Day-Ahead Scheduling With Renewable Generation Considering Shared Energy Storage

Xv, Anqi; He, Chuan; Zhang, Min; Wang, Tengxin

doi:10.1109/aeees54426.2022.9759703

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…In order to fully tap the application potential of energy storage, some studies propose the concept of shared energy storage (Zhao et al, 2020). Some studies propose a business model for utility-scale shared energy storage systems (Ben-Idris et al, 2021), while other studies analyze the complementary and controllable capabilities of energy storage that promote new energy consumption, and study the multiple energy storage sharing mechanism (Xv et al, 2022). Further, some studies use a hybrid storage system for energy sharing within the prosumers' community (Mussadiq et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Allocating the capacity of shared energy storage for wind farm groups based on the over-limit power export risk

Wang

Song²,

Gu³

et al. 2023

Front. Energy Res.

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Energy storage in wind farms can stabilize the fluctuation of wind power output. Shared energy storage can reduce the construction cost of energy storage devices and stimulate the enthusiasm of wind farms to invest in energy storage. The wind power base is composed of multiple wind farm groups. Existing research methods did not consider how to allocate shared energy storage among wind farm groups in the wind power base. This paper proposes an energy storage capacity allocation method for wind farm groups. Firstly, a bilevel model for the shared energy storage allocation is established. The upper-level model optimizes the shared energy storage allocation of each wind farm group with the goal of minimizing the over-limit power export risk in the wind power base; The lower-level model calculates the over-limit power export of each wind farm group according to the energy storage capacity allocation and transfers the value of over-limit power export to the upper-level model. The bilevel model can be converted to a two-stage model that can be solved sequentially; The wind power base in Belgium is used for numerical simulation to verify the effectiveness of the proposed model. Finally, the sensitivity of confidence level, total energy storage capacity, and risk preference factor are analyzed.

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Section: Introductionmentioning

confidence: 99%

Allocating the capacity of shared energy storage for wind farm groups based on the over-limit power export risk

Wang

Song²,

Gu³

et al. 2023

Front. Energy Res.

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Optimal Scheduling for Shared Energy Storage Users Considering Both Construction and Operation of the Shared Energy Storage Station

Xu,

Shan,

et al. 2024

2024 9th Asia Conference on Power and Electrical Engineering (ACPEE)

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Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks

Zhong,

Ye,

Yang

et al. 2024

Processes

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As global energy demand rises and climate change poses an increasing threat, the development of sustainable, low-carbon energy solutions has become imperative. This study focuses on optimizing shared energy storage (SES) and distribution networks (DNs) using deep reinforcement learning (DRL) techniques to enhance operation and decision-making capability. An innovative dynamic carbon intensity calculation method is proposed, which more accurately calculates indirect carbon emissions of the power system through network topology in both spatial and temporal dimensions, thereby refining carbon responsibility allocation on the user side. Additionally, we integrate user-side SES and ladder-type carbon emission pricing into DN to create a low-carbon economic dispatch model. By framing the problem as a Markov decision process (MDP), we employ the DRL, specifically the deep deterministic policy gradient (DDPG) algorithm, enhanced with prioritized experience replay (PER) and orthogonal regularization (OR), to achieve both economic efficiency and environmental sustainability. The simulation results indicate that this method significantly reduces the operating costs and carbon emissions of DN. This study offers an innovative perspective on the synergistic optimization of SES with DN and provides a practical methodology for low-carbon economic dispatch in power systems.

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Day-Ahead Scheduling With Renewable Generation Considering Shared Energy Storage

Cited by 3 publications

References 13 publications

Allocating the capacity of shared energy storage for wind farm groups based on the over-limit power export risk

Allocating the capacity of shared energy storage for wind farm groups based on the over-limit power export risk

Optimal Scheduling for Shared Energy Storage Users Considering Both Construction and Operation of the Shared Energy Storage Station

Deep Reinforcement Learning-Based Joint Low-Carbon Optimization for User-Side Shared Energy Storage–Distribution Networks

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