Energy peak shaving with local storage

Johnson, Matthew; Bar-Noy, Amotz; Liu, Ou; Feng, Yi

doi:10.1016/j.suscom.2011.05.001

Cited by 33 publications

(24 citation statements)

References 8 publications

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“…Both papers mention the problems with scaling distributed energy storage to many consumers, but neither i) explores the full implications of large scale adoption, including the decreasing ROI for consumers with storage as adoption scales nor ii) proposes or evaluates a solution to the problem. Johnson et al [12] formulate the peak shaving problem based on a pricing plan where customers are billed for their peak usage. The authors present an optimal offline algorithm, and a competitive online algorithm for solving the problem.…”

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

“…Generally speaking, it could be possible to consider the effects of aging by dividing the planning problem into different time intervals, each one characterized by a specific set of storage properties (e.g., the battery's capacity or efficiency), depending on the aging effects versus time. However, to the best of our knowledge, this approach has never been used in the relevant literature in case of the BESS sizing; rather, the pertinent literature only takes into account the problem of the charging/discharging cycles, as was the case for our paper [4,5,22].…”

Section: Formulation Of the Optimization Problem For Calculating The mentioning

confidence: 99%

“…Storage devices, in fact, are key components of micro grids, which are a cluster of loads and distributed resources optimally integrated and controlled in order to maximize technical and economic benefits [5]. The services BESS can provide can be enlarged further if optimal sizing and operation of BESSs are guaranteed.…”

Section: Introductionmentioning

confidence: 99%

“…In the most general case, reducing the electricity bill can involve both energy [energy charge (EC)] and peak power (demand charge) [5,8]. This paper refers only to the EC, so reducing the electricity bill means only the ability of the end-user to increase its energy required from the grid during the lower price hours and decrease the energy required from the grid when the price is higher.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist

et al. 2014

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Demand response (DR) can be very useful for an industrial facility, since it allows noticeable reductions in the electricity bill due to the significant value of energy demand. Although most industrial processes have stringent constraints in terms of hourly active power, DR only becomes attractive when performed with the contemporaneous use of battery energy storage systems (BESSs). When this option is used, an optimal sizing of BESSs is desirable, because the investment costs can be significant. This paper deals with the optimal sizing of a BESS installed in an industrial facility to reduce electricity costs. A four-step procedure, based on Decision Theory, was used to obtain a good solution for the sizing problem, even when facing uncertainties; in fact, we think that the sizing procedure must properly take into account the unavoidable uncertainties introduced by the cost of electricity and the load demands of industrial facilities. Three approaches provided by Decision Theory were applied, and they were based on: (1) the minimization of expected cost; (2) the regret felt by the sizing engineer; and (3) a mix of (1) and (2). The numerical applications performed on an actual industrial facility provided evidence of the effectiveness of the proposed procedure.

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“…[17][18][19][20][21][22][23][24]. Furthermore, with fast development of material science, researches on new energy storage technologies such as graphen based energy storage technologies, are also carried out.…”

Section: Introductionmentioning

confidence: 99%

Challenges and progresses of energy storage technology and its application in power systems

Yao

Yang

Cui

et al. 2016

J. Mod. Power Syst. Clean Energy

196

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As a flexible power source, energy storage has many potential applications in renewable energy generation grid integration, power transmission and distribution, distributed generation, micro grid and ancillary services such as frequency regulation, etc. In this paper, the latest energy storage technology profile is analyzed and summarized, in terms of technology maturity, efficiency, scale, lifespan, cost and applications, taking into consideration their impact on the whole power system, including generation, transmission, distribution and utilization. The application scenarios of energy storage technologies are reviewed and investigated, and global and Chinese potential markets for energy storage applications are described. The challenges of large-scale energy storage application in power systems are presented from the aspect of technical and economic considerations. Meanwhile the development prospect of global energy storage market is forecasted, and application prospect of energy storage is analyzed.

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Energy peak shaving with local storage

Cited by 33 publications

References 8 publications

Scaling distributed energy storage for grid peak reduction

Scaling distributed energy storage for grid peak reduction

Optimal Sizing of Battery Storage Systems for Industrial Applications when Uncertainties Exist

Challenges and progresses of energy storage technology and its application in power systems

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