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The future grid will include a high penetration of distributed generation, which will have an impact on its security. This paper discusses the latest trends, components, tools, and frameworks aimed at 100% renewable energy generation for the emerging grid. The technical and economic impacts of renewable energy sources (RES)-based distributed generation (DG) on the emerging grid security are also discussed. Moreover, the latest approaches and techniques for allocating RES-DG into the distribution networks using specific performance indices based on recent literature were reviewed. Most of the methods in recent literature are based on metaheuristic optimization algorithms that can optimally allocate the RES-DGs based on the identified network variables. However, there is a need to extend these methods in terms of parameters considered, objectives, and possible ancillary support to the upstream network. The limitations of existing methods in recent literature aimed at ensuring the security of the integrated transmission-active distribution network under high RES-DG penetration were identified. Lastly, the existing coordination methods for voltage and frequency control at the transmission and active distribution system interface were also investigated. Relevant future research areas with a focus on ensuring the security of the emerging grid with high RES-DG penetration into the distribution networks are also recommended.

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The proliferation of renewable energy sources distributed generation (RES-DG) into the grid results in time-varying inertia constant. To ensure the security of the grid under varying inertia, techniques for fast security assessment are required. In addition, considering the high penetration of RES-DG units into the modern grids, security prediction using varying grid features is crucial. The computation burden concerns of conventional time-domain security assessment techniques make it unsuitable for real-time security prediction. This paper, therefore, proposes a fast security monitoring model that includes security prediction and load shedding for security control. The attributes considered in this paper include the load level, inertia constant, fault location, and power dispatched from the renewable energy sources generator. An incremental Naïve Bayes algorithm is applied on the training dataset developed from the responses of the grid to transient stability simulations. An additive Gaussian process regression (GPR) model is proposed to estimate the load shedding required for the predicted insecure states. Finally, an algorithm based on the nodes’ security margin is proposed to determine the optimal node (s) for the load shedding. The average security prediction and load shedding estimation model training times are 1.2 s and 3 s, respectively. The result shows that the proposed model can predict the security of the grid, estimate the amount of load shed required, and determine the specific node for load shedding operation.

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