A Decentralized Multi-Agent Approach for Service Restoration in Uncertain Environment

Sharma, Anurag; Srinivasan, Dipti; Trivedi, Anupam

doi:10.1109/tsg.2016.2631639

Cited by 73 publications

(52 citation statements)

References 27 publications

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“…The distributed MAS is proposed in [73] - [77]. In [73], switch agents, load agents, and generator agents communicate with each other to make decisions without a negotiation agent.…”

Section: ) Masmentioning

confidence: 99%

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Review of Service Restoration for Distribution Networks

Shen

Xue³

2020

Journal of Modern Power Systems and Clean Energy

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With the rapid deployment of the advanced metering infrastructure (AMI) and distribution automation (DA), selfhealing has become a key factor to enhance the resilience of distribution networks. Following a permanent fault occurrence, the distribution network operator (DNO) implements the selfhealing scheme to locate and isolate the fault and to restore power supply to out-of-service portions. As an essential component of self-healing, service restoration has attracted considerable attention. This paper mainly reviews the service restoration approaches of distribution networks, which requires communication systems. The service restoration approaches can be classified as centralized, distributed, and hierarchical approaches according to the communication architecture. In these approaches, different techniques are used to obtain service restoration solutions, including heuristic rules, expert systems, metaheuristic algorithms, graph theory, mathematical programming, and multi-agent systems. Moreover, future research areas of service restoration for distribution networks are discussed.

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“…The distributed MAS is proposed in [73] - [77]. In [73], switch agents, load agents, and generator agents communicate with each other to make decisions without a negotiation agent.…”

Section: ) Masmentioning

confidence: 99%

“…In [76], load agents, EV aggregator agents, DG agents, and switch agents are developed in the MAS where these agents negotiate to determine the configuration of microgrids and operation conditions of EVs and DG units. The MAS is improved in [77] by modelling the uncertainties of loads and DERs with the Monte Carlo method.…”

Section: ) Masmentioning

confidence: 99%

Review of Service Restoration for Distribution Networks

Shen

Xue³

2020

Journal of Modern Power Systems and Clean Energy

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“…It considers distributed ESS support in which a switch agent restores the load after detecting, locating, and isolating a fault while a distributed ESS agent supports the distribution system in both grid-connected and islanded modes. Two fully distributed MAS-based approaches for service restoration were presented in [23] and [24]; they considered controlled DG islanding and the uncertainty of load demand together with DGs, respectively. A distributed MAS-based communication scheme using the average consensus algorithm was developed in [25] via local communications between agents for global information discovery as inputs of the optimization-based service restoration module, which would thus become robust to a disastrous event.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Framework for Service Restoration in Distribution Systems With Distributed Generators and Static/Mobile Energy Storage Systems

Prabawa

Choi

2020

IEEE Access

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This paper presents a multi-agent system (MAS)-based approach for service restoration in a distribution system with distributed generators (DGs), static energy storage systems (SESSs), and mobile energy storage systems (MESSs). In comparison with existing MAS-based service restoration approaches in a two-layer cyber-physical architecture, excluding the dispatch of MESSs, we propose a three-layer framework that consists of cyber, physical, and transportation layers corresponding to the communication scheme for the MAS, electric distribution system, and transportation network for MESSs, respectively. In the proposed MAS framework, agents communicate and cooperate with each other for service restoration without violating system operation constraints by conducting the following actions: i) Kruskal-algorithmbased island reconfiguration (switch agent), ii) generation of switching sequence and dispatches of DGs and SESSs (DG and static battery agents) under monitored loading condition (load agent), and iii) dispatch of MESSs based on optimal road routing using the Dijkstra algorithm (mobile battery agent). Case studies were carried out in an IEEE 33-bus distribution system, and the results validated the performance of the proposed approach in terms of number of restored loads and restoration time steps, voltage level, and state of charge of the SESSs and MESSs with different numbers of fault lines and SESSs/MESS, and different extents of damaged roads.

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“…The decentralized MAS for distribution network restoration was investigated in ref. 21 in an uncertainty environment.…”

Section: Introductionmentioning

confidence: 99%

“…According to the above‐mentioned studies and Table 1, there have been three main research gaps in the available literature on restoration process as follows: In many studies, 13‐21 the restoration technique has been applied to the distribution network while disregarding CNMGs. It is worth noting that CNMGs are implemented in the future outline of distribution network 9 and, in turn, the restoration service seems necessary for the proposed system during fault condition to enhance the reliability of both distribution network and MGs. Although the MG generally includes RES, ESS, and load in most studies, 5‐10 there will be EVs in future MGs.…”

Section: Introductionmentioning

confidence: 99%

Two‐stage coupling of coordinated energy management and self‐healing strategies in a smart distribution network considering coupling neighbouring microgrids according to autonomous decentralized restoration technique

Zaheri

Khederzadeh

2020

Int Trans Electr Energ Syst

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One of the central approaches in the smart grid is to restore the smart distribution network (SDN) including different renewable sources and storage systems as microgrid formwork under fault conditions. Therefore, the automatic restoration processes need optimal power flow results in the SDN at fault times. Hence, this study proposed the optimal two‐stage coordinated energy management approach (CEMA) and self‐healing strategy (SHS) in the SDN in the presence of coupling neighbouring microgrids (CNMGs) according to autonomous decentralized restoration technique (ADRT). In the first stage, the SDN operation cost receiving from the upstream grid was minimized to obtain flexible and reliable network, followed by addressing network power flow equations, CNMG constraints including the renewable energy sources (RESs), energy storage systems (ESSs) and electric vehicles (EVs), and SDN operating limits. In the second stage, the optimal model of the ADRT formwork was defined based on the multi‐agent system (MAS). Also, the suitable restoration path was obtained with different zone agents distributed across the network CNMGs considering the minimization of the difference between the number of switching operation and priority loads restored as the normalized objective function. Furthermore, the scenario‐based stochastic programming was used to model the uncertainty of energy price, load, RES power, and EVs parameters. Finally, the proposed problem was solved by the Grey Wolf Optimization (GWO) algorithm and simulated on 33‐bus radial distribution network using MATLAB software. According to numerical results, the proposed strategy can offer the self‐healing and flexible‐reliable SDN in addition to the well‐suited overloading management for CNMGs.

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A Decentralized Multi-Agent Approach for Service Restoration in Uncertain Environment

Cited by 73 publications

References 27 publications

Review of Service Restoration for Distribution Networks

Review of Service Restoration for Distribution Networks

Multi-Agent Framework for Service Restoration in Distribution Systems With Distributed Generators and Static/Mobile Energy Storage Systems

Two‐stage coupling of coordinated energy management and self‐healing strategies in a smart distribution network considering coupling neighbouring microgrids according to autonomous decentralized restoration technique

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