Impact of energy storage in micro-grid systems with DGs

Kwhannet, U.; Sinsuphun, N.; Leeton, Uthen; Kulworawanichpong, Thanatchai

doi:10.1109/powercon.2010.5666059

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…The daily estimated output of the DFIG is shown in Fig. 6 and the detailed parameters are given in [37][38][39]. The capacity of transformer T 3 is extended to 6 MW accordingly.…”

Section: Assumptions In the Example Microgridmentioning

confidence: 99%

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Cai

Chen

Guan

et al. 2018

Prot Control Mod Power Syst

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Microgrid as an important part of smart grid comprises distributed generators (DGs), adjustable loads, energy storage systems (ESSs) and control units. It can be operated either connected with the external system or islanded with the support of ESSs. While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the unbalance between the daily load curve and generation curve. In this paper, a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge (SOC) of EV batteries. The optimization problem is proposed to obtain the economic operation for the microgrid based on this model. In dayahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming. With the optimal charging/discharging scheduling of EVs, the daily load curve can better track the generation curve. The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.

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“…The daily estimated output of the DFIG is shown in Fig. 6 and the detailed parameters are given in [37][38][39]. The capacity of transformer T 3 is extended to 6 MW accordingly.…”

Section: Assumptions In the Example Microgridmentioning

confidence: 99%

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Cai

Chen

Guan

et al. 2018

Prot Control Mod Power Syst

View full text Add to dashboard Cite

show abstract

“…In radial distribution grids with DG, it is often very expensive at the end nodes to deliver energy from the system to the power supply, since the transport of this energy causes considerable Joule losses. Therefore, in these particular cases it is a very efficient option to place energy storage near the DG in order to store and reuse the generated energy, at times of system instability [45] . In the same way, there are systems of micro-grids with hybrid energy storage, using in addition to the banks of batteries, capacitors that allow the loading and unloading depending on the operation of the grid in active and reactive power [46] .…”

Section: Main Textmentioning

confidence: 99%

Optimal reactive power compensation in electrical distribution systems with distributed resources. Review

Téllez

López

Isaac

et al. 2018

Heliyon

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In this paper an exhaustive bibliographical revision of the mathematical methods used for the optimal selection and location of reactive power compensating elements is developed, the results obtained by different authors for different objective functions are analyzed and a scientific problem in the conflict that the electric variables show when analyzed individually is identified; thus demonstrating the need to analyze this problem in a multi-criteria way and taking into account topologies of distribution grids with distributed generation and energy storage. This research demonstrates that reactive power compensation in distribution grids with distributed resources is a problem that must be analyzed from multiple criteria that consider several objective functions to be optimized; thus achieving a global solution that contemplates an optimal location and dimensioning of reactive power compensating elements that contribute to the joint improvement of the voltage profiles, minimization of power losses, harmonic mitigation, increased line capacity, voltage stability and power factor improvement, all of them to a minimum investment cost. A theoretical heuristic is also proposed to solve the described problem, based on the multicriteria optimization method.

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“…The optimal configuration of capacity plays a crucial role in the comprehensive control and energy management of microgrids [1] . Energy configuration has an important impact on the efficient use of renewable energy and the stability and economics of microgrids, especially for off-grid microgrids, if the grid does not have sufficient energy storage capacity, the energy generated by wind turbines or photovoltaic (PV) panels Excess electricity will not be adequately stored, resulting in wasted energy.…”

Section: Introductionmentioning

confidence: 99%

Optimal configuration of wind, photovoltaic and hydrogen storage system considering the whole life cycle

Qiu,

Han,

Fan

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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The optimal configuration of energy storage system capacity is one of the effective measures to reduce the cost of Microgrid. A method for optimizing the capacity allocation of wind, photovoltaic and hydrogen energy storage hybrid systems considering the whole life cycle economic optimization was established. Firstly, this paper establishes various benefit and cost models, and establishes a hybrid system capacity allocation model considering the charging and discharging power constraints of the energy storage system (ESS). Secondly, the ESS control strategy is designed based on the capacity allocation model, including the judgment of ESS action timing and the selection of electrolyzer operation mode, ensuring a high degree of matching between the control strategy and capacity allocation. Finally, the particle swarm algorithm is used to solve the capacity optimization configuration model of the energy storage hybrid system, and the optimal configuration of the system is obtained. In the calculation example, the impact on capacity allocation under different control strategies is analyzed, and the optimal configuration scheme of the system is obtained. The results of the calculation example show that using the capacity allocation method proposed in this paper, when the wind resources are determined and the parameters remain unchanged, there is an approximately linear relationship between the wind turbine power, configuration results and the total system revenue. At the same time, it provides a reference for the optimal configuration of microgrid energy storage capacity.

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Impact of energy storage in micro-grid systems with DGs

Cited by 15 publications

References 8 publications

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Day-ahead optimal charging/discharging scheduling for electric vehicles in microgrids

Optimal reactive power compensation in electrical distribution systems with distributed resources. Review

Optimal configuration of wind, photovoltaic and hydrogen storage system considering the whole life cycle

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