The impact of electricity storage on wholesale electricity prices

Nyamdash, Batsaikhan; Denny, Eleanor

doi:10.1016/j.enpol.2012.11.033

Cited by 34 publications

(18 citation statements)

References 35 publications

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“…The results further revealed an increase in consumer surplus which was larger than the decrease in producer surplus, and thus resulting in a net increase in the social welfare. Similarly, the authors in [11] investigated the impact of large-scale energy storage operation on the wholesale electricity price. However, no mathematical models were described for determining either the optimal operation strategy of the storage units or the market-clearing price.…”

Section: Introductionmentioning

confidence: 98%

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Impact of Energy Storage Systems on Electricity Market Equilibrium

Awad¹,

Fuller²,

EL-Fouly³

et al. 2014

IEEE Trans. Sustain. Energy

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Integration of large-scale energy storage systems (ESSs) is desirable nowadays to achieve higher reliability and efficiency for smart grids. Controlling ESS operation usually depends on electricity market prices so as to charge when the price is low and discharge when the price is high. On the other hand, the market-clearing price itself is determined based on the net demand, i.e., including energy storage output, at every hour. Therefore, it is crucial to develop a mathematical model to determine the optimal ESS operation as well as the market-clearing prices. The problem is formulated as a mixed complementarity problem (MCP) that allows the representation of special (incentive) prices, which cannot be represented in a single optimization model. The proposed model is useful for power system operators to determine the optimal storage dispatch simultaneously with the market-clearing price in addition to the conventional generation dispatch. The impact of energy storage size and location on market price, total generation cost, energy storage arbitrage benefit, and total consumer payment is further investigated in this paper. The latter analysis provides some guidelines for power system planners to identify the optimal size and location for installing large-scale ESSs.Index Terms-Energy storage systems (ESSs), mixed complementarity problem (MCP), smart grids.

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Section: Introductionmentioning

confidence: 98%

“…The impact on electricity price was instead approximated by observing the effect of storage units on the generation cost of marginal conventional plants. Like [9] and [10], [11] did not model the power network, so locational effects were not considered.…”

Section: Introductionmentioning

confidence: 99%

Impact of Energy Storage Systems on Electricity Market Equilibrium

Awad¹,

Fuller²,

EL-Fouly³

et al. 2014

IEEE Trans. Sustain. Energy

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show abstract

“…Many other tools exist that focus on the power system (examples are WILMAR [26,33,34] (Wind Power Integration in Liberalised Electricity Markets), E2M2 [35], LTS [22], EUPowerDispatch [36], URBS-EU [27], PowerACE-Europe [37], the European Climate Foundation model [38], COMPETES [39] (COmprehensive Market Power in Electricity Transmission and Energy Simulator)). Their level of description is somehow similar to the models presented.…”

Section: Modelling Tools For the Power Systemmentioning

confidence: 99%

Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools

Després

Hadjsaïd

Criqui

et al. 2015

Energy

153

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“…The arbitrage application is widely discussed in the literature, both from a system (e.g., [10][11][12][13][14][15]) and from an individual storage plant's PBUC perspective, the latter being the focus of this article. Generally, there are two important assumptions in PBUC arbitrage models: the first is related to the storage operator's assumed knowledge of future prices, i.e., the (im)perfect price foresight assumption, while the second is related to whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or price-making assumption [16,17].…”

Section: Scope and Approachmentioning

confidence: 99%

“…The DA arbitrage objective function is now (14), which is still subject to (3) - (11), and is now additionally subject to (15) - (20). The resulting problem is a mixed-integer nonconvex quadratic program (nonconvex MIQP) which is solved in GAMS using the SBB solver [52]:…”

Section: Including Time-varying Piecewise-linear Market Resilience Fumentioning

confidence: 99%

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

et al. 2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Electricity storage plants can be used for many applications, with one of the most studied applications being arbitrage in the day-ahead market. Terms of use: Documents inAlthough the arbitrage value is related to the presence of price spreads, it also depends on the effect of (dis)charge actions on prices, as arbitrage generally reduces price spreads by increasing off-peak prices when charging and decreasing peak prices when discharging. As such, there are two important assumptions in price-based unit commitment arbitrage models: first, whether the storage operator is assumed to have perfect knowledge of future prices, and second, whether they recognize that their (dis)charge actions may affect those prices, i.e., the price-taking or pricemaking assumption. This article proposes a comprehensive formulation of the arbitrage problem including detailed operating constraints, and focuses on relaxing the price-taking assumption by considering real-world price-effect data, published in the form of hourly piecewise linear relationships between quantity and price based on submitted bids, which are referred to as "market resilience functions". These can be used to (1) evaluate the price-taking and price-making assumptions based on simplified price-effects, and to (2) provide an upper limit to the arbitrage value under the assumption that prices and price-effects are known at the decision stage. In addition, a stepwise approximation to the piecewise linear functions is developed to reduce computation time, i.e., from mixed-integer nonconvex quadratic programming to mixed-integer linear programming, while providing lower-and upper bound approximations to the arbitrage value. The developed models are applied to the Belgian day-ahead market for 2014, and show that the price-effect has a strong impact on the operation and arbitrage value of large-scale storage.

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The impact of electricity storage on wholesale electricity prices

Cited by 34 publications

References 35 publications

Impact of Energy Storage Systems on Electricity Market Equilibrium

Impact of Energy Storage Systems on Electricity Market Equilibrium

Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools

Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects

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