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With the active participation of numerous end-users in the development of low-carbon energy ecosystems, the continuous expansion of the Energy Internet diminishes the timeliness of energy transmission and increases the complexity of energy scheduling, which leads to reduced energy efficiency. To solve it, a partitioning approach based on dual-stage agglomeration for Energy Internet is proposed in this article. First, the entropy weight of Energy Internet is proposed to assess the line significance of energy transmission, while establishing a uniform criterion of judgment by considering the energy loss of heterogeneous energy sources. Second, as the first stage of partitioning, the local expansion and boundary detection mechanism is proposed to realize localized node agglomeration and generate small-scale regions while ensuring all nodes contained in subregions. Furthermore, the hierarchical region agglomeration mechanism is proposed as the second stage of partitioning, which can aggregate the generated small-scale regions and improve the quality of the partitioning result based on flexible partitioning. Through the above stages, the proposed partitioning approach improves energy allocation, transmission and global efficiency of Energy Internet. Finally, case studies of an Energy Internet with 171-node are presented to validate the proposed approach.

Localizing small leakage within pipeline systems represents a significant challenge due to the unclear characteristics of the associated signals. This article proposes a novel localization method designed to accurately pinpoint small leakages by exploiting the attenuated characteristics of the negative pressure wave (NPW). This approach transforms the localization problem into an optimal consensus problem, where the precise distance to the leakage point is determined by achieving consensus in the attenuated NPW magnitudes. Importantly, our method eliminates the necessity for estimating the NPW data at the leakage point, as it is based on the attenuation model of NPWs at both ends. In the solving process, a recursive variable is designed that obviates the necessity for distance summation calculations for convergence assessment, thereby reducing the complexity of the consensus problem. Subsequently, the pipeline small leakage localization algorithm is developed by adaptive dynamic programming (ADP), employing an actor-critic structure to resolve the optimal consensus problem. Finally, the effectiveness of the algorithm is demonstrated through extensive testing with real small leakage data, and then, a thorough error analysis is conducted. The results show that our proposed method effectively achieves accurate localization of the small leakage with low complexity.

Since most of the existing models based on the microgrids (MGs) are nonlinear, which could cause the controller oscillate, resulting in the excessive line loss, and the nonlinear could also lead to the controller design difficulty of MGs system. Therefore, this article researches the distributed voltage recovery consensus optimal control problem for the nonlinear MGs system with $N$ -distributed generations (DGs), in the case of providing stringent real power sharing. First, based on the distributed cooperative control concept of multiagent systems and the critic neural networks (NNs), a novel distributed secondary voltage recovery consensus optimal control protocol is constructed via applying the backstepping technique and nonzero-sum (NZS) differential game strategy to realize the voltage recovery of island MGs. Meanwhile, the model identifier is established to reconstruct the unknown NZS games systems based on a three-layer NN. Then, a critic NN weight adaptive adjustment tuning law is proposed to ensure the convergence of the cost functions and the stability of the closed-loop system. Furthermore, according to Lyapunov stability theory, it is proven that all signals are uniform ultimate boundedness in the closed loop system and the voltage recovery synchronization error converges to an arbitrarily small neighborhood of the origin near. Finally, some simulation results in MATLAB illustrate the validity of the proposed control strategy.

In traditional leak location methods, the position of the leak point is located through the time difference of pressure change points of both ends of the pipeline. The inaccurate estimation of pressure change points leads to the wrong leak location result. To address it, adaptive dynamic programming is proposed to solve the pipeline leak location problem in this article. First, a pipeline model is proposed to describe the pressure change along pipeline, which is utilized to reflect the iterative situation of the logarithmic form of pressure change. Then, under the Bellman optimality principle, a value iteration (VI) scheme is proposed to provide the optimal sequence of the nominal parameter and obtain the pipeline leak point. Furthermore, neural networks are built as the VI scheme structure to ensure the iterative performance of the proposed method. By transforming into the dynamic optimization problem, the proposed method adopts the estimation of the logarithmic form of pressure changes of both ends of the pipeline to locate the leak point, which avoids the wrong results caused by unclear pressure change points. Thus, it could be applied for real-time leak location of long-distance pipeline. Finally, the experiment cases are given to illustrate the effectiveness of the proposed method.