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• An improved method considering correlated stochastic variables is proposed for PLF. • A twice-permutation technique is proposed to ensure the desired correlations. • Singular value decomposition extends the scope of the method. As the increasing integration of large-scale renewable energy sources in power systems, the stochastic characteristics of loads and renewable energy systems become much more complex and impacts power systems much more than ever. Probabilistic load flow analysis is a powerful tool to discover the stochastic characteristics of power systems. There are two important issues for probabilistic load flow analysis based on Monte Carlo simulation: (i) How to generate random samples with the specific distribution and correlation; and (ii) how to make the simulation method to work well even when the correlation matrices are not positive definite. In order to handle the two issues, Nataf transformation combined with Latin hypercube sampling and singular value decomposition method is proposed for solving probabilistic load flow problems with correlated variables in this paper. By using the singular value decomposition (SVD), the proposed method works well even when the correlation matrices are not positive definite. And the twice-permutation technique based on SVD ensures that the samples have the desired correlations. The investigation on modified IEEE 14-bus system and modified IEEE 118-bus system shows that the proposed method is accurate and efficient. [ABSTRACT FROM AUTHOR]

• A risk-averse deep learning method for real-time emergency load shedding. • Deep learning algorithm to enhance prediction accuracy on load shedding strategy. • A shedding amount inference model to speculate the stability after load shedding. • A risk-averse loss function to minimize the risk of load under-cutting events. • Experiments on multiple power systems to verify the practical values of the method. Emergency load shedding is an effective and frequently used emergency control action for power system transient stability. Solving the full optimization models for load shedding is computational burdensome and thus slow react to the intense system variations from the increasing renewable energy sources and the more active demand-side behavior. Other sensitivity-based methods impair the control accuracy and may not guarantee global optimality. Artificial intelligence methods, as the data-driven approaches, have recently been well-recognized for its real-time decision-making capability to tackle the system variations. The existing artificial intelligence methods for emergency load shedding are based on shallow learning algorithms and can lead to both load under-cutting and over-cutting events. However, when the loads are under-cut, the power system will be exposed to a high risk of post-control instability that can propagate into cascading events, which incurs significantly higher cost than an over-cutting event. Being aware of such unbalanced control costs, this paper proposes a risk-averse deep learning method for real-time emergency load shedding, which trains deep neural network towards the reluctance to load under-cutting events, so as to avoid the huge control cost incurred by control failure. The case studies on two renewable power systems demonstrate that, compared to the state-of-the-art methods, the proposed risk-averse method can significantly improve the control success rate with negligible increase in prediction error, ending up with lower overall control cost. The results verify the enhanced control performance and the practical values of the proposed method. [ABSTRACT FROM AUTHOR]

Renewable energy resources and transport electrification have become essential components of modern and future distribution planning. Despite the impetus of economic and environmental benefits, the uncertainty brought in poses challenges. In this paper, we propose an integrated expansion planning framework based on a multiobjective mixed-integer nonlinear program. The aim is to minimize the net present value of investments considering feeder routing, substation alterations and construction while maximizing the utilization of proposed charging stations. Distributed generation and load uncertainties, recast in two-stage stochastic programming, are tackled with a scenario generation and reduction technique using a probabilistic approach with Kantorovich metrics. The final number of the scenarios are validated with an alternative clustering method. A flow-based location–allocation theory with user equilibrium traffic assignment model is exploited to site charging stations. The sizing problem is determined using continuous-time Markov chain modeling. The proposed framework is solved with a multiobjective Tchebycheff decomposition-based evolutionary algorithm and tested on a modified 54 bus distribution network and 25 transportation node system. Numerical results demonstrate the capability of the proposed method. Distribution planning authorities can benefit from the presented approach to make intertemporal investment decisions while maintaining the quality of system performance. • Optimal Integrated distribution plan coupled with road electricity is identified. • Uncertainty is handled using scenario generation and reduction approach. • The final branches of scenario tree are verified by k-means clustering method. • Stochastic modeling with Markov Chain solves charging station planning problem. [ABSTRACT FROM AUTHOR]

Networking of microgrids has received increasing attentions in recent years, which requires the uncertainty management associated with variations in the system. In this paper, a two-stage energy management strategy is developed for networked microgrids under the presence of high renewable resources. It decomposes the microgrids energy management into two stages to counteract the intra-day stochastic variations of renewable energy resources, electricity load and electricity prices. In the first stage (hourly time scale), a hierarchical hybrid control method is employed for networked microgrids, aiming to minimize the system operation cost. The mean–variance Markowitz theory is employed to assess the risk of operation cost variability due to the presence of uncertainties. In the second stage (5-min time scale), the components in microgrids are optimally adjusted to minimize the imbalance cost between day-ahead and real-time markets. Simulation study is conducted on an uncoordinated microgrids system as well as on the proposed networked system. According to the simulation results, the proposed method can identify optimal scheduling results, reduce operation costs of risk-aversion, and mitigate the impact of uncertainties. [ABSTRACT FROM AUTHOR]