An efficient probabilistic-chronological matching modeling for DG planning and reliability assessment in power distribution systems

In recent decades, power distribution systems have encountered a considerable shift toward utilizing renewable resource based distributed generation (DG) systems. This is due to the proven ability of DGs to reduce fossil fuel consumption, which reduces harm done to the environment. In this paper, a new state reduction algorithm is proposed to determine the minimum number of states required to describe or represent the behavior of wind speed and solar irradiance in DG planning problems and reliability analysis. This algorithm could be generalized to incorporate any planning problem where wind or PV power is part of its parameters. Moreover, an adequate time representation that mimics the fluctuation of renewable resource based DGs and chronologically matches the fluctuations in system demand is presented. Three different data clusters are applied (monthly, seasonal and yearly) to investigate the variability of DG power output and electricity demand on both DG planning problems and reliability assessment. These models are evaluated considering DG siting and sizing problems, as well as a supply adequacy-based reliability assessment. The proposed model measures the deviations in annual energy losses (AEL), total DG penetration, loss of load expectation (LOLE), and loss of energy expectation (LOEE).