TY - CONF
T1 - Modeling the uncertainties in renewable generation and smart grid loads for the study of the grid vulnerability
T2 - 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
Y1 - 2016/09//
SP - 1
EP - 5
A1 - Mir Hadi Athari
A1 - Zhifang Wang
KW - RM14-003
AB - The uncertainties of power systems are becoming more and more important due to power systems restructuring and ever increasing renewable generation integration into the grid. In the literature several uncertainty modeling approaches have been proposed to facilitate decision making process for efficient operation of power systems. These approaches mainly focus on deriving a statistical representation for different sources of uncertainty such as load, generation, and line capacities. However, there is a need for an uncertainty model which can capture the frequency components of uncertainty signals for applications such as voltage control, frequency control/ stability, and grid vulnerability analysis. In this paper we proposed an uncertainty modeling approach that captures the high dynamics of renewable generation uncertainty and smart grid loads and reveals their frequency domain characteristics. We tested the proposed model on IEEE 300 bus system to evaluate the impact of uncertainty on line flows.
JF - 2016 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
PB - IEEE
CY - Minneapolis, MN, USA
DO - 10.1109/ISGT.2016.7781265
ER -
TY - CONF
T1 - A Multi-objective Optimization Algorithm for Bus Type Assignments in Random Topology Power Grid Model
T2 - 2016 49th Hawaii International Conference on System Sciences (HICSS)
Y1 - 2016/01//
SP - 2446
EP - 2455
A1 - Seyyed Hamid Elyas
A1 - Zhifang Wang
KW - RM14-003
AB - Our previous work proposed a random topology power grid model, called RT-nested-smallworld, formulated based on a comprehensive study of the real-world grid topologies and electrical properties. The model can be used to produce a sufficiently large number of power grid test cases with scalable network size and featuring the same kind of small-world topology and electrical characteristics found in realistic grids. However, the proposed RT-power grid model has a shortcoming that is its random assignment of bus types (i.e. Generation, Load, or Connection), which is inconsistent with that of a realistic grid. Because our recent study found that the bus type assignment in a realistic power grid is not random but a correlated one. With the help of Bus Type Entropy, a novel measure which provides a quantitative means to better represent the correlation of bus type assignments in a grid topology, we propose a multi-objective optimization algorithm for the bus type assignments in the random topology power grid modeling. The proposed search algorithm is able to locate the best set of bus type assignments for a given random "electrical" topology generated by RT-nested-smallworld, so that each bus type assignment in the target set will have a Bus Type Entropy value close to that of a realistic grid with comparable network size. In order to demonstrate the performance of the proposed multi-objective algorithm, we experiment our algorithm on three sets of realistic power systems, namely, the IEEE-300 bus system, the NYISO system and the MPC system. The obtained results confirm the validity of our proposed method.
JF - 2016 49th Hawaii International Conference on System Sciences (HICSS)
PB - IEEE
CY - Koloa, HI, USA
DO - 10.1109/HICSS.2016.306
ER -