Dynamic battery loss evaluation and its application for optimal online wind‐storage integrated scheduling

Zhou, Yun; Su, Hao; Gui, Qiang; Gan, Lei; Feng, Donghan; Yan, Zheng; Fan, Yue Yue

doi:10.1049/iet-rpg.2020.0344

Cited by 12 publications

(10 citation statements)

References 19 publications

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“…Fig. 4 shows the timeline of the two-stage real-time optimal electricity dispatch strategy for residential quarter based on the receding horizon optimization process [20]. In each advanced optimization horizon () T Sk, the RLA assumes its responsibility to collect the implemented results and new arrival/departure EVs' data of last discrete time interval t  , update the forecasted load data in the forthcoming horizon.…”

Section: Advanced Optimization Horizonmentioning

confidence: 99%

“…Referring to the linearization steps in [20], the charging satisfiability constraint ( 17) can be converted to equivalent linear constraints. And with the economic objective function in (7), the nonlinear constraint in ( 14) can be relaxed.…”

Section: Fig 5 Schematic Diagram Of Advanced Optimization Horizon And...mentioning

confidence: 99%

“…For the kth advanced optimization horizon shown in Fig. 4, the objective of the second-stage optimization model shown in (20) is to minimize the peak-valley load difference in the advanced horizon. In (20), avg k P is the average RLA purchasing electricity power (residential quarter's net load) in the kth advanced optimization horizon.…”

Section: ) Second-stage Electricity Dispatch Peak-valley Difference O...mentioning

confidence: 99%

“…4, the objective of the second-stage optimization model shown in (20) is to minimize the peak-valley load difference in the advanced horizon. In (20), avg k P is the average RLA purchasing electricity power (residential quarter's net load) in the kth advanced optimization horizon. The typical residential peak-valley TOU price standard in Shanghai City is summarized in Table 3.…”

Section: ) Second-stage Electricity Dispatch Peak-valley Difference O...mentioning

confidence: 99%

See 3 more Smart Citations

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

Zhou

et al. 2023

Energy

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Section: Advanced Optimization Horizonmentioning

confidence: 99%

Section: Fig 5 Schematic Diagram Of Advanced Optimization Horizon And...mentioning

confidence: 99%

Section: ) Second-stage Electricity Dispatch Peak-valley Difference O...mentioning

confidence: 99%

Section: ) Second-stage Electricity Dispatch Peak-valley Difference O...mentioning

confidence: 99%

See 2 more Smart Citations

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

Zhou

et al. 2023

Energy

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“…In view of the limited number of cycles for BESS, minimizing the loss of lifetime to prolong its service life is extremely important [19][20][21]. The number of CDC is frequently used to measure the loss [22].…”

Section: Literature Review and Research Gapmentioning

confidence: 99%

Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Chen

Wang

et al. 2022

IET Power Electronics

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In allusion to the issues of high loss of lifetime and neglecting scheduling potential, a control scheme to extend BESS lifetime for assisting wind farm to track power generation plan based on wind power feature extraction through improved whale optimization algorithm (IWOA) optimized swing door trending (IWOA‐SDT) is proposed. Firstly, an IWOA is designed to optimize the compression offset of the swing door trending algorithm. The preliminary wind power feature is extracted by IWOA‐SDT and is further corrected with actual output of wind power to acquire the corrected wind power feature. Secondly, BESS is divided into two groups and the second battery group is subdivided into three battery clusters, and their capacities are configured by the deviation between preliminary feature and forecasted power. The grouping state of battery units is dynamically adjusted based on devised dynamic grouping method at the corresponding time of corrected wind power feature. The power coordinative allocation approach of the two battery groups is designed. A power coordinative allocation strategy for battery groups is devised to obtain the power regulation signals of two battery groups. The power allocation in battery units of the first group is calculated by the number of the units, and the double‐layer power allocation in battery units of the second group coordinated with multiple rules is devised. Finally, the proposed control scheme is implemented in a wind farm to track the power generation plan, and the results show that the wind farm with BESS can accurately track the power generation plan, the service life of BESS is significantly extended and the scheduling potential is sufficient for the next period.

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Bi-level decomposition algorithm of real-time AGC command for large-scale electric vehicles in frequency regulation

Feng

Zhao

et al. 2023

Journal of Energy Storage

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Dynamic battery loss evaluation and its application for optimal online wind‐storage integrated scheduling

Cited by 12 publications

References 19 publications

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

Two-stage real-time optimal electricity dispatch strategy for urban residential quarter with electric vehicles’ charging load

Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Bi-level decomposition algorithm of real-time AGC command for large-scale electric vehicles in frequency regulation

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