Optimizing Storage Siting, Sizing, and Technology Portfolios in Transmission-Constrained Networks

Wogrin, Sonja; Gayme, Dennice F.

doi:10.1109/tpwrs.2014.2379931

Cited by 157 publications

(107 citation statements)

References 23 publications

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“…Optimal planning of energy storage under different conditions and objectives have been studied in [10,[16][17][18][19][20][21][22]. In addition, [23][24][25][26][27] investigated joint optimal allocation and sizing of energy storage. In [28] the authors also show that energy storage is beneficial for renewable generation expansion and that joint optimization of renewable generation and flexibility sources including energy storage results in much higher cost savings than when investment planning is procured separately.…”

Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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Many countries have the ambition to increase the share of renewable sources in electricity generation. However, continuously varying renewable sources, such as wind power or solar energy, require that the power system can manage the variability and uncertainty of the power generation. One solution to increase flexibility of the system is to use various forms of energy storage, which can provide flexibility to the system at different time ranges and smooth the effect of variability of the renewable generation. In this paper, we investigate three questions connected to investment planning of energy storage systems. First, how the existing flexibility in the system will affect the need for energy storage investments. Second, how presence of energy storage will affect renewable generation expansion and affect electricity prices. Third, who should be responsible for energy storage investments planning. This paper proposes to assess these questions through two different mathematical models. The first model is designed for centralized investment planning and the second model deals with a decentralized investment approach where a single independent profit maximizing utility is responsible for energy storage investments. The models have been applied in various case studies with different generation mixes and flexibility levels. The results show that energy storage system is beneficial for power system operation. However, additional regulation should be considered to achieve optimal investment and allocation of energy storage.

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Section: Knowledge Gapmentioning

confidence: 99%

Optimal Investment Planning of Bulk Energy Storage Systems

2018

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“…First, the initial membership value must meet requirement (5). Secondly, according to (6), the vector of cluster center for iteration k can be computed:…”

Section: The Fuzzy-means Clustering Techniques For Pv Powermentioning

confidence: 99%

“…BESS is an attractive technology to solve problems concerning the high penetration of PV stations. Based on prior studies, a number of approaches to optimizing the capacity of BESS are reported from several aspects, such as better utilization of PV [5] and reduction of generation costs [6]. The technical and economic model of the PV-battery [7] and lifecycle-based cost modeling for batteries [8] are also investigated.…”

Section: Introductionmentioning

confidence: 99%

Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

Yang

Ren

et al. 2018

Mathematical Problems in Engineering

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Under the application scenario of smoothing photovoltaic (PV) power fluctuation, a novel typical daily power curve mining method is developed for a battery energy storage system (BESS) that utilizes the power probability distribution and Bloch spherical quantum genetic algorithm. The charging/discharging of BESS is analyzed by applying fuzzy-means clustering techniques. In the mining approach, at any sample time, those distribution intervals containing concentrated power points are individually located by using probability distribution information and Bloch spherical quantum genetic algorithm. Character power for the specified interval can also be determined using Bloch spherical quantum genetic algorithm. Next, a roulette principal is employed, to determine one value from the character power data as a typical value of the mined power curve at the sample time. By connecting the typical power at each sample time, the typical daily power curve for BESS is achieved. Based on typical power curve, decision-maker can master the important operating parameters of BESS and analyze optimal capacity allocation. By error evaluation indexes between the mined typical daily power curve and power curve under different weather patterns, the simulation results verify that the mined power curve can address the operating power of the BESS under different weather patterns.

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“…Many studies have investigated the allocations of DGs including REs and ESSs in transmissions systems [1][2][3], but these studies did not consider contingencies. In Ref [1], the authors formulated a problem to determine the allocation and scheduling of ESSs by employing a bi-level programming problem, and in Ref [2], the stochastic framework to enhance reliability and operability was proposed considering both power shortages and surpluses.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref [1], the authors formulated a problem to determine the allocation and scheduling of ESSs by employing a bi-level programming problem, and in Ref [2], the stochastic framework to enhance reliability and operability was proposed considering both power shortages and surpluses. In Ref [3], the authors determined the siting and sizing, in addition to the technology portfolios of ESSs. Moreover, some researchers have studied the allocation of DGs considering the contingencies in power systems [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

Konishi

Takahashi

2017

Energies

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There is a growing need to connect renewable energy systems (REs), such as photovoltaic systems (PVs), to the power grid for solving environmental problems such as global warming. However, an electricity grid with RE is vulnerable to problems of power shortage and surplus owing to the uncertainty of RE outputs and grid failures. Energy storage systems (ESSs) can be used to solve supply reliability problems, but their installation should be minimized considering their high costs. This study proposes a method to optimize the allocations of PVs and ESSs based on vulnerability analysis, and utilizes our proposed concept of "slow" and "fast" ESSs, which can reflect the influences of both uncertainties: PV outputs and grid failures. Accordingly, this paper demonstrates an optimal allocation of PVs and ESSs that minimizes the amount of ESSs while satisfying the PV installation target and the constraints on supply reliability indices for power shortage and power surplus in the event of a grid failure.

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Optimizing Storage Siting, Sizing, and Technology Portfolios in Transmission-Constrained Networks

Cited by 157 publications

References 23 publications

Optimal Investment Planning of Bulk Energy Storage Systems

Optimal Investment Planning of Bulk Energy Storage Systems

Typical Daily Power Curve Mining for Energy Storage Systems under Smoothing Power Fluctuation Scenarios

Optimal Allocation of Photovoltaic Systems and Energy Storage Systems based on Vulnerability Analysis

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