Optimal energy storage control policies for the smart power grid

Koutsopoulos, Iordanis; Hatzi, Vassiliki; Tassiulas, Leandros

doi:10.1109/smartgridcomm.2011.6102369

Cited by 157 publications

(94 citation statements)

References 6 publications

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“…Koutsopoulos et al [13] and Su et al [12] examine the operation of a single storage device, thus neglecting the network constraints. On the other hand, Kanoria et al in [14], Gayme et al in [15] and Chandy et al in [16] explicitly model the role of the networks in the operation of distributed storage resources.…”

Section: Introductionmentioning

confidence: 99%

Optimal large-scale storage placement in single generator single load networks

Thrampoulidis

Bose

Hassibi

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Large-scale storage will play an increasingly important role in future power grids. As a result, how to optimally place storage in such networks, is an important investment problem. Furthermore, since the allocation of storage resources is static, i.e., it is not feasible to move storage around in a dynamic fashion, it is important to derive optimal such allocations that are robust to the values of the load profiles and other network parameters, such as the line flow constraints. For a single generator single load network, and for a cost of generation that is quadratic in the generation power, we show that, for any given amount of storage resources, placing storage at the demand node is always optimal. This result is true regardless of the demand profile and flow constraints, and therefore is robust. As a byproduct of this result, for a fixed demand profile, we characterize the dependence of the optimal production cost on the flow constraints and on the available storage resources.

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Section: Introductionmentioning

confidence: 99%

Optimal large-scale storage placement in single generator single load networks

Thrampoulidis

Bose

Hassibi

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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“…One way to reduce peak demand that has received significant attention in the research community is leveraging energy storage to shift some demand from peak to off-peak periods. To shift demand, prior work proposes to store energy during off-peak periods, which increases offpeak demand, and use it during peak periods, which then decreases peak demand [4,7,11,13,22,23,24].…”

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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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“…The work studies the profitability of a variety of different pricing schemes, and their effectiveness in decreasing grid demand peaks at scale. Koutsopoulos et al [20] explore the problem from the perspective of a utility operator. In this case, the utility controls when to charge and discharge battery-based storage to minimize generation costs, assuming the marginal cost to dispatch generators increases super-linearly as utilities move up the dispatch stack to satisfy increasing demand.…”

Section: Related Workmentioning

confidence: 99%

GreenCharge: Managing RenewableEnergy in Smart Buildings

Mishra

Irwin

Shenoy

et al. 2013

IEEE J. Select. Areas Commun.

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Abstract-Distributed generation (DG) uses many small onsite energy harvesting deployments at individual buildings to generate electricity. DG has the potential to make generation more efficient by reducing transmission and distribution losses, carbon emissions, and demand peaks. However, since renewables are intermittent and uncontrollable, buildings must still rely, in part, on the electric grid for power. While DG deployments today use net metering to offset costs and balance local supply and demand, scaling net metering for intermittent renewables to a large fraction of buildings is challenging. In this paper, we explore an alternative approach that combines market-based electricity pricing models with on-site renewables and modest energy storage (in the form of batteries) to incentivize DG. We propose a system architecture and optimization algorithm, called GreenCharge, to efficiently manage the renewable energy and storage to reduce a building's electric bill. To determine when to charge and discharge the battery each day, the algorithm leverages prediction models for forecasting both future energy demand and future energy harvesting. We evaluate GreenCharge in simulation using a collection of real-world data sets, and compare with an oracle that has perfect knowledge of future energy demand/harvesting and a system that only leverages a battery to lower costs (without any renewables). We show that GreenCharge's savings for a typical home today are near 20%, which are greater than the savings from using only net metering.

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Optimal energy storage control policies for the smart power grid

Cited by 157 publications

References 6 publications

Optimal large-scale storage placement in single generator single load networks

Optimal large-scale storage placement in single generator single load networks

Scaling distributed energy storage for grid peak reduction

GreenCharge: Managing RenewableEnergy in Smart Buildings

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