2016
DOI: 10.1109/tste.2016.2581167
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Day-Ahead Smart Grid Cooperative Distributed Energy Scheduling With Renewable and Storage Integration

Abstract: Day-ahead scheduling of generation units and storage devices is essential for the economic and efficient operation of a power system. Conventionally, a control center calculates the dispatch schedule by gathering information from all of the devices. However, this centralized control structure makes the system vulnerable to single point of failure and communication failures, and raises privacy concerns. In this paper, a fully distributed algorithm is proposed to find the optimal dispatch schedule for a smart gr… Show more

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Cited by 83 publications
(57 citation statements)
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References 30 publications
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“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”
Section: Application Scenarios Solution Methods Optimization Goals LImentioning
confidence: 99%
See 2 more Smart Citations
“…Renewable energy generation Stochastic optimization Handling date uncertainties of renewable energy [10][11][12] Robust optimization [14][15][16][17] Wind power forecasting Linear methods Increasing the accuracy of prediction model [19,20] Nonlinear methods [24][25][26][27] Microgrid management Ordinary decision theory Optimizing energy-scheduling strategies [28][29][30] Noncooperative games [33][34][35][36] Cooperative games [37][38][39][40] and robust optimization [9]. On the one hand, stochastic optimization provides an effective framework to optimize statistical objective functions while the uncertain numerical data are assumed to follow a proverbial probability distribution.…”
Section: Application Scenarios Solution Methods Optimization Goals LImentioning
confidence: 99%
“…The authors developed a cooperative distributed energy-scheduling algorithm to optimize the energy dispatch problem while considering the integration of renewable generation and energy storage in Ref. [39]. In Ref.…”
Section: Application Scenarios Solution Methods Optimization Goals LImentioning
confidence: 99%
See 1 more Smart Citation
“…The lower control level concentrates on power system modelling and aims to control BESS to track the power reference. An optimal distributed control scheme should be flexible, robust and address the problem with a costeffective way (Kumar Nunna & Srinivasan, 2016;Morstyn, Hredzak, & Agelidis, 2014;Morstyn, Hredzak, Member, & Vassilios, 2016;Xu, Zhang, Hug, Kar, & Li, 2015;Zhang, Rahbari-asr, Duan, & Chow, 2016;Zhao & Ding, 2018).…”
Section: Control Strategy For Distributed Mas-based Microgridmentioning
confidence: 99%
“…In order to protect the privacy of each participant, Zhao & Ding (2018) presented another cooperative optimal solution for multiple battery energy storage system in a microgrid to maintain the supply-demand mismatch under the wind power generation uncertainties. Similarly, Zhang et al (2016) presented an offline cooperative distributed energy scheduling algorithm, which allowed limited information for agent communication and facilitated reconfiguration of the distributed system. Likewise, in (Kumar Nunna & Srinivasan, 2016), a dynamically updated energy management schedule was presented by altering the operation of storage devices and controllable demand response load to overcome the day-ahead forecasting error and system uncertainty.…”
Section: Control Strategy For Distributed Mas-based Microgridmentioning
confidence: 99%