Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability

Liu, Yi; Yuen, Chau; Hassan, Naveed Ul; Huang, Shisheng; Yu, Rong; Xie, Shengli

doi:10.1109/tie.2014.2371780

Cited by 134 publications

(50 citation statements)

References 35 publications

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“…It is used for the computation of incentives and pricing in communication networks and has been previously used for incentive design for participatory sensing [26] (though not reward computation). It can be used to minimize dynamic costs [27] and is suitable for rapid changes over time in the environment in which it is applied [28]. These attributes are directly relevant given the desire by service providers that budget consumption be optimized.…”

Section: Lyapunov Optimizationmentioning

confidence: 99%

“…over the time slots. Furthermore, unlike other scenarios typically modelled using Lyapunov Optimization (for example, [27]), forfeit ( ) is, for every time slot t, independent of queue backlogs from previous timeslots:…”

Section: Modelling the Environment For Budget Optimizationmentioning

confidence: 99%

“…The goal rather is to minimize the upper bound on the right hand side of (27). Therefore, the reward allocation algorithm observes the queue backlog ( ) in every time slot and adapts the Lyapunov Optimization approach [35] to choose the budget consumption ( ) as the solution to the following problem:…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

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Adaptive Reward Allocation for Participatory Sensing

Connolly

Dusparić

Iosifidis

et al. 2018

Wireless Communications and Mobile Computing

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Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. However, as an environment is constantly changing (for example, an accident causing a buildup of traffic and elevated pollution levels), the value of a given data item to the service provider is likely to change significantly over time, and therefore an incentivization scheme must be able to adapt the rewards it offers in real-time to match the environmental conditions and current participation rates, thereby optimizing the consumption of the service provider's budget. This paper presents adaptive reward allocation (ARA), which uses the Lyapunov Optimization method to provide adaptive reward allocation that optimizes the consumption of the service provider's budget. ARA is evaluated using a simulated participatory sensing environment with experimental results showing that the rewards offered to participants are adjusted so as to ensure that the data captured matches the dynamic changes occurring in the sensing environment and takes the response rate into account while also seeking to optimize budget consumption.

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Section: Lyapunov Optimizationmentioning

confidence: 99%

Section: Modelling the Environment For Budget Optimizationmentioning

confidence: 99%

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Adaptive Reward Allocation for Participatory Sensing

Connolly

Dusparić

Iosifidis

et al. 2018

Wireless Communications and Mobile Computing

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“…In this optimization problem, the objective is to find the maximum value of D l ðtÞ such that summation of all demands from N users is less than or equal to the available supply b DðtÞ as given in constraint (12) and to satisfy constraint (13). Algorithm 3 that is inspired from [23] can be used to solve this optimization problem.…”

Section: Power Limit Methodsmentioning

confidence: 99%

“…consumption management techniques and use neural network techniques to develop distributed algorithms. The authors in [12,13] develop demand response algorithms in micro-grids in the presence of renewable and distributed energy resources in the system. In [14], the authors developed an algorithm to predict day-ahead prices in order to reduce peak load while targeting maximization of the electricity provider's profits.…”

Section: State Of the Artmentioning

confidence: 99%

Framework for minimum user participation rate determination to achieve specific demand response management objectives in residential smart grids

Hassan

Khalid

Yuen

et al. 2016

International Journal of Electrical Power & Energy Systems

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False data injection attacks and detection on electricity markets with partial information in a micro‐grid‐based smart grid system

Jin

Dou

2020

Int Trans Electr Energ Syst

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Background: The smart grid systems with advanced cyber technologies are vulnerable to the false data injection attacks. The attacks will bypass the online state estimation monitoring, steal energy by a false dispatch in the electricity markets, and gain financial benefits. In this paper, the FDI attack case in the electricity markets of a smart grid power system is considered with micro-grids connected. The estimated transmitted power is modified, leading to the change of electricity prices which will bring the benefit to the attacker. Further, influence on the optimization output of energy management for a Micro-grid has been discussed. By trading online electricity with the main grid, the microgrids are able to maintain power balance and reduce generation cost of the whole smart grid system power system. Then different optimal generation

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Electricity Cost Minimization for a Microgrid With Distributed Energy Resource Under Different Information Availability

Cited by 134 publications

References 35 publications

Adaptive Reward Allocation for Participatory Sensing

Adaptive Reward Allocation for Participatory Sensing

Framework for minimum user participation rate determination to achieve specific demand response management objectives in residential smart grids

False data injection attacks and detection on electricity markets with partial information in a micro‐grid‐based smart grid system

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