integrate and utilize various advanced energy technologies such as renewables, cogeneration, power-to-X, electric vehicles, and energy storage. The problem scale of multi-energy networks is increasing rapidly with the accelerated expansion and integration of regional and urban energy systems. For these reasons, the analysis and optimization of multi-energy networks are faced with significant challenges. New methods are urgently required to address the basic computational challenges in multi-energy networks such as the multi-temporal-spatial scales, nonlinearity, uncertainty, combinatorial property of mixed continuous and discrete variables, and the solving of large-scale systems in a multi-stakeholder context. This special issue mainly covers papers on the computational theories and methods that can be applied in multi-energy networks. The aim is to present a state-of-the-art collection of innovative models, algorithms, approaches, and tools for the control, operation, design, simulation, and analysis of multi-energy networks. The special issue provides an opportunity for researchers and practicing engineers to share their latest discoveries and best practices in these areas.

The core of self-supervised point cloud learning lies in setting up appropriate pretext tasks, to construct a pre-training framework that enables the encoder to perceive 3D objects effectively. In this article, we integrate two prevalent methods, masked point modeling (MPM) and 3D-to-2D generation, as pretext tasks within a pre-training framework. We leverage the spatial awareness and precise supervision offered by these two methods to address their respective limitations: ambiguous supervision signals and insensitivity to geometric information. Specifically, the proposed framework, abbreviated as PointCG, consists of a Hidden Point Completion (HPC) module and an Arbitrary-view Image Generation (AIG) module. We first capture visible points from arbitrary views as inputs by removing hidden points. Then, HPC extracts representations of the inputs with an encoder and completes the entire shape with a decoder, while AIG is used to generate rendered images based on the visible points’ representations. Extensive experiments demonstrate the superiority of the proposed method over the baselines in various downstream tasks. Our code will be made available upon acceptance.

The operation of Unmanned Aerial Vehicles (UAVs) is often subject to state-dependent alterations and unstructured uncertainty factors, such as unmodelled dynamics, environmental weather disturbances, aerodynamics gradients, or changes in inertia and mass due to payloads. While a large number of autopilot solutions have been proposed to operate UAVs, none of these solutions is able to counteract the effects of state-dependent and unstructured uncertainties online by parameter estimation and adaptive control techniques. This work presents a systematic integration of adaptive control into ArduPilot, a popular open-source autopilot suite maintained by a large community of UAV developers. Adaptation features are embedded in the ArduPilot control structure without altering the original architecture, to allow users to use the autopilot suite as usual. Tests show that the proposed adaptive ArduPilot provides consistent improved performance in several uncertain flight conditions. The source code of the proposed adaptive ArduPilot is released at https://github.com/Friend-Peng/Adaptive-ArduPilot-Autopilot.

Adaptive integral sliding mode control (AISMC) is an extension of adaptive sliding mode control which is a way to ensure sliding motion while handling system uncertainties. However, conventional AISMC formulations require to different extent a priori knowledge of the system uncertainty: either the upper bound of the uncertainty or of its time derivative are assumed to be bounded a priori, or the uncertainty is assumed to be parametrized by some structure-dependent factorization. This work proposes a variant of AISMC with reduced a priori knowledge of the system uncertainty: it is shown that Euler-Lagrange dynamics typical of sliding mode literature admit a structure-independent parametrization of the system uncertainty. This parametrization is not the result of structural knowledge, but it comes from basic properties of Euler-Lagrange dynamics, valid independently on the structure of the system. The AISMC control method arising from this parametrization is analyzed in the Lyapunov stability framework, and validated in systems with different structures: a surface vessel and an aerial vehicle.

Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e. artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This work formulates an artificial-delay control method with adaptive gains in the presence of nonlinear (Euler-Lagrange) under-actuation. The appeal of studying Euler-Lagrange dynamics is to capture many robotics applications of practical interest, as demonstrated via stability and robustness analysis and via robotic ship and robotic aerial vehicle test cases.