Japan's Pivot to Resilience: How Two Microgrids Fared After the 2011 Earthquake

Marnay, Chris; Aki, Hirohisa; Hirose, Keikichi; Kwasinski, Alexis; Ogura, Saori; Shinji, Takao

doi:10.1109/mpe.2015.2397333

Cited by 76 publications

(24 citation statements)

References 2 publications

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“…The resilience is important for electricity systems because they need high reliability and availability. Hence, there are many studies and projects focused on the resilience [13], [14], [15], [16], [17], [18], [19]. These works are divided into two categories.…”

Section: The Resilience Of Electricity Systemsmentioning

confidence: 99%

“…These works are divided into two categories. The works [13], [14], [15], [16], [17] in the first category improved the resilience of existing electricity systems by utilizing microgrids. The Sendai microgrid [14] is built for the electricity system of Tohoku Fukushi University.…”

Section: The Resilience Of Electricity Systemsmentioning

confidence: 99%

“…where C is the time and spatial average of c i (t) for 1 ≤ i ≤ n. According to Eqs. (16) and (17), A and C are approximately proportional to κ A , and the difference between them is determined with λ GC . If λ GC is a small value, the consumption rates are relatively large compared with the generation rates, and the microgrid will face a severe energy shortage situation without the energy supply from an external power grid.…”

Section: Time-varying Modelmentioning

confidence: 99%

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Fast Directional Energy Interchange Used in MCMC-based Autonomous Decentralized Mechanism toward a Resilient Microgrid

Sato

Taniguchi

2020

Journal of Information Processing

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In order to utilize renewable energy effectively, the generated surplus energy should be stored in batteries, and transferred to distant places with high demand in a microgrid. As a scalable mechanism for such energy transfer (energy interchange), we proposed an autonomous decentralized mechanism (ADM) based on Markov Chain Monte Carlo (MCMC), and clarified that our ADM accomplishes the global objective to quickly supply energy appropriately for energy demand all over the microgrid. In this paper, toward a resilient microgrid, we propose a method of directional energy interchange used in our ADM. We first design a method of the directional energy interchange to be able to quickly transfer energy in an appropriate direction on the basis of the advection-diffusion equation used in physics. Then, we investigate the performance of the proposed method through a simulation experiment considering energy shortage and emergency situations. Simulation results show that the proposed method (a) can quickly supply energy from a traditional centralized grid to a microgrid under energy shortage situations, and (b) can quickly gather distributed energy to a specific place (e.g., safe shelter) under emergency situations.

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Section: The Resilience Of Electricity Systemsmentioning

confidence: 99%

Section: The Resilience Of Electricity Systemsmentioning

confidence: 99%

Section: Time-varying Modelmentioning

confidence: 99%

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Fast Directional Energy Interchange Used in MCMC-based Autonomous Decentralized Mechanism toward a Resilient Microgrid

Sato

Taniguchi

2020

Journal of Information Processing

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“…For example, Bragado (2016) investigated the problem of infrastructure system downtime estimation after earthquakes, where an empirical model was developed on the basis of damage data of earthquakes during the last hundred years. Marnay et al (2015) studied Japan's energy system resilience after earthquakes and highlighted the significance of microgrids.…”

Section: Energy Systemsmentioning

confidence: 99%

System resilience enhancement: Smart grid and beyond

Huang¹,

Wang²,

Chen³

et al. 2017

Front. Eng

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Boosting the resilience of power systems is a core requirement of smart grids. In fact, resilience enhancement is crucial to all critical infrastructure systems. In this study, we review the current research on system resilience enhancement within and beyond smart grids. In addition, we elaborate on resilience definition and resilience quantification and discuss several challenges and opportunities for system resilience enhancement. This study aims to deepen our understanding of the concept of resilience and develop a wide perspective on enhancing the system resilience for critical infrastructures.

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“…It caused difficulties to rescue teams for providing assistance. Moreover, victims who lived in their homes could not get warm as their heaters were not able to operate at the time of the disaster [1]. To solve such a situation, facilities are needed to provide electricity at the time of a blackout or disaster.…”

Section: Introductionmentioning

confidence: 99%

RBF neural network-based online intelligent management of a battery energy storage system for stand-alone microgrids

et al. 2016

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Background: An offline optimization approach based on energy storage management response in a microgrid was not fast and not reliable enough to control and adjust the system efficiently after the loss of the utility grid. Thus, it causes system inefficiency and collapse in the presence of violent changes of loads or outage of distributed generations. To solve such a problem, more real-time management is needed. Any changes in loads/generations should be compensated successfully by a battery energy storage system (BESS) in a short period of time.Methods: This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN) incorporating particle swarm optimization (PSO) to prevent the stand-alone microgrid from instability and system collapse. BESS is centrally controlled by a controller developed by the proposed RBFNN. PSO is used to determine the optimized active and reactive power at every load/generation changing situation to monitor the effect of system frequency, voltage, and reference power regulation. These optimized power data are then employed as target data for the RBFNN generalization and training process. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process. With an appropriate RBFNN training, the optimum active and reactive power can directly be obtained without the necessity of performing the PSO optimization process at any change of load/generation. Results: The results show that the predictive results of the proposed RBFNN model only slightly differed from the target results based on PSO and have a minimum statistical error compared to the predictive results based on the multilayer perceptron neural network (MLPNN) model. Conclusions: The proposed RBFNN is suitable for the online estimation of the active and reactive power of BESS and can be used for real-time energy storage management as an online controller.

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Japan's Pivot to Resilience: How Two Microgrids Fared After the 2011 Earthquake

Cited by 76 publications

References 2 publications

Fast Directional Energy Interchange Used in MCMC-based Autonomous Decentralized Mechanism toward a Resilient Microgrid

Fast Directional Energy Interchange Used in MCMC-based Autonomous Decentralized Mechanism toward a Resilient Microgrid

System resilience enhancement: Smart grid and beyond

RBF neural network-based online intelligent management of a battery energy storage system for stand-alone microgrids

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