A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

Ghofrani, M.; Arabali, A.; Etezadi-Amoli, M.; Fadali, M. Sami

doi:10.1109/tste.2012.2227343

Cited by 212 publications

(154 citation statements)

References 24 publications

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“…In particular, the intermittent nature of the power produced by renewable energy sources (RESs) is threatening the stability of power systems, increasing the risk of instabilities and failures. In order to overcome this issue, various studies have proposed the exploitation of energy storage system (ESS) technologies for the management of RES power fluctuations [2][3][4][5][6][7]. The proposed solutions include the use of ESSs for the provision of spinning reserves, frequency and voltage regulation, peak shaving, and load following.…”

Section: Introductionmentioning

confidence: 99%

Aging Cost Optimization for Planning and Management of Energy Storage Systems

et al. 2017

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In recent years, many studies have proposed the use of energy storage systems (ESSs) for the mitigation of renewable energy source (RES) intermittent power output. However, the correct estimation of the ESS degradation costs is still an open issue, due to the difficult estimation of their aging in the presence of intermittent power inputs. This is particularly true for battery ESSs (BESSs), which have been proven to exhibit complex aging functions. Unfortunately, this collides with considering aging costs when performing ESS planning and management procedures, which are crucial for the exploitation of this technology. In order to overcome this issue, this paper presents the genetic algorithm-based multi-period optimal power flow (GA-MPOPF) procedure, which aims to economically optimize the management of ESSs by taking into account their degradation costs.The proposed methodology has been tested in two different applications: the planning of the correct positioning of a Li-ion BESS in the PG& E 69 bus network in the presence of high RES penetration, and the definition of its management strategy. Simulation results show that GA-MPOPF is able to optimize the ESS usage for time scales of up to one month, even for complex operative costs functions, showing at the same time excellent convergence properties.

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Section: Introductionmentioning

confidence: 99%

Aging Cost Optimization for Planning and Management of Energy Storage Systems

et al. 2017

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“…[5] proposed a marketbased probabilistic optimal power flow (POPF) with energy storage integration and wind generation to minimize hourly social cost and maximize wind power utilization. [6] proposed a vulnerability assessment and found the optimal locations and capacities of ESSs for power system vulnerability mitigation.…”

Section: Introductionmentioning

confidence: 99%

Utilization of Energy Storage System based on the Assessment of Area of Severity in Islanded Microgrid

Lee¹,

Yoon

Park

et al. 2017

Journal of Electrical Engineering and Technology

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-This paper proposes a method to utilize an energy storage system (ESS) based on the assessment of an area of severity (AOS) to voltage sag. The AOS is defined as a set of the fault positions that can cause voltage sags at many buses simultaneously. The assessment of AOS helps to determine an optimal location of ESS installation to minimize the expected sag frequency (ESF) at concerned buses. The ESS has the ability not only to play traditionally known roles but also to mitigate voltage sag impact on renewable energy sources (RES) in the islanded microgrid. Accordingly, using the proposed method the ESS has additional features to prevent the operation failure of RESs and improve the stability of the microgrid. In order to verify the presented method, a case study was conducted on the sample microgrid system that is modified from an IEEE 57-bus system.

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“…Other studies incorporate operational and physical constraints through the optimal power flow (OPF) formulation, which is a nonconvex problem. Since the OPF is nonconvex and NP-hard, numerous studies solve an approximation; references [8], [9] and [10] solve a lossless approximation to assess optimal sizing and charge scheduling. This approach is problematic because actual power systems operate on alternating current (AC) networks, for which losses due to A. Castillo is with the Department of Geography and Environmental Engineering, Johns Hopkins University, Baltimore, MD 21218 USA anya.castillo@jhu.edu…”

Section: Introductionmentioning

confidence: 99%

Profit maximizing storage allocation in power grids

Castillo¹,

Gayme²

2013

52nd IEEE Conference on Decision and Control

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Abstract-This work investigates the interaction between nodal price signals and the optimal allocation and operation of distributed energy storage systems (ESS) in alternating current (AC) power networks. We model a multi-period optimal power flow (OPF) problem with charge and discharge dynamics for energy storage collocated with load and/or generation. We then apply a convex relaxation based on semidefinite programming (SDP) and derive the storage subproblem from the Lagrangian dual. We use the storage subproblem to investigate the relationship between the storage variables and the locational marginal prices (LMPs), which are market-based price signals defined by the dual variables associated with the nodal active power flow balance. Our theoretical results prove that LMPs drive charging and discharging dynamics, and that storage is allocated and operated to maximize the storage operator's profits, i.e. minimize the system costs in a purely competitive market. We then use this framework to illustrate LMP-based storage dynamics.

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A Framework for Optimal Placement of Energy Storage Units Within a Power System With High Wind Penetration

Cited by 212 publications

References 24 publications

Aging Cost Optimization for Planning and Management of Energy Storage Systems

Aging Cost Optimization for Planning and Management of Energy Storage Systems

Utilization of Energy Storage System based on the Assessment of Area of Severity in Islanded Microgrid

Profit maximizing storage allocation in power grids

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