Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty

Krishnamurthy, Dheepak; Uçkun, Canan; Zhou, Zhi; Thimmapuram, P.; Botterud, Audun

doi:10.1109/tpwrs.2017.2685347

Cited by 181 publications

(94 citation statements)

References 19 publications

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“…The charging and discharging efficiencies of battery can be given by (4) and (5), respectively. Fig.…”

Section: A Battery Energy Storage Modelmentioning

confidence: 99%

“…In [2] and [3], a mixed integer linear approach was developed to optimise the storage dispatch that can maximise the profits in real-time markets in the United States and Germany, respectively. In order to handle the uncertainty in electricity price, a scenario-based stochastic formulation was developed in [4] for battery energy arbitrage in both day-ahead and realtime market. The authors of [5] present a bidding mechanism based on two stage stochastic programming for a group of storage that participate in the day-ahead reserve market.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

Cao

Harrold

Fan

et al. 2020

IEEE Trans. Smart Grid

177

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Accurate estimation of battery degradation cost is one of the main barriers for battery participating on the energy arbitrage market. This paper addresses this problem by using a model-free deep reinforcement learning (DRL) method to optimize the battery energy arbitrage considering an accurate battery degradation model. Firstly, the control problem is formulated as a Markov Decision Process (MDP). Then a noisy network based deep reinforcement learning approach is proposed to learn an optimized control policy for storage charging/discharging strategy. To address the uncertainty of electricity price, a hybrid Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) model is adopted to predict the price for the next day. Finally, the proposed approach is tested on the the historical UK wholesale electricity market prices. The results compared with model based Mixed Integer Linear Programming (MILP) have demonstrated the effectiveness and performance of the proposed framework.

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“…The charging and discharging efficiencies of battery can be given by (4) and (5), respectively. Fig.…”

Section: A Battery Energy Storage Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

Cao

Harrold

Fan

et al. 2020

IEEE Trans. Smart Grid

177

View full text Add to dashboard Cite

show abstract

“…However, they assumed a constant round-trip system efficiency. In [36], a stochastic optimisation algorithm leveraging a MILP formulation for the day-ahead bidding market was proposed and compared to a traditional LP. They used binary variables to express acceptance or rejection of their bid, and it was shown that MILP was superior to LP, in terms of cumulative revenue.…”

Section: Literature Reviewmentioning

confidence: 99%

Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming

et al. 2019

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To achieve maximum profit by dispatching a battery storage system in an arbitrage operation, multiple factors must be considered. While revenue from the application is determined by the time variability of the electricity cost, the profit will be lowered by costs resulting from energy efficiency losses, as well as by battery degradation. In this paper, an optimal dispatch strategy is proposed for storage systems trading on energy arbitrage markets. The dispatch is based on a computationally-efficient implementation of a mixed-integer linear programming method, with a cost function that includes variable-energy conversion losses and a cycle-induced battery capacity fade. The parametrisation of these non-linear functions is backed by in-house laboratory tests. A detailed analysis of the proposed methods is given through case studies of different cost-inclusion scenarios, as well as battery investment-cost scenarios. An evaluation with a sample intraday market data set, collected throughout 2017 in Germany, offers a potential monthly revenue of up to 8762 EUR/MWh cap installed capacity, without accounting for the costs attributed to energy losses and battery degradation. While this is slightly above the revenue attainable in a reference application—namely, primary frequency regulation for the same sample month (7716 EUR/MWh cap installed capacity)—the situation changes if costs are considered: The optimisation reveals that losses in battery ageing and efficiency reduce the attainable profit by up to 36% for the most profitable arbitrage use case considered herein. The findings underline the significance of considering both ageing and efficiency in battery system dispatch optimisation.

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“…Balancing markets have been receiving increasing attention among researchers looking at strategic behavior, optimal positions and market design issues. For example, Weber [33] investigated the incentives of market participants (statistical arbitrage potential) in the German electricity balancing mechanism, Ding et al [34] proposed a two-stage stochastic model for an integrated strategy of day-ahead offering and real-time operation policies to maximize their overall profit, and in reference [35], bidding strategies for storage owners in the day-ahead and real-time market were analyzed. In reference [36], a risk-constrained trading strategy using logistic regression forecasts is presented, and in reference [37], a general methodology for optimal bidding strategies based on probabilistic wind generation was formulated.…”

Section: Optimal Imbalance Positionsmentioning

confidence: 99%

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

2018

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This paper applies a multi-factor, stochastic latent moment model to predicting the imbalance volumes in the Austrian zone of the German/Austrian electricity market. This provides a density forecast whose shape is determined by the flexible skew-t distribution, the first three moments of which are estimated as linear functions of lagged imbalance and forecast errors for load, wind and solar production. The evaluation of this density predictor is compared to an expected value obtained from OLS regression model, using the same regressors, through an out-of-sample backtest of a flexible generator seeking to optimize its imbalance positions on the intraday market. This research contributes to forecasting methodology and imbalance prediction, and most significantly it provides a case study in the evaluation of density forecasts through decision-making performance. The main finding is that the use of the density forecasts substantially increased trading profitability and reduced risk compared to the more conventional use of mean value regressions.

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Energy Storage Arbitrage Under Day-Ahead and Real-Time Price Uncertainty

Cited by 181 publications

References 19 publications

Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

Deep Reinforcement Learning-Based Energy Storage Arbitrage With Accurate Lithium-Ion Battery Degradation Model

Ageing and Efficiency Aware Battery Dispatch for Arbitrage Markets Using Mixed Integer Linear Programming

A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market

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