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The high penetration of photovoltaic (PV) panels in a distribution system could cause sharp fluctuations in bus voltages. This phenomenon could induce abnormal operations in high-precision equipment or result in erroneous computer memory functions. To handle this issue, this paper proposes a voltage control to smooth out a critical bus voltage, without utilizing the local controllable devices, while maintaining the bus voltages across the distribution system within the allowable range. First, a smoothing out control of voltage is proposed and theoretically proved to mitigate the voltage fluctuations at the critical bus by the first-order inertia. Second, an optimization model is proposed for the coordinated voltage control, which coordinates the actions of discrete control devices with the smoothing out control of PV inverters. A novel convex approximation method is proposed to transform the nonconvex model into an approximate convex model. It is theoretically proved that the solutions obtained by solving the approximation model can control the bus voltages across the distribution system within the allowable range. Last, the proposed voltage control is simulated in 15-bus and 51-bus distribution systems. The 6-minute and 24-hour simulations show that a single voltage fluctuation and the sum of squared voltage fluctuations at a critical bus are reduced by 22.4% and 36.5%, respectively, as compared to the results offered by using the existing benchmark control method.

With the development of urban water supply systems, the leakage detection of water supply pipe networks is of great significance for the safe operation of urban water supply systems. In practice, due to the short of the important data, traditional detection models often fail to achieve good detection results. Therefore, this article proposes a novel pipeline attention integrating the long short-term memory (LSTM) to detect the leakage. The density-based spatial clustering of applications with noise (DBSCAN) method divides the water supply network into several regions according to the leakage characteristics of pipelines. Then, the LSTM extracts dynamic time-varying hydraulic features of pipelines, and the pipeline attention extracts the dynamically changing features between pipelines. Finally, the Attention- LSTM model is used to detect the leak region of urban water supply systems. Compared with multilayer perceptron classifier, $K$ neighbors classifier, decision tree classifier, support vector machine classifier, multiscale fully convolutional network, one-dimensional multichannel convolution neural network, and variational autoencoder, the F1-Score of the Attention- LSTM is greatly improved, which are 116%, 120%, 254%, 180%, 21%, 26%, and 27%, respectively. Therefore, the proposed model can accurately detect the leakage of water supply network and effectively reduce the waste of resources.

This paper presents a privacy-preserving operational decision-making approach for autonomous microgrids networked via a power distribution system, where the distribution system operator (DSO) and microgrid master controllers (MMCs) make independent decisions as different stakeholders. First, the scheme of bilevel optimization (i.e., the Stackelberg leader-followers game) is applied to investigate the sequential interactions between DSO and MMCs. Then, given the coexistence of continuous and binary variables in the lower-level problems, an exact strong-duality-based reformulation and decomposition framework is customized to cope with the nonconvex nature of the bilevel mixed-integer optimization. Meanwhile, a fast-enhanced Benders decomposition algorithm is proposed to realize local privacy-preserving decision-making, where multiple unified Benders cuts are generated once and the strongest one is selected back to Benders mater problem. Mathematically, the proposed algorithm will not change the equilibrium point and corresponding optimal solution after finite iterations. Finally, through numerical experiments on a simplified two-bus test system and a modified IEEE 123-bus system, we demonstrate the effectiveness of privacy preservation, as well as the robustness and scalability of the proposed approach.

Developing efficient defensive strategies against cyber-attacks is a major concern of modern power system research. With the goal of minimizing the expected load loss, this article proposes a robust probabilistic substation-based defender-attacker-defender (DAD) model to allocate the substation's defensive resources. The proposed model aims to minimize the risk of cyber-attacks on intelligent electronic devices (IEDs). The game between the defender and the attacker concerning multiple substations, IEDs, and their connected lines is particularly modeled. In addition, we extend the proposed probabilistic DAD model to an observability-ensured DAD model where the existing phasor measurement units in the power grids are considered in the defensive resource allocation. Integrated with the logarithmic transformation and piecewise linearization techniques, a customized column-and-constraint generation algorithm is developed to solve the proposed model. Finally, the numerical results on IEEE RTS 24-bus and 118-bus systems validate the proposed model.