Coordinated Optimization of Multiservice Dispatch for Energy Storage Systems With Degradation Model for Utility Applications

Baggu, Murali; Nagarajan, Adarsh; Cutler, Dylan; Olis, Dan; Bialek, Tom; Symko-Davies, Martha

doi:10.1109/tste.2018.2853673

Cited by 22 publications

(10 citation statements)

References 16 publications

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“…𝑑 𝛽 7 (1) where, 𝑄 𝐹 denotes capacity fade in percentage, 𝛽 1 to 𝛽 7 are fitting parameters, 𝑇 is the temperature, and 𝑑 is the time duration in months. Equation (2), which is a quadratic relationship between the depth of discharge (DOD) and degradation, was proposed by [64] for distribution-sited Li-ion batteries. ∆𝑄 𝐹 = 𝑘 × 𝐷𝑂𝐷 2 (2) where ∆𝑄 𝐹 is the capacity degradation per cycle.…”

Section: Modeling a Battery System For Economic Dispatchmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Rouholamini

Wang

Nehrir

et al. 2022

IEEE Trans. Smart Grid

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Section: Modeling a Battery System For Economic Dispatchmentioning

confidence: 99%

“…In addition to [63]- [64], [61]- [62] and [65]- [68] also aimed to propose a dispatch model for batteries. A battery economic dispatch with time-coupling constraints was given in [65], and a generic dispatch model was suggested in [66].…”

Section: Modeling a Battery System For Economic Dispatchmentioning

confidence: 99%

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Rouholamini

Wang

Nehrir

et al. 2022

IEEE Trans. Smart Grid

View full text Add to dashboard Cite

“…The other constraints of ADNs include power balance constraint, voltage constraint, power source limit constraint, and current constraint, these constraints can be referred to. 20…”

Section: Operational Constraintsmentioning

confidence: 99%

Strategy optimization of distributed battery energy storage systems for improving dynamic performances of VRB in active distribution networks

Lei

2022

Intl J of Energy Research

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This paper proposed an improved genetic algorithm-based operational strategy for vanadium redox flow battery (VRB) energy storage systems (ESSs) in active distribution networks for improving the dynamic performances of batteries. Firstly, the accurate model of VRB considering the influences of external factors, such as temperature, electrolyte flow rate, ion exchange membrane, catalyst, polarization, self-discharge, and leakage current are constructed. By the test of the accurate model, the dynamic performances Ф of VRB consisting of efficiency η, self-discharge rate λ, utilization rate ψ u , maximum discharge depth D oD , and cycle life κ are reasonably proposed. And then, the mathematical framework for the operational strategy optimization of ESSs was developed considering both the dynamic performances Ф and the external benefits of VRB ESSs. Finally, case studies based on a modified IEEE 123 Node Test Feeder verified the safe and reasonable operational states of battery ESSs with higher efficiencies, utilization rate, cycle life and lower self-discharge rate, and maximum discharge depth. The dynamic performances of battery ESSs are enhanced by about 32.4%.

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“…Photovoltaic cells and wind turbines in the energy conversion part convert renewable energy solar and wind energy into electrical energy, gas boilers convert natural gas energy in conventional energy into heat energy, and micro gas turbines can convert natural gas energy into a part of heat energy and electricity. The unit can use the electric energy in the system to selectively convert it into heat energy or cold energy according to the demand of the load, and the absorption refrigeration unit can convert the waste heat generated by the micro gas turbine into cold energy [13][14]. The energy management and distribution are in charge of the energy management center, which realizes the optimal scheduling and control of the entire DE system through communication and control signals.…”

Section: Composition and Structure Of De Systemmentioning

confidence: 99%

Distributed Energy Scheduling Problem Based on Learning Algorithm

2020

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Compared with traditional fossil energy, new energy has the advantages of large reserves, great development potential, and no pollution. For the current rapidly growing electricity demand, it is of great significance to fully develop and utilize new energy. In this context, the necessity of new energy development and the difficulty of new energy utilization drive the rapid development of distributed energy(DE) optimization scheduling methods. Since the efficient operation of DE optimal scheduling relies on advanced communication technology, this paper implements DE scheduling in the cloud environment. In this paper, the objective optimization scheduling simulation experiment of the economic cost and environmental protection benefit of the DE system is carried out, and the DE system is optimized by three learning algorithms including Bayesian, decision tree and improved decision tree, and the DE economic scheduling problem is solved , and compared with the other two algorithms, the effect of improving the decision tree algorithm to reduce the economic cost is more obvious.

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Coordinated Optimization of Multiservice Dispatch for Energy Storage Systems With Degradation Model for Utility Applications

Cited by 22 publications

References 16 publications

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

A Review of Modeling, Management, and Applications of Grid-Connected Li-Ion Battery Storage Systems

Strategy optimization of distributed battery energy storage systems for improving dynamic performances of VRB in active distribution networks

Distributed Energy Scheduling Problem Based on Learning Algorithm

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