Implementation of Risk-Aggregated Substation Testbed Using Generative Adversarial Networks

Capturing the anomalies of a cyber system in power control networks would promote operational awareness. Correlation of such events, e.g., intrusion attempts, traffic flow, and other signatures, together with control alarm events gives operators an in-depth understanding in order to make an informed decision. This paper proposes a threat inference framework to promote real-time vulnerability assessment associated with cyber intrusions on power communication networks. Wasserstein Generative Adversarial Networks (WGAN) is proposed to estimate the performance of the adversarial model. Additionally, a machine-learning framework is introduced to model the filtering process of the security devices, i.e., firewalls, isolation, and encryption devices, and the posterior fitting method is incorporated to establish an accurate probabilistic formulation. Finally, a testbed is established to coordinate system evaluation. Verification of the intrusion model is part of the implementation to quantify system risks based on the anomalies using (1) the open-source emulator, and (2) an externally imported system analyzer to characterize resulting impacts. The effectiveness and feasibility of the generative models are verified in a comparison study where the proper parameter settings could be obtained. The proposed framework is justified with extensive studies of substation networks using real-world settings

Distributed optimization method for economic dispatch of active distribution networks via momentum with historical information and forecast gradient

In the context of vigorously developing new energy sources, the economic dispatch(ED) of active distribution network (ADN) is essential. Due to the single fault affecting the global and high communication cost in centralized scheduling, we propose a distributed gradient method, namely the fast Nesterov accelerated gradient method(FNAGM), which can solve the economic dispatch problem(EDP) in ADN. The distributed architecture does not need to collect global information and only uses a sparse communication network to complete communication exchanges. It is the distributed architecture that can protect users’ private information and reduce communication pressure. By constructing the acceleration matrix based on the upper limit of the second derivative, the convergence speed can be effectively improved while satisfying the equality constraints. Eventually, the FNAGM combined with the historical momentum information and the forecast gradient, which is simulated in the bi-layer model of ADN via MATLAB. The verification results show that the algorithm with the historical momentum information and the forecast gradient can complete the optimal scheduling of controllable distributed generator(DG). What is more, the convergence efficiency performance is greatly improved

Energy management method for microgrids based on improved Stackelberg game real-time pricing model

With the rapid development of microgrids with distributed generations (DGs) and energy storage system (ESS), it is important to study energy management methods to improve the operation economy of microgrids. However, there is currently a lack of research on microgrid’s energy management models including multi-party groups such as wind turbines, photovoltaics and ESS. This paper proposed an energy trading management method of microgrids based on Stackelberg game real-time pricing mechanism, which can solve the more complex optimization operation problem of microgrids. First, the rolling optimization was carried out to determine the charging and discharging behavior of ESS for maximizing the total benefit microgrid in the next few time slots. Further, a Stackelberg game real-time pricing model was built. The electricity prices of different entities in the microgrid in the next time slot were optimized by microgrid operator (MGO) to determine the load demand of each DG, preference parameter in the utility function of DGs was improved to promote the internal energy interaction and the economic benefits of the microgrid. Finally, the results show that our method can effectively improve DGs’ total utility and stimulate energy trading within the microgrid. Compared with no optimization and traditional method, the daily profit of MGO obtained by our method was increased by 31.89% and 5.4% respectively, verifying the economics of the proposed method