Compared with other natural or man-made networks, electric power grid assumes distinct "electric" topology with special small-world properties and electrical parameter settings. In this paper we study the scaling property of power grid in terms of both topology measures and electric parameters, with a number of realistic power grid test cases of different size. The examined measures and parameters include average node degree, average path length, algebraic connectivity, the bus type entropy that characterize relative locations of generation and load buses, generation capacity, total demand, and transmission capacity. Interpreting and testing the scaling property of power grid will help us better understand the intrinsic characteristics of electric energy delivery network of this critical infrastructure; and enable the development of an appropriate synthetic modeling that could be utilized to generate power grid test cases with accurate grid topology and electric parameters.

1 aWang, Zhifang1 aElyas, Seyyed, Hamid uhttp://aisel.aisnet.org/hicss-50/es/monitoring/4/01963nas a2200157 4500008003900000022001400039245008000053210006900133260001200202300001000214520145600224653001301680100002501693700001801718856006901736 2016 d a0885-895000aImproved Synthetic Power Grid Modeling with Correlated Bus Type Assignments0 aImproved Synthetic Power Grid Modeling with Correlated Bus Type c12/2016 a1 - 13 aThis paper presents our study results on the correlated assignment of generation, load, or connection buses in a given grid topology and the development of an optimized search algorithm to improve the proposed synthetic grid model, called RT-nestedSmallWorld. A numerical measure, called "Bus Type Entropy", was proposed in an initial study on this subject to characterize the correlation of bus type assignments in realistic grids. In this paper its definition has been redefined and improved with the help of some newly obtained data of realistic grids so that the scaling property of a real-world grid's entropy value versus the network size can be effectively captured with a curve-fitting approach. With the help of the derived scaling function, we will be able to determine an appropriate target entropy value that a correlated bus type assignment should assume in a specific Nbus grid network. Therefore our previously proposed synthetic power grid modeling has been enhanced with a direct search procedure for the best bus type assignments in a specific Nbus grid topology, saving the mandatory, but most the time unattainable, requirement of a set of realistic grid data with a comparable network size for identifying the search target. Finally the performance of the proposed approach is examined based on some available grid data including the IEEE test cases, the NYISO-2935, the ERCOT-5633 and the WECC-16994 systems.

10aRM14-0031 aElyas, Seyyed, Hamid1 aWang, Zhifang uhttps://certs.lbl.gov/publications/improved-synthetic-power-grid01569nas a2200145 4500008003900000245011600039210006900155260004000224300001000264520102400274653001301298100002201311700001801333856007201351 2016 d00aModeling the uncertainties in renewable generation and smart grid loads for the study of the grid vulnerability0 aModeling the uncertainties in renewable generation and smart gri aMinneapolis, MN, USAbIEEEc09/2016 a1 - 53 aThe 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.

10aRM14-0031 aAthari, Mir, Hadi1 aWang, Zhifang uhttps://certs.lbl.gov/publications/modeling-uncertainties-renewable02172nas a2200145 4500008003900000245010600039210006900145260003400214300001600248520163800264653001301902100002501915700001801940856006801958 2016 d00aA Multi-objective Optimization Algorithm for Bus Type Assignments in Random Topology Power Grid Model0 aMultiobjective Optimization Algorithm for Bus Type Assignments i aKoloa, HI, USAbIEEEc01/2016 a2446 - 24553 aOur 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.

10aRM14-0031 aElyas, Seyyed, Hamid1 aWang, Zhifang uhttps://certs.lbl.gov/publications/multi-objective-optimization01471nas a2200145 4500008003900000245008100039210006900120260004000189300001000229520095700239653001301196100002501209700001801234856007301252 2016 d00aStatistical analysis of transmission line capacities in electric power grids0 aStatistical analysis of transmission line capacities in electric aMinneapolis, MN, USAbIEEEc09/2016 a1 - 53 aThis paper mainly focuses on statistical analysis of transmission line capacities in terms of both topology and electrical parameters. We expand our earlier work on random-topology power grid modeling as a way to generate synthetic grid data to test new concepts and designs. We examine transmission line capacities based on both network topology metrics and a newly proposed electrical index called “neighboring capacity ratio”. The obtained results show that the issue of transfer capacity assignment not only emerges as an electrical optimization concern, but some topological metrics must be considered to find the best line capacity assignment that is consistent with what is manifested in real-world grid. Statistical experiments conducted on the ERCOT and WECC system data help appropriately characterize the transmission line capacity. And the results can be utilized to improve our synthetic power grid models in the future.

10aRM14-0031 aElyas, Seyyed, Hamid1 aWang, Zhifang uhttps://certs.lbl.gov/publications/statistical-analysis-transmission02742nas a2200157 4500008003900000245006500039210006200104260002900166520215900195653001002354653002802364653001302392100001802405700002302423856013802446 2015 d00aOn Bus Type Assignments in Random Topology Power Grid Models0 aBus Type Assignments in Random Topology Power Grid Models aKauai, HIbIEEEc01/20153 aIn order to demonstrate and test new concepts and methods for the future grids, power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments. If the random networks are truly representative and if the concepts or methods test well in this environment they would test well on any instance of such a network as the IEEE model systems or other existing grid models. Our previous work [1] proposed a random topology power grid model, called RT-nested-small world, based on the findings from a comprehensive study of the topology and electrical properties of a number of realistic grids. The proposed model can be utilized to generate a large number of power grid test cases with scalable network size featuring the same small-world topology and electrical characteristics found from realistic power grids. On the other hand, we know that dynamics of a grid not only depend on its electrical topology but also on the generation and load settings, and the latter closely relates with an accurate bus type assignment of the grid. Generally speaking, the buses in a power grid test case can be divided into three categories: the generation buses (G), the load buses (L), and the connection buses (C). In [1] our proposed model simply adopts random assignment of bus types in a resulting grid topology, according to the three bus types' ratios. In this paper we examined the correlation between the three bus types of G/L/C and some network topology metrics such as node degree distribution and clustering coefficient. We also investigated the impacts of different bus type assignments on the grid vulnerability to cascading failures using IEEE 300 bus system as an example. We found that (a) the node degree distribution and clustering characteristic are different for different type of buses (G/L/C) in a realistic grid, (b) the changes in bus type assignment in a grid may cause big differences in system dynamics, and (c) the random assignment - f bus types in a random topology power grid model should be improved by using a more accurate assignment which is consistent with that of realistic grids.10aCERTS10areliability and markets10aRM14-0031 aWang, Zhifang1 aThomas, Robert, J. uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7070137&refinements%3D4255068112%26filter%3DAND%28p_IS_Number%3A7069647%2902664nas a2200157 4500008003900000245008200039210006900121260004200190300001000232520211000242653001302352100001802365700002502383700002302408856007502431 2015 d00aA novel measure to characterize bus type assignments of realistic power grids0 anovel measure to characterize bus type assignments of realistic aEindhoven, NetherlandsbIEEEc06/2015 a1 - 63 aElectric power engineers and researchers need appropriate randomly generated grid network topologies for Monte Carlo experiments to test and demonstrate new concepts and methods. Our previous work proposed a random topology power grid model, called RT-nested-small-world, based on a comprehensive study of the real-world grid topologies and electrical properties. The proposed model can be used to produce a sufficiently large number of power grid test cases with scalable network size 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. And our recent study has shown that the bus type assignment of a realistic power grid is not random but a correlated one. Generally speaking,the buses in a power grid can be grouped into three categories: generation buses (G), load buses (L), and connection buses (C). When studying the dynamics of a grid we need to take into account not only its "electrical" topology but also the generation and load settings including their locations, which are equivalent to the bus type assignments in our model. In this paper we define a novel measure to characterize typical bus type assignments of realistic power grids. The proposed measure will enable the recognition of the specific set of bus type assignments, consistent with that of a realistic grid, from those generated from random permutation. This will prove useful for designing an optimal algorithm to improve our random topology power grid modeling. The proposed measure, called the Bus Type Entropy, incorporates both bus type ratios and the link type ratios. Therefore it provides a quantitative means to identify the presence of correlation among the bus type assignments of a realistic grid. We then experiment with this entropy measure on a NYISO system and the IEEE 300-bus system. The numerical results from both test cases verify the effecti- eness of the proposed measure to characterize the bus type assignment of a real-world power grid.

10aRM14-0031 aWang, Zhifang1 aElyas, Seyyed, Hamid1 aThomas, Robert, J. uhttps://certs.lbl.gov/publications/novel-measure-characterize-bus-type01469nas a2200265 4500008003900000022001400039245009000053210006900143260001200212300001200224490000700236520067700243653001000920653002700930653002400957653002800981653001301009653001501022100002301037700001301060700001801073700002001091700001901111856007301130 2012 d a1053-588800aDemand-Side Management in the Smart Grid: Information Processing for the Power Switch0 aDemandSide Management in the Smart Grid Information Processing f c09/2012 a55 - 670 v293 aOver the course of several decades after their introduction, power systems merged into large interconnected grids to introduce redundancy and to leverage on a wider pool of generation resources and reserves. As the system grew in size and complexity, a cyberphysical infrastructure was progressively developed to manage it. Traditionally, general-purpose computing and communication resources have been used in power systems, specifically to serve two needs: 1) that of monitoring the safe operation of the grid and logistics of power delivery, and 2) that of gathering information required to dispatch the generation optimally and, later on, to operate the energy market.10aCERTS10ademand-side management10aelectricity markets10apower system monitoring10aRM11-00710asmart grid1 aAlizadeh, Mahnoosh1 aLi, Xiao1 aWang, Zhifang1 aScaglione, Anna1 aMelton, Ronald uhttps://certs.lbl.gov/publications/demand-side-management-smart-grid