A New Class of MDS Erasure Codes Based on Graphs

Maximum distance separable (MDS) array codes are XOR-based optimal erasure codes that are particularly suitable for use in disk arrays. This paper develops an innovative method to build MDS array codes from an elegant class of nested graphs, termed \textit{complete-graph-of-rings (CGR)}. We discuss a systematic and concrete way to transfer these graphs to array codes, unveil an interesting relation between the proposed map and the renowned perfect 1-factorization, and show that the proposed CGR codes subsume B-codes as their "contracted" codes. These new codes, termed \textit{CGR codes}, and their dual codes are simple to describe, and require minimal encoding and decoding complexity.Comment: in Proceeding of IEEE Global Communications Conference (GLOBECOM

Optimizing Generation Maintenance Scheduling Considering Emission Factors

Conventional generation maintenance scheduling (GMS) is a solution to increase the reliability of power systems and minimize the operation and maintenance costs paid by generation companies (GenCos). Nonetheless, environmental aspects, such as zero carbon emissions, have attracted global attention, leading to emission costs being paid by electricity generators. Therefore, to obtain GMS plans that consider these factors, this paper proposes multi-objective GMS models to minimize operation, maintenance, and emission costs by using lexicographic optimization as a mathematical tool. A demand response program (DRP) is also adapted to decrease emission generation and operational expenditures. The probability that no generation unit (GU) fails unexpectedly and the average net reserve value, comprising the system reliability with and without considering the GU failure rate, are demonstrated. Numerical examples are implemented for the IEEE 24-bus reliability test system. A GMS algorithm presented in a published work is run and compared to verify the robustness of the proposed GMS models. Our results indicate that this paper provides comprehensive approaches to the multi-objective GMS problem focusing on operation, maintenance, carbon, and DRP costs in consideration of technical and environmental aspects. The use of lexicographic optimization allows for the systematic and hierarchical consideration of these objectives, leading to significant benefits for GenCos