no code implementations • 2 May 2024 • Yiqian Wu, Bolun Xu, James Anderson
We present a framework to differentiate strategic capacity withholding behaviors attributed to market power from inherent competitive bidding in storage unit strategies.
no code implementations • 26 Apr 2024 • Saud Alghumayjan, Jiajun Han, Ningkun Zheng, Ming Yi, Bolun Xu
This paper presents an integrated model for bidding energy storage in day-ahead and real-time markets to maximize profits.
no code implementations • 8 Mar 2024 • Xin Qin, Ioannis Lestas, Bolun Xu
This paper derives a theoretical framework to study the economic capacity withholding behavior of storage participating in competitive electricity markets and validate our results in simulations based on the ISO New England system.
no code implementations • 26 Jul 2023 • Liudong Chen, Bolun Xu
Simulation using real-world consumer data shows that our equitable tariffs protect low-income consumers from price surges while effectively motivating consumers to reduce peak demand.
1 code implementation • 20 Jun 2023 • Yuexin Bian, Ningkun Zheng, Yang Zheng, Bolun Xu, Yuanyuan Shi
Energy storage are strategic participants in electricity markets to arbitrage price differences.
no code implementations • 28 Jan 2023 • Joshua Jaworski, Ningkun Zheng, Matthias Preindl, Bolun Xu
We incorporate nonlinear battery models and price uncertainty into the V2G management design to provide a realistic estimation of cost savings from different V2G options.
no code implementations • 2 Jan 2023 • Yousuf Baker, Ningkun Zheng, Bolun Xu
We also test a transfer learning approach by pre-training the bidding model using New York data and applying it to arbitrage in Queensland, Australia.
no code implementations • 14 Nov 2022 • Ningkun Zheng, Xiaoxiang Liu, Bolun Xu, Yuanyuan Shi
This paper proposes a novel energy storage price arbitrage algorithm combining supervised learning with dynamic programming.
no code implementations • 14 Jul 2022 • Ningkun Zheng, Xin Qin, Di wu, Gabe Murtaugh, Bolun Xu
Combined with an optimal bidding design algorithm using dynamic programming, our paper shows that the SoC segment market model provides more accurate representations of the opportunity costs of energy storage compared to existing power-based bidding models.
no code implementations • 2 Sep 2021 • Yuanyuan Shi, Bolun Xu
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals.
no code implementations • 30 Oct 2019 • Yuxiao Liu, Bolun Xu, Audun Botterud, Ning Zhang, Chongqing Kang
Results identify how the bounds decrease with additional power grid physical knowledge or more training data.