A price signal prediction method for energy arbitrage scheduling of energy storage systems

Zamani-Dehkordi, Payam; Chitsaz, Hamed; Rakai, Logan; Zareipour, Hamidreza

doi:10.1016/j.ijepes.2020.106122

Cited by 13 publications

(9 citation statements)

References 26 publications

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“…However, in order to maximize the benefit of energy arbitrage, it is extremely important to have an insight into the price alteration of forthcoming hours in the day‐ahead and real‐time market. A proper optimization platform can significantly increase the profit of BESS for energy arbitrage 174 . BESS can be designed to provide multiple services and in that case, synergies can maximize the techno‐economic benefit of BESS, that is, minimum revenue reduction during frequency response to maximize energy arbitrage benefit 175 .…”

Section: Bess Application In Renewable Energy Systemmentioning

confidence: 99%

A review of key functionalities of battery energy storage system in renewable energy integrated power systems

2021

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Renewable energy sources (RES), such as photovoltaics (PV) and wind turbines have been widely applied as alternative energy solutions to address the global environmental concern and satisfy the energy demand. The large‐scale amalgamation of intermittent RES causes reliability and stability distress in the electric grid. To mitigate the nature of fluctuation from RES, a battery energy storage system (BESS) is considered one of the utmost effective and efficient arrangements which can enhance the operational flexibility of the power system. This article provides a comprehensive review to point out various applications of BESS technology in reducing the adverse impacts of PV and wind integrated systems. The key focus is given to battery connection techniques, power conversion system, individual PV/wind, and hybrid system configuration. The application of BESS is categorized into three areas, active, reactive, and active‐reactive power features. The key findings of the existing research of BESS application are summarized and discussed along with several simulation results. By taking a thorough review, this article identifies the key challenges of BESS application including battery charging/discharging strategy, battery connection, power conversion efficiency, power converter, RES forecast, and battery lifetime and suggests future research directions that could be explored during the design, operation, and implementation of BESS technology in the power system.

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Section: Bess Application In Renewable Energy Systemmentioning

confidence: 99%

A review of key functionalities of battery energy storage system in renewable energy integrated power systems

2021

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“…The BESS energy arbitrage model is based on [8,14,15,20], where the objective is to maximize the profits that an energy storage system can obtain when buying and selling energy throughout the simulation horizon. The objective function and the constraints of the problem are described bellow.…”

Section: Mathematical Modelmentioning

confidence: 99%

“…This price forecast is obtained from a Recurrent Neural Network (RNN) using a Long Short-Term Memory (LSTM) architecture. As in [8], the RNN was trained using the date-time data, the system load forecast, and the pre-dispatch price data of the previous day, as can be seen in Figure 5. This arbitrage strategy limits the knowledge of prices to the next 24 h; however, in order to compare this case with the other case studies, the simulation was iteratively carried out until reaching a horizon of 365 days.…”

Section: Day-ahead Forecast Case (Case B)mentioning

confidence: 99%

“…One of the most important elements when executing energy arbitrage is knowing the behavior that the market will have in the future to obtain the greatest amount of profits. This is a complex problem due to the number of variables that affect the behavior of the electrical system [8]. Commonly, energy markets work with a day-ahead structure, where the ideal operation is established one day before the system's actual operation.…”

Section: Introductionmentioning

confidence: 99%

“…Prediction models such as those presented in [8,19] show that hybrid models based on neural networks and reinforcement learning can offer, for some cases, better economic performance results than conventional models, depending on the simulation horizon, the market, and the application.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Grid-Scale Battery Energy Storage for Arbitrage Purposes: A Colombian Case

Penaranda¹,

Romero-Quete²,

Cortés³

2021

Batteries

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This study seeks to determine a suitable arbitrage strategy that allows a battery energy storage system (BESS) owner to obtain the maximum economic benefits when participating in the Colombian electricity market. A comparison of different arbitration strategies from the literature, such as seasonal, statistical, and neural networks-based models, is performed. To determine BESS’s optimal operation, a Mixed Integer Linear Programming (MILP) optimization problem is formulated, including a battery degradation model based on an upper piecewise linear approximation method. A financial evaluation of the different arbitrage strategies is carried out, resulting, for all the analyzed cases, in a negative net present value (NPV); thus, the results show that the income obtained from BESS when only performing energy arbitrage in the Colombian market do not compensate the investment costs. Results have also shown that strategies based on statistical and prediction models have a better performance than seasonal strategies, especially in atypical circumstances such as COVID-19.

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Economic evaluation of energy storage integrated with wind power

Zhang

Lü

et al. 2023

Carb Neutrality

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Energy storage can further reduce carbon emission when integrated into the renewable generation. The integrated system can produce additional revenue compared with wind-only generation. The challenge is how much the optimal capacity of energy storage system should be installed for a renewable generation. Electricity price arbitrage was considered as an effective way to generate benefits when connecting to wind generation and grid. This wind-storage coupled system can make benefits through a time-of-use (TOU) tariff. A proportion of electricity is stored from the wind power system at off-peak time (low price), and released to the customer at peak time (high price). Thus, extra benefits are added to the wind-storage system compared with wind-only system. A Particle Swarm Optimization (PSO) algorithm based optimization model was constructed for this integrated system including constraints of state-of-charge (SOC), maximum storage and release powers etc. The proposed optimization model was to obtain the optimal capacity of energy storage system and its operation control strategy of the storage-release processes, to maximize the revenue of the coupled system considering the arbitrage. Furthermore, the energy storage can provide reserve ancillary services for the grid, which generates benefits. The benefits of energy storage system through reserve ancillary services were also calculated. A case study was analyzed with respect to yearly wind generation and electricity price profiles. The benefit compared with no energy storage scenario was calculated. The impact of the energy storage efficiency, cost and lifetime was considered. The sensitivity and optimization capacity under various conditions were calculated. An optimization capacity of energy storage system to a certain wind farm was presented, which was a significant value for the development of energy storage system to integrate into a wind farm.

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A price signal prediction method for energy arbitrage scheduling of energy storage systems

Cited by 13 publications

References 26 publications

A review of key functionalities of battery energy storage system in renewable energy integrated power systems

A review of key functionalities of battery energy storage system in renewable energy integrated power systems

Grid-Scale Battery Energy Storage for Arbitrage Purposes: A Colombian Case

Economic evaluation of energy storage integrated with wind power

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