Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Chang, Weiguang; Dong, Wei; Yang, Qingliang

doi:10.1016/j.apenergy.2023.120827

Cited by 23 publications

(2 citation statements)

References 43 publications

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“…However, the uncertainties are not considered in the proposed frameworks by the authors of [24,25]. The authors of [26] propose two service modes including energy storage and electricity trading for its customers in a centralized energy management manner and consider market price uncertainty in their proposed model.…”

Section: Related Workmentioning

confidence: 99%

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Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

Salehi,

Rastegar

2023

IET Generation Trans & Dist

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Residential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers’ costs in the proposed model.

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Section: Related Workmentioning

confidence: 99%

“…Detailed comparisons among the research work [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] reported in the literature review and this paper from various perspectives are shown in Table 1. This paper proposes a novel TE management framework for residential users and CES.…”

Section: Novelties and Contributionsmentioning

confidence: 99%

Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

Salehi,

Rastegar

2023

IET Generation Trans & Dist

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Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

Chang,

Yang

2023

Applied Energy

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An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

Wang,

Yang,

et al. 2023

Applied Energy

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Day-ahead bidding strategy of cloud energy storage serving multiple heterogeneous microgrids in the electricity market

Cited by 23 publications

References 43 publications

Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks

Low carbon oriented collaborative energy management framework for multi-microgrid aggregated virtual power plant considering electricity trading

An optimization-based partial marginal pricing method to reduce excessive consumer payment in electricity markets

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