Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid

Nguyen, Hung Khanh; Song, Ju Bin; Han, Zhu

doi:10.1109/infcomw.2012.6193526

Cited by 142 publications

(96 citation statements)

References 9 publications

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“…The incentive issue has been previously considered in work that focuses on demand response in general power grid networks [13]- [15]. In [16], the authors proposed a game theoretic demand response scheme that achieves the distributed load prediction through the participation of customers.…”

Section: Related Workmentioning

confidence: 99%

A contract-based approach for ancillary services in V2G networks: Optimality and learning

Gao

Chen

Wang

et al. 2013

2013 Proceedings IEEE INFOCOM

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Abstract-With the foreseeable large scale deployment of electric vehicles (EVs) and the development of vehicle-to-grid (V2G) technologies, it is possible to provide ancillary services to the power grid in a cost efficient way, i.e., through the bidirectional power flow of EVs. A key issue in such kind of schemes is how to stimulate a large number of EVs to act coordinately to achieve the service request. This is challenging since EVs are self-interested and generally have different preferences toward charging and discharging based on their own constraints. In this paper, we propose a contract-based mechanism to tackle this challenge. Through the design of an optimal contract, the aggregator can provide incentives for EVs to participate in ancillary services to power grid, match the aggregated energy rate with the service request and maximize its own profits. We prove that under mild conditions, the optimal contract-based mechanism takes a very simple form, i.e., the aggregator only needs to publish an optimal unit price to EVs, which is determined based on the statistical distribution of EVs' preferences. We then consider a more practical scenario where the aggregator has no prior knowledge regarding the statistical distribution and study how should the aggregator learn the optimal unit price from its interactions with EVs. Simulation results are shown to verify the effectiveness of the proposed contract-based mechanism.

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Section: Related Workmentioning

confidence: 99%

A contract-based approach for ancillary services in V2G networks: Optimality and learning

Gao

Chen

Wang

et al. 2013

2013 Proceedings IEEE INFOCOM

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“…Let λ be the thermal conductivity of the walls. 1 Ultimately, we want to compute energy requirement of the room E. E can be written as an equation E = f (λ, a, b, c, . .…”

Section: ) Modeling Thermal Storage and Roomsmentioning

confidence: 99%

“…Electricity prices also fluctuate. This is because some power plants cannot stop power generation or some power sources are dynamic (e.g., solar), and peak hour demand leads not only to more electricity loss in power generation/delivery but also to power plant damage and fast deterioration [1]. As such, dynamic price can encourage usage in low demand time and penalize usage in high demand time.…”

mentioning

confidence: 99%

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

Pan

Wang

Cao

et al. 2013

2013 IEEE 34th Real-Time Systems Symposium

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Abstract-Energy is a global concern and the electricity bills nowadays are leading to unprecedented costs. Electricity price is market-based and dynamic. In this paper, we investigate how to cut the electricity bills of commercial buildings in a dynamic power market. The building thermal systems (e.g., air-conditioning), which dominate electricity bills, has a special property of thermal storage, i.e., the energy will not immediately dissipate from thermal air/water. Intuitively, with storage, the energy can be "stored" in the thermal system, making it possible to purchase electricity in low price and use it at appropriate time. The building thermal supply and electricity purchasing surely depends on human activities that the building should support such as class and meeting schedules. To minimize electricity bills, we develop a holistic planning of electricity purchasing schedule with thermal storage management, and appropriate room assignment schedules for classes/meetings usage.The computing algorithms require inputs of physical modeling on energy consumption. We develop wireless sensing systems to collect fine-grained data which are used to assist the cross-disciplinary physical modeling. We conduct validation through real experiments. We formulate an optimization problem and show that it is NP-complete. Our primary focus is to minimize electricity bills, which matches the incentives of the commercial buildings. We show that this does not coincide with energy conservation. We further investigate the relationship of minimization of electricity bills and minimization of energy consumption. We develop algorithms for our problem and our evaluation shows that we can achieve a 40% cost reduction.

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“…To have a manageable scale of system behavior, demand-side management (OSM) is becoming one of the most important research topics in the smart grid community. Via OSM, load shifting [1] has been considered as a means of adjusting power demand to reduce the peak load [2] or the peak-to-average ratio [3], [4]. Loads that are suitable for load shifting management include water heaters, washing machines or dishwashers, plug-in electric vehicles, refrigerators, air conditioners (thermostatic loads [5]), etc.…”

Section: Introductionmentioning

confidence: 99%

“…In [3], Nguyen et al considered an approach that minimizes the peak-to-average ratio in a distributed energy storage sys tem, and a pricing model was investigated. In contrast to a noncooperative design, a cooperative DSM system was proposed in [10] to improve the energy utilization efficiency.…”

Section: Introductionmentioning

confidence: 99%

Demand-side energy storage system management in smart grid

Chiu

Sun

Poor

2012

2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)

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Publisher's copyright statement: c 2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-An economical way to manage demand-side energy storage systems in the smart grid is proposed by using an Hoo design. The proposed design can adjust the stored energy state economically according to the price signal, while tolerating a certain degree of system uncertainty and having physical constraints on the stored energy level satisfied. Roughly speaking, batteries in the proposed design are charged during a low-price period while being discharged during a high-price period for cost control. Simulations show that the proposed energy storage system can meet the real-time power demand and save money in the long term in contrast to energy storage systems using constant-state schemes.

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Demand side management to reduce Peak-to-Average Ratio using game theory in smart grid

Cited by 142 publications

References 9 publications

A contract-based approach for ancillary services in V2G networks: Optimality and learning

A contract-based approach for ancillary services in V2G networks: Optimality and learning

Minimizing Building Electricity Costs in a Dynamic Power Market: Algorithms and Impact on Energy Conservation

Demand-side energy storage system management in smart grid

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