Sizing Energy Storage to Accommodate High Penetration of Variable Energy Resources

Makarov, Yuri V.; Du, Pengwei; Kintner-Meyer, Michael Cw; Jin, Chunlian; Illian, H.F.

doi:10.1109/tste.2011.2164101

Cited by 235 publications

(143 citation statements)

References 18 publications

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“…Equations (18) and (19) show that the active/reactive flow from bus to bus of line between these two buses is equal to the sum of the flows of other lines which are connected to the bus plus the net injection on the bus and the losses over the line. The following constraints define the current line flow constraints: The constraint (20) is relaxed version of the original one that is equality instead of inequality.…”

Section: ) Dsss Installation Constraintsmentioning

confidence: 99%

“…The objective was to find the best siting, rating, and control strategy of storage systems, in order to minimize the overall investments and network costs (network upgrade and Joule losses). In [19], the problem of the calculation of the total reserve provided by storage systems of a noninterconnected power network, with large penetration of renewables, is formulated. The peculiarity of the proposed approach consists in the use of the discrete Fourier transform (DFT) to determine the required balancing power in different time-spans.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

370

221

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Abstract-Dispersed storage systems (DSSs) can represent an important near-term solution for supporting the operation and control of active distribution networks (ADNs). Indeed, they have the capability to support ADNs by providing ancillary services in addition to energy balance capabilities. Within this context, this paper focuses on the optimal allocation of DSSs in ADNs by defining a multi-objective optimization problem aiming at finding the optimal trade-off between technical and economical goals. In particular, the proposed procedure accounts for: 1) network voltage deviations; 2) feeders/lines congestions; 3) network losses; 4) cost of supplying loads (from external grid or local producers) together with the cost of DSS investment/maintenance; 5) load curtailment; and 6) stochasticity of loads and renewables productions. The DSSs are suitably modeled to consider their ability to support the network by both active and reactive powers. A convex formulation of ac optimal power flow problem is used to define a mixed integer second-order cone programming problem to optimally site and size the DSSs in the network. A test case referring to IEEE 34 bus distribution test feeder is used to demonstrate and discuss the effectiveness of the proposed methodology.Index Terms-Ancillary services, energy storage, mixed integer second order cone programming, power distribution, power system planning. NOMENCLATURE: DSSDispersed storage system. DG Distributed generation.Variables: 1 Binary variable associated with the presence of energy storage at bus .

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Section: ) Dsss Installation Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Nick

Cherkaoui

Paolone

2014

IEEE Trans. Power Syst.

370

221

View full text Add to dashboard Cite

show abstract

“…The capital costs of an ESS can be broken down into an energy and a power component. The energy cost for storage is the cost of the devices that actually store the energy (storage medium) [10,31]. The power cost would include the synchronous machines in a PHS and the power electronics in a battery system [10].…”

Section: Economic Evaluationmentioning

confidence: 99%

Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications

et al. 2017

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Abstract:In the last few years, several investigations have been carried out in the field of optimal sizing of energy storage systems (ESSs) at both the transmission and distribution levels. Nevertheless, most of these works make important assumptions about key factors affecting ESS profitability such as efficiency and life cycles and especially about the specific costs of the ESS, without considering the uncertainty involved. In this context, this work aims to answer the question: what should be the costs of different ESS technologies in order to make a profit when considering peak shaving applications? The paper presents a comprehensive sensitivity analysis of the interaction between the profitability of an ESS project and some key parameters influencing the project performance. The proposed approach determines the break-even points for different ESSs considering a wide range of life cycles, efficiencies, energy prices, and power prices. To do this, an optimization algorithm for the sizing of ESSs is proposed from a distribution company perspective. From the results, it is possible to conclude that, depending on the values of round trip efficiency, life cycles, and power price, there are four battery energy storage systems (BESS) technologies that are already profitable when only peak shaving applications are considered: lead acid, NaS, ZnBr, and vanadium redox.

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“…As the integration of large-scale storage into a system can significantly improve renewable generation usage, it recently has been the focus of considerable scholarly attention [6][7][8][9]. Although energy storage appears to be a feasible option with which to increase system flexibility for the accommodation of large-scale intermittent renewable generation, the high upfront costs of this advanced storage technology merit consideration.…”

Section: Introductionmentioning

confidence: 99%

Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems

2018

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Abstract:From an environment perspective, the increased penetration of wind and solar generation in power systems is remarkable. However, as the intermittent renewable generation briskly grows, electrical grids are experiencing significant discrepancies between supply and demand as a result of limited system flexibility. This paper investigates the optimal sizing and control of the hydrogen energy storage system for increased utilization of renewable generation. Using a Finnish case study, a mathematical model is presented to investigate the optimal storage capacity in a renewable power system. In addition, the impact of demand response for domestic storage space heating in terms of the optimal sizing of energy storage is discussed. Finally, sensitivity analyses are conducted to observe the impact of a small share of controllable baseload production as well as the oversizing of renewable generation in terms of required hydrogen storage size.

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Sizing Energy Storage to Accommodate High Penetration of Variable Energy Resources

Cited by 235 publications

References 18 publications

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Optimal Allocation of Dispersed Energy Storage Systems in Active Distribution Networks for Energy Balance and Grid Support

Break-Even Points of Battery Energy Storage Systems for Peak Shaving Applications

Sizing Hydrogen Energy Storage in Consideration of Demand Response in Highly Renewable Generation Power Systems

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