Active consumers who engage in energy consumption, production and provide ancillary services in a dynamic and interactive manner will be an integral part of the future grid. This paper models and analyzes interaction between active consumers and aggregators with a specific focus on consumer actions in response to real-time electricity pricing and their impacts on grid voltage. Firstly, the paper introduces a unique prospect theory based consumer behavior model that captures wide range of consumers each with their individual preferences. Secondly, the interaction between consumers and aggregators is modeled as a Stackelberg game. However, unlike existing game theoretic efforts that assume rational behavior of consumers, the prospect theory based models systematically incorporate realistic consumer behavior including irrationality. Another unique aspect of the formulation is the inclusion of the physical grid constraints (in terms of voltage violations) within the analysis. In contrast to prior approaches that limit themselves to economic aspects, the proposed techno-economic perspective provides an understanding of the impact of large scale penetration of active consumers on the physical grid. The proposed model is analyzed using simulations on the IEEE 69 bus test system and the impact of irrationality on both consumer/aggregator payoffs and voltage violations is quantified.

This paper presents a novel hierarchical multilevel decentralized optimal power flow (OPF) for power loss minimization in three-phase unbalanced large-scale distribution systems via optimal reactive power scheduling of rooftop photovoltaics generators. The system is decomposed into three levels corresponding to: 1) the primary; 2) the lateral; and 3) the secondary feeder subnetworks of distribution systems. A distributed sequential coordination scheme based on analytical target cascading method is developed to minimize power losses while considering the operational constraints. Results based on the proposed method are compared with centralized OPF for validation. Control based on the proposed method is compared with no reactive power control and local reactive power control to identify its effectiveness. Further, a virtual feeder is introduced to separate the coupled subnetworks into decomposed layers, which enables parallel processing of the optimization problems to reduce computational complexity and provide faster solution. A 559-node large-scale distribution network built based on the IEEE 37 node test system is used to demonstrate performance of the proposed algorithm.

Due to increasing environmental concerns and demand for clean energy resources, photovoltaic (PV) systems are becoming more prevalent. Considering that in several instances, customers pay for both energy and power, PV installations not only must reduce the customers’ energy purchases but also lower their peak demand for maximum financial benefits. However, in many cases, the peak demand does not coincide with the peak of photovoltaic generation. To address this issue, excess energy generated during low-demand periods can be stored in a battery, which can then be used to meet peak demand. Determining the optimal size of photovoltaic and battery components while ensuring system performance and financial benefits is significantly challenging. This study proposes a novel statistical methodology for optimizing PV-battery system size. In the proposed method, the PV-battery system must meet peak demand thresholds with a specific probability. Further, cost and benefit functions are used for financial evaluation. Finally, Monte Carlo simulations, developed using time series clustering and a Bayesian model are utilized to assess system performance and financial feasibility.

This paper introduces Moving Target Defense (MTD) in distribution system against False Data Injection (FDI) attacks on the supervisory control and data acquisition (SCADA) system. Based on the AC power flow model, a hidden MTD (HMTD) strategy is constructed in combination with network reconfiguration by minimizing the system loss and line power flow differences before and after the HMTD. The proposed HMTD-based network reconfiguration is formulated as a mixed-integer nonlinear programming (MINLP) problem. A refined Genetic Algorithm (GA) is proposed to solve it. Numerical test is conducted in a modified IEEE-66 bus system. The simulation results show that the proposed model reduces the power loss introduced by the HMTD as well as yields a stealthy MTD to the attackers. The impact of HMTD on the system performance is also compared with that of the existing MTD strategies.

Faults caused by animals are a significant factor in the reliability of distribution systems. Examination of historical data on outages recorded by a utility in Kansas showed that the occurrences of animal-caused faults, most of which are caused by squirrels, are dependent on weather conditions and the time of the year. It was observed that the majority of animal-caused outages take place on fair weather days, which are the days with temperature between 40 degrees F and 85 degrees F with no other weather activity. Also, the breeding cycle and behavioral patterns of squirrels over the year has a strong correlation with the animal-caused outages in different months of the year. Population density of squirrels will have an influence on the number of outages caused by animals in overhead distribution systems, but reliable data on animal population density could not be found, thus an adequate direct relationship could not be established. In this paper, a Bayesian model is constructed for prediction of animal-caused outages in overhead distribution systems given the type of month and the number of fair weather days per week. The model uses historical data for training purposes and predicts the number of animal-caused outages per week given a combination of month type and number of fair weather days per week. A confidence interval is built around the predicted values of animal-caused outages, and the percentage of observed values lying within the confidence limits are computed in order to obtain accuracy of the predictions. Also, the time-sequences of monthly averaged predictions are compared with the observed values. The model is illustrated with seven years of data obtained for a service territory of a utility in Kansas

Moving target defense (MTD) using distributed flexible AC transmission system (D-FACTS) devices is a promising defense strategy to detect stealthy false data injection (FDI) attacks against the power system state estimation. However, all existing studies myopically perturb the reactance of D-FACTS lines without considering the system voltage stability. In this paper, we first illustrate voltage instability induced by MTDs in a three-bus system. To address this issue, we further propose a novel MTD framework that explicitly considers system voltage stability by using continuation power flow and voltage stability indices. We mathematically derive the sensitivity matrix of voltage stability index to line impedance, on which an optimization problem for maximizing voltage stability index is formulated. This framework is tested on the IEEE 14-bus and the IEEE 118-bus transmission systems, in which net load redistribution attacks are launched by sophisticated attackers. The simulation results show the effectiveness of the proposed framework in circumventing the voltage instability while maintaining the detection effectiveness of MTD. We conduct case studies with and without the proposed framework under different MTD planning and operational methods. The impacts of the proposed two methods on attack detection effectiveness and system economic metrics are also revealed.