This paper elaborates a new protection scheme based on dual-setting DOCRs for automated distribution networks. In this course, the reduction in relays operation time saturates as the number of these relays increases. Thus, optimal deployment of dual-setting DOCRs should be specified in an efficient manner. To do so, a multi-objective optimization approach is established which compromises the reduction of total operation time and the number of dual-setting DOCRs. Coordination constraints are accommodated and non-standard inverse-time characteristics are established to intensify flexibility of the proposed strategy. The proposed model lies within a non-linear programming fashion which is tackled by particle swarm optimization (PSO). Moreover, the augmented -constraint approach is utilized to reach the Pareto-optimal solutions following which the fuzzy decision making process determines the best compromised solution. Detailed simulation studies are carried out to interrogate performance of the proposed approach.

Photovoltaics (PVs) and plug-in electric vehicles (PEVs) could instigate voltage violation issues, if not controlled properly. Besides, these components highly contribute to economic efficiency of microgrids (MGs). Being motivated to enhance technical and economic issues, this study develops an energy management system (EMS) that is able to coordinate voltage control devices, say under load tap changers (ULTCs), PVs, PEV aggregators, and dispatchable distributed generations (DDGs). Active and reactive power provision of PEVs and DDGs along with voltage control of ULTC lessens the plausible violations. So, higher renewable energy is contributed by PVs and extra monetary saving is reflected. In this manner, augmented Epsilon-constraint method followed with fuzzy decision making is applied to operation cost and voltage deviation minimizations. The proposed approach is formulated as a mixed-integer non-linear multi-objective problem and tested on a modified IEEE 33-bus medium-voltage MG. Results are discussed in depth.

This study proposes a multi-objective approach for daily scheduling of microgrids (MGs) contributing to a higher penetration of photovoltaics (PVs). In this process, the existing control devices including distribution flexible ac transmission system (D-FACTS) and under-load tap changer (ULTC) are integrated in Volt/VAr control process. Besides the substantial role in enhancing the service quality, a proper support of reactive power by distribution static compensator (DSTATCOM) annihilates the overvoltage/undervoltage issues and increases the power provision share of PVs. Thus, a higher monetary saving is attained. Moreover, a moderated tap changing operation of ULTC is noticed and the dispatchable distributed generations (DDGs) mainly participate in active power processes. In addition, the hourly reconfiguration of MG assists to a proper feeding path to higher dispatch of cost-effective resources and enhancing the technical metrics. The proposed approach is formulated within a mixed integer linear programming (MILP) being tackled by an augmented e-constraint method. Efficient linearization techniques are deployed to lower the computational burden of the problem. Detailed simulation studies are conducted to assess performance of the proposed approach.

This paper addresses the joint stochastic energy and reserve scheduling problem in microgrids (MGs). The established approach proposes a novel high-performance energy management system (EMS) making use of automatically controlled switches (ACSs). Accordingly, besides the optimal scheduling of active elements namely distributed generations (DGs) and responsive loads (RLs), the optimal topology of the network for each of the scheduling intervals is determined as well. Likewise, the effects of the reconfiguration process in probable variations of the scheduled energy patterns in DGs, RLs, and grid purchases are thoroughly assessed to highlight the alterations in unallocated capacities of these resources. Moreover, the uncertainties associated with both the load and wind speed forecasting errors are suitably accommodated through the reserve allocations. The proposed optimization procedure is formulated as a mixed-integer non-linear problem and resolved using a genetic algorithm (GA). The effectiveness of the projected framework is verified utilizing a typical MG, and the obtained numerical results are discussed in depth. Copyright © 2016 John Wiley & Sons, Ltd.