With the increase in data volume, more types of data are being used and shared, especially in the power Internet of Things (IoT). However, the processes of data sharing may lead to unexpected information leakage because of the ubiquitous relevance among the different data, thus it is necessary for data owners to conduct preventive audits for data applications before data sharing to avoid the risk of key information leakage. Considering that the same data may play completely different roles in different application scenarios, data owners should know the expected data applications of the data buyers in advance and provide modified data that are less relevant to the private information of the data owners and more relevant to the nonprivate information that the data buyers need. In this article, data sharing in the power IoT is regarded as the background, and the mutual information of the data and their implicit information is selected as the data feature parameter to indicate the relevance between the data and their implicit information or the ability to infer the implicit information from the data. Therefore, preventive audits should be conducted based on changes in the data feature parameters before and after data sharing. The probability exchange adjustment method is proposed as the theoretical basis of preventive audits under simplified consumption, and the corresponding optimization models are constructed and extended to more practical scenarios with multivariate characteristics. Finally, case studies are used to validate the effectiveness of the proposed preventive audits.

Ensuring that the outputs of neural networks satisfy specific constraints is crucial for applying neural networks to real-life decision-making problems. In this paper, we consider making a batch of neural network outputs satisfy bounded and general linear constraints. We first reformulate the neural network output projection problem as an entropy-regularized linear programming problem. We show that such a problem can be equivalently transformed into an unconstrained convex optimization problem with Lipschitz continuous gradient according to the duality theorem. Then, based on an accelerated gradient descent algorithm with numerical performance enhancement, we present our architecture, GLinSAT, to solve the problem. To the best of our knowledge, this is the first general linear satisfiability layer in which all the operations are differentiable and matrix-factorization-free. Despite the fact that we can explicitly perform backpropagation based on automatic differentiation mechanism, we also provide an alternative approach in GLinSAT to calculate the derivatives based on implicit differentiation of the optimality condition. Experimental results on constrained traveling salesman problems, partial graph matching with outliers, predictive portfolio allocation and power system unit commitment demonstrate the advantages of GLinSAT over existing satisfiability layers. Our implementation is available at https://github.com/HunterTracer/GLinSAT.

The real-time control of modern power systems faces cyber risks owing to the deep coupling of cyber systems and physical systems. Attack detection plays an important role in cybersecurity issues. In load frequency control systems, for example, detecting whether malicious data are injected guarantees the stability of the frequency. As an effective active attack detection algorithm, the dynamic watermarking algorithm faces challenge of parameter uncertainty in real-world applications. Here, we quantitatively analyze the influence of uncertain parameters for the dynamic-watermarking-based detection algorithm. By introducing the parameter identification algorithm, we propose an active attack detection framework for LFC systems with uncertain parameters, which is constructed for an online application. The proposed framework is validated in a real-world three-region power system simulation. The results show that the introduction of the parameter identification steps ensures the validity of the detection algorithm and can effectively avoid the malfunction of the detection mechanism caused by uncertain parameters.

Currently, the rapid development of Internet of Things (IoT) technology is promoting the development of smart grids. However, because of the numerous loads present and the surge of data in smart grids, network congestion, transmission delay, and insufficient channel resources pose pressing challenges for accurate, real-time transmission in load control communication systems. Mobile edge computing (MEC) and nonorthogonal multiple access (NOMA) technology, as new types of communication architectures, can significantly improve the communication quality in such network system. In this article, we first establish an NOMA-MEC system model with relay assistance based on cloud-edge collaboration in smart grids, aiming to minimize the energy consumption cost of the system while satisfying constraints on relay power and computation latency. In addition, we formulate a powerful four-slot transmission strategy to support cooperative computation and communication in the proposed NOMA-MEC system with relay assistance. Since the optimization problem is nonconvex, an efficient joint cooperative computation and communication algorithm with relay assistance is developed to solve it. Numerical results show that the proposed resource allocation strategy can significantly improve the communication transmission quality and reduce the energy consumption cost of the system.

With the development of the Internet of Things (IoT) and big data technology, the data value is increasingly explored in multiple scenarios. However, the isolation of IoT data among entities makes it difficult to optimally allocate data and convert them into economic value, thus it is necessary to introduce the IoT data sharing mode to drive data circulation. To enhance the accuracy and fairness of IoT data sharing, the heterogeneity of participants is sufficiently considered, and data valuation and profit allocation in IoT data sharing are improved based on electricity retail. Data valuation is supposed to be relevant to attributes of IoT data buyers, where risk preferences of electricity retailers are selected as characteristic attributes and data premium rates are proposed to depict their impacts. Profit allocation should measure the marginal profit shares of electricity retailers and data brokers (DBs) fairly, thus an asymmetric Nash bargaining model is used to guarantee that they receive reasonable profits based on their contributions to the coalition of IoT data sharing. Considering the heterogeneity of participants comprehensively, the proposed IoT data sharing fits a large coalition of IoT data sharing with multiple electricity retailers and DBs. To demonstrate the applications of IoT data sharing, case studies are utilized to validate the data value for electricity retailers with different risk preferences and the efficiency of profit allocation using the asymmetric Nash bargaining model. Finally, the proposed method can promote the efficiency of using IoT data and guide for potential large-scale IoT data transactions.

Integrated energy systems (IESs), in which various energy flows are interconnected and coordinated to release potential flexibility for more efficient and secure operation, have drawn increasing attention in recent years. In this article, an integrated energy management system (IEMS) that performs online analysis and optimization on coupling energy flows in an IES is comprehensively introduced. From the theory perspective, an energy circuit method (ECM) that models natural gas networks and heating networks in the frequency domain is discussed. This method extends the electric circuit modeling of power systems to IESs and enables the IEMS to manage large-scale IESs. From the implementation perspective, the architecture design and function development of the IEMS are presented. Tutorial examples with illustrative case studies are provided to demonstrate its functions of dynamic state estimation, energy flow analysis, security assessment and control, and optimal energy flow. From the application perspective, real-world engineering demonstrations that apply IEMSs in managing building-, park-, and city-scale IESs are reported. The economic and environmental benefits obtained in these demonstration projects indicate that the IEMS has broad application prospects for a low/zero-carbon future energy system.

Nontechnical losses (NTLs) are estimated to be considerable and increasing every year. Recently, high-resolution measurements from globally laid smart meters have brought deeper insights on users' consumption patterns that can be exploited potentially by NTL detection. However, consumption-pattern-based NTL detection is now facing two major challenges: the inefficiency of harnessing high dimensionality and the severe lack of fraudulent samples. To overcome them, an NTL detection model based on deep learning and anomaly detection is proposed in this article, namely bidirectional Wasserstein GAN and support vector data description-based NTL detector (BSBND). Motivated by the powerful ability of generative adversarial networks (GANs) to learn deep representation from high-dimensional distributions of data, in the BSBND, we utilized a BiWGAN for feature extraction from high-dimensional raw consumption records, and a one-class classifier trained only on benign samples-SVDD-is adopted to map features into judgments. Moreover, a novel alternate coordinating algorithm is proposed to optimize the cooperation between the upstream BiWGAN and the downstream SVDD, and also, an interpreting algorithm is proposed to visualize the basis of each fraudulent judgment. Case studies have demonstrated the superiority of the BSBND over the state of the arts, the powerful feature extraction ability of BiWGAN, and also the effectiveness of the proposed coordinating and interpreting algorithms.

Probability density forecast offers the whole distributions of forecasting targets, which brings greater flexibility and practicability than the other probabilistic forecast models such as prediction interval (PI) and quantile forecast. However, existing density forecast models have introduced various constraints on forecasted distributions, which has limited their ability to approximate real distributions and may result in suboptimality. In this paper, a distribution-free density forecast model based on deep learning is proposed, in which the real cumulative density functions (CDFs) of forecasting target are approximated by a large-capacity positive-weighted deep neural network (NN). Benefiting from the universal approximation ability of NNs, the range of forecasted distributions has been proven to contain all the distributions with continuous CDFs, which is superior to existing models' considering both width and accordance with reality. Three tests from different scenarios were implemented for evaluation, i.e., very-short-term wind power, wind speed, and day-ahead electricity price forecast, in which the proposed density forecast model has shown superior performance over the state of the art.

As nontechnical losses in power systems have recently become a global concern, electricity fraud detection models attracted increasing academic interest. The wide application of smart meters has offered more possibility to detecting fraud from user's consumption patterns. However, the performances of existing consumption-based electricity fraud detection models are still not satisfactory enough for practice, partly due to their limited ability to handle high-dimensional data. In this paper, a deep-learning-based model is developed for detecting electricity fraud in the advanced metering infrastructure, namely, the multitask feature extracting fraud detector (MFEFD). The deep architecture has brought MFEFD a powerful ability to handle high-dimensional input, through which consumption patterns inside load profiles can be effectively extracted. Another challenge is that the insufficiency of labeled data has restricted the generalization of existing models since they are mostly based on supervised learning and labeled data. MFEFD is trained in a semisupervised manner, in which multitask training was implemented to combine the supervised and unsupervised training, so that both the knowledge from unlabeled and labeled data can be made use of. Real-world-data-based case studies have demonstrated MFEFD's high detection performance, robustness, privacy preservation, and practicability.

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.