Preprints
https://doi.org/10.5194/wes-2024-96
https://doi.org/10.5194/wes-2024-96
12 Aug 2024
 | 12 Aug 2024
Status: this preprint is currently under review for the journal WES.

Enabling Efficient Sizing of Hybrid Power Plants: A Surrogate-Based Approach to Energy Management System Modeling

Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das

Abstract. Sizing of Hybrid Power Plants (HPPs), which include wind power plants and battery energy systems, is essential to capture trade-offs among various technology mixes. To accurately represent these trade-offs, an Energy Management System (EMS) is introduced to model the operation of a battery when participating in any market, resulting in realistic operational revenues and costs. However, traditional EMS models are computationally expensive to solve, a challenge that intensifies when integrating these models into sizing processes. This research paper aims to address the critical need for a computationally efficient, accurate, and comprehensive operational model that enables quantitative assessment of HPPs. A novel methodology is introduced to approximate a state-of-the-art EMS model for HPPs involved in spot market power bidding. This approach utilizes singular value decomposition for dimension reduction and a feed-forward neural network as a regression. The accuracy of our methodology is evaluated, showing a root mean square error of 0.09 in predicting hourly operational time series. This method proves effective in accurately evaluating the operation of HPPs across various geographical locations and hence on multiple sizing problems. Furthermore, we utilized the surrogate to evaluate the profitability of several HPPs sizing, achieving a root mean square error of 0.010 on the profitability index. This shows that the developed surrogate is suitable for HPP sizing for given cost and financial assumptions.

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Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-96', Anonymous Referee #1, 07 Sep 2024
    • AC1: 'Reply on RC1', Charbel Assaad, 01 Nov 2024
    • AC3: 'Reply on RC1 - Please disregard the first Reply on RC1', Charbel Assaad, 04 Nov 2024
  • RC2: 'Comment on wes-2024-96', Anonymous Referee #2, 21 Sep 2024
    • AC2: 'Reply on RC2', Charbel Assaad, 01 Nov 2024
    • AC4: 'Reply on RC2 - Please disregard the first reply on RC2', Charbel Assaad, 04 Nov 2024
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das
Charbel Assaad, Juan Pablo Murcia Leon, Julian Quick, Tuhfe Göçmen, Sami Ghazouani, and Kaushik Das

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Short summary
This research develops a new method for assessing Hybrid Power Plants (HPPs) profitability, combining wind and battery systems. It addresses the need for an efficient, accurate, and comprehensive operational model by approximating a state-of-the-art Energy Management System (EMS) for spot market power bidding using machine learning. The approach significantly reduces computational demands while maintaining high accuracy. It thus opens new possibilities in terms of optimizing the design of HPPs.
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