Optimization of Customer-Side Battery Storage for Multiple Service Provision: Arbitrage, Peak Shaving, and Regulation

Su, Hao; Feng, Donghan; Zhao, Yi; Zhou, Yun; Zhou, Quan; Fang, Chen; Rahman, Usama

doi:10.1109/tia.2022.3145330

Cited by 22 publications

(9 citation statements)

References 29 publications

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“…Under the constraint conditions such as charging and discharging efficiency, SOC operating range and power balance, the temporal power demand data of the energy storage system are calculated by simulation. After considering confidence intervals or weighing energy storage investment and application effect, the rated power and capacity of the energy storage system are calculated based on the temporal power demand data samples [84][85][86].…”

Section: Configuration Methodsmentioning

confidence: 99%

“…User-side energy storage can be charged and discharged in an orderly manner according to the difference of electricity price at different times of the day, so as to gain profits from the price difference. In order to improve the marketization benefits under this mode, relevant studies analyzed the potential benefits of distributed energy storage in various aspects from exploring the rational income mode of user-side energy storage [85,86]. In this application mode, energy storage is generally used as a demand-side response resource to participate in the capacity market.…”

Section: Participate In the Demand-side Responsementioning

confidence: 99%

See 1 more Smart Citation

Review on the Optimal Configuration of Distributed Energy Storage

Liu,

Su,

Quan

et al. 2023

Energies

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With the large-scale access of renewable energy, the randomness, fluctuation and intermittency of renewable energy have great influence on the stable operation of a power system. Energy storage is considered to be an important flexible resource to enhance the flexibility of the power grid, absorb a high proportion of new energy and satisfy the dynamic balance between the supply and demand of a system. At present, the cost of energy storage is still high, and how to achieve the optimal energy storage configuration is the primary problem to be solved. Therefore, the current research progress in energy storage application scenarios, modeling method and optimal configuration strategies on the power generation side, grid side and user side are summarized in this paper. On this basis, the shortcomings that still exist of energy storage configuration research are summarized, and the future research direction for energy storage configuration is prospected. This review can provide reference for the latest development and future research and innovation direction for energy storage configuration.

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Section: Configuration Methodsmentioning

confidence: 99%

Section: Participate In the Demand-side Responsementioning

confidence: 99%

Review on the Optimal Configuration of Distributed Energy Storage

Liu,

Su,

Quan

et al. 2023

Energies

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show abstract

“…Another challenge resides in the complexity of the peak shaving problem in view of the underlying multi-objective optimisation and inter-dependencies between the multi-agents involved (Su et al, 2022) (Wynn et al, 2022). It follows that solving optimisation problems related to peak shaving is computationally expensive and requires lengthy processes.…”

Section: Digital Twin For Residential Energy Demand Controlmentioning

confidence: 99%

IoT and machine learning for enabling sustainable development goals

Jaber

2023

Front. Comms. Net.

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The United Nations drafted an agenda for 2030 to achieve sustainable development with 17 well-defined goals which are an urgent call for action requiring collaboration and innovation across countries and organisations. The year 2023 marks the midpoint toward fulfilling the proposed agenda but the world is still behind in attaining any of the set goals. This article discusses the opportunities offered by the rise of the Internet of Things and advances in artificial intelligence in ushering these goals with tangible use cases that relate to health, energy, and cities. Moreover, the article exposes the challenges that emerge from applying these technologies in the realisation of sustainable development goals (SDG) with underlined risks related to bias, security, data privacy, and the multi-objective optimisation of often competing SDGs.

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“…At present, research pays attention to optimizing the size of BTM BESS aiming for providing load management [9], [10]. The research related to BESS providing frequency regulation and energy arbitrage mostly focuses on how to design the operation strategy [11], [12]and cost-economic analysis [13], [14]. Nevertheless, it is rare to concentrate on how to design and optimize the size of BESS that offer stackable servicesenergy arbitrage and frequency regulation [15], especially for BTM BESS.…”

Section: Introductionmentioning

confidence: 99%

“…The factors affecting the cost and benefits of customer-sider BESS providing load management were quantitatively analyzed in [10] to design the optimal size. A three-level management framework of BTM BESS was presented for providing energy arbitrage, arbitrage, and frequency regulation in [11]. It achieved optimal operation of BESS under various operating conditions by decomposing the original problem from different time scales.…”

Section: Introductionmentioning

confidence: 99%

Optimal sizing of behind-the-meter BESS for providing stackable services

Zhang

Anvari‐Moghaddam

Peyghami

et al. 2022

2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG)

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The behind-the-meter (BTM) battery energy storage system (BESS) is mainly utilized for providing load management. But the saved electricity bill hardly offsets the high upfront investment cost. The multirevenue streams created by certain stackable services can offset the initial cost by reasonably designing the size and operation strategy of BESS. Therefore, to maximize the return rate on BESS investment, a two-stage optimal model for optimizing the power and energy capacity of a BTM BESS is proposed in this paper. The provided stackable services by BESS include energy arbitrage and frequency regulation. A hybrid algorithm combining the genetic algorithm and a mixed-integer linear programming model is employed to co-optimize the size and operation strategy of BESS. The real load data from the plastic manufacturing industry and the frequency regulation information from the PJM market are employed as databases to validate the availability and effectiveness of the proposed model and the hybrid algorithm.

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Optimization of Customer-Side Battery Storage for Multiple Service Provision: Arbitrage, Peak Shaving, and Regulation

Cited by 22 publications

References 29 publications

Review on the Optimal Configuration of Distributed Energy Storage

Review on the Optimal Configuration of Distributed Energy Storage

IoT and machine learning for enabling sustainable development goals

Optimal sizing of behind-the-meter BESS for providing stackable services

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