A Monte Carlo simulation technique evaluates the reliability indices of restructured power systems with a hybrid market. A model for optimal transaction curtailment for a contingency state in a hybrid market is developed to incorporate the changes brought about by deregulation. The objective of the contingency optimal transaction curtailment for each individual generation company is to minimise its revenue loss. The problem is formulated as a linear programming problem and solved using an optimisation technique. Customer load curtailment, which is the basic parameter for reliability evaluation, is determined using a load-shedding philosophy which is based on the results of the optimal transaction curtailment by the genco. Supply and demand transactions of the market participants are represented by a transaction matrix. The impact of the firm and nonfirm bilateral and reserve contracts on customer reliabilities have been studied. The technique has been illustrated by application to the IEEE Reliability Test System.
This paper presents the application of fuzzy clustering technique on large load data to greatly reduce calculations in reliability evaluation of restructured power systems. The method involves: first grouping a large load data into few clusters, secondly calculating partial membership value of each load point in each cluster, thirdly calculating reliability indices for each cluster and finally, expressing the reliability indices at each load point in terms of the reliability index of the cluster and the membership value that the load point has in that cluster. A non-sequential Monte Carlo simulation technique based on this framework has been proposed to evaluate the customer reliability of restructured power systems.
Statistical static timing analysis (SSTA) has been used in practice as an extension to regular static timing analysis (STA) to analyse for the impact of process variations on timing in new process nodes. However, the use of statistical timing in design optimization is a challenge that chip design teams face. In this paper, we propose some approaches to enable the comprehension of statistical timing behaviour during optimization. We indicate how the design standard-cell library can be analysed for variation, to help in understanding the robustness of cells, and in enabling the use of the correct logic architectures. We then propose an approach to comprehend the statistical timing behaviour in conventional (static-timing-based) optimization engines. We show how this method helps in optimising the overall area of the design, while simultaneously improving the timing characteristics. We also indicate a complementary approach of incrementally optimising the design using the statistical characteristics of the library as an added cost for sizing. We show how these approaches resulted in a 9.1% reduction in design area, and a direct improvement in statistical timing of a complex real-life design.
This paper presents a time sequential Monte Carlo simulation technique to evaluate customer load point reliability in multi-bilateral contracts market. The effects of bilateral transactions, reserve agreements, and the priority commitments of generating companies on customer load point reliability have been investigated. A generating company with bilateral contracts is modelled as an equivalent time varying multi-state generation (ETMG). A procedure to determine load point reliability based on ETMG has been developed. The developed procedure is applied to a reliability test system to illustrate the technique. Representing each bilateral contract by an ETMG provides flexibility in determining the reliability at various customer load points.
In a bilateral contracts market while selecting contracts to meet their reliability requirements, probability distributions of load point reliability indices considering multi-bilateral contracts with Gencos can provide useful information for the customers. This paper utilizes the modeling of each bilateral contract as an ETMG (equivalent time varying multi-state generation) to evaluate the load point reliability. The priority order in which the generating companies serve various contracts in case of generator outages is considered in modeling the ETMG. A procedure to determine the probability distributions of reliability indices based on the ETMG at various customer load points is proposed. A test system has been analyzed to illustrate the procedure and also the factors that affect the load point reliability.
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