Showing papers by "Robert J. Thomas published in 2015"

Subspace Methods for Data Attack on State Estimation: A Data Driven Approach

Abstract: Data attacks on state estimation modify part of system measurements such that the tempered measurements cause incorrect system state estimates. Attack techniques proposed in the literature often require detailed knowledge of system parameters. Such information is difficult to acquire in practice. The subspace methods presented in this paper, on the other hand, learn the system operating subspace from measurements and launch attacks accordingly. Conditions for the existence of an unobservable subspace attack are obtained under the full and partial measurement models. Using the estimated system subspace, two attack strategies are presented. The first strategy aims to affect the system state directly by hiding the attack vector in the system subspace. The second strategy misleads the bad data detection mechanism so that data not under attack are removed. Performance of these attacks are evaluated using the IEEE 14-bus network and the IEEE 118-bus network.

Probabilistic Forecast of Real-Time LMP and Network Congestion

Abstract: The short-term forecasting of real-time locational marginal price (LMP) and network congestion is considered from a system operator perspective. A new probabilistic forecasting technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability distribution of the real-time LMP/congestion is obtained. The proposed method incorporates load/generation forecast, time varying operation constraints, and contingency models. By shifting the computation cost associated with multiparametric programs offline, the online computation cost is significantly reduced. An online simulation technique by generating critical regions dynamically is also proposed, which results in several orders of magnitude improvement in the computational cost over standard Monte Carlo methods.

On Bus Type Assignments in Random Topology Power Grid Models

Abstract: In 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 of 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.

Probabilistic Forecast of Real-Time LMP via Multiparametric Programming

Abstract: The problem of short-term probabilistic forecast of real-time locational marginal price (LMP) is considered. A new forecast technique is proposed based on a multiparametric programming formulation that partitions the uncertainty parameter space into critical regions from which the conditional probability mass function of the real-time LMP is estimated using Monte Carlo techniques. The proposed methodology incorporates uncertainty models such as load and stochastic generation forecasts and system contingency models. With the use of offline computation of multiparametric linear programming, online computation cost is significantly reduced.

A novel measure to characterize bus type assignments of realistic power grids

Abstract: Electric 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-smallworld, 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

A Detailed Power System Planning Model: Estimating the Long-Run Impact of Carbon-Reducing Policies

Abstract: In this paper, a much more detailed representation of the nation's electricity system than has been traditionally used in policy models is employed. This detailed representation greatly increases the computational difficulty of obtaining optimal solutions, but is necessary to accurately model the location of new investment in generation. Given the proposed regulation of CO2 emissions from US power plants, an examination of economically efficient policies for reducing these emissions is warranted. The model incorporates realistic physical constraints, investment and retirement of generation, and price-responsive load to simulate the effects of policies for limiting CO2 emissions over a twenty-year forecast horizon. Using network reductions for each of the three electric system regions in the U.S. And Canada, an optimal economic dispatch, that satisfies reliability criteria, is assigned for 12 typical hour-types in each year. Three scenarios are modeled that consider subsidies for renewables and either CO2 emissions regulation on new investment or cap-and-trade. High and low gas price trends are also simulated and have large effects on prices of electricity but small impacts on CO2 emissions. Low gas prices with cap-and-trade reduce CO2 emissions the most, large subsidies for renewables alone do not reduce carbon emissions much below existing levels. Extensive retirement of coal-fired power plants occurs in all cases.