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• Dynamically reconfiguring the structure of multi-microgrid systems to enhance the penetration of distributed energy resources. • Vehicle-to-grid concept is deployed to capture the intermittency of renewable resources and load demand. • To make the model more accurate, the uncertainty of loads and renewables is handled by using stochastic programming. • A mathematical-based convex optimization model is established to reap the benefits of CPLEX and GUROBI as solvers. • Results illustrate that both technical and economic aspects of multi-microgrid system improved significantly. On the one hand, the inherent intermittency in demands and renewable energy sources (RES) frequently bring challenges such as overload or surplus generation within microgrids. On the other hand, electric vehicle aggregations (EVAs) have garnered substantial attention as a pivotal strategy to address climate change and serve as a sustainable substitute for petroleum-based vehicles. However, the uncoordinated deployment of EVAs within microgrids, especially in the face of the intermittent nature of RES, poses a potential threat to the secure operation of microgrid systems. To tackle the mentioned issues, this research concentrates on interconnecting a group of scattered microgrids to create a multi-microgrid system. In more detail, by developing an energy management strategy to reconfigure the interconnections among microgrids, the efficient exchange of power among these multi-microgrid systems is facilitated, addressing the variability in load demands amidst the stochastic generation patterns of RESs. Besides, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) concepts of EVAs are synchronized within the reconfigurable microgrid structure to enhance the flexibility of the model. To evaluate the model under realistic situations, a scenario-based method is also employed to reflect the effects of uncertainties on the model. The proposed approach, characterized by its mathematical convexity, allows for employing efficient solvers like CPLEX, ensuring the attainment of a feasible global solution within a finite timeframe. The effectiveness of the proposed method is demonstrated through its implementation on a modified 33-bus test system operated as multi-microgrid system. The results show the effectiveness of the proposed approach as a promising tool for optimizing the operation of reconfigurable multi-microgrid systems in the presence of EVAs, leading to operational cost reduction and voltage profile enhancement. [ABSTRACT FROM AUTHOR]

• An accurate power flow is formulated by utilizing a dynamic approximation. • A robust approach is used to model uncertain parameters, including RESs, loads, and market price. • A mixed-integer linear programming approach is provided for taking the powerful solvers into account. • The proposed linear framework is faster than the previous methods with less error. Decarbonization legislated the risen integration of renewable energy sources (RESs) in the presence of energy storage systems (ESS) in distribution networks. Nonetheless, these resources may impose substantial costs to the utilities for managing frequency. In the present era, the concept of active distribution networks (ADNs) has been taken into consideration in the study of grid penetration of RESs and ESSs. Accordingly, ADN requires a sophisticated AC optimal power flow (OPF) to solve such a complex decision-making process. This paper aims to develop an accurate and effective OPF in ADNs. To this end, this paper contributes to developing a highly efficient tool to operate ADNs under uncertainties in load and renewables. Firstly, a highly accurate and dynamic linear load flow is presented to compensate for the restrictions of nonlinear OPF, such as scalability and robustness. In more detail, the floating tangent surface method is utilized to approximate better the nonlinear and nonconvex equations in the OPF formula. For the iterative process of the dynamic linear approximation, its local convergence can be derived. Then, to make the model much more realistic, the uncertainty of RESs, load, and price is accounted for in a robust approach which alleviates their vulnerability in decisions. The proposed approach is finally formulated as a mixed integer linear problem and implemented in various case studies. After assessing the proposed strategy under different circumstances, the average error of different case studies is 3.30E-06 and 6.24E-02, respectively, for the proposed method and another approach in the literature, which means the proposed method has less error than other methods. From the results, it can be observed that the presented OPF solving method is vastly superior to other prior approaches in terms of more accurate results. [ABSTRACT FROM AUTHOR]

• Mapping second-order cone AC OPF equations via semi-Lorentz transformation. • Transforming both variables and network parameters in Model-A. • Applying the proposed linear transformation on the network parameters in Model-B. • Developing an empirical analysis to obtain a good value for the semi-Lorentz factor. • Improving both optimality gap and relaxation error compared to other methods. Optimal power flow (OPF) has always been one of the most crucial tools for power system operations. OPF problem formulation involves non-linear alternative current (AC) power flow equations, and a wide range of challenges occur as a result. This is because the resulting non-convex optimization problems are not only complex and time-consuming, but also difficult to find a global optimum as many local optimums are present. So far, different relaxations have been provided to address these issues. One of the most effective strategies for convexifying such formulations is second-order cone programming (SOCP). Although SOCP is an efficient instrument for convexifying AC OPF equations, it is unable to reach the global optimal solution compared to other methods. The aim of this paper is therefore to provide a new method to approach the global optimum of AC OPF relaxed by SOCP. This method is obtained with the aid of a new linrear tranfsormation called semi-Lorentz transformation as it similar to the Lorentz transformation in the special relativity theory. In this method second-order cone AC OPF equations are mapped to a new model via semi-Lorentz transformation. In addition, an approximation approach is also presented to reach the best semi-Lorentz factor, the main driver in semi-Lorentz transformation, for each particular problem based on the network parameters. From the comparative analysis in case studies, the proposed OPF solution method has robust precision and higher efficiency while consuming less computing time. [ABSTRACT FROM AUTHOR]

• A bi-objective economic dispatch for a wind-thermal-energy storage system. • Pumped hydroelectric storage system to alleviate the peak-time pressure. • Hybrid optimization algorithm: whale optimization enhanced by wavelet mutation. • Trade-off between operation cost and water consumption as the problem's objectives. This paper proposes a novel approach to discover an optimal solution of multi-area dynamic economic dispatch integrated with wind power and pumped hydroelectric storage. Minimizing of water consumption associated with power production in thermal power plants is yet to be addressed in economic dispatch problem. Thus, it is crucial to modernize the conventional power industry to save the water utilization in the process of generating electric energy. As a result, making a feasible power generation schedule by finding a trade-off between total generation cost and water consumption is the main focus of this paper. Toward this end, this study introduces an effective optimization approach for decreasing the contaminants ‎produced by the combustion-based power plants via reflecting the impacts of wind power and storage systems in the proposed problem. An efficient optimization algorithm considering the merits of whale optimization algorithm and wavelet mutation is also proposed to handle the complications of the trade-off between generation cost and water consumption. The proposed approach is applied to 40-unit 4-area IEEE system by 1) scrutinizing the performance of the hybrid framework from different standpoints, 2) achieving an improvement about 5.4% in the operation cost, and 3) promoting the decarbonized economy by implementing ‎renewable energy. [Display omitted] [ABSTRACT FROM AUTHOR]