Description:
This study presents a reliability-constrained adaptive-robust multi-resolution model for generation maintenance scheduling (GMS) problem considering the uncertainty sources of electricity demand, wind power generation, and equipment unavailabilities. In the proposed tri-level adaptive-robust model, a polyhedral uncertainty set is used to model the electricity demand and wind power generation fluctuations. In addition, equipment unavailabilities as discrete uncertainty sources are modelled in the reliability sub-problem where the expected energy not supplied is determined as a reliability criterion. Accordingly, the proposed model obtains a robust maintenance schedule for generating units immunised against the worst realisation of electricity demand and wind power generation while satisfying the reliability constraint considering equipment unavailabilities. Moreover, maintenance and operation periods are specifically modelled using different resolutions in the proposed multi-resolution GMS approach. To solve the proposed reliability-constrained adaptive-robust multi-resolution model, a new solution approach including Benders cut, reliability cut, and block coordinate descent method is presented. Numerical results on two test systems show the effectiveness of both the proposed GMS model and the proposed solution approach.
Description:
In mature electric power systems, growth in generation/demand, integration of renewable energy, and system expansion may elevate short-circuit levels beyond the rating of existing components. Thanks to technological advancements in materials, superconducting fault current limiters (SFCLs) can effectively alleviate excessive fault currents without affecting normal operation of power systems as they are invisible in non-faulted conditions. However, due to their rather high prices, SFCL optimal placement (SOP) comes to attention. The effectiveness of SOP depends on optimally siting of SFCL resistive/inductive types, which vary in transmission and distribution networks due to different X/R ratios. In this study, an SOP is proposed to determine optimal locations and types of SFCLs taking into account short-circuit level of buses. In addition, a complex-valued artificial bee colony (CABC) algorithm is introduced to efficiently solve complex-valued optimisation problems such as power system applications, including SOP. The proposed SOP with CABC is examined on transmission and distribution test cases to evaluate its effectiveness. It is found that by employing the proposed complex decision vector, the CABC algorithm exhibits an enhanced exploration capability and convergence rate due to halving decision vector length and considering mutual effects of real and imaginary parts of decision variables.