The future smart grid encompasses ac–dc clusters known as ac–dc microgrids. For reliability and security purposes, each microgrid hosts a mix of synchronous-based and converter-based distributed resources. However, synchronous-based generators, in particular, are characterized by their limited reactive power capabilities because of the limitation of their excitation systems, which could lead to voltage instability problems when the hosting microgrid is islanded. AC–DC microgrids also comprise controllable thermal and electrical loads. Most modern ac–dc loads have power electronics interfaces that control the voltage at their ends, exhibiting constant power characteristics. AC–DC microgrids with high penetrations of constant power ac–dc loads are vulnerable to voltage collapse. As with other power system architectures, microgrids are subject to contingencies, e.g., a line circuit outage, during which their loadability can be jeopardized. For this reason, the work reported in this paper involved the investigation of the steady-state voltage stability of islanded ac–dc hybrid microgrids (HMGs) during contingencies. Voltage stability analysis was carried out on a 12-bus ac–dc HMG. Several case studies were designed to reveal the likelihood of voltage instability/collapse in microgrids under severe contingencies. The voltage stability analysis also shows that interfacing ac and dc microgrids does not always enhance loadability. The analysis was carried with the goal of raising awareness among microgrid planners and operators of the possibility that a voltage collapse phenomenon can develop in islanded ac–dc microgrids under extreme events. Contingency analysis should therefore be incorporated into planning and operations criteria for islanded ac–dc microgrids.

Recently, renewable energy resources (RESs) and electric vehicles (EVs), in addition to other distributed energy resources (DERs), have gained high popularity in power systems applications. These resources bring quite a few advantages for power systems—reducing carbon emission, increasing efficiency, and reducing power loss. However, they also bring some disadvantages for the network because of their intermittent behavior and their high number in the grid which makes the optimal management of the system a tough task. Virtual power plants (VPPs) are introduced as a promising solution to make the most out of these resources by aggregating them as a single entity. On the other hand, VPP’s optimal management depends on its accuracy in modeling stochastic parameters in the VPP body. In this regard, an efficient approach for a VPP is a method that can overcome these intermittent resources. In this paper, a comprehensive study has been investigated for the optimal management of a VPP by modeling different resources—RESs, energy storages, EVs, and distributed generations. In addition, a method based on bi-directional long short-term memory networks is investigated for forecasting various stochastic parameters, wind speed, electricity price, load demand, and EVs’ behavior. The results of this study show the superiority of BLSTM methods for modeling these parameters with an error of 1.47% in comparison with real data. Furthermore, to show the performance of BLSTMs, its results are compared with other benchmark methods such as shallow neural networks, support vector machines, and long short-term memory networks.

This paper presents a new explicit stochastic hydro‐economic optimization model for reservoir‐irrigation district systems design affected by multiple interdependent sources of uncertainties. The model solution determines both optimal design and long‐term operation policies of the systems while accounting for uncertainties of reservoir inflow, rainfall, crop yield, crop price, and production costs. The problem is formulated as a chance‐constrained program in which the dependence structure of discrete and continuous random coefficients is accounted for using copula. In addition to design variables such as the sizing of reservoir capacity and the irrigable area, optimal reservoir operation policies and irrigation management strategies are determined. The model performance and significance is tested in the case study of Chamshir hydrosystem in Iran, consisting of Chamshir Dam and downstream irrigation districts. Various scenarios assessing baseline conditions, risk attitude, and the impacts of change in climatic inputs and upstream conditions are also evaluated. The model results are used to quantify the interrelationships and trade‐offs among optimal values of design parameters, optimal irrigation policy, decision‐makers' risk‐attitude, uncertainty levels of agro‐economic factors, and target reliability levels of meeting crop yield and water requirement. The results demonstrate that smaller sizes of the reservoir and the irrigation district and stress‐avoidance irrigation policies, rather than deficit‐irrigation policies, are preferred when the decision maker is risk‐averse and uncertainties in agro‐economic factors and reliability levels are large. Additionally, we show how the optimal cropping pattern and irrigation strategies are affected by climate change‐induced rainfall variations and the alteration of conditions upstream of the project.