Optimum Network Ageing and Battery Sizing for Improved Wind Penetration and Reliability

Abstract

A multiobjective framework that optimises the uprating of the line's real-time thermal rating and capacity of battery storage against wind curtailment, network ageing and reliability is proposed. The two enhancements are limited to the accumulated expected amount and duration of wind power losses of each wind farm bus. In the framework, actual conductor properties, line failures due to thermal effects, weather data, battery operation policy and wind farm model are considered. The trade-off of the optimisation criteria, the Pareto front, is solved using the non-sorting genetic algorithm and fuzzy decision-making method. Results show that the conductor maximum allowable temperature affects all the three optimisation parameters, but battery efficiency only affects wind curtailment level.

Demand Respond Program and Dynamic Thermal Rating System for Enhanced Power Systems

“…The Maher study clearly shows that deterioration of failure rates as equipment ages hurts reliability indices. It is vital to consider overhead line age when evaluating reliability for both load points and system indices [45]; research result shows that either a feeder bus is considered repairable or non-repairable components, the failure rate increase directly as feeder bus aged consistently; the studies also show that the failure rate increased sharply after a certain threshold of feeder bus age is reached.…”

DC Microgrid Planning, Operation, and Control: A Comprehensive Review

“…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.…”

Optimizing renewable-based energy supply options for power generation in Ethiopia

“…Despite these challenges, rising electricity demand and awareness of the need to keep global warming below 1.5˚C in order to meet internationally agreed climate change targets necessitate a major transformation of energy systems that dramatically increases the integration of intermittent renewable energy sources such as wind [39][40][41][42] and solar [43][44][45][46]. The dynamic thermal rating (DTR) and energy storage systems [47][48][49] as well as demand response to actively manage load profiles [50][51][52][53][54][55] are pioneering approaches to enhance the reliability of the power network and increase the penetration of such variable renewable energy sources into power systems.…”

“…Grid level batteries can store energy when there is excess generation from wind and solar and discharge it to meet variable peak demand. Optimized battery energy storage system can minimize the power curtailment, network ageing, and increase reliability [40], reduces the renewable energy systems curtailment considering power flow constraints [61] and increase the reliability of generation by allowing the direct load reduction due to equipment shutdown as well as load redistribution during the day [39]. Employing a DTR system in a power network is therefore the potential to improve network capacity, increase the utilization of existing assets of solar and wind energy sources.…”