Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility

Joe‐Wong, Carlee; Sen, Soumya; Ha, Sangtae; Chiang, Mung

doi:10.1109/jsac.2012.120706

Cited by 188 publications

(108 citation statements)

References 18 publications

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“…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. In [15], the authors proposed an algorithm which in the presence of special ECS (Energy Consumption Scheduling) devices deployed in the smart meters of each home, determine the optimal energy consumption schedule.…”

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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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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“…(16), (17) it follows that: i.e. the cost of the schedule Z is obtained by adding to the cost of the matching E a positive quantity which depends only on the cardinality of N 1 .…”

Section: Performance Assessmentmentioning

confidence: 99%

“…Joe-Wong et al [17] combine a convex optimization formulation for computing day-ahead energy prices and an algorithm for estimating and refining EVs' user reaction to the prices. The algorithm allows the provider to dynamically adjust the offered prices based on the EVs' behavior.…”

mentioning

confidence: 99%

Complexity Analysis of Optimal Recharge Scheduling for Electric Vehicles

Rottondi

Neglia²,

Verticale

2016

IEEE Trans. Veh. Technol.

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Abstract-The massive introduction of Electric Vehicles (EVs) will make fleet managers spend a significant amount of money to buy electric energy. If energy price changes over time, accurate scheduling of recharging times may result in significant savings. In this paper we evaluate the complexity of the optimal scheduling problem considering a scenario with a fleet manager having full knowledge of the customers' traveling needs at the beginning of the scheduling horizon. We prove that the problem has polynomial complexity and provide complexity lower and upper bounds. Moreover, we propose an online sub-optimal scheduling heuristic that schedules the EVs' recharge based on historical travelling data. We compare the performance of the optimal and sub-optimal methods to a benchmark online approach that does not rely on any prior knowledge of the customers' requests, in order to evaluate whether the additional complexity required by the proposed strategies is worth the achieved economic advantages. Numerical results show up to of 35% cost savings with respect to the benchmark approach.

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“…In this case, the decision-making of a single foresighted consumer is formulated as a stochastic control problem aiming to maximize its long-term utility [11][12][13]. Alternatively, in [15,16], multiple myopic consumers aim to maximize their utility, and their decisions are formulated as static optimization problems among cooperative users.…”

Section: Related Workmentioning

confidence: 99%

Incentive-compatible demand-side management for smart grids based on review strategies

Schaar

2015

EURASIP J. Adv. Signal Process.

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Demand-side load management is able to significantly improve the energy efficiency of smart grids. Since the electricity production cost depends on the aggregate energy usage of multiple consumers, an important incentive problem emerges: self-interested consumers want to increase their own utilities by consuming more than the socially optimal amount of energy during peak hours since the increased cost is shared among the entire set of consumers. To incentivize self-interested consumers to take the socially optimal scheduling actions, we design a new class of protocols based on review strategies. These strategies work as follows: first, a review stage takes place in which a statistical test is performed based on the daily prices of the previous billing cycle to determine whether or not the other consumers schedule their electricity loads in a socially optimal way. If the test fails, the consumers trigger a punishment phase in which, for a certain time, they adjust their energy scheduling in such a way that everybody in the consumer set is punished due to an increased price. Using a carefully designed protocol based on such review strategies, consumers then have incentives to take the socially optimal load scheduling to avoid entering this punishment phase. We rigorously characterize the impact of deploying protocols based on review strategies on the system's as well as the users' performance and determine the optimal design (optimal billing cycle, punishment length, etc.) for various smart grid deployment scenarios. Even though this paper considers a simplified smart grid model, our analysis provides important and useful insights for designing incentive-compatible demand-side management schemes based on aggregate energy usage information in a variety of practical scenarios.

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Optimized Day-Ahead Pricing for Smart Grids with Device-Specific Scheduling Flexibility

Cited by 188 publications

References 18 publications

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

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

Complexity Analysis of Optimal Recharge Scheduling for Electric Vehicles

Incentive-compatible demand-side management for smart grids based on review strategies

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