Co‐optimisation model for the long‐term design and decision making in community level cloud energy storage system

Li, Xiangyu; Chen, Guo; Zhang, Dong

doi:10.1049/iet-rpg.2020.0612

Cited by 14 publications

(8 citation statements)

References 27 publications

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“…In [16,17], the feasibility of the CES model is evaluated economically in the background of the electricity market in Germany and the United Kingdom, respectively. Aiming at minimizing the total investment and operating cost, paper [18] established a long-term collaborative optimization model for CES systems with multiple ESSs. Reference [19] proposed an improved CES service mechanism considering battery life loss and energy transaction between users.…”

Section: Literature Surveymentioning

confidence: 99%

Optimal configuration of energy storage capacity in wind farms based on cloud energy storage service

Chen

Yang

Jiang

et al. 2021

IET Renewable Power Gen

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In wind farms, the energy storage system can realize the time and space transfer of energy, alleviate the intermittency of renewable energy and enhance the flexibility of the system. However, the high cost limits its large‐scale application. Cloud energy storage (CES) can provide users with leasing energy storage service at a relatively lower price, and can provide energy trading service. Wind farms can lease CES and participate in energy transaction to reduce the cost of energy storage and suppress wind power fluctuations. This paper proposes a framework of wind farm system based on CES service, and designs a power allocation strategy. Considering whole‐life‐cycle cost of the self‐built energy storage, leasing and trading cost of the CES and penalty cost of wind abandonment and smooth power shortage, an optimal configuration model of combined energy storage capacity in wind farms based on CES service was established to minimize the total annual cost. Taking a wind farm in Germany as a case study, it is verified that the model in this paper can effectively reduce the cost, extend the service life of self‐built energy storage equipment and suppress the wind power fluctuations.

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Section: Literature Surveymentioning

confidence: 99%

Optimal configuration of energy storage capacity in wind farms based on cloud energy storage service

Chen

Yang

Jiang

et al. 2021

IET Renewable Power Gen

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“…In [15], an online control approach for real-time energy management of distributed ESS is proposed. The authors of [16] established a long-term collaborative optimization model for CES systems with multiple.…”

Section: Introductionmentioning

confidence: 99%

“…Previous study like [8][9][10][11][12][13][14][15][16] focused on demand-side CESS and distributed energy. Few papers study the generation-side CESS.…”

Section: Introductionmentioning

confidence: 99%

A two‐stage robust optimal configuration model of generation‐side cloud energy storage system based on cooperative game

Wang

Zhang

Xia

et al. 2022

IET Generation Trans & Dist

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Cloud energy storage system (CESS) can effectively improve the utilization rate of the energy storage system (ESS) and reduce the cost. However, there is a lack of a model designed for large-scale renewable energy power plants (REPPs). Due to the volatility and intermittency of renewable energy power generation, as well as the demand of following scheduling plan and market arbitrage, it is also necessary to configure ESS for REPPs. However, if the REPP builds ESS by itself, the investment is relatively high. Therefore, the application of CESS on the renewable energy generation side can reduce the investment cost and increase the revenue by utilizing the difference between actual output and demand.Considering the uncertainties of renewable energy, this paper proposes a robust optimal configuration model of CESS based on the cooperative game. Firstly, the CESS model on the generation side is developed to describe the formation mechanism of ESS supply and demand. Then, the proposed model aims at maximizing the revenue of REPPs. The participants of the coalition are each REPP. By taking the renewable power uncertainty into consideration, the novel nested column-and-constraint generation (nested C&CG) method is utilized to solve the proposed model based on the min-max-min form. Furthermore, the Shapley-value method is used to distribute the benefits to each member of the grand coalition. Finally, case studies verify the rationality and validity of the proposed model.

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“…The communication safety and security requirements of the CESS system are met by [21] through designing and developing a node controller which controls the connection of the public user to the CESS. Based on [22], the cost of battery degradation and uncertainty of the price should be considered in the design and decision-making processes of the CESS in a long-term manner. Moreover, the decisions of investment and operation of the CESS operator and the consumers are considered in [23] to express the benefits of this type of storage system.…”

Section: Introductionmentioning

confidence: 99%

Transient stability enhancement in multiple‐microgrid networks by cloud energy storage system alongside considering protection system limitations

Ebrahimi

Yazdaninejadi

Golshannavaz

2022

IET Generation Trans & Dist

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The gas turbine synchronous generators (GTSGs) are widely deployed as distributed generations (DGs) in countries with massive natural gas production owing to their low prices. However, due to the low inertia time constants of these synchronous‐based DGs, they are more susceptible to power grid faults which stands as a transient stability issue in networks with multiple microgrids (MGs). On the other hand, the cloud energy storage system (CESS) is a new concept that centralizes the individual distributed energy storage of one or more MGs. Here, the employment of CESS with synchronverter grid connections creates a suitable opportunity for improving the transient stability of the network by providing higher inertia. This is while the fault current contribution of the synchronverter‐based CESS imperils protection constraints in these networks. Therefore, a proper protection coordination index (PCI) is considered in the conducted study to identify the optimal size of the synchronverter‐based CESS through a two‐stage optimization algorithm that preserves the protection constraints among protective devices. Finally, the transient stability of the network with synchronverter‐based CESS is assayed by calculation of the critical clearing time (CCT) for faults. Numerical studies are carried out on the IEEE 33‐bus test system. Results are discussed in depth.

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Co‐optimisation model for the long‐term design and decision making in community level cloud energy storage system

Cited by 14 publications

References 27 publications

Optimal configuration of energy storage capacity in wind farms based on cloud energy storage service

Optimal configuration of energy storage capacity in wind farms based on cloud energy storage service

A two‐stage robust optimal configuration model of generation‐side cloud energy storage system based on cooperative game

Transient stability enhancement in multiple‐microgrid networks by cloud energy storage system alongside considering protection system limitations

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