Today, the electricity power system is the most complicated engineering system has ever been made. The integrated power generating stations with power transmission lines has created a Network, called complex power Network. The reliability estimation of such complex power Networks is a very challenging problem, as one cannot find any immediate solution methods in current literature. In this paper, we advanced a new method for estimating the reliability of such Networks, which is based on 1) decomposition of the whole Network into subNetworks called islands, 2) estimating each island’ s reliability in exact form using the Network reliability theory, and 3) assembling the islands back together to estimate the whole Network reliability, again in exact form. We applied the new method on Iran’ s power Network with 105 generation stations and 16460 kilometres of transmission lines.

Tehran is the capital of Iran and is one of the largest cities in the world. With its history of earthquakes, high population density, and a population of over 8.5 million, it must be prepared to cope with disaster. This article introduces a new method for estimating the reliability of a roadway Network that considers the physical characteristics of the road and link capacity degradation. The proposed method was applied to a real road Network and the connectivity reliability of the city was calculated. The proposed technique for computing connectivity reliability is sensitive to the characteristics of the road and adjacent buildings. The results indicate that northern Tehran, with a low population density and newer buildings and highway Network, shows higher reliability for origin and destination trips than other regions. This region has a higher probability of connectedness to other regions after a disaster. The weakest area of the Network lies in the southern and central regions with older buildings and no highway Network. These regions show low connectivity reliability and high population density. These results provide useful insights into the vulnerability of the Tehran Network in response to a major earthquake.

In the study of system reliability and survival analysis, the reliability function, the mean residual lifetime, and the hazard rate are important measures factors. Besides, they provide helpful tools to analyze the maintenance policies and burn-in of a system. This study considers a Network containing n components with two states, up and down, showing whether an element is connected to other parts or not. It is supposed here that the system is subject to shocks that may cause component failure in which the number of crashes at each shock follows a truncated binomial distribution, and the process of shocks is associated with nonhomogeneous Poisson. The reliability function, the mean residual lifetime, and the hazard rate of this Network are investigated under the shock model by experimenting them on a real system.

Several regulation schemes such as Rate of Return (ROR) and Price Cap (PC) lack incentives for efficient investment. ROR lead to overinvestment and PC on the other hand lead to underinvestment. Regulator employs various forms of reward/penalty schemes (RPS) to cover inherent weaknesses in PC. Adding RPS allow utilities to make cost effective reliability improvements in response to changes in technology and costs but since this schemes result in consumers purchasing reliability and delivery as a single, optimal level of investment can’t be insured. To solve this problem, in the recent year, the reliability Insurance Scheme (RIS) has been introduced which through it the reliability and delivery services are unbundled and so the efficient investment is insured.In this paper, firstly, the investment structure under RIS is compared to that of traditional regulatory schemes and, according to this comparison; the advantages of RIS are illustrated. In the second step, under RIS, the effects of free-riding and investment delay on the investment dynamics are evaluated and finally the centralized method for alleviating these effects is proposed.