Moein Manbachi

This paper proposes an advanced solution for the energy management of the islanded AC/DC microgrids using an Intelligent Agent (IA) approach. This approach provides islanded AC/DC microgrids with three main operating agents (i.e., AC microgrid, DC microgrid and system operator agents) communicating with each other at each operating time interval to increase system levels of performance, efficiency, and reliability. Bidirectional communication allows data collection and control command flow between AC microgrid, DC microgrid and system operator to not only minimize AC and DC operational costs but also minimize AC/DC conversion costs, perform optimal demand shifting and minimize load shedding using a progressive strategy. For the optimization purpose, this paper uses an advanced multi-objective particle swarm optimization engine to effectively solve the problem of each agent. To test the precision and operational performance of the proposed solution, a 33-node islanded AC/DC microgrid with diverse AC/DC generating resources and loads is studied.

This paper presents a new approach for quasi real-time and adaptive energy management for islanded AC/DC microgrids using Advanced Metering Infrastructure (AMI) data. The paper presents a sophisticated multi-objective algorithm by employing AMI-based energy conservation and optimization techniques, tailored for different operational scenarios. The paper argues that optimal operation can be achieved by defining new concepts, such as Operation Value Factor (OVF), that can inherently reduce system operational costs. Moreover, the results of this paper demonstrate how utilizing accurate load models can improve AC/DC microgrid efficiency. To test the performance and the applicability of the proposed solution, a modified 33-node islanded AC/DC microgrid is used as a case study in the presence of various types of AC/DC generating units and loads.

This paper aims to investigate Community Energy Storage (CES) impacts on AMI-based Volt-VAR Optimization (VVO) solutions for advanced distribution networks. CES is one of the technologies employed to improve system stability, reliability and quality. As such, it could have considerable impacts on voltage control, reactive power optimization and energy conservation. Conservation Voltage Reduction (CVR) is one of the main tasks of advanced VVO engines in distribution networks. Moreover, in order to check the performance of the discussed VVO engine in the presence of CES during peak time intervals, 33-node distribution feeder is employed. The results of this paper show significant improvement in the performance of the VVO engine when CES is forced to discharge in peak times. Moreover, the results present how CES could affect Volt-VAR Control Component (VVCC) switching and how it affects the energy conservation efficiency.

This paper presents an implementation of an IEC 61850-based real-time co-simulation platform for verification of the performance of a volt-VAR optimization (VVO) engine for smart distribution networks. The proposed VVO engine is able to minimize grid loss, volt-VAR control asset operational costs, and conservation voltage reduction operational costs through its comprehensive objective functions, weighted by fuzzification using advanced metering infrastructure (AMI) data. The optimization engine receives the AMI data stream through measurement aggregators. Moreover, it sends control commands to volt-VAR control components modeled in real-time digital simulator (RTDS) through DNP.3 protocol. To check the performance and the precision of proposed VVO, a fault scenario is imposed upon the system. IEC 61850 GOOSE messages are generated and sent to change the status of specified breakers, while the VVO engine receives system reconfiguration commands via IEC61850 Manufacturing Message Specification (MMS) protocol. The results of the study on 33-node feeder showed adequate performance of proposed VVO in grid operating scenarios.

This paper investigates a novel approach for maintenance scheduling of volt-VAR control components (VVCCs) of distribution networks with the aid of new generation of volt-VAR optimization (VVO) solutions called quasi-real-time VVO. The new quasi-real-time VVO technique optimizes distribution network using advanced metering infrastructure (AMI) data of each quasi-real-time stage. As this VVO performs automatically and online, it is necessary for VVCCs to undergo maintenance without disturbing VVO performance. Moreover, the lost benefits that could be gained by online VVO have to be minimized. Hence, this paper proposes an AMI-based VVO consisting of a VVO engine and a maintenance scheduling engine (MSE) that operate in tandem to optimize distribution network and find the optimal maintenance scheduling of different VVCCs. To test the accuracy and the applicability of the proposed solution, a 33-node distribution feeder is employed. Furthermore, five different maintenance scenarios are investigated to check the proposed VVO performance. The results prove that the integration of VVO with MSE could be a reliable approach that can solve maintenance scheduling of VVCCs without interrupting and/or resetting VVO.