A storage expansion planning framework using reinforcement learning and simulation-based optimization

Tsianikas, Stamatis; Yousefi, Nooshin; Zhou, Jian; Rodgers, Mark D.; Coit, David W.

doi:10.1016/j.apenergy.2021.116778

Cited by 18 publications

(22 citation statements)

References 47 publications

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“…Each user's local storage stores some resources, which can be pictures, audio, video, etc. [13][14][15]. Each resource can be shared through the Internet "O2O mode" to users who need this resource.…”

Section: Resource Sharing Model Of Distancementioning

confidence: 99%

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Remote English Teaching Resource Sharing Based on Internet O2O Model

Hou

2022

Scientific Programming

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Aiming at the problems of low success rate, delay, and high communication cost in distance English teaching resource sharing, this paper puts forward a method of distance English teaching resource sharing based on Internet O2O mode. Based on the model of distance English teaching resource sharing, this paper designs four processes: query, reply, resource substitution, and resource sharing optimization. Experimental results show that the proposed method can achieve high success rate of resource sharing, low latency, communication cost, and high transmission efficiency. Therefore, it is an effective method.

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Section: Resource Sharing Model Of Distancementioning

confidence: 99%

“…. , l n 􏼈 􏼉 T , then the resource components in distance English teaching are filtered by means of formula (13) combined with principal component analysis [26][27][28], and the data resource components with large variance and much information are retained:…”

Section: Remote Mapping Of English Teaching Resourcesmentioning

confidence: 99%

Remote English Teaching Resource Sharing Based on Internet O2O Model

Hou

2022

Scientific Programming

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“…Tsianikas et al. proposed a long‐term microgrid expansion planning model, aiming at minimizing the investment and power shortage cost, and made a battery investment plan for a microgrid 7 . Wang et al.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

Pang

Zhou

Tsianikas

et al. 2022

Quality & Reliability Eng

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Microgrid has attracted more and more attention to provide backup power for customers in the case of power grid outages. Microgrid expansion planning is significant to handle the increasing customer demand and to enhance power resilience. Current research about long‐term microgrid expansion planning rarely if ever considered the uncertainties associated with energy storage and power generation units, for example, battery cycle degradation. These factors have important influence on the performance of microgrid expansion planning in reality. In this paper, a long‐term microgrid expansion planning model with multiple energy resource is presented. Deep reinforcement learning method is used to obtain the cost‐effective microgrid expansion policies to enhance power resilience. In the case study, optimal microgrid expansion planning is achieved based on the proposed model. The impacts of battery degradation and resilience constraint on microgrid expansion policy optimization are also investigated. The simulation results prove the effectiveness of the proposed method on economic and resilient microgrid expansion planning.

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“…Even the degradation process is not well-known, though some studies have tried to consider the degradation of batteries technically in the optimization of EP. On the other hand, some studies consider EP without ESS degradation in the recent literature (Ansari et al, 2021;Larsen and Sauma, 2021;Saber et al, 2018;Trpovski et al, 2020;Tsianikas et al, 2021). The operational cost of the ESS may cover degradation resulting in capacity loss.…”

Section: Introductionmentioning

confidence: 99%

An expansion planning method for extending distributed energy system lifespan with energy storage systems

Tercan

Elma

Gökalp

et al. 2021

Energy Exploration & Exploitation

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The recent advances in the modern power grids, such as growing energy demand and penetration of higher amounts of distributed energy generators like renewable energy resources, caused additional grid integration challenges for the distributed energy system operators. Besides, deep electrification impacts triggered by a growing share of electric vehicles as additional electric loads made it essential for the distributed energy system operators to re-investigate their upgrade plans in terms of distributed energy system lines and corresponding infrastructure investments. An energy storage system offers the opportunity to use energy flexibly, resulting in deferring the inevitable upgrade costs of the distribution grid elements and increasing the power quality. In this study, a new method is proposed to extend the lifespan of distributed energy systems with an energy storage system and reduce line upgrade costs. The proposed method is tested on the IEEE-33 with different case studies. The findings of this study indicated that the investigated energy storage option has a positive impact on the distributed energy system components and assets in terms of extending their lifespan and helping to mitigate growing demand peaks because of the load increase. According to the results, the proposed method reduces the total cost by up to 66%. Furthermore, the power losses are reduced by an average of 34.8%, and the voltage profiles are improved with the energy storage system.

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A storage expansion planning framework using reinforcement learning and simulation-based optimization

Cited by 18 publications

References 47 publications

Remote English Teaching Resource Sharing Based on Internet O2O Model

Remote English Teaching Resource Sharing Based on Internet O2O Model

Deep reinforcement learning for resilient microgrid expansion planning with multiple energy resource

An expansion planning method for extending distributed energy system lifespan with energy storage systems

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