A Bi-Level Program for the Planning of an Islanded Microgrid Including CAES

Zhang, Jun; Li, Kejun; Wang, Mingqiang; Lee, Wei-Jen; Gao, Hongxia; Zhang, Chenghui; Li, Ke

doi:10.1109/tia.2016.2539246

Cited by 102 publications

(41 citation statements)

References 28 publications

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“…A bi-level programming model is a hierarchical system optimisation model in which the upper and lower levels have their own mathematical models. 19,20 The lower-level programming problem is based on the scheme given by the upperlevel decision-making process. The optimal value of the lower-level decision-making process is fed back to the upper level, and the upper-level decision-making process conforms to the global optimal benefit according to the optimal value response of lower-level decision-making process.…”

Section: Mathematical Formulation With Bi-level Programmingmentioning

confidence: 99%

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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To facilitate the development of active distribution networks with high penetration of large‐scale distributed generation (DG) and electric vehicles (EVs), active management strategies should be considered at the planning stage to implement the coordinated optimal allocations of DG and electric vehicle charging stations (EVCSs). In this article, EV charging load curves are obtained by the Monte Carlo simulation method. This article reduces the number of photovoltaic outputs and load scenarios by the K‐means++ clustering algorithm to obtain a typical scenario set. Additionally, we propose a bi‐level programming model for the coordinated DG and EVCSs planning problem. The maximisation of annual overall profit for the power supply company is taken as the objective function for the upper planning level. Then, each scenario is optimised at the lower level by using active management strategies. The improved harmonic particle swarm optimisation algorithm is used to solve the bi‐level model. The validation results for the IEEE‐33 node, PG&E‐69 node test system and an actual regional 30‐node distribution network show that the bi‐level programming model proposed in this article can improve the planning capacity of DG and EVCSs, and effectively increase the annual overall profit of the power supply company, while improving environmental and social welfare, and reducing system power losses and voltage shifts. The study provides a new perspective on the distribution network planning problem.

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Section: Mathematical Formulation With Bi-level Programmingmentioning

confidence: 99%

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Liu

Yan

et al. 2020

Int Trans Electr Energ Syst

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show abstract

“…Moreover CAES can moderate fluctuations of renewable energy production due to the large-scale storage capacities, high ramp rate, and quick start-up time and therefore are an attractive technology for smart grids. 67 Although CAES is a mature and attractive technology for grid energy storage systems there are shortcomings that need to be considered. The CAES efficiency is much lower than similar technologies such as HPS, attributed to the fact that during compression, thermal energy is wasted.…”

Section: • the Electrification Of Southern Cameroon Is Analyzed Bymentioning

confidence: 99%

On the integration of the energy storage in smart grids: Technologies and applications

et al. 2019

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Smart grids are one of the major challenges of the energy sector for both the energy demand and energy supply in smart communities and cities. Grid connected energy storage systems are regarded as promising solutions for providing ancillary services to electricity networks and to play an important role in the development of smart grids. The aim of the present article is to analyze the role of storage systems in the development of smart grids. The article includes an analysis and a list of energy storage systems that are applied in smart grids. Various energy storage systems are examined raging from electrical, electrochemical, thermal, and mechanical systems. Two case studies are presented that show the role of energy storage in effective management of energy demand and supply.

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“…The WT and PV models are derived as in [17], while the BESS is modelled by their equivalent circuit models. To fully utilise wind and solar energies, both the WTS and the PVS are operating in the maximum power point tracking (MPPT) mode.…”

Section: System Configuration and Modellingmentioning

confidence: 99%

Distributed charging management of multi‐class electric vehicles with different charging priorities

Alsabbagh

Yin

2019

IET Generation, Transmission & Distribution

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A Bi-Level Program for the Planning of an Islanded Microgrid Including CAES

Cited by 102 publications

References 28 publications

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

Optimal allocation of distributed generation and electric vehicle charging stations based on intelligent algorithm and bi‐level programming

On the integration of the energy storage in smart grids: Technologies and applications

Distributed charging management of multi‐class electric vehicles with different charging priorities

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