In the active distribution networks (ADNs), the emerging trend of high penetration of sustainable energy sources such as wind turbines, photovoltaics, and the forthcoming integration of energy storage devices and additional dynamic loads like plug-in-electric vehicles (PEVs) influences to change the nature of electric distribution networks. Consequently, it deteriorates power quality and reliability in terms of unacceptable voltage rise-drop, harmonic distortion, voltage unbalance, and excessive power loss. In this context of transition into the active distribution network, the probabilistic optimal power flow (P-OPF) in the ADN is considered an important tool that helps the distribution grid operators for defining the optimal settings of the system's control variables. However, the optimal solution of P-OPF becomes highly complex while considering the multiple variables, timevarying characteristics and operational uncertainties associated with sustainable resources, consumer behaviour and electricity market prices. This review aims at promoting the research and discussion of P-OPF studies in the context of ADNs characteristics and challenges. First, we perform a data-driven and visualized scientometric analysis of 1988 quality documents retrieved from the Scopus during the last ten years using VOSviewer software. Second, an in-depth investigation of the recent and emerging techniques for solving the P-OPF problem is presented. Third, a set of recommendations and highlights of future challenges for P-OPF in ADNs are offered. The review findings showed high interest among the scientific community in sustainable energy source integration, given the recent rise in publications and citations. Also, there is still a lack of innovative optimization techniques that can adequately handle the current issues associated with P-OPF and objectives with fair computational efficiency and accuracy. Finally, this paper offers a guide for P-OPF researchers to navigate the recent studies considering the sustainable sources, spot the new research frontiers, and identify the most impactful countries and publishers.

Microgrids (MG) cluster are isolated from the utility grid but they have the potential to achieve better techno-economic performance by using joint energy and reserve sharing among MGs. This paper proposes a techno-economic framework for the optimal operation of isolated MGs-cluster by scheduling cooperative energy sharing and real-time reserve sharing for ancillary services based on the cooperative game theory. In the day-ahead scheduling, a coalitional sharing scheme is formulated as an adjustable robust optimization (ARO) problem to optimally schedule the energy and reserves of distributed generators (DGs) and energy storage systems (ESSs), thereby responding to the uncertainties of photovoltaic systems, wind turbines, and loads. These uncertainties are the main reason for power system imbalance which is mitigated by regulating the frequency in real-time and a dynamic droop control process is used to realize the reserves in a distributed manner. This control process is embedded into the ARO problem, which is formulated as an affine ARO problem and then transformed into a deterministic optimization problem that is solved by off-shore solvers Apart from the reduction in the operation cost, the frequency restoration can be improved jointly, resulting in the coupled techno-economic contribution of the MGs in the coalition. The contribution of each MG is quantified using shapely value, a cooperative game approach. Simulations are conducted for a case study with 4 MGs and the results demonstrate the merits of the proposed cooperative scheduling scheme.

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