Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers

Neely, Michael J.; Tehrani, Arash Saber; Dimakis, Alexandros G.

doi:10.1109/smartgrid.2010.5621993

Cited by 191 publications

(105 citation statements)

References 18 publications

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“…However, these finite-horizon problems are proven to be NP-hard, and the algorithms only achieve optimality when the delay constraint is arbitrarily loose. In [5], the authors develop an energy allocation algorithm to minimize the total electricity cost. However, they do not allow renewable energy to be saved for future use, which our work takes into account.…”

Section: A State-of-the-artmentioning

confidence: 99%

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Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy

Chen

Shroff

Sinha

2013

IEEE J. Select. Areas Commun.

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Abstract-The smart grid is the new generation of electricity grid that can efficiently utilize new distributed sources of energy (e.g., harvested renewable energy), and allow for dynamic electricity price. In this paper, we investigate the cost minimization problem for an end-user, such as a home, community, or a business, which is equipped with renewable energy devices when electrical appliances allow different levels of delay tolerance. The varying price of electricity presents an opportunity to reduce the electricity bill from an end-user's point of view by leveraging the flexibility to schedule operations of various appliances and HVAC systems. We assume that the end user has an energy storage battery as well as an energy harvesting device so that harvested renewable energy can be stored and later used when the price is high. The energy storage battery can also draw energy from the external grid. The problem we formulate here is to minimize the cost of the energy drawn from the external grid while usage of appliances are subject to individual delay constraints and a long-term average delay constraint. The resulting algorithm requires some future information regarding electricity prices, but it achieves provable performance without requiring future knowledge of either the power demands or the task arrival process. Moreover, we analyze the influence of the assumption that energy can be sold from the battery to the grid. An alternative algorithm is proposed to take advantage of the ability to sell energy. The performance gap between our proposed algorithm and the optimum is shown to diminish as energy selling price approaches the electricity price.

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Section: A State-of-the-artmentioning

confidence: 99%

“…Furthermore, it is worth pointing out that in JTSES the task scheduling and energy management have been decoupled due to the removed Eqn. (5).…”

Section: Energy Sellingmentioning

confidence: 99%

Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy

Chen

Shroff

Sinha

2013

IEEE J. Select. Areas Commun.

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“…However, these schemes are limited to ergodic network models, which may not hold true in our highly dynamic WSN-HR. The recent universal scheduling framework developed by Neely [28] optimizes finite-horizon general network utility with arbitrary dynamic network processes, which has been used in P2P networks [29] and smart electricity markets [30]. OBSEA is the first to combine the universal scheduling framework [28] and network science principles and then applying it urban WSNs using data mules.…”

Section: Related Workmentioning

confidence: 99%

“…Inequality (30) holds only when r x (t) = 0, as Q x (t)/V − I ′ x (0) > 0 (the condition of Lemma 1). Then, the rate controller must set r x (t) = 0 to maximize (15).…”

mentioning

confidence: 99%

Selfish Mules: Social Profit Maximization in Sparse Sensornets using Rationally-Selfish Human Relays

Yang

Adeel

McCann

2013

IEEE J. Select. Areas Commun.

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Abstract-Future smart cities will require sensing on a scale hitherto unseen. Fixed infrastructures have limitations regarding sensor maintenance, placement and connectivity. Employing the ubiquity of mobile phones is one approach to overcoming some of these problems. Here, mobility and social patterns of phone owners can be exploited to optimize data forwarding efficiency. The question remains, how can we stimulate phone owners to serve as data relays? In this paper, we combine network science principles and Lyapunov optimization techniques, to maximize global social profit across this hybrid sensor and mobile phone network. Sensor data packets are produced and traded (transmitted) over a virtual economic network using a lightweight socialeconomic-aware backpressure algorithm, combining rate control, routing, and resource pricing. Phone owners can get benefits through relaying sensor data. Our algorithm is fully distributed and makes no probabilistic/stochastic assumptions regarding mobility, topology, and channel conditions, nor does it require prediction. The global social profit achieved by our algorithm can perform close to (or better than) an ideal algorithm with perfect prediction-proven by rigorous theoretical analysis. Simulation results further demonstrate that the proposed algorithm outperforms pure backpressure and social-aware schemes; highlighting the advantage of building systems combining communication with other types of networks.

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“…Though it is simple and there is no need for information exchange process among consumers, the maximized utilities for consumers and the optimal social welfare for the networks cannot be always achieved. If cooperations between consumers are permitted, more benefits can be obtained correspondingly, such as the centralized optimization scheme [5]. However, it requires perfect information sharing among all consumers or a central controller to collect consumers information to make decision, and therefore not practical for large-scale networks.…”

Section: Introductionmentioning

confidence: 99%

Cooperative power consumption in the smart grid based on coalition formation game

Ling

Ren

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-The power consumption schemes of consumers is an important issue in energy management process in smart grid. The non-cooperative methods which are always considered cannot achieve the maximized performance for consumers and networks. In this paper, we propose a cooperative power consumption scheme for consumers based on coalition formation game, which is suitable for the general electricity markets in smart grid. The advantage is that it can utilize the cooperative relationships among each other for payment savings and meanwhile take the social welfare into consideration. It is realized according to the pricing model used by power provider, the welfare function of the consumer coalitions, as well as the coalition formation algorithm based on the modified Pareto order which are proposed in this paper. Simulation results show that a stable consumers' partition can be formed in the concerned area and the higher utility for consumers and social welfare can be obtained comparing with the non-cooperative methods.

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Efficient Algorithms for Renewable Energy Allocation to Delay Tolerant Consumers

Cited by 191 publications

References 18 publications

Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy

Heterogeneous Delay Tolerant Task Scheduling and Energy Management in the Smart Grid with Renewable Energy

Selfish Mules: Social Profit Maximization in Sparse Sensornets using Rationally-Selfish Human Relays

Cooperative power consumption in the smart grid based on coalition formation game

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