Synthetic Modeling of Power Grids Based on Statistical Analysis

The development of new concepts and methods for improving the efficiency of power networks needs performance evaluation with realistic grid topology. However, much of the realistic grid data needed by researchers cannot be shared publicly due to the security and privacy challenges. With this in mind, power researchers studied statistical properties of power grids and introduced synthetic power grid topology as appropriate methodology to provide enough realistic power grid case studies. If the synthetic 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. In the past, power researchers proposed a synthetic grid model, called RT-nested-smallworld, based on the findings from a comprehensive study of the topology properties of a number of realistic grids. This 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, in the proposed RT-nested-smallworld model the approaches to address some electrical and topological settings such as (1) bus types assignment, (2) generation and load settings, and (3) transmission line capacity assignments, are not sufficient enough to apply to realistic simulations. In fact, such drawbacks may possibly cause deviation in the grid settings therefore give misleading results in the following evaluation and analysis. To address this challenges, the first part of this thesis proposes a statistical methodology to solve the bus type assignment problem. This method includes a novel measure, called the Bus Type Entropy, the derivation of scaling property, and the optimized search algorithm. The second part of this work includes a comprehensive study on generation/Load settings based on both topology metrics and electrical characteristics. In this section a set of approaches has been developed to generate a statistically correct random set of generation capacities and assign them to the generation buses in a grid. Then we determine the generation dispatch of each generation unit according to its capacity and the dispatch ratio statistics, which we collected and derived from a number of realistic grid test cases. The proposed approaches is readily applied to determining the load settings in a synthetic grid model and to studying the statistics of the flow distribution and to estimating the transmission constraint settings. Considering the results from the first two sections, the third part of this thesis will expand earlier works on the RT-nested-smallworld model and develop a new methodology to appropriately characterize the line capacity assignment and improve the synthetic power grid modeling

On the Scaling Property of Power Grids

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

Optimized Household Demand Management with Local Solar PV Generation

Demand Side Management (DSM) strategies are of-ten associated with the objectives of smoothing the load curve and reducing peak load. Although the future of demand side manage-ment is technically dependent on remote and automatic control of residential loads, the end-users play a significant role by shifting the use of appliances to the off-peak hours when they are exposed to Day-ahead market price. This paper proposes an optimum so-lution to the problem of scheduling of household demand side management in the presence of PV generation under a set of tech-nical constraints such as dynamic electricity pricing and voltage deviation. The proposed solution is implemented based on the Clonal Selection Algorithm (CSA). This solution is evaluated through a set of scenarios and simulation results show that the proposed approach results in the reduction of electricity bills and the import of energy from the grid