This paper proposes a scenario optimization based algorithm in order to allocate charging station of plug in electric vehicles (PEVs) within a commercial area with the aim of increasing penetration level of photovoltaic (PV) panels as well as decreasing side effects of vehicular loads. A multivariate stochastic modeling methodology based on the notion of copula is provided in order to develop a probabilistic model of the load demand due to PEVs. The suggested method considers the correlation issue among the related random variables of the PEVs. This model, in addition to the models provided for PV generation and commercial loads, is utilized to construct synthetic data required for the optimal allocation of the station. Considering the prepared scenarios, particle swarm optimization (PSO) algorithm is utilized to minimize energy loss as well as voltage deviation in the distribution system. The proposed methodology has been applied on a test distribution network in order to demonstrate its efficiency in simultaneous integration of vehicular loads and PV generations.

This paper proposes a self-healing strategy for two neighboring islanded microgrids (MGs) that are connected together through a normally open interconnecting static switch (ISS). Each MG is expected to support the other during power deficiencies, which is facilitated by the developed self-healing agent. This agent operates based on either a centralized approach, when data communication system is available, or a decentralized approach, when data communication system does not exist or is temporarily unavailable. The necessity and possibility of the MGs interconnection or isolation is determined based on instantaneous power generation of the distributed energy resources in both MGs (in the centralized approach) and local frequency measurements at either side of ISS (in the decentralized approach). The dynamic performance of the MGs with the proposed strategies is evaluated by PSCAD simulation studies. Furthermore, the stability of the system of coupled MGs is investigated through small-signal stability and modal analyses in MATLAB.

The proliferation of distributed energy resources in distribution systems has given rise to a new concept known as Microgrids (MGs). The effective control of MGs is a crucial aspect that needs to be prioritized before undertaking any implementation procedure. This article provides a comprehensive overview of hierarchical control methods that ensure efficient and robust control for MGs. Specifically, it focuses on the secondary controller approaches (centralized, distributed, and decentralized control) and examines their primary strengths and weaknesses. The techniques are thoroughly discussed, deliberated, and compared to facilitate a better understanding. According to functionality, the hierarchical-based control scheme is allocated into three levels: primary, secondary, and tertiary. For secondary control level, the MG communication structures permit the usage of various control methods that provided the significance of the secondary controller for consistent and reliable MG performance and the deficiency of an inclusive recommendation for scholars. Also, it gives a review of the literature on present important matters related to MG secondary control approaches in relation to the challenges of communication systems. The problem of the secondary level control is deliberated with an emphasis on challenges like delays. Further, at the secondary layer, the distributed control techniques for reducing communication system utilization and then reducing communication system delays are conferred. Furthermore, the benefits and limitations of various control structures, such as centralized, decentralized, and distributed are also discusses in this study. Later a comparative analysis of entire control approaches, the best methods of control according to the author’s perspective are also discussed.

The storage units decrease the operation cost of active distribution network considerably if they are managed optimally. In this paper, the short-term optimal scheduling of stationary batteries is presented. The point estimate method is used for considering uncertainty of load, wind-based distributed generation and plug-in electric vehicles as well as their influence on optimal scheduling. The optimal scheduling consists of minimizing cost objective function under technical constraints. In this paper, the cost objective function is composed of operation and reliability costs which are minimized using Tabu search algorithm. The storage units are used for several objectives, i.e., peak shaving, voltage regulation, and reliability enhancement. The numerical studies show the advantages of batteries for energy management in active distribution network, and the impact of uncertainties on optimal scheduling.

This paper proposes a power exchange strategy for sustainable microgrids (MGs) of remote areas that have no access to a utility grid. This strategy manages the MG during power deficiencies under a decentralised approach (DA), which is the most probable case for remote MGs due to the lack of communication systems, or a centralised approach if a data communication system is available. Under each approach, a two‐level (internal/external) control is established. The internal support is provided through power exchange with local energy storage while the external support is provided through power exchange with a neighbouring MG, after their temporary coupling. Appropriate conditions and constraints, under which the necessity and possibility of internal/external power exchange can be determined, are defined and formulated. These terms are based on local frequency measurements (for DA) and instantaneous power generation of the dispatchable distributed energy resources (for centralised approach). The dynamic performance of a remote system, composed of two MGs, operating with this strategy is evaluated by simulation studies in PSCAD/EMTDC. Furthermore, the small signal stability of such a system is investigated in MATLAB.