2017
DOI: 10.1049/iet-gtd.2016.2076
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Operating strategy and optimal allocation of large‐scale VRB energy storage system in active distribution networks for solar/wind power applications

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Cited by 50 publications
(27 citation statements)
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“…Unlike the conventional unit commitment problem which depends on a priori information, this method is not as more suitable for practical implementation as it does not require prior RES and load information. A day ahead unit commitment operation is solved in [81] using a heuristic optimization technique to minimize the total operation cost and carbon dioxide while scheduling the [56] Battery cost minimization total energy consumption is reduced reliability is not improved load management and ESS location MILP [57]- [59] Not specified cost minimization (investment and operation) reduction in power conversion loss -DE [60], [61] battery and supercapacitor battery life cycle maximization and cost minimization the microgrids configuration is optimized SOC is not well managed Compro mise Programming (CP) [62] battery daily worth maximization and cost minimization effective sizing with minimal cost system operational requirements are not considered PSO [63]- [65] battery minimization of annualized capital cost, and operation loss of power supply probability is reduced, assumption is made based on & maintenance cost limited RES sensitive analysis [66] not specified maximization control performance and optimal node selection for ESS variation of the grid constructions minimization power losses mitigation of power and energy variation and parameters are not considered GWO [67], [68] battery minimization net present cost optimized configuration is selected -DP optimization [69] vanadium redox battery ESS cost load uncertainty improvement PQ issues are unsolved NSGA-II [70] hybrid SMES-flywheel maximize the power delivered, cost reduction and performance improvement solution procedure is minimize power fluctuation and costs time-consuming probabilistic approach [71], [72] battery investment cost minimization optimal size of battery when time-of-use sensitivity analysis with random (ToU) is used uncertainties are well handled input variables should be investigated linear programming [73] hydrogen storage cost and carbon emission minimization reduced carbon emission size of hydrogen storage is larger than battery power among different microgrids units. This approach also effectively eliminates congestion according to congestion signals by optimally scheduling different units.…”
Section: A Unit Commitmentmentioning
confidence: 99%
“…Unlike the conventional unit commitment problem which depends on a priori information, this method is not as more suitable for practical implementation as it does not require prior RES and load information. A day ahead unit commitment operation is solved in [81] using a heuristic optimization technique to minimize the total operation cost and carbon dioxide while scheduling the [56] Battery cost minimization total energy consumption is reduced reliability is not improved load management and ESS location MILP [57]- [59] Not specified cost minimization (investment and operation) reduction in power conversion loss -DE [60], [61] battery and supercapacitor battery life cycle maximization and cost minimization the microgrids configuration is optimized SOC is not well managed Compro mise Programming (CP) [62] battery daily worth maximization and cost minimization effective sizing with minimal cost system operational requirements are not considered PSO [63]- [65] battery minimization of annualized capital cost, and operation loss of power supply probability is reduced, assumption is made based on & maintenance cost limited RES sensitive analysis [66] not specified maximization control performance and optimal node selection for ESS variation of the grid constructions minimization power losses mitigation of power and energy variation and parameters are not considered GWO [67], [68] battery minimization net present cost optimized configuration is selected -DP optimization [69] vanadium redox battery ESS cost load uncertainty improvement PQ issues are unsolved NSGA-II [70] hybrid SMES-flywheel maximize the power delivered, cost reduction and performance improvement solution procedure is minimize power fluctuation and costs time-consuming probabilistic approach [71], [72] battery investment cost minimization optimal size of battery when time-of-use sensitivity analysis with random (ToU) is used uncertainties are well handled input variables should be investigated linear programming [73] hydrogen storage cost and carbon emission minimization reduced carbon emission size of hydrogen storage is larger than battery power among different microgrids units. This approach also effectively eliminates congestion according to congestion signals by optimally scheduling different units.…”
Section: A Unit Commitmentmentioning
confidence: 99%
“…In most of the literature studies, the scheduling of storage technologies is governed either by the objective function to be optimized or sometimes by time-dependent fixed operating strategies. In [6,14,15], dynamic programming is employed for optimal scheduling of BESSs under uncertain environment. e problem is evaluated for minimization of a cost function to ensure flexible and economical utilization of BESSs.…”
Section: Introductionmentioning
confidence: 99%
“…A comparative study of some redox electrolyte systems with respect to their suitability as test systems for the evaluation of cell designs has been reported [44]. The use of RFBs in a changing energy landscape including growing use of renewable energy sources from the grid level down to localized applications, including details like CO 2 -footprint has been highlighted [45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64]. Availability of raw materials for RFBs has been addressed [65].…”
Section: Introductionmentioning
confidence: 99%