Abstract
The intermittency and variability of permeated distributed generators (DGs) could cause many critical security and economy risks to distribution systems. This paper applied a certain mathematical distribution to imitate the output variability and uncertainty of DGs. Then, four risk indices—EENS (expected energy not supplied), PLC (probability of load curtailment), EFLC (expected frequency of load curtailment), and SI (severity index)—were established to reflect the system risk level of the distribution system. For the certain mathematical distribution of the DGs’ output power, an improved PEM (point estimate method)-based method was proposed to calculate these four system risk indices. In this improved PEM-based method, an enumeration method was used to list the states of distribution systems, and an improved PEM was developed to deal with the uncertainties of DGs, and the value of load curtailment in distribution systems was calculated by an optimal power flow algorithm. Finally, the effectiveness and advantages of this proposed PEM-based method for distribution system assessment were verified by testing a modified IEEE 30-bus system. Simulation results have shown that this proposed PEM-based method has a high computational accuracy and highly reduced computational costs compared with other risk assessment methods and is very effective for risk assessments.
Highlights
The extensive penetration of renewable-type distributed generators (DGs) in distribution networks could bring many benefits to the grid, as they are alternative to conventional generators [1,2]
DGs connected to a distribution system could cause some critical risks to distribution systems from security and economy aspects
In order to reasonably assess the risks of distribution systems with penetrating DGs, four risk indices—EENS, PLC, EFLC, and SI—were used in this paper to reflect the system risk level in distribution systems
Summary
The extensive penetration of renewable-type distributed generators (DGs) (e.g., wind and PV) in distribution networks could bring many benefits to the grid, as they are alternative to conventional generators [1,2]. In [13,14,15], EENS, PLC (probability of load curtailment), EFLC (expected frequency of load curtailment), and SI (severity index) were presented as risk indices to assess the risk level of distribution system. The randomness of wind and PV can be imitated by some mathematical formulas These other uncertainty methods mentioned are not applicable for the risk assessment of a distribution system with the penetration of probabilistic DGs. For probabilistic DGs, probabilistic methods—including the Monte Carlo method, scenario-based decision-making method, and point estimate method—can be suitably applied. For the certain mathematical distribution of the DGs’ output power, an improved PEM-based method was proposed to calculate these four system risk indices. Simulation results demonstrate that total generation capacity, type, location, dispersion, and capacity proportion of DGs have great influences on the system risk indices
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