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Energy storage systems (ESS) are indispensable building blocks of power systems with a high share of variable renewable energy. As energy-limited resources, ESS should be carefully modeled in uncertainty-aware multistage dispatch. On the modeling side, we develop a two-stage model for ESS that respects the nonanticipativity of multistage dispatch, and implement it into a distributionally robust model predictive control scheme. The ESS model features multiple selective operational modes, which enable its power interval to be scheduled in chronological dispatch. On the algorithm side, we propose to evaluate the future worst-case cost expectation over a convex relaxation of the non-convex and discrete value function of the recourse. This gives rise to a novel algorithm that efficiently solves a typical class of two-stage distributionally robust optimization problems, which are equipped with discrete recourse and infinite support. The advantage of the algorithm over the state-of-the-art nested column-and-constraint generation method is theoretically interpreted. Simulation is performed on the modified IEEE 118-bus system and 300-bus system. Results show that ESS function well on the basis of the proposed model and control scheme, and also demonstrate the superiority of the novel algorithm.

Nowadays, rooftop photovoltaic (PV) panels are getting enormous attention as clean and sustainable sources of energy due to the increasing energy demand, depreciating physical assets, and global environmental challenges. In residential areas, the large-scale integration of these generation resources influences the customer load profile and introduces uncertainty to the distribution system’s net load. Since such resources are typically located behind the meter (BtM), an accurate estimation of BtM load and PV power will be crucial for distribution network operation. This article proposes the spatiotemporal graph sparse coding (SC) capsule network that incorporates SC into deep generative graph modeling and capsule networks for accurate BtM load and PV generation estimation. A set of neighboring residential units are modeled as a dynamic graph in which the edges represent the correlation among their net demands. A generative encoder–decoder model, i.e., spectral graph convolution (SGC) attention peephole long short-term memory (PLSTM), is devised to extract the highly nonlinear spatiotemporal patterns from the formed dynamic graph. Later, to enrich the latent space sparsity, a dictionary is learned in the hidden layer of the proposed encoder–decoder, and the corresponding sparse codes are procured. Such sparse representation is used by a capsule network to estimate the BtM PV generation and the load of the entire residential units. Experimental results on two real-world energy disaggregation (ED) datasets, Pecan Street and Ausgrid, demonstrate more than 9.8% and 6.3% root mean square error (RMSE) improvements in BtM PV and load estimation over the state-of-the-art, respectively.

Distribution service restoration plays a vital role in mitigating the adverse impacts of power outages stemming from extreme weather conditions. With incentives toward reducing the carbon footprint of power generation on the environment and the reliance on fossil fuel resources, the future distribution networks are equipped with distributed renewable energy sources. This paper reviews the state-of-the-art frameworks proposed for service restoration in the distribution networks with renewable generation technologies. First, the objectives of the distribution service restoration and the challenges to accomplishing this task are discussed. Later, the existing approaches to restoring the distribution networks and the control architectures are presented. The contribution of microgrid technology and the models used to incorporate renewable energy resources in distribution service restoration are discussed. In this context, the application of artificial intelligence to distribution service restoration is highlighted, and potential research opportunities in distribution service restoration are presented.