Evaluating the Performance of Small Autonomous Power Systems Using Reliability Worth Analysis

The analysis and design of a small autonomous power system (SAPS) that contains renewable energy sources (RES) technologies can be challenging, due to the large number of design options and the uncertainty in key parameters. Renewable power sources add further complexity because their power output may be intermittent, seasonal, and nondispatchable. Due to this characteristic, reliability evaluation of a RES based SAPS cannot be implemented using the traditional deterministic and analytical methods. Moreover, in order to be complete, this evaluation has to be done within a cost-benefit framework. This chapter investigates the effect of reliability worth in the optimal economic operation of SAPS that is based on RES technologies, considering different scenarios. The optimization procedure is implemented with a combined genetic algorithm (GA) and local search procedure. In addition, this chapter examines the effect of considering SAPS components forced outage rate in the obtained optimal solutions via Monte Carlo simulation (MCS). The performance of the proposed optimization methodology is studied for a large number of alternative scenarios via sensitivity analysis, which study the effect on the results due to the uncertainty on weather data and cost data. The results show that the optimal operation of a RES based SAPS depends largely on the consideration of reliability worth as well as the inclusion of components forced outage rate.