On the stability of wholesale electricity markets under real-time pricing

Roozbehani, Mardavij; Dahleh, Munther A.; Mitter, Sanjoy K.

doi:10.1109/cdc.2010.5718173

Cited by 91 publications

(68 citation statements)

References 9 publications

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“…Prior work also highlights the effect of variable rate pricing on grid stability [17,18], showing that real-time pricing has the potential to create an unstable closed feedback loop. We show this experimentally in Figure 5 in the presence of large-scale energy storage.…”

Section: Related Workmentioning

confidence: 99%

Scaling distributed energy storage for grid peak reduction

Mishra

Irwin

Shenoy

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

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Reducing peak demand is an important part of ongoing smart grid research efforts. To reduce peak demand, utilities are introducing variable rate electricity prices. Recent efforts have shown how variable rate pricing can incentivize consumers to use energy storage to cut their electricity bill, by storing energy during inexpensive off-peak periods and using it during expensive peak periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at scale. In this paper, we present the storage adoption cycle to describe the issues with incentivizing energy storage using variable rates. We then propose a simple way to address the issues: augment variable rate pricing with a surcharge based on a consumer's peak demand.The surcharge encourages consumers to flatten their demand, rather shift as much demand as possible to the lowprice period. We present PeakCharge, which includes a new peak-aware charging algorithm to optimize the use of energy storage in the presence of a peak demand surcharge, and use a closed-loop simulator to quantify its ability to flatten grid demand as the use of energy storage scales. We show that our system i) reduces upfront capital costs since it requires significantly less storage capacity per consumer than prior approaches, ii) increases energy storage's ROI, since the surcharge mitigates free riding and maintains the incentive to use energy storage at scale, and iii) uses aggregate storage capacity within 18% of an optimal centralized system.

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Section: Related Workmentioning

confidence: 99%

Scaling distributed energy storage for grid peak reduction

Mishra

Irwin

Shenoy

et al. 2013

Proceedings of the Fourth International Conference on Future Energy Systems

View full text Add to dashboard Cite

show abstract

“…In [13], a demand response scheme is analyzed in which a social planner determines the real-time wholesale electricity prices so to minimize generators' cost functions. The consumers then adjust their consumption based on this price to optimize their utilities.…”

Section: Related Work and Discussionmentioning

confidence: 99%

Mean-field-type optimization for demand-supply management under operational constraints in smart grid

Tembiné

2015

Energy Syst

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The electricity business depends significantly on continuous, cost-effective sources of electricity, the efficient reaction to the demand and storage cost. But the increasing demands and alternative renewable energy sources on the power grid are causing instability and price volatility. Smart grid technology and programs are emerging to address these problems. In this paper we study demand-supply management under operational constraints and outage scheduling. We consider a model of an electricity market in which finite number of suppliers offer electricity. Each supplier has several power plants with different maximum capacity of production and different cost depending on the operational constraints. The response of the market to these offers is the quantities bought from the suppliers. The objective of the market is to satisfy the electricity demand at minimal cost. We develop a theoretical framework for cooperative production strategies between electricity producers. We formulate the problem as an optimal control problem under constraints. Using maximum principle techniques, we provide closed-form expressions of the joint production efforts between the electricity producers. Applying inf-convolution technique to the Hamiltonian we transform the multiple variable optimization problem into a single-variable optimization problem, reducing significantly the curse of dimensionality. In order to capture the random nature of most of the renewable energy sources, we introduce stochastic demand and stochastic production variabilities. We derive closed-form expressions using mean-field-type optimization techniques. The framework is shown to be flexible enough to include both risk-neutral and risk-sensitive behavior of a decision-maker when facing uncertainties. B Hamidou Tembine

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“…Recently, dynamic pricing based demandresponse strategy is gaining attention as a coordination mechanism. When the households are very flexible (for instance, by using their DSs), their responsiveness to the dynamic price might lead to a closed-loop feedback that could cause volatility, as demonstrated in [5]. Thus, in addition to reflecting the aggregate load profile, a dynamic pricing model needs intelligence to capture the flexibility of the households to respond to price signals.…”

Section: Introductionmentioning

confidence: 99%

Distributed Storage Management Using Dynamic Pricing in a Self-Organized Energy Community

Negeri

Baken

2012

Self-Organizing Systems

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Abstract. We consider a future self-organized energy community that is composed of "prosumer" households that can autonomously generate, store, import and export power, and also selfishly strive to minimize their cost by adjusting their load profiles using the flexibly of their distributed storage. In such scenario, the aggregate load profile of the selforganized community is likely to be volatile due to the flexibility of the uncoordinated selfish households and the intermittence of the distributed generations. Previously, either centralized solutions or cooperation based decentralized solutions were proposed to manage the aggregate load, or the load of an individual selfish household was considered. We study the interplay between selfish households and community behavior by proposing a novel dynamic pricing model that provides an optimal price vector to the households to flatten the overall community load profile. Our dynamic pricing model intelligently adapts to the intermittence of the DGs and the closed-loop feedback that might result from price-responsiveness of the selfish households using its learning mechanism. Our dynamic pricing scheme has distinct import and export tariff components. Based on our dynamic pricing model, we propose a polynomial-time distributed DS scheduling algorithm that runs at each household to solve a cost minimization problem that complies with the selfish nature of the households. Our simulation results reveal that our distributed algorithm achieves up to 72.5% reduction in standard deviation of the overall net demand of the community compared to a distributed scheduling with two-level pricing scheme, and also gives comparable performance with a reference centralized scheduling algorithm.

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On the stability of wholesale electricity markets under real-time pricing

Cited by 91 publications

References 9 publications

Scaling distributed energy storage for grid peak reduction

Scaling distributed energy storage for grid peak reduction

Mean-field-type optimization for demand-supply management under operational constraints in smart grid

Distributed Storage Management Using Dynamic Pricing in a Self-Organized Energy Community

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