Sensitivity analysis of the economic benefits from electricity storage at the end consumer level

Ahlert, Klaus-Henning; Dinther, Clemens van

doi:10.1109/ptc.2009.5282245

Cited by 32 publications

(22 citation statements)

References 12 publications

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“…Traditionally, most of the studies address the optimal operation of a storage system based on linear programming [8][9][10][11], nonlinear programming [12], dynamic programming [13][14][15][16] and multipass iteration particle swarm optimization approach [17]. Other charge/discharge strategies are described in [18][19][20][21][22][23][24][25].…”

Section: Current Literaturementioning

confidence: 99%

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A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

2015

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Abstract:Price arbitrage involves taking advantage of an electricity price difference, storing electricity during low-prices times, and selling it back to the grid during high-prices periods. This strategy can be exploited by customers in presence of dynamic pricing schemes, such as hourly electricity prices, where the customer electricity cost may vary at any hour of day, and power consumption can be managed in a more flexible and economical manner, taking advantage of the price differential. Instead of modifying their energy consumption, customers can install storage systems to reduce their electricity bill, shifting the energy consumption from on-peak to off-peak hours. This paper develops a detailed storage model linking together technical, economic and electricity market parameters. The proposed operating strategy aims to maximize the profit of the storage owner (electricity customer) under simplifying assumptions, by determining the optimal charge/discharge schedule. The model can be applied to several kinds of storages, although the simulations refer to three kinds of batteries: lead-acid, lithium-ion (Li-ion) and sodium-sulfur (NaS) batteries. Unlike literature reviews, often requiring an estimate of the end-user load profile, the proposed operation strategy is able to properly identify the battery-charging schedule, relying only on the hourly price profile, regardless of the specific facility's consumption, thanks to some simplifying assumptions in the sizing and the operation of the battery. This could be particularly useful when the customer load profile cannot be scheduled with sufficient reliability, because of the uncertainty inherent in load forecasting. The motivation behind this research is that storage devices can help to lower the average electricity prices, increasing flexibility and fostering the integration of renewable sources into the power system.

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Section: Current Literaturementioning

confidence: 99%

“…In [9], the authors determine the optimal charge/discharge schedule by using a linear optimization model of the battery systems (based on Li-ion and lead-acid technology) for arbitrage accommodation. They found that the cost and the efficiency of the storage systems have the highest impact on simulation results.…”

Section: Linear and Nonlinear Programmingmentioning

confidence: 99%

A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

2015

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“…Similar work by Harsha and Dahleh (2015) considered the effect of wind uncertainty in U.S. dwellings, and reported 7-13 % in cost savings from wind and storage penetration. Other studies, such as Ahlert and van Dinther (2009) put an emphasis on price uncertainty and noted savings up to 17 % for different battery sizes in German residential buildings. A more recent study by Mishra et al (2012), with similar focus, but applied to a different set of American households, ascertains the value of energy storage to be 10-15 % .…”

Section: Related Models For Energy Storage Valuationmentioning

confidence: 99%

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

2015

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The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis-what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: "Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?". We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University's campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated 123 6 P. Crespo Del Granado et al.value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation.

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“…Each DS is battery storage and is described by the parameters that are summarized below [9]. Φ: the maximum energy storage capacity (in kWh).…”

Section: Distributed Storage (Ds)mentioning

confidence: 99%

“…Lead-acid battery storage is used as a reference, since it is regarded as the most economical choice [9]. The parameter specifications for lead-acid suggested in [9] are listed in Table 1. The data for the demand and electricity production of a household are acquired from Alliander 2 .…”

Section: Simulationmentioning

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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Sensitivity analysis of the economic benefits from electricity storage at the end consumer level

Cited by 32 publications

References 12 publications

A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

A Simple Operating Strategy of Small-Scale Battery Energy Storages for Energy Arbitrage under Dynamic Pricing Tariffs

The impact of wind uncertainty on the strategic valuation of distributed electricity storage

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

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